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BRIDGING THE
SCIENCE-POLICY INTERFACE:
REFLECTIONS AND LESSONS FROM A STUDY OF NATURAL RESOURCE MANAGEMENT
POLICY-MAKING IN AUSTRALIA
GERALDINE PASQUAL
BACHELOR OF SCIENCE (AGRICULTURE) (HONOURS), UNIVERSITY OF
WESTERN AUSTRALIA GRADUATE DIPLOMA OF COMPUTING, CURTIN UNIVERSITY MASTER OF ELECTRONIC MARKETING AND INFORMATION
MANAGEMENT, UNIVERSITY OF WESTERN AUSTRALIA
JANUARY 2010
THIS THESIS IS PRESENTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
AT
THE UNIVERSITY OF WESTERN AUSTRALIA
SCHOOL OF EARTH AND ENVIRONMENT
FACULTY OF NATURAL AND AGRICULTURAL SCIENCES
PERTH, AUSTRALIA
ii
THESIS DECLARATION
This thesis does not contain work that I have published, nor work under review for
publication. The thesis is completely the result of my own work, and was conducted
during the period of candidature.
Signed:__________________________
(Geraldine Pasqual)
2009, Geraldine M. Pasqual.
iii
ABSTRACT
In an attempt to sharpen the conceptual basis of the science-policy interface this thesis
reflects on the socio-political dynamics of science use in natural resource management
policy-making, based on a multi-method approach of ethnographic fieldwork, semi-
structured interviews and an online questionnaire. It is one of the first comprehensive
analyses of the science-policy interface in the Australian natural resource management
sector.
Environmental degradation caused by agricultural practices in Australia is considered
one of the pressing policy challenges that government faces in the 21st century. Over the
past twenty years, the significant investment by Australian governments in policies and
programs to improve natural resource condition has met with limited success. One of
the most significant contributors to policy limitations is the difficulty in translating
research findings into improved decisions and concrete policy actions – the difficulty of
bridging the science-policy interface. There are many reasons for this shortfall but a
significant issue facing scholars and practitioners is how to conceptualise the interface
and its defining elements.
More recently, the notion of the interface has shifted from a linear, one-dimensional
flow of information from science to policy, to social processes that include relations
between scientists and policy-makers that allow for exchanges and co-production of
knowledge. Concomitantly, the focus of researchers in this field has shifted from
information quality to the social dynamics of science use. A deeper understanding of the
interface is required for improvements to the planning of science use in policy-making
and this is explored in this thesis.
By applying an analytical framework to reflect on the dynamics of science use across
micro-organisational, organisational and system perspectives, the socio-political
constructs of power and trust emerged as common elements. A micro-organisational
perspective using structuration theory as an analytical approach, highlighted issues of
institutional power and structures of control that would influence information use in
organisations (e.g. communication norms and resource control). An organisational
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perspective based on an interaction model of research use, indicated that the use of
science by NRM policy-makers would be more likely in conditions of high, relational
power (i.e. interactions between scientists and policy-makers) and low, political power
(i.e. conflicts of values and interests). A system perspective, based on a supply chain
approach, pointed to the use (or non-use) of science for power and political positioning
in policy-making.
The system perspective also indicated potential for the development of trust between
science ‘suppliers’ and policy-making ‘customers’ to be an element in enhanced shared
learning. The organisational perspective called attention to social capital in the
development of trust. Policy-makers who were more frequent science users generally
interacted more with researchers than less frequent users, were more interconnected
through personal networks, and were less exposed to socio-political conflicts. The
production and maintenance of social capital appeared important for research transfer
and use.
To summarise, the use of science by NRM policy-makers in Australia varied depending
on information quality (e.g. access, relevance and availability), gender, organisational
form (regional, State, federal), and importantly on the socio-political dynamics of
connectivity and conflict in the science-policy interface. Any policy or strategy
developed to enhance bridging of the science-policy interface has to take into account
not only issues of science supply (e.g. quality and availability of science at relevant
scales) but also issues of policy-maker demand (e.g. trust and power). The analyses
undertaken in this thesis highlighted the need for future studies to also consider how
social capital can be developed and maintained to support a viable science-policy
interface.
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ACKNOWLEDGEMENTS
Many people and organisations have provided support and encouragement over the
period that this thesis has been in the making. My doctoral supervisor Matthew Tonts
provided an ideal balance of freedom to think and great support and guidance. To him
go my sincere thanks and appreciation, especially for imparting a positive attitude and
necessary focus.
Many individuals responded with generous contributions of information, insights and
time. I am particularly grateful to more than thirty scientists and policy-makers who
agreed to be interviewed in the course of the project. Without their cooperation this
thesis could not have been written and unfortunately for reasons of anonymity it is not
possible to thank them all by name. However the contributions of others can be
acknowledged and I would like to thank them for their assistance in a variety of ways:
David Bennett, David Bowran, Jim Dixon, David Hartley, John Holley, Bob Nulsen,
Dan Petty, Daryll Richardson, Geoffrey Syme and Heather Tomlinson.
A grant provided by the University of Western Australia to undertake the doctoral study
is gratefully acknowledged. The Cooperative Research Centre of Plant-based Solutions
to Dryland Salinity also assisted with a grant. The supportive work environment of the
Western Australian Department of Agriculture and Food enabled this opportunity to be
realised. Staff in Commonwealth, State and regional natural resource management
organisations provided much appreciated assistance and gainful interactions. My
colleagues in the School of Earth and Environment provided a source of inspiration and
learning, as well as the much needed light relief that comes with shared friendships and
experiences. To my family, thanks for the great support and making sure the question
‘Are we there yet?’ was never forgotten.
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TABLE OF CONTENTS
THESIS DECLARATION ......................................................................................................... ii ABSTRACT................................................................................................................................ iii ACKNOWLEDGEMENTS ....................................................................................................... v TABLE OF CONTENTS .......................................................................................................... vi LIST OF TABLES ..................................................................................................................... ix LIST OF FIGURES ................................................................................................................... xi LIST OF PLATES ................................................................................................................... xiv LIST OF TEXT BOXES .......................................................................................................... xv LIST OF ACRONYMS AND ABBREVIATIONS ............................................................... xvi CHAPTER 1: INTRODUCTION.........................................................................................- 1 -
1.1 The Environmental Imperative ................................................................................- 3 - 1.2 The Policy-making Dilemma.................................................................................- 11 - 1.3 The Research Problem and Aims ..........................................................................- 15 - 1.4 Organisation of Thesis...........................................................................................- 17 -
CHAPTER 2: THE SCIENCE-POLICY INTERFACE -EXPLORING CONCEPTUAL STRUCTURES......................................................................................................................- 20 -
2.1 Introduction ...........................................................................................................- 20 - 2.2 Role of Science for NRM Policy-making..............................................................- 20 - 2.3 Conceptual Structures for Science in Policy-making ............................................- 26 -
2.3.1 Meaning and measurement of ‘use’ ..................................................................- 28 - 2.3.2 A communication perspective...........................................................................- 32 - 2.3.3 An organisational interest perspective ..............................................................- 39 - 2.3.4 An interactive perspective.................................................................................- 41 -
2.4 An Analytical Framework .....................................................................................- 48 - 2.4.1 System perspective............................................................................................- 51 - 2.4.2 Organisational perspective ................................................................................- 56 - 2.4.3 Micro-organisational perspective ......................................................................- 64 - 2.4.4 Dimensions of the analytical framework - learnings.........................................- 68 -
2.5 Conclusion.............................................................................................................- 71 - CHAPTER 3: RESEARCH METHODS ......................................................................- 73 -
3.1 Introduction ...........................................................................................................- 73 - 3.2 Data Collection and Analysis ................................................................................- 77 -
3.2.1 Micro-ethnography/participant-observation study ............................................- 77 - 3.2.2 Contextual analysis ...........................................................................................- 79 - 3.2.3 Semi-structured interviews................................................................................- 80 - 3.2.4 Online questionnaire .........................................................................................- 83 -
3.3 Conclusion.............................................................................................................- 84 - CHAPTER 4: THE CONTEXT OF NRM POLICY-MAKING IN AUSTRALIA ……- 86 -
4.1 Introduction ...........................................................................................................- 86 - 4.2 The Australian Research Context ..........................................................................- 87 - 4.3 The Changing Context...........................................................................................- 94 -
4.3.1 The external policy-making context..................................................................- 94 - 4.3.2 The internal policy-making context ................................................................- 104 - 4.3.3 Contemporary NRM programs........................................................................- 125 -
4.4 Conclusion...........................................................................................................- 133 -
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CHAPTER 5: A SYSTEM PERSPECTIVE ....................................................................- 135 - 5.1 Introduction .........................................................................................................- 135 - 5.2 Characteristics of the Supply Chain ....................................................................- 136 -
5.2.1 Supply base management ................................................................................- 137 - 5.2.2 Quality management .......................................................................................- 148 - 5.2.3 Customer relations ..........................................................................................- 161 - 5.2.4 Performance ....................................................................................................- 166 - 5.2.5 Competitive environment................................................................................- 171 -
5.3 Discussion............................................................................................................- 186 - 5.4 Conclusion...........................................................................................................- 189 -
CHAPTER 6: A MICRO-ORGANISATIONAL PERSPECTIVE ON SCIENCE USE....................................................................................................................................……..- 190 -
6.1 Introduction .........................................................................................................- 190 - 6.11 General structure of NRM in the Commonwealth government ............................- 191 - 6.12 Overview of structuration theory ..........................................................................- 193 -
6.2 Signification ........................................................................................................- 196 - 6.2.1 Information control ..............................................................................................- 196 - 6.2.2 ‘Business’ modes of thought................................................................................- 197 - 6.2.3 Interaction practices .............................................................................................- 202 -
6.3 Power...................................................................................................................- 206 - 6.3.1 Tension of autonomy-dependency .......................................................................- 207 - 6.3.2 Resource asymmetry ............................................................................................- 208 -
6.4 Legitimation ........................................................................................................- 209 - 6.4.1 Managerialism norms...........................................................................................- 210 -
6.5 Conclusion...........................................................................................................- 213 - CHAPTER 7: AN ORGANISATIONAL PERSPECTIVE.............................................- 216 -
7.1 Introduction .........................................................................................................- 216 - 7.2 Explanation of Variables .....................................................................................- 217 - 7.3 Assessment of Measures......................................................................................- 218 -
7.3.1 Descriptive statistics of sample .......................................................................- 224 - 7.3.2 Frequency of use at different stages ................................................................- 225 -
7.4 Differences in Science Use Among Policy-makers.............................................- 228 - 7.4.1 Comparing federal, State and regional organisations......................................- 228 - 7.4.2 Comparing groups with different levels of social connectivity.......................- 229 - 7.4.3 Comparing male and female groups................................................................- 231 -
7.5 Empirical Relationships: Correlation Analyses...................................................- 235 - 7.6 Empirical Relationships: Regression Analyses ...................................................- 236 -
7.6.1 Dependent variable..........................................................................................- 237 - 7.6.2 Explanatory variables......................................................................................- 239 - 7.6.3 Regression analysis process ............................................................................- 241 - 7.6.4 Reduced regression model ..............................................................................- 242 -
7.7 Factor Analysis ....................................................................................................- 246 - 7.8 Discussion............................................................................................................- 253 - 7.9 Conclusion...........................................................................................................- 257 -
CHAPTER 8: CONCLUSION...........................................................................................- 259 -
8.1 Introduction .........................................................................................................- 259 - 8.2 Research Summary ..............................................................................................- 260 -
8.2.1 Science use and NRM policy-making.............................................................- 260 - 8.2.2 Science use and the Australian NRM policy context ......................................- 261 - 8.2.3 An analytical framework for science use ........................................................- 262 - 8.2.4 Socio-political dynamics of science use..........................................................- 265 -
8.3 Relevance and Conclusion...................................................................................- 267 - BIBLIOGRAPHY...............................................................................................................- 269 -
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APPENDIX A: KEY ATTRIBUTES OF INTERVIEWEES......................................- 292 - APPENDIX B: GUIDE QUESTIONS USED IN THE SEMI-STRUCTURED INTERVIEWS …………………………………………………………………………- 293 - APPENDIX C: ONLINE QUESTIONNAIRE .............................................................- 295 - APPENDIX D: INTERNAL RELIABILITY COEFFICIENTS (CRONBACH’S ALPHA) FOR VARIABLES INCLUDING MULTIPLE ITEM SCALES...................- 307 -
APPENDIX E:.....................................................................................................................- 308 - a) EXPLANATION OF INDEPENDENT VARIABLES................................................- 308 - b) DEFINITIONS OF INDICES .......................................................................................- 310 -
APPENDIX F: SELECTED INTERVIEW ABSTRACTS .........................................- 312 -
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LIST OF TABLES
Page
Table 2.1 Seven standards of utilisation (Knott and Wildavsky, 1980).
31
Table 2.2
The determinants of science use (adapted from Belkhodja et al., 2007)
35
Table 2.3 A classification of the analytical framework 51
Table 3.1 Research framework 76
Table 3.2
Number of interviewees, by affiliation, sector and state classification
82
Table 6.1 Key components and symbols of social structures (institution) or human action framed in the structuration analysis.
195
Table 7.1 Explanatory variables and models of use 218
Table 7.2 Summary of responses to questions in the Semi-structured Interviews.
220
Table 7.3: Alignment of themes with perceived social reality (percentages)
223
Table 7.4: Descriptive statistics for policy-maker population (per cent)
224
Table 7.5: Frequency distribution by stages of science use
227
Table 7.6: Use of science by organisation type
229
Table 7.7: Use of science by relationship strength
230
Table 7.8: Use of science by gender
232
Table 7.9: Mean values of science use and dimensions of use by gender
233
Table 7.10: Relationships among variables
237
Table 7.11: Correlation coefficients for continuous independent variables
240
Table 7.12 Descriptive statistics for variables used in regression analyses
240
Table 7.13 Predictions regarding the impact of the independent variables on science use.
240
x
Table 7.14 Contributions of each independent variable (Standardised coefficients - Beta values)
241
Table 7.15 Contributions of each independent variable (Standardised coefficients - Beta values) by organisation type
243
Table 7.16 Correlation coefficients for dimensions of the Intensity of Use Index
243
Table 7.17 Organisational and information dimensions of science use: Rotated Factor Loadings
248
Table 7.18 Use of science in policy model
252
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LIST OF FIGURES
Page Figure 1.1: Some types and causes of land degradation (Source:
http://anra.gov.au)
8
Figure 2.1: A conceptual model of research utilisation - based on the ‘interaction’ model (e.g. Landry et al., 2003) and the Knott and Wildavsky (1980) scale of knowledge utilisation.
45
Figure 4.1 Land use in Australia (NLWRA, 2002).
88
Figure 4.2 Distribution of land degradation factors (i.e. soil acidity and salinity) induced by agricultural practices in Australia.
90
Figure 4.3 Australia’s 57 NRM regions
93
Figure 5.1 Perceived value of scientific information in daily policy-making activities (Online Questionnaire 2007, Appendix C)
138
Figure 5.2 Frequency of using social science information sources for daily policy-making activities in the past year (Online Questionnaire 2007, Appendix C).
142
Figure 5.3 Perceived intra-organisational proximity of researchers and policy-makers (Online Questionnaire 2007, Appendix C)
143
Figure 5.4 Perceived importance of membership on scientific panels and committees (Online Questionnaire 2007, Appendix C)
146
Figure 5.5 Agreement with the statement ‘In general NRM scientific reports and presentations tend to be so complex or technical that they are hardly worth paying attention to’ (Online Questionnaire 2007, Appendix C).
150
Figure 5.6 Frequency of use of the Australian Agricultural and Natural Resources Online (AANRO) database (Online Questionnaire 2007, Appendix C).
151
Figure 5.7 Perceived importance of integration of scientific findings when deciding to use scientific reports (Online Questionnaire 2007, Appendix C).
152
Figure 5.8 Frequency of use of GIS or models for daily policy-making activities (Online Questionnaire 2007, Appendix C).
153
Figure 5.9 Perceived importance of product quality attributes when deciding to use scientific reports (Online Questionnaire 2007, Appendix C).
156
xii
Figure 5.10 Frequency of use of informal networks for daily policy-
making activities (Online Questionnaire 2007, Appendix C)
158
Figure 5.11 Perceived importance of science available on the World Wide Web via the internet (Online Questionnaire 2007, Appendix C).
160
Figure 5.12 Frequency of use of the World Wide Web for daily policy-making activities (Online Questionnaire 2007, Appendix C).
161
Figure 5.13 Perceived strength of relationship with scientists by policy-makers based in regional, State and federal organisations (Online Questionnaire 2007, Appendix C).
162
Figure 5.14 Perception that commissioned scientific work is more useful than academic research in policy-making activities (Online Questionnaire 2007, Appendix C).
163
Figure 5.15 Perception that research by NRM scientists is too heavily influenced by concern with academic prestige than with the relevance of the research outcome (Online Questionnaire 2007, Appendix C).
166
Figure 5.16 Perception that sufficient resources are reserved by their organisation to obtain research information for policy-making (Online Questionnaire 2007, Appendix C).
170
Figure 5.17 Frequency of person-to-person contact between policy-makers and different research organisations (Online Questionnaire 2007, Appendix C).
174
Figure 5.18 Policy-makers perception of the importance of formalised interactions with researchers in terms of professional satisfaction (Online Questionnaire 2007, Appendix C).
175
Figure 5.19 Perceived outcome of clash between minister and organisation (n = 70, Online Questionnaire 2007, Appendix C).
180
Figure 5.20 Perceived outcome of clash between scientific and social considerations (Online Questionnaire 2007, Appendix C).
181
Figure 5.21 Perceived importance of support for organisational policy when deciding to use scientific reports (Online Questionnaire 2007, Appendix C).
184
Figure 7.1 Scatter plot of the two principal components showing their influence on use of science by policy-makers (High, Low).
249
Figure 7.2: Distribution of the research experience of policy-makers (High, Moderate, Low) in regard to the two principal
251
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components. Not all the labels for the data are loaded as some data elements overlap.
xiv
LIST OF PLATES
Page
Plate 1.1 Salinity affected land in the Great Southern Region, Western Australia (Commonwealth of Australia, 2006b)
6
xv
LIST OF TEXT BOXES
Page
Text Box 3.1 Examples of analytical categories 82
xvi
LIST OF ACRONYMS AND ABBREVIATIONS
ABARE Australian Bureau of Agricultural and Resource Economics
ANZLIC Australia New Zealand Land Information Council
ABARE Australian Bureau of Agricultural and Resource Economics
ABS Australian Bureau of Statistics
ANAO Australian National Audit Office
BOM Bureau of Meteorology
BSE Bovine Spongiform Encephalopathy
CRC Cooperative Research Centre
CSIRO Commonwealth Scientific and Industrial Research Organisation
DAFF Department of Agriculture Fisheries and Forestry
EPA Environmental Protection Authority
IAASTD International Assessment of Agricultural Knowledge, Science and
Technology for Development
IS Information system
LLWRDC Land and Water Resources Research and Development Corporation
LWA Land and Water Australia
NAPSWQ National Action Plan for Salinity and Water Quality
NHT Natural Heritage Trust
NRM Natural resource management
R&D Research and development
RDC Research and Development Corporation
US United States of America
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CHAPTER 1: INTRODUCTION
Widespread environmental degradation, increasing pollution, over-exploitation of
resources and resource scarcity are symptomatic of worldwide shortcomings in natural
resource management1 (NRM) policy-making and planning (e.g. Ascher, 1999; Mercer
et al., 2007; Hamblin, 2009). One of the most significant contributors to policy
limitations is the difficulty in translating research findings into improved decisions and
concrete policy actions – the difficulty of bridging the science-policy interface (e.g.
Committee for the National Institute for the Environment, 1994; Kendy, 2004; Porder,
2004; Sallenave and Cowley, 2006; van den Hove, 2007).
There are many reasons for this shortfall but a significant issue facing scholars and
practitioners is how to conceptualise the interface and its defining elements (e.g. Landry
et al., 2003; Cummings et al., 2007; Belkhodja et al., 2007; Sarewitz and Pielke, 2007).
To sharpen the conceptual basis of the science-policy interface this thesis aims to
elaborate an analytical framework to reflect on the social dimensions of science use,
particularly in relation to NRM policy-making in Australia.
A traditional view is that science is objective and value-free, providing opportunities to
reduce uncertainty in decision-making. But science can add to policy uncertainty,
scientific conflict can complicate decision-making, and science driven by political
interests can confuse policy development (Pielke, 2007). Many scholars agree that,
ultimately, the decisions made depend more on the collective values of the policy-
makers than any scientific or technical verification (e.g. Allen and Gould, 1986;
Sarewitz, 2004; Pielke, 2007). In line with this view, Dovers (1995: 143) asserts that
policy-making is primarily a political process:
the case made here for more research, data-gathering and especially monitoring should not be taken as implying a possible future where
1 The term natural resource management (NRM) concerns activities that use, develop and/or conserve natural resources, and the systems they form (e.g. forestry, agriculture, water allocation and tourism). NRM includes catchment and landscape-scale management strategies, and engages with biodiversity conservation, control of pest plants and animals, and maintenance of soil and water quality (Conacher and Conacher, 2000; Lockwood et al., 2009). NRM in Australia often means managing environment issues associated with agriculture with little attention to other industries such as mining. Across the OECD the term ‘agri-environment’ programs or policies is more common (Hajkowicz, 2009).
- 2 -
science can adequately inform all decisions. It never will: decisions will always in the final analysis be political....
The socio-political dynamics of the policy-making environment is, thus, pivotal to
understanding the difficulty of translating research findings into effective policy.
Inherent in this complexity is the recognition that a traditional view of policy-making as
a rational process is perhaps too simplistic. Thus, researchers are challenged to fully
understand the science-policy interface in a way that is sensitive to issues of social and
political context. A growing body of literature not only tackles this issue from an
interaction perspective, emphasising the importance of social processes, but also
attempts to understand the broader social, organisational and political context within
which science transfer and use in policy-making occurs (see Section 2.3).
This thesis contemplates linking aspects of socio-political context with science use in
one of Australia’s most important policy areas: natural resource and environmental
policy. It begins by outlining the profound stresses on the Australian landscape and the
difficulty in translating research findings into improved policy to counter this. It then
traces a likely evolution of conceptual structures for the ‘science-policy interface’, and
elaborates an analytical framework to reflect on the socio-political dynamics of the
policy-making process that may shape science use. The use of science in the Australian
NRM policy-making system illustrates specific attributes and examples relating to the
application of this framework.
For the purpose of this thesis, science means any natural science, social science and
economic research that are formally expected to feed into Australian environmental
strategies, policies and legislation (Commonwealth of Australia, 1992). Policy means
the outcome of a series of decisions involving multiple interactions among many players
and can involve inaction (Hogwood and Gunn, 1984); and science-policy interfaces are
defined as social processes that include relations between scientists and other actors in
the policy process, and that allow for exchanges, co-evolution, and joint construction of
knowledge with the aim of enriching decision-making (van den Hove, 2007).
In this thesis the terms information and knowledge are used interchangeably to reflect
common usage, but it is important to recognise that their meanings are different.
Information is processed data (discrete facts) or data endowed with relevance and
- 3 -
purpose. Knowledge is a combination of information together with experiences, values,
context and insights (Fitzroy and Hulbert, 2005). The term information is broadly
interpreted, encompassing monitoring and research, data-bases and information
systems, communication, dissemination and ownership of information describing
natural systems and human interactions with them (Dovers, 1995). Furthermore, NRM
knowledge has been categorised into: formal, expert, scientific knowledge; experiential
or tacit knowledge; local knowledge; and indigenous knowledge (Campbell, 2006). The
focus here is on scientific knowledge.
1.1 The Environmental Imperative
Land, water, air, minerals, plants and animals are natural resources2 vital to our survival,
providing humanity with food, shelter, fibre, goods and services. The management of
natural resources is one of the most difficult problems facing humanity because of the
combination of many decision attributes, such as complexity, uncertain and conflicting
values, incomplete and uncertain knowledge, long time horizons, multiple goals, multi-
scale management, and time pressures. The problem dates back to ancient civilizations –
the decline of ancient Mesapotamia and Greece have been attributed to salinity and soil
degradation (Doyle and Kellow, 1995). Today, almost two billion hectares and three
billion people are affected by significant levels of land degradation (IAASTD, 2008).
Policy shortcomings have led to degradation of the resource base in both developed and
developing countries: soil erosion in North America and Australia; soil acidification in
Europe; deforestation and desertification in Asia, Africa, and Latin America; and waste
and pollution of water almost everywhere (e.g. World Commission on Environment and
Development, 1987; Samson and Knopf, 1994; Ascher, 1999; Brouwer and Lowe,
2000; Hamblin, 2009). Natural resources are often exploited for immediate gains,
without offsetting long-term benefits for society (Ascher, 1999; Howes, 2005).
According to Ascher (1999: 3):
Despite all the predictions that the importance of natural resources would recede rapidly in our increasingly technological world, our
2 Some resources can be renewable if not overused (e.g. water, timber and food; fish and soil are considered conditionally renewable (i.e. close to being not renewable)). Resources such as land are fixed in quantity and once used for one purpose often cannot be used for another. Resources such as fossil fuels and minerals are non-renewable (Beder, 2006).
- 4 -
industrial and agricultural bases are still strongly rooted in raw materials.
Growing population, economic growth and rising incomes have increased the demand
for manufactured goods, food, water, housing and energy (Ascher, 1999; Meinzen-Dick
and Ringler, 2006). These all require greater quantities of natural resource materials,
further increasing pressure on ecosystems.
Based on systems theory, the resource system can be considered a discrete element in
the ecosystem (Conacher and Conacher, 2002). In management practice, it is becoming
more difficult to separate the concept of the resource system from that of the wider
environment. Over the past two decades the problem frame of managing natural
resources has dramatically shifted in Australia, straying into the more holistic realm of
environmental management. There has been a transition from an early focus on the
engineering, technical and economic aspects of resource management, to an emphasis
on the human dimensions of management.
The change seems to be linked not only to the rise of the sustainability paradigm and the
analytical notion of the ‘triple bottom line’ (i.e. economic, social and ecological
sustainability), but also to the decentralisation of environmental governance and a
recognition of the need for institutional change (Bellamy and Johnson, 2000;
Brunckhorst, 2002; Lane et al., 2004). It is becoming easier to imagine a management
environment where the dimensions of resource and environmental management are
linked, especially since resource use appears to easily morph into an environmental
threat, as exemplified by the environmental degradation (e.g. soil erosion, salinity – see
Plate 1.1) caused by the intensification of agriculture in Australia (Commonwealth of
Australia, 1991; Beresford et al., 2001; Lyle and Ostendorf, 2005).
Before Europeans arrived in Australia there was no agricultural practice of cultivating
the land or raising livestock. The Aboriginal people were tribal nomads who obtained
their food from the native flora and fauna (Burvill, 1979). Campbell (1994: 11)
emphasised how ‘the Aboriginal inhabitants at the time of European settlement in
Australia saw themselves as part of the land, and their culture and systems of land
management evolved with the land over tens of thousands of years’. In contrast, Tonts
(2002) points to how European farming systems (sheep, cattle and wheat) were imposed
- 5 -
on the Australian landscape in pursuit of the ‘yeoman ideal’, despite evidence of
environmental limitations (e.g. low rainfall, noxious weeds and poor soils). Arguably,
the early development of land use policy in Australia has been informed by a societal
ideal envisaged by policy-makers, rather than by ecological constraints imposed by the
landscape. With the benefit of science and technology, the early production failures
were overcome by the introduction of fertilisers and changes in farming methods. But
there is no such panacea for the current magnitude of environmental degradation caused
by agricultural practices in Australia.
Rapid growth in land and water use for agriculture in Australia3 since the 1950s has
greatly increased pressure on the environment (e.g. induced land degradation, reduced
biodiversity), depleting the resource legacy for future generations and increasing risks
of environmental damage and loss in farm production (NLWRA, 2001a; NLWRA,
2001b;Curtis and Lockwood, 2000; Morton et al., 2002; Nelson et al., 2004). The scope
of the problem is immense. For example, 5.7 million ha of agricultural land are
estimated at risk or already degraded by salinisation. This could spread to 17 million ha
by 2050, causing the loss of some 18,000 species of plants and animals (NLWRA,
2001a; Morton et al., 2002). As the repair bill for land and water degradation mounts
up, estimated at between A$ 2 to A$ 6.5 billion annually, it has become increasingly
apparent that the environment has been poorly valued in economic terms, and that
natural resources are not limitless (Morton et al., 2002). There is a growing recognition
by Australian governments that wiser management of natural resources has become
imperative.
3 Today agriculture covers around 60% of Australia’s land area (ABS, 2007) and the agricultural sector accounts for about 65% of water consumption (ABS, 2006).
- 6 -
Plate 1.1: Salinity affected land in the Great Southern
Region, Western Australia (Commonwealth of
Australia, 2006b)
Within studies of resource management, there is a growing realisation that wise
management is not only about managing natural resources per se, but as much about
managing human actions. Allen and Gould (1986: 23) suggested that increased efforts
to focus on social dimensions is the key to successful NRM, ‘People are what make
problems ‘wicked’4. Emphasis on people within the organisation and on external
customers is the central element when ‘wicked’ problems are successfully handled’. On
this issue Fiske (1990: 16) observed that:
Increasingly, biologists, ecologists and resource managers are coming to agree that natural resource management is really ‘human management’, that we are not just managing resources, we are managing people.
The idea that management of natural resources is actually management of human
behaviour is also shared with Freudenburg and Gramling (2002: 122): 4 Mason and Mitroff, (1981) identified wicked problems as being typified by complexity, uncertainty, ambiguity, conflict, and societal constraints.
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Most of the people we call ‘natural resource managers’ actually do very little managing of natural resources. Instead, what many of them actually try to manage most of the time are the behaviours of the one species that few resource managers have ever studied, namely, Homo sapiens.
In line with this view, the causes of land degradation identified by Reeves et al., (1998)
relate to issues of human behaviour and actions, in both the environmental (e.g.
vegetation removal and over-cropping) and the socio-political (e.g. institutional and
political issues) contexts (Figure 1.1).
The role of governments in managing the relationship between resource use behaviour
and the environment has received increasing attention in recent years (Dovers, 1995;
Ascher, 1999; Moore and Rockloff, 2006). This includes a number of approaches, such
as studies of how governance arrangements have caused, or helped deal with, NRM
problems and analyses of how poor NRM practices can be prevented or controlled by
use of policy instruments (e.g. laws, procedures, regulations, market mechanisms and
institutional change). It is generally assumed that government will have the capacity and
legal standing to prevent poor resource practice behaviour, and increase the adoption of
practices that are socially desirable.
To this end, Australian governments have shifted from an early focus on regulatory
mechanisms to more inclusive, regionally based NRM. Investment by successive
Australian Commonwealth governments in environmental and resource management
over the past thirty years is significant (e.g. A$ 6.51 billion spent since 1990), and
increasing (e.g. A$ 1.7 billion in 2001/02 and A$ 4 billion in 2006/07). This is
paralleled by investments at State and local government levels (e.g., local governments
spent A$ 268 million on NRM in 1998/99 and A$ 422 million 2002/03)
(Commonwealth of Australia, 2008b; Hajkowicz, 2009). The fundamental challenge of
these policies and programs is to repair past mistakes, and improve the way natural
resources are used and managed in the long-term.
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Figure 1.1: Some types and causes of land degradation
(Source: http://anra.gov.au)
Over the past twenty years, Australian governments have invested mainly in voluntary
approaches, based on education and grants schemes, to stimulate and support the
adoption of more sustainable land-use practices. These have been described under the
umbrella of Landcare (Moore and Rockloff, 2006). Landcare has been successful in
raising levels of landholder awareness and changing farmer attitudes towards
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conservation farming practices, but less successful in achieving measurable change on
the ground in natural resource condition (Curtis and De Lacy, 1996; Lockie and
Higgins, 2007; Wallington and Lawrence, 2008; Hajkowicz, 2009); land degradation
continued to increase throughout agricultural Australia during the ‘decade of Landcare’
in the 1990s (Wallace, 2003). More recently, the Australian government’s focus has
shifted to modifying the governance arrangements associated with land-use, as a means
of encouraging more rapid movement towards sustainable practices. Investment in a
decentralised, regional delivery model is central to these changes (Moore and Rockloff,
2006). Impacts of these changes on the condition of natural resources, or trends of land
degradation at the landscape scale are not yet clear (Commonwealth of Australia,
2008a).
The use and management of resources has come under greater scrutiny by society, as
awareness grows of environmental issues, and higher standards of environmental
management are demanded (Curran, 2000). Fear of irreversible environmental
degradation is a significant concern of many governments. Natural resource use has
produced many benefits but at a cost, particularly in terms of environmental damage
(Yencken and Wilkinson, 2000). Yet, some scholars are skeptical of the environmental
risks involved and have suggested that the longer term trends suggest that the state of
the world’s environment is improving (e.g. Lomborg, 2001). Others are concerned
about the risk of losing the resilience of our ecological systems on which our economies
depend (Arrow et al., 1995).
Debate is ongoing as to whether a legacy of ecosystem disturbance will be offset by
continuing material improvements in quality of life (Schneider, 1997b). The political
system is charged with the difficult task of finding the balance of values across this
cultural dichotomy, and in the past forty years has turned to the concept of sustainability
for help. The term ‘sustainable development’ is defined by the World Commission on
Environment and Development, 1987, Chapter 2: 1) as:
Development that meets the needs of the present without compromising the ability of future generations to meet their own needs.
This definition does not go so far as to suggest the current generation should limit the
rates and methods of resource use (Bennett, 2001). This relegates ‘sustainable
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development’ to a role that describes the trade-offs between economic growth and the
environment (Dovers, 1995), and aligns it with prevailing economic and social agendas
(Robertson, 1993).
However, the evidence is difficult to refute that rapid industrial growth and increases in
population densities in the past century have accelerated the speed of resource use and
increased the scale of human impact on the environment (United Nations, 2001).
Ehrlich and Holdren (1971) noted that the impact of humans on the environment is a
combination of population, resource use per person (affluence) and environmental
damage per unit of resource used (technology). Accordingly, a report by the United
Nations (2001: 1) commented that:
The twentieth century has been a century of unprecedented population growth, economic development and environmental change….Moreover population growth and economic development occurred simultaneously with increasingly unsustainable utilisation of the earth’s physical environment.
Sterner (2003: 1) described how fast global population is growing, ‘almost 80 million
people per year – and has doubled since 1960… This population growth poses
considerable challenges for resource and environmental management.’ Notably, besides
population, levels of consumption and choice of technology are other important
determinants of human impacts on the environment (Sterner, 2003). Rather than being
discussed in terms of limits to economic and population growth, ecological
sustainability is often described in terms of carrying capacity (i.e. choosing a limiting
resource and estimating how many people it will support) or ecological footprints (i.e.
amount of nature needed to support a human population) (Beder, 2006).
Beder (2006) pointed to analyses that showed humanity’s ecological footprint had
exceeded Earth’s ecological limits by the 1980s and continues to rise, as evidence of
major environmental degradation throughout the world. She emphasied that although
many economists still argue that technological change and international trade will
ensure that there are always enough resources to meet carrying capacity, the
technologies that extend carrying capacity often come at a price (e.g. agri-chemicals to
increase crop yields have significant environmental impacts), and when technology
makes use more efficient, it may encourage greater use rather than less use (e.g. modern
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fishing technologies enable catches to be increased and depletion of stocks to be
accelerated).
Public concern about the environment rose in the late 1980s, fuelled by widening
debates about scientific discoveries such as ozone depletion, chemical contaminants and
genetically modified organisms and heightened public awareness of environmental,
food and health risks (e.g. salinity, pollution, Bovine Spongiform Encephalopathy
(BSE)). In response there was a global surge of environmental policy and legislative
innovation (Beder, 2006). In Australia this was associated with a concomitant need for
accountability in administrative decision-making, increased media attention on
environmental issues, and the emergent role of scientists as sources of authoritative
information. Policy questions were posed that needed scientific advice, and there was an
imperative that policies were not perceived as politically self-serving and
unsubstantiated (Christoff, 1994). The demand for science was endorsed in public
policy documents (e.g. agreements between governments) and made explicit by
government enquiries into the use of science to inform policy (see Section 1.3). But
there are barriers to the effectiveness of science use in environmental policy-making
that concern both scientists and policy-makers.
1.2 The Policy-making Dilemma
A traditional view of natural resource planning has been based on the rational-
comprehensive model. This approach, according to Lachapelle et al., 2003), is a
scientifically based, expert-driven approach to management and allocation decisions
that implicitly assumes consensus on a singular objective, availability of all data needed
to support a decision, and seemingly unlimited financial resources and time. The poor
performance of this model has been attributed to the increasingly complex world in
which NRM policy-makers function; a world characterised by multiple objectives,
limited finances, compressed timelines, frequent conflicts over goals, increased
temporal and spatial scales of analysis and significant uncertainty about the effects of
management actions (Lachapelle et al., 2003).
Under the rational-comprehensive model science has long been viewed as objective and
value-free, thereby providing opportunities to reduce uncertainty in decision-making.
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Some scholars point to the benefits of using scientific knowledge in environmental
policy-making, such as its contribution to problem identification, the development and
evaluation of appropriate strategies to deal with the problem, the modeling of likely
futures with and without intervention, and the monitoring and reporting on
environmental impacts (e.g. Canter, 1996; Cullen, 2006). Science has become a widely
recognised information source for sound policy about natural systems (Sarewitz et al.,
2000). At an international level it is considered vital to policy-making for sustainable
development:
Achieving development and sustainability goals calls for national and international regulations to address the multiple economic, environmental and social dimensions of these transboundary issues. These policies need to be informed by broad-based evidence from natural and social sciences with multistakeholder participation. (IAASTD, 2008: 12)
Pielke (2004) argued that science may defuse political debate (and gridlock) by
revealing new choices and directions, rather than just fuelling opposing sides of a
political conflict. Science may also draw attention to resource issues to get them on the
policy and political agenda. For example, the experience of the Wentworth Group of
Concerned Scientists in Australia5 demonstrated how science can move resource
management issues like water reform higher on the political agenda (Cullen, 2006). The
call for evidence-based, resource and environmental policies (i.e. policy based on some
form of technical evidence) continues by some policy-makers, interest groups, citizens,
and scientists6. Briggs (2006) suggests that knowledge to help guide the integration of
science and policy is in demand by NRM organisations in Australia.
But when values and interests are conflicting, scientific ‘facts’ can be deconstructed into
socially constrained interpretations (Jasanoff, 1990). Hence, scientific uncertainty about
consequences may add to policy indecision, scientific conflict over ‘facts’ may
5 The Wentworth Group describes itself as a ‘group of Australia’s leading environmental scientists’ who ‘are advocating ‘radical and fundamental reform to halt further degradation of Australia’s landscapes’ (The Wentworth Group, 2002: 2). The Group is convened by World Wildlife Australia, a branch of an international, non-government conservation organisation. 6 Some of the organisations making such calls include the United States (U.S.) Federal government, U.S. National Oceanic and Atmospheric Administration, U.S. Global Change Research Program, The World Bank and the U.S. National Research Council (McNie, 2007), the Australian Federal Government (Commonwealth of Australia, 2004a; ANAO, 2004), the Australian Commonwealth Scientific and Industrial Research Organisation and Bureau of Meteorology (CSIRO and BOM, 2004).
- 13 -
complicate decision-making and scientific knowledge constrained by political interests
may confuse policy development (Pielke, 2007). Moreover, as policy-making is a
political process, ultimately the decisions made depend more on the collective values of
the policy-makers than on scientific evidence (Allen and Gould, 1986; Mercer, 2000;
Sarewitz, 2004; Pielke, 2007). Accordingly, Walker et al. (1999b: 61) argued that
effective NRM may rely more on an effective political process than on science:
Research addressing natural resource management issues is based on an unwarranted confidence that it will lead to ‘objective and rational management of natural resources’. This implies that there are demonstrably correct solutions to natural resource management problems; that is that we can achieve decisions based on unanswerable and irrefutable logic. This is naive, in that it does not recognise that natural resource management is inherently a political process in which different needs and interests are balanced… Effective natural resource management can occur without formal research but not without an effective political process.
Thus, a foundation for NRM policy choices might be considered deficient if based
solely on scientific and technical knowledge. In line with this view, Sexton et al. (1999)
and Allison and Hobbs (2004) suggest that viewing NRM problems only from a
scientific perspective may increase the risk of policy failure. There are questions raised
by some scholars, such as Harding (2002) and Marsden and Dovers (2002), about
whether scientific information can provide the whole picture. This included a normative
view that a wide range of alternative perspectives and considerations – non-technical,
local, ethical, social, political – should also be included in the decision-making process
to ensure a more informed decision that can attract widespread support. This view is
also shared by Herrick and Jamieson (1995: 105) who argued that ‘it is a mistake to
suppose that ‘good science’ can always provide a ‘right answer’ for science-based
policy disputes.’ As Holling et al. (1998: 352) points out, current science is neither able
to ‘prescribe sustainable outcomes’ nor ‘explain resource collapses’, because there are
no quick and simple solutions or explanations for problems that behave in complex and
unpredictable ways.
Regardless of the various criticisms leveled against using science as the foundation of
policy decisions, science is still seen by some as a vital ingredient for improved policy
outcomes, as Cullen (2006: 1) states:
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So many of the big issues facing society are ‘science-intensive’, and beneficial outcomes are unlikely unless science can be actively engaged in the development and assessment of appropriate policies.
In a similar vein, Lubchenco (1998: 495) commented, ‘science alone does not hold the
power to achieve the goal of greater sustainability, but scientific knowledge and wisdom
are needed to help inform decisions that will enable society to move toward that end.’
Herrick and Jamieson (1995: 111) further expands on this notion by stating that ‘science
plays a crucial role in setting agendas, framing problems, and supplying concepts and
vocabularies.’ But they also emphasised that the role of science in policy-making is
essentially limited to these activities. Herrick and Jamieson (1995: 111) concluded from
a study of involving science in the acid rain policy process that:
science cannot ‘answer’ policy questions. It can provoke, structure, and inform the debate, but it cannot bring it to closure. Science should be viewed more as an enabling device, helping to define problems, and less as a closure device, helping to establish and evaluate conclusions.
It is in this light that some may caution that as a source of knowledge to inform policy
debate, the role of science is limited in complex, socio-political contexts. As pointed out
by Healy and Ascher (1995), in a complex and political, policy-making environment the
use of science is not certain. Apart from external influences, there are also internal
attributes of credibility and uncertainty that may also diminish the role science can play
in policy. For example, a report commissioned by the Australian government noted
concerns by regional organisations about the flood of, sometimes conflicting, scientific
information (CSIRO and BOM, 2004: 37):
Catchment Management Authorities (CMAs) and researchers alike highlighted the vast array of scientific literature and information available, some of it conflicting...CMAs stated that there was uncertainty over what science could deliver.
These concerns imply that rather than increase confidence and reduce debate in policy-
making, science may add to conflict and uncertainty, thereby negating the view by some
- 15 -
that it is axiomatic that science is free from ambiguity. Scientists also share these
concerns, as an Australian scientist explained (Interviewee No 13, 2007)7:
I think there’s a real disconnect and that’s always been that the scientific process is an ugly process because it relies on people disagreeing with each other, that’s how it works. Policy doesn’t accommodate that very well. They want to hear what the answer is, they don’t want to hear people disagreeing and saying it’s wrong.
Overlooking concerns that scientific certainties are fragile, several reports
commissioned by the Australian government have recommended immediate
improvements to linkages between salinity science and policy at regional and national
levels (e.g. CSIRO and BOM, 2004; Commonwealth of Australia, 2004a; RM
Consulting Group, 2006). Not surprisingly, attempts to improve linkages between NRM
science and policy have generally not been successful to date (e.g. McAlpine et al.,
2007). There may be many reasons for this shortfall but exactly what these are in the
Australian context is unclear as, to date, little or no research of the socio-political
processes of science use in Australian federal, State and regional NRM policy-making
organisations has been conducted. The existing reports and commentary, written largely
by natural scientists (e.g. Walker et al., 2006; Briggs, 2006) tends to treat science use in
NRM policy from a communication perspective without reference to socio-political
processes, and with little regard to the broader social, institutional and political context
within which both policy-making and institutional change occurs.
1.3 The Research Problem and Aims
Considerable bodies of scientific knowledge in areas as diverse as climate change, air
and water pollution and forest management, have emerged with the explicit aim of
enabling effective decision making and resolving political disputes (Sarewitz, 2004).
Thus, policy tends to turn to science for some of the answers, yet both scientists and
policy-makers have expressed concern about the often ineffective coupling of science
and policy; simply linking a scientist to decision-making processes is not generally
considered a satisfactory option by organisations. Why are policy-makers not giving
7 The system for coding interviewees in this project is outlined in section 3.3.2 and attributes of the interviewees are presented in Appendix A.
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science the attention it deserves? What influences the transfer and use of science?
Finding a conceptualisation of the system that can be shared by those who have to deal
with it, which includes researchers and policy-making organisations, will be an
important focus to build collective understanding. As Dunn, 1980) points out, to
improve the practical consequences of the use of knowledge it is important to examine
and build on the theory and research of knowledge use.
Since the 1980s, many studies have attempted to develop theoretical models to explain
research use by policy-makers (Huberman, 1987; Lester, 1993; Oh and Rich, 1996;
Landry et al., 2001b, 2003; Belkhodja et al., 2007). These draw on information and
organisational factors to explain use, but no general theoretical model has emerged
(Landry et al, 2003; Belkhodja et al., 2007). Nor has any model managed to capture the
complexity and dynamics of knowledge use, giving attention to the product of
interactions between the organisational context and individuals (Nonaka and Toyama,
2003). New analytical perspectives may shed greater light on our understanding of use.
Weiss (1979: 438) points out how a single perspective of knowledge use carries the risk
that the other factors and processes will be obscured or overlooked:
As with the blind man of the parable, each analysis of the relationship between social research and social policy may reach conclusions different from the others because each has groped a different piece of a large, wrinkled elephant of a problem.
Researchers are still learning how to build a coherent, conceptual model of science use
in policy. In practice this knowledge gap can make it difficult to establish an effective
and acceptable path for science to inform NRM policy-making. Thus, to sharpen the
conceptual basis of the science-policy interface this thesis will elaborate an analytical
framework to reflect on the socio-political dynamics of science use, particularly in
relation to NRM policy-making in Australia. As some researchers concerned about the
use of ecological indicators assert, paying attention to the policy-making context is
important:
It is our view that, in order to understand development and use of indicators, it is crucial to have insight in the specific characteristics and dynamics of the management and policy processes in which they are embedded (Turnhout et al., 2007:216).
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Within this broad umbrella of conceptual development, the thesis takes into
consideration the socio-political dynamics of knowledge use. In recent years, attention
has turned to the social dynamics of policy-making processes in organisations and how
this shapes research use (Cummings et al., 2007; Belkhodja et al., 2007; Sarewitz and
Pielke, 2007; Turnhout et al., 2007).
Given this, and the need for more detailed research on the use of science in NRM
policy-making, the following specific aims were established for this thesis:
1) Examine the science-policy interface and the underpinning conceptual
perspectives on this subject;
2) Explore the context of resource and environmental policy-making as played
out in Australia, to review the wider changes in the policy arena and the way
these can affect science use;
3) Elaborate an analytical framework to explore and reflect on the socio-
political dynamics of science use;
4) Examine the influence of a range of factors on science use by NRM policy-
makers.
1.4 Organisation of Thesis
The thesis consists of eight chapters that cover three thematic areas:
i. The research challenge, research context and methods, including the rationale
for the conceptual and analytical frameworks that were applied;
ii. The analysis of the data;
iii. The major findings, discussion and overall conclusion.
Chapter One introduced the notion of an environmental imperative as governments
grapple with problems of environmental degradation associated with NRM policy
failures and the difficulty in bridging the science-policy interface to help counter this;
particularly in an increasingly complex world.
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The concept of knowledge use is discussed in Chapter Two. The evolution of
conceptual structures of science use and the emergence of an interactive perspective,
emphasising the links between producers and users is outlined. Associated with a
deepening appreciation of the role of context (e.g. political, social factors), Chapter Two
also introduces an analytical framework used in later chapters to reflect on the socio-
political dynamics of use across micro-organisational, organisational and system
perspectives.
Chapter Three outlines the multi-strategy approach utilised in the conduct of the
research, drawing on an information system (IS) research framework for bridging scales
and levels in policy context (i.e. micro-organisational to system level). It presents the
research design and outlines the diversity of methods and techniques used in the
collection of the data as well as the methodology adopted in analysing the data.
The role of the Australian NRM policy-making context (e.g. institutions, policy-
processes) in shaping the interactions between science and NRM policy-making is
discussed in Chapter Four. A broad overview of recent changes in the external and
internal context of the Australian NRM policy-making environment is also provided in
this chapter. This chapter offers an Australian context within which to view and
interpret the local data collected.
With a system perspective, Chapter Five applies a ‘supply chain’ framework to track the
network of activities, processes and links targeted at the generation and use of science in
policy-making. The ‘supply chain’ concept invites reflection on science use with respect
to a broader socio-political context of science production and transfer.
Focusing on an micro-organisational perspective, Chapter Six draws on structuration
theory to reflect on the relationship between social structures and individuals in a
Commonwealth government NRM agency with regard to research use. The chapter
discusses how structuration theory can offer new insights about information use by
organisations for policy-making, particularly by exposing aspects of power and trust.
In Chapter Seven attention is turned to examining the influence of a range of factors,
including informational, organisational and social, on science use by NRM policy-
makers in Australia. Measures of research use using quantitative data collected by an
- 19 -
Online Questionnaire were analysed with a range of explanatory variables drawn from
the literature. The chapter presents a multidimensional framework of social dynamics
and political power, suggested by the empirical evidence, for understanding the use of
science by policy-makers.
Chapter Eight is the integration of the research findings, aimed at providing insights
towards the building of science-policy interfaces that more effectively meet emerging
knowledge and policy initiatives. This final chapter will synthesise the information and
analyses presented in the previous chapters and present the general conclusions of the
research.
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CHAPTER 2: THE SCIENCE-POLICY INTERFACE -EXPLORING CONCEPTUAL STRUCTURES
2.1 Introduction
This chapter explores the science-policy interface and examines the underpinning
conceptual perspectives on this subject. It begins with an introduction to how science
use in policy has been approached over the past twenty years, discussing the role of
science in a traditional rational process of policy and focusing on distortions to this
process that often alter its role. It then considers interdisciplinary efforts and explores
how they have contributed to conceptual understanding about the science-policy
interface, and elaborates the notion of what science use in policy-making means to
researchers. It calls attention to criticisms of conceptual structures of information use in
policy-making and planning. The chapter also considers perspectives from business
studies, organisational science and social science, and explores how they can be used in
an analytical framework to reflect on the science-policy interface in a way that is
sensitive to issues of context and scale.
2.2 Role of Science for NRM Policy-making
Tracing the connections between knowledge, economic productivity and managing
change, Drucker (1992: 95) noted a shift to the ‘knowledge society’, suggesting that in
this society ‘knowledge is the primary resource for individuals and for the economy’.
To deal with changing environments, Drucker (1992) and Nonaka (1994) considered
knowledge and learning to be of prime importance. The availability of good
information, Sarokin and Schulkin (1991) emphasised, is also central to effective and
equitable decision-making. Accordingly, Sarewitz and Pielke (2000) observed that
environmental analysis and policy have called on the efforts of science for knowledge
about human impacts on the environment and the implications of alternative policies.
Evidence shows that scientific information may matter for policy-making (see, for
example, Jasanoff, 1990;Norse and Tschirley, 2000; Turnhout, 2003). Policy-makers
and scientists have included scientific findings in recent policy statements and debates
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on major resource and environmental issues such as global climate change (e.g. Stern,
2006), water use and management in Australia (e.g. The Wentworth Group, 2006), and
the health of natural ecosystems (e.g. Australian State of Environment Committee,
2006). At an international level, scientific information is considered vital to policy-
making for sustainable development (IAASTD, 2008). As Szerszynski et al., (1996: 1)
observed:
Widespread public concern over the effects of human activity on the natural world has produced a broad consensus between scientists, policy-makers and other ‘authoritative’ commentators about the need for more reliable information about the present condition of the environment, the status of current threats, and the imperative for appropriate responses.
Not surprisingly then, governments over the past twenty years, have accorded priority to
the use of resource and environmental information for policy-making (Thomas, 2007).
Much of this recent development can be explained by: the growth in resource and
environmental institutions and compliance with legislation (Thomas, 2007); the
tendency to call for greater accountability in research investments (Hanney et al., 2003);
and to the rise of the concept of ‘sustainable development’ and associated demand for
‘triple bottom line’ information (Dovers, 1995).
The past 20 years has seen a revitalisation of interest in reforming the NRM policy-
making process in Australia, with greater emphasis on ‘science informed or evidence-
based’ policy. Many recent public policy documents have articulated how NRM policy
should be developed with an awareness and integration of scientific evidence. For
example, in the National Forest Policy Statement (NFPS), the Commonwealth and the
States agreed that a sound scientific basis was needed for sustainable forest
management, and strategic land use decision-making for forests should be based on
comprehensive evaluation of the potential use and values of particular forest areas
(Ashe, 2002). Forestry Tasmania (2002: 1) also maintained that ‘scientific
understanding and research play a central role in the planning and development of forest
management systems and practices’. Additionally the Bilateral agreement between the
Commonwealth of Australia and Western Australia for the extension of the Natural
Heritage Trust, dated December 17, 2002, stated:
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The following broad principles should apply at national, state, regional and local levels, to policy and planning relating to natural resources, and will provide a basis for the development of national standards defining best practice management of natural resources:… decision-making based on good science, economic analysis, the best available information and, where appropriate, predictive modeling.
Similarly the Framework for Future NRM Programmes endorsed by the Australian
Natural Resource Management Ministerial Council (2006: 9) on 24 November 2006,
included this principle to guide the development of future NRM:
The establishment of decision-making processes and structures that are informed by the best available scientific and socio-economic information and advice, and that provide for the timely review of this information and advice.
Although some policy-makers and scientists strongly believe that environmental policy-
making is very dependent on science, and that it is the differences in language and
operating rules perceived between the ‘two cultures’ of science and policy that make
integration difficult (Briggs, 2006), others are less of this view. According to Innes
(1998) this conventional view of the role of information for policy is flawed. She argued
that, ‘in this view, experts develop information in response to questions from decision-
makers or to solve problems that decision-makers have identified’ but ‘a substantial
literature on knowledge utilisation documents how little, on the whole, formal
information and analysis influence decisions’ (Innes, 1998: 3). Accordingly, Bridgman
and Davis (2004: 48) observed that ‘experience suggests rationality is an unusual thing
in the complex, quasi-political world of public policy.’
According to Sarewitz (2004), the uncertainty of scientific knowledge, the wickedness
of problems and the tensions between competing scientific claims make its use in
policy-making difficult, contributing to confusion, delays and political manipulation in
the policy-making process. As Jasanoff (1987: 195) pointed out, ‘in areas of high
uncertainty, political interest frequently shapes the presentation of scientific facts and
hypotheses to fit different models of reality.’ Miller (1993: 563) asserted that ‘science is
fallible and while of some use in [natural resource] decision-making, should not play a
dominant role’ and Herrick and Jamieson (1995: 105) argued that ‘it is a mistake to
suppose that ‘good science’ can always provide a ‘right answer’ for science-based
policy disputes. Indeed, good science may not even be able to provide a range of right
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answers for a given policy question.’ Because the rational-comprehensive model of
planning and its reliance on scientific rationality is not designed to deal with political
differences, the role of science and analysis in policy-making may be limited for
complex environmental problems (Allen and Gould, 1986; Cullen, 2006). As observed
by Cullen (2004: 14), the lack of scientific agreement on cause-effect relationships,
dissension about measures of significance and the distribution of political power are
what defines the management of water use the Murray-Darling Basin8 in Australia as a
wicked problem:
There is no simple trade-off between wealth creation and environmental degradation. The environmental damage may be experienced hundreds of kilometres from where the wealth is created, and there may be time lags of 50 to 80 years before the consequences of actions become apparent, as was the case with dryland salinity. There is argument as to what is significant degradation.
Also associated with conflicting values and interests is an apathy towards science.
Hamblin (2009) observed that it was decades before scientific proof about
environmental degradation in the Basin received public and political recognition, and
that despite understanding of the consequences, regulators continued to allow increases
in water extraction from the Murray-Darling Basin to the point where there is now a
permanent crisis of supply. Not surprisingly, despite wider attempts by the Australian
government to institutionalise the use of science in NRM policy-making through the
policy process and organisational change, there is little evidence that the efforts have
been successful or that policy has gained any benefits (e.g. Ashe, 2002; ANAO, 2004;
Commonwealth of Australia, 2004a).
For rural NRM policy-making, the move to decentralised, regional delivery of NRM
resulted in information gaps and caused tensions between the use of scientific
knowledge and other forms of knowledge, and between ‘technocracy and democracy’
(Lane et al., 2004). Generally, information at regional scales is often lacking, access to
data and technical support is limited, and organisational capacity to comprehend, and
learn how to capitalise on scientific knowledge is often inadequate (Morton et al., 2002;
Commonwealth of Australia, 2004a; Commonwealth of Australia, 2008a; Hajkowicz,
8 Represents three quarters of irrigated land in Australia (Hamblin, 2009).
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2009). Accordingly, Ewing (2003: 408) commented ‘… asymmetries exist in the power
and knowledge structures framing ICM (Integrated Catchment Management); there is
often a mismatch of powers and funds and difficulty in accessing relevant information
and skills’. Delivering science informed policy through this policy-making environment
is very difficult. Moving to decentralised, regional delivery also gave prominence to
other forms of knowledge, specifically local and experiential knowledge, in
environmental management (Lane et al., 2004). The problem then, as Lane et al., (2004:
112) pointed out, ‘is to identify ways in which different, even competing, knowledges
are to [be] reconciled in a single, planning decision or process.’
In the case of the NFPS, Ashe (2002: 175) attributed shortcomings in the science-policy
interface to political differences and the availability and quality of data:
Although the strength of the process is that it has been underpinned by sound scientific objectives and principles, there have been difficulties in incorporating scientific endeavors into a political process with wider economic and social objectives. Moreover, despite good intentions, scientific weaknesses have been identified for some assessments, aggravated by tight timeframes and the availability and quality of data.
Accordingly, two opposing views on the role of science in environmental policy-making
have emerged. Based on a rational-comprehensive model of policy-making, one view
according to Miller (1993: 563) is:
the widely held belief in technical rationality, a way of thinking that claims a primary role for objective, scientific information in decision-making. This ideological position is one that places great faith in the value of science, the importance of leaving decisions to experts, and the assumptions that properly conducted science is impartial and value-free.
Conversely, science is asserted to be socially constructed, not impartial and often used
to serve the interests of those in positions of power (Fischer, 2005). Scientific
knowledge is open to uncertainties and speculations making it vulnerable to being
‘cherry-picked’ for political gain (Pielke, 2007). These uncertainties also give rise to
scientific conflicts that tend to increase confusion in the policy process by introducing
technical controversy into an already complex situation (Collingridge and Reeve, 1986).
Accordingly, Szerszynski et al., (1996: 7) asserted: ‘Not only does scientific knowledge
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produce unanticipated consequences [in a complex and unpredictable future], but the
knowledge itself is indeterminate and uncertain.’ Conflicts over scientific knowledge
can arise from ‘a lack of information, misinformation, scientific uncertainty, different
interpretations of the same information or different opinions as to what information is
relevant’ (Christie, 2008: 17). This draws attention to how scientific uncertainty will
allow political discourse to play a key role in sanctioning which scientific knowledge is
accepted as true, or which can be legitimately articulated and included. Dovers, 2005a
(2005: 29) for example, has pointed to the socially constructed nature of knowledge in
policy-making:
Information in policy is not a value-free commodity – different groups and interests have fundamentally varied views of the world, assumptions about how things work, and claims and rejections of the validity of knowledge – and different rationalities emerge associated with a range of ‘discourses.
Sarewitz (2004: 385) holds a similar view, proposing how:
science supplies contesting parties [in environmental controversies] with their own bodies of relevant, legitimated facts about nature, chosen in part because they help make sense of, and are made sensible by, particular interests and normative frameworks.
Sarewitz (2004: 389) describes this condition an ‘excess of objectivity’. As science is so
prolific and diverse, Sarewitz, 2004 argues that a set of scientifically legitimated facts
can be compiled to support many, and sometimes contradictory, value positions. Miller
(1993: 563) suggested that ‘both viewpoints are correct but only in different contexts’.
There is increasing emphasis on the importance of social and political context for
understanding the disparate roles of science in policy-making; as a source of
information to resolve environmental problems on the one hand and a potential source
of conflict on the other (Christie, 2008).
Science may be very useful, according to Miller (1993), when the policy-making
context is ‘tame’ (i.e. one that is relatively restricted, about which much is known and
that can be tackled with available methods with assumed consensus on a singular
objective), and the focus is on the technical merits of particular management
interventions. However, the policy-making process is rarely rational and the policy-
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making context typically involves multiple stakeholders, multiple objectives and
considerable uncertainty then the resulting scientific conflicts and political disputes pose
limitations to the usefulness and use of science for policy-making in ‘wicked’ contexts
(Allen and Gould, 1986; Caldwell, 1993; Dovers, 1995; Martin, 1998; Sarewitz et al.,
2000). As Pielke (2004: 405) observed, ‘From the perspective of the public or policy
makers, scientific debate and political debate on many environmental issues already
have become indistinguishable, and such cases of conflation limit the role of science in
the development of creative and feasible policy options.’
New collaborative and communicative approaches to policy-making and planning are
emerging to handle the ‘wickedness’ of problems. There is a trend towards
environmental decision-making and policy models which reflect principles of
integration, information exchange and broad citizen participation (Perry and
Vanderklein, 1996). Traditional planning that focused on problems, information and the
right solution is becoming more focused on interests, relationships and support for a
solution. Under these circumstances, successful planning involves a focus on learning,
relationship building, ownership, skills in bargaining, negotiation and mediation,
improved social and political acceptability and more effective information management
(e.g. education) (Perry and Vanderklein, 1996; Lachapelle et al., 2003). It also
recognises that the understanding and knowledge of local and other stakeholders are a
legitimate and important as scientific and technical expertise. In a similar vein, Innes
(1998) contends that by using communicative rationality as the underlying conceptual
structure for planning processes (i.e. planning as primarily communicative action rather
than analysis prepared for decision makers) then the role of information is not limited to
scientific knowledge - but information may be more influential ‘by becoming embedded
in the thought, practices and institutions of a community, and thereby influencing
actions’ rather than by being only used as evidence (Innes, 1998: 1).
2.3 Conceptual Structures for Science in Policy-making
Contributions from many different research traditions have fuelled current knowledge
about the production, transfer and use of research in policy and practice. Over the past
50 years, the fields of business, communications, economics, marketing, geography,
philosophy, political science, public health and sociology have conducted theoretical
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and empirical research on aspects of knowledge production, acquisition, utilisation and
application to organisational and individual, problem solving processes. This has led to
a plethora of theories and models offered to explain the phenomenon (e.g. see reviews
by Weiss, 1977a; Beyer and Trice, 1982; Oh, 1996a; Rich, 2001; Greenhalgh et al.,
2004; Estabrooks et al., 2006; Jacobson, 2007; Nutley et al., 2007).
In general, researchers operate within their own frameworks and paradigms, and use
different language and metaphors to describe the phenomenon, including: knowledge
utilisation, knowledge dissemination and diffusion, knowledge translation, knowledge
transfer and exchange, the science-policy interface and the co-production of science and
policy. Thus, the plural theories, models and definitions, and lack of an integrating
framework have hindered development of a comprehensive conceptual framework of
knowledge use (Beyer and Trice, 1982; Rich, 2001). This suggests the need for an open
and diversified approach and interdisciplinary awareness in engaging with studies in
this field.
Traditionally, the knowledge use literature focused on the application of social research
in decision-making by governments and government agencies (Weiss, 1977a: 1). A
century of social research has been used for purposes such as prison reform, social relief
for the poor and financial improvements for working-class families in Europe (Weiss,
1978). Prior to the 1960s, according to Weiss (1977a) and Lynn (1978), social
researchers assumed that policy-makers regularly used research in policy-making, but in
the 1960s, reports commissioned by the United States federal government indicated that
policy-makers seldom used social and economic research in their decision-making.
Later, in the 1970s, increased government interest in social and economic research for
policy (e.g. social and economic indicators, surveys of need, policy analysis) revived
expenditure on social research and stimulated studies of research use for policy (Weiss,
1977a; Lynn, 1978).
Generally, the assumptions underpinning early models of science use, such as rational
planning approaches, a linear model of science production and instrumental use of
science have been recognised as flawed (Oh, 1996a; Landry et al., 2001a, Landry et al.,
2003; Estabrooks et al., 2006; Belkhodja et al., 2007). Due to weaknesses in these
assumptions and an increasingly messy and ‘wicked’ policy-making context, conceptual
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structures used by scholars to explore and explain the science-policy interface are
increasingly moving from:
· ‘knowledge-driven’ and ‘problem-solving’ functions of use to ‘enlightenment’
and ‘interactive’ functions, and from ‘instrumental’ to ‘conceptual’ use because
these are considered more realistic representations of the ‘use’ of research;
· rational-comprehensive planning approaches to collaborative approaches, in
which ‘scientists, managers and the public jointly pursue problem solving’
(Lachapelle et al., 2003: 476) and the multifaceted nature of contemporary
policy-making contexts may be better accommodated (e.g. see Chapter 2.1);
· a linear model of science feeding into policy (i.e. instrumental use) to a co-
production model ;
· a ‘two-communities’ model of the science-policy interface that focused on
linkage breakdowns and cultural differences between scientists and policy-
makers, to an ‘interaction’ model of the interface, emphasising social processes,
social learning theory and the concept of the co-production of science;
· ‘science push’, ‘demand pull’ and ‘dissemination’ models to the current
‘interaction’ model, that integrates elements of all the models, emphasising the
social dimensions of the use of science.
This section further explores these shifts in conceptual thinking. Operationally, there
was also a focus on the measurement of ‘use’, and calls for scales and indices that could
include some of the non-instrumental aspects of use (Landry et al., 2001b). Recent
studies are significant for their broad review and critique of the literature about the
modeling and measurement of knowledge use (Oh, 1996a; Rich, 2001; Landry et al.,
2001a, Landry et al., 2003; Belkhodja et al., 2007).
2.3.1 Meaning and measurement of ‘use’
The term use has been often undefined in the literature and since many interpretations
are possible this had led to some confusion (Webber, 1991). Conceptually, the term
knowledge use is very broad. In her work on knowledge use, Weiss, 1979a identified
seven meanings, (or as some call them ‘models’ - e.g. Hanney et al., 2003), of ‘use’ in
terms of its function in policy-making:
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1) knowledge-driven - the application of basic research; suggests a linear sequence
in which research generates knowledge that impels action;
2) problem-solving - the communication of research to policy-makers on a
problem agreed with them;
3) enlightenment - research is used by the gradual accumulation of insights,
theories, concepts and perspectives;
4) political - research is used to bolster support or provide ammunition in an
adversarial system of policy-making;
5) tactical - requesting additional information to delay action;
6) interactive - multiple, competing information sources that suggest a set of
interactions between researchers and users rather than a linear sequence from
research to decisions;
7) intellectual enterprise - policy research is one of many types of intellectual
pursuits.
Of these the most common meaning of ‘knowledge use’, according to Weiss (1978) and
Innes (1998), is ‘problem-solving’ or the application of specific findings to specific
decisions (i.e. the instrumental use of knowledge). In this case, Rich (2001) suggests a
‘tracer’ approach may be appropriate to trace outputs back to discrete inputs.
Instrumental use is, however, rare according to Weiss (1977c) and Rich (2001) and
conceptual use more likely (i.e. the accumulation of knowledge from multiple sources
that promotes a gradual shift in opinions and shapes the way policy-makers think about
problems). This is consistent with the view of Brewer and Stern (2005: 51):
Individual research projects are seldom translated directly into policy choices. However, an accumulation of findings, including challenges to existing assumptions and interpretations of fact, can make a difference in policy.
And Stone (2000: 54):
Research by itself is rarely sufficiently convincing or comprehensive to the prime source of policy innovation. Instead, the accumulation of scientific findings gradually alters the perceptions of decision-makers.
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The emergent view in the knowledge use literature is that the enlightenment and
interactive functions of ‘use’ and a conceptual mode of ‘use’ are more realistic
representations of science use in policy-making (e.g. Knott and Wildavsky, 1980).
Regarding measurement of use, Landry et al., (2003) observed that studies in the field
of knowledge use are based on two designs: the discrete event design and the process
design. The discrete event design asks respondents to identify how the findings of a
single study affect a discrete decision (i.e. instrumental use). One of the problems with
the discrete event design is that it tends to suggest that specific research findings feed
directly into policy-making to affect a single decision-making event (Landry et al.,
2001; Brewer and Stern, 2005). As Rich (1997: 20) pointed out, ‘in measuring
utilisation, one is not examining a single discrete event at one point in time. Knowledge
utilisation occurs gradually over an extended period of time.’
Apart from scientific uncertainty and the multi-step policy process, there are also socio-
political dynamics at play that undermine the discrete event design, particularly with
regard to the interaction of policy-maker and political interests. In government agencies,
for example, differences in information use between ‘technical’ and ‘political’ policy-
making contexts mean that many policy-makers may expand or curtail information use
according to political conditions (Oh, 1996b). Thus, research may not necessarily be
used directly as suggested by the discrete event design but can experience a variation, in
which use can alternate with non-use depending on fluctuations in political interests. As
Knott and Wildavsky (1980: 545) and Oh (1996b) have suggested, while there may be
perception that use is contingent on information content, equally important are the
political processes that can weaken use and need to be accounted for:
Excellent analysis is often ignored. Information is one, but only one, input into the bargaining process that yields policy decisions. Political power, special skill, and organisation capabilities to act all enter in to the final outcome. Failure to adopt a new idea or to implement it frequently has nothing to do with the way information came to the organisation or its form. Delimitation of a role for dissemination, therefore, requires keeping levels of utilisation distinct.
Based on this view, a revised conceptualisation of information use incorporates stages
of use in the policy-process. The process design views use in terms of the influence of
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information across the policy-making process. The various levels, according to Knott
and Wildavsky (1980), may each be usefully conceived as a stage (see Table 2.1) in
which each is a link in the chain of utilisation. While these include use in terms of the
information reaching the desk of a policy-maker, they also incorporate attempts to apply
the information to change policy. The Knott and Wildavsky (1980) scale has been used
and validated by Lester (1993) and Landry et al. (2001a, 2003).
According to Landry et al. (2007), the scale is meant to capture not only the extent to
which information is processed cognitively by policy-makers but also its consequence
in the policy process. Referring to the scale’s seven stages of knowledge use: reception,
cognition, reference, adaptation, effort, influence and application of knowledge, Lester
(1993: 274) points out that ‘the entire scale is cumulative in the sense that all these
stages are important indicators and build on each other.’ Landry et al. (2003) argued
that, whist it would be premature to restrict research to a single measure of ‘use’, the
advancement of knowledge would be facilitated by several applications of the same
specification of use, rather than single applications of different specifications of use.
Table 2.1 : Seven standards of utilisation (Knott and Wildavsky, 1980).
Reception Utilisation takes place when policy-makers or advisers receive policy-relevant
information. When the communication comes to rest in the ‘in-basket’, so that the
data ‘reach’ the policy-maker rather than remain on an analyst’s desk or in the files of
a distant consultant firm, utilisation is complete.
Cognition The policy-maker must read, digest, and understand the studies. When he has done
so, utilisation has occurred.
Reference If frame of reference is the criterion, then utilisation somehow must change the way
the policy-maker sees the world. If information changes his preferences, or his
understanding of the probabilities or magnitude of impacts he fears or desires,
utilisation is a reality.
Effort To make a real difference, information must influence the actions of policy-makers. If
they fight for adoption of a study’s recommendations, we know a real effort was
made even if political forces or other events block it.
Adoption What is essential is not whether policy-relevant information is an input to the policy
process but whether it goes on to influence policy outcomes. Policy results, not
inputs, is the proper standard.
Implementation Policy adoption is critical but, if adopted, policy never becomes practice, information
has no chance to affect action. Adoption without implementation is a hollow victory.
Impact A policy may be implemented but fail to have the desired effects. Hence it may be
(and is) argued that only when policy stimulated by information yields tangible
benefits to the citizen has utilisation taken place.
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Many conceptual structures have contributed to current understanding of the
phenomenon of research use in policy-making and their development is ongoing. As a
theory of knowledge use is inexact, a useful distinction can be made between studies
that are influenced by:
1) a communication perspective;
2) an organisational interest perspective; or
3) an interactive perspective.
Different conceptual structures explaining the policy-making process (e.g. rational-
comprehensive model) and the process of research use in policy-making (Table 2.2)
underlie each of these perspectives, and are elaborated in the following section.
2.3.2 A communication perspective
Starting with early studies in related research fields of communication and the spread of
innovations, the initial theories of research use were developed. According to Rogers
(1962) and Caplan (1979) the problem of poor information use may be conceptualised
as one of gaps and breakdowns in linkages between information suppliers and users. In
early conceptual models of research use, this notion was expressed in a belief of a
cultural gap between scientists and policy-makers, leading to a breakdown in
communication and consequently, to low levels of knowledge use (Caplan, 1975, 1979;
Weiss, 1977a, Weiss, 1977c; Sabatier, 1978, and Dunn, 1980, 1983). This became
known as the ‘two-communities theory or metaphor’9. Most communication-related
studies, observed Oh and Rich (1996), take the two-communities metaphor as the
underlying conceptual structure for their work. For example, the dissemination model of
research use (Table 2.2) is underpinned by the concept of two-communities (Landry et
al., 2001a, 2003).
The dissemination model was developed in response to the recognition that knowledge
transfer was not automatic. It was based on the assumption that researchers and users
have different interests (i.e. two-communities metaphor), and the resulting 9 Dunn (1980) argued that the ‘two-communities’ concept lacked clearly defined terms and empirically testable statements therefore metaphor rather than theory is a more appropriate description.
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communication deficit is expected to reduce the use of research, exacerbated by the use
of complex, technical language (Caplan, 1979; Oh, 1996c; Landry et al., 2003). This
model suggested that a step should be added to research activities by developing
dissemination mechanisms to identify useful knowledge and transfer it to potential
users.
In many cases, the products of research never get widely disseminated and, thus, have
little significant impact. The assumed, one-way flow of information and traditional
dissemination approaches, Landry et al. (2001a) observed, have not proven to be
effective in encouraging the adoption and implementation of new research results.
Scholarly journals were inconvenient since they neglected to adapt to content, calendar,
form, and mode of diffusion to meet the particular needs of the users. According to
Landry et al. (2001a), the type of research results and dissemination efforts (adaptation
of research results to the needs of users and user’s own attempts to acquire results) were
the main determinants. Indicators of adaptations included efforts to make reports more
readable and easier to understand, efforts to make conclusions and recommendations
more specific and operational and efforts to focus on to make reports more appealing
(Landry et al., 2001a). However, criticisms of the ‘two-communities metaphor’,
focusing on the weaknesses of underlying assumptions, including a linear model of use
and rational-comprehensive approaches to planning, have reduced support for the
‘dissemination’ model.
A key feature of the two-communities metaphor is that the problem of knowledge use is
recognised as a problem of a breakdown in linkages between science and policy-making
communities. This linkage has been commonly related to information attributes:
availability of information or data, relevance of the information, the form in which it is
delivered and its timeliness (Norse and Tschirley, 2000). Since the late 1970s, this view
of knowledge use as a problem of linkage breakdown and the assumption that research
feeds into policy in a linear manner began to be challenged, with calls for approaches
that could capture the more diffuse and indirect nature of knowledge use (Beyer and
Trice, 1982). A sharp boundary between ‘use’ and ‘non-use’, where knowledge directly
contributes to a decision, was not supported by later studies (Weiss, 1977c; Webber,
1991).
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The ‘two-communities metaphor’ contends that the gulf between science and policy-
making communities is similar to the communication gap between science and the
humanities, described by Snow (1971) as the ‘two cultures’. In his description of the
‘two cultures’ theory, Snow (1971: 21) articulates that ‘the scientific edifice of the
physical world’ is ‘the most beautiful and wonderful collective work of the mind of
man’. In so doing he alludes to a concept of science use that focuses on a faith in
scientific expertise and a rational-comprehensive model of policy-making. Scientists, he
adds, are morally ‘the soundest group of intellectuals we have,’ while literary
intellectuals are ‘natural Luddites’.
Although, rational choice theory has been commonly applied in studies of knowledge
use, the weaknesses of its assumptions have been widely criticised, and traditional
rational-comprehensive planning processes appear to function poorly (for example, see
Yin and Gwaltney, 1981; Hogwood and Gunn, 1984; Oh and Rich, 1996; Lachapelle et
al., 2003). The main attributes of this theory are:
· a scientifically based, expert-driven approach to management and allocation
decisions (Lachapelle et al, 2003);
· decision-makers are perceived as rational and logical thinkers (Porter, 1995);
· policy-making is ‘rational’ in the sense that the process is separated into rational,
sequential steps, generally described as agenda setting, decision-making,
adoption, implementation and evaluation (Porter, 1995).
Rational choice theory assumes policy-making is ‘comprehensive’, in that it canvasses,
assesses and compares all options, calculating all the social, political and economic
costs and benefits of public policy (Davis et al., 1993). The most obvious criticism,
observed Hogwood and Gunn (1984: 47), is that ‘models of rational policy-making are
unrealistic or impractical’.
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Table 2.2: The determinants of science use (adapted from Belkhodja et al, 2007) Models of Research Use
(alternative names) Key attributes Criticisms Explanatory factors Past studies
‘Science Push’ Model or ‘Knowledge Driven’ Model or ‘Technological’ Model
Researchers are the source of ideas for directing research. Users are receptacles for results of research. Linear sequence from supply of research to use. Advancement of research determines extent of use. Corresponds to rational model of policy-making
Uptake of research is considered independent of the oganisational and social context. Transfer of knowledge is not automatic. No one assumes responsibility for transfer. Raw research information is not usable knowledge. No relation between technical quality of research and use.
‘ SCIENCE SUPPLY’ 1. Content attributes: efficiency,
compatibilty, complexibility, observability, trialibility, validity, reliability, relevance, radicalness, access to, credibility.
2. Types of research (basic/applied), research domains and disciplines and scale of research results.
Dunn (1983); Huberman (1987); Lomas (1990); Dearing and Meyer (1994); Rich (1997); Oh (1997a); Landry et al. (2001; 2003).
‘Demand Pull’ Model or ‘Decision-Driven’ Model of Research or ‘Organisational interests’ explanations
Focus on user needs and context Users are the main source of ideas for directing research. Linear sequence starts with identification of the research problem by users.
Focus on the instrumental and neglects other uses Too much stress on user needs only Fails to consider impact of organisation on knowledge use
‘ SCIENCE DEMAND’ 1. Organisational structures, size
of agencies, types of policy domains, positions (professionals or managers) and needs of organisations
2. Users organisational contexts: research relevance, research coincides with needs
Yin and Moore (1988); Rich (1991); Landry et al. (2001).
Dissemination model or ‘Two-communities’ explanations
Dissemination mechanisms used to identify useful knowledge and transfer it to potential users
Potential users are neither involved in the selection of transferable information, nor involved in the production of research results
‘ DISSEMINATION’ 1. Adaptation of the products to
meet user needs 2. Dissemination or acquisition
efforts
Huberman (1987); MacLean (1996); MacLean (1996); Oh and Rich (1996); Lomas (1997); Landry et al. (2002a).
Interaction model or ‘Interaction’ explanations
More attention on interactions and relationships between reasearchers and users at the different stages of knowledge production, dissemination and utilisation
Can lead to selective use of research. Can be difficult to establish due to time and turnover issues. Overomes the criticisms of previous models.
‘ INTERACTION’ Explanatory factors identified in the prior models: 1. Types of research and discipline 2. Needs and organisational
interests of users 3. Adaptation, dissemination and
acquisition efforts 4. Insitutional and social linkage
mechanisms
Dunn (1980); Oh (1997);Yin and Moore (1988 ) Landry et al. (2001; 2003).
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Other criticisms of the application of rational choice theory to understand science use in
policy include:
· knowledge is assumed to be used in an isolated, linear manner but the process of
knowledge use can be diffuse and difficult to separate from the social context;
· little analytical attention is given to the social and political context of decision-
making, with its different interests and power differentials between participants
(Lachapelle et al., 2003);
· a direct relationship is incorrectly assumed between knowledge acquisition and
its use, and its does not deal with the legitimate non-use of information (Oh,
1996a; Rich, 2001);
· scientifically valid information is assumed to always be used, but empirical
studies suggest that policy-makers are less likely to use counter-organisational
information even if it is valid (Nelson et al., 1987; Oh, 1996a);
· availability of all data needed to support a decision is assumed but there may
little scientific agreement about cause-effect relationships, limited time and
resources, lack of information and structural inequities in access to information
(Lachapelle et al., 2003);
· consensus is assumed on singular objectives, whereas there can be multiple and
competing goals (Lachapelle et al., 2003).
Thus, complete rationality is not achievable. It is thwarted by a lack of knowledge,
skills and consistency in values and goals and conflicts of interests. Complex
organisational structures may hinder problem solving and information flow. There may
be time and budget constraints on information collection and biased information may
reach the decision-maker - coloured by the preferences of those providing it. There may
be time lags that mean information is out of date by the time it arrives. The
‘wickedness’ of the problem and the uncertainty of science make it very difficult to use
a rational planning approach to predict the consequences of interventions, and compare
alternatives. As Yin and Gwaltney (1981) observed, scientific results carry some degree
of uncertainty so the political goal of a reduced range of alternatives (often a single
preferred) involves considerations beyond scientific knowledge. Without the ability to
predict the outcomes of policies, the policy-maker is confronted with no rational basis
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for choice. Also discounted is the legitimate non-use of information and it does not
acknowledge that information can have other functions apart from instrumental
purposes (e.g. political gain) (Innes, 1998; Rich, 2001).
Many scholars have argued that rational choice theory is weakened by the inaccurate
assumption that research feeds into policy in a direct or linear manner (e.g. see Weiss,
1977a). The linear model portrays science as a vast pool of knowledge that flows from
basic research to applied research, and then ultimately to societal benefits (Pielke,
2007). The linear model is sometimes referred to as the ‘science push’ model of
research use (Table 2.2). The science push model, according to Landry et al. (2001a),
assumes researchers are the source of ideas for directing the research and users are
simple receptacles for the results. The focus of the model is on attributes of the
information product and past studies suggest these may include (Landry et al., 2001a):
· content attributes: efficiency, compatibility, complexity, observability,
trialability, validity, reliability, divisibility, applicability and radicalness;
· types of research: basic/applied, general/abstract, quantitative/qualitative,
particular/concrete and research domains and disciplines.
The early assumptions of rationality, instrumental use of knowledge and linear process
of science production weakened support for the ‘science push’ model. Notably, Landry
et al. (2001a) observed that some empirical studies have found no relation between
technical quality of research results and utilisation, and also suggested that the impact of
the types of research products (e.g. quantitative or qualitative research reports) on
utilisation is indeterminate (e.g. the knowledge value of quantitative studies can be
outweighed by higher comprehension costs which can make use of quantitative research
reports less likely).
Significantly, under the science push model the effect of the socio-political context of
research demand on use is ignored; science only serves as an information resource and
is not connected to the policy process, giving the normative view of science divorced
from the messiness of politics, as observed by Pielke (2007: 143):
Even though scholars who study science in society have successfully critiqued as normative and descriptive theory the linear model and its
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implication that science and politics can be cleanly separated, it persists through the scientific enterprise because it reflects a perverse incentive structure that shapes science in politics’
The linear model persists, Pielke (2007) claims, because of its success in protecting
bureaucratic and political interests; politicians are more than happy to pass the onus of
resolving a highly political dispute to the scientist, while the scientific community are
happy to be recipients of the resources offered by the policy-makers to perform the
research (Pielke, 2007). However, in studies of knowledge use, the shift in analytical
focus from information factors to the social and organisational context of the end user
demonstrates weakened support for the linear model notion that ‘science and politics
can be cleanly separated’ (Pielke, 2007: 143).
This analytical shift is tied to the emergence of two new conceptual structures for
science use:
· an ‘integrative’ science (also called ‘stakeholder’ or ‘postnormal science’)
(Funtowicz and Ravetz, 1993; Steel et al., 2004; Pielke, 2007); and
· a ‘co-production’ process (Jasanoff and Wynne, 1998; Lemos and Morehouse, 2005;
Lövbrand, 2007).
This analytical shift is also tied to the stronger notion of scientific processes engaged
with policy-making systems. In a similar vein Lövbrand (2007: 41) suggested, ‘an
understanding of science as a situated and contingent social activity’, and Funtowicz
and Ravetz (1993: 739-740) emphasised a shift to a more interactive model of scientific
discovery:
The reductionist, analytical worldview which divides systems into ever smaller elements, studied by even more esoteric specialism, is being replaced by a systemic, synthetic and humanistic approach…The model for scientific argument is not a formalised deduction but an interactive dialogue.
Thus, there is deepening disillusion with the linear model of science and many
researchers consider it superseded by a co-production model (Byerly, 1995; Jasanoff
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and Wynne, 1998; Gibbons, 1999; Lemos and Morehouse, 2005; Lövbrand, 2007). This
co-production model has been defined by Lövbrand (2007: 41) as:
‘the institutionalised practices by which ‘usable science’ best is produced in interaction between scientists, policy-makers and the public’, and ‘a dynamic process by which science and society continually shape, constitute and validate one another’.
2.3.3 An organisational interest perspective
The science push model and the two-communities metaphor have been criticised for
their neglect of social and organisational influences on science use (e.g. Oh and Rich,
1996; Slob et al., 2007). Characteristics of the research itself, (e.g. technical quality)
were not found to be consistently related to use (e.g. Landry et al., 2003). In response, a
number of researchers then turned their focus to end users and their social and
organisational context. In a similar vein, Graham Allison (1971) proposed in the
Essence of Decision what he termed the ‘organisational model of policy-making’.
Instead of viewing policy decisions as a product of rational choice by an actor, this
model views them as the outputs of standard, organisational processes. For example,
organisational routines may be designed to minimise the influence on decisions of
particular individuals or groups or to maintain an internal equilibrium among competing
interests. From the focus on organisational context have emerged the organisational
interest perspective and the demand pull model (Table 2.2).
The organisational interest perspective has gained weight among scholars, who argued
that organisational characteristics are also important determinants of science use (Beyer
and Trice, 1982; Oh, 1996a; Rich, 2001; Landry et al., 2001a, 2003; Jacobson et al.,
2004; Cummings et al., 2007; Belkhodja et al., 2007). According to this perspective,
research results are more likely to be used when they meet the needs of users and
support the interests and the goals of the organisation (Oh, 1996a; Rich, 2001). It
assumes that users make choices about information for the benefit of their organisation
(e.g. budget, personnel or mission). As pointed out by Oh (1996a) greater attempts by
organisations to secure, maintain and increase organisational power can result in a
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tendency by decision-makers to rarely consult information from external sources.
Aligned with the organisational interest perspective is the demand pull model.
According to Landry et al. (2001a), the demand pull model assumes users are the major
source of ideas for directing research. This model follows a linear sequence and use is
explained by user demand. Use is, thus, expected to increase when the objectives of
users are also included in research planning (Landry et al., 2001a). Of primary interest
in studies of research use have been organisational structures, agency size, types of
policy domains, occupational status and attributes of the organisational context, such as
learning and culture (Oh, 1996a; Landry et al., 2001a; Jacobson et al., 2004; Belkhodja
et al., 2007).
Prior empirical studies have suggested that use of knowledge increases as users consider
research pertinent, as research coincides with their needs, as users' attitudes give
credibility to research and as results reach users at the right time (Landry et al., 2001a).
Oh (1996a) suggested that the organisational position of users may also affect the use of
information. Those in higher positions are expected to consult internally more due to
easy access, as well as the belief that information from their own agency is in line with
interests of the organisation. Decision makers in higher positions may receive or request
less information than those in lower positions, mainly due to the limited time to process
all the information received. Oh (1996a) points out that the demand pull model assumes
that the under-use of research mainly lies in the conflict between research findings and
the organisational interests of the users. Landry et al. (2001a) outlined criticisms of this
model that included:
· a focus on the instrumental use of research that does not account for more
gradual impacts of accumulated knowledge;
· excessive emphasis on the egotistical interests of the users; and
· omitting the interaction between producers and users of research findings.
It is noteworthy that the social or organisational perspective that has evolved in studies
of science use are mainstream in the research tradition of the sociology of science. For
example, Jasanoff (2004: 14) observed the interplay between science and socio-political
dynamics: ‘the dynamics of politics and power, like those of culture, seem impossible to
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tease apart from the broad currents of scientific and technological change.’ The concept
of the social construction of science, based on sociological understandings, can be
considered closely aligned to the organisational interest perspective of science use.
Despite the apparent connection between the research utilisation work and sociology of
knowledge studies, in general, the literature has paid little attention to it. Yet
recognising this convergence may support a more holistic understanding about the
process of science use in policy. Following an early focus on the ‘supply’ side of the
science production system, scholars in the sociology of science have since pointed to the
interactive nature of the ‘co-production of science’10, and the importance of political and
cultural dimensions in shaping science use by policy-makers (Jasanoff and Wynne,
1998; Lahsen and Nobre, 2007). This parallels recent shifts in research utilisation
studies, from a focus on information factors to organisational determinants of use, and
from a focus on the theory of diffusion (product is transferred from inventor to user
without change) to the concept of an ‘interaction’ model of use, emphasising the social
and political context of the policy-making process (Oh, 1996a; Rich, 2001; Lahsen and
Nobre, 2007; Lahsen and Nobre, 2007; Lövbrand, 2007; Belkhodja et al., 2007). In a
similar vein, Lachapelle et al. (2003) describe recent shifts in studies of natural resource
planning processes, from a focus on scientific expertise and traditional rational-
comprehensive planning processes to approaches that recognise a need for collaborative
processes in the ‘wicked’ contexts commonly found in NRM policy-making.
2.3.4 An interactive perspective
A new perspective has recently emerged, according to Nutley et al. (2007), that attends
to the complex and context-bound nature of knowledge use, and the importance of
linkages between researchers and users and the role of actors in the wider policy
process. This perspective integrates current knowledge about the kinds of factors that
seem to influence the use of research, pointing to the linkages between researchers and
users and focusing on exchanges between them (Landry et al., 2001a, 2003; Nutley et
10 The term co-production also carries the notion of a dynamic process by which science and society continually shape, constitute and validate one another (Jasanoff and Wynne, 1998). This idea is supported by lessons from science and technology studies that knowledge creation and use by organisations is a dynamic and social process. The organisation interacts with individuals and the environment to develop and use knowledge (Nonaka and Toyama, 2003).
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al., 2007). This interactive perspective has provided a new way of conceptualising
knowledge use, based on more holistic criteria and evaluation. The ‘interaction’ model
derived from this perspective conceptualises the use of research as a dynamic process,
rather than a single event (Table 2.2). Closely tied to this shift to an interaction model of
science are recent developments in parallel research streams. Particularly relevant are
research developments that have built on the concept of ‘diffusion of innovations’
(Rogers, 1995). Diffusion theory was first applied in the field of rural sociology and
perceived diffusion as the spread of ideas among individuals. According to O’Keefe and
Marks (1998: 88), ‘individuals make adoption (or rejection) decisions and the
accumulation of these individual decisions over time results in the diffusion of the
innovation through the population.’ However, classical diffusion theory has been found
to be wanting.
There was a tendency, according to Greenhalgh et al. (2004), to focus on the product
attributes and individual characteristics, and to believe patterns of adoption reflected
fixed personality traits and adoption behaviour. In doing so this ignored, as Midgley and
Dowling (1978: 230) pointed out, ‘the dynamic social processes that characterise the
diffusion of innovations’. Consequently, new framings of adoption generally recognise
that this behaviour is a function of the interaction between the individual decision-
making unit, the innovation and the social system (Midgley and Dowling, 1978; 1993).
Similar models have been reported by researchers in evidence-based medicine
(Greenhalgh et al., 2004) and agricultural extension practices (O'Keefe and Marks,
1998).
Current research suggests that a linear model of innovation diffusion is not valid (as per
the linear model of science) and social factors are important, since the evidence base
may be uncertain and contested in a political environment (Greenhalgh et al., 2004).
Other parallels can also be drawn between the increasing importance of social
ingredients in the theoretical evolution of innovations, innovation diffusion and of
knowledge use in organisations. For example, increasingly, the innovation process is
regarded as a non-linear and interactive process of learning and exchange between the
organisation and its environment: moving from information based theories of innovation
to social network theories (Landry et al., 2000; Kaufmann and Tödtling, 2001). The
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modeling of how innovations spread has evolved, as pointed out by Nutley et al. (2002:
9), from science push to interaction models:
In the early models the emphasis is on the innovation itself, and on the channels of communication through which knowledge passes. Later models incorporate not only ‘science push’ but also ‘demand pull’, where research is called to for to meet problem solving needs amongst users. In later models the process of interaction between the innovation and a group of potential adopters becomes the focus of attention. These interactions are usually brought together in some form of model of the various stages of the diffusion process.
These developments are considered to be closely aligned with the evolution of models
of knowledge use (also from science push to interaction - see Chapter 2.2.4). Hence, the
use of evidence in practice and the use of research in policy may be conceptualised less
as a straightforward, linear process of product adoption, and more as a non-linear,
interactive process.
The lack of interaction between researchers and their potential audiences has been
identified as the main problem in under-utilising research findings (Landry et al., 2003).
Accordingly, Dilling (2007: 49) observed that ‘Information that is created and
disseminated without awareness of and engagement with intended users generally fails
to be usable’. The main shortcoming, however, of the ‘dissemination’ model was that
potential users were neither involved in the selection of the transferable information, nor
involved in production of the research results (Table 2.2). To overcome criticisms of
previous models the ‘interaction’ model was developed, based on the idea that use is a
socially mediated process and depends on interpersonal relations between researchers
and users.
The interaction model predicts that the more sustained and intense the interaction
between researchers and users, the more likely use will occur. Unlike prior models, it
suggests giving greater attention to the relationships between researchers and users at
different stages of knowledge production, dissemination and utilisation. It combines the
determinants of the previous models with a new category of determinants consisting of
formal and informal linkage mechanisms (Landry et al., 2001a, 2003).
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Hence, the conceptual models for research use have evolved from science push to
interaction models (Table 2.1) with an associated shift in emphasis from a focus on
information factors, to today’s focus on organisational and social factors. It is widely
accepted and reports exist to support the view that socio-organisational factors influence
research use (Landry et al., 2001a, 2003; Greenhalgh et al., 2004, Belkhodja et al.,
2007; Estabrooks, 2007; Meijers, 2006). The relationship between these factors and
knowledge use is depicted in Figure 2.1.
To explore and explain the use of science in policy-making, researchers have developed
a range of analytical frameworks. Beginning with a science push or technological
model, there has been a shift to a demand pull model, a dissemination model and to the
current interaction model, that integrates elements of all the models with added
emphasis on the relationships between researchers and users (Weiss, 1979a; Kline and
Rosenburg, 1986; Buxton and Hanney, 1996; Landry et al., 2001a, 2003). Another
integrated framework that also, according to Nutley et al. (2007: 111), ‘builds on, and
integrates much previous work in the field to provide a more sophisticated framework
that moves our understanding of research-policy relationships beyond uni-dimensional,
rational-linear approaches and simplistic, two-communities approaches is the ‘context,
evidence and links’ framework for examining the research-policy relationship (Crewe
and Young, 2002; Crewe et al., 2005).
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Knowledge factors, such as: · Content attributes (e.g. reliability,
complexity) · Research types (e.g. basic/applied) Organisation11 factors, such as: · Organisational size · Organisational culture Dissemination factors, such as: · Results presented in a non-technical
language · Relevance of the information for policy
use Interaction factors, such as: · Formal linkages between research
producers and users (e.g. involvement in research projects).
· Informal linkages (e.g. casual personal contacts)
Figure 2.1: A conceptual model of research utilisation
- based on the ‘interaction’ model (e.g. Landry et al.,
2003) and the Knott and Wildavsky (1980) scale of
knowledge utilisation.
The ‘context, evidence and links’ framework highlights the importance of context, the
nature of the research evidence and the links between key actors in modeling when and
how research is used in the policy-making process. According to Crewe and Young
(2002: 2) policy-making is ‘structured by a complex interplay between political
interests, competing discourses and the agency of multiple actors.’ This process may
vary across contexts, for example the political strategies and power relations at play, or
the influence of broad paradigms on the types of knowledge regarded as useful or
legitimate. Based on a synthesis of research in the field they suggest a three-dimensional
framework for understanding the link between research and policy, as illustrated in
Figure 2.2. Policy uptake of research is viewed as an interaction of these three factors:
11 In this study the emphasis is on the user organisation though it is recognised that the attributes of the research producing organisation may also be important.
Use of research by policy-makers: · Seeking research · Receiving research information· Comprehension of research · Referencing of research
reports · Adapting the format of
research for decision-makers · Promoting the adoption of
research · Using research to make
professional choices and decisions
· Contribution to new or improved products or services
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1) Context: politics and institutions. Research use in policy will be shaped by
political and institutional structures of the broader policy context, and the
changing power relations. The interests and roles of key policy makers and
researchers are also important, as are the organisational culture and pressures
within which they operate. Finally, research use will be shaped by the way
policies are adapted, developed or distorted when implemented.
2) Evidence: credibility and communication. The credibility of the research (how it
was gathered, who by and whether it was perceived as accurate), and its
communication will affect its use by policy-makers. The quality of the research,
its source (e.g. people accept information more readily from those they trust),
the way it is packaged and the degree it challenges received wisdom are all
important. The model assumes that policy-makers actively engage with research
and suggests that research will be shaped through policy-makers’ knowledge,
values and experiences.
3) Links: influence and legitimacy. The model emphasises the importance of links
between policy-makers, researchers and other stakeholders (e.g. media). Crewe
and Young (2002: 14) suggest that the use of research will be greater where
researchers and policy-makers ‘forge close personal links, with appropriate
chains of legitimacy to those they represent.’ Issues of trust, legitimacy,
openness and formalization of networks are considered important for influencing
research uptake. Also considered important are the identity of key actors, the
roles played (supportive or blocking), the links between them, and the extent to
which the research methodology gave the findings legitimacy.
Highlighting the dynamic system of research use, Crewe et al (2005) noted that the
value of the framework lies not in trying to understand the dimensions in isolation from
each other, but in the questions it raises about the interactions between the three
dimensions. Accordingly, Nutley et al (2007: 111) write that it ‘represents the use of
research as a dynamic, complex and mediated process, which is shaped by formal and
informal structures, by multiple actors and bodies of knowledge, and by the
relationships and play of politics and power that run through the wider policy context.’
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The evidence Links between policy-makers and researchers
The political context
Figure 2.2: The ‘context, evidence, links’ framework (Crewe et al, 2005)
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2.4 An Analytical Framework
Developments in ways of thinking about knowledge use in different research traditions
have tended to converge over the past 40 years. Theories and models of decision-
making, science production and research use have shifted in emphasis from an
analytical focus on information factors of ‘supply’ to an integrated analytical framework
emphasising the relationship between ‘suppliers and customers’. Even researchers
working outside the mainstream knowledge field are beginning to recognise this. Based
on an analysis of impact assessment frameworks, Nykvist and Nilsson (2009: 15)
suggested that:
to enhance the potential for integrating sustainability concerns, it seems less fruitful to develop more advanced and complex assessment frameworks and models than strengthening institutional arenas for social learning.
There is a growing view that a traditional, ‘rational-linear’ model of use (i.e. science
production and transfer, innovation diffusion or innovation process) has been
superseded by an interaction model that acknowledges the iterative and interactive
nature of much research use (see Chapter 2.2 and Nutley et al., 2007). The resultant
interaction model involves a significant change from a traditional, information product
focus. The interaction model includes a range of factors – organisational, cultural,
informational and political – and growing recognition that they are important in shaping
research use. Notably, this does not deny that individual characteristics are also
important, as Mowday and Sutton (1993: 208) point out, ‘the organisational context
may have little or no influence when individuals or groups are buffered from or simply
ignore contextual forces’, nor that individuals can also shape organisational context.
One of the elements not often discussed in the literature on contextual factors is the role
of scale, where scale refers to the intra- and inter-organisational levels. Yet the
dimensions of context operate at various scales. Rich (1979: 334) writes that
‘Knowledge is, however developed in a context; the context is made up of internal and
external linkages as well as the interactions between the two.’ The internal linkages
may be characterised by an intra-organisational context, and external linkages by an
inter-organisational context. These relationships and their interactions are influenced by
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context at different scales. How can the different contexts be conceptualised? In the
absence of an overarching framework, context may be studied at intra- and inter-
organisational scale using three approaches drawn from other research traditions. First,
the ‘supply chain’ framework, drawn from business management research, may shed
light on contextual factors at the inter-organisational level, including possible linkages
between research organisations and the demands of wider society. Second, an
organisational perspective, based on organisational and management research, may
highlight contextual influences at the intra- and inter-organisational levels. Thirdly,
structuration theory may provide an analytical perspective for examining possible intra-
organisational influences of knowledge use. These elements provide three pillars of an
analytical framework to understand the internal and external contextual influences on
use. Whilst theory steered the development of the three pronged analytical framework it
did not dictate it.
Based on a consideration of theoretical developments in the field of knowledge use, and
observations and experiences in practical aspects of NRM policy-making, a set of
principles for analysing the science-policy interface were developed:
a) Connective – considers the multiple levels of interactions between research and
policy (e.g. micro-organisational, organisational and system perspectives);
b) Comprehensive - an inclusive approach that takes into account the social,
political, economic, technological and institutional contexts of the science-policy
interface and the nested nature of NRM policy-making, namely the components
of the policy cycle (e.g. across a range of federal, State and regional policy-
making contexts; across the science-policy ‘supply chain’);
c) Collaborative - grounded in an ‘interactive’ approach, emphasising the social
dimensions of science use in policy.
These principles guided development of the analytical framework for this thesis that
drew on work in other disciplines (e.g. information systems research, business
management and sociology) to help conceptualise the science-policy interface. This
analytical framework grew out of initial on-site observations and interviews, and was
refined throughout the process in an iterative interaction between the empirical material,
and the theoretical and contextual perspectives drawn from the literature. Thus, the
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analytical framework was not pre-ordained but was allowed to emerge in alignment
with the empirical material and literature. For example, structuration theory could have
been predestined for a study of contextual factors at inter-organisational level (e.g.
structuration theory has been used to analyse the coordinating mechanism of supply
chains) (Goldbach, 2003). However, the concept of ‘networks’ emerged from the
literature as integral to the inter-organisational context, suggesting that frameworks,
such as ‘supply chains’, that can accommodate this concept would be more appropriate.
Thus, the distinction drawn between the three pillars is one considered appropriate to
the context of this study, but not set in stone.
A fundamental challenge to conceptualising the use of research in policy-making is
extending the idea of micro-organisational behaviour to a macro-organisational level
(i.e. extending the perception of an inherently micro-organisational-level phenomenon
to the organisational level, and to inter-organisational and society levels). Hence, this
thesis seeks to understand the dimensions of knowledge use at different socially
constructed scales for a more holistic view of the social interactions shaping use. A
multilevel, analytical framework (Table 2.3) is presented as a holistic approach for
understanding contextual influences in a system, through analysis of the conditions at
intra- and inter-organisational levels:
1) A system level perspective: introduces an analytical focus on organisational
networks by using a ‘supply chain’ framework drawn from business
management research to examine how whole populations of organisations and
society may interact;
2) An organisational level perspective: introduces an analytical focus on whole
organisations, by examining the dimensions of organisational context (e.g.
culture, strategies, linkages) on science use;
3) A micro-organisational level perspective: introduces an analytical focus on
individual and group dynamics in an organisational setting, by drawing on
features of structuration theory to conceptualise the interplay between individual
and organisation.
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Table 2.3: A classification of the analytical framework
Perspective Level of Relationship Analytical focus
System Inter-organisational and
organisation/society
External context
Organisation Inter-organisational and
individual/organisation
Internal and external context
Micro-organisational Individual/organisation Internal context
2.4.1 System perspective
Mansbridge, 1990 (1990: 121) pointed out that in 1986 James Buchanan won the Nobel
Prize in economics for applying to politics a ‘rational’ choice model based on self-
interest. In the same year, however, he rejected the single motive of self-interest in
favour of looking at context. ‘There has not been enough attention paid’, he concluded
then, ‘to the interdependence between the predicted patterns of political outcomes and
the rules or institutions that constrain the political actors.’ Both those who imagine a
world of benevolent public servants following only the public interest and those who
‘have modeled politicians and bureaucrats as self-interested maximisers’ share ‘a fatal
flaw’. This ‘fatal flaw’, Mansbridge (1990) asserted, is the neglect of context.
Early studies of knowledge use were equally flawed. The focus was on the narrow
contribution of information with little recognition of the implications of the broader
institutional and social context. Subsequent work on knowledge use in policy
recognised that use is a complex process, strongly influenced by the context in which it
takes place (e.g. Huberman, 1994; Crewe and Young, 2002). In search of a less narrow
view of use, Sarewitz and Pielke (2007: 6) borrowed the concepts of ‘supply and
demand’ from economics to:
conceptualise science in terms of a ‘supply’ of knowledge and information, societal outcomes in terms of a ‘demand’ function that seeks to apply knowledge and information to achieve specific societal goals
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The concept of ‘supply and demand’ is popular with other researchers in this field.
Landry et al. (2001a: 400) used ‘supply and demand’, and the idea of the ‘market for
knowledge’, in their analysis of the transaction costs incurred in knowledge supply.
McNie (2007: 17) also used the concepts of supply and demand to describe the problem
of ‘reconciling the supply of scientific information with user demands’, focusing on an
exploration of the historical context of the problem. This thesis builds on this idea, and
informed by the business management literature (e.g. Spekman et al., 2001; Bessant,
2003; Hugos, 2006), applies the concept of ‘supply chain’ to explore the network
context (i.e. links in the chain) of knowledge use, by analysing the chain of knowledge
production and use activities. In this case, the links in the chain involve those entities
that are involved in the process of generating scientific knowledge for use by policy-
making ‘customers’. According to Stadtler (2008), a supply chain framework overturns
a traditional notion that a single organisational unit is responsible for the
competitiveness of products. It applies system thinking to include the supply chain as a
whole. The aim here is not to dissect all the activities along the science-policy chain, but
to use the supply chain framework to reflect on the often overlooked dynamics of inter-
organisational relationships, and the broader interactions between research
organisations, policy players and society.
Although the more common term ‘supply chain’ is used in this study, a preference for
the term ‘supply network’ is recognised. As Christopher (2005: 5) argued, ‘the word
‘chain’ should be replaced by ‘network’ since there will be normally multiple suppliers
and, indeed, suppliers to suppliers, as well as multiple customers and customers’
customers to be included in the total system’. Hence, the ‘supply chain’ does not portray
a simple relationship between research and policy-making organisations, but also
extends to links between research funding bodies, research organisations, lobby groups
and politicians. Interestingly, this focus on understanding linkages between stakeholders
is in contrast to the approach of the two-communities metaphor that focuses on the
cultural gaps between science and policy-makers (see Section 2.2.1). For example,
rather than view the problem as one of a ‘gap’ the problem may be viewed as one of
‘linkage’. This has potential practical benefits - parties may be more likely to work
together to strengthen linkages that, according to Jason and Powell (2004: 5) ‘channel
and direct flows of information and resources from position to position within a social
structure’, rather than if they viewed each other as the problem across a ‘gap’.
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Thus, the supply chain has been defined as (Christopher, 2005: 17):
a network of organisations that are involved, through upstream and downstream linkages, in the different processes and activities that produce value in the form of products and services in the hands of the ultimate customer.
In a broad sense, Stadtler (2008) noted, a supply chain consists of two or more legally
separate organisations being linked by material, information and financial flows. These
organisations may be producers, service providers and even the target group – the
ultimate customer. Hakansson and Johanson (2002: 373) pointed to different types of
bonds between supply chain members;
there may be technical bonds which are related to the technologies employed by the firms, knowledge bonds related to the parties’ knowledge about their business, social bonds in the form of personal confidence, administrative bonds related to the administrative routines and procedures of the firms, and legal bonds in the form of contracts between the firms.
Importantly, a ‘supply chain’ framework overturns a traditional focus on a uni-
directional flow of information and resources from supplier to customer – there is also
an emphasis on customer demands flowing back the other way. This incorporation of
end users in the supply chain may be aligned with the ‘interactive’ perspective of
knowledge use (Section 2.3.4) lending support to applying the ‘supply chain’
framework to study knowledge use.
However, applying the ‘supply chain’ concept to the production of knowledge products
and services is not new. Turnhout et al. (2007: 220) writes that ‘the notion of
knowledge transfer implies a ‘chain of knowledge’ from production, through translation
and transfer, to the use of knowledge’. Arguably, by focusing on the activities
throughout the ‘chain of knowledge’, and thus, adopting an inter-organisational
perspective of context, an enhanced broader, understanding of use may be gained.
Furthermore, Landry et al. (2006) successfully applied a similar, ‘knowledge value
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chain12’ approach to conceptualising the knowledge transfer process in public health
organisations, although the focus was on intra-organisational activities. Nevertheless,
Landry et al. (2006: 597) highlighted the role of organisational context, pointing out
that:
More concretely, knowledge transfer is about creating, transferring and transforming knowledge from one social or organisational unit to another in a value-creating chain: it is a complex interactive process that depends on human beings and their context.
Drawing on the management literature, they used the concept of knowledge as ‘the
resource with the highest, strategic value for organisations.’ Thus, knowledge is treated
as a resource that can be managed, and that can help improve organisational processes
through the common management concept of a ‘knowledge value-chain’. Landry et al.
(2006: 597) identified some knowledge translation problems:
The transfer of knowledge from one community or organisational unit to another usually faces five problems: knowledge access, knowledge incompleteness, knowledge asymmetry, knowledge valuation and knowledge incompatibility.
Elaborating on these problems, Landry et al. (2006) pointed out that socio-political
themes of social networks, trust and social capital are important contextual elements:
1) Knowledge access applies to the ability about the existence of knowledge and
the ability to retrieve it in a timely and usable form; issues include problems of
‘information overload’ and the ‘digital divide’.
2) Knowledge incompleteness occurs when the attributes of the transferred
research knowledge are not completely specified; a gap may exist between the
abstract principles that knowledge is based on and proof of its practical
application.
3) Knowledge asymmetry occurs when knowledge users know more about the
problems that need solving and knowledge producers know more about the
solutions.
12 The terms ‘supply chain’ and value chain are used interchangeably in this thesis. Both terms are widely used in the management literature and the key concepts are demand-pulled not supply-pushed cooperation.
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4) Knowledge users may be sceptical about the multiple solutions offered, while
knowledge producers might feel undervalued. They argue that development of
trust between users and producers is important to close this gap.
5) Knowledge valuation refers to people exchanging knowledge when the value
gained by the parties is greater than the costs involved. Although difficult to put
an overall value on knowledge because it is intangible, largely uncodified or
spread over groups of people, they allude to social capital as a key component of
more efficient knowledge transactions.
6) Knowledge incompatibility arises when knowledge producers or intermediaries
attempt to transfer to organisations or communities knowledge that is not
compatible with their mission, historical context, values, skills, resources and
prior investments in technologies. This problem is closely aligned with the
‘organisational interests’ perspective of knowledge use, that suggests research
results are more likely to be used when they meet the needs of users and support
the interests and the goals of the organisation (Section 2.3.3).
Thus, extending the ‘supply chain’ and ‘knowledge as a resource’ concept to inter-
organisational links in the knowledge production chain may improve understanding of
how the wider, external context (e.g. social networks) may affect the decision by
‘markets’ (e.g. policy-makers) to ‘purchase’ (e.g. use) a ‘product’ (e.g. scientific
knowledge).
For example, Webster (2002) points out that the demands of any market in a supply
chain can be related to the themes of responsiveness (e.g. speed), reliability (e.g. meet
customer needs) and relationships (e.g. all business partners). Thus, the concept of
‘supply chain’ complements the ‘interaction’ model of knowledge use, highlighting how
the use of science by policy-makers does not necessarily operate in a vacuum nor
according to a one-way, linear model of research transfer. Use may be facilitated by
‘customer’ norms and mutual exchanges of information. Thus, the focus of studies of
supply chain systems is the management of relationships between organisations in the
chain (Webster, 2002). The ‘supply chain’ framework emphasises the inter-
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organisational, context13 of science use, highlighting the participants, relationships and
interactions of knowledge transfer, from one social or organisational unit to another in a
‘knowledge chain’. The application of the ‘supply chain’ framework to the context of
knowledge use in policy-making is introduced and discussed in Chapter 5.
2.4.2 Organisational perspective
The importance of organisational context (such as culture and norms) to understanding
knowledge use has recently gained weight (e.g. Oh, 1996a; Rich, 2001; Landry et al.,
2003; Jacobson et al., 2004; Cummings et al., 2007; Belkhodja et al., 2007; Sarewitz
and Pielke, 2007). Sarewitz and Pielke (2007: 13) sketch the likely association:
‘decisions emerge within institutional contexts; such contexts, in turn, help to determine
what types of information may be useful for decision-making.’ In a similar, more
operational mode, Belkhodja et al. (2007: 380) explains that the organisational
perspective is ‘based on the idea that organisational structures, tasks, roles, procedures
and routines are essential elements in understanding the acquisition, dissemination, and
utilisation of information and, in particular, knowledge’. Rich (2001: xlv) argued ‘that
to understand the use of information in public policy-making one must first understand
the bureaucratic and political conditions under which public officials operate’. To
illustrate his point, he used the example of use of social science information by United
States public officials from 1972 to 1974 which revealed that the levels of use observed
were related more to standard bureaucratic rules and procedures, than to information
factors such as, timeliness, relevance and quality of the data presented. The focus solely
on information variables that influence use highlights a deficiency of the ‘science push
model’ (see Section 2.2.4). By using the more inclusive and integrated approach of the
‘interaction’ model outlined in Section 2.2.4, this study aims to overcome this
deficiency. Whilst the focus in this study is on the context of the user organisation it is
recognised that the policies and practices of research supplier organisations (e.g.
promotion and tenure) are also relevant to knowledge production, transfer and use
(Murray, 2001; Jacobson et al., 2004).
13 However, context in this case does not involve specific settings, events or individuals (e.g. Chapter 4 describes the specific context of NRM policy-making in Australia), but provides a ‘thick description’ of the internal and external environment within which scientists and policy-makers carry out their business.
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Despite increased awareness that an organisational perspective is important, there has
been limited adoption of related concepts and language, such as organisational learning
and culture, from organisational research. Drawing on a narrow conceptual base can be
disadvantageous by limiting the range of factors that are studied. According to
Estabrooks et al. (2003) one reason why the research community has not made more
progress in understanding nurse’s use of research has been the reliance on one theory –
Rogers innovation diffusion framework (Rogers, 1995). To avoid similar drawbacks,
Belkhodja et al. (2007) aimed to bridge the gap between the research fields of
organisation research and knowledge utilisation, arguing that if knowledge use is to be
appropriately modeled then an improved understanding of organisational factors is
needed.
In support of this more holistic view, Belkhodja et al. (2007) adopted concepts from the
organisation sciences, focusing on organisational absorptive capacity, organisational
learning and organisational culture, to study the impact of organisational influences on
research use by health service managers. They drew widely on organisational,
operations management and innovation management literature to explore these concepts
in detail. It is not intended to duplicate their efforts here. In brief, organisational
absorptive capacity refers to the capacity of the organisation to assimilate and reproduce
new knowledge acquired from external sources (e.g. indicators include size of unit and
people in the unit paid to do research); organisational learning is generally accepted as
stemming from the learning of individuals and the institutionalisation of knowledge in
(old and new) organisational routines, memory and structure (e.g. indicators include
activities to facilitate learning and research experience of professionals); organisational
culture is defined as ‘a specific set of standards, values, attitudes, beliefs, traditions,
language and ways of doing things that are particular to a given organisation (Belkhodja
et al., 2007: 391) (e.g. indicators include preference for research as a source of
information and the intensity research sources are used). Additionally, by using these
and similar concepts that have already been well defined, albeit in another research
field, researchers can operate from a consistent base, making it easier to share and
compare findings. This study builds on this approach by examining other concepts
which may be closely aligned to the current ‘interactive’ perspective of use.
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The ‘interactive’ perspective sees knowledge use as a socially mediated process and
emphasises the importance of relationships. This theme is shared with recent advances
in knowledge management that attend to a new social emphasis on the importance of
people sharing and learning knowledge; shifting from a focus on human capital (i.e.
individual learning, individual knowledge) and technical solutions (e.g. knowledge
repositories or content management systems), to social capital (i.e. networks of
goodwill, shared norms, social trust and mutual support that people can derive value
from)14 (Huysman and Wulf, 2004). Accordingly, Bresnen et al. (2004b: 1539)
observed that:
Recent theory and research on the diffusion of knowledge and learning within and between organisations has increasingly emphasized the socially embedded and situated nature of knowledge and learning and the associated difficulties in attempting to transfer knowledge and learning from one context to another.
In a similar vein, Wagner (2005: 17) argued that ‘an organisation's knowledge grows
out of ongoing and social interaction with other organisations and not simply by
transferring resources from one organisation to another’. Some literature reviews on
organisational factors that impact research use have suggested that relationships and
social interaction are important (Estabrooks et al., 2003; Greenhalgh et al., 2004).
Clearly, the increasing importance of the role of ‘the social’ in shaping knowledge
transfer and use is a widely shared, emergent theme. Stacey (2002, cited in Parsons,
2002) observed that ‘knowledge inheres in interactions between people. It is the product
of relationships, not something discrete, locked away in individual heads.’ Rarely,
though, have social dimensions gained detailed attention from scholars in the field of
research utilisation, although variables that affect knowledge transfer and use have been
posited (such as absorptive capacity and organisational interests) and ‘linkage
mechanisms’ between researchers and users have been identified as important
determinants of knowledge transfer and use (e.g. Landry et al., 2001a; Belkhodja et al.,
2007; Landry et al., 2007). In contrast, the allied concept of social capital has been
widely recognised in organisational and knowledge management studies as an important 14 Also mirrored in developments in information systems. Recently the information systems field has adopted approaches to structure and technology that focus more on social structures than on formal structure and production technologies as evidenced by recent adaptations of structuration theory (Orlikowski, 1992; 2000)
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ingredient for knowledge development in and between organisations (e.g. Cohen and
Prusak, 2001; Inkpen and Tsang, 2005). By explicitly linking social capital to
knowledge use, a more nuanced understanding of the ‘interactive’ perspective of
research use may be gained.
Social Capital and Knowledge Use
Social capital refers to ‘network ties of goodwill, mutual support, shared language,
shared norms, social trust and a sense of mutual obligation that people can derive value
from. It is understood as the glue that holds together social aggregates…’(Huysman and
Wulf, 2004: 1). The concept has recently gained prominence in a variety of research
fields, including political science (Putnam, 1995), sociology (Coleman, 1988),
education (Bourdieu, 1983), knowledge management (Yli-Renko, 2001) and
organisational theory (Cohen and Prusak, 2001). The concept is quite fluid as there is no
universal definition. Some researchers consider it to be membership in groups and
social networks (e.g. Belliveau, 1996); others consider it an umbrella term,
encompassing concepts including social cohesion and social exchange (Adler and
Kwon, 2002). Distinctions have been drawn between bridging (wider, overlapping
networks – ties between different groups) and bonding (refers to closed networks – links
within a group) social capital (Tonts, 2005; Estabrooks et al., 2006). More concretely:
Social capital consists of the stock of active connections among people: the trust, mutual understanding, and shared values and behaviors that bind the members of human networks and communities and make cooperative action possible (Cohen and Prusak, 2001: 4)
This definition emphasises that social capital exists at the group level by focusing on the
active connections between group members. Fischer et al. (2004: 359) contends that
such connections may generate better knowledge sharing and transfer within a group,
due to the established trust, shared language and goals and informal ties. Recognising
that social capital also exists at individual level, Nahapiet and Ghoshal (1998: 243)
defined social capital as ‘the sum of the actual and potential resources embedded within,
available through, and derived from the network of relationships possessed by an
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individual or social unit. This definition presents a more holistic view - that the social
capital of a group depends on the social capital held by its individual members (i.e. their
relations with other actors). This does not mean that it is a form of capital that is
possessed individually, but instead it is embedded within the structure of relationships
that connect people together (Bresnen et al., 2004a).
Nahapiet and Ghoshal (1998) provided a useful framework for understanding the
dimensions of social capital, arguing that it consists of three distinct but closely
interrelated dimensions:
· Structural: the network of relationships that connects people and helps individuals
to find people for assistance or cooperation. These connections are important
because they provide channels to knowledge that is unevenly spread within and
across organisations. Notably it is not the size of the network that is important but
its diversity (Burt, 1995);
· Relational: the kinds of bonds, such as respect or friendship, and the sense of trust
that individuals share. Of particular interest are the norms that underpin cooperation
and control within groups, such as trust and reciprocity (Bresnen et al., 2004a);
· Cognitive: the bonding force, such as shared understanding, languages, narratives,
codes or interests that binds the group. Bresnen et al. (2004a) pointed out that
‘communities of practice’, in which individuals collaborate and share ideas through
narration or joint work, depends heavily on cognitive social capital.
In a similar vein Paldam (2000) proposed three families of social capital 1) trust (i.e.
relational), 2) ease of co-operation (i.e. cognitive) and 3) network (i.e. structural). Many
different methods and indicators have been proposed to measure these dimensions of
social capital, and no single one can adequately represent the concept of social capital.
Comparing established network measures that may be used to measure structural social
capital, Borgatti and Jones (1998) suggested that it is important to recognise differences
in unit of analyses (i.e. individual or group) when choosing network measures. They
point out that such differences may also mask another difference, an outward focus (e.g.
external ‘bridging’ measures for individuals) versus an inward focus (e.g. internal
‘bonding’ measures for groups), and increase the tendency to overlook the possibility of
ties between groups (i.e. external ‘bridging’ measures for groups) For example, to
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represent the network dimension for individuals connected to others in a group they
suggest measures of heterogeneity (variety of connections) and quality (usefulness of
connections), and to measure the network dimension for groups connected to groups
they suggest measures of group degree (number of outsiders tied to at least one group
member) and closeness (total distance of the group to all non-members).
Apart from network (or relational) measures, there are also measures associated with
structural (roles, rules, procedures and networks that facilitate coordinating efforts,
creating expectations and lowering transaction costs) and cognitive (e.g. norms, values,
attitudes, beliefs) forms of social capital (Landry et al., 2000). For example, in a study
of regional innovation Landry et al. (2000) used as indicators of structural social capital:
1) network assets – sources of information for innovative actions (business, information
and research networks), 2) relational assets – degree of acquaintance with different
economic actors (e.g. managers in government agencies involved in the promotion of
economic development at regional level) and 3) participation assets – degree of
participation in meetings, associations and networks of manufacturing firms; and as an
indicator of cognitive social capital: trust – the extent to which reciprocal trust is
important as a condition of success in relationships with clients, suppliers and agencies
involved in the promotion of innovation. In a study of research use in policy-making,
Belkhodja et al. (2007: 410) used the term ‘relational capital index’ in regard to an
indicator of structural social capital (i.e. degree of acquaintance with researchers in
different types of research organisations), but did not define on expand on the concept.
The term ‘relational capital’ is popular in studies of evidence-based medical practice
and considered similar to the concept of social capital (e.g. Estabrooks, 2007;
Cummings et al., 2007). Cummings et al., 2007(2007: S37) reported that relational
capital was ‘important in influencing unit-level characteristics [increasing staff
development and reducing nurses' emotional exhaustion] that influence nurses’ research
utilisation’, They defined ‘relational capital’ as ‘a combination of positive and
collaborative working relationships among staff who work together across departments
and programs (physicians, registered and licensed practical nurses, and unlicensed
assistant personnel)’ (Cummings et al., 2007: S29). Thus, in studies of research use by
clinicians the concept of social capital likely refers to relationships between clinicians
that may be considered a more closed network, compared to a concept of a bridging
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network of relationships between researchers and users. Thus, ‘social capital’ will be
used in preference to ‘relational capital’, which is a less widely applied and understood
concept.
In general, social capital has been regarded as providing benefits for organisational
knowledge management. Knowledge acquisition has been identified as a direct benefit
of social capital (Adler and Kwon, 2002; Nahapiet and Ghoshal, 1998). However, there
is also a ‘darker side’ to social capital and knowledge use; the accumulation of social
capital does not always benefit an organisation. Adler and Kwon (2002) outlined the
potential costs and risks of social capital from the perspective of both individuals and
organisations. One of the main risks for both is that social capital can be a costly and
inefficient way of obtaining information as it involves considerable investment in
establishing and maintaining relationships. Particularly at risk is the transfer of explicit
types of knowledge which are more efficiently transferred via weak ties, compared with
strong ties more likely to be needed by the transfer of complex, tacit knowledge
(Hansen, 1999). Social capital can also inhibit the process of knowledge use by creating
conditions of dependency for an individual. For example, strong, internal ties combined
with weak, external ones, may favour more internal information searches, and inhibit
the acceptance of new ideas that are externally generated. Accordingly, Newman and
Dale (2005: r2) noted that ‘not all social capital is created equal’, and pointed out that,
Bonding social capital has the potential to hinder innovation by 1) cutting off actors from needed information, and 2) imposing social norms that discourage innovation. Bridging social capital allows actors to access outside information and overcome social norms with support from outside the local network, in addition to increasing access to diverse forms of other capital. Because bridging capital brings in new and potentially novel information, it is here that bonding capital provides the group resilience needed to absorb the benefits of bridging capital; the two capitals are complementary. The sheer amount of social capital is not likely to be a good indicator of how well a community will be able to engage problems. It is a dynamic balance of bonding and bridging social capital that builds resilience.
Thus, at an organisational level, high levels of bonding social capital would have the
potential to reduce the flow of research into a policy-making group and hamper research
use (Adler and Kwon, 2002). An extreme example of this can be seen in the condition
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of ‘group think’, a term coined by Janis and Mann (1977) to describe the negative effect
of in-group pressures on decision-making, where there is bias in assessing research and
little outside advice is sought. Thus, while social capital can have a positive effect on
knowledge sharing and use, it can also have an exclusionary effect that leads to the
rejection of new sources of knowledge that fall outside the existing social network
(Bresnen et al., 2004a). This is of particular relevance to the examination of the
relationship between gender and organisational social networks.
In emphasising a gender dimension to social capital and knowledge transfer and use,
Gidengil et al. (2003), cites that ‘politics is still very much a man’s world, and gendered
forms of social capital may mean that men get exposed to more information about that
world than women do.’ Similarly, in terms of much higher levels of male participation
in policy-making and programmes, NRM may also be regarded very much a man’s
world (Agarwal, 2000; Section 4.3.2). Gender has been identified as a key factor
shaping social interactions and thus, people’s access to and use of information resources
(Gidengil et al., 2003.). Several studies have found that men and women may have
different kinds and qualities of social capital based on differences in their social
networks, values of collaboration, levels of conflict and capacity for conflict
management (Agarwal, 2000; Molyneux, 2002, Westermann et al., 2005).
Researchers have pointed out the difference in social contexts in which men and women
operate - women often depend more on informal relations and so form stronger kinship
and friendship relations than men, who tend to rely more on formal relationships
(Agarwal, 2000; Molyneux, 2002, Gidengil et al., 2003.). Evidence suggests that men’s
social networks are better positioned than women’s to benefit from social relationships,
in terms of a diversity of information resources that may accrue from social ties
(Agarwal, 2000; Molyneux, 2002, Gidengil et al., 2003.). Thus, there may be no gender
difference in amounts of social capital but females may have less effective social capital
for effective information use (i.e. less knowledge about information services and less
social opportunities to access information, to build information resources and influence
the sourcing, shaping or using information). As illustrated by the observation of
Gidengil et al. (2003: 1) that ‘men and women with equivalent amounts of social capital
can have very different stocks of political information.’ Furthermore, female sources of
knowledge that fall outside the existing social network may be neglected. Agarwal
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(2000) emphasised that the lack of female participation in NRM (also see Section 4.3.2
for Australian context) may cause communication problems, resulting from failings in
information flow along gender lines. In a study of rural environmental management
groups in South East Asia, Agarwal (2000: 288), illustrates this point by emphasising
that ‘information about the rules framed, or changes in rules, such as in membership
eligibility conditions or on other aspects of forest management, do not always filter
down to the women, nor is there any inbuilt mechanism for their feedback.’
By failing to tap women’s knowledge of diverse species for enhancing biodiversity,
Agarwal (2000: 290), argues that this has had ‘negative efficiency implications’ for
rural policy. Thus, the gendered nature of social capital has implications for NRM
information transfer and use. Moreover, men are more accepting of technology than
women, suggests Miller et al. (2007). The general consensus from the risk perception
literature is, according to Miller et al. (2007), that men view risks as smaller and less
problematic than women, and report greater levels of trust in formal institutions,
particularly science, technology and government. Generally, women rate environmental
concerns more highly than men (Burger et al., 2000). Consequently, men may be more
likely to transfer and use new scientific findings than women. However, by overlooking
different perspectives about risk, important risk and uncertainty issues may be neglected
and the science needed to inform their management may not be sourced. If only one
kind of person asks the questions and interprets the results, then policy information
sources and analysis may be narrow and inbred. Thus, gender differences in social
networks, information use and risk perception may lead to the suppression of different
or disconfirming evidence.
2.4.3 Micro-organisational perspective
Using an analogy of a sports team, one could say that the organisational perspective
focuses at the organisational level on how the ‘team’ operates (e.g. organisational
culture), whereas a micro-organisational perspective emphasises ‘the rules of the game’
or institutional factors (e.g. organisational norms). It is based on the idea drawn from
structuration theory (Giddens, 1984), that social practices, such as information use in
policy, are organisational ‘structures’ that result from interactions between the
individual policy-maker and the organisational context (i.e. organisational power,
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resources and norms). Clark et al. (1990: 25) summarised the core of structuration
theory (from Giddens, 1984):
· Social practices lie at the root of the constitution of both individuals and society,
shifting the focus of social theory away from individual actions and experiences of
an individual actor and the existence and requirements of some kind of societal
totality;
· Human agents are knowledgeable and have the capacity to exercise their powers to
accomplish a social practice;
· These social practices are routinised and recursive, i.e. ordered and stable across
space and time people draw on `structural properties' (i.e. rules and procedures),
which are institutionalised properties of society, to construct the visible patterns
(social practices) that make up society;
· Structure is both the medium and outcome of a process of ‘structuration’, (i.e. it is
activity dependent, as seen in the production and reproduction of practices across
time and space.)
Structures, according to Giddens (1984: 25) only exist in people’s minds and ‘the social
systems in which structure is recursively implicated…comprises the situated activities
of human agents, reproduced across time and space.’ ‘Structures’ facilitate human
agency, as illustrated by Healey (1997: 47): ‘as individuals, we live in webs of relations
through which structuring forces bear in on us.’ In turn, ‘structures’ change intrinsically
as human agency changes, in response to external events and the agency’s recursive and
reflexive monitoring of them. Giddens (1984: 25) suggested that ‘analysing the
structuration of social systems means studying the modes in which such systems,
grounded in the knowledgeable activities of situated actors who draw upon rules and
resources in the diversity of action contexts are produced and reproduced in action.’ The
micro-organisational perspective adopted in this study is about examining ways in
which the social systems of policy-making, grounded in the activities of policy actors
who draw on rules and resources, are produced and reproduced in social practices of
information use and policy creation.
Thus, structuration theory (Giddens, 1984) provided an analytical approach and an
interpretative basis for the micro-organisational perspective, drawing on observations
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and experiences captured as part of an exploratory micro-ethnographic study of
information use in a policy-making organisation (See Chapter 6). Healey (1999) pointed
out that Giddens' analysis of structure emphasises the key relations that carry structure
forward: the authoritative structures (i.e. formal and informal rules), allocative
structures (i.e. the way resources are distributed), and knowledge and cultural structures
(i.e. ideas which frame how actions develop and are legitimated) – and a key
implication that changes in one category or structure result in changes in others. Thus, if
there are changes in the belief systems or norms, new resources arise to bring actions
into correspondence; if new resources are introduced, the underlying beliefs and norms
will shift. According to Healey (1999) the consequence of this perspective is that the
relation between knowledge and action is not linear, but continually interactive,
implying that ‘interaction models’ of developing knowledge resources are likely to be
more appropriate than linear ones. This view is not inconsistent with the emergence of
similar ‘interactive’ theories and conceptual models of knowledge production, transfer
and use (see Section 2.2).
Structuration theory emerged as a significant development in European sociology in the
late 1970s. Generally only the work of Giddens (1984) has received any significant
attention (Ritzer, 1996). The conceptual core of structuration theory lies in the ideas of
structure, system and the duality of structure. Structure, is defined as ‘the structuring
properties [rules and resources]…the properties which make it possible for discernibly
similar social practices to exist across varying spans of time and space and which lend
them systemic form’ (Giddens, 1984: 17). It is both ‘the medium and the outcome of
action’ (Giddens, 1984: 374). A key concept that Giddens developed is that social
structures are virtual, describing them as ‘traces in the mind’, which exist only through
the actions of humans. Thus, structures themselves do not exist in time and space.
Rather social phenomena have the capacity to become structured. Some scholars had
some difficulty with the virtuality of Gidden’s structure concept. Orlikowski (1992)
tried to imbue technology with structure but later revised this assertion (Orlikowski,
2000).
More specifically Giddens (1984) identifies three dimensions of structure which he
describes as signification, domination and legitimation. These are seen as interacting
through modalities of, respectively, interpretative schemes, resources, and norms,
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involving the communication of meaning, the exercise of power and the evaluation and
judgement of conduct as shown in Figure 2.3. The separation of these dimensions is
simply for analytical convenience, since they are, in practice, seen to be intimately
interlinked. For example, the operation of norms depends on power relationships for its
effectiveness and is deployed through symbolic and linguistic devices of
communication.
Structure
Signification
Domination Legitimation
modalities interpretive scheme
resources norms
Interaction Communication
Power Sanction
Structure(s) System(s) Structuration
Rules and resources, or set of transformation relations, organized as properties of social systems
Reproduced relations between actors or collectives, organized as regular social practices
Conditions governing the continuity or transmutation of structures, and therefore the reproduction of social systems
Figure 2.3: Giddens’ Model of Structuration (Giddens,
1984: 29)
Actors in structuration theory may be individuals or organisations. Individual actors in
organisations orientate their actions towards organisational practices that if frequently
used and accepted, turn into established organisational routines. Such practices and
routines, referred to as organisational ‘structures’ in the following, are constituted by
action and merely exist in the memories and expectations of their constituting actors
(Goldbach, 2003). This duality between individual and collective phenomena is at the
heart of structuration theory – social structure is seen as being drawn on by human
agents in their actions, while the actions of humans in social contexts serve to produce,
and reproduce, the social structure (Jones, 1999).
Structure constitutes both the condition for and the result of action. Beginning from an
action perspective, this implies that action is influenced by structure whilst
simultaneously reinforcing it (Goldbach, 2003). The largely abstract nature of
structuration theory has led to few empirical studies, though some exceptions are
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Orlikowski (1992) and DeSanctis and Poole (1994) use of structuration theory as a
framework to examine the introduction of advanced information technologies (e.g.
computer aided design and group decision support systems). Barley (1997) noted that
structuration theory ‘provides little guidance on how to investigate the way in which
everyday action revises or reproduces an institution.’ Nevertheless, structuration theory
has been recognised as a useful approach by researchers who have used it to examine
more deeply how human agency and organisation interact in both knowledge creation
and transfer processes.
For example, Goldbach (2003) used structuration theory to discuss power and trust as
coordination mechanisms that influence material and information flows in supply
relationships, by building up shared norms. For example, actors possessing power over
others may influence the latter to adapt their own values, and in practice this may
translate to use of only sanctioned information. Also if actors are prepared to trust one
another, then shared values may be set up, and in practice this may translate to an
increased openness to use scientific knowledge. In a different vein, Nonaka and Toyama
(2003: 4) used structuration theory to consider how the dynamic interplay between
structure and human agency can synthesise the medium from which knowledge evolves,
arguing that ‘structure and humans are two ways of considering social action and they
interplay in defining and reproducing each other. Knowledge is created through such
interactions between human agency and social structures.’ In this framework structure
and agency are viewed, not as independent or conflicting elements but as a mutually
interacting duality. Social structure is seen as being drawn on by human agents in their
actions, while the actions of humans in social contexts serve to produce and reproduce
the social structure.
2.4.4 Dimensions of the analytical framework - learnings
The above discussion has been a brief examination of theoretical developments in the
field of knowledge utilisation. There has been an evolution of conceptual models with
which to examine the use of knowledge in policy-making. Early research studies
assumed a linear, science push model, focusing on knowledge supply or information
factors. Later researchers focused on the communication and demand aspects of
knowledge use. More recently, an interaction model has been proposed that takes both
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views into account; implicit in the model is the importance of understanding context.
Using this integrated approach, some researchers have assumed internal, organisational
context may be an important determinant of use (e.g. organisational culture), while
others have focused on external factors (e.g. prevailing paradigms) affecting the ‘co-
production of science’.
Thus, prior conceptualisations may have each focused selectively on contextual
influences at one level of knowledge use. This study suggests that an interactive
perspective is incomplete if only applied to understand the interactions between
research and policy at a single contextual level (e.g. intra-organisational). An analytical
framework was proposed to reflect on the dynamics of science use across three
perspectives (system – supply chain, organisational – interaction model and micro-
organisational – structuration theory). Their potential contribution to our understanding
of knowledge use in policy is outlined below, drawing on the previous discussion.
Arguably, no single perspective can capture the dynamic, complex and multi-leveled
nature of the context of knowledge use. The potential of each perspective to shed light
on a different facet of context follows:
1. A system level perspective: using a supply chain framework, it portrays knowledge as
a strategic, organisational resource and the relationships between knowledge transfer
and use as a network; the term extends a traditional concept of a uni-directional flow of
information and resources from supplier to customer. There is an emphasis on customer
demands flowing back the other way, and on a wider network of organisations involved
in channeling and directing information resources. This emphasis on a bi-directional
connection between supplier and customer suggests strong alignment with the
interaction model of knowledge use. Unlike the other two perspectives, it emphasises
external influences that may be drawn from interactions with the external socio-
organisational conditions (e.g. availability of science depends on external social
conditions shaping investment in research (Lubchenco, 1998)). Thus, wider socio-
organisational themes of social networks, trust and social capital emerge as important
contextual influences.
2. An organisational level perspective: focusing on dimensions of organisational
context at the intra-organisational (e.g. organisational culture of users) and inter-
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organisational level (e.g. linkages) on science use, an organisational level perspective
highlights the importance of social context for knowledge use. It also facilitates theory
development by drawing on a wider body of established theory in related disciplines,
such as organisational management. For example, it can be used to link organisational
management concepts, such as social capital, to knowledge use and thus, gain a more
nuanced understanding of the interactive view of research use. As Newman and Dale
(2005: r2) noted ‘not all social networks are created equal’. Consequently, simply
increasing interactions between policy-makers and scientists may not lead to increased
research use if the bonding links within the organisation are too weak to absorb benefits
from the research. Thus, a dynamic balance between bonding and bridging links may be
more important than the amount of interactions.
3. A micro-organisational level perspective: Unlike the other two perspectives, this one
introduces an analytical focus at the intra-organisational level on the influence of
organisational rules and norms, using structuration theory to provide a lens to examine
the interplay between institutional context and individual agency on the practice of
information use. Importantly, it captures the dynamic nature of knowledge use and
focuses on issues of power and conflict which are not well captured by the other
perspectives. It can offer a new, more holistic and dynamic way of conceptualising
science use in policy-making, one that looks beyond the isolated characteristics of
policy-makers, organisations or of scientific information, instead, emphasising the
potential of interaction and the duality of individuals (agencies) and organisations
(structures).
These three perspectives should not be seen as replacing other frames or methods.
Whilst they are useful tools for illuminating the issues associated with science use at
neglected contexts and scales, they cannot provide answers to problems of under-use or
mis-use. However, they may assist the process of science transfer and use by
highlighting the particular contextual conditions influencing use, and the promotion of a
more transparent process of science use in policy-making.
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2.5 Conclusion
As Chapter 2 points out, there is a growing rhetoric about the use of science to underpin
policy and practice, even as critics call attention to the weakness of the underlying
assumptions of rationality and instrumental use (e.g. Lachapelle et al., 2003; Sarewitz,
2004). There are tensions between the often assumed perception of a rational world of
policy-making that suggests science feeds directly into policy, and the wicked and
messy world that policy-making generally inhabits (Lachapelle et al., 2003). Research
on practice demonstrates how experts and policy-makers tend to disagree and how,
generally, little formal information and analysis influence decisions (Innes, 1998: 55).
Furthermore, science is asserted by some to be socially constructed, not impartial and
often used to serve the interests of those in positions of power (Fischer, 2005).
The NRM policy-making environment in Australia has not been untouched by the wide-
ranging call for evidence-based resource policy. The increasing emphasis on science in
the natural resource policy agenda brings to the fore, asymmetries of power and
knowledge, particularly in regard to regional organisations where there is often a
mismatch of powers and funds and difficulty in accessing relevant information and
skills (see Ewing, 2003; Hajkowicz, 2009). Delivering science informed policy through
this policy-making environment is very difficult.
The notion and measurement of knowledge use applied in this thesis were presented in
this chapter. Early conceptions of the relationship between research and policy were
based on the idealised assumption that specific findings were being applied to specific
decisions (i.e. the instrumental use of knowledge) (Weiss, 1978; Innes, 1998). In
opposition to the rationalistic ideal, conceptual use or the enlightenment model has been
suggested – in which knowledge infiltrates policy-making in a diffuse manner,
promoting a gradual shift in opinions and shaping the way policy-makers think about
problems (Weiss, 1977c; Rich, 2001).
A historical perspective since the 1980s of conceptual structures for the study of
science-policy interface pointed to the weaknesses in the assumptions that underpinned
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early models of science use, such as rational planning approaches, a linear model of
science production and instrumental use of science (Oh, 1996a; Landry et al., 2001a,
2003; Estabrooks et al., 2006; Belkhodja et al., 2007). The science push model and the
two-communities metaphor were criticised for their neglect of social and organisational
influences on science use (e.g. Oh, 1996a; Slob et al., 2007). Researchers then turned
their focus to a demand pull model arguing that research results were more likely to be
used when they supported personal interests and the goals of the organisation (e.g. Oh,
1996a; Rich, 2001; Landry et al., 2001a, 2003; Jacobson et al., 2004; Cummings et al.,
2007; Belkhodja et al., 2007). The interaction model of science use has recently
emerged to overcome criticisms of the previous models.
Rather than focusing narrowly on factors such as information quality or political
objectives, an interaction model provides an opportunity for researchers and
practitioners to look at the intensity and maintenance of social interactions between
scientific and policy-making systems. The interaction model suggested that knowledge
transfer and use is a socially mediated process which depends on interpersonal relations
between researchers and users. However, despite the general utility of the interaction
model, it is unable to easily account for the complexity in the socio-political dynamics
of the science-policy interface.
Interpreting the complexity of the interface requires a more holistic approach. A central
issue facing researchers is how to conceptualise the interface in a way that is sensitive to
issues of scale and context, recognising that it is a social process occurring in a context.
Against an increasingly complex policy-making context and the need for bridging
various scales, a multi-level, analytical structure was formulated. Three approaches
(system – supply chain, organisational – context and micro-organisational –
structuration theory) were proposed to reflect on the socio-political dynamics of science
use in NRM policy-making in Australia.
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CHAPTER 3: RESEARCH METHODS
3.1 Introduction
Land and water degradation accounted for more than A$ 1.7 billion of Australian
Commonwealth government’s investments in NRM programs in 2001/02. With
degradation leading to increasing on and off-farm damage, investment in these
programs increased to A$ 4 billion in 2006/07 (Chapter 1). This has significant
implications for NRM policy-making processes across federal, State and regional levels
of governance, with new investment fuelling rapid institutional change and policy
development. There is, however, evidence to suggest that the effectiveness of NRM
policy-making is being hindered by the difficulty of bridging the science-policy
interface, particularly in an increasingly ‘messy and wicked’ NRM planning
environment. No longer can it be assumed that attempts to include science will
automatically improve the effectiveness of NRM policy-making (e.g. see Chapters 1
and 2).
Yet there are few systematic studies by geographers and other social scientists of
science use in Australian federal, State and regional NRM policy-making organisations.
The existing reports and commentary, written largely by natural scientists (e.g. Walker
et al., 2006, Briggs, 2006) tend to treat science use in NRM policy from a
communication perspective (Section 2.3.2), with little regard to the broader social
context within which both policy-making and institutional change occurs.
The general lack of scholarly attention to the social processes of science use in
environmental policy-making in Australia, is in sharp contrast to an extensive European,
Canadian and American literature that deals with issues associated with the social
dynamics of science use in social policy (e.g. education, health) (e.g. Oh, 1996a; Rich,
2001; Landry et al., 2001a, 2003; Belkhodja et al., 2007), and in environmental policy
(e.g. Freudenburg and Gramling, 2002; Lachapelle et al., 2003; Lövbrand, 2007). A
growing body of literature deals with questions related to the use of research in policy
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from an interaction perspective, emphasising the importance not only of ongoing
relationships between producers and users, but also the broader social context of use
(see Section 2.3.4).
To deal with issues of context and scale, the overall research approach is a combination
of case study15 and cross-sectional analysis. As described by Yin (1994), the former
allows for a context-rich description of the phenomenon in focus; the latter allows for
the discovery of relationships between a range of variables (Bryman, 2004). As science
use in policy and the Australian policy-making context are not easy to separate, a multi-
case study design16 allows the study to be extended over multiple contexts (i.e. federal,
State (WA and NSW), regional (WA and NSW)) in Australia, whilst the cross-sectional
design (within the case study design) enabled variation in use within the case to be more
closely studied.
Doing interpretative research means bringing a researcher’s bias, world-view and
background to the interaction with empirical material. To help ensure rigour some
scholars propose engaging in reflexivity (i.e. reflection on text production and language
use) (Poszzebon, 2004). To this end, I recognised that my own ideas about the interplay
between science and policy, about the role of science in NRM, and my role as a
researcher would affect this study. Using a reflexive approach, the thesis makes visible
my reflections on how my personal experiences shape the research so that the reader can
take them into account. I have also tried to adopt the role of an informed and self-aware
researcher by seeking appropriate literature and people to broaden considerations of the
research process and context and reduce the risk of researcher bias.
The study is based on an inductive approach; it began with a general aim and stayed
open to new ideas, interactions and understandings as the study progressed, similar to
the view of an evolving case study (Stake, 1995). A case study based on an inductive
15 Case studies are often used to illustrate problems or indicate good practice, but here, case study means a social research method ‘that lies in the explicit recognition, in the latter usage, of an underlying methodological philosophy about how we know the social world, and its linkage to a body of theory and practice in literature’ (Blaxter et al., 2006: 72). 16 Key NRM policy initiatives in Australia are funded by federal and state governments, and delivered through regional organisations. Therefore using a multi-case design, the use of science in NRM policy-making was studied in Commonwealth, State and regional organisations in two States (WA and NSW). At the time of the study, WA had a non-statutory approach with six NRM catchment management organisations, whereas NSW had a statutory approach and 13 catchment management authorities, each managed by committees that are skills-based, rather than based on geographic or agency representation.
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approach leaves open the question of how to best organise the empirical data so it can
provide a meaningful basis for analysis and conclusions.
A multi-strategy research approach that drew on an information system (IS) research
framework was used, to assist in defining the scope and guiding the organisation of data
collection and analyses (Ives et al., 1980). Although this work is not about research in
IS, the choice was based on the view that the scope of the framework would enable a
comprehensive treatment of the significant role that context plays in facilitating science
use by policy-makers (e.g. organisational contextual factors). This framework presented
a typology of five IS environments that defined the operating context of the information
system user:
1) the external environment includes social, political, cultural, economic and
resource considerations;
2) the organisational environment is marked by the organisational goals, tasks,
culture, structure, volatility and management philosophy;
3) the user environment is the environment surrounding and including users and
can be described by characteristics of the user and of the users tasks;
4) the IS development environment consists of the development methods and
techniques, design personnel and their characteristics and the organisation and
management of IS development; and
5) the IS operations environment incorporates the resources needed for IS
operations (e.g. software, hardware, personnel) (Ives et al., 1980).
Rather than focusing narrowly on individual level factors, the framework facilitated a
wider appreciation of the policy-making context by emphasising both the external (e.g.
prevailing ideology), and internal (e.g. cultural contexts) environmental contexts of
policy-making. It also highlighted the development and operations environments of
‘science production’ that can influence knowledge transfer and use. Using this
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framework (Table 3.1) allowed for a number of alternative methodological and
analytical approaches to be organised in a meaningful way.
Table 3.1: Research framework
Environment
Methods
Perspective
Analyses
External
· Literature analyses
· System level
Chapters 4 and 5
Organisational
· Literature analyses · Micro-ethnography · In depth semi-
structured interviews · Online questionnaire
· System level · Organisational
level · Micro-
organisational level
Chapters 4 and 5 Chapters 6 and 7
User (i.e. policy-maker)
· Literature analyses · Micro-ethnography · In depth semi-
structured interviews · Online questionnaire
· System level · Organisational
level · Micro-
organisational level
Chapters 4 and 5 Chapters 6 and 7
Development and operations (of science)
· Literature analyses
· System level
Chapter 5
Theoretical concerns steered but did not dictate the analytical perspectives developed.
Based on the approaches of Allison (1971), Hisschemoller et al. (2001) and Cohen
(2006)17, three different perspectives were used to examine science use in the Australian
resource and environmental policy-making system, with particular regard to context and
structural complexity: a system level perspective; an organisational level perspective
and a micro-organisational level perspective. Each provided a different lens to view the
process and they were organised into an analytical framework (See Section 2.4). The
data collected and analysed were used to illustrate specific attributes and examples
relating to the application of this analytical framework (see Section 2.4).
17 This approach is based on the analytic method used by Allison (1971) to examine the events of the Cuban missile crisis (i.e. three models -the rational actor, organisational processes and government politics); by Hisschemoller et al., (2001) to explore environmental policy analysis (ie. multiple perspectives including political science, international relations studies, knowledge utilisation studies and science and technology studies) and; by Cohen (2006) to analyse environmental problems (i.e. five dimensions - values, politics, technology and science, policy design and economics, management).
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3.2 Data Collection and Analysis
A case study approach allows for a range of data sources and methods to be used in
collecting and analysing information. This allows for different facets of the study to be
explored and merged, and for a triangulated approach to be used (Bryman, 2004). The
study used four main paths of inquiry: a micro-ethnographic18 study, contextual
analysis, semi-structured interviews and an online questionnaire. Importantly, the
cooperation and consent of the research subjects and institutions was gained for access
to documents, people and institutions.
3.2.1 Micro-ethnography/participant-observation study
Micro-ethnography/participant-observation was used to provide insights into processes
and perspectives unlikely to be shared in structured interviews or elaborated in
documents. Using this method at the start of the research process was timely. It allowed
the researcher to get closer to the phenomenon at an early stage and reflect on research
ideas and interpretations as they emerged. Research introspection is an inherent quality
of the process, as Eisenhart (1988: 106) observed,
‘The ethnographer regularly records the kinds of things that are happening to her or to him in the research situation. In this manner, the ethnographer tries to account for sources of emergent interpretations, insights, feelings, and the reactive effects that occur as the work proceeds.’
The method supported a dynamic research approach, and improved the opportunity to
adapt the research in response to the emerging contours of the issue and contextual
understandings. Rather than approach the phenomenon with established research ideas
and questions, these were allowed to evolve from the process, not unlike the approach
18 the ethnographic study is termed ‘micro’ to reflect the short time (a month on a full-time basis) spent collecting data. (Wolcott, 1995, cited Bryman and Bell, 2007: 442). Resource and practical limitations did not allow a full-scale ethnography over many years, generally associated with ethnographic studies of different cultures. This is not a reflection on the quality of data collected as there is no guarantee that a longer-term study would result in more useful data. A longer study could also increase the risk of bias as the researcher could become more of a participant than an observer (Bernard, 2002).
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described by Stange et al. (1994: 278): ‘In the dynamic research process, the level of
understanding is constantly evolving, and the research questions change as
understanding unfolds.’ The method also allowed a more holistic view of the issue and
its social and organisational dimensions, as Eisenhart (1988: 106) observed, ‘Once in
the field, researchers employ their [ethnographic] methods flexibly in an attempt to
maximize opportunities to view the scene from many different perspectives and
comprehend it holistically.’
The micro-ethnographic, exploratory study took place in 2005 in the Natural Resource
Management Business Unit of a Commonwealth government department. I worked full
time for a month on an internship basis within the department to gain an understanding
of the national context of NRM policy-making and to progress research ideas for the
thesis. My expected contribution, as explained by one manager, was to: ‘assist with the
development of our thinking on future NRM approaches’. I was exposed to the normal
agency processes and meetings, including opportunities to interact with and learn from
people across the agency. For example, I worked with people from different policy-
making sections and teams (e.g. Joint NRM Team, Future Directions Team) to
formulate policy questions and participated in intra- and inter-agency meetings to
review NRM policy directions.
Gaining access to people in a closed social setting like organisations is not necessarily
easy (see Bryman, 2004). Several factors probably contributed to smoothing my path.
Having a position within the organisation meant that I was kept notified of relevant
events to attend and useful people to meet. My introduction included that I was a PhD
candidate from the University of Western Australia, an affiliation pointing to
engagement in a credible activity. Another key element may have been a strong,
professional standing based on over two decades of research and advisory roles in the
agri-food sector and in government. Access to people was probably smoothed by having
shared professional networks, shared understandings about their problems and insights
into the way government ‘does business’.
I considered my work colleagues in the Division as members of a ‘policy-making
cultural group’ and as a participant observer I collected field notes about issues of
interest observed in the agency’s natural setting. This was done with a view to turn these
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exploratory research encounters with NRM policy-makers into research ideas and to
attempt to understand how a group of public sector employees ‘use information to make
NRM policy’. The guiding question was ‘How is information use in the NRM policy
process socially constructed among the members of the Division?’, supplemented by
‘What factors in the process can influence science use?’. The approach was that of an
observer operating largely without a theoretical construct. The micro-ethnographic
study was exploratory and conducted before an extensive literature review had been
undertaken, reducing bias that may result from using a pre-existent conceptual
framework, or a premature focus on certain data.
I also gained insights into individual and organisational factors that may affect the use
of science in NRM policy-making and have implications for the development of a
conceptual framework. The NRM Division had its own distinctive set of shared beliefs,
attitudes, practices, assumptions and expectations. It had its own language and networks
which made an ethnographic study of the ‘culture’ of NRM policy-making possible.
Observation was used in conjunction with other sources of data to contextualise and
extend the analysis conducted. Throughout the field work I was provided with access to
policy reports and documents, was engaged in policy formulation and spoke with many
individuals who acted as informants of the NRM policy process.
3.2.2 Contextual analysis
The second line of enquiry was a contextual analysis. The aim was to provide a ‘thick
description’ of the internal and external environment within which scientists and policy-
makers carry out their business, and more specifically of the NRM policy-making
context in Australia. The focus on issues specific to the Australian context, included
recent changes in the NRM institutions and processes that may affect the relationship
between policy-making and science use. This was not intended to be a historical review,
so no attempt was made to analyse primary documents. Reports by government
departments and research from published studies were selected based on their relevance
to the contextual analysis.
The contextual analyses complemented the collection of data by other methods. An
iterative strategy allowed interplay between the different sources of data, their analysis
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and interpretation. A major advantage of the iterative strategy was that it opened new
windows into understanding the data. From the micro-ethnography emerged a pattern of
information use by policy-makers. The analysis pays attention to how the pattern can be
framed by using structuration theory and also, how the pattern can be understood by
using a contextual analysis of public sector change in Australia. The contextual analyses
provided an opportunity to develop alternative perspectives to reflect on ways of
interpreting the data.
3.2.3 Semi-structured interviews
From September, 2006 to April, 2007, semi-structured interviews with 24, executive
level policy-makers and seven, senior scientists were conducted. Of these, 25 were male
and six were female; 13 were from Western Australia, 13 from New South Wales and
six from the Australian Capital Territory; 17 operated in State organisations, eight in
regional bodies and seven in Commonwealth organisations. Qualitative interviewing
allowed the collection of information about the participants’ own understanding and
meaning of the phenomenon, which is difficult to do with quantitative research
techniques that generally reflect the researcher’s concerns (Bryman, 2004). Interviewing
enhanced the scope and breath of the participant observation study, allowing
interactions with a broader range of people. Importantly, it provided an opportunity for
the researcher and participants to explore the questions and the answers in an in-depth,
interactive and flexible way, which is not often the case with other methods (Bryman,
2004; Patton, 2002). Interviewees were able to talk about what they saw as relevant or
important, and interview questions could be adjusted to seek clarification or respond to
interview direction.
An open-ended questionnaire format enabled the researcher to cover an extensive range
of issues. Interviewees were asked to talk about broad themes regarding aspects of:
i) The meaning and importance of using science in NRM policy-making;
ii) The use of science in NRM policy development and implementation;
iii) The importance of interaction between NRM scientists and policy-makers;
iv) The demand for science by policy makers;
v) The quality of the relationship between policy-makers and scientists.
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Scientists and policy-makers were asked to elaborate on their background, their
experiences and their perceptions in relation to these broad themes (see Appendix B for
the guide questions used in the interviews). The interview guide was piloted with six
policy-makers and scientists from an NRM organisation (not part of final sample) to
trial the questions and to practise interviewing technique. Shaped by ethical
considerations, the interviews were recorded with consent of participants, after
providing them with a brief background on the research topic and aims. It was also
agreed that the names of participant would not be revealed. The interviews were
conducted in the interviewee’s workplace and ranged in length between one to two
hours. The interviews were transcribed for analysis, which was undertaken using QSR
NVivo qualitative data analysis software. This software assisted with the physical task
of coding and manipulation of the data (e.g. relating the occurrence of a coded theme to
a particular attribute of the interviewee, such as gender or organisational location), and
the identification of key themes.
Thirty one individuals were interviewed based on a maximum variation (heterogeneity)
sampling strategy (Patton, 2002). By selecting a small sample of great diversity, any
common patterns that emerge would be valuable in capturing the shared dimensions of
the phenomenon (Patton, 2002). Hence, to facilitate variation the researcher sought
views of interviewees who differed from each other in terms of NRM affiliation,
geographic location and their ‘sector’. The target groups, selected purposively, were
scientific and policy-making members of Commonwealth, State and regional NRM
boards, committees or research organisations. Table 3.2 presents a breakdown of the
interviewees. Appendix A lists key attributes of the interviewees.
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Table 3.2: Number of interviewees, by affiliation, sector and state classification
Affiliation Sector Total Community 1 Commonwealth Government 4
Commonwealth NRM board/committee/research organisation Commonwealth Science 3
WA NSW Community - 1 Community - 2 3 State Government - 4 State Government - 1 5
State NRM board/committee/research organisation State Science - 2 State Science - 2 4
Community - 1 Community - 3 4 State Government - 2 State Government - 2 4
Regional NRM board/committee/research organisation State Science - 2 State Science - 1 3
From the early attempts to code the interview data (as per guidelines of Miles and
Huberman (1994) and Bryman (2004)) it became quickly apparent that the themes
emerging were shared with system and organisational analytical perspectives (see
Section 2.2). Thus, the interview data were compiled into two sets of analytical
categories, focusing on supply chain and organisational dimensions respectively and
based on an iterative interaction between the empirical material and the literature. These
categories were subdivided and examples are given in Table 4.3 (for explanation of
categories see Chapter 5 for system and Section 7.6 for organisational categories).
Text Box 3.1: Examples of analytical categories Organisational categories, such as:
• informal and formal linkages between policy-makers and researchers
• research culture support
• policy-making roles
• use of external information sources
• information relevance for policy-making needs
• research experience of policy-makers
• support of organisational interests
• efforts to adapt research for policy use.
System categories, such as:
• issues of science supply
· uncertainty of science
· availability of science.
• issues of science demand
· ‘wickedness’ of NRM policy problems
· policy-making environment (e.g. restructuring, outsourcing, political conditions).
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To help uncover differences between the categories some quantification of the data was
undertaken. This approach is in line with the methodology used by Silverman (1984) in
a social analysis of doctor-patient interactions in public and private oncology clinics.
Using limited quantification of some of the data, Silverman (1984: 193) argued that
‘Observational methods necessarily concentrate on particulars deemed ‘interesting’
according to certain theoretical or practical frames of references…when wedded, as here
to simple methods of quantification, it seeks to combine theoretical insight with
methodological rigour’. Thus, this approach aims to combine the theoretical insights of
the three analytical perspectives with a practical appreciation of the prevalence of issues
raised by interviewees.
3.2.4 Online questionnaire
Primary data were collected from an online questionnaire of NRM policy-makers in
Commonwealth, State and regional organisations in two states, WA and NSW, in 2007.
To collect the data a questionnaire was generated that was pre-tested and discussed with
five policy participants in representative NRM organisations. To generate the sample of
policy-makers, each NRM policy-making organisation was contacted and a list of NRM
policy-makers was obtained from them, or an agreement that they would forward the
questionnaire to their policy-makers was made. The questionnaire (see Appendix C)
provided data regarding the use of science by policy-makers in Australian NRM
organisations (see Chapter 7 for details of variables, measurement and analysis).
MySource content management system software facilitated online delivery of the
questionnaire, and protocols suggested by Dillman (2000) for internet questionnaire
design were followed. The invitation was emailed to each policy-maker in the sample
asking them to access the questionnaire and respond by a specific date. To improve the
response rate, follow-up emails and phone calls were used as recommended by Dillman
(2000). The email invitation was sent to an estimated 426 NRM policy-
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makers19, and 80 usable questionnaires were returned. The total response rate was 19 per
cent. The valid responses indicated that most were from State government (50 per cent)
compared with Commonwealth government (17.5 per cent), regional organisations (27.5
per cent) and other NRM organisations involved in policy-making (e.g. State
government utility, federal government funded incorporated organisation) (five per
cent). Respondents were from WA (66.3 per cent) and ACT/NSW (33.8 per cent).
The data were processed and analysed using the Statistical package for the Social
Sciences (SPSS) Version 12.0. The statistical analyses used descriptive statistics, such
as frequencies to assess the range and differences in use of science in the sample.
Relationships between variables were explored using correlation and regression
analyses and this process and the measures used are detailed in Chapter 7. Since the
variables consisted of continuous and categorical types the study used statistical
methods that were appropriate (e.g. Pearson’s correlations for dependent interval/ratio
variables; Spearman’s Rho for dependent categorical variables; multiple regression for
continuous dependent variables). To identify patterns of factors influencing use of
science by NRM policy-makers, and to corroborate the findings of the regression model
a factor analysis was also conducted (See Section 7.8).
3.3 Conclusion
This chapter began by reviewing some of the gaps in knowledge about the use of
science in policy-making, particularly in regard to the paucity of Australian studies
about the social processes and context of science use in NRM policy-making
organisations. It then shifted to the research design that guided the research, focusing
on:
1) the research framework for organising a number of alternative methodological
and analytical approaches in a meaningful way; and
19 The target population comprised 25 per cent Commonwealth, 31 per cent State and 44 per cent regional organisations, with 42 per cent from WA and 58 per cent from the ACT/NSW.
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2) the analytical framework that was adopted to examine science use in the
Australian NRM policy-making system, with particular regard to issues of
context and structural complexity (see Section 2.4).
This Chapter also described the range of methods that were used in the collection and
analysis of data. The data collection methods included an extensive literature review, a
micro-ethnography, semi-structured interviews and an online questionnaire of NRM
policy-makers. The analysis of the multiple data collected was also included in the
discussion of the data collection methods.
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CHAPTER 4: THE CONTEXT OF NRM POLICY-MAKING IN AUSTRALIA
4.1 Introduction
An increasing number of studies have focused on the connection between research use
and organisational context (see Chapter 2). However, NRM policy-making does not
occur in an organisational vacuum but within an extended context, shaped by
institutional, political, economic, and ideological influences. The context of Australian
policy-making is unique. Its policy-making institutions and processes are shaped by
Australian political culture, influenced by a distinctive legal and economic system, and
by the institutional arrangements of the public sector (Davis et al., 1993).
Decisions about land clearing, tree logging, drainage or environmental rehabilitation are
made by people in particular settings, and under pressure from a range of stakeholders,
organisations and other influences. Research evidence has to compete with not only the
political ideology and the agenda of the government of the day, but also with financial
restraints, public preferences, economic factors, political practicality, intellectual trends
and other influences (Black, 2001; Bowen, 2005). Indeed, some authors go so far as to
suggest that research use in policy-making would seem to be almost completely
dependent on the policy-making context (e.g. Stone et al., 2001).
The purpose of this chapter is to examine the role of the Australian NRM policy-making
context (e.g. institutions, policy-processes) in shaping the interactions between science
and NRM policy-making. It is not possible in one chapter to map all of the changes or
review all features of Australian NRM policy-making; its aim is to provide a broad
overview of how contextual change can influence the use of science in policy-making.
The Chapter also reflects on the interplay between science and policy-making in three,
contemporary NRM programs (i.e. Landcare, National Action Plan for Salinity and
Water Quality and the National Heritage Trust).
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For a comprehensive coverage of contextual factors, a framework delineating both
internal and external factors important to the Australian policy context is applied:
· the external environment (e.g. prevailing ideologies; technological
development);
· policy stakeholders and pressure groups (e.g. advisory committees and media);
and
· internal business strategising and planning (e.g. public sector reforms, expertise,
policy process).
Thus, this Chapter sets out to explore the particular context of resource and
environmental policy-making as played out in Australia, to review the wider changes in
the policy arena, institutions and policy processes, and to map their effects on the
interplay between the broad policy-making context and the use of science in policy-
making. This Chapter, therefore, aims to provide an Australian local context within
which to view and interpret the data collected.
4.2 The Australian Research Context
Australia is a region of particular interest to the study of the use of science in NRM
policy-making. There are several reasons for this. First Australia is an ecologically
unique continent, characterised by high levels of biodiversity (80 per cent of which is
found nowhere else) and a fragile landscape under pressure (Nelson et al., 2004;
Hamblin, 2009). It has an economy based more on natural resources than many other
countries: agriculture and mining account for over 61 per cent of export earnings
derived from trade in commodities (OECD, 2007). Agriculture is by far the largest user
of land and water resources in Australia. Today agriculture covers around 60 per cent of
Australia’s land area (ABS, 2007) (see Figure 4.1) and the agricultural sector accounts
for about 65 per cent of water consumption (ABS, 2006). Yet Hamblin (2009: 1197)
points out that this does not mean that the match between agriculture and the Australian
envionment is ideal: ‘Non-irrigated farm production levels are low by world standards
because of the low and erratic nature of the rainfall and the infertile, acid, alkaline or
salty soils.’ But not only is agriculture is under pressure from the environment, it also
places stresses on the already fragile environment, exacerbating land degradation and
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declines in water availability that constitute major challenges for Australia (Industry
Commission, 1998; Hamblin, 2009).
Figure 4.1: Land use in Australia (NLWRA, 2002).
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Based on visual and anecdotal evidence, Campbell (1994: 16) emphasised that ‘Salinity,
soil acidification, soil structure decline, vertebrate pests, weed infestation, rural tree
decline, water quality decline and species extinction are all increasing, if not
accelerating.’ In 2001, a national land and water assessment (NLWRA, 2001b: viii)
verified this by reporting that:
Soil acidification looms as a major soil degradation issue in all Australian States...Water-borne soil erosion is a major and continuing issue for Australian agriculture and catchment management and impacts on river, estuary and marine resources.
Unrealistic expectations of long-term resource capability and water availability,
combined with inappropriate management have, over time, led to the decline of land,
water and vegetation resources in many Australian rural areas, reducing biodiversity and
natural habitats (Australian State of Environment Committee, 2006; Hamblin, 2009).
According to a farmer survey in 2002, two million hectares on 20,000 farms across
Australia showed some signs of salinity (Australian Bureau of Statistics, 2002).
Similarly, the National Land and Water Resources Audit in 2001 estimated that a total
of 2.4 million hectares were saline, of which 1.6 million hectares were in Western
Australia, with a total of 5.4 million hectares predicted to be at risk of salinisation.
About 30 per cent of Western Australia’s south-west is considered to be at risk of
becoming saline by 2050 (NLWRA, 2001a). Soil acidity is also a serious concern across
large areas of rural Australia, mainly because of agricultural practices. After many years
of acidification under legume-based pastures and related cropping rotations, about 50
million hectares of Australia’s agricultural land (around half the total area) have a
surface soil pH value less than 5.5. (Australian State of Environment Committee, 2006)
(Figure 4.2).
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Source: NLWRA, 2002
Figure 4.2: Distribution of land degradation factors
(i.e. soil acidity and salinity) induced by agricultural
practices in Australia.
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The cost of NRM is high; estimated at A$ 3.5 billion annually (ANAO, 2004), or 10%
of agricultural GDP (OECD, 2008). Farmers invested about A$ 220 million in 1999-
2000 in NRM and environmental protection (mainly on fencing, earthworks and weed
management), or about A$ 2.60 for every dollar invested by the government
(Commonwealth of Australia, 2003c). Rural town residents also share these costs, as
salinity impacts not only water quality, productive land and bio-diversity, but also
infrastructure (Commonwealth of Australia, 2004a). Because most agricultural
production systems rely heavily on the condition and productivity of the natural
resource base, the declining quality and availability of water and ongoing land
degradation are real concerns to the Australian agriculture industry.
The second reason why Australia is of particular interest is that scientific knowledge for
managing resources, including both the natural and social sciences, is expected to feed
into Australian environmental strategies, policies and legislation (Commonwealth of
Australia, 1992). But this transfer is complicated by issues of ‘supply’ (e.g. shortage of
science at relevant scales) and issues of ‘demand’ (e.g. the political nature of the policy-
making context). Referring to Australian environmental policy, Mercer, 2000 (2000: 21)
points to ‘the interplay between the dictates of political and scientific rationality’ (italics
in original) and emphasises that ‘more often than not the conclusion is inescapable that
it is political considerations that determined final policy outcomes rather than scientific
findings, however valid.’ Currently, at least a billion dollars per year is invested in
publicly funded research into NRM20 and agricultural science in Australia (Campbell,
2006). Despite this, serious information defects have been highlighted in the NRM
policy-making system, emphasising the poor quality of data and science to inform NRM
policy (Productivity Commission, 2004; Commonwealth of Australia, 2008a;
Hajkowicz, 2009). Due to a lack of adequate data, auditors reported being unable to
measure the impact of programs on the condition of natural resources at the landscape
scale (Commonwealth of Australia, 1997; Commonwealth of Australia, 2008a).
Third, the urgency for more effective natural resource planning and policy-making is
rapidly growing. Despite recent significant investments by government in NRM
20 The collective investment by Australian rural research and development corporations to tackle NRM issues was estimated at A$78.5 million during 2004/05, representing more than 20 per cent of the corporations’ research investment (Joint RRDCs NRM Working group, 2005).
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research and in major NRM policies and programs21, environmental problems continue
unabated amid increasing concern about the difficulty in translating research findings
into improved decisions and concrete policy actions22. Some scientists and policy-
makers suggest that, as a result, NRM policy is less robust, and returns on research and
NRM program investment are weakened (Cullen, 1990; Committee for the National
Institute for the Environment, 1994; Hajkowicz, 2009). Recent reports commissioned by
the Australian government have recommended immediate improvements to linkages
between salinity science and policy at regional and national levels (CSIRO and BOM,
2004; Commonwealth of Australia, 2004a; RM Consulting Group, 2006).
Fourth, Australian NRM is also an interesting arena in relation to resource governance
and the use of science in policy-making. Over the past few decades, Australia has seen
the emergence of many new NRM institutions and organisations. In the past decade a
rapid area of institutional development has been the regional delivery of NRM and
associated increase in community environmental organisations. Today, Australia has 57
regional natural resource management bodies each with a community-based board of
management with responsibilities for integrated management of the region’s natural
resources, supported by a regional NRM body (Robins and Dovers, 2007) (Figure 4.3).
Many of the regional groups now have statutory powers and functions under State
government legislation. This devolution of responsibility is confronting regional groups
with ‘wicked’ NRM problems, and a concomitant need for better knowledge, and better
knowledge distribution, to support ‘the formation of sustainable decision-making
partnerships between scientists, society and management agencies’ (Rogers, 2006: 278).
21 In 1989 the Australian Government announced the National Landcare Program with a funding package of A$ 340 million (Thomas, 2007). The Natural Heritage Trust (NHT) was announced in 1996 with a funding package of A$ 1.3 billion for the next 5 years (phase 1). Two subsequent extensions of the NHT have been announced of A$ 1.2 billion in 2001 (phase 2) and A$ 2 billion in 2007 (phase 3). Another program commencing in 2000 was the National Action Plan for Salinity and Water Quality (often referred to as the ‘NAP’). The NAP was funded by State and Federal governments at A$ 1.4 billion over a 7-year period. 22 The Natural Heritage Trust Phase 1 Final Evaluation report commented on the lack of scientific information to inform decision-making (Hassall and Associates Pty Ltd, 2005) and the House of Representatives Standing Committee on Science and Innovation reported risks for the adequate use of science in regional plans (Commonwealth of Australia, 2004a).
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Source: http://www.environment.gov.au/biodiversity/threatened/nrm-regions-map.html
Figure 4.3: Australia’s 57 NRM regions
The regional delivery model appears highly conducive to the spread of knowledge.
Jennings and Moore (2000: 189) concluded that ‘The key value of regionally organising
for natural resource management is providing for information broking and networking.’
However, although this view holds promise for regional delivery to support the
distribution and use of scientific information in policy, the reality is that ‘the inability of
some regions to access adequate data and analysis at a useable scale on key issues has
hindered the progress of quality planning for these NAP regions.’ (Commonwealth of
Australia, 2004a: 15) and ‘the absence of sufficient scientific data has limited the ability
of regional bodies to link the targets in their plans to program outcomes’
(Commonwealth of Australia, 2008a: 16). Regions surveyed by the Australian National
Audit Office (ANAO) in 2007 commented on the lack of adequate scientific data. Of
the 50 regional bodies that responded only 12 (24 per cent) agreed that the level of
scientific knowledge and technical data has been adequate for developing plans,
investment strategies and measurable regional targets (Commonwealth of Australia,
2008a). This is consistent with the view of Robins and Dovers (2007) that limited
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access to the internet, experts and relevant information has made it difficult for the
planning processes of regional policy communities in Australia to be informed by
current science and thinking.
Thus, at regional level, issues of access and availability of scientific information have
been recognised as barriers to use. However, responsibility for NRM also rests with
federal and State governments. The notion that planning and decision-making processes
be informed by current science and thinking has been at the heart of agreements
between all three entities. For example, the Intergovernmental Agreement on a National
Action Plan for Salinity and water quality, between the Commonwealth and the states,
specifies that the integrated catchment/regional plan and catchment/regional targets will
be based on scientific analyses (Commonwealth of Australia, 2000). This multifaceted,
variable policy-making context makes Australia of particular interest, as the use of
science in NRM policy-making happens across different contexts (e.g. geographic,
institutional) and in organisational environments that are in an ongoing state of flux.
Germane to the context of this thesis is the use of science in NRM policy-making in
Australia across the three tiers of governance: regional, State and federal.
4.3 The Changing Context
4.3.1 The external policy-making context
The external policy-making environment (e.g. prevailing ideologies; technological
development) matters because it shapes aspects of policy-making (e.g. political, social
and technological) that impact on the supply of and demand for science and the
acceptance of scientific claims. For example, some new external economic, societal and
technological factors that may be driving change in the Australian NRM policy-making
context include new information and communication technologies, globalisation of the
Australian economy and managing a sustainable environment (Commonwealth of
Australia, 2004c). This section identifies themes in the external, NRM policy-making
context of Australia to guide exploration of the interactions between policy-making and
science use from about 1890 to the present day. It draws on studies that explored the
wider context that NRM operates in (e.g. Dore et al., 2003).
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Prevailing ideologies
An ideology has been defined as ‘coherent sets of ideas’ that ‘link values to programs of
action.’ (Doyle and Kellow, 1995: 38). Throughout most of the history of Australia,
economic development has been a major political ideology and the exploitation of
natural resources has always been central to economic expansion (Yencken and
Wilkinson, 2000). Agricultural development has been a most pervasive economic
activity (over sixty per cent of Australia is farmed and grazed), generating much wealth
but also leaving a legacy of widespread soil structure decline (about 26 million ha at
risk), soil erosion, soil acidity (over 55 million ha at risk), dry and wetland salinity
(more than 17 million ha at risk) (Yencken and Wilkinson, 2000; NLWRA, 2001a).
To meet the aspirations of economic development, Australian governments between
1870 and 1930 decided that government needed to play a more proactive role, as
observed by Stokes (2004: 14):
This ideology [of state development] prescribes a role for the state in generating economic growth and is based upon a view of the ideal relations between government and business within a particularly harsh and isolated geographic context...State development represented a form of collective action required because private individuals and corporate action were insufficient for the tasks of building the wealth of the colonies and the nation.
An example of this approach is provided by Western Australia. Following the granting
of self government to Western Australia in 1891, the ideology of state development
drove the formulation of an agricultural development policy. Notably, early agricultural
development appeared driven by a desire by government to create a cultural landscape
and social class, the pursuit of the ‘yeoman ideal’ as a policy objective (Tonts, 2002).
Western Australian agricultural policy increasingly targeted management of the
productive function of the landscape. The Homesteads Act, 1893 gave land away on
condition it be actively cleared of vegetation and cultivated. This was complemented by
the Agricultural Bank Act, 1894 that provided the basis for the Agricultural Bank of
Western Australia that made loans to farmers for clearing and cultivating the land. The
State was able to engage in these paternalistic behaviours because of its stronger
financial position following the discovery of gold in Western Australia during the early
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1890s. Additionally, the Royal Commission on Agriculture, 1887 prescribed the
functions of the agricultural landscape and provided insights into what it regarded was
appropriate. Wheat was favoured as the major crop but mixed-farming was encouraged.
(Burvill, 1979; Tonts, 2002).
The overarching political agenda of ‘economic development’ generated a demand for
science focused on agricultural production and profitability. As noted by Tonts (2002:
110), a Bureau of Agriculture was established by the Forrest government in the 1890s
‘to improve the scientific basis of farming in Western Australia’. The bureau was
renamed the Department of Agriculture in 1898 and, as well as, providing technical and
scientific advice to farmers, it advised the government on aspects of agricultural policy
(Tonts, 2002). The Department conducted research aimed at developing new methods of
farming to improve production and profitability (Glynn, 1975). The importance of
wheat production as a policy objective shaped the direction of research to focus on
improving wheat yields. Research findings from the 1900s demonstrated the benefits of
superphosphate fertiliser application on pastures and wheat and made the expansion of
agriculture in Western Australia possible as the soils are nutritionally poor (Burvill,
1979).
During these early years, the ‘supply’ of science seemed largely driven by the demand
for knowledge to improve agricultural production. Taking a long term view of the
potential environmental costs of agriculture was generally not considered in the policy
or research equation. Whilst science had seemingly created an agricultural solution to
the policy problem of economic expansion, it would also introduce another policy
problem of environmental degradation that a new generation of policy-makers would be
seeking solutions for. It was not until the 1930s when the ‘dust bowl’ conditions of the
United States of America raised alarm bells about soil erosion that Australian States
began to take notice of the impacts of farming on the environment (Conacher and
Conacher, 2002). In Western Australia, the Soil Conservation Act 1945 set up the Soil
Conservation Service that aimed to conserve soil resources and mitigate erosion.
Science then targeted soil conservation issues and developed techniques to manage the
problem. From 1949-69 the cleared land on farms doubled with the advent of new land
clearing methods. The total area of farmland remained at about 13 million ha from 1930
to 1951, but rose steadily to 19.5 million ha by 1968. Cereal growing and sheep
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husbandry provided 75 per cent or more of the gross value of rural production. Then this
was mirrored in the production focus of scientific research into legumes, nitrogenous
fertilisers, animal disease control and breeding, new chemicals and plant pathogens
(Burvill, 1979).
The Commonwealth government became more active from the 1970s, commissioning
major inquiries or reports dealing with land use and rural policy and proposing national
and State structures. The findings of the Commonwealth Government’s 1978 national
collaborative study on soil erosion in Australia was significant in raising government,
agency and public awareness of the nature and extent of Australia’s growing land
degradation problems (Conacher and Conacher, 2002). By the late 1980s the National
Soil Conservation Strategy was released and funding was made available under the
National Soil Conservation Program (NSCP) for research and community projects to be
conducted with the States (Conacher and Conacher, 2002). An ideology of
‘sustainability’ was also emerging, largely driven by the report Our Common Future, by
the World Commission on Environment and Development (1987). New, and sometimes
tangential, ideologies complicated the policy-making process, making the path less
smooth for scientific findings to be translated into resource management policy.
The different views of managing resources led to coordination problems in resource
management policy between the Commonwealth and States, and within States, at
agency level. An example was the attempt to limit vegetation clearance in Western
Australia in the early 1980s. State environmental policy may have been working
towards that aim, but land settlement policies still required clearance as a condition of
settlement. This policy was reinforced at the Commonwealth level by taxation
provisions that made expenditure on clearing deductible (Kellow, 1996). Furthermore,
Doyle and Kellow (1995) argued that although soil degradation loomed as the most
significant environmental problem, it had been neglected in part due to the
overemphasis by the environment movement in Australia on wilderness preservation.
They contend that this had led to the neglect of the rural environment which has less
‘sex appeal’ and ‘which, paradoxically, includes those areas that suffer most
degradation on an everyday basis.’ (Doyle and Kellow, 1995: 12). In contrast, Moore
and Rockloff (2006: 260) point to the neglect of resources with little economic value
and contend that ‘The strong focus by the Australian government on the sustainability
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of economically important natural resources, such as agriculture and water, reflects its
developist paradigm.’ Differing views have made the path for science into the
environmental policy-making process less smooth, as the process tries to accommodate
parallel ideologies of ‘economic and state development’, ‘sustainability’ and
‘wilderness preservation’ and the different views, interests and values of different
stakeholders (e.g. governments, State agencies, interest groups).
Additionally, there were differences between the management and research ideologies
of eastern Australia and Western Australia (WA), as illustrated by the case of salinity.
Passioura (2005) highlighted that some 25 years ago the government policy view in WA
maintained that solutions to salinity were at hand and that it was reasonable to release
further large areas for clearing for agriculture. In contrast, the Victorian Salinity
Committee of the Parliament of Victoria was deeply concerned about salinity and
considered the possibility of using the Murray as a drain to export the salt from
catchments to the sea. Such differences between eastern Australia and WA persist to
this day, reflected by their ‘diverging perspectives on appropriate management
interventions and research and development (R&D priorities).’ (Commonwealth of
Australia, 2004a: xxxvii). Therefore, the process of formulating NRM policy and
research priorities (that dictates what science is available for policy) represents a
balancing act of opposing ideological pressures.
Concern about the declining state of the nation’s land and water resources grew in the
1980s; the ideology of ‘economic development’ was now strongly paralleled by an
ideology of ‘sustainability’ and its long-term integration of economic, social and
environmental imperatives (Dovers, 2003c). This resulted in a paradigm shift in
thinking about the research needed for policy-making; from biophysical research
focusing on production issues, to economic and social research. Accordingly, a
Commonwealth government report noted that, ‘A full understanding of agricultural
systems and any changes needed to move towards sustainability will also need to
encompass economic and social factors’ (Commonwealth of Australia, 1991: 189). This
report emphasised that:
scientific, economic and sociological research should be accepted as essential components of policy to achieve ecologically sustainable development of Australian agriculture… The problems that must be
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solved to approach ecological sustainability in our agricultural systems are many, complex and difficult, both scientifically and socially. Were this not so, we would not be in our present predicament.
To match this emerging policy context of sustainability, the National Research Priorities
defined by the Australian Government in 2005 presented a goal of sustainability rather
than simply profit: ‘An environmentally sustainable Australia – Transforming the way
we utilise our land, water, mineral and energy resources through a better understanding
of human and environmental systems and the use of new technologies…’ (Joint RRDCs
NRM Working Group, 2005: 4). Concurrently, the ideology of regionalisation23, as
applied to NRM, was becoming increasingly common worldwide and in Australia
(Farrelly, 2005). Governments, resource managers, planners and developers were
responding to the problems of resource exploitation and conservation by ‘seeking more
integrated approaches that will enable their projects and programs to deliver as many
benefits as possible, within acceptable limits of social and environmental impact, and
with minimum conflict and cost’ (Lang, 1986 cited in Williams, 2007a: 131).
Australia is now formally divided into 57 NRM regions, each with a community-based
Board of management with responsibilities for integrated management of the region’s
natural resources, supported by a regional NRM body (Moore and Rockloff, 2006). The
regional strategic and investment plans were the basis for regional investment from both
the Natural Heritage Trust and the National Action Plan for Salinity and Water Quality
(Keogh et al., 2006). A condition of funding was that plans be underpinned by ‘the best
available science’ (Keogh et al., 2006: 18). However, although the government
investment protocols supporting regionalisation stipulated the use of scientific data by
regional planners – the supply of data did not meet demand (Commonwealth of
Australia, 2004a; Commonwealth of Australia, 2004b). Accordingly, McAlpine et al.
(2007: 38) noted that:
A key challenge facing all regions [in Queensland’s rangelands] is critical gaps in the resource condition information base, which prevented regional planners developing meaningful and measurable
23 Moore and Rockloff (2006) point out that regionalisation is distinct from regionalism, the former is usually guided by industry or government, whereas the latter is initiated and developed by community members.
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resource condition and management action targets for native vegetation and biodiversity.
Thus, regional planning, driven by the ideology of regionalisation, may be envisaged as
a case of putting ‘the cart before the horse’ – beginning to plan before committing to
build an adequate knowledge-base to draw on. Without adequate knowledge, including
science, regionalisation per se lacks the information base widely considered essential
for the successful planning and design of polices and strategies for sustainable natural
resource use (Dovers, 1995; ANAO, 2004; Commonwealth of Australia, 2006;
McAlpine et al., 2007; Robins and Dovers, 2007). Before the regional model had been
developed this need was already recognised. A Commonwealth government discussion
paper (Commonwealth of Australia, 1999: 79) on developing a national policy for
regional delivery asserted that:
The knowledge gained through research and from information on the condition of natural resources is central to improving natural resource management. Moreover, understanding what motivates people to adopt or reject new management practices – the interplay between economic, social and environmental factors – is important.
A more recent Commonwealth report (Commonwealth of Australia, 2006: 144)
supported this view, stating that:
Adequate access to information, particularly scientific information, is a fundamental tenet of good environmental decision making. It is difficult for governments or landholders to make the right decisions without the ability to measure accurately trends in resource use and condition against baseline data. This is not to say that decision making should be delayed until perfect information is available — that would mean no decisions would ever be made — but adequate information must be available for decision makers, and for those affected by, or interested in, the decision.
Thus, though the rhetoric of regionalisation required ‘the best available science’ this has
not been matched by a commitment to invest in its generation - so science may be
considered to be ‘the best available’, but as so many have pointed out, not necessarily
adequate for the task (e.g. Commonwealth of Australia, 2008a; McAlpine et al., 2007;
Robins and Dovers, 2007). As pointed out by the Chief Executive Officer of the CRC
for Plant-based Management of Dryland Salinity, Kevin Goss (Goss, 2005: 2), in a
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submission to the Senate inquiry into the extent and economic impact of salinity in the
Australian:
Many of the issues associated with national programs are to do with their administration and pre-occupation with 'getting dollars on the ground'. Our analysis has identified situations where viable salinity management options are not available, that further investment in R&D or no action may be a better option than incentives or regulation.
Thus, the ideology of regionalisation emphasised regional-based planning and delivery
of NRM programs and 'getting dollars on the ground', but it is becoming increasingly
apparent that this is based, as in the case of dryland salinity, on ‘a mistaken presumption
that economically viable solutions are available for widespread adoption’
(Commonwealth of Australia, 2004a: xxxiii). Investment in science generation has not
been well aligned with investment in NRM plans and goals, limiting the information
available to support regional planning and the development of innovative responses to
the problem. More recently, regionalisation has led to a more explicit process to devolve
power to other forms of knowledge, apart from science. A Commonwealth program
called Caring for Our Country commenced on 1 July 2008. Its business plan focused on
investing in NRM projects ‘that draw together the use of traditional ecological
knowledge, local knowledge and western science to protect, restore and better manage
the environment and productive agricultural lands’ (Commonwealth of Australia,
2008b: 22). It follows that there will be less risk of science being privileged over other
forms of knowledge in future NRM projects, but simultaneously, this may also increase
the risk of science being marginalised. Driven by an ideology of regionalisation, the
delivery model in Australia has re-shaped the internal policy-making environment,
adding another layer of complexity to NRM policy-making with new inputs of local
knowledge, new, unmet, demands for science at regional scale and wider community
values for policy-makers and scientists to consider.
International market pressures: demand for ‘clean and green’
Growing demand in world food and fibre markets for ‘clean and green’ products and the
threat of trade barriers may significantly impact on the need for research to underpin
NRM policy-making. Market demand for food and fibre grown in a sustainable way is
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growing, and so is the international pressure on Australian agriculture to substantiate
‘clean and green’ claims (Harris, 2002). Many of the practices of western agriculture are
not sustainable, according to Harris (2002: 202), and cause damage to the environment.
In arid regions like Australia, Walker et al. (1999a) pointed out that water use efficiency
is at a premium and ‘leaky’ western crops tend to lead to severe NRM problems like
dryland salinity.
Although GATT rules prohibit one country from taking trade action against another
country to protect natural resources, countries may establish international agreements to
ensure that their consumers are given an opportunity to purchase only goods which
comply with their values. The emergence, and ‘increasing strength, of green consumer
preferences’ (Mech and Young, 2001: 55), raises concern about the environmental
issues of food production. Increasingly, certification of ecological and environmental
integrity of rural commodities is becoming a significant market force (Mech and Young,
2001; Chang and Kristiansen, 2006). To develop sustainable land use systems to
maintain Australia’s ‘clean and green’ image, Yencken and Wilkinson (2000) argue that
better information is needed for environmental and resource policy-making (e.g. digital
land use and land management data, integration of process and spatial models, social
and cultural effects on land management behaviour).
Information and communication technologies
Many new approaches and information capacities in sustainable development are
underpinned by increasingly powerful information technologies (Dovers and Wild
River, 2003a). The Internet, coupled with increased computer power, object
programming, visualisation software, public dissemination policies and information
connection services have generated an entirely new context to access data and
information for NRM policy-making. For example, NRM policy-makers in Western
Australia can access the Shared Land Information Platform (SLIP) which delivers
online, real-time access to spatial data across government agencies, addressing in part
the problem of information fragmentation (Landgate, 2009). The National Land and
Water Resources Audit, commissioned in 1996, has also compiled an accessible
Australia-wide information base on land, vegetation and water (NLWRA, 2001b).
National datasets compiled by the Audit and the Bureau of Rural Sciences (BRS) are
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available for download through the Australian Natural Resources Data Library. The
Australian Spatial Data Directory gives users descriptions of many datasets, together
with contact and access details. However, despite these improvements in information
delivery, access issues of cost, interoperability and scale still exist (NLWRA, 2004).
Many have concluded that a significant difficulty facing natural resource managers in
Australia is the absence of necessary data at sufficient detail for policy-making, in
particular at a regional or local level (Industry Commission, 1998; Bellamy et al., 2002;
Campbell, 2006; Keogh et al., 2006; Commonwealth of Australia, 2008a). Accordingly,
one recent federal government report points out that, ‘Insufficient baseline data and
underpinning science for regional target-setting has led to inconsistency in the way
projects are designed and monitored’ (Keogh et al., 2006: 24). Furthermore, McLain
and Lee (1996: 438) observed that, ‘The ability of institutions to respond to new
knowledge depends on whether they have access to new information and whether they
have the will and capacity to act on that information.’ Thus, though advances in
information and communication technologies have improved access to and management
of NRM knowledge, the capacity to acquire and use scientific knowledge is also a
critical aspect of developing a policy response.
Stakeholders and pressure groups
Policy-making stakeholders and pressure groups (e.g. government agencies, advisory
committees, media, non-government organisations) matter because they may facilitate
the selective use of science in policy-making. They may define the boundaries of what
science inputs are acceptable in policy-making as well as the ways (e.g. entry points,
communication media) science can enter the policy process. The point at which
environmental issues emerge as problems depends on successful claims-making by
pressure group activities, scientific authorities, governmental actors, media accounts and
public evaluations (Irwin, 2001b) (also see Section 5.2.2). For example, in the case of
government action on salinity, assembling the claim involved the research community,
Australian government officials and efforts of the media. All of these were important in
building its political profile and public awareness and crucially distinguishing salinity
from wider soil problems.
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Government officials at Commonwealth and State levels produced a raft of reports
recommending programs and policy directions to tackle salinity (e.g. Government of
Western Australia, 1996; Prime Minister's Science Engineering and Innovation Council
Working Group (PMSEIC), 1999), researchers called attention to the issues (e.g.
George et al., 1997; Beresford, 2001) and the media drew the attention of the public to
the problem (e.g. the media made pointed references to ‘the politics behind the
paralysis’ in government funding of a salinity package in Western Australia (Beresford
et al., 2004: 142)). Of significance here is the process by which claim-makers use
science to influence those in power to recognise definitions of environmental problems
and to take responsibility for them. What environmental problems, such as salinity,
mean in terms of policies, actions and outcomes is structured and fashioned partly
through the powers and predispositions of key social institutions. Science can be caught
in a political process of claims and counter-claims. As Grant and Papadakis (2004: 158)
point out, ‘A major problem with securing trust is convincing stakeholders of the
science behind environmental problems and solutions.’ Thus, the stakeholders and
pressure groups operating in the political web may influence the use of science in
policy, depending on their level of support of the science in question.
4.3.2 The internal policy-making context
NRM governance: a complex system
When Australia was federated in 1901, powers were divided under the Constitution
between the federal and State governments; resulting in the Commonwealth sphere, six
State governments and two Territory governments (Keary, 1995; Williams, 2007a).
Notably the powers conferred on the Commonwealth government did not include a
general responsibility for management of natural resources, planning, environment, and
regulation of land use development. Apart from circumstances relating to ‘matters of
national environmental significance’ (as defined by the Environment Protection and
Biodiversity Conservation Act 1999), general powers in this field reside with the State
and Territory governments (Harris and Perkins, 1984; Williams, 2007a). Under the
Australian Constitution, responsibility for the legislative and administrative framework
within which natural resources are managed lies with the State and Territory
governments. In turn, the State and Territory governments have traditionally devolved
some responsibilities particularly relating to land use and development planning to local
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governments. Thus, the current resource management functions are principally State
responsibilities, though the manner of their performance varies substantially from state
to state (Bellamy, 2006). These functions are embodied in a multitude of acts,
regulations, programs and by-laws, developed and implemented by an NRM policy
system which differs between the levels of government and also between States and
between regional organisations (Williams, 2007a). However, the Commonwealth
government can invoke its funding, taxation, and international trade powers to make
inroads into state affairs and have an important indirect influence on environmental
policy (Bates, 2003; Gurran et al., 2005). It can play an important role in national policy
making, by setting policies directly, and through national government councils (such as
the Council of Australian Governments and the Natural Resource Ministerial Council)
(Odgers, 2003). Furthermore, the Commonwealth government collects over 80% of all
government revenues, and State governments are dependent on average on federal fiscal
transfers for half of their budget revenue (Harris and Perkins, 1984).
The fiscal power of the Commonwealth government gives them the ability to intervene
in resource management matters. Incentives may be provided through legislation,
mainly in the form of grants to the State (Bates, 2003; Saunders, 1996). The Natural
Resources Management (Financial Assistance) Act 1992, authorises grants to encourage
an integrated, uniform approach to national problems. However, NRM legislative
schemes do not require the effects of policies, programs and decisions to be monitored,
and as Bates (2003: 256) observed, ‘on this score, natural resource legislation fails the
tests of purposefulness and information richness and sensitivity (italics in original)’.
Accordingly, early national NRM programs neglected the importance of monitoring and
evaluation of policies and programs and the associated role of science. Bates (2003:
286) drew attention to the adverse findings of the Commonwealth Auditor General: ‘the
Commonwealth is still unable to indicate in any detail the outcomes that have been
achieved from natural resources management and environment programs such as
Landcare, Save the Bush, One Billion Trees and other programs’.
An NRM monitoring and evaluation framework has been recently developed in 2009 to
overcome these criticisms of implementing NRM policy. The framework document
promises a bigger role for science in NRM policy-making by stating that ‘all parties
involved in designing and implementing NRM programs will be assisted in this
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adaptive management process through improved access to current science, research and
information about on-ground experience.’ (Commonwealth of Australia, 2009: 17).
However, monitoring data will need to be gathered across sectors in such a way as to
meet multiple organisational and sectoral objectives and the data itself may become
politically contentious. As NRM policy-making environments become increasingly
‘messy and wicked’ this may raise issues of the social or political acceptability of the
data. These issues may impact on whether science would be regarded as a valid or
trustworthy basis for further decision-making (Lachapelle et al., 2003). Other pathways
also exist for federal interventions and include some 43 treatises and conventions
relating to the environment which override state powers (Lynch and Galligan, 1996).
The Commonwealth has entered into international agreements and conventions relating
to the environment (e.g. The Declaration of the United Nations Conference on
Environment and Development (UNCED) and Agenda 21 (1992); The Convention for
the Protection of World Cultural and Natural Heritage (1972); The Convention on
Wetlands of International Importance (Ramsar Convention, 1971)). The extent and
significance of these international commitments towards environmental management
means that State and local governments are operating in a very complex policy and
legislative context, and managers of environmental and resource organisations need to
be aware when scientific advice is needed and have the capacity to access the science
and understand the legal principles involved.
The current management of Australia’s natural resources involves three tiers of
government (local, State and federal), each developing NRM policy initiatives
independently of each other in an ad hoc way. Consequently, there is a diversity of
NRM institutional arrangements existing and within any State/Territory or region. For
Bellamy (2006), these fragmented institutional arrangements may involve competing
objectives and interests, giving rise to intergovernmental conflict. For example, State
boundaries often cut across natural geographical regions like the Murray-Darling river
system, exposing differences in state institutional arrangements that may undermine
coordinated policy-making, on issues such as water rights or salinity management
(Dalton et al., 1996). Moreover, such fragmented institutional arrangements may lead to
imbalances in science investment and use in developing policy responses to NRM
problems, as reported by a House of Representatives inquiry (Commonwealth of
Australia, 2004a: xxxviii) into the use of science in salinity:
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Evidence suggested there is an imbalance in the Australian Government’s salinity science investments towards mapping, at the expense of developing new land and water use systems, including engineering systems and new industries for saline resources. There were strongly divergent views in the evidence: between national NRM agencies, which argued for the efficacy of highly targeted interventions (at least in eastern Australia) aided by mapping technologies, versus a range of submitters who argued that research findings point to the need for large scale land use change and, hence, the need for profitable land use options that can be widely adopted by landholders. (parentheses in original)
However, more collaborative arrangements (e.g. intergovernmental agreements) may
have an important role to play in developing uniformity, or at least consistency, in
science use and supply among the States (Saunders, 2002). Since 1990, the
Commonwealth has sought a more cooperative relationship with the States. (Doyle and
Kellow, 1995). As Bates (2003) pointed out:
The Commonwealth and States have now adopted a more cooperative approach to environmental protection and natural resource management … This ‘co-operative’ federalism has been reflected in recent years through the Commonwealth Government basically adopting an initiation and co-ordination role with respect to the development of national policies for resource management and environmental protection (Bates, 2003: 284).
The shared responsibility between the Commonwealth and the States referred to as
‘cooperative federalism’ is reflected, for example, in the Intergovernmental Agreement
on the Environment that was signed by the Commonwealth and all States and
Territories in 1992 (Industry Commission, 1998). The purpose of this agreement was to
achieve sound environmental management through a system of parallel and
complementary legislation. Under this agreement, consultation between the
Commonwealth, State and Territory governments in practice is formalised through
ministerial councils, standing committees and a range of consultative committees that
also include key industry, scientific and local government representatives (e.g. Federal-
State councils of ministers, the Australian and New Zealand Environmental
Conservation Council, the Agriculture and Natural Resource Management Council of
Australia and New Zealand) (Bellamy, 2006). Also born out of the ‘co-operative
federalism’ is the emergence of new, nationally orchestrated organisations influencing
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NRM science supply (e.g. Cooperative Research Centres contribute to maintaining
supply amid research downsizing) and NRM science demand (e.g. regional
organisations represent a new tier of responsibility for NRM). The regional scale has
become an increasingly important focus for NRM research, with regional funding
dependent on the quality of regional plans, and of the underpinning science.
Contemporary Public Sector Management
The nature of organisational change in the Australian public sector24 is important to
understand science use in policy. From a personal perspective, my experience of
working in the agri-food sector in the State public service over the past twenty years
suggested that the adoption of new public sector management approaches and the
restructuring of the workforce were critical in terms of both the capacity and potential
for information supply and use. Thematic analyses of data collected during the Semi-
structured Interviews were in line with this view (see Appendix F). The theme of public
sector management (or organisational restructuring) emerged strongly in their
perceptions about science use in policy-making. In the words of one male, State-based
policy-maker (Interviewee No. 26, 2007):
we use government agencies where they’re appropriate. Quite often though the skills sets in the agencies are not strong enough to answer these questions. And this is one of the big problems; that science for policy is a problem in the sense that the agencies have lost some of the capacity in science. So we are depending more and more on universities and so on.
The availability of science for policy-making had been undermined by the growing
tendency to scale back the workforce. The public sector change observed in Australia
has followed world trends. Since the late 1980s, there has been a strong emphasis on
changing the organisational culture to better reflect that of private sector companies
(Bradley and Parker, 2006).
At the State level, the impacts of restructuring were observed in downsizing trends as
exemplified by: declining numbers of agricultural agency staff (e.g. retired professional 24 Although ‘government departments’ are the focus of attention here, Dalton et al. (1996) describes six types of public sector organisations: legislatures, political parties, the courts, departments, public trading enterprises and general government enterprises.
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staff not being replaced); declining numbers of extension staff providing specialist
advice to farmers (i.e. individual one-on-one extension practices were progressively
replaced by group extension); scientific leadership positions assuming more business
management responsibilities (e.g. moves to a ‘funder-purchaser-provider’ model
requiring project management of ‘contracted’ internal and external staff); and increased
(but not always successful) attempts to include other knowledge sources and widen
consultation in the policy process (e.g. Industry Partnership Groups were established to
contribute to the formulation of strategic research policy). The rise of the ‘new
managerialism’ in the State public service is mirrored by similar structural and
institutional changes at Commonwealth level.
The Federal Australian Public Service (APS) has been reorganised over the past thirty
years to play a stronger managerial role rather than to be principal providers of policy
advice. This has been a move attributed to financial pressure and an apparent attempt by
the administration to adopt a more rational and comprehensive approach to public
decision-making (Edwards, 2002). One of the outcomes is that generic management
skills began to be valued above specialist ones (Dovers and Wild River, 2003a). Expert
policy advice was more likely to be drawn from outside government and would not be
expected to reside in the official policy advising capability of the public service.
Consequently the number of consultants working with the APS and their pervasiveness
in public sector policy-making has also been growing since the 1980s (Martin, 1998).
Emphasis has shifted from rule enforcement and administration to the attainment of
results through mission statements, performance management and performance-based
rewards (Bradley and Parker, 2006). Increasingly the APS works through contractors,
charges users and runs agencies as commercial business operations and ‘public servants
also now require strong technical skills, especially in contract management, financial
and risk management, information technology and communications’ (Commonwealth of
Australia, 2003b: 165). There is increased ministerial control of the policy agenda,
decision making and senior appointments. Outsourcing and contractual arrangements
have become the norm (Davis and Rhodes, 2000; Teicher et al., 2006). The effect of the
new managerialism is the increased risk of policy failure imposed by the loss of
specialist skills. As Hess and Adams (2002: 70) asserted:
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The problems this creates for agencies which have gone a long way down the new public management road are evident in the loss of both centralised knowledge and institutional memory through competitive tendering and contracting out with the attendant loss of personnel. One of the ironies of the recent decades of public sector reform is that the hollowing out of public agencies under new public management has lessened their capacity to develop and value the knowledge required by the positivist approach they continue to pursue. Consequently the new public managers are being called upon to do without having the capacity to first know.
This view is also shared by Wiseman (2005: 59):
Numerous critiques of managerialist public sector direction-setting and change management strategies have demonstrated their limitations in a world where the knowledge and capacity needed to predict and address increasingly complex policy problems comes from many sources.
Thus, the capacity of the APS, including the organisations with NRM responsibilities, to
engage with scientific knowledge and to assess the scientific soundness, quality and
value of research commissioned for NRM policy-making may have been greatly
weakened by the move to managerialism. As Dovers and Lindenmayer (1997: 71)
pointed out:
a reasonably high level of ecological literacy and proficiency would be required in policy and management staff, in both the general sense of the management requirements of natural systems.
Furthermore, the Productivity Commission report, Cost Recovery by Government
Agencies, (Productivity Commission, 2001) commented that in a private sector model
departments generally operate on a cost recovery basis for the provision of data,
information and communication services to other federal or State organisations.
According to this report (Productivity Commission, 2001: 114) the higher cost of
requesting disaggregated and regional data from some Government agencies may limit
and discourage research for regional planning. This change to a private sector agenda
means that some organisations may not be able to afford to access scientific information
that may be needed to underpin NRM policy, and regional organisations may be
particularly disadvantaged.
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Under a private sector model, the concomitant change to performance based contracts
for policy-makers also has implications for science use. During the 1980s and 1990s the
Westminster tradition25 was increasingly challenged by Commonwealth and State
governments to move away from the system of career public servants and place senior
policy-makers on contracts that could be terminated if performance was deemed
unsatisfactory (Dalton et al., 1996). Accordingly, Davis and Rhodes (2000: 14)
observed that ‘Governments promote those public servants to departmental secretary
who are seen as most competent at translating their policies into effective action.’ Under
these circumstances it would not be unreasonable to assume that public policy-makers
would try to provide policy advice which would be seen to ‘toe the line’ rather than risk
their position, increasingly using science to support policy decisions already made
rather than raise a contrary view.
Approaches to managing policy knowledge by the public sector are also changing in
response to structural shifts in political imperatives. Hess and Adams, 2002; Hess and
Adams, 2005 Hess and Adams (2002, 2005) argue that contemporary changes in public
administration, ‘in particular the retreat from economic rationalism and the embracing
of social and human capital ideas with the search for ‘third ways’ and ‘triple bottom
lines’ (Hess and Adams, 2005: 68), have been characterised by moves from positivist to
constructivist knowledge frames. The positivist approach has experts searching for the
‘right ideas’ and applying objective expertise to solve a problem. In contrast, the
constructivist approach suggests that it is within the relationships and discourse between
policy players that the appropriate knowledge develops, implying ‘a focus on iteration
and learning in which knowledge is not owned by experts’ (Hess and Adams, 2005:
230).
According to Hess and Adams (2005: 239), the constructivist approach calls for
‘forward and outward-looking policy processes based on verifiable evidence’ that relies
on ‘a breadth of disciplinary knowledge across many social sciences spanning
economics, political science, sociology, history, geography, demography, organisational
theory and management’. Hess and Adams (2005) observed that the research and 25 The Westminster system of government means that Australian government policy-makers operated in the context of a clear division of responsibilities and powers between elected members of parliament, the public service and the judiciary. This has traditionally meant that public servants were supposed to be able to provide policy advice to their minister without fear of being sacked or disciplined if the advice was not welcomed (Dalton et al., 1996).
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information needed to address the complexity of policy issues is seen as requiring a
combination of qualitative and quantitative data. They suggest that this points to a need
for a wider public sector skills base to understand how these data can be collected and
analysed for policy use. In a study of public management of an NRM project, Boxelaar
et al. (2006: 121) further emphasised that policy-makers will need new skills to cope
with increasing levels of complexity in the NRM policy environment:
public servants are dealing with multiple and competing knowledge frames that are brought to bear on issues as diverse stakeholders become involved in the rural development process…The findings presented in this article support the view that existing practices and structures within government struggle to support the shift towards collaborative community-based approaches and that change is required.
New skills and processes (e.g. conflict resolution, communication, knowledge
management) are needed to accommodate the integration of ‘rational/expert’ knowledge
with new sources of knowledge (Edwards, 2002; Hess and Adams, 2002). The
contemporary shift in public management implies a new skills and disciplinary base,
quite different from that needed under a market-oriented public administration.
Boxelaar et al. (2006: 124) also pointed out that tensions exist between the
consolidation and management of market-based policy approaches (i.e. ‘new public
management’ approach) to NRM, and collaborative policy approaches, simultaneously
embraced by governments:
the discourse of collaboration and community engagement does not necessarily take public administration down a constructivist path…Analysis of the DSC project suggests that the constructivist aspirations for the project were subsumed within prevailing, positivist regimes of practice.
Boxelaar et al. (2006) observed that the shift from positivist to constructivist frames of
knowledge in public management is neither smooth nor certain; changing long-standing,
public management practices to match the changing policy-making rhetoric can be a
lengthy and complex process. For example, the Australian National Audit Office (2008:
18) noted that ‘the lack of mature workforce planning processes impairs agencies’
capacity to address challenges arising from the changing workforce environment.’ A
lack of policies, strategies or frameworks to support workforce capability goals over the
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next five years was recently identified in about half of APS agencies (Australian
National Audit Office, 2008). Thus, the current workforce planning practices of the
APS are poorly positioned to support alignment with the new, more interactive,
collaborative policy-making model that is emerging in workforce environments like
NRM (Reddel and Woolcock, 2004). The danger is that if collaborative policy-making
approaches are simply constructed within the constraints of an existing positivist model,
they will be marginalised. The following discussion of staff selection practices of the
APS further illustrates this.
Policy-making demographics
The APS employs professionals from many different backgrounds, including business,
accountancy, arts, humanities, social sciences, natural sciences, engineering and law.
Scientifically trained policy researchers and policy makers have traditionally been a
minority group in the APS. Matheson (2001: 52) noted that the cumulative effect of a
longstanding preference for economists has been to increase the proportion of senior
officers who hold economics and business degrees:
Between 1965 and 1995 the proportion who held degrees in economics/arts/social sciences increased from 45 per cent to 69 per cent while those holding professional qualifications or degrees in natural science declined from 53 pre cent to 20 per cent. Those holding degrees in economics or business accountancy made up almost half (49 per cent) of Gregory’s sample in 1995.
This trend is expected to continue, with graduate recruitment statistics in 2001 showing
a high proportion of graduates in the APS from a business/administration/economic
background (46.5 per cent) compared with science (9.2 per cent) (Commonwealth of
Australia, 2003a). In 2005, Commonwealth agencies reported problems in recruiting
and retaining staff with qualifications and expertise in specialist science areas such as
agricultural science (for the Department of Agriculture, Forestry and Food)
(Commonwealth of Australia, 2005).
Because professionals bring to an organisation ideas about how things should be done
this apparent imbalance in professional skills in the APS may have implications for
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science use. Each professional brings their own set of paradigms (i.e. model, pattern or
frame of reference built up by experience) and specific problem-solving abilities. For
example the problem of managing salinity could be tackled by economists as an
investment problem, by hydrologists as a water movement problem and by social
scientists as a problem of human behaviour. Thus, the discourse about the problem and
potential solutions will differ. The domination of the business/economic paradigm in the
APS suggests business/economic input is likely to be highly influential in NRM policy
discourse and debate, thereby limiting the opportunity for other disciplines to effectively
contribute to the discourse. Accordingly, Keary (1995: 12) emphasised the importance
of adopting a broad, integrative approach to develop a unifying framework for water
resources management in Victoria in the 1980s:
This framework was the design of a team of hydrologists, economists, engineers, lawyers, mathematicians, environmental scientists, hydrogeologists etc. The group brought together professional skills that contribute to thought in the water industry. Each person had their own set of paradigms and specific problem solving abilities. When they worked as a group, many divergent paradigms were brought to the table.
Thus, many paradigms were unified under the umbrella of the legislative framework; a
framework that Keary (1995) recognised would be deficient and weakened if supported
by only one paradigm. Similarly, Hess and Adams (2005) pointed out that from the
1990s under a market approach for public administration based on economic reasoning,
usable knowledge for policy was limited; market-type knowledge, such as competition
and price signals was valued, but altruistic and non-market ideas struggled to make an
impact on policy. Consequently, the decline in professional diversity of the APS may
weaken its capacity to develop a national NRM knowledge framework for policy-
making; reducing its ability to seek, engage with, understand, and use multiple
(including scientific) paradigms and information in a timely manner for policy, and to
assess the scientific soundness, quality and value of research commissioned for policy-
making. A view that less professional diversity would also weaken personal
relationships and information sharing between scientists and policy-makers emerged
from the semi-structured interviews with Australian NRM policy-makers and scientists
(Appendix B); one State-based scientist noted that it was easier to interact and share
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information with policy-makers who had an appreciation of the science (Interviewee
No. 12, 2007):
quite a few of our policy people have come through a practical background or a scientific background of sorts or a regulatory background. So they’ve been in an operational role perhaps before, not everyone, but quite a few so that the policy people understand the implications and understand the practicalities of the policies they are developing so that makes the relationship with the scientists much more amicable because they can talk on the same wavelength, you know they can both stretch a bit to each others fields, whereas if you have a specialised policy group that’s dealing today with natural resources, and tomorrow with crime and the next day with health which is what often happens in groups like the Cabinet offices or somewhere you know, where policy people are working on government policy but not necessarily specialising in a field. Its much more difficult for science to connect with them. So having policy people that understand the operational side of the policy is really good.
Another impact of the APS recruitment policy is the likely increasing degree of
consensus on policy issues, as observed by Matheson (2001: 56):
it is likely that this preference for economists has excluded talented non-economists from senior administrative posts and narrowed the range of policy advice received by governments, given that an economics degree is the strongest predictor of policy attitudes. For example, SES officers maintain that the spread of economists throughout line departments has produced an increasing degree of consensus on policy issues.
For the APS the increasing degree of consensus may be a desired outcome since it
simplifies and fast tracks what could otherwise be a policy process that is more lengthy
and complex than needed. The addition of science into the policy-making discourse may
introduce more complexity than the APS can manage, and hence science use may not be
necessarily encouraged. The change processes in APS have also produced an ageing
workforce, as the baby boomers who were recruited in the 1970s and 1980s move into
the 45 to 54 age range (Commonwealth of Australia, 2005). There are also gender
related changes related to public sector restructuring that may have implications for
social capital and the use of science to inform NRM policy (see Section 2.3.2). Over the
past 15 years, women’s representation in the APS has increased from 47.4 per cent to
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56.6 per cent of ongoing employees (Commonwealth of Australia, 2007). However,
Commonwealth NRM policy-making remains largely male dominated. For example, in
2006/07 the senior executive service of the Department of Agriculture, Forestry and
Fisheries (DAFF) comprised 69 per cent males and 31 per cent females
(Commonwealth of Australia, 2007), and DAFF records suggested a similar ratio of
males to females in the NRM Business Unit.
Generally, women have been under-represented across all levels in the NRM policy
system, as exemplified in the Agriculture, Forestry and Fisheries policy area of the
NSW public sector by 32.1 per cent in 2007 (NSW Government, 2008). Examples can
also be found in regional NRM policy-making; in 2004/05, women represented 17 per
cent of the Swan Catchment Council (SCC, 2005) and 27 per cent of the South West
Catchments Council (SWCC, 2005). Similarly, in a case study of rural restructuring in
Northern Ireland, Shortall (2002: 160) pointed out that:
Rural development initiatives are understood as offering a new form of governance that is more inclusive, more representative, and more transparent. They are seen as providing scope to include women in political structures in a way that has not previously been achieved. However…ideological and cultural barriers exist, such that agricultural and rural restructuring unfolds in a gendered fashion, which support the status quo.
Some may argue that the continuing widespread decline in the natural resource base in
Australia is evidence that something is wrong with the status quo. One compelling
reason to support this claim is that policy-making endeavors can only be improved by
having a greater diversity of perspectives in the search for knowledge and solutions to
human problems.
The shifts in the public sector in Australia over the past thirty years to a managerial
focus, business skilled workforce and downsizing have contributed to the loss of
specialist skills, emergence of new responsibilities and increased degree of consensus.
These could be considered symptomatic of the ‘pathology of natural resource
management’ defined as ‘when the range of natural variation in the system is reduced,
the system loses resistance’ [italics in original] (Holling and Meffe, 1996: 330).
Imposing new responsibilities on an organisation, Briggs (2003: 162) noted, ‘without
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building its capacity to deal with the new responsibilities appears to be a strong
predictor of the pathology’. As Holling and Meffe (1996: 331) pointed out:
Bureaucracies are an exercise in variance reduction through regulation and control, eliminating extreme behaviour and promoting conformity to a set of standards…But deeply entrenched bureaucracies are characteristically unresilient to new challenges because the system discourages innovation or other behavioural variance.
Institutions managed by command and control, Holling and Meffe (1996) observed,
have low resilience to new challenges, because the system discourages the exchange of
new information and ideas and penalises alternative views. In light of the Holling and
Meffe (1996) argument, by exhibiting features of the pathology, the Australian public
sector is at risk of becoming myopic, stagnant and less resilient to respond to new
challenges.
NRM R&D Management
Information often has public good characteristics. This has led to a significant degree of
government involvement and investment in the generation and dissemination of
agricultural and environmental information (Industry Commission, 1998). An analysis
of the Australian NRM knowledge system by Campbell (2006) revealed that more than
40 organisations and funding programs established and funded by the Australian
government are charged with purchasing, managing or delivering NRM science (e.g.
Australian Bureau of Statistics, CSIRO, Greenhouse Office, Bureau of Rural Sciences,
Department of Environment and Heritage). If non-government organisations (e.g.
National Farmers Federation, Greening Australia, Australian Conservation Foundation,
Australian Bush Heritage Fund) are included then at the national level there would be
more than 100 players. If all the NRM science players across the States and territories
are included (e.g. State NRM agencies, universities, intergovernmental coordination
mechanisms like the Australia New Zealand Land Information Council (ANZLIC)),
then Campbell (2006) suggests that more than several hundred organisations could be
investing public monies in the generation and management of formal scientific
knowledge in NRM.
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The information base for sound resource planning depends on funding research to
generate the information (e.g. data from resource appraisals such as soil surveys and
vegetation mapping). From the 1920s to the 1940s, Australian Commonwealth and
State governments conducted extensive land and resource appraisals for soils, forests,
climate, fisheries, minerals, water and land capability to begin to build such an
information base. This was encouraged by the establishment of government statutory
bodies (departments of forestry, agriculture, fisheries, wildlife and mines) and the
national research organisation, the Commonwealth Scientific and Industrial Research
Organisation (CSIRO) (Conacher and Conacher, 2002). A division of labour was
established in 1927 between CSIRO and State departments of agriculture, where CSIRO
was responsible for conducting strategic research in agriculture and the State
departments for applying it in the field (extension). This policy encouraged a scientific
culture in CSIRO that elevated strategic agricultural research, but gave less attention to
adoption and use of research than was appropriate given the provisions of their Act.
The CSIRO Board in 1990 endeavoured to change this culture by endorsing a modified
policy to advocate that CSIRO ‘has primary responsibility to facilitate or encourage the
application of the results of its research’ and ‘that scientists who are responsible for
managing research are also responsible for ensuring that the appropriate extension links
for their results are in place’ (Coffey, 2000: 1). Coffey (2000) pointed out that, since
1990, CSIRO has gained further experience in technology adoption, including policy
advice to government departments and policy committees. This is exemplified by
CSIRO recently providing scientific advice direct to national NRM policy committees
(CSIRO and BOM, 2004; Walker et al., 2006). The Board now deems it unnecessary to
have a specific policy dealing with extension (Coffey, 2000). However, having
encouraged links between science and policy, CSIRO then recognised that policies were
also needed to manage the links to avoid conflicts being played out in public between
scientists and government. Accordingly, CSIRO has tried to manage the links between
science and policy by enforcing a policy of scientists not commenting on government
policy, as the Chief Executive of CSIRO (Garrett, 2006: 1) noted:
Our approach is to publish our science irrespective of its implications, even if the findings may be politically sensitive. However, we do not provide direct commentary - negative or positive - on existing, past or proposed government policies. This has been the stance taken by the organisation for many years…
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CSIRO uses its science to inform the development of policy. As its leading scientific adviser, CSIRO works with government, often behind the scenes, to provide a scientific base for policy. CSIRO informs policy, it does not write policy. Governments take on board CSIRO’s scientific advice and integrate it with social, economic, political and other considerations to make policy decisions… Were a CSIRO scientist to make prescriptive, public statements about policy, CSIRO would be in danger of losing its integrity and acting as a lobby group rather than as a respected science organisation. Should individual scientists want to comment on government or opposition policy, they can do so as private individuals.
Importantly, Garrett (2006) highlights the view that CSIRO, as a supplier of scientific
information, has a role to inform policy, and not to write policy, or else it risks acting as
a lobby group. Some science commentators have cautioned that CSIRO may already be
acting as a lobby group given the reliance of research on funding from both government
and industry policy-making stakeholders (e.g. Dennis, 2006; Miller, 2006). The erosion
of government funding and pressures on agencies like the CSIRO to find external
industry funds and partners, may increase the risk of scientific suppression
(Gunningham and Sinclair, 2002). Accordingly, Dennis (2006) and Miller (2006)
reported claims by some CSIRO scientists that findings of climate research were being
suppressed by political pressure because of conflicts with government policy. Over the
past twenty years, other stakeholders participating in the supply of scientific information
for NRM have emerged.
Under the Primary Industries and Energy Research and Development Act 1989, 15
Rural Research and Development Corporations (RRDCs) and one council were
established within the Commonwealth Primary Industries and Energy Portfolio (now
the Department of Agriculture, Fisheries and Forestry). RRDCs are the major vehicle
for rural R&D funding, not conducting the research directly but acting as ‘research
brokers, organisers and catalysts’ (Commonwealth of Australia, 2006: 80). The RRDCs
operate a contestable system of research funding, their task being to allocate funding
and manage projects as effectively and efficiently as possible (Commonwealth of
Australia, 2006). Providers of research to the RRDCs include State departments of
agriculture, CSIRO, universities, Cooperative Research Centres, the Australian Bureau
of Agricultural and Resource Economics (ABARE), the Bureau of Rural Sciences
(BRS), industry owned research institutions, private consultants, and industry
businesses and groups.
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With the exception of the Land and Water Resources Research and Development
Corporation (LWRRDC, later called Land and Water Australia, LWA), the RRDCs
were all commodity specific or market focused. The RRDC model is strongly weighted
towards large-scale, traditional agricultural industries through dollar-for-dollar matching
of industry levies by the Australian Government. Morrison et al. (2004) emphasised
that, consequently, the research questions being funded by RRDCs may not be aligned
with the highest priorities for NRM, nor may the cross-disciplinary, integrative
approaches needed to effectively inform NRM policy be applied. The following
interview data from the Semi-Structured Interviews (see Chapter 3 for methods)
underscores this. A member of a regional Catchment Management Board in New South
Wales commented on the imbalance in research investments (Interviewee No. 14,
2007):
so the GRDC or the MLA then which drag a lot of private R&D dollars out of growers. They’re probably saying, ‘well we’ll just go more to our production type outcomes’. But the strong linkage is between the BMPs [Best Management Practices] which is, when you are using the resource, you try and not to abuse it. But they’re the best practices and the industries have to become involved in those. And that relationship [between industry policy-makers and NRM scientists] at the moment is poor.
This comment highlights the production and industry sector emphasis of research
investments amid growing concerns about broader management of the resource base.
Not only is research investment viewed from a perspective of technological
improvements, but as a change agent for minimising negative impacts on the resource
base.
LWRRDC was established specifically to tackle cross-sectoral sustainability issues,
such as land degradation and water quality and was totally funded by Commonwealth
appropriations (Mobbs and Dovers, 1999). However, a recent report for the RRDCs
NRM Working Group emphasised that priority setting for NRM research was a joint
exercise across sectors, reporting that the RRDCs collectively invested $78.5 million
during 2004/05 to tackle NRM issues (Joint RRDCs NRM Working Group, 2005). It
analysed NRM expenditure over all the RRDCs, describing the breakdown of
expenditure as: ‘using frontier technologies and innovation (18%), investigating
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environmental priorities such as water, soils, biodiversity and climate (57%), and
transforming Australian industries (25%)’ (Joint RRDCs NRM Working Group, 2005:
4). Thus, a major aim of the Act appears to have been met – to shift agricultural
research and development (R&D), including NRM research, from being supply driven
by researchers to being demand driven by users (Commonwealth of Australia, 2006).
On the supply side, over the past two decades the Cooperative Research Centre (CRC)26
model has offered the RRDCs an opportunity to develop R&D infrastructure, that had
been eroded as the State agriculture departments have employed the strategy of
economic restructuring (downsizing) (Coffey, 2000; Centre for International
Economics, 2003). The Centre for International Economics27 (2003) in a report for the
Department of Education, Science and Training, noted that the temporary nature of the
CRCs may pose a risk for the RRDCs, and industry owned research institutions are
filling this role for some RRDCs. Research priorities were established by an extensive
process of consultation with industry, researchers and increasingly policy makers and
many RRDCs have developed R&D priorities within a broader industry plan and
(Centre for International Economics, 2003). However, the unintended consequences of
the commercial focus of the CRCs may be that science may be used to inform policy
only if commercial interests are supported, as exemplified by the case cited by Miller
(2006: 117), that Australian climate and biofuel scientists claimed they were being
‘silenced’ while key commercial stakeholders had been given the opportunity to shape
Australia’s climate policy.
The R&D strategic plans developed by the RRDCs and the program plans and annual
operating plans that sit under them, dictate the areas of R&D that will be funded. Calls
for R&D projects without specified priorities have largely disappeared (Centre for
International Economics, 2003). Significantly, LWA, the RRDC with the aim of
achieving the sustainable management and use of Australia’s natural resources has
recently been abolished (Land and Water Australia, 2009). For Mobbs and Dovers
(1999: 8) the establishment of LWA meant:
26 A CRC is a company formed through a collaboration of businesses and researchers. This includes private sector organisations (both large and small enterprises), industry associations, universities and government research agencies such as the Commonwealth Scientific and Industrial Research Organisation (CSIRO), and other end users (DIISR, 2009). 27 The Centre for International Economics describes itself as ‘a private economic research agency that provides professional, independent and timely analysis of international and domestic events and policies’.
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an emerging appreciation of the complex nature of the ‘sustainability’ paradigm; it recognised that priority issues for research were not simply related to questions about biophysical processes, or the economics of production…and more fundamentally it could provide the national perspective that was deemed necessary and could take the lead on issues unlikely to be funded by industry focused RRDCs.
This view was shared by a recent Productivity Commission report (Productivity
Commission, 2007: 433) into public support for science and innovation in Australia:
In the case of RRDCs undertaking significant public good research, such as the environmental research sponsored by Land and Water Australia (LWA), strong grounds for large public subsidies remain because that research is unlikely to take place in their absence.
Thus, the closure of LWA in June 2009 may signal an emerging imbalance in
investment in NRM research for policy (e.g. greater risk of neglecting social and
institutional research), and consequently, an imbalance in the resulting adequacy of
NRM research available to inform policy choice. Future government and industry
investment into NRM will be, according to the LWA Chairman, Ms Bobbie Brazil
(LWA media release, May 5, 2009), focused on remediation and on-ground works and
‘operating on the mistaken assumption that the research has been done, that we know
what to do’. In contrast, some may believe that Land and Water is no longer needed,
having been successful in mainstreaming NRM. The RRDCs are increasingly linking up
the dimensions of sustainability, according to the Centre for International Economics
(2003: xiv), noting ‘there appears to be greater involvement by some RRDCs in
informing policy growing out of their need to consider industry sustainability from an
environmental as well as economic perspective’.
The operating environments of RRDCs are continually evolving and present a greater
degree of uncertainty and complexity than probably desired by many stakeholders.
Without a coherent, national policy framework for NRM R&D there is potential for
national communication, coordination and coherence to be thwarted by tensions
between research investment in commodity production versus soil/water management,
and the emphasis on biophysical versus social and institutional R&D. The need for more
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coherent national institutional arrangements for ecologically sustainable development
and NRM has been expressed since the 1980s (e.g. in the field of public health - the
Australian Institute of Health and Welfare and the National Health and Medical
Research Council) (Dovers, 1999).
The RRDC model relies heavily on government–industry partnerships; planning and
funding from compulsory and voluntary industry levies matched by the Commonwealth,
much of the agricultural R&D conducted in Australia (Centre for International
Economics, 2003). The matched levy element of the model has advantages of ensuring a
contribution from industry, facilitating industry involvement in priority setting and
project selection for R&D, and enabling RRDCs to establish long term research
agendas. Compulsory levies are used to ensure that individual farmers contribute to the
cost of research on the commodities they produce, with government providing
substantial additional funding. An estimated $1 billion is invested in primary industry
R&D each year by governments, higher education institutions, R&D corporations and
the private sector. Apart from supporting the RRDCs by the collection of industry levies
and the provision of matching funding, the Australian Government also makes direct
grants to public research institutions such as CSIRO and sets the institutional
framework and the system of intellectual property rights that encourage private research
(Commonwealth of Australia, 2006).
However, support to primary industries and producers for R&D is not limited to the
Australian Government’s matching contribution; other federal and State/Territory
government monies find their way in through research agencies such as the CSIRO,
higher education institutions, CRCs and State departments of agriculture (Productivity
Commission, 2007). The State and Territory Governments are also significant
supporters of NRM research in Australia. At this level of government, agriculture is,
according to the Productivity Commission (2007), a traditional area of involvement,
citing the example of the NSW Department of Primary Industries Division of Science
and Research employing over 900 staff, managing over 700 projects, with a budget of
over $130 million. Similarly, the Victorian Department of Primary Industries has a
scientific strength of some 950 staff.
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The funding arrangements for NRM research in Australia are complex and multi-
layered; funding agencies channel industry levies matched by federal government
contributions to major research agencies at State and national level, with added
investment by State and Territory governments. Adding to the complexity is the
emergence of a new level of ‘funding agency’ at regional level, channeling government
monies into research to underpin NRM planning at regional scale.
Regional delivery model
Operating within a deregulatory paradigm, Australian governments have, over the past
decade, developed NRM programs which propose the development of
government/community partnerships and the establishment of markets in various
environmental services to manage resources. According to Lyster (2002: 37), ‘these
partnerships are at once the result of downsizing in the public sector which is now
expected to operate as efficiently as the corporate sector, as well as a so-called “desire”
by government to regulate “in partnership with the community” in the area of natural
resource management.’
A model of service delivery that favours using many contractors necessarily creates
fragmentation. Functions once consolidated in a few public institutions are now shared
across a market of providers. Traditionally Commonwealth NRM funding would be
allocated through existing programs run by State NRM departments. However, the
States can now be bypassed in favour of regional organisations and Davis and Rhodes
(2000: 13) argued that this model is flawed, suggesting:
this approach highlights the several issues common to fragmented service delivery systems: the costs of co-ordination, equalizing territorial service quality, the high political and administrative costs of monitoring and evaluation; haggling about implementation and feedback loops and barriers to policy learning by the Commonwealth government. With little institutional infrastructure at community level how can it co-ordinate, monitor, evaluate and ensure the lessons learnt from the field inform future policy choices?
Although institutional capacity in the regions has been strengthened over the past
decade the problems of fragmentation persist. For example, Robins and Dovers (2007)
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highlight disparities between the allocation of resources between regions and
differences in their capacity to access research information.
The creation of 57 regions and regional bodies raises issues about information exchange
and transfer, including fragmentation, volume, relevance, accessibility, the need for
two-way exchange, and the need for sharing across regions and up to the
Commonwealth level and back (Campbell, 2006; also see Section 4.2). In addition, the
complexity of environmental problems requires multi- or inter-disciplinary solutions.
CSIRO and BOM (2004: 2) pointed out that achieving such disciplinary integration in
an effective way is a difficult issue:
natural resource management requires integration and interpretation of scientific theory and knowledge across specific scientific disciplines that have often operated in relative isolation from one another. While there is often detailed knowledge of specific research subjects and sites, and knowledge of broad scale processes, there are significant challenges in integrating current knowledge across the range of scales needed to apply it to landscape, regional and paddock scales of resolution.
4.3.3 Contemporary NRM programs
Sections 1.1 and 4.2 revealed the significant investment by the Australian government
over the past two decades in NRM programs, specifically the National Heritage Trust,
the National Action Plan for Salinity and Water Quality and the National Landcare
Programs. By contemplating the interplay between science use and policy-making in
these NRM programs, insights about the influence of the Australian context may be
derived. Stages of the social policy process, proposed by Dalton et al. (1996), provide a
practical way to organise the ensuing discussion about this interplay.
Problem identification, analysis and program design
Social policy-making, according to Dalton et al. (1996), is a contest over social issues,
over social purposes and goals. The discourse of NRM policy-making opens up the
view that that NRM policies are essentially social policies involving social
arrangements. The proponents of NRM programs point to their contributions to building
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cooperative relationships and partnerships between people. For example, Campbell
(1994: 1) describes Landcare as ‘a unique national program which is a partnership of
government, farmers and community groups.’, Lee and Wood (2004) described how
management targets of NAP were ‘established within a dialogue between the regional
body and governments’, and Cullen (2005), reflecting on a decade of regional delivery
of NAP/NHT, described the challenges of developing ‘workable partnerships’.
Furthermore, the NAP policy document emphasised that ‘partnerships are essential to
the success of this important initiative’ (Council of Australian Governments, 2000: 3)
and articulated the social basis of NAP policy (Council of Australian Governments,
2000: 5):
The goal of this Action Plan is to motivate and enable regional communities to use coordinated and targeted action to:
· prevent, stabilise and reverse trends in dryland salinity affecting the sustainability of production, the conservation of biological diversity and the viability of our infrastructure
· improve water quality and secure reliable allocations for human uses, industry and the environment.
As one Commonwealth policy manager (Interviewee No. 21, 2007) observed, ‘when
you think about what the Australian Government’s trying to achieve, in reality, we don’t
actually deal with biophysical, we are not actively managing on ground and so on.
Virtually everything we do is working with people.’ The prevailing discourse, with its
emphasis on social change, social arrangements and goals, suggests opportunities
existed for social and organisational research to play an important role in informing
design of these programs, including ‘analysing or working out what is going on in the
historical, social, organisational and inter-organisational context’ (Dalton et al, 1996: 3).
However, it is difficult to produce empirical evidence that socio-organisational research
played a role in the design of these NRM programs28. In fact, as will be demonstrated,
the evidence points to the contrary, resulting in more narrowly informed policy that
carried a higher risk of failure.
28 Socio-organisational research in this case refers to studies of both intra- and inter-organisational behaviour. Of note are the studies of the processes and institutions for resource and environmental management in Australia that can contribute to socio-organisational research in this area (e.g. Dovers, 2001; Dore et al., 2003).
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Two key assumptions of program design contributed to the overlooking of socio-
organisational research. The first was the assumed priority of social change to improve
on-ground action (e.g. a focus on capacity building and economic instruments) that
overlooked the social change needed to improve the management of NRM partnerships
(e.g. focus on organisational planning) for program implementation (see Section 4.3.2).
For example, a 2005 review of regional NRM planning in Queensland pointed to the
importance of recognising the social elements of the NRM planning context,
recommending that ‘regional NRM bodies and scientific communities in regions would
benefit from maintaining and building on relationships developed through the plan
development process.’ (McDonald et al., 2005).
Furthermore, in 1999, the Commonwealth government released a discussion paper for
the purpose of developing a national policy on natural resource management that
advocated ‘the development of regional institutional arrangements for devising and
overseeing the implementation of regional strategies for natural resource management’
(Commonwealth of Australia, 1999: 29), yet the paper failed to acknowledge the
corresponding need for change in government institutional arrangements to better align
with the proposed government/regional communities partnership framework. The
needed change in organisational and institutional elements of governments (e.g.
workforce planning, expertise, policy process) to better align public sector management
with more collaborative approaches to NRM policy-making had not been recognised
(see Section 4.3.2). Thus, the need for, and use of socio-organisational and institutional
research to underpin and inform the making of a social policy for NRM, appears to have
been overlooked across the multi-layered NRM policy-making system.
The second was the assumed importance of biophysical, economic and production
related research to inform planning processes (e.g. Commonwealth of Australia, 1999;
CSIRO and BOM, 2004; Joint RRDCs NRM Working Group, 2005; Walker et al.,
2006). For example, in December 1999, the Commonwealth government Department of
Agriculture, Forestry and Fisheries released a discussion paper entitled ‘Managing
Natural Resources in Rural Australia for a Sustainable Future’ for the purpose of
developing a national policy on natural resource management. The stated research
priorities in that paper were biophysical and socio-economic: ‘knowledge gained
through research and from information on the condition of natural resources is central to
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improving natural resource management’ (Commonwealth of Australia, 1999: 79). The
significant role that socio-organisational research could play in informing processes to
achieve the social goals defined in these programs had been overlooked.
The paper also maintained that ‘there has been a strong move towards the development
of regional plans during the past decade, and catchment and regional planning is well
advanced in many regions.’ (Commonwealth of Australia, 1999: 35). The Coastal CRC
(2002) signaled an early concern that this was not the case and that NAP was being
implemented without ‘established guidelines of the structures and processes for
achieving outcomes’, rightly predicting that consequently, ‘there is a real risk that
achievements under the NAP will be sub-optimal’. Many reports confirmed that many
regions were not well equipped or supported to develop and implement regional plans
(Lyster 2002; ANAO, 2004; Robins and Dovers, 2007; Commonwealth of Australia,
2008a). The rationale for designing these NRM programs did not appear to have
included a process that involved understanding the social and institutional processes and
their interactions.
Over the past twenty years the NRM policy paradigm has shifted from regarding land
degradation as a biophysical problem that can be solved with technological thinking, to
regarding land degradation as a social issue requiring wide-spread attitudinal change.
The NRM research orientation has, however, failed to keep pace with the policy shifts
and continues to focus on biophysical and economic processes, failing to recognise the
importance of researching social processes and interactions (e.g. changing institutional
cultures).
Turning to the question of the funding of science, Robins and Dovers (2007: 277)
pointed out that research fads and over-promotion can lead to inequities in funding
allocations that contribute to this imbalance: ‘ideas and technologies are funded that
may be overstated, such as airborne geophysics in the case of salinity management’,
whist funding of socio-organisational research may be overlooked; capacity ‘typologies
have been used to inform the design and delivery of NRM programs and research in
Australia in the case of landholders and local governments, but not in the case of
regional organisations’. As a result, observed Robins and Dovers (2007), four years
after resource allocations through NAP had commenced, NRM program designers still
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had limited information about the capacity and social and institutional foundations of
the regional NRM organisations. The lack of a ‘baseline’ capacity also raises questions
about the ability for the effectiveness of the programs to be evaluated.
Over the past decade, research to inform NRM policy has been generally dominated by
biophysical and economic agendas, making it difficult to inform a holistic approach to
NRM policy that includes social dimensions. Recently, the Framework for Future NRM
Programmes endorsed by the Natural Resource Management Ministerial Council on 24
November 2006 stated that:
An effective approach to NRM entails the application of a wide range of policy and programme responses, including capacity-building initiatives, grants, on-ground works and regulation, and is likely to entail an increased emphasis on the use of market-based instruments in the future (Natural Resource Management Ministerial Council, 2006: 1)
Some may be concerned that without an understanding of the social challenges
contained in this framework, the stated effective approach is based on a mistaken
assumption that the organisational and institutional arrangements across
Commonwealth, State and regional organisations are adequate and well aligned to
implement the policy and program responses. For example, soon after the Landcare
Program was established, Campbell (1992) reported that traditional government
institutional cultures were constraining the effectiveness of Landcare groups. Inadequate
training in participatory processes, a productionist orientation, hierarchical lines of
command and an empire building culture were all cited as examples of institutional
difficulties in coping with community participation.
Since then, Landcare has raised levels of landholder knowledge, awareness of issues
and adoption of best management practices (Curtis and De Lacy, 1996), but it has not
resulted in the scale and speed of social change needed to make a difference to resource
condition at the landscape level (Wallington and Lawrence, 2008; Hajkowicz, 2009).
Local projects were often poorly coordinated, small-scale, and potentially undermined
by the actions of farmers elsewhere in the catchment (Wallington and Lawrence, 2008).
By gaining a deeper understanding of the gaps in the organisational and institutional
arrangements that underpinned a social policy like the Landcare Program, then policy
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responses may have been more successful at the outset and the impact of the policy
more lasting and effective.
Policy implementation
The implementation of Landcare, NAP and NHT has been largely through a ‘regional
delivery’ model (Keogh et al., 2006). Hartley, 2005(2005) discusses three competing
paradigms of governance and public management that are conducive to reflecting on the
interplay between science use and policy-making in implementing this model. The first
paradigm is the ‘traditional’ public administration approach, largely based on a
legislative, bureaucratic approach with power and authority lying in the government; the
second is termed the ‘New Public Management’ approach (see Section 4.3.2),
underpinned by a different set of assumptions in management theory, and introducing
concepts of business processes, such as ‘purchaser-provider’ and ‘customer focus’ into
government; the ‘networked governance’ approach shifts emphasis to less control and
more steering and community governance.
The establishment of a ‘regional delivery’ model in Australia, where power is
transferred to the regions, is well aligned with the latter ‘citizen centred governance’, or
‘networked governance’ approach. Under this paradigm of ‘continuously changing
context, diverse population, and complex problems’, the regional bodies may be
expected to have a larger role as co-producers of scientific knowledge and innovation
(Hartley, 2005: 29). Accordingly, a government report into the coordination of science
in managing salinity, stated that, ‘the new NRM context has altered the research supply-
demand relationship, with catchment management authorities now having greater power
to determine research priorities’ (Commonwealth of Australia, 2004a: xxxviii).
However, although the policy-making rhetoric implies more collaborative approaches to
scientific production, governments and the regional bodies have a preference for
immediate on-ground action (Yencken and Wilkinson, 2000; Commonwealth of
Australia, 2004a; Natural Resource Management Ministerial Council, 2006). This
preference is seemingly at odds with the parallel objective that regional organisations
make decisions based on ‘the best available science’ (Keogh et al., 2006: 18) that
requires investment in long term research.
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Soon after the implementation of NAP/NHT, gaps in institutional arrangements to
support science use in policy-making were revealed. Processes and systems to meet
demand for regional scientific knowledge were lacking, as observed by Robins and
Dovers (2007), ‘[regional] research is often ‘curiosity-driven’ and focused nearby,
governed by researchers’ agendas and priorities,’ suggesting regional bodies do not play
a significant role as co-producers of knowledge in the regional delivery model.
Likewise the House of Representatives’ Science Overcoming Salinity Report
(Commonwealth of Australia, 2004a) identified decision-making and data availability
and quality issues with the National Action Plan for Salinity and Water Quality (NAP);
of particular relevance is the finding that the architecture of the NAP:
i. inhibited national research coordination;
ii. did not have a charter to fund salinity research;
iii. had geographic gaps by focusing on only 21 regions; and
iv. lacked a rigorous scientific basis for the allocation of funds to regions.
Despite the good intentions of the Commonwealth and State agreed principle that
decisions would be based on good science, this was not translated into practical effect in
the NAP architecture. A regional policy-maker (Interviewee No 16, 2007) felt that the
actual policy that shaped the geography of NAP was based on politics as an obvious
response to strong lobbying:
See, a while ago, we’re talking about the NAP, you know the National Action Plan, and all the science behind that so all this salinity’s out there, big program, multi-million dollar programs, then you hear the other day, for example, a lot of that science was based on how much science. In the fact that maybe some of its political, ‘we are really badly off in Western Australia, we are really badly off. We need this money in Queensland. We need the money,’ so a lot of it, its been suggested, was talked up about that process and is salinity really bad as it is being made out to be? Then, you’ll hear other people say, no, it was but due to the drought, we’re not getting salinity like we use to, you know because of the water tables and all of that sort of stuff. Tell me where’s the science behind it? Where’s the truth? Probably in the middle, I don’t know. But they’re the sort of things that, that all have political spin, it all has that process.
The House of Representatives report (Commonwealth of Australia, 2004a) also
highlighted a failure to incorporate key research findings into salinity programs and
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considerable variation across regional bodies in the uptake of science, suggesting
breakdowns in the knowledge transfer process. The regional model may have also
increased the risk of investments in regional science at the expense of science needed to
service the knowledge needs of State and Commonwealth NRM policy-makers. The
supply of science for State or federal policy may have been stymied by the channeling
of resources into somewhat independent and isolated research efforts at a regional scale,
whilst overlooking a wider research need at State and federal scale. Organisations may
have been likely to be interested in (and therefore gather information about) phenomena
which might be of little relevance to each other, or it may be impractical to generalise
from, or to integrate regional results, to service State or federal policy questions (e.g.
different tests used). Without processes and systems to facilitate inter-regional co-
operation and an inter-regional science policy, regions may restrict data availability (i.e.
intellectual property concerns) or to collect data that is unable to be shared (e.g. lacks
interoperability). Furthermore, the regional delivery model adds the challenge for
research and development organisations to effectively transfer information across 57
regions, particularly in a time of State government downsizing, and to manage multiple
research relationships (Keogh et al., 2006).
The development of regional policy, including strategic and investment plans, calls for
science at an equivalent local scale. This opens the possibility that research
organisations may need to manage relationships with up to 57 regional bodies, and
regional bodies need to manage relationships with a number of research providers.
According to one research organisation, CSIRO Land and Water, ‘the challenge in the
regionally distributed model of NRM is to establish effective processes for dispersed
research and development organisations to interact with regional groups spread across
the whole country.’ (Keogh et al., 2006: 73). Failure to manage these relationships, may
lead to information problems identified by regional bodies, including fragmentation,
low volume, lack of relevance and accessibility, poor two-way exchange, and poor
sharing across regions (Campbell, 2006). The effectiveness of collaborative NRM
relationships, ensuring NRM science supply meets demand, may greatly depend on an
improved understanding of the social and institutional arrangements needed for
effective policy and process design. Thus, the challenges facing NRM, and the use of
science in NRM policy-making, in the Australian context are not confined to
understanding the condition and behaviour of natural resources per se – understanding
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the condition and behaviour of social resources appears equally critical to achieving
NRM goals.
4.4 Conclusion
Australian landscapes are not well suited to many current past and current land uses and
management practices. Few of Australian soils are naturally suited to intensive
agriculture, being shallow, low in nutrients and high in salt. Consequently, much of
Australia’s agricultural land is under potential pressure from a range of degradation
problems, including water and wind erosion, salinity, soil acidity and structural
degradation.
Scientific knowledge for managing resources, including both the natural and social
sciences, is expected to feed into Australian environmental strategies, policies and
legislation (Commonwealth of Australia, 1992). But this transfer is complicated by
issues of ‘supply’ (e.g. shortage of science at relevant scales) and issues of ‘demand’
(e.g. the political nature of the policy-making context and the local socio-cultural
environment). Serious information defects have been highlighted in the NRM policy-
making system, emphasising the poor quality of data and science to inform NRM policy
(Productivity Commission, 2004; Commonwealth of Australia, 2004a; Commonwealth
of Australia, 2008a; Hajkowicz, 2009).
According to Bellamy (2006) the current management of Australia’s natural resources is
multi-layered and fragmented. With three tiers of government in Australia (local, State
and federal), each developing NRM policy initiatives independently of each other in an
ad hoc way, there is a diversity of NRM institutional arrangements existing across
Australia. Furthermore, Australia is now formally divided into 57 NRM regions, each
with a community-based board of management with responsibilities for integrated
management of the region’s natural resources, supported by a regional NRM body
(Moore and Rockloff, 2006; Williams, 2007a). Driven by an ideology of
regionalisation, the regional delivery model in Australia has re-shaped the internal
policy-making environment, adding another layer of complexity to NRM policy-making
with new inputs of local knowledge, new, unmet, demands for science at regional scale
and wider community values for policy-makers and scientists to consider.
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This chapter also considered aspects of structural change in the APS over the past two
decades through examination of contemporary trends in public management practices. It
demonstrated that the capacity of the APS to handle knowledge for policy-making is
threatened by practices of outsourcing and the associated loss of specialist skills. The
chapter showed that the resulting narrower disciplinary base, associated with positivist
scientific philosophy may reduce the effectiveness of knowledge use in an increasingly
complex NRM policy-making system. The chapter contended that these movements
pointed to the complexities of science use in the federal NRM sector, particularly during
a period of significant investment in NRM programs.
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CHAPTER 5: A SYSTEM PERSPECTIVE
5.1 Introduction
Chapter 2 described how interpreting the use of science in policy-making has been
tackled by a range of conceptual structures, evolving from models focused on the
information attributes of science, to models attuned to interactions between researchers
and users. Chapter 2 also presented an analytical approach to reflect on the socio-
political dynamics of science. Three approaches drawn from other research traditions
are suggested as being useful: 1) a supply chain approach, to shed light on system-wide
interactions (e.g. links between research funding bodies, research organisations and
interest groups); 2) an organisational perspective based on an interaction model of
research use; and 3) a micro-organisational perspective based on structuration theory to
invite consideration of the socio-political dynamics of information use by policy-
making actors in organisations.
Recognising the importance of a wider understanding of the socio-political dynamics
influencing science use in policy-making, this chapter applies a supply chain approach
to understanding science use in the NRM policy-making system. Chapter 2 introduced
the concept of supply chain to explore the network context (i.e. links in the chain) of
knowledge production and use activities. In this case, the links in the chain refers to
entities that are involved in the production, transfer and use of scientific knowledge.
The term ‘scientific knowledge’ is interpreted to mean a ‘product or service’ that is
produced by researchers for use by policy-making customers. The supply chain concept
is applied not to dissect all the activities along the science-policy chain, but to invite
reflection on science use in regard to a broader socio-political context of science
production and transfer.
This chapter applies the supply chain concept to track the network of activities,
processes and links targeted at the generation and use of science in policy-making. The
chapter begins with an introduction to the supply chain framework used, discussing its
application in the context of this thesis. It then considers the management of the
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‘science supply base’ and the science production process, with a focus on issues of
constraints to chain integration and information flow. Attention is then turned to the
quality aspects of the scientific ‘product’, focusing on ease of access and user-
friendliness of tools that can improve product quality.
The chapter also examines the influence on science use of customer ‘demand’, and in
turn, how this is affected by the broader social and political environment. Three main
data collection methods were used to illustrate specific attributes and examples relating
to the application of the supply chain approach in the Australian NRM policy-making
system: an online questionnaire of Australian NRM policy-makers; a micro-
ethnography study in an Australian federal NRM department; and Semi-structured
Interviews of Australian NRM scientists and policy-makers (see Chapter 3). In addition
to these three main sources, the research drew on qualitative data gained from many
years of experience working as part of agri-food research and policy advisory teams in
the State public service.
5.2 Characteristics of the Supply Chain
Supply chains are increasingly being regarded as an important conduit of sharing
information and learning from other organisations (Tan et al., 1999; Spekman et al.,
2001). Whilst not referring explicitly to a concept of supply chain, a number of studies
described in Chapter 2 also suggested that supply chain is an important vehicle for the
transfer and transformation of information (e.g. Landry et al., 2006; Turnhout et al.,
2007). This thesis adopts a broad conceptualisation of the supply chain that spans
science suppliers’ suppliers (e.g. funding bodies) to policy customers’ customers (e.g.
lobby groups).
To identify processes and activities in the ‘science-policy supply chain’ that may be
important for science use in policy, this section draws on a framework that blends five
supply chain management constructs proposed by Tan et al. (1999) and eight core
supply chain processes recognised by the Global Supply chain Forum (Croxton et al.,
2001) (Table 5.1). In the context of this research, the ‘product’ is scientific information,
the ‘customers’ are the policy-makers, the ‘suppliers’ are the researchers. The supply
chain framework is applied to the Australian ‘science-policy supply chain’ using data
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collected by an Online Questionnaire of NRM policy-makers and Semi-structured
Interviews of scientists and policy-makers (See Chapter 3).
Table 5.1: A framework for the ‘science-policy supply chain’ adapted from Tan et al. (1999) and Croxton et al. (2001).
Supply chain Constructs (Tan et al., 1999)
Supply chain Processes (Croxton et al., 2001)
Description
Supply base management · supplier relationship management
(procurement)
· product development and
commercialisation.
This process includes the forging of
relationships with suppliers and the
integration of customers and suppliers into
the product development process.
Quality management · order fulfillment This process requires effective integration
of the production and delivery plans.
Customer relations · customer relationship
management
· customer service management
A process to maintain and develop
relationships with the customer and to
provide a source of customer information,
such as product availability.
Performance · manufacturing flow management
· returns management (returns)
This process includes all activities
necessary for managing the product flow
through the production facility.
Competitive Environment · demand management The demand management process needs
to balance the customers’ requirements
with the supply capabilities (e.g. forecast
demand) in order to keep up with
competitors.
5.2.1 Supply base management
Product development – supplier integration
Product development generally begins with a financial driver. As Harris (2002) points
out, science operates according to a ‘purchaser-provider’ model. The suppliers to
science include the purchasers (e.g. funding bodies) that generate the financial flows to
produce and maintain science supply. Chapter 4 described how research for the
Australian ‘science-policy’ supply chain is fuelled by funding distributed by Rural
Research and Development Councils (RRDCs). Thus, the integration of suppliers in the
supply chain is critical to maintain the science supply base for policy. Suppliers, via
their purchasing power, can have an impact not only on the volume of science that is
developed but also its quality.
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The importance of integrating suppliers into the ‘science-policy’ chain was highlighted
by a question in the Online Questionnaire that asked how often the statement scientific
information meets my needs and expectations applied in their daily policy-making
activities. Of 77 respondents, a total of two (three per cent) indicated never and 51 (66
per cent) indicated sometimes (Figure 5.1). One reason for the somewhat poor quality of
the science supply base for policy may be that integration of suppliers in the chain is
weak and resources are directed to initiatives that are unlikely to yield positive
contributions to policy-making. Stronger integration may offer the benefit of inter-
organisational relationships with policy organisations that can bring in research
objectives that are new to the supplier. According to Harris (2002) another reason for
the inadequate quality may be that many suppliers to science do not have the skills or
knowledge to effectively invest in research priorities and projects of relevance to policy-
making.
0% 20% 40% 60% 80% 100%
Scientific information meets
my needs and expectations (n =
77)
never sometimes often very often
Figure 5.1: Perceived value of scientific information in
daily policy-making activities (Online Questionnaire
2007, Appendix C)
Harris (2002: 203) cautioned that suppliers to science may lack appropriate capacity
with potential declines in the quality of science available for policy-making:
the much vaunted ‘purchaser-provider’ model of science requires that the purchaser actually knows something about that which he or she is purchasing. In my experience, particularly in some of the more complex areas of NRM this is rarely the case. Many R&D managers do not understand what constitutes the ‘state of the art’ at the time
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and frequently support second rate science and sub-optimal outcomes
Others caution that the suppliers to science are operating according to a traditional
research model and focusing on single research paradigms that may not be able to tackle
the more holistic concerns of ‘wicked’ NRM problems. Historically, NRM research
funding in Australia has favoured the pursuit of a biophysical understanding of resource
use and management (Mobbs and Dovers, 1999). Referring to integration research for
NRM in Australia, Bammer et al. (2005: 10) observed that ‘on the whole the disciplines
have largely kept apart and there is still a considerable gulf between biophysical
scientists and social scientists’ and that ‘the large number of [research] players provides
some insight into why effective integration is still more the exception than the rule.’
Greater recognition of the importance of the human dimension in NRM and the
difficulty of matching single discipline views to multidimensional problems, suggests
more integrative approaches (e.g. geography, an ‘interdisciplinary discipline’29) are
needed that transgress narrow disciplinary boundaries (Barnett et al., 2003; Cohen,
2006). In line with this view, Cohen (2006: 12) cautioned that ‘when analysing an
environmental issue ignoring other fields is an obstacle to better solutions’.
Thus, the poor quality of the supply base might be due to an immature funding model
that has yet to act on a recognition that the design and implementation of policy, for
‘wickedly complex problems’, relies on a wider analytical and interpretative view than
can be provided by a single discipline (e.g. see Bennett and Thomas, 1982; Allen and
Gould, 1986; Mobbs and Dovers, 1999; Harris, 2002; Brown, 2003), as summed up in
the following statements from a State and a federal government policy-maker
respectively:
Because the questions that we need answered are difficult. They are complex and they are not just one discipline (Interviewee No. 26, 2007).
I think there’s been a tremendous level of frustration and mismatch between the policy needs and the science, the capacity of the science
29 Geographers are well placed to contribute to the sustainable management of coastal environments given their integrated view of physical and human systems, and their focus on spatial and geomorphic perspectives and timescales that are appropriate to resource issues. These scales sit between those of other disciplines such as geologists, ecologists and engineers (Kennedy et al., 2008).
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to deliver what policymakers want. That’s because scientists have tended to be too narrow, too narrowly focused on their own particular discipline rather than on the broader multi-disciplinary needs which are so paramount in the regions of Australia. So I think the policymakers have been frustrated, the scientists have been frustrated because they can’t give the policymakers what they want (Interviewee No. 24, 2007).
Accordingly, Bennett and Thomas (1982) recognised the limitations of a single
discipline approach, arguing that:
Technological solutions to salinity problems cannot be rationally implemented without considering the social and economic consequences. A biophysical ‘solution’ may not be economically efficient, as social changes may turn out to be less costly than technological fixes (Bennett and Thomas, 1982: 8).
Accordingly, in a review of LWRRDC’s research portfolio (also see Section 4.3.2),
Mobbs and Dovers (1999: 11) argued that the traditional R&D model was deficient,
failing to address the multiple dimensions of sustainability, ‘wherein research tasks are
narrowly construed as technical problems which fall within the domains of biophysical
science or neo-classical economic, tackled in isolation…’. The largely technical and
biophysical30 orientation of the research, according to Mobbs and Dovers (1999),
focused on understanding the symptoms rather than the underlying causes of
environmental problems:
It is widely accepted that the most crucial barriers to sustainable NRM are not scientific or technical, but rather social, institutional, political, economic and cultural. Without more attention to social and institutional issues, Australia will not be examining the underlying causes of unsustainable patterns of resource use (Mobbs and Dovers, 1999: 114).
Mobbs and Dovers (1999) suggested that the asymmetrical research investment strategy
had exacerbated the disciplinary divide31 and lack of connections between the
biophysical and the social science, and a less simplistic view of NRM that recognised
30 In simple terms, biophysical research explores events, processes and relationships within and between elements of the biological and physical world. Research in the humanities and social sciences explores human individual and social behaviour and culture (Mobbs and Dovers. 1999). 31 A divide not unlike that suggested between the sciences and humanities, commonly referred to as the ‘two cultures theory’ (Snow, 1971; also see Section 2.2.1).
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multiple dimensions of the resource problem was needed. Consistent with this view
Harris (2002: 203) contends ‘there is tension between the ‘hard’ science of the
researchers and modellers, and the ‘soft’ science of those concerned with the
community, with adoption and outcomes’. Accordingly, Mobbs and Dovers (1999: 14)
cited the example of an R&D Director who remarked that ‘in his experience…social
and institutional research is not deemed ‘real-science’’. Thus, social science approaches
may remain unconvincing to some. Interviews of policy-makers and scientists suggested
that the social sciences were not always recognised as legitimate scientific sources of
information for NRM policy-making. The sentiments expressed by two policy-makers
(one is a former scientist) in this regard are:
I’m sure I could have an argument with my biophysical focused colleagues who would say that it is not science it is all guesswork and opinion what you do in social science (Interviewee No. 1, 2006).
a lot of fuzziness in social science and economics which to my mind exclude them from being sciences. For example if you can’t reproduce a result when you do an experiment and you do an experiment 10 times and you get 10 different results you are not talking science, and that is what you get with economics and social science; that is my view you can’t go through the normal scientific process of observation, hypothesis, testing and then continue that loop again everytime they test they come up with a different result you can’t box people up and say if you answered that question today then a person like you will answer that way tomorrow (Interviewee No. 31, 2006).
This is consistent with the view, that some research funders and scientists do not
recognise the social sciences, or emerging multidisciplinary sciences to be valid
(Charlesworth et al., 1989; Freudenburg and Gramling, 2002). Nevertheless, a question
in the Online Questionnaire that asked how often different information sources were
used in daily policy-making activities indicated that social science information was in
demand by policy-makers. Of 80 respondents, 21 (31 per cent) indicated using social
science information often to very often (Figure 5.2). Importantly, Chapter 4 noted that
the closure of LWA32 in June 2009 may signal an emerging imbalance in investment in
NRM research for policy (e.g. greater risk of neglecting social and institutional
32 Land and Water Australia was a Rural Research and Development Corporation that was significant in its funding of social and institutional research related to sustainable management and use of Australia’s natural resources.
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research), and consequently, an imbalance in the resulting adequacy of NRM research
available to inform policy choice.
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
How often did you usesocial science
information sourcesfor your policy-making
activities in the pastyear? (n = 80)
never sometimes often very often
Figure 5.2: Frequency of using social science
information sources for daily policy-making activities
in the past year (Online Questionnaire 2007, Appendix
C).
Product development – integration of customers
Closer integration of organisations in the supply chain is a means of improving
competitiveness and product quality (Stadtler, 2008). The spatial constraints to
integration were highlighted by a question in the Online Questionnaire that asked
policy-makers about their perception of intra-organisational proximity of scientists and
policy-makers. In regional organisations, 30 per cent (six of 20) indicated that
researchers are never located near policy-makers, compared with five (two of 39) per
cent of State organisations and seven per cent (one of 14) of federal NRM organisations.
The intra-organisational proximity of scientists and policy-makers in federal and State
organisations would help enable a closer integration compared with regional
organisations.
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0% 20% 40% 60% 80% 100%
Regional (n = 20)
State (n = 39)
Federal (n = 14)Generally in my organisation
researchers are located near policy-
makers
never sometimes often very often
Figure 5.3: Perceived intra-organisational proximity
of researchers and policy-makers (Online
Questionnaire 2007, Appendix C)
In the context of this thesis, during the Semi-structured Interviews a federal policy-
maker (Interviewee No. 21, 2007) noted the importance of inter-organisational
proximity between regional organisations and research centers for ‘customer’ access to
policy-relevant science:
CSIRO is always influential because it’s big and it’s distributed so it’s quite interesting to look at the way some of the regions use science, and those regions that are approximate to cities that have significant university capacity or CSIRO base or whatever will often get the benefit of a whole heap of research that’s actually being done on their turf. So if you’re around Townsville you will get the benefit of the CSIRO crew there. Certainly around Murrumbidgee, you have got the benefit of having ANU and University of Canberra and CSIRO and others here as well.
This indicates the importance of spatial dimensions to the integration of policy
customers into the supply chain and the increased difficulty of integrating regional
organisations that are more remote to research centers, with variable internet access and
limited expertise base, compared with State and federal NRM organisations. Integration
might be easier and require less effort if regional policy customers are near to the
science producers. Thus, it would appear that one of the reasons that research is not
used by regional organisations is the spatial and structural impediments to integration.
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These limit the intra- and inter-organisational research information flows required for
product development. Hargadon and Sutton (1997) suggest that the ability of a supply
chain partner to learn from its partners is affected by its ability to harness the
information and transform/transfer it internally among organisational members as
knowledge. It would appear that the ability of regional organisations to gain knowledge
from the information flows in a supply chain is limited by spatial and structural
constraints to chain integration. Notably, integration in the science-policy supply chain
is particularly critical for regional ‘customers’ as their internal scientific capacity is
weak (Robins and Dovers, 2007). Accordingly, during the Semi-structured Interviews a
regional policy-maker (Interviewee No. 29, 2007) pointed to the dependence of the
regional organisation on State agencies to not only collect data, but also provide the
expertise to interpret the information:
We are reliant, as I said earlier, on the input, the science really from the other agencies. And we need to have a service level agreement with Department of Natural Resources that they can actually do a lot of the scientific interpretation for us and a lot of the science monitoring. That data, we’re going to need, and even the interpretation of the data, I mean even if they collect the data, we don’t have the scientists in our organisation to be able to say what that means. We’ve got people who’ve got a fair bit of science, we can sort of say well that looks like it should mean this. But that’s not always good enough. You’ve got to have somebody who can do that proper interpretation. And so we need that backup, we need that support.
Regional bodies often expect State agencies will fund the science needed for policy, as
one regional policy-maker (Interviewee No. 7, 2006) explained:
that sort of regional surveillance type of resource condition monitoring is not something that the regional groups are mainly community-based recognise as a priority. Many of the community people that are in the decision-making role at regional level with those funds see that as a State agency responsibility so they tend not to want to put a lot of money into it.
But a scientist (Interviewee No. 15, 2007) points out that this expectation is problematic
because the data collection funded by the States may not be at the scale needed for
regional level planning:
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the CMAs have an expectation that they will also be able to use the data and that the data will be sufficiently detailed, I suppose for them to answer some of their questions. And we’re still trying to work through that because that’s not always going to be possible because the scales are so very different and the CMAs will obviously be focused very much on their management actions and the outcomes from there, but the specific management actions, no Statewide monitoring program is going to be able develop those things.
Not surprisingly, with multiple NRM policy-making and scientific organisations
operating across three tiers of governance (federal, State and regional) in Australia there
is institutional confusion about management of the science supply base (e.g. issues of
resourcing and regional community involvement) which contributes to the inadequacy
of science, particularly for regional policy (see Section 4.2).
Product development – ‘co-production’ of science
The perceived value of being involved in the production of science was highlighted by a
question in the Online Questionnaire that asked policy-makers about the professional
satisfaction derived from their membership on research committees. Of the 77
responses, a total of 44 (57 per cent) indicated that their membership on expert panels
was important in terms of their professional satisfaction (Figure 5.4). The survey
findings were supported by data collected during face-to-face interviews of policy-
makers and scientists. Participation by policy-makers in expert or technical panels was
seen as generally leading to science that is more policy-relevant. As one regional policy-
maker (Interviewee No. 9, 2006) pointed out:
there’s the representation like me on the [research organisation] board to ensure that science is relevant.
and similarly, another regional policy-maker (Interviewee No. 7, 2006) noted:
so they included in the membership of each of those groups some community group employees or members on those groups to try and inject a strategic view as well as a science and technical view into the development of the programmes and investment plan.
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A male, State-based scientist also pointed out that early engagement with policy-makers
was valuable and important to ensure availability of a quality scientific product:
the communication thing is the absolute essential as well so that we keep, we talk to each other often and early and that you know when policy, you start to think of policy initiatives that they engage with the scientists right early on from the word go when they’re starting to think about something new, that dialogue starts going and that we develop those things in partnership. Frequently things have failed because policy have decided on a course of action and they’ve developed a policy until it was almost done and then have said ok now we need you scientists to provide us the support, the scientific support for it and sometimes that couldn’t done and that’s something that we need to avoid at all costs (Interviewee No. 15, 2007).
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
How important is yourmembership on expert
panels, andcommittees involving
researchers?
not important at allsomewhat unimportantneither important nor unimportantsomewhat importantextremely important
Figure 5.4: Perceived importance of membership on
scientific panels and committees (Online
Questionnaire 2007, Appendix C)
These findings are in line with the concept of the ‘co-production of science’ introduced
in Section 2.3.2 that refers to ‘the institutionalised practices by which ‘usable science’
best is produced in interaction between scientists, policy-makers and the public’, and ‘a
dynamic process by which science and society continually shape, constitute and validate
one another’ (Lövbrand, 2007: 41). This is also consistent with the argument of Gibbons
et al. (1994) that due to a fundamental change in recent years, in the ‘mode’ of
knowledge production, new research relationships and institutional structures are also
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emerging to influence the ways in which scientific knowledge is produced (Gibbons et
al., 1994).
According to Gibbons et al. (1994) the key change is that science production is
becoming a less self-contained activity, citing examples of the need for several different
skills to solve problems and the spread of knowledge production from academia to
many different types of institutions. Gibbons et al. (1994) concluded that scientific
knowledge production has become a more socially distributed process and key trends in
science production over the past thirty years provide some evidence of this. There has
been a shift from pure research carried out by individuals or small teams, to large inter-
disciplinary research teams characterised by strong linkages to government and
commercial outcomes (Harris, 2002). For example, the Australia’s government-industry
funded Cooperative Research Centre program combines researchers from different
research areas and has adopted a commercial focus (Wasson and Dovers, 2005). There
is also a shift to ‘technique cross-fertilisation’ - applying new techniques to a wide
variety of unrelated problems and the integration of knowledge (e.g. using spatial
analytical methods (Hill et al., 2006), integrated assessment techniques (Gough et al.,
1998) or collective narratives of expertise (Gibbons, 1999)).
Within a wider supply chain view of science production, there exist social, institutional,
technological and political drivers of these trends to consider. These include scarcer
funds (Gibbons, 1999), stronger linkages between government policy and international
environmental conventions (e.g. oceans policy, biodiversity, global warming) (Harris,
2002), increased recognition of ‘triple bottom line’ information demands (Harris, 2002;
Croke et al., 2007) and new information technologies and agency agreements enabling
the integration of data (Sarewitz and Pielke, 2000; NLWRA, 2004; Croke et al., 2007).
The narrow, scientific paradigm, inwardly focusing on technical publications and peer
review, is being supplanted by an outreach model of science supply that includes
managing relationships with a wide range of users to ensure access, availability and
awareness of the scientific product (e.g. Australian research organisations entering into
supplier relationships with regional NRM organisations, see Section 4.3.3).
Importantly, the ‘co-production’ of science will not ensure that science directly
translates into policy. There are many reasons for this, such as issues of politics and
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values that are discussed in the sections below. As Caldwell (1993) points out, the
science process per se does not directly deliver policy, and science based analyses do
not necessarily determine, nor are they essential for, a policy choice (see also Section
1.1). Data collected from the Semi-structured Interviews with policy-makers and
scientists (n = 31) support this view. The short-term NRM program funding cycles, for
example, often require Australian NRM policy-making organisations to make hasty
decisions, usually in the absence of scientific knowledge as pointed out by a male, State
policy-maker (Interviewee No. 31, 2006):
The other thing is and this is big problem with programs like NAP the money arrived, the money is there and has to be spent by mid 2008 so you can’t sit back and say well hang on guys we haven’t quite got the science sorted out and the policies aren’t in place and we say no to this lot of money and hope some more comes, what we say is yep we will wing it and that is what we do you wing it and you try and fill the gaps while you are actually doing the work and that is not just us that is across the board.
5.2.2 Quality management
In the scope of quality management are processes that transform scientific products into
a state that meets customer demand (i.e. fulfil the order). It raises questions about
product quality and promotion, such as ‘how appropriate is the scientific product (i.e.
data, information and knowledge33) to the customer need?’ and ‘does the customer know
about the product?’. Successful supply chain management has been linked to the
frequency and quality of information flows, reflected in the quality and amount of
information shared. For example, Mohr and Nevin (1990) showed empirically that
greater communications led to better cooperation and outcomes among channel
partners.
Order fulfilment - integration needed
The importance of the user-friendliness of the scientific product was highlighted by a
question in the Online Questionnaire that asked for the level of agreement with the
33 Data refers to discrete objective facts about events without any meaning in themselves. Information is processed data or data endowed with relevance and purpose. Knowledge is a combination of information together with experiences, values, context and insights (Fitzroy and Hulbert, 2005).
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statement that in general NRM scientific reports and presentations tend to be so
complex or technical that they are hardly worth paying attention to. Of the 80
respondents to the question, 46 (57 per cent) disagreed with the statement but 23 (29
percent) were neutral and 11 (14 per cent) agreed (Figure 5.5). The survey findings were
supported by data collected during face-to-face interviews with policy-makers. The use
of science in policy was rarely viewed as a simple action by policy-makers, but more as
requiring additional effort to adapt a scientific product for policy. Importantly, this
effort was generally seen as leading to the analysis and integration of discrete findings
to generate knowledge products. As one male, State policy-maker pointed out
(Interviewee No. 17, 2007):
A piece of science in isolation without having an analysis to then provide knowledge products is of very little value to the practitioners.
In a similar vein, another male, State policy-maker suggested that the way science
information is used in policy requires a more integrated and broader view than the
narrow disciplinary approach of science (Interviewee No. 1, 2006):
I think alot of our scientists become reductionists in their thinking; they will focus in on the minutiae of a particular problem, they are really delving into quite complex detail, scientific technical issues and that is important that they do, that they develop the science and they develop information for us but in terms of how that gets used is abit different.
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0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
In general NRM scientificreports and presentationstend to be so complex or
technical that they arehardly worth payingattention to (n = 80)
agree neutral disagree strongly disagree
Figure 5.5: Agreement with the statement ‘In general
NRM scientific reports and presentations tend to be so
complex or technical that they are hardly worth
paying attention to’ (Online Questionnaire 2007,
Appendix C).
The importance of integrating NRM data to meet end user needs has been recognised by
Australian NRM government agencies and led to the development of an online NRM
database called the Australian Agriculture and Natural Resources On-line (ANNRO),
described on its website (http://www.aanro.net/L3.html) as ‘an integrated knowledge
discovery tool for agriculture and natural resources.’ According to the website it was
designed with the aim of being ‘a national single entry point to agriculture and natural
resource management research information.’ However, the importance of promotion
was highlighted by a question in the Online Questionnaire that asked policy-makers
how often they used the tool. Of the 80 respondents to the question 72 (90 per cent)
reported never using ANNRO for their policy-making activities in the past year (Figure
5.6).
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0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
The AustralianAgricultural and
Natural ResourcesOnline (AANRO)database (n = 80)
never sometimes
Figure 5.6: Frequency of use of the Australian
Agricultural and Natural Resources Online (AANRO)
database (Online Questionnaire 2007, Appendix C).
This could indicate poor promotion and hence, a lack of awareness of the product, as an
Australian scientist explained to me during fieldwork conducted in a Commonwealth
government NRM agency (See Chapter 6), ‘so there are a lot of clever little toys out
there that can potentially be used by the regions. But nobody knows about them…’. It
may also reflect the magnitude of the task facing policy-makers to integrate and manage
complex, disaggregated information, despite the claims of the online tool. This is not
easy, observed a State-based policy-maker (Interviewee No. 17, 2007), as scientific
findings are generally communicated in a chaotic, piecemeal fashion, thereby limiting
their use in policy:
there’s a program in NSW called WISE which compiles all pieces of knowledge and science in NRM and most catchments in NSW might have 2,000 or so pieces of information, critical pieces of information in natural resources…the interesting thing though is with all those studies is that what they say is that a lot of those studies don’t provide a lot of good science product to actually use for a practitioner. So they are science but they are isolated as an end result. Its science that can’t be adopted in a range of forms.
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0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Results integratedacross range of
scientific disciplines (n =76)
not important at allsomewhat unimportantneither important nor unimportantsomewhat importantextremely important
Figure 5.7: Perceived importance of integration of
scientific findings when deciding to use scientific
reports (Online Questionnaire 2007, Appendix C).
The importance of integrating scientific information to improve the quality of the
information product for use in policy-making was supported by findings from the
Online Questionnaire (Appendix C). Of a total of 76 respondents, 60 (75 per cent)
considered the integration of information across disciplines an important factor in their
decision to use a scientific report (Figure 5.7). This is consistent with the view of Croke
et al. (2007) that resource management involves integrated management of a catchment,
watershed or river basin and a more integrated way of communication may result in a
more resilient information product for policy-making. This sentiment was shared by
several policy-makers and exemplified by the following extract (Interviewee No. 20,
2007):
the lack of integration if you like, as a policy maker, getting hold of the scientific information that is current and relevant can be challenging. We pay for a lot of it and we don’t manage that knowledge very effectively.
The importance of integrating science for quality improvement was also marked by a
question in the Online Questionnaire that asked how often models and geographic
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information systems34 (GIS) were used (Figure 5.8). Of the 80 respondents to the
question 31 (40 per cent) used GIS or models often or very often for their daily policy-
making activities. These tools can integrate disparate data sets across databases and
agencies, helping to ensure data is easier to access, use and display (e.g. user-friendly
maps and outputs) and contributing to their popularity among policy-makers. As Lang
(2001: 1) noted about the role of GIS:
By putting their [planners’] spatial data in an integrated system where it can be organised, analysed and mapped, they find patterns and relationships that were previously unrecognised. This in turn gives them a deeper understanding of the issues they face, and lets them bring more information and less conjecture to the problem solving process.
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Systemsanalysis/modelling or
geographicinformation systems
(n = 80)
never sometimes often very often
Figure 5.8: Frequency of use of GIS or models for
daily policy-making activities (Online Questionnaire
2007, Appendix C).
One Australian government policy-maker (Interviewee No. 20, 2007) explained that
GIS was an important tool for quality improvement, providing the means to integrate
production and environmental data for NRM policy-making:
34 Geographic information systems (GIS) produce map-based information by storing, manipulating, analysing and integrating, physical, biological, social and economic data linked to a specific location (Steinberg and Steinberg, 2006).
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We hold a huge amount of data about biodiversity, about vegetation and environmental type information. And then we have got Bureau of Resource Science’s agricultural data. It’s very closely allied and we both need both sets of information to make sense of natural resource management…. so we are working across them and with CSIRO and ABARE at the moment on how to spatially integrate all that information.
Importantly, this suggests that NRM data are carried in disconnected databases, stored
in diverse formats and held in different NRM government agencies, and that steps have
only recently been taken by the agencies to ensure wider access and a more user-
friendly product. As a report by the NLWRA (2004) points out, there are differences
between agencies in data standards, data priorities and technical capabilities that have
hindered the sharing of data. Furthermore, the speed of integration of raw spatial data
for the quality management of NRM information is closely linked to the pace of
institutional change. According to NLWRA (2004) and McDonald et al. (2005) the
limiting factors to integration are also institutional issues, as agencies tend to vie for
cost recovery and resources, and operate under a ‘silo mentality’ (e.g. ‘if it’s not ours it
can’t be good’).
Another regionally-based NRM policy-maker (Interviewee No. 9, 2006) pointed out that
contemporary restructuring of the public sector workforce (e.g. see downsizing issues
raised in Chapter 4) was an institutional issue leading to the loss of research data and
gaps in resource data coverage:
government people when they have done the research, not so much now, but it used to be, take their research with them. If they leave, it’s theirs, they take it. This does not seem to happen now but it used to be really, really frustrating that you could be dealing with a person with expertise in something and they move on and then you have lost the ability to access their research because they took it with them.
Order fulfilment – readability and interpretation needed
The importance of readability of scientific reports was highlighted by a question in the
Online Questionnaire (Appendix C) that asked respondents to nominate how important
the use of non-technical language was in their decision to use a report. Of the 80
responses to the question, 71 (88 per cent) suggested that use of a non-technical
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language was important (Figure 5.9). For many researchers the end point of research is
often perceived as publication in a journal, as quantitative measures of publications (e.g.
bibiliometrics) are often used to evaluate the quality of research outputs of scientists and
their organisations (see Butler, 2008). Researchers, thus, may tend to communicate in a
technical language that is often ambiguous and unfamiliar and designed for scholarly
journals.
Thus, it would appear that one of the key reasons for research not being used is its poor
readability and the need for interpretation. A similar set of findings emerged from data
collected during the Semi-structured Interviews. The end point of the research supply
chain was not viewed simply as a journal publication but more as a form of a
communication that is more accessible, more user-friendly and with a focus on
outcomes (i.e. making a difference). As two, State-based policy-makers pointed out:
And the key thing is how do you express the science, it’s got to be expressed in the community’s terms of reference in something they understand. A complex index based on multifactoral analysis, is absolutely useless. If you say that our index has gone from 5.3 to 6.7. Nothing. If you say that you can’t drink the water in 2030, that’s relevant. So the key thing is how you communicate science and scientists hate this bit because it’s reducing this in-depth understanding back to some glib phrase. But that’s what the community focuses in on. (Interviewee No. 11, 2007).
Having good science is a great start but what we need are science communicators as well as scientists who can break it down into easily understandable language, make it relevant, use it to persuade land managers to change the way they manage land (Interviewee No. 31, 2006).
A federal government scientist (Interviewee No. 22, 2007) spoke of preparing
information for decision-makers who would make the final decision:
If the results are too arcane and they’re not distilled down to key messages … the shorthand we use in our world is ‘Ronald Reagan’ briefing notes … if you cannot get the key findings on one A4 page, 14 point font, dot points, double-spaced, right? … it’s not going to get past them. And there’s an art in taking what is inherently a complex scientific or environmental issue and distilling the results down to that level. But you have to do that. And it’s not because these people are not intelligent. Some ministers even read the
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primary scientific literature on occasion but when they need the information it has to be really clear to them.
Here the transfer and use of science was to do with developing key messages and
filtering the information to satisfy a higher policy-making authority:
The responses to the Online Questionnaire also indicated that having examples of use
(89 per cent), the credibility of the source (82 per cent) and the relevance of the product
(92 per cent) were also widely regarded as important attributes of information content
quality needed to ‘fulfil the order’ (Figure 5.9). However, the appeal of the report (i.e.
packaging) was perceived by only 41 per cent of respondents as important (Figure 5.9).
Some studies have found that efforts to adapt research reporting to users’ needs are
strongly associated with increased use of the information (Greenberg and Mandell,
1991; Landry et al., 2001a, 2003). It would appear from the results of the Online
Questionnaire that these efforts would be more effective if they were focused on
integrating and interpreting findings for end-users rather than on sophisticated
‘packaging’ of research.
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Appeal of reports and products (graphics, colour,packaging) (n = 79)
Results integrated across range of scientificdisciplines (n = 76)
Presentation of results in non-technical language (n =80)
Relevance of the information for policy use (n = 79)
Credibility or prestige of the source (n = 80)
Examples or demonstrations of how to use researchresults (n = 80)
not important at allsomewhat unimportantneither important nor unimportantsomewhat importantextremely important
Figure 5.9: Perceived importance of product quality
attributes when deciding to use scientific reports
(Online Questionnaire 2007, Appendix C).
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Order fulfilment – trust and credibility needed
Of 80 respondents to the Online Questionnaire, a total of 66 (83 per cent) indicated that
the credibility or prestige of the source was an important factor in their decision to use a
scientific report (Figure 5.9). The sense of trust and confidence in the product is
important for its use, noted Harris (2002: 203), and as highlighted in Chapter 2, the
development of close personal relationships between scientists and policy-makers can
form the basis of trust to share and learn knowledge. This has the potential to be an
element not only in enhancing shared learning in supply chains but also in the
development of social capital. Data collected during the Semi-structured Interviews
supported the Questionnaire findings. The following sentiment about trust in science
(Interviewee No. 28), giving particular importance to the personal interaction between
policy-makers and scientists, was echoed by a number of NRM policy-makers:
I would be relying on a summary of scientific advice or evidence and in addition I would always want to know where it has come from and whose looked at it because I would also do relying on the fact of the level of trust and the bond I have established with the person who had transmitted that knowledge to me.
The notion of trust in a product can be extended across the partners in a supply chain.
Stadtler (2008) points out that the basis of long-term partnerships in supply chains is a
certain degree of trust. As a State-based policy-maker pointed out (Interviewee No. 17,
2007):
Trust is an extremely important issue. If you want to get best practice in science, I mentioned before that model of linking the policy makers right down to the end users, if there’s not trust to go down to that end user and I don’t think you get as good an outcome because it requires hard work, it requires trust and a lot of hard work to make it work. You’ve got to be open to views that may not be the same view as what you have as a policy maker, that requires a lot of work to work through that process.
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This calls attention to an improved understanding of the institutional arrangements and
organisational processes that may be needed to support the building and maintenance of
trust in science-policy supply chains.
Order fulfilment – interaction needed
The importance of research information transfer via informal networks was highlighted
by a question in the Online Questionnaire that asked how often informal networks were
used as an information source. Of the 80 respondents, a total of 53 (66 per cent) used
them often to very often (Figure 5.10). Thus, it would appear that one of the central
reasons for information transfer is having a source of frequent interactions and
connections with people. The production and maintenance of social capital appears
important for research transfer in the science-policy supply chain. The focus on
connections between policy-makers and scientists is also central to the interaction
perspective of knowledge use introduced in Chapter 2.
0% 20% 40% 60% 80% 100%
Informal networks(n = 80)
never sometimes often very often
Figure 5.10: Frequency of use of informal networks
for daily policy-making activities (Online
Questionnaire 2007, Appendix C).
The survey findings were supported by data collected during face-to-face interviews
with policy-makers and scientists. The importance of formal and informal processes to
maintain interactions between scientists and policy-makers was one of the most
unequivocally shared themes that emerged. ‘It’s more than just sort of reading that
papers and going to a conference. We are involved in joint projects with people and
some of them worked for us previously … it’s that personal network level as well’ said
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a State policy-maker (Interviewee No. 11, 2007). A State scientist echoed a similar view
(Interviewee No. 12, 2007):
I think the lines of communication depend quite heavily on personal contacts, knowing who’s around and knowing what people are working on and that’s why we try and build up an ongoing relationship with our policy people so they know who to contact, or give them a central point to contact, so that we can get the right people.
Another policy-maker based regionally, pointed to a process used to build science-
policy relationships that enabled information sharing and learning from each other
(Interviewee No. 10, 2006):
so the regional body, us, said hey you fellows [scientists] lets have a meeting and try to sort this out, so we kept them to the table and meeting monthly, and kept them to a meeting schedule, even though we didn’t have any money, so we didn’t do full minutes we just did dot points and had a limited amount of time on it, but at least we got them to the table meeting face to face and thing started to really roll, some usable stuff and some not so usable stuff, we got them outside their comfort zone and made them answer lots of questions, it could have been done much better if we did it again. It was a learning process.
This extract is consistent with the view of Gough et al. (1998) that, instead of
integrating through the use of models alone35, integrating through social processes may
result in a scientific product that is more effective for informing policy by allowing a
two-way information flow, and the sharing of insights into model limitations that
obscure underlying values and assumptions. There is growing concern, according to
Gough et al. (1998), about the over-quantification and dependency on models to inform
policy-making (also see Schneider, 1997).
35 The literature describes many other approaches to integrate information, from frameworks and methods for integrated modelling (e.g. expert systems, agent based methods - see Croke et al., 2007), to the use of supercomputers to simulate various possible futures at global scales (Gough et al, 1998), and to a large range of sociological tools and techniques (Syme et al., 1999).
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Order fulfilment – speed and ease of access needed
The importance of speed and convenience for internet delivery of science ‘products’
was highlighted by a question in the Online Questionnaire (Appendix C) that asked
NRM policy-makers their view on the importance of the availability of science on the
World Wide Web. Of the 78 responses to the question, a total of 58 (73 per cent)
considered web-based access to science important (Figure 5.11). This was consistent
with responses to another question that asked NRM policy-makers how often were
world-wide web and online discussion groups/bulletin boards used.
Of the 80 responses to the question, a total of 61 (76 per cent) used the world-wide web
and online discussion groups/bulletin boards in the past year for their daily policy
activities (Figure 5.12). The internet offers a means for policy end users to access
science in a quick and cost-effective way. However, quality issues associated with the
science that unregulated websites may carry can cause confusion and conflict in the
policy process, leading to delays and poorly informed policy.
0% 20% 40% 60% 80% 100%
Science availableon World Wide
Web via theinternet (n = 78)
not important at allsomewhat unimportantneither important nor unimportantsomewhat importantextremely important
Figure 5.11: Perceived importance of science available
on the World Wide Web via the internet (Online
Questionnaire 2007, Appendix C).
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0% 20% 40% 60% 80% 100%
World Wide Webvia the internet andonline discussion
groups/bulletinboards (n = 80)
never sometimes often very often
Figure 5.12: Frequency of use of the World Wide Web
for daily policy-making activities (Online
questionnaire 2007, Appendix C).
The internet allows larger quantities of knowledge to be sent over greater distances at
decreasing cost, improving the speed, cost-effectiveness and ease of delivery of science
products to policy-making customers (Scott, 2006). However, the Web presents both
opportunities and challenges for users of science information. At times, policy-makers
accessing the internet may find themselves with more information than they can handle,
as a scientist in an Australian NRM organisation remarked to me, ‘type salinity in
Google and you get absolutely floored by the information’. Because of the ease of
posting information on the Web it also makes it difficult for quality science sites to be
differentiated from less credible ones, as Treise et al. (2003: 310) noted:
Knowing which of the many sites available contains information high in quality, currency and accuracy is a much greater challenge. Because anyone can post information on the Web, finding science information that is useful, accurate, and current may be difficult.
5.2.3 Customer relations
Customer relationship management - process and transparency needed
In the scope of customer relations are processes to manage and maintain relationships
with the customer. The importance of spatial and institutional differences on the
strength of customer relationships between policy-makers and science was highlighted
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by a question in the Online Questionnaire that asked respondents to indicate the strength
of their relationships with policy-makers, ranging from ‘arms length’ to ‘total
partnership’ (Figure 5.13). Fifty one per cent (10 of 19) of regional policy-makers
considered their relationships with scientists to be adequate, compared with 61 per cent
(eight of 13) federal policy-makers and 77 per cent (30 of 39) State policy-makers.
Notably, no state or federal policy-makers considered their relationships with
researchers to be at arms length, but nine per cent (two of 19) regional policy-makers
did. Regional NRM policy-making ‘customers’ in Australia are generally more
geographically and institutionally remote from the science production process compared
with State and federal counterparts (see Chapter 4). Consequently, regional ‘customers’
may often place considerable emphasis on the way that the science process seems to
neglect them.
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
All (n = 75)
Regional (n = 19)
State (n = 39)
Federal (n = 13)
arms length limited discussion and information flowadequate discussion and information flow close relationship and good information flowtotal partnership
Figure 5.13: Perceived strength of relationship with
scientists by policy-makers based in regional, State
and federal organisations (Online questionnaire 2007,
Appendix C).
This was supported by data collected during face-to-face Semi-structured Interviews
with policy-makers. As one male, regional policy-maker (Interviewee No. 14, 2007)
explained how divorced the science process was from engaging with the regional,
policy customer:
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a model target which was theoretically built on a few, well it was built on a few assumptions which have since shown to be incorrect. But we, at the time, the Board, were uncomfortable with the assumptions. And at the time there was also conflicting evidence around. Now that reference, that evidence, conflicting evidence was actually not referenced in, when they did the modeling.
Seen through a traditional prism of a reductionist science process and positivist
philosophy, community participation is not a methodology standard in science, and
consequently, customer integration in the science process might be side stepped.
Another regional policy-maker (Interviewee 10, 2006), mentioned that the development
and review of a regional environmental plan by scientists represented an opaque,
exclusive process:
the same scientists that set it [the plan] up reviewed it, not a good idea, and said that they were absolutely right, that it was the farmers’ fault and the agencies’ fault for not implementing the things they said they should do - the agencies hadn’t done a catchment plan, hadn’t got the community to change and the science was right. Then we found out doing [Project X] that the assumption was wrong and the plan was flawed.
According to Schneider (1997) a lack of transparency in the scientific process is an
important source of controversy about the use of science in policy-making. As
Schneider (1997: 230) cautioned, building complex models that do not make explicit
‘their value-laden assumptions’ negates the intended rational approach to policy-
making:
To the extent that integrated assessment models (IAMs) inform that value laden process of decision-making, they can educate our intuitions and make our decisions ‘more rational’. To the extent that, in a haze of analytic complexity, IAMs obscure values or make implicit cultural assumptions about how nature or society works (or the modelers’ beliefs about how they ‘should’ work), IAMs can thus diminish the openness of the decision-making process. And, to the extent that openness is proportional to rationality, diminished openness would render policy-making even ‘less rational’.
Failures in the science ‘supply chain’ to ensure transparency and integration of regional
‘customers’ in the process, points to issues of knowledge asymmetry that denies power
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to ‘demand pull’ elements (i.e. end-users). Cognisant of this general neglect, many
researchers have advocated more effective communication between scientists and
policy-makers as critical to the success of the science process to inform policy
discussions (Norse and Tschirley, 2000; Parrotta and Campos Arce, 2003; Janse and
Konijnendijk, 2007; Totlandsdal et al., 2007).
The importance of using non-technical language for communication was indicated by a
regional policy-maker (Interviewee No. 14, 2007) who noted a breakdown in
relationships with science suppliers due to the exclusiveness of scientific discourse. He
expressed frustration at being excluded from the scientific process and an inability to
communicate what he believed to be important issues to scientists:
Yeah, well it’s just that you know, I suppose you need to trust what you’ve been told. Unless you’ve been trained to ask that sort of scientific question you don’t know what to ask. And while you might ask it, you know, it’ll come out as blunt as ‘well I don’t believe you’. And then of course the scientists just say, just go into a whole sort of ramble about why you should believe them. But in the end you are sitting there and thinking, ‘well I still don’t understand’.
Technical jargon played a key role in barring his participation in an expert led
discourse, consistent with the view of Fischer (2005) that the level of science
sophistication required to engage in the process is beyond most stakeholders, making it
easier for them to be excluded. This interview extract embodies the concept of the ‘co-
production of science’ introduced in Section 2.2.1 (e.g. Jasanoff and Wynne, 1998;
Lahsen and Nobre, 2007). It reveals that the flow of scientific information from research
organisations to regional NRM bodies might be considered aligned with a traditional
supply chain concept of a uni-directional flow of information and resources from
supplier to customer – there is little emphasis on ‘customer’ demands flowing back the
other way and no evidence of ‘co-production’. Technical jargon and the lack of
transparency of the process make it difficult for information to be shared, trust to be
built and for policy-makers and scientists to learn from each other, and in turn, makes it
difficult for science to be responsive to end user needs and consequently for it to be
useful for policy.
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To some extent this was reflected in responses to the Online Questionnaire (Appendix
C). Australian NRM policy-makers that had engaged in the ‘co-production’ of science
by commissioning reports, generally found them more useful than academic research.
For 40 per cent of regional (eight of 20), 56 per cent of State (22 of 39) and 46 per cent
of federal (six of 13) NRM policy-makers, the statement commissioned scientific work,
studies and reports are more useful to me than academic research applied ‘often’ to
‘very often’ in their policy-making activities (Figure 5.14). This may indicate a
perception that academic research may be less relevant for policy than it could be, with
27 per cent of all respondents agreeing with the statement that research by NRM
scientists is too heavily influenced by concern with academic prestige than with the
relevance of the research outcome (Figure 5.15).
0% 20% 40% 60% 80% 100%
Federal (n = 13)
State (n = 39)
Regional (n = 20)Commissioned scientific work,
studies and reports are more useful to me than academic
research
never sometimes often very often
Figure 5.14: Perception that commissioned scientific
work is more useful than academic research in policy-
making activities (Online Questionnaire 2007,
Appendix C).
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0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Research by NRMscientists is too heavilyinfluenced by concernwith academic prestigethan with the relevanceof the research outcome
(n = 80)
strongly agree agree neutral disagree strongly disagree
Figure 5.15: Perception that research by NRM
scientists is too heavily influenced by concern with
academic prestige than with the relevance of the
research outcome (Online Questionnaire 2007,
Appendix C).
It could also indicate that science, as Law (2004) observed, can be used to carry and
enact social, scientific and political agendas. By using their power and resources to
commission and use only science that supports decisions or organisational interests (see
evidence presented in Section 5.2.3 Customer relations: Customer relationship
management - process and transparency needed), policy-makers may continue to fuel a
perception that commissioned work is more useful than academic studies. Knowledge
incompatibility (e.g. conflict with organisational goals) and power relations (e.g.
advocating a scientific agenda or funding priorities) in a supply chain may distort the
production of science, affecting such attributes as availability, type, methods and
location of the science conducted.
5.2.4 Performance
Manufacturing flow management – conflict and uncertainty
In the scope of the Performance construct (Table 5.1) are processes that transform
science intermediates and products into their next state to meet demand (Croxton et al.,
2001). Data collected during face-to-face interviews with scientists and policy-makers
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indicated that the ‘manufacture’ of science was rarely viewed as a simple linear process,
but more as a setting for turbulence and conflict among scientists as alluded to by a
federal policy-maker (Interviewee No. 27, 2007):
And indeed, as you know, you can get two or three opinions on any item in science. We have it all the time… so I mean it’s a contestable marketplace.
Similar sentiments were expressed by a State scientist based in NSW (Interviewee No.
13, 2007) who noted the difficulty policy faces of trying to reconcile issues of scientific
conflict about scientific ‘facts’:
I think there’s a real disconnect and that’s always been that the scientific process is an ugly process because it relies on people disagreeing with each other, thats how it works. Policy doesn’t accommodate that very well. They want to hear what the answer is, they don’t want to hear people disagreeing and saying it’s wrong.
According to Johnson (2005: 32), ‘science is a social activity in that it takes several
scientists, analysing and criticising each other to produce more reliable knowledge’.
Within a ‘supply chain’ context several science ‘suppliers’ may be involved in
generating a science ‘product’. Without general agreement on the science there cannot
be a clear way forward for policy. Jasanoff (1990) suggests that mechanisms for
reconciling alternative constructions of science may be a serious deficiency in the
supply of science for a science advisory process in policy-making.
The manufacture of science, therefore, may be viewed not just as a passive registration
of the facts of nature but as an active process of social construction shaped by
individual, organisational and societal values (e.g. funding priorities) (e.g. Jasanoff,
1990; Shove and Redclift, 2001). Jasanoff (1990) noted that the perceptions of scientific
‘reality’ are always coloured by features of the individual, such as the scientist’s
professional, institutional, political and cultural affiliations. Accordingly, Blaxter et al.
(2006: 14) pointed to:
the ‘standard’ view of research is that of a detached scientist examining the facts of the case coolly and unemotionally. However, this standard view of research belies the extent to which, as we have
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suggested, research is a social activity that can be powerfully affected by the researcher’s own motivations and values. It also takes place within a broader social context, within which politics and power relations influence what research is undertaken, how it is carried out and whether and how it is reported and acted upon.
In a similar vein, Toman (1998: 6) argues that ‘science is not and cannot be entirely
value-free’ and the scientific product cannot be described as ‘exact’, regardless of how
objective science may claim to be; statements made have only probabilistic, not exact
confirmation leaving open the possibility of new evidence or new thought. The
production process, according to Bennett and Thomas (1982), allows arbitrary
assumptions to be introduced into analyses (e.g. cost-benefit or land use systems
analyses) when exact knowledge is lacking.
Importantly, the lack of transparency and the inherent uncertainties might hinder the
integration of policy-making ‘customers’ in the science production process, leading to
their reduced trust in science. The notion of knowledge incompleteness introduced in
Section 2.4.1 covers issues of credibility and trust due to the uncertainty of science. The
uncertainty of science was a consistent theme raised by thirty-five per cent of the NRM
scientists and policy-makers interviewed (n = 31, Appendix F). They did not express
uncertainty in terms of quantified measures as science does, instead they pointed out
their inability to trust science to be sufficiently consistent and accurate for policy use.
As noted by one scientist based in a State research organisation: ‘and this is where it
gets really difficult because science is uncertain’ (Interviewee No. 3, 2006), and a State
policy-maker (Interviewee No. 16, 2006) who commented on how delays and debates
are important effects of using science:
Having scientists can slow down the process…[scientists say] we don’t know the science behind it, we’ll have to do some work on this, we’ve got to get some stuff from overseas….But there’s science working, one saying, ‘I don’t think it really is safe’, not that they said it wasn’t but they weren’t comfortable with it, and another one says its fine.
According to Bellamy and MacLeod (1998) uncertainties need to be managed and
effectively communicated so it becomes a recognised input to evaluating policy options.
This is a view also shared by Cimorelli and Stahl (2005: 281):
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It is important for policy makers to know whether inherent uncertainties would result in their making a different policy choice. Likewise it is also important for policy makers to know whether other policy options might be made if uncertainty estimates were different than currently believed or calculated.
However, knowledge flows between organisations may not only be hindered by inherent
uncertainties in the science production process, but also by the poor communication of
them. Keeney and Van Winterfeldt (1986) point out that the performance of the science
community in communicating uncertainties has been relatively poor. In line with this
view one State policy-maker remarked (Interviewee No. 11, 2007):
Science tends to be shades of grey, policy is black and white. And so there is this sort of clash. Some scientists can understand that environment policy works in and can communicate effectively into it. A lot of scientists can’t. And so for me, I’ve got to be careful about who I bring in to talk. Because you can bring someone in who absolutely loses it because they’re talking about all these different factors, all these shades of grey, and the person that you are trying to influence is wanting to make the decision, ‘do I do this or that’ or don’t I? And they’re just faced with this, in their eyes uncertainty. And if something’s uncertain you don’t do it.
Susskind (1994) has argued that, unable to connect with stakeholder36 concerns (e.g.
communicating uncertainties) scientists are not having sufficient impact on policy-
making.
Manufacturing flow management – resource availability
Scientific products presented as aids to policy-making (e.g. cost-benefit analyses,
decision frameworks and models) often demand a great deal of information before they
can be applied to a particular policy issue. As Healy and Ascher (1995) point out, the
scientific research needed to generate and analyse this information (i.e. manufacture the
product) may be expensive. Resource constraints, thus, may limit the capacity of policy
organisations to play a constructive role in the manufacture of scientific information
products.
36 Includes decision-makers and stakeholders with economic or political influence.
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The importance of institutional policies for resourcing the manufacture of science for
policy was highlighted by a question in the Online Questionnaire that asked NRM
policy-makers whether sufficient resources were reserved by their organisation to obtain
research information for policy-making. Of 69 respondents, a total of seven (nine per
cent) indicated never and 42 (58 per cent) said sometimes (Figure 5.16). Thus, it would
appear that the policies for allocating resources to science manufacture are sometimes
not successful to develop a scientific response that matches policy demand.
Importantly, the objective and funding agenda of research are not generally required to
demonstrate a match with policy needs, unless the research has been specifically
commissioned by policy-makers. Data collected during Semi-structured Interviews of
scientists and policy-makers (n = 31) are in line with this view. As one male, State-
based policy-maker noted (Interviewee No. 17, 2007):
Policy makers always demand, there’s always a need of science but how that matches with what science wants to provide or what science has the funding to provide is a pretty critical issue and they don’t often come together, and its not a perfect world of you know, the scientists being available just to respond to policy makers, they usually have their own area of expertise, and funding cycles in science and research are often so long and drawn out that it takes them a while to respond to new or emerging policy issues as well.
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Sufficient resourcesare reserved toobtain research
information for policymaking (n = 69)
never sometimes often very often
Figure 5.16: Perception that sufficient resources are
reserved by their organisation to obtain research
information for policy-making (Online Questionnaire
2007, Appendix C).
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5.2.5 Competitive environment
The Competitive Environment construct (Table 5.1) includes building an understanding
of competitors (Tan et al, 1999) but in the context of this thesis there is a focus on the
demand management process – building an understanding of end user environments for
a competitive edge. According to Croxton et al. (2001), this process includes the
activities and tools that recognise customer needs and customer value; it aims to balance
the policy customers’ requirements with the science supply capabilities (e.g. forecast
demand). For the purpose of this thesis, the ‘customers’ in a science-policy supply chain
are the policy-makers in regional, State and federal NRM organisations. They include
representatives on regional, State and federal NRM Committees and Boards that plan
and make decisions about program investments. Also included are the customers’
customer (i.e. the Minister). It is useful to briefly consider the differences between the
customer environments, both across organisational types and between States, as they
significantly point to different groups of customer needs and values. In a supply chain
sense, the presence of different customer groups would suggest that there is a need to
shift from a commodity approach to science towards a more differentiated product that
better meets the needs of its target customers.
As discussed in previous sections in this chapter, policy ‘customers’ in regional
organisations are less well resourced and integrated with science ‘suppliers’ compared
with their State and federal counterparts. Policy ‘customers’ in regional organisations
are also less likely to be formally trained in science than their State and federal
counterparts (e.g. see Chapter 7). Policy ‘customers’ in regional organisations include
those that are members of the regional board and management. Regional NRM bodies
and Boards, as Robins and Dovers (2007: 277) point out, ‘have a role to play in
debating priorities and determining resource sharing between and within
States/Territories’. Although the selection processes for members differs between States
there is generally a requirement for different social sectors to be represented (e.g. Moore
and Rockloff, 2006).
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Importantly, the regional policy environment would appear to be aiming to be a
microcosm of society rather than a subset of a government agency. With a more diverse
mix of policy ‘customers’ (e.g. representing local, agricultural, environmental, tourism
and government interests) the regional policy environment has more potential to be
‘wicked and messy’ than in State and federal government organisations. Although the
policy environment in State and federal organisations may be a less volatile mix, the
power relations between policy customers have more potential to be asymmetric (see
Figure 5.19). Findings from the micro-ethnography (e.g. see Chapter 6) and the Online
Questionnaire (Figure 5.19) indicate that power relations exist between policy
‘customers’ and ‘customers’ customers’ (i.e. the Minister and lobby groups), and are
asymmetric in the customers’ customers’ favour. This was supported by data collected
during the Semi-structured Interviews (Appendix C). Several shared the sentiment of
one State policy-maker (Interviewee 1, 2006):
It [Major NRM project] was launched contrary to advice to the minister. This is where politics come into it where the Minister wants to believe it will make a difference great photo opportunity for him and his commonwealth counterpart.
Referring to the Australian political process, Doyle and Kellow (1995: 115) pointed out
that ‘much of the political process also involves the interaction of politicians and
bureaucrats with formally advanced groups in society which are seeking to protect or
advance particular interests.’ These interest groups or policy communities have power,
according to Doyle and Kellow (1995: 119), because the Government ‘needs to secure
re-election’ and their ‘co-operation is so necessary for the implementation of policy that
their lack of co-operation could render the policy ineffective’. By successfully lobbying
politicians such groups can negate the use of science by organisations, especially if
policy-makers perceive that politicians set the key value objectives (see Figure 5.19).
Thus, for supply chain development, the diverse ‘customer’ environment in regional
organisations and the asymmetric power relationships in State and federal government
organisations point to different challenges to demand management for ‘science-policy’
chain integration.
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Demand management – organisational culture
Norms and behaviours form part of the culture of policy organisations. They may hinder
or facilitate the flow of information in supply chains. Relationship building between
chain members is a behaviour that supports information flow (Tan et al., 1999). It is
noteworthy that the ‘interaction’ perspective of knowledge transfer and use introduced
in Section 2.3.4 also pointed to the importance of linkages between researchers and
users for the transfer and use of knowledge (e.g. Landry et al., 2001a, 2003; Nutley et
al., 2007). From an organisational perspective (Section 2.4.2), the building of
relationships between researchers and users in a ‘science-policy’ supply chain has the
potential to be an element in the development of social capital.
The importance of the strength of inter-organisational relationships was highlighted by
questions in the Online Questionnaire that examined the influence of informal and
formal linkages between policy-makers and scientists on science use (Appendix C).
Five items from the online questionnaire were combined to come up with a measure of
informal linkages (Informal Linkage Index - Appendix E). These items related to the
frequency of person-to-person contact with researchers (on a scale from 0-6, where 0 =
does not apply, 1 = never to 6 = more than once a week) in five types of research
organisations. The responses of NRM policy-makers that make up the informal linkage
index are shown in Figure 5.17. Person-to-person contact was most frequent with State
government agencies. Of 80 respondents, a total of 66 (83 per cent) indicated that they
had person-to-person contact with State government agencies at least once a month.
This could reflect that of the 80 respondents, half of them worked in State government
agencies and thus, having some geographic advantage from co-location.
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0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Universities and Co-operative research centres(CRCs) (n = 80)
State government agencies (e.g. environmentaland agricultural departments) (n = 80)
Federal government agencies (e.g. CSIRO) (n =78)
Private firms (n = 78)
Non government organisations (NGOs) orcommunity groups (n = 80)
nevera few times a yearabout once a month2 to 3 times a monthabout once a weekmore than once a week
Figure 5.17: Frequency of person-to-person contact
between policy-makers and different research
organisations (Online Questionnaire 2007, Appendix
C).
Seven items from the online questionnaire were combined to come up with a measure of
formal linkages (Formal Linkage Index – Appendix E). These items related to the
importance of formalised, linkage delivery mechanisms in terms of their professional
satisfaction (on a scale from 0 to 5, where 0 = no opinion, 1 = not important at all, to 5 =
extremely important). The responses of NRM policy-makers that make up the formal
linkage index are shown in Figure 5.18. Generally, respondents indicated that each of
these linkages were important for their professional satisfaction; about half of the
respondents (n = 80) indicated that each of the linkages was considered important.
Notably, information delivered via the internet and the ability to network and learn
about science first hand at conferences and workshops was considered more important
to their professional satisfaction than receiving newsletters directly or being actively
involved in research projects. This could indicate that policy-makers perceived that
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professional satisfaction was greater when the information was independently sourced
rather than being targeted recipients of uncontrollable amounts of material.
0% 20% 40% 60% 80% 100%
Your active involvement in research projects(n = 75)
Your participation in professionalconferences and workshops involving
researchers (n = 78)
Your membership on expert panels, andcommittees involving researchers (n = 77)
Newsletters and research delivered directlyto you (n = 78)
Research reports sent to you by email (n =78)
Science available on World Wide Web via theinternet (n = 78)
Science delivered through professionalassociations and other venues (n = 75)
not important at allsomewhat unimportantneither important nor unimportantsomewhat importantextremely important
Figure 5.18: Policy-makers perception of the
importance of formalised interactions with researchers
in terms of professional satisfaction (Online
Questionnaire 2007, Appendix C).
Thus, it would appear that policy-makers interact and engage with researchers on a
fairly regular basis and most regard the professional benefits offered by the relationship
as important. This has the potential to be an element in development of social capital in
‘science-policy’ supply chains. These findings were supported by data collected during
the Semi-structured Interviews. The importance of interactions between scientists and
policy-makers was often raised in regard to the transfer and use of science (Appendix
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F). The view of one male, federal scientist (Interviewee No. 22, 2007) who pointed to
the transfer of science as an exercise in relationship management, was resonated by a
number of interviewees:
But in fact it’s about relationship management. I develop and maintain relationships with the key policy makers, or the key staffers of policy makers in most of the jurisdictions in Australia and the federal jurisdiction.
Influences on intra-organisational relationships are also important for information flows.
Organisational structures, values and procedures can influence internal information
transfer (e.g. Kettl, 1993; Thorngate, 2001). Data collected during the Semi-structured
Interviews is in line with this view. As scientific briefing notes move up the
organisational hierarchy they may be seriously edited, according to one policy-maker
(Interviewee No. 28, 2007), and more likely to lose the key messages:
the only problem with departmental science is that sometimes you talk to a departmental official and say ‘I had no idea that’s what you really thought’ and then they go ‘I wrote that in the briefing note’ but it gets a bit edited as it went up the system. I don’t think for any bad reason or for any censorship but I suspect the person who’s been around 15 years thinks they know a bit more than the person who arrived two weeks ago, and the person who’s been there 20 years thinks let’s put this in perspective and let’s make this a bit slimmer.
This is consistent with the view of Kettl (1993) that organisational hierarchy can mean
that communication is filtered and is a one way process – up the ladder. Scientific
knowledge and their uncertainties may be omitted or filtered out to reduce the
information load upwards. Crucial knowledge, thus, may be lost because its importance
is not recognised. According to Thorngate (2001) knowledge can also be distorted or
given a new slant depending on how individuals frame that information. This might
depend on their background and experience. Scientists, for example, tend to see
problems as research ones while engineers define tasks in engineering terms.
Knowledge may also be hoarded and used selectively to achieve power and position
(Kettl, 1993; Harding, 2002).
Demand management - ‘customer’ needs
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Strongly integrated relationships in supply chains suggest a focus on the needs of the
customer and a greater tailoring of products and services (e.g. Croxton et al., 2001).
However, as the science needs of Australian NRM policy-makers appear to be only
sometimes met (Section 5.2.1 - Supply base management, Figure 5.1) it follows that the
science-policy relationships in the chain may be weakly integrated. Furthermore,
Section 5.2.2 - Quality management pointed to specific deficiencies in policy customer
needs in regard to the quality of the science product (e.g. integration, uncertainty).
With a broader view of the needs of the policy customer, data collected during the
Semi-structured Interviews (Appendix B) suggested that science was important in
policy-making to:
· meet reporting requirements: to report to government about the state of natural
resources (e.g. statewide monitoring and evaluation program to report on natural
resource condition);
· meet legislative requirements (e.g. science underpinning land clearing
decisions);
· feed into and inform government policy, providing a level of confidence for
politicians to act on, and to help formulate longer term strategic questions;
· manage risk by improving understanding of the condition of natural resources,
how the system works and the impacts of various management practices on the
natural and social environment.
These perceived needs for science in policy generally correspond to several categories
proposed by Oh (1996a) and Weiss (1977c). They appear consistent with an
instrumental need for empirical evidence to help solve a policy problem, a conceptual
need for a deeper understanding of the NRM system and wider need to synthesise
evidence. According to Sexton et al. (1999) decision makers use science as a ‘compass’
to help frame issues, set priorities and fashion responses, and Grundmann (2007: 428)
noted that science can be used as ‘a basis for the legitimation of political decisions’.
Thus, the need for science in policy-making is not always informational and could be
political. This corresponds, as suggested by Oh (1996), to a justification or political
need for support of a predetermined policy decision. The political need for science is
discussed below in the section titled Demand management – ‘customer’ values.
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The process of demand management also required an examination of how trends in the
external business environment might affect customer needs. Research priorities are
continuously changing in response to the shifting agendas of society and politics.
Sarewitz and Pielke (2007) points out that investment has increased over the past twenty
years in new research programs in response to heightened social concerns or potential
social benefits and outcomes (e.g. economic growth, agricultural production);
computing, climate change and biotechnology are some research areas that have been
assigned a higher priority. Data collected during the Semi-structured Interviews concurs
with these observations. As one policy-maker interviewee (Interviewee No. 11, 2007)
noted about the higher priority recently accorded to climate science:
The actual science wasn’t changing it was slowly accreting, slowly getting more, then we got the drought, then we had not enough water for cities to drink and suddenly all this policy work went off like a rocket. And now everyone everywhere was trying to do things with this climate change. So you could do all the good science but to actually get it to the stage where politicians take notice of it, needs some of these social drivers. You’ve got to convince society first… And so when you are posing a question of how does science influence policy at that high Government level, Governments’ responding to democracy, they’re responding to community pressures, so it really becomes how does science influence the community and so the community influences the Government.
This is in line with the view of Gibbons (1999: 14) that shifting social perceptions
gradually translate into changed scientific and institutional priorities for science
production:
the sites at which problems are formulated and negotiated have moved from their previous institutional locations in government, industry and universities into the 'agora' – the public space in which both 'science meets the public', and the public 'speaks back' to science.
This is supported by the view of Toman (1999: 65) that:
increased information about the natural world and human impacts leads to a reconsideration of values, which in turn leads to a refocusing of science as needed to address emerging policy issues.
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Thus, it would appear that the demand for science is dynamic and fluctuates in response
to broader social and political agendas. This has potential to be an important
consideration in the development of the responsiveness of the ‘science-policy’ supply
chain (i.e. the ability of the complete supply chain to react according to changes in the
marketplace (Surie and Wagner, 2008: 54).
Demand management - ‘customer’ values
NRM policy-makers are involved in making choices between competing problem
frames and in messy and wicked contexts (see Chapters 1 and 2). According to Pusey
(1991: 68) it can be ‘mistakenly assumed that their choices and decisions are decided on
technical criteria and are, in this sense, ‘value-free’ or neutral.’ The importance of how
the values of policy-maker customers are set was highlighted by a question in the
Online Questionnaire that asked policy-makers about their view on this statement:
In cases where there in cases where there is a clash between a ministers political preference and what the organisation sees as rational policy it is my view that it is usually the minister that gets his/her own way.
About 64 per cent (n = 70) of respondents indicated that the minister’s preference would
prevail. Pusey (1991: 70) put a similar statement to members of the Senior Executive
Service of the Australian Public Service and found that half indicated that politicians are
influential in setting the key value objective.
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
In cases where there is aclash between a ministers
political preference andwhat the organisation
sees as rational policy itis my view that (n = 70):
usually the minister who gets his/her own wayit could go either waythe view of the organisation usually wins acceptance
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Figure 5.19: Perceived outcome of clash between
minister and organisation (n = 70, Online
Questionnaire 2007, Appendix C).
Questions were also raised as to what knowledge is considered most influential by
policy-makers in deciding what happens (Appendix C). The following statement was
put to NRM policy-makers from federal, State and regional organisations:
in situations where scientific and technical considerations clash with social values, at the end of the day it is usually the scientific and technical considerations that prevail.
The responses are shown in Figure 5.20. On their perceptions about the relative
strengths of science and social pressures in deciding what is done, only eight per cent of
State, seven per cent of federal and 23 per cent of regional NRM policy-makers
surveyed in 2007 agreed with the statement. The corresponding percentage of regional
NRM policy-makers was higher which could indicate a lower level of engagement in
expert led discourse. Seventy eight per cent of State policy-makers disagreed with the
statement, compared with 57 per cent of federal and 50 per cent of regional policy-
makers who disagreed. This could indicate a policy-making position for State policy-
makers that is open to a wider spectrum of political interests. There was a significant
difference in the responses of the three policy-making groups to this question.37
37 An Analysis of Variance was conducted to compare the level of agreement by Federal, State and regional policy-makers with the statement that ‘in situations where scientific and technical considerations clash with social values, at the end of the day it is usually the scientific and technical considerations that prevail’. There was a significant difference (p < 0.05) in responses for Federal, State and regional policy-makers (df = 2, 73) = 3.4, p = .03). Post hoc
comparisons using the Tukey HSD test indicated that the mean score for State policy-makers ( = 3.85, σ = 0.77)
was significantly different from the regional policy-makers ( = 3.32, σ = 0.89). Federal policy-makers ( = 3.5, σ = 0.65) did not differ significantly from either State or regional policy-makers.
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0% 20% 40% 60% 80% 100%
State (n = 40)
Federal (n = 14)
All (n = 76)In situations where
scientific and technical
considerations clash w ith social
values, it is usually the scientific and
technical considerations that
prevail
agree neutral disagree strongly disagree
Figure 5.20: Perceived outcome of clash between
scientific and social considerations (Online
Questionnaire 2007, Appendix C).
This corresponds to the findings of Pusey (1991: 71) who asked members of the Senior
Executive Service in the Australian Public Service in the late 1980s to respond to the
statement that,
In situations where technical and administrative considerations clash with social values, at the end of the say it is usually the technical and administrative considerations that prevail.
He found that, only about a third agreed with the statement. The pattern emerging is that
there are inherent political tensions in the policy-maker role. There is a common thread
in respondent’s assessments of the considerable strength that political factors and social
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pressures have in setting the value objective, what information is considered most
influential and in deciding what is done. This is consistent with the view of Cohen
(2006) that resource management problems are complex, encompassing not only
scientific issues but also issues of human values. It is the different sets of values, goals
and policy options that make NRM policy problems ‘wicked’, distorting an idealised
view of a linear, rational policy process and upsetting the often assumed importance of
scientific information in deciding what is done (see Section 1.1).
Consistent with this view, several interview participants noted that science was only one
part of the NRM policy equation and in their view should not carry more weight. One
federal policy-maker remarked that:
Policy can be made without science, it’s an input to it. It doesn’t have to be taken into account and it can be formally listened to and discounted (Interviewee No. 31, 2007).
and another mentioned that:
I mean sciences are only one part of the puzzle. I don’t for a second think that science should be put in charge. That’s a recipe for disaster too (Interviewee No. 18, 2007).
A male, federal research manager also emphasised that science had a vital role to play,
as a decision aid but not for setting the policy agenda:
it needs to be a science informed rather than science determined business. Because while there are important scientific understandings to bring to bear, ultimately decision-making in natural resource management is not just about what the science tells you, it’s also about social implications. The costs and benefits of making investment. And these are all value-laden decision making processes which governments make (Interviewee No. 23, 2007).
Several participants spoke about how they had to contend with many different values
and competing goals in the NRM policy process. The following sentiment (Interviewee
No. 18, 2007) was echoed by a number of NRM policy-makers at regional, State and
federal levels:
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Everybody agrees water’s a problem, the scarcity for water. There’s whole different views about how you solve the problem that’s called water shortage. And some people value, you know, recreation above agriculture or agriculture above environment, or environment above everything. So it’s really difficult having these competing views to come to a conclusion about that
NRM problems involve value conflicts, uncertain information and fuzzy goals, which
make them difficult to solve using science and analysis alone. Allen and Gould (1986)
mirror this view: ‘science and analysis are of secondary assistance to politics when it
comes to public decision-making’. Accordingly, one NRM policy-maker noted that:
…and then you overlay a political process, where once you get to a political process you might see that decisions aren’t made based necessarily on science (Interviewee No.13, 2007).
and another observed that:
I felt that perhaps the science may have disappeared a little bit out of some of it and it got down to my assets is more important than your assets, rather than necessarily keeping it purely scientific (Interviewee No. 9, 2006).
Thus, it would appear that, in supply chain terms, one of the key reasons for the
exchange of science to be ineffective is that in certain situations there is a tendency for
science-policy, supply chains to be oriented towards traditional business thinking of
self-interest, independence and limited information sharing. This is acutely apparent
when policy-making becomes more wicked and messy (see Section 2.1). Under wicked
conditions, ‘decisions are essentially ethical and political, not factual’ (Haller and
Gerrie, 2007: 142) and thus, require little or no scientific data. Using a supply chain
metaphor, the relationships in a science-policy chain are especially, weakly integrated in
wicked situations; lacking maturity in supply chain attitudes (e.g. balancing cooperation
and power) and processes (e.g. development of social relationships).
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Demand management – customer conditions
Central to the process of demand management is consideration of the demand drivers
that exist in the operating environment of the policy customer. One such driver is the
organisational interest perspective introduced in Section 2.3.3. According to an
organisational interest perspective, the exchange of science in a chain is more likely to
be effective when it meets the needs of users and support the interests and the goals of
the organisation. Consistent with this view, of 72 respondents to the Online
Questionnaire, a total of 31 (42 per cent) indicated that the support for organisational
options of a report’s findings is an important factor when deciding to use a report
(Figure 5.21).
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Report findings supportthe limited options of the
organisation (n = 72)
not important at allsomewhat unimportantneither important nor unimportantsomewhat importantextremely important
Figure 5.21: Perceived importance of support for
organisational policy when deciding to use scientific
reports (Online Questionnaire 2007, Appendix C).
Akin to organisational interests and the notion of self-interest is the use of information
for power and political positioning (Healy and Ascher, 1995). As discussed in the
section 5.2.5 Competitive environment: Demand management - customer needs the need
for science in policy can be political. When politics prevail, the policy customer(s) with
most power would represent the target, customer group. For all other customers the
exchange of science might be ‘ineffective’, meaning that the volume of shared
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information might be adequate but not necessarily its content, which may be biased or
‘cherry picked’ for political gain (see Section 2.2). As noted in the above section, in
supply chain terms, one of the key reasons for the exchange of science to be ineffective
is that sometimes science-policy supply chains tend to be oriented towards traditional,
business thinking of self-interest and limited information sharing, rather than towards
chain optimisation and extensive information sharing. It would appear also from
findings previously discussed (e.g. Section 5.2.5 customer needs and customer values)
that under politically charged, customer conditions this tendency might be exacerbated.
Observations were made along these lines as an invited observer to a steering meeting
between federal, State and regional actors from scientific and policy-making
organisations, where the process of NRM planning took place. At these meetings
decisions are made about how funds should be invested in NRM projects and concerns
were being expressed about the political expediency behind the push in the policy
process for support of a particular project:
[Regional policy-maker A] I struggle to find too much community good with this one….this one has not had any evidence of this [use of science and investment principles]. The timeframes and milestones are totally unrealistic, they will need to go through the EPA. These guys just want to do [project X] and worry about governance later.
The political imperative became the signifier of issues that directed the discussion. In a
supply chain sense, the power relations were asymmetric in the ‘customers’ customer’
favour and the demand management process was focused on a political need for science.
[Federal policy-maker A] This proposal is about putting the debate on a rational basis. If we cannot get some discussion about the project it will rattle around as a political noise and we will get political use of funds. We need to get it going to ventilate the potential impacts. It is high on the political radar and we need to move quickly.
[State policy-maker A] This is a bit of a shot-gun marriage between [organisation X] and [lobby group Y]. There are dangers of engaging with this in the regional model concept.
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[State policy-maker B] The biggest danger to the model is not to engage with it. This sends a dangerous signal that all you need to do is make enough noise and ignore the model.
Within the steering group is a set of social rules that prescribes legitimate policy-
making processes – the ‘regional model concept’. In supply chain terms these represent
the formal processes and procedures for the exchange of information to inform policy.
The meeting participants were reluctant but regarded it necessary to breach these rules
to legitimise a supply chain for a political need. This observation is consistent with the
findings of an organisational study by Riley (1983: 428) indicating that ‘rule violations
are legitimised in particular contexts in which political practices are accepted’. Success
in these contexts, according to Riley (1983: 428):
Appears to be generated primarily by risk-reducing strategies and structures that produce satisfying outcomes, even if the rules have to be bent or ignored. The key to this process is not upsetting those with power.
It was a regional policy-maker who prompted a return to the political imperative:
[Regional policy-maker A] I don’t want to resort to the ‘squeaky wheel’ but I understand the politics behind this – keeping the Minister happy and at the same time of doing things right.
This is in line with observations of Healey and Ascher (1995: 17) that, ‘in the policy
process, information becomes raw material for use (and manipulation) by a host of self-
interested actors, not an eagerly awaited source of consensus.’ Thus, in supply chain
terms, a more powerful, policy customer could lead to a supply chain that is more
competitive but not necessarily more effective, with information exchange restricted
and biased to satisfying political objectives.
5.3 Discussion
The analysis of science use using a supply chain framework presented in this chapter
showed potential to facilitate understanding on a different scale, of the broader
processes and contexts that are involved in the production, transfer and use of scientific
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knowledge. By exploring supply chain processes that spanned science suppliers’
suppliers (e.g. funding bodies) to policy customers’ customers (e.g. lobby groups),
insights were gained into how factors well outside the policy-making organisation can
come to influence use. Beginning with an examination of factors affecting the
management of the supply base, the framework then shifted focus to product quality
management issues, before moving to considerations of managing customer
relationships and meeting the demand for research. The quality of the science supply
base for NRM policy was perceived to be wanting by NRM policy-makers who
participated in the Online Questionnaire.
Emerging from the supply chain analysis presented in this chapter was an emphasis on
integration factors, suggesting that information products would be used when the
integration of findings and connectedness of people in the research-policy network are
well supported. In the context of this research, these foci included the integration of:
1. research from different disciplines for a wider analytical and interpretative view.
The importance of integrating scientific information to improve the quality of
the information product for use in policy-making was supported by findings
from the Online Questionnaire (Appendix C). Of a total of 76 respondents, 60
(75 per cent) considered the integration of information across disciplines an
important factor in their decision to use a scientific report.
2. policy-making customers in the research supply chain. Respondents to the
Online Questionnaire indicated their support for closer involvement in the
production of science. Of 77 responses, a total of 44 (57 per cent) indicated that
their membership on expert panels was important in terms of their professional
satisfaction. The survey findings were supported by data collected during face-
to-face interviews of policy-makers and scientists. Participation by policy-
makers in expert or technical panels was seen as generally leading to science
that is more policy-relevant. Social science information was in demand by
policy-makers - of 80 respondents to the Online Questionnaire, 21 (31 per cent)
indicated using social science information often to very often.
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3. funding ‘suppliers’ in the research supply chain. The importance of integrating
suppliers into the ‘science-policy’ chain was highlighted by a question in the
Online Questionnaire that asked how often the statement scientific information
meets my needs and expectations applied in their daily policy-making activities.
Of 77 respondents, a total of two (three per cent) indicated never and 51 (66 per
cent) indicated sometimes (Figure 5.1). Weaker integration of suppliers in the
chain may direct resources to initiatives that are unlikely to yield positive
contributions to policy-making, whereas stronger integration may offer the
benefit of inter-organisational relationships with policy organisations that can
bring in research objectives that are new to the supplier. According to Harris
(2002) another reason for the inadequate quality may be that many suppliers to
science do not have the skills or knowledge to effectively invest in research
priorities and projects of relevance to policy-making.
An emphasis on the context in which policy-making occurs was also indicated by the
supply chain analysis. Intuitional factors in the social context can influence the use of
science. The capacity of customers to engage in the science-policy supply chain was not
distributed equally. This was attributed in part to the variation in contexts (i.e. spatial
and institutional) and highlighted by a question in the Online Questionnaire that asked
respondents to indicate the strength of their relationships with policy-makers, ranging
from ‘arms length’ to ‘total partnership’. Fifty one per cent (ten of 19) regional policy-
makers considered their relationships with scientists to be adequate, compared with 61
per cent (eight of 13) federal policy-makers and 77 per cent (30 of 39) State policy-
makers. Notably, no State or federal policy-makers considered their relationships with
researchers to be at arms length, but nine per cent (two of 19) regional policy-makers
did. Regional NRM policy-making ‘customers’ in Australia are generally more
geographically and institutionally remote from the science production process compared
with State and federal counterparts (see Chapter 4). Consequently, regional ‘customers’
may often place considerable emphasis on the way that the science process seems to
neglect them. There were spatial constraints to the integration of researchers with
policy-makers (e.g. co-location). In regional organisations, 30 per cent (six of 20)
indicated that researchers were never located near policy-makers, compared with five
(two of 39) per cent of State organisations and seven per cent (one of 14) of federal
NRM organisations. The intra-organisational proximity of scientists and policy-makers
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in federal and State organisations would help enable a closer integration compared with
regional organisations.
5.4 Conclusion
Parallels can be drawn between the system dynamics of a science-policy, supply
network presented in this chapter that focused on factors of ‘integration, context and
exchange’, and the argument put forward by general systems theory for an emphasis on
the dynamics of ‘connectedness, context and feedback’ in system dynamics (Cundill et
al, 2005). Parallels can also be drawn between the interactive and information exchange
components of supply chains and the notion of social capital. The findings of the
science-policy, supply chain analysis demonstrated that policy-makers interact and
engage with researchers on a fairly regular basis and most regard the professional
benefits offered by the relationship as important. This has the potential to be an element
in development of social capital in science-policy, supply chains.
The system perspective based on the supply chain approach also pointed to the potential
use of information for power and political positioning. An emerging pattern indicated
tensions between the use of science to fulfil political agendas and to underpin a sound,
policy-making process.
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CHAPTER 6: A MICRO-ORGANISATIONAL PERSPECTIVE ON SCIENCE USE
6.1 Introduction
By using structuration theory, this chapter introduces an analytical focus on the socio-
political dynamics of information use for policy-making at the micro-organisational
level. This is consistent with the view of Silverman (2001) and Ison (1993) that an
active understanding of human behaviour requires an understanding of both process
(e.g. how policy knowledge is created) and context (e.g. organisational influences on
this process). This chapter draws on structuration theory to reflect on the policy-making
dynamics of an NRM policy-making organisation that can influence the social
construction of information use.
The relationship between institutions and individuals in a Commonwealth government
NRM agency was examined using data generated from participant observation,
interviews with key informants and secondary source material (see Chapter 3 for
rationale and data collection methods), set against change in the wider Australian
policy-making context (see Chapter 4). Recent organisational and institutional changes
in the Australian NRM policy-making system in the context of a more ‘business-like’
Australian public sector that are likely to impact on information use in this case were
outlined in Chapter 4. In summary, these changes were the outsourcing of information
for policy-making and the favouring of generic management skills and
economic/administrative backgrounds. Thereby, weakening the capacity of the
Australian public service to use a range of (including scientific) paradigms and
information for policy, and to assess the scientific soundness, quality and value of
research commissioned for policy-making;
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6.11 General structure of NRM in the Commonwealth government
Over the past twenty years, the Department of Agriculture, Fisheries and Forestry, like
other Australian government agencies, had moved to new business approaches to public
management, manifested in the rise of business plans and the prominence given to
performance monitoring and stricter reporting arrangements (Robins and Dovers, 2007).
At the time of the study in 2005 the Department base in Canberra numbered about 1400
people, organised into seven divisions (or business units), plus the Bureau of Rural
Sciences, the Bureau of Agricultural Economics, the Australian Quarantine Inspection
Service and Biosecurity Australia. This case concerned the Natural Resource
Management (NRM) Business Unit of the Department.
According to the Department (Commonwealth of Australia, 2004d) the NRM Business
Unit was working towards, ‘innovative agriculture, fisheries and forestry sectors that
use natural resources profitably and sustainably to produce safe food, fibre and wood
products for domestic and overseas markets’, and contributed to five key areas:
· Improving security of access to natural resources;
· Establishing and maintaining the regional natural resource management
approach;
· Reporting on primary industries use of natural resources;
· Encouraging improved use of natural resources; and
· Ensuring community and stakeholders have the information and skills to make
informed decisions about how they use and manage natural resources
(Commonwealth of Australia, 2004d: 2).
The Department reported that the NRM Business Unit contributed to these five key
areas by focusing on:
· Strategic Investments
Administering programs that strategically invest in activities that promote and facilitate greater adoption of sound natural resource management practice particularly at the catchment or regional scale.
· National Policies
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Developing and implementing national policy frameworks, strategies and agreements for achieving sustainable resource management and use in agriculture, forestry and fisheries in particular, frameworks for secure access to natural resources for sustainable use of public resources of water, vegetation and fisheries while meeting the needs of the environment and reconciling differences among competing resource uses.
· Information for Resource Managers
Building the information bases to support improved natural resource management including products and processes that integrate economic, scientific, social and environmental information in a consistent and transparent manner, allow improved understanding of the costs and benefits of different resource management options and support monitoring and reporting on natural resources management performance.
· Decision Making Tools
Providing information products, decision support, monitoring and reporting tools to enhance the management of Australia’s natural resources and support Australian objectives for responding to climate change and variability.
· Building Capacity
Contributing to building institutions, communities and resource managers that have the capacity to develop and implement strategic and integrated approaches to improved natural resources management.
· Strategic Influencing
Ensuring portfolio and Australian Government priorities and perspectives are taken into account in policies and strategies related to natural resources that are developed outside the portfolio both domestically and internationally (Commonwealth of Australia, 2004d: 4).
The NRM Business Unit was therefore, engaged not only in formulating national policy
but also played a role in development of regional policy (e.g. providing information for
regional planning) and the implementation of regional policy (e.g. administration of
regional programs). In 2005, the NRM Business Unit initiated planning for future NRM
national policy frameworks. Within this area of policy-making, observational
experience and field data about how information use may be socially constructed
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pointed to a potential interplay of human actions and dimensions of social systems
(structure). A conceptual framework that might articulate the dynamic relations between
actions and institution was required.
6.12 Overview of structuration theory
Section 2.3.3 introduced structuration theory to conceptualise how the institution of
information use is formed, reproduced and modified through interplay of action and
structure. Structuration is a metatheory that aims to connect human action with
structural explanation in social analysis (Giddens, 1984). Structures, as Riley (1983:
415) points out are:
the rules and resources people use in interaction, and they are analysed as dualities: they are both the medium and the outcome of interaction. They are the medium because structures provide the rules and resources individuals must draw on to interact meaningfully. They are its outcome because rules and resources exist only through being applied and acknowledged in interaction – they have no reality independent of the social practices they constitute.
Structuration is thus, the production and reproduction of social systems by the
application of rules and resources. The focus here is on how structuration theory can
offer new insights about information use by organisations for policy-making.
It should be noted that structuration is a general theory of social organisation, rather
than a theory specific to information use in policy-making. To the extent, however, that
the ‘information use in policy-making’ system is seen as social system, then
structuration offers potentially significant insights on this phenomenon. It is not a theory
that easily lends itself to empirical analyses, as it is about describing and analysing
patterns of relationships, requiring longitudinal observation and identification of
persistent institutional change that were not possible in the scope of this study.
However, by drawing on evidence of institutional change (e.g. Chapter 4 outlines
change in the Australian policy-making context) it was possible to engage in some
interpretation of the field experiences. While it cannot be claimed that the application of
structuration theory is a complete analysis of all the factors involved, it is offered as a
useful technique for gaining insight into the events that occurred in the field setting.
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Similar and supporting applications of this type of theory have been reported in Section
2.2.3.
At the heart of the theory is an attempt to treat human action and social structure as a
duality. Walsham (2002: 361) emphasised that:
the crucial point here is that structure, defined in this way, is seen as rules of behaviour and the ability to deploy resources, which exist in the human mind itself, rather than as outside constraints. The actions, therefore, of a human being draw on these rules and resources and, in so doing, reproduce structure in the mind.
Thus, for example, a policy-maker who condenses a policy document is drawing on the
concept of the length of policy briefs, the rule that the document should be a concise
‘one pager’ and the perceived ability for deploying the resource of time to modify the
document. Giddens (1984) posited that a given action arises from the interplay of
categories of structure (power, communication and sanction) that are reinforced or
modified in the course of the action. ‘Power’ gives people the resources to produce the
intended outcomes, ‘communication’ filters how the world appears to individuals, and
‘sanction’ calls out what actions are appropriate. It is the power over the formation of
rules of behaviour and power over the flows of material resources, Giddens (1984)
argued, that is manifested as the ‘structures’ that people live and work within. A key
implication of Gidden’s model is that changes in one dimension of structure result in
changes in the others. If there are changes in the belief systems or norms, new resources
arise to bring action into association; if new resources are introduced, the underlying
beliefs and norms will shift. As Healey (1999: 46) noted, ‘our ‘context’ is thus, actively
constituted through our actions’. A template, based on the three categories of structure
was used for this analysis (Table 6.1).
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Table 6.1: Key components and symbols of social structures (institution) or human action framed in the structuration analysis.
Realm of Social Structure
SIGNIFICATION
· Structure of
signification: modes of communication
DOMINATION
· Structure of
domination: political and economic institutions
LEGITIMATION
· Structure of
legitimation: norms and social routines
Modalities
Interpretative
Schemes (e.g. assumptions,
categories, knowledge)
Facility
(e.g. resources)
Norms
(e.g. protocols, codes of conduct)
Realm of Human Action
COMMUNICATION
POWER
SANCTION
Synopsis of Micro-ethnography Observations
· Information control · ‘Business’ modes
of thought · Interaction
practices
· Tension of
autonomy-dependency
· Resource asymmetry
· · Managerialism
The analytical approach, based on this template, was to frame a certain part of the
structuration process. For example, the analysis of information use activity takes
institutions as a backdrop and focuses on how actors draw on and reproduce the
structures of the system in social practices. The analysis of institutions (structure)
assumes that information use is going on but focuses on the structural characteristics of
institutions. Poole and DeSanctis (2004: 12) argued that ‘it is not only legitimate but
necessary to parse the coevolving aspects of structure and action, focusing first on one
and then on the other’. Each approach brackets, or frames, a certain part of the
structuration process and uses the rest as an unanalysed ground for its object of interest.
For clarity the three major structural features of institutions, signification, domination
and legitimation are used to organise this chapter into Sections 6.2, 6.3 and 6.4
respectively. While the three fundamental elements of social interaction: meaning,
power and norms are highly interdependent and not separable in practice, Orlikowski
and Robey (1991: 404) argued that they can be treated as distinct and that each can be
examined from the perspective of human agency and institutionalised properties.
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6.2 Signification
According to Riley (1983), when studying signification it is the codes or modes of
coding used, as expressed in symbolic language (e.g. metaphors, names) and action (e.g.
strategies, processes), that are of interest. Information control and business processes
were two symbols of signification that emerged from the micro-ethnography.
6.2.1 Information control
An image of rational bureaucracy is generated through structures that legitimate rule-
following behaviour. Reporting hierarchies, organisational knowledge and standard
operating procedures are examples of organisational ‘structures’ that legitimise and
direct the way that policy is written. These ‘structures’ may be reproduced or they may
be modified or enhanced. From the data collected during participant observation
emerged the view that information control was an important structuring property of the
agency.
Strategies of information control included using the ambiguity of language and control
of the policy-making process. As one writer of NRM policy pointed out, the words used
in a policy document destined for agreement by the States were considered to be
‘hooks’ to encourage initial acceptance of the policy. It was explained that the words
used were kept deliberately broad or vague to provide further opportunities to mould the
policy to suit their organisation. In a study of organisational culture, Riley (1983: 426)
suggests that, ‘insight into the ‘grey area’ – where rules were ambiguous – was a form
of power’. Thus, the insights of the Commonwealth agency into the ‘grey area’ - where
policy language is ambiguous - may be interpreted as a way of managing their power
relations with the States.
Another strategy of information control was the observed action taken to institutionalise
a system of agency acceptable language. There was a management directive that policy
officers would ‘commence a process of instituting a language around the Department’s
view of NRM…’. Information control seemed to be also evident from the organisational
‘structures’ that constrained information use to internal policy documents. For example,
to begin working on a preliminary planning process for future NRM programs I
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requested background information about current programs. This was partly met by the
provision of internal policy documents that had been developed or commissioned by the
NRM Business Unit, and which in turn referenced other internally produced reports.
Crucially, Briggs (2003: 161) points out that such institutions that have closed cultures
which suppress ideas in the organisation and resist new information from outside ‘are
averse to risk, partly because they do not have the expertise to judge and manage it.’
The use of internal organisational texts reinforced the production of material that
preserved the organisational ‘view of NRM’, providing evidence that information flow
is constrained to support organisational interests, filtering information, avoiding debate
and limiting the entry of new ideas and information into the policy-making process. It
would appear that the constraints of the reporting system also restricted officers to
engage in patterns of activity that strengthen existing status systems, such as serving to
validate the organisation’s dominance as the source of expertise.
6.2.2 ‘Business’ modes of thought
Healey (1997: 45) argued that of the elements embodied in structuration theory,
peoples’ ‘modes of thought perhaps carry the greatest power of all, for example, the
power to imagine that the public sphere was the domain of men and the private the
domain of women’. The decline in professional diversity of the APS, as a result of new
public sector management approaches introduced over the past twenty years, could be
seen as a development of a ‘business’ mode of thought that imagined that the public
service was the domain of administrators (Chapter 4). This view was supported by
documentary evidence of administrative approaches (e.g. business frameworks and
processes; business naming such as ‘NRM Business Unit’) and data collected from
discussions with officers in the NRM Business Unit. Many revealed they were career
public servants, having a background in economics, policy science, management or
administration but not necessarily science, or research management. The following
response from an NRM policy manager sums up a typical working background
(Interviewee 20, 2007):
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I started off my working life in the social welfare sector, working for Social Security...I guess I am a career public administrator. My degrees are in human resource development and in economics.
This was in line with the view of another federal policy-maker who pointed out that the
practice of generic public administrators circulating among agencies was common
(Interviewee No. 21, 2007):
As we get to a more senior level, people tend to move around the Public Service, across departments and so on and they don’t automatically come with a science base.
Section 4.3.3 pointed out that economics and business increasingly dominates the skills
base of the APS workforce. This has potential to underpin a collective ‘business’ mode
of thinking about NRM problems that in a structuration sense, can mean that a business
system of meaning is continually being reinforced. It would appear that as a result of the
workforce change, the stimulus for conflict between policy-makers was reduced.
Members of the NRM Business Unit used similar terms and concepts to describe the
NRM problem in business terms (e.g. program delivery, partnerships with industry). For
example, the NRM outcome of the allocation of funds had become obscured through the
use of administrative discourse; the Departments’ Natural Resource Management
Business Plan 2004/05 (DAFF, 2004: 19) emphasised the importance of cost-sharing
partnerships as a ‘key deliverable’:
In addition to achieving program objectives, leveraging further investment from the private sector, State/Territories, local government is also an important outcome for NRM programs.
Thus, funding allocations were regarded as investments in the development of cost-
sharing partnerships with the States and regional groups, blurring their importance for
resource change. Department policy-makers reproduced the business culture of NRM
policy-making that concurrently is used by individual officers for policy advancement.
The power to imagine the NRM problem and ways to address the problem were
generally restricted by the business focus of policy-makers. Thus, in the structuration
process, the signification of business ‘modes of thought’ has favoured the framing of
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NRM policy primarily in administrative terms, and reinforced this structure by
concomitantly limiting the capability to engage with other modes of thought. A
comment by a policy manager, for example, about liking the idea of adaptive
management ‘because it meant you could just start doing things’ suggested a lack of
awareness of gaps in NRM understandings. By concentrating intellectual power in
business and management acumen, there is likely to an imbalance in the diversity of
intelligences in the public sector that limits the communication frames or paradigms
available to inform NRM policy (also see Section 4.3).
In structuration terms, the policy-making practices are viewed as operating as dualities;
a recursive relationship existing between the policy-making ‘structure’ and agency.
Thus, the NRM policy-making structures that are ‘shaped’ by agency and that, in turn,
‘shape’ agency are captivated by the key structuring business ‘modes of thought’. For
example, a scientist who had been on secondment for about a year to the NRM Business
Unit to examine the science needs for sustainable and productive agriculture, admitted
that NRM policy was still not a clear concept, ‘NRM policy, I still don’t know what
exactly what the word ‘policy’ actually means, you know exactly.’ But it seemed that
the institutional structure was shaping the human agency to reproduce the business
modes of thought because he then spoke of NRM policy in terms of administrative
processes:
Now you are better off with policy, you know that is supposed to improve the way we operate at different levels. Can be at the very administrative level. The way you manage the NHT and NAP, you know, at the administrative level. How do you interact with the regions. But it can also be land use management and descriptions about how you do things. It’s very, very broad actually.
However, he also added that NRM policy is also about ‘land-use management’ and
land-use practices. It would appear that there is potential for social structures to change
as a result of the agent introducing an alternative policy discourse. This demonstrates
how the duality of administrative structure can operate, shaping the culture of human
agency, and in turn, how structure can be shaped by agency.
Hence, in terms of the structuration process, this ‘duality of structure’ may be viewed as
a mutual unfolding and two way link, between the action of communicating business
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meanings, and a signification structure that sanctions business ‘modes of thought’. This
might have the unintended consequences of reproducing a policy-making system that
feeds back only business ideas. Ideas are considered critical by Giddens to shaping
structure, as Healey (1999: 114) points out:
for Giddens the key relations that carry structure forward, and which agents interpret and shape, are authoritative structures (formal and informal rules), allocative structures (the way resources are distributed), and ideas (knowledge and cultural structures, which frame how actions are developed and legitimated).
For example, as observed by one federal policy-maker, introducing another way of
thinking, such as science, may be needed to alter the practice of policy-making to
generate new policy choices (Interviewee No. 21, 2007):
I think what we really see though is for those people who are working in a policy area and have got an issue, a policy issue that they have got to solve or develop a solution to, will then go, the better ones of those will say ‘what do I need to fix this?’ And, will say, ‘okay, well clearly I need some science in here because I don’t know if I can fix this issue just by throwing money at it or whether it’s a regulatory issue or whether it’s an information or capacity building issue, or approach that will actually give us the solution’. So, I think it’s, part of it’s the [science] background.
Hence, with the addition of intellectual diversity in the workforce new policy meanings,
in this case science, might be communicated and linked to signification structures that
sanction new modes of thought.
Thus, the change in the Federal public service over the past twenty years to a more
business-skilled workforce could be construed as an increased concentration of power in
business approaches to the NRM problem. The business ‘mode of thought’, dominant in
agency, seemed to lead to easy consensus on administrative intent. For example, the
NRM Business Unit had managed the implementation of the $3 billion Natural Heritage
Trust 1 from 1996/97 to 2001/2002. Whilst supporting administrative and political
intent, the funding model was largely disconnected from achieving any long-term,
resource change outcomes (Hassall & Associates Pty Ltd, 2005). The evaluation report
commissioned by the Australian Government notes ‘a focus on on-ground works and a
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yearly funding cycle in some areas appears to have supported a culture of securing
grants rather than outcomes’ (Hassall and Associates, 2005: vii). This is highlighted by
data collected during the Semi-Structured Interviews (see Chapter 3 for methods).
Several interviewees expressed similar sentiments about the disconnection between
administrative funding cycles and resource management outcomes - one policy-maker
commented (Interviewee No. 25, 2007):
There was a real push to get the money out of the door. There were funding cycles. The money had to go out of the door and be used within that funding cycle whether there was a drought or not. There were a huge number of trees that were planted that just died. Because in order to get the money you had to plant the trees and whether they were going to survive or not was irrelevant.
In a similar vein, a regional policy-maker based in NSW described the political impetus
to spend the money (Interviewee No. 29, 2007):
It’s a political requirement that you keep getting the money out. So to start up projects this is really important. Just to keep spending the money is really important. To be able to say ‘yes, we’re doing this which is addressing this issue’ is important.
This comment emphasises the imperative at regional level to comply with funding
cycles that are a product of the business ‘modes of thought’ and political intent. Thus,
the dominance of business-type frames would have constrained the institutional
‘structures’ of policy-making routines. Rather than resource outcomes, administration
and political intent became the focus. In structuration terms, agents were restricted in
questioning and changing routines.
In terms of structuration, these business approaches to NRM (e.g. programs, like the
Natural Heritage Trust) can be portrayed as an interpretative scheme which mediates
between the signification structures (rules, concepts) and the social interaction, in the
form of communication between policy-makers. The signification structures (rules,
concepts) in this case include the notions of finance and management – the ‘business
mode of thought’ applied to administration of NRM programs. Thus, NRM programs
may be considered to embody the norms of an organisational business culture and to
communicate a set of values and ideals about what is approved and what is not. As such
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NRM programs are not an objective and neutral way of conveying policy information to
decision-makers. They are deeply occupied in the reproduction of values that are
aligned with business and economic meanings. The structuring forces are seen as the
‘business ideas’ that permeate the thinking of the agency and reinforce the ‘business’
culture, thus, making the flow of power and resources to new modes of thought
difficult. By being aware of these ‘structuring constraints bearing in on use’, Healey
(1999: 49) argues that we ‘can change the flow of resources, and, most significantly, by
changing the way we think about things.’
6.2.3 Interaction practices
For Giddens (1984: 25) the duality of structure refers to the notion that ‘the structural
properties of social systems are both medium and outcome of the practices they
recursively organise’. Thus, in structuration terms, the interaction practices, used by
groups in the Unit to collaborate and work together, is a structural property that
produces, and reproduces ‘structure’ - the rules and resources linked to the practices. As
the interaction practices represent the means by which relations develop they are akin to
the concept of building social capital (see Section 2.4.2). Thus, the potential exists to
use social capital to extend a structurational perspective in this direction. In social
capital terms, an observed weakness in bridging social capital (i.e. ties between different
groups - see Section 2.3.2) would indicate an interaction practice that produced and
reproduced a ‘structure’ that limits information sharing.
A weakness in horizontal communication between groups in the organisation pointed to
limited bridging social capital. Discussions with officers working in different teams and
units about their roles and responsibilities revealed a lack of awareness of project
overlaps and linkages. This is consistent with the notion of ‘information silos’ whereby
separate collections of information are used by different groups in organisations without
knowledge of one another. Thus, efforts can be duplicated and valuable, available
information prevented from being located and utilised across silos (Webster, 2004).
Viewed through structuration theory, the weak links between different groups indicated
limited interpretative schemes for officers to draw on; a shared conformity in thinking
that encouraged extension of the status quo rather than a shift to fundamentally different
relationships for working together. This would serve to reinforce present relations and
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understandings as well as the established configuration of limited horizontal
communication.
The interaction practices of the policy-makers in the Unit were not observed to be
highly formalised. Policy-makers in the Unit did not appear to be engaged in formal
science-policy interaction practices (e.g. joint science-policy teams or working groups).
Viewed in structuration terms, the rules and resources were not organised to inform and
define formal interactions between policy-makers and researchers. This is not to say that
organised interaction did not happen at executive level nor that informal
communications by email, telephone and face-to-face meetings did not occur. As one
manager pointed out, a formalised process of interaction existed between senior
executives of the Bureau of Rural Science and other areas of the organisations:
each six months, engaging in a formal round of discussions between the Bureau and the various different areas of the Department to talk about what their forthcoming needs might be in terms of the work that we do. But also to share with them our thoughts as to what we think might be on the horizon or what are some of the capabilities that we’ve got that might be able to inform them in their policy advising and program delivery role (Interviewee No. 23, 2006).
Nevertheless, at some stages of the federal NRM policy-making process it seemed that
organisational routines, or ‘structures’, constrained science-policy interactions and
information exchange. Thus, a myopic ‘structure’ was reproduced; with limited ability
to draw on ‘the richness of knowledge and understanding available to people in their
different worlds’ (Healey, 1999: 49). This would have heightened the effort and
resources needed to create new structures that can shape new interactions for
information exchange. Sometimes efforts were devoted to altering information
exchange processes and thus invent ‘structure’, as alluded to by a policy officer
describing a case of interactive culture building between scientists and policy-makers
that departed from the norm:
for example, the Department set up a taskforce to deal with [Issue X]…And in that case we actually had a number of people from the Bureau of Rural Sciences seconded to that taskforce (Interviewee No. 23, 2006).
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A more concrete attempt to invent ‘structure’ to alter information exchange practices
could be perceived in the efforts devoted to organise rules and resources into an internal
science-policy interaction ‘structure’. Officers of the Department explained that the
Bureau of Rural Sciences (BRS) is an agency established within the Department that is
expected to act as a conduit between science and policy-making by reproducing
relations between scientists and policy-makers. Hence, BRS represents a ‘structure’ that
is the medium and outcome of practices to provide ‘scientific advice to government’
(BRS, 2008). However, a distinct view about this ‘structure’ that emerged from
discussions with officers in the Department was that BRS tried to do the science
because this was perceived as more prestigious and professionally rewarding, rather
than to act as an integrator and interpreter of science for the policy-maker. To the extent
that BRS is apparently ‘doing’ rather than ‘advising’ the science, supports the
contention of Giddens (1984) that it is difficult to assume that a ‘structure’ will always
be reproduced, and such science-policy interaction ‘structures’ can be modified and
altered by the actions of the actors involved – in this case, the researchers based in BRS.
According to structuration theory, organisations are the regularised practices of
individuals. The individual actors are the agents of these practices, and organisations,
therefore do not have agency. But ‘the apparent ability of organisations to act consists
of the agency of its constituent members’ (Berends, 2003: 1041). Ongoing, significant
organisational change in the Department appeared to have weakened its agency and the
ability of the organisation to act. In structuration terms, the workforce restructuring
could be perceived as a strategy to remake systems and fundamentally change the
practice of producing NRM policy. But observations made during the field work
suggest that the repositioned agency did not have the capability to alter policy-making
systems; repositioned agency was not able to draw on the ‘structure’ of insitutionalised
practices to effect radical change.
The Department was still going through a period of considerable restructuring and
change during my field work. The 18 months previous to this study had seen a turnover
of almost 55 per cent of the senior executive service, and even during this one month
case period, the NRM Head and the associated Deputy Secretary were soon to be
replaced. I was based in the NRM Future Policy Program within the NRM Strategies
Branch. The head of the NRM Strategies Branch had occupied the position for six
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months. Over the past three months the NRM Future Policy Program had three different
Program heads. The current Program manager had been in her position for less than two
weeks when I arrived – most of the other section managers were in similar situations.
Viewed through structuration, diminished stores of knowledge about interaction (e.g.
NRM language) and the work environment (e.g. professional networks, previous plans),
were apparent in the dimension of signification (communication). Uncertainty about
how to make sense of the working environment was expressed by the actions of agency;
workshops had been commissioned by the Unit to raise understanding of concepts such
as ‘sustainable agriculture’ to assist in workplace communications. Furthermore, many
Program plans were in a state of flux. For example, in my Program each of the previous
three Program heads over the past three months had expressed a different view of the
objectives and actions needed. From discussions with officers in the Unit there was a
perception that the radical restructuring had created organisational problems including,
loss of corporate memory, information gaps and networks (internal and external) that
limited the sharing of knowledge. As Hardcastle et al. (2005: 226) pointed out, ‘the
more knowledgeable an agent is about their social context and the social structures
available to them, the more capacity they are assumed to have in exercising their
agency’. The capability of newly located human agency tended to be weakened by a
lack of adequate knowledge and insufficient clarity about ways to act. These
weaknesses rendered the agency, and ultimately the organisation, less capable of action
and challenging structural problems.
Social structures that contributed to the weakness of bridging capital between
Departments responsible for the joint development of strategic NRM policy were also
observed. Policy officers in the two NRM Departments responsible for the joint
development of strategic NRM programs (i.e. Department of Environment and Heritage
and the Department of Agriculture, Forestry and Food) operated by the ‘rule’ that a
specific policy group would source, coordinate and integrate strategic NRM policy
advice. Each department separately provided advice which the group then attempted to
integrate. By acting as an information gatekeeper between two NRM Departments, this
group could decide what advice was important or worth including and their actions
reaffirmed the pattern of information use and institutional practice. The reproduction of
this social structure also denied opportunities for trust and direct links forming between
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policy groups of these departments (i.e. reducing the formation of bridging social
capacity), and hence, also for more effective and direct sharing of information,
including science. In the context of structuration theory, the application of structural
properties (the rules and resources) for joint policy formulation served to build the
capacity for action by the integrating policy agents. The rules and resources also
influenced agency to act in a way that continued to reproduce the social structure.
6.3 Power
Giddens (1984) argues that power involves action, proposing that it is through the
actions of agents within a social context that power is exercised. Both agency and
structure are considered integral to power analysis. Power is not itself a resource but is
understood to be exercised through the medium of resources (Giddens, 1984: 16). For
Giddens (1984: 16), people are never powerless and the balance of power can alter over
time; a changing power he termed the dialectic of control in social systems:
But all forms of dependence offer some resources whereby those who are subordinate can influence the activities of their superiors. This is what I call the dialectic of control [italics in original] in social systems.
Control, according to Giddens (1984: 283), is the ‘capability that some actors, groups
and types of actors have of influencing the circumstances of action of others. In power
struggles the dialectic of control always operates . . .’. Hence, the ‘subordinate’ tries to
acquire control, and the ‘powerful’ attempt to maintain control. This is done, notes
Hardcastle et al. (2005: 226), ‘via social structures (rules and resources), the power
alternating between levels of autonomy (e.g. directing others), and dependency (e.g.
being directed), from one time–space encounter to the next.’ This section employs
structuration theory to explore the exercise of power in the Department. Attention is
turned to the power dynamics of social systems, the structures of domination and
actions of control and their implications for information use.
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6.3.1 Tension of autonomy-dependency
Cohen (1989: 150) defines relational power as ‘the capability of actors to secure
outcomes where the realisation of these outcomes depends on the doings of others’ and
asserts that administered systems involve the intentional exercise of relational power.
The internal reporting hierarchy directed the way policy was ‘made’ and written, and
embedded in this social system is the exercise of relational power. Interactions with the
NRM Business Unit suggested that the Minister was perceived to be in a position of
superordinance and exercising relational power.
Superordinate agents, according to Cohen (1989: 151), ‘control facilities to achieve
outcomes’. In structuration terms, the exercise of power through resources means that
those controlling facilities (i.e. resources) would also control the means of deploying
power. Thus, to maintain access to resources and a capability to exercise some sort of
power it was in the interest of the Unit to ensure close contact with the Minister’s
advisers to keep up-to-date with the Minister’s ideas, concerns, interests and problems.
This would enable them to anticipate and respond in a timely manner to any potential
conflict. It followed that a perceived ‘rule’ was to avoid providing policy advice to the
Minister that would cause conflict. In structuration terms, the resource medium
available for the subordinates to exercise power was perceived to be under control of the
superordinate agent. The action of creating conflict with superiors forms the basis of
potential change in the resource medium. What may happen is that in the event of
conflict superiors can engage in new practices to remove the medium by which
subordinates can deploy power. Thus, the perception among subordinates that they are
too dependent on superiors to be able to influence their actions constrains the capability
of subordinates to change the structure or invent new structure.
By reproducing a ‘no conflict’ institutional ‘structure’, the risk of conflict would have
been reduced and the dependence of subordinates preserved. Some members of the Unit
noted that the appointment of a new Minister meant that they had to learn ‘how to
handle the new Minister’. Consequently, attempts were made to source information
about his background from his advisers so as to better understand his views, previous
decision-making preferences and potential expectations. One officer explained that ‘the
Minister liked to have lots of little projects’ because by ‘funding on ground works
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directly this could provide lots of opportunities to see evidence of funding.’ The agents
did not appear capable of exercising power in this situation. There was no critical
discussion or analysis about the impact on resource outcomes of funding ‘lots of little
projects’. There was no attempt by agency to try and change the course of events as
dictated by the superior. Viewed through structuration theory, the capability of agents to
act was constrained by their perception that they were not able to influence the activities
of their superiors. Thus, in this situation there is a level of agreement as to who has
control and who is dependent in the context of social interaction. By acquiescing to a
position of dependence the subordinates chose not to engage in actions of autonomy.
This preserved the continuity of social structures, including power relations.
6.3.2 Resource asymmetry
Intergovernmental relations (e.g. different portfolios) were perceived by informants as a
hindrance, citing ‘institutional and ideological turf wars’ and ‘potentially conflicting
policies/programs’. Central to structuration theory, is the idea that power in
organisations is mediated via the organisational resources that members mobilise within
the interaction. Hence, information is a resource that functions as a medium of power
and members in different government Departments with access to more or better quality
information (i.e. information asymmetry) will be able to exercise power more:
All social systems are marked by an asymmetry of resources, and the existing structure of domination is reaffirmed through the use of resources (R'), It is only when the existing asymmetry of resources is explicitly challenged or countered, that the existing structure of domination may be modified. (Orlikowski and Robey, 1991: 149)
The delivery of two NRM funding programs (NAP and NHT - Chapter 4) by the
Department appeared to represent an exercise in relational power to address the issue of
resource asymmetry. Rather than engaging in actions to preserve the social structures,
action had been taken to create new relations of power. Two ideologically different
Commonwealth Departments are responsible for delivery of natural resource
management programs: the Department of Agriculture Fisheries and Forestry (DAFF)
and the Department of Environment and Heritage (DEH), focusing on agricultural
production and natural resource conservation and repair respectively. In structuration
terms, the funding programs delivered by these Departments are resources (e.g.
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information, people and money) through which power is exercised. Thus, if structures
of domination that contribute to resource asymmetry were built into these social
institutions, then agents in either Department could become less capable of action. A
new social structure called the Joint Team had been invented as a joint venture between
DAFF and DEH with responsibility for management of the Natural Heritage Trust and
the National Action Plan for Salinity and Water Quality. The programs were delivered
by mixed teams of staff of the NRM Division of DAFF and the NRM Programmes
Division of DEH. The Team reported jointly to the heads of these Divisions:
The NAP is delivered by the ‘Australian Government NRM Team’, which is a joint initiative between DAFF and DEH. The NRM team comprises more than 100 DAFF and DEH employees working together to deliver the NAP and the NHT. The team works directly to two Australian Government Ministers, the Minister for Environment and Heritage and the Minister for Agriculture, Fisheries and Forestry (ANAO, 2004: 79).
The Joint Team was described by informants as ‘building trust between parties’. This
draws parallels with the notion of building social capital where trust and mutual
understanding make cooperative action possible (Section 2.4.2). The new social system
could be interpreted, in structuration terms, as a way to counter resource asymmetry,
seemingly to enable agency for both Departments rather than privileging one over
another. By creating a new structure, it could be understood that agents in both
departments were provided with equal levels of access to the same resources and rules
and thus, an equal capability to act (Giddens, 1984: 14).
6.4 Legitimation
Structures of legitimation are the rules relating to social interaction, that ‘relate on the
one hand to the constitution of meaning, and on the other to the sanctioning of modes of
social conduct.’ [italics in original] (Giddens, 1984: 18). For example, rules may be
social norms that govern the way language is used or how a process is conducted.
Norms help determine what can be sanctioned in human interaction, which iteratively
produce structures of legitimation.
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6.4.1 Managerialism norms
The Department had adopted a business approach to NRM policy, reflected in its
Corporate Plan discourse (i.e. describing Mission and Vision Statements and the core
business of Units). This has contributed to the structure of legitimation through
sanctioning a particular way of working (e.g. outsourcing) and propagating a set of
norms of acceptable professional social practice. The official narrative portrays the
‘NRM problem’ as one of effective design of funding programs: ‘Administering
programs that strategically invest in activities that promote and facilitate greater
adoption of sound natural resource management practice particularly at the catchment or
regional scale’ (Commonwealth of Australia, 2004d: 3). Traditionally, resource
management functions are principally State (and local government) responsibilities
(Chapter 4). Thus, the role of Commonwealth government as an ‘NRM funding
program designer’ is not in conflict with the function of the States and may be
considered aligned with the business approach adopted.
However, the business-like nature of the discourse that was observed to dominate the
policy-making process may weaken other discourses, consequently limiting the sharing
and use of new knowledge for analysis and debate. For example, during meetings the
NRM outcome was generally described in terms of ‘program delivery and integration’
and discussion frequently centred on issues of resource allocation and returns on
investment, such as ‘getting the biggest bang for your buck’, ‘ongoing government
intervention in the market’ and ‘exit strategies for programs’. Management concerns
such as ‘ensuring maximum return on investment’, ‘how long will funding continue?’
‘harmonised reporting’ or ‘risk management of the regional model’ also prevailed in the
discourse. A discourse can, according to Dryzek (1997: 8), ‘provide the basic terms for
analysis, debates, agreements, and disagreements in the environmental area no less than
elsewhere.’ In structuration terms, central to this ‘new public management’ discourse
was a rule that sanctioned the priority of language about compliance, accountability and
value for money, giving secondary importance to language about achieving a resource
outcome.
The business approach also sanctioned interactions to build strong internal bonds in the
unit by developing social practices that facilitated internal communication channels. For
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example, learning the ‘rules’ of policy communication, such as a policy language (e.g.
highly dependent on acronyms – e.g. NAP, NHT, NLP, MBI, MER) and an
understanding beyond a text book view of who to communicate with and how (e.g.
complex network – e.g. Ministerial Reference Groups, Policy and Program Committees,
Advisory Committees, Policy Task Forces), strengthened my internal bonds in the
Department and facilitated my engagement in the policy process. However, the lack of
transparency of the ‘rules’ of engagement may constitute a barrier to building external
communication channels, weakening ‘bridging social capital’ and decreasing the
efficiency of communicative action between groups, making entry of new information
into policy more difficult and slower.
In a structuration sense, the rise of public sector managerialism (Chapter 4) can be
interpreted as the introduction of new business norms, including outsourcing. This
would be reflected in a shared understanding (i.e. structures of signification) that
consultants were legitimate contributors and reviewers of central components of
Australian public policy as expressed in a review of public sector reform by the
Australian Public Service Commission:
Scientific management has given way to business management as the inspiration for new thinking on operations…Supporting activities have been ‘out-sourced’ to contractors on a relatively major scale…It has also included policy work of various kinds as the growing role of consultancy organisations with public sector clienteles demonstrates (Commonwealth of Australia, 2003b: 16).
Outsourcing may also be used to protect organisational interests from political
adversaries. Martin (1998: 204) argued, ‘the way in which consultants work provides a
shield which insulates government and administration from the immediate demands of a
critical and undemanding public’. However, outsourcing also increases the risk of a
public sector populated by ‘generalists and purchasers’ – the so called ‘hollow
government’ (Commonwealth of Australia 2003b: 165).
Chapter 4 outlined the change in the Australian Public Service to a new public sector
management framework based on purchaser-provider models and private-public
partnerships. The Australian Public Service (APS) has been reorganised to play a
stronger managerial role with expert policy advice more likely to be sourced from
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outside government. From numerous Departmental contracting documents, observations
of consultants working on site, and reports by commissioned consultants emerged a
view that supports this trend towards the outsourcing of policy advisory services. As
noted by one Commonwealth policy-maker:
Mostly Australian government departments will commission their work externally from consultants, universities, CSIRO and other providers (Interviewee No. 23, 2007).
Consultants sometimes worked on site and were provided access to resources as
functioning members of the organisation. Consultants contributed to policy formulation
and to policy review and evaluation, as one officer noted:
there are specific evaluations that start to look at the outcomes of …those programs…Usually we often use consultants to do them (Interviewee No. 21, 2006).
However, the norm of replacing expert, long-standing and committed personnel with
consultants carries a risk:
Loss of institutional and management memory is likely, lessening the ability, to learn from past research and management lessons (Dovers and Lindenmayer, 1997: 71).
Thus, a tension is apparent between the norms of short-term contracts and arrangements
and that of developing expertise and long term commitment some believe essential for
effective policy. Drawing on structuration theory, outsourcing can be construed as a
social structure that reflects an ‘accepted norm’ of a rational planning approach and a
‘language rule’ that research use in policy is understood only in instrumental terms (i.e.
the application of specific findings to specific decisions). Instrumental use is, however,
rare according to Weiss (1977a) and Rich (2001) and conceptual use more likely (i.e.
the accumulation of knowledge from multiple sources that promotes a gradual shift in
opinions and shapes the way policy-makers think about problems). Importantly, this
indicates that the gradual impacts of accumulated knowledge on policy practice were
ignored by a structure of signification based on instrumental use. With respect to
structuration, this apparent neglect of the capacity-building of policy-makers was
manifested by an ‘outsourcing social structure’ that diverts resources (i.e. information
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and money) from policy-makers to consultants, giving consultants a greater medium to
exercise power. Policy-makers would participate less in directly sourcing and evaluating
information and over time would accumulate less knowledge; constituting a barrier to
organisational learning that would result in poorly scrutinised commissioned studies. By
reproducing the structure, policy-makers, hence, can increasingly lose the capability to
analyse and influence the actions of consultants. With this loss of power policy-makers
become more dependent on consultants for advice.
Viewed through structuration theory, outsourcing, as a new social action has potential to
constrain a structure of signification to instrumental ‘use’ and to transfer the medium
(i.e. resources) to exercise power to the consultant, by drawing on a structure of
domination that provides resources (i.e. money and information) to the consultant.
Hence, by not directly engaging in the analysis of policy information, the capability of
agency (i.e. member of the Department) to store and transfer knowledge is weakened,
the agency becomes more dependent on the consultant and less capable to act (e.g.
assess quality of commissioned studies) and knowledge relations become more
asymmetric. Consequently, with a weakened agency the quality of policy advice may be
poorer.
6.5 Conclusion
A micro-ethnographic study conducted in 2005 in the Commonwealth Department of
Agriculture, Fisheries and Forestry reiterated the personal experience gained over
twenty years of working in a State agricultural department, that generally, the Australian
public sector has adopted a more business and managerial focus. In terms of
structuration theory, as a result of the move to managerialism, structures of information
use had developed that were less resilient and more myopic; dominated by business
modes of thought and more adverse to risk (i.e. lacking the expertise to judge and
manage information to inform policy and limiting the entry of new ideas and
information). According to Holling and Meffe (1996: 331) these may be regarded as
symptoms of institutions that suffer from what they termed the ‘pathology of natural
resource management’, defined as the ‘loss of system resilience when the range of
natural variation in the system is reduced’:
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Bureacucracies are an exercise in variance reduction through regulation and control; their purpose is elimination of extreme behaviour through regulation to promote conformity to a specific set of standards, which to some degree is certainly desirable in a civilised society. But deeply entrenched bureaucracies are characteristically unresilient to new challenges because the system discourages innovation or other behaviour variance.
Holling and Meffe (1996) warned that institutions that are dominated by cultures of
control, resistance to new ideas and an unwillingness to change contribute to on-going
degradation of ecosystems. Ongoing, significant organisational change in the
Department appeared to have weakened its agency and the ability of the organisation to
act. In structuration terms, the workforce restructuring could be perceived as a strategy
to remake systems and fundamentally change the practice of producing NRM policy.
But the capability of newly located human agency tended to be weakened by a lack of
adequate knowledge and insufficient clarity about ways to act. These weaknesses
rendered the agency, and ultimately the organisation, less capable of action and
challenging structural problems.
Importantly, structuration theory called attention to the power dynamics of the micro-
organisational social context, the bureaucratic patterns of information control and the
implications of these matters for information use. From the data collected during
participant observation emerged the general view that the organisational structures of
the Department retained power over the way information was used. Information control
was evident from organisational structures that constrained information use to internal
policy documents. The power to imagine the NRM problem and ways to address the
problem were generally restricted by the business focus of the policy-makers.
Intellectual power was concentrated in business and management acumen, limiting the
communication frames or paradigms available to inform NRM policy. The ability to
share knowledge within the organisation was restricted by weaknesses in networks and
pointed to limited bridging social capital. Viewed through structuration theory, this
indicated constrained interpretative schemes for officers to draw on; a shared
conformity in thinking that encouraged extension of the status quo rather than a shift to
fundamentally different relationships for working together. The internal reporting
hierarchy seemed to direct the way policy was made and written, and embedded in this
social system was the exercise of relational power. In structuration terms, the exercise
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of power through resources means that those controlling facilities (i.e. resources) would
also control the means of deploying power, including access to information and sources
of information.
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CHAPTER 7: AN ORGANISATIONAL PERSPECTIVE
7.1 Introduction
Until recently, the notion of the science-policy interface focused on information factors
and a one-way linear process of science feeding direct into policy. However, emphasis
has shifted to a social process that involves interactions between scientists and policy-
makers and a ‘co-production’ of knowledge. Concomitantly, the research focus has
shifted from information quality to the social dynamics of research use. No longer are
attributes of information considered sufficient to understand knowledge use (Chapter 2).
Exploring the social context of research use has been shown in studies to have
consequences for understanding science use (e.g. Oh, 1996a; Rich, 2001; Landry et al.,
2001a, 2003); Belkhodja et al., 2007).
This chapter focuses on social dimensions of organisational context on science use. The
importance of organisational context (such as culture and norms) to understanding
knowledge use has recently gained weight (see Section 2.4.2). Adopting an
organisation-level perspective highlights the importance of organisational structures,
tasks, roles, procedures and routines in understanding the acquisition, dissemination,
and utilisation of information (Belkhodja et al., 2007). The scope of the analysis
presented in this chapter includes both the internal and external organisational context
but does not extend to levels of relationship between the policy organisation and wider
society.
This chapter examines the influence of a range of factors, including informational,
organisational and social, on science use by NRM policy-makers in Australia. It begins
by considering how use was measured and then shifts to an analysis of the extent and
dynamics of use through the stages of policy-making. The chapter then considers how
science use might be influenced by gender and organisational form (regional, State,
federal). It then moves to an analysis of the influence of information, organisational and
social factors on the use of science, and uses a factor analysis to identify patterns of
factors that may offer greater explanatory power.
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7.2 Explanation of Variables
Chapter 2 introduced the meaning and measurement of the dependent variable ‘use’
(Section 2.3.1). Overall, there have been two different approaches to measuring
knowledge use (Belkhodja et al., 2007). One is a product perspective based on an
instrumental use of knowledge. The second is a process perspective based on the
knowledge use scale whose stages vary according to the different aspects and stages of
the decision-making process (Landry et al., 2001a). However, as instrumental use is rare
and a conceptual mode of use more likely, a stage-based process was favoured to
measure ‘use’ for the Online Questionnaire (Weiss, 1977a; Rich, 2001; also see Chapter
2). Thus, in this thesis the dependent variable ‘use’ is a derived index of the Knott and
Wildavsky scale (Knott and Wildavsky, 1980) that comprised stages of use: seeking,
reception, cognition, reference, adaptation, effort, influence and application.
Chapter 2 also introduced a range of explanatory variables used in previous studies of
use (summarised in Table 2.2). From the Online Questionnaire data, the research use of
policy-makers was analysed with the explanatory variables described in Table 7.1.
Putting the Online Questionnaire in context, Table 7.1 illustrates the alignment between
the explanatory variables used and previously reported models of ‘use’. These variables
and their operational definitions (i.e. specific dimensions that make up each index) are
further explained in Appendix E.
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Table 7.1: Explanatory variables and models of use
Models of Use Explanatory Variables Description
Science Push Information Relevance Index the extent to which scientific information received appears to meet policy-making needs.
Content Index the importance of organisational interest factors
Intensity of Use Index
the level of frequency that external information sources are sued for daily policy activities (e.g. universities and co-operative research centres, government research agencies, scientific journals, the internet, popular media, informal networks, joint science-policy teams or think-tanks, commissioned research products).
Organisational Culture Index the level of individual and organisational commitment to promote research culture within an organisation
Policy Role Index the extent to which the policy-maker is engaged in policy-making activities
Demand Pull
Research Experience Index measures previous involvement in professional research activities (e.g. been principal investigator or co-investigator, collected data, contributed data from their organisation for a research study, carried out an in depth review of the literature).
Dissemination
Adaptation Efforts Index
the importance given by the policy-maker to the effort of adapting research products ((i.e. access to in-house advisors or experts to interpret this information, confidence in the reliability and validity of the methodology employed, relevance of the information for policy use, credibility or prestige of the source, capacity to verify the quality of research results, strength of working relationships with researchers.)
All the above plus linkage mechanisms between researchers and policy-maker users:
Formal Linkage Index
a measure of the importance of the formal linkages between NRM policy-maker users and research suppliers (e.g. active involvement by policy-makers in research projects; participation in professional conferences and workshops involving researchers; membership on expert panels, and committees involving researchers;)
Interaction
Informal Linkage Index
a measure of the intensity of the informal person-to-person links between research suppliers (i.e. universities and cooperative research centres, State and federal government agencies, private firms and non-government organisations) and NRM policy-maker users
7.3 Assessment of Measures
Many of the variables in the Online Questionnaire consisted of items that were multiple
scale. These carried several advantages, including that a number of items were more
likely to capture the totality of a broad concept than a single question and finer
distinctions could be drawn between people (Bryman and Cramer, 2005). To assess the
reliability of the scales an item analysis of the inter-utilisation components was
conducted. The internal consistency of each factor was tested using the Cronbach’s
alpha test. Although no specific criteria exist for evaluating the results of the test,
Nunnally (1978, cited Belkhodja et al., 2007), recommends a minimum level of 0.50 for
new constructs and a minimum level of 0.7 for constructs that are well established in the
literature. Appendix D shows the Cronbach alpha for the dependent variable science
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‘use’ (0.90) and for each of the explanatory variables measured on the multiple items
scale. The values of the Cronbach alpha coefficient were all above 0.05, indicating that
the multiple item scales used in the study were reliable.
The negative value of skewness (-.153) indicated a slight clustering of scores on the
continuous variable (use of science index) at the high end, and the negative value for
kurtosis (-.306) indicated a distribution that was relatively flat. The Kolmogorow-
Smirnov statistic is 0.200 (p<0.05) which indicated normality (Pallant, 2001). Hence the
distribution of scores for the ‘use of science’ dependent variable was relatively normal.
Generally, researchers who have examined science use in policy have presupposed the
perceived importance of science for policy-making. Much previous work has also
emphasised the importance of interactions between policy-makers and scientists in
increasing the likelihood of attention being given to the research produced (Hanney et
al, 2003). The variables used were grounded in these perceptions and assumptions, thus,
their validity was explored during the Semi-structured Interviews. Data collected during
the Semi-structured Interviews of scientists and policy-makers provided indications as
to the perceptions and assumptions concerning the importance of science for NRM
policy-making. The scientists (n = 7) were employed by State and Commonwealth
research agencies, and the policy-makers (n = 24) were based in regional, State and
Commonwealth NRM organisations. Table 7.2 is a summary of their responses to
questions regarding the importance of science use and of the interactions between
scientists and policy-makers (Appendices B and C list questions). Represented in the
table is the common denominator found in the responses of scientists and policy-
makers. Respondents shared the view that science was important in NRM policy-
making. However, several recognised that science was just one of the factors to be
considered in NRM policy-making, as one policy-maker, based in a federal NRM
organisation commented (Interviewee No. 27, 2007):
I think it’s important, but let’s be clear, it’s one input, it’s not the only input and it cannot be the ultimate input on which the decisions are all made. As I said there are other decisions to be taken into account, other inputs that are equally as valid.
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Not surprisingly then, a variable demand for science was also highlighted by
respondents.
The interactions between scientists and policy-makers were also perceived as crucial
and important for science use. A male, State based, policy-maker spoke of how a lack of
personal contact between scientists and researchers would lead to misinterpretations and
mishandling of information (Interviewee No. 5, 2006):
it’s helped by having that sort of technical people there with the people doing the management and planning… that’s very critical and that’s what we’ve found, look if you don’t have your stakeholders understanding where you’re coming from, then you could be wasting your time
However, respondents also pointed to the variable quality of relationships between
scientists and policy-makers. The difference was attributed to a range of factors, such as
personalities and power structures, as one male, State policy-maker (Interviewee No.
17, 2007) remarked: ‘that (relationships with scientists) varies who you’re working
with. Its personalities, its institutional arrangements…’. Thus, science use in policy-
making and the interactions between scientists and policy-makers were perceived as
important. But it could not be assumed that the interactions between scientists and
policy-makers were always automatic nor that science was the only legitimate source of
information for policy-making.
Table 7.2: Summary of responses to questions in the Semi-structured Interviews.
Interviewees Importance of science? Importance of interaction between scientists and policy-makers?
Degree of demand for science?
Quality of relationships between scientists and policy-makers?
Scientists Important, essential crucial. Several mentioned complications from personal opinion and scientific debates. Science is occasionally marginalised. Has a fair influence.
Crucial, very important
Not automatic demand. In normative terms, science should not be completely driven by policy needs but responsive to future.
Uneven, can be poor to good.
Policy-makers Critical, fundamental, important. Several mentioned its influence is relative - it is not the only factor considered in policy-making. Some suggest that science hasn’t been the influence that it should have been.
Crucial, essential, critical, fundamental. Has to be close for ideas to be exchanged.
Demand is variable. Supply does not meet demand. Several indicated that matching science to policy needs has been problematic.
Variable, improving, not enough.
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The strength of alignment between researcher defined variables, drawn from previous
studies of use (e.g. Landry et al., 2001a, 2003; Belkhodja et al., 2007), and data
collected during the Semi-structured Interviews, as measured by the level of thematic
consistency in interviews (Table 7.3), suggested that these factors seemed to reflect the
perceived social reality of science use in NRM policy-making. By ‘consistent’ it is
meant that the interviewees had raised issues about these variables or provided
examples of them in the context of science use by policy-makers. Selected interview
extracts were provided in Appendix F as evidence that the meanings of responses
attributed to themes seemed to reflect the theme concerned. For instance, a process of
formal arrangements between regional policy-makers and scientists was emphasised by
a regional, male policy-maker, who spoke of the importance of partnering with
researchers to help ensure understanding and trust of the data for regional policy use
(Interviewee No. 14, 2007):
Well ‘no I don’t believe the research, don’t believe outcomes because I haven’t been involved personally involved in it,’ is a fundamental one. And it really is, both in the farming system sense and also in a catchment wide sense. So we still prefer to pilot in any research program.’
A federal based policy-maker drew attention to the effect of informal, face-to-face
communication on improving understanding and uptake of science (Interviewee No. 10,
2007):
This is the matter with science of trying to deal with the huge amount of information that’s coming through, trying to pull out the pertinence, knowing whom you can rely on for advice, going to the odd inspirational thing. Go to good conferences, networking is an important thing in this area, its amazing what gets done over a casual conversation at the conference or at a Christmas do or something like this.
There was also significant evidence to suggest that the efforts needed to modify science
for policy-making figured strongly in their perception of science use. In the words of a
male policy-maker based in a State NRM organisation (Interviewee No. 17, 2007): ‘A
piece of science in isolation without having an analysis to then provide knowledge
products is of very little value to the practitioners’.
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Thematic analysis of the interview data, however, was not limited to researcher-defined
variables drawn from previous studies of use. The strength of alignment between
themes drawn from a ‘supply-demand’ network view of science use (e.g. ‘order
fulfilment’ and ‘demand management’, see Chapter 5), as measured by the level of
thematic consistency in interviews (see Appendix F for selected extracts), suggested that
supply chain thinking could be a strong component of the perceived social reality of
science use in NRM policy-making. By identifying this alignment, a measure of
credence could be attributed to the supply chain perspective adopted (Section 2.4.1). For
instance, the theme of demand management was signalled in several responses, as
commented by one regional policy-maker (Interviewee No. 9, 2006):
I felt that perhaps the science may have disappeared a little bit out of some of it and it got down to my assets is more important than your assets, rather than necessarily keeping it purely scientific.
The comment emphasises a variable demand for science that is responsive to the
political conditions of the policy-making ‘customer’. Accordingly, Jasanoff (1987: 198)
observed that, ‘Policy-makers have an overriding interest in responding flexibly to
changing political currents’. Tapping into the notion of ‘demand management’ means
that science use is understood as an interaction along a supply chain that is sensitive to
the political context of the ‘customer’.
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The strength of alignment between themes drawn from the Australian NRM policy-
making context (see Chapter 4), as measured by the level of thematic consistency in
interviews (Appendix F), suggested that contextual elements also added to the perceived
social reality of science use in NRM policy-making. A context-centred view of science
use emerged, emphasising the interplay between the dynamics of institutional change
and science use. Along these lines, a State-based policy-maker spoke of the impact of
public sector restructuring (see Chapter 4) (Interviewee No. 26, 2007):
We use Government agencies where they’re appropriate. Quite often though, the skills sets in the agencies are not strong enough to answer these questions. And this is one of the big problems that science policy is a problem in the sense that the agencies have lost some of the capacity in science. So we are depending more and more on Universities and so on.
Table 7.3: Alignment of themes with perceived social reality (percentages)
Themes Description Policy-makers (n = 21)
Scientists (n = 10)
Per cent of interviews consistent
(n = 31)
Formal linkages
formal linkages between NRM policy-maker users and research suppliers (e.g. involvement in research projects, membership on committees)
100 88 90.6
Informal linkages
informal person-to-person links between research suppliers and NRM policy-maker users
96 75 87.5
Policy role
engagement in the policy-making cycle (e.g. involvement in problem formulation and/or policy implementation)
43 38 40.6
Organisational culture
commitment to promote research culture within an organisation
83 63 81.2
Intensity of Use sourcing external information sources by policy-makers for their daily policy activities
70 63 65.6
Information Relevance
the extent to which scientific information received appears to meet policy-making needs
83 75 78.1
Research experience
previous involvement in professional research activities
30 50 34.3
Content the importance of organisational interest factors (e.g. findings support the organisational goals)
61 63 59.3
Adaptation the effort of adapting research products for policy-making.
91 88 87.5
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The comment points to the increased difficulty of science use in a context of downsizing
in government departments that has curtailed the capacity for science exchange. This
quote highlighted an important theme running through the interview data: science use is
a process occurring in a particular context, thus, understanding the dynamics of the
process requires consideration of its context too.
7.3.1 Descriptive statistics of sample
Table 7.4 presents statistics that describe the composition of the sample. Of the 81
questionnaires, 80 were valid. Of the 80 valid responses 62.5 per cent were from males
and 37.5 per cent were from females. This reflects the largely male-dominated nature of
NRM policy-making as pointed out in Chapter 4. Their mean age was 45.5 years, the
mean number of years they had been involved in NRM was 15.6 years, and the mean
number of years working in policy-making was 7.8 years. In 87.5 per cent of the cases
these NRM policy-makers had more than five years of NRM experience and in 66.2 per
cent of the cases more than five years of policy-making experience.
Table 7.4: Descriptive statistics for policy-maker population (per cent)
Total
sample (n = 80)
Federal (n = 14)
State (n = 40)
Regional (n = 22)
Organisational level Executive Director/CEO 20.0 21.4 12.5 32.0 Manager/senior policy officer 38.8 22.0 55.0 22.7 Policy/research officer 22.5 22.0 22.5 18.1 Council/community member 5 0.0 0.0 13.6Gender Female 37.5 50.0 35.0 36.0 Male 62.5 50.0 65.0 64.0Education Tertiary educated 90.0 100.0 98.0 77.0 Degree with natural science
component 77.5 64.0 87.5 63.0
Average age 45.5 42.5 45.3 48.0Average years involved with NRM 15.6 10.1 18.3 14.3Average years working in NRM policy-making 7.8 5.4 8.9 6.1
Organisation size More than 500
More than 500
Less than 100
In 38.8 per cent of the cases, the positions occupied were manager or senior policy
officer, in 22.5 per cent of the cases the positions occupied were policy or research
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officer, in 20 per cent of the cases the positions occupied were Executive Director,
Director, CEO, Secretary or Chair and in five per cent the positions occupied were
Council or Community member. The valid responses indicated that 17.5 per cent were
in federal government, 50 per cent were in State government, 27.5 per cent were in
regional organisations and five per cent in other NRM organisations involved in policy-
making (e.g. State government utility, federal government funded incorporated
organisation). Respondents were from WA (66.3 per cent) and ACT/NSW (33.8 per
cent)38.
Of the 80 valid responses, 77.5 per cent of these respondents held university degrees or
diplomas that included a natural science component (e.g. environmental, geographical,
physical, chemical and earth sciences, agriculture science, veterinary science, biological
science or biotechnology), while 12.5 per cent held university qualifications that did not
include any natural science component. All federal respondents held science degrees (n
= 14, natural science = 64 per cent, social science = 36 per cent), compared with 73 per
cent of catchment management respondents (n = 22, natural science = 64 per cent,
social science = nine per cent) and with 100 per cent of State respondents (n = 40,
natural science = 90 per cent, social science = 10 per cent). It is worth noting, however,
that the proportion of federal policy-makers (n = 14) with natural science degrees at 64
per cent was considerably higher than the 20 per cent reported in the APS workforce in
1995 (Matheson, 2001: 52), or the 9.2 per cent of graduates recruited into the APS in
2001 (Matheson, 2001: 52). One possible explanation for this difference is that the level
of responses reflected the scientific values and interests of the participants, and those
without a background in the natural sciences may be less likely to be interested in
participating in a survey about the use of NRM science. Clearly, it would be difficult to
use the data to generalise up to the wider policy-maker population. Ten per cent of
respondents had no university qualification.
7.3.2 Frequency of use at different stages
By using the Knott and Wildavsky scale (Knott and Wildavsky, 1980) to measure
knowledge use, the Online Questionnaire employed a stage-based design of the process.
Section 2.3.1 outlined the rationale for adopting the process design and the Knott and 38 The population comprised 25 per cent Federal, 31 per cent State and 44 per cent regional organisations, with 42 per cent from WA and 58 per cent from the ACT/NSW.
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Wildavsky (Knott and Wildavsky, 1980) scale of knowledge use. The use scale was
composed of seven process stages: reception, cognition, reference, adaptation, effort,
influence and application. To include the practice of seeking information, an eighth
stage called seeking was added (Table 7.5 describes each stages’ content). The Use of
Research Index was based on the frequency of use at each stage (Section 7.6.1). It
should, of course be recognised that this is a conceptual device, and the process of
science use can follow a less step-wise pattern. By conceptualising use as a process,
differences in the frequency of ‘use’ at different stages could be measured. For example,
measuring use in terms of reception and cognition may in fact represent research that
does not end up making a difference to the policy choice.
The majority of respondents frequently (i.e.‘often’ or ‘very often’) sought and received
science information relevant to their work (80 per cent and 74 per cent respectively -
Table 7.4). About 24 per cent indicated that they ‘sometimes’ received relevant science
information. This suggested that user needs for science, specifically in terms of
information availability and relevance, were met more often than may be generally
assumed.
About half (56.3 per cent) of the 80 respondents to the Online Questionnaire indicated
that they frequently used science that has led, or will lead to, concrete changes in the
programs or services provided by their organisation. This is not surprising, given the
considerable intellectual capacity of the respondents to comprehend and adapt science
for policy. The majority were scientifically trained (77.5 per cent held a degree with a
natural science component - Table 7.4) and had substantial experience in research (68.9
per cent had been involved in at least four types of research activities as measured by
the Research Experience Index - Appendix E). A study of research use by managers and
professionals in health service organisations is in line with this view, with its conclusion
that research experience is an important determinant of research use (Belkhodja et al.,
2007).
A large proportion of the science information that was received did not seem to have an
impact on policy-making. The percentage of respondents who use science information
‘often’ or ‘very often’ showed a marked decline from the cognition stage (80 per cent)
to the influence (52.6 per cent) and application (56.3 per cent) stages. This is not
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surprising, since as emphasised by Knott and Wildavsky (1980: 541), ‘despite the
tremendous growth in the number of analytical studies, producers of advice are
frequently discouraged by the lack of impact on the formulation of policy’. The
effective use of research by policy-makers measured in terms of influence was often
irregular. For 42.5 per cent of the 80 respondents the science received ‘sometimes’
influenced their decisions and choices, while for 35 per cent, the information received
‘sometimes’ influenced changes in programs or services.
Table 7.5: Frequency distribution by stages of science use
Frequency of Science Use Does not
apply, don’t
know and missing
data Never
(1) Sometimes
(2) Often
(3) Very Often
(4) Total
Average on 1 to 4
scale (Standard Deviation)
Stages of Use
Frequency (Percent)
Frequency (Percent)
Frequency (Percent)
Frequency (Percent)
Frequency (Percent)
Frequency (Percent)
1 Seeking I have sought science information relevant to areas of my responsibility 0
(0) 1
(1.3) 15
(18.8) 28
(35.0) 36
(45.0) 80
(100) 3.24
(0.79) 2 Reception I have received science information relevant to areas of my responsibility
1 (1.3)
1 (1.3)
19 (23.8)
25 (31.3)
34 (42.5)
80 (100)
3.16 (0.84)
3 Cognition I have read and understand science information received 1
(1.3) 0
(0) 15
(18.8) 32
(40) 32
(40) 80
(100) 3.22
(0.75) 4 Reference I have cited science reports to colleagues, managers or politicians as a reference in my work
1 (1.3)
6 (7.5)
39 (48.8)
21 (26.3)
13 (16.3)
80 (100)
2.52 (0.86)
5 Adaptation I have adapted the format of science reports to provide information useful to decision-makers 3
(3.8) 9
(11.3) 35
(43.8) 22
(27.5) 11
(13.8) 80
(100) 2.45
(0.88) 6 Effort (Adoption)
I have made efforts to promote the adoption of science information
1 (1.3)
4 (5.0)
28 (35.0)
30 (37.5)
17 (21.3)
80 (100)
2.76 (0.85)
7 Influence
I have used science information to make professional choices and decisions that would not have been made otherwise
2 (2.5)
2 (2.5)
34 (42.5)
23 (28.8)
19 (23.8)
80 (100)
2.76 (0.85)
8 Application I have used science information that has led, or will lead to concrete changes in the programs or services provided by my organisation
3 (3.8)
4 (5.0)
28 (35.0)
28 (35.0)
17 (21.3)
80 (100)
2.75 (0.86)
Thus, while science seems to have been frequently sought and received by NRM policy-
makers, a large proportion of the science received seemed to have negligible impact on
policy-making. Only about half of received research results were progressed through the
reference, adaptation, effort, influence and application stages. There appeared to be a
shared perception that the knowledge received was far more than the knowledge
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eventually applied in policy-making. This suggested that the nature of science use
tended to be asymmetrical. This notion of asymmetrical science use:
· showed similarity with the ‘enlightenment’ model of research use in policy -
science use is perceived as a gradual assimilation of research and some filtering
of the received science could be expected before it could be applied – thus, the
science received would be more than the science applied (See Section 2.3.1);
· was consistent with studies that suggested scientific conflicts and political
disputes would limit the application of the science received by policy-makers
(e.g. see Allen and Gould, 1986; Caldwell, 1993; Dovers, 1995; Martin, 1998;
Sarewitz et al., 2000).
7.4 Differences in Science Use Among Policy-makers
7.4.1 Comparing federal, State and regional organisations
A Chi-square test for independence indicated that NRM policy-makers based in federal
government were less likely to use science than those based in State and catchment
management organisations (χ2 = 7.02, df = 2, n = 76, p<0.05) (Table 7.6). This may be
related to individual factors, that suggested the respondents in federal and regional
organisations were less experienced and educated in NRM matters (Table 7.4); and to
structural changes over the past twenty years in the federal public service that has seen
the rise of economic and business professionals and the trend to outsourcing expertise in
federal NRM policy-making (see Section 4.3.2). This was exemplified by the lower
levels of NRM policy-making experience in federal organisations (Table 7.4), and
accorded with findings of the participant observation study (Chapter 6), that indicated
these organisational factors may have an important influence on information sharing
and use by federal policy-makers.
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Table 7.6: Use of science by organisation type
Use of Science
Low High
Federal Government (%) 78.6 21.4
State Government (%) 37.5 62.5
Organisation Type
Catchment Management (%) 50.0 50.0
The higher proportion of science use in State government departments may be explained
by their traditional scientific base and responsibility for resource management functions
(see Section 4.3.2) that emphasised production research, scientific reporting and
environmental responsibilities. All State government respondents held science degrees
compared with 76 per cent of respondents of catchment management organisations, who
were less likely to use science. The political pressures of group decision-making that
affects policy-making in community based organisations may have also influenced
science use by regional policy-makers (Chapter 4). Qualitative evidence from the semi-
structured interviews lends some support to this view by indicating that regional local
politics was considered important among policy-makers based in regional organisations
(consistent in 87 per cent of interviewees, n = 8), in contrast, the issue of regional
politics was not mentioned by any federal interviewee. The significance of the influence
of regional politics on science use was pointed out by one regional policy-maker:
that’s why we went specifically to sub-catchment approaches. Now that created a political problem for us then. Because it meant that we were withdrawing public dollars from some areas that historically had them. Now we’ve had to with, and we have, we’ve sort of stood our ground and said, “sorry”, you know, this is a scientific approach, this is what it is telling us. And we’ve got to meet an outcome. And that hasn’t been easy, you know. Because it comes from all sorts of directions. (Interviewee No. 14, 2007)
7.4.2 Comparing groups with different levels of social connectivity
NRM policy-makers who perceived their relationships with scientists were based on
limited discussion and information flow were less likely to use science than those who
perceived their relationships with scientists were based on adequate discussion and
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information flow (χ2 = 13.7, df = 1, n = 75, p<0.05) (Table 7.7). This highlighted the
importance of social connections for science use.
According to Fischer et al. (2004: 359) such connections may generate better
knowledge sharing and transfer within a group, due to the established trust, shared
language and goals and informal ties. These networks of relationships have been
interpreted as critical in the formation of social capital (Bresnen et al., 2004a). This was
in line with studies suggesting that social capital – the network ties or the stock of active
connections among people – facilitates the transfer and use of knowledge (see Section
2.3.2). It also lends weight to the argument that the use of policy information is
positively related to the interaction between researchers and decision-makers (Dunn,
1980; Nelson et al., 1987).
Table 7.7: Use of science by relationship strength
Use of Science Low High
Limited discussion and information flow 79.2 20.8 Policy-maker’s perception of relationship with researcher Adequate discussion and information
flow 33.3 66.7
There is a view, that the interaction between scientists and policy-makers is difficult
owing to differences in language and operating rules between the ‘two communities or
cultures’ of science and policy (Section 2.2.1). This was consistent with data collected
during the Semi-structured Interviews and summed up by these comments:
You’ve certainly got to get the ideas from both ways, so policy makers letting scientists know what their priorities are, where are their gaps and where are they likely to invest money, etc. and scientists providing that. The catch is the mode of operations are so different that there’s this clash of philosophies and things (male, State-based, policy-maker, Interviewee No. 11, 2007).
Scientists and policymakers don’t talk the same language and I guess that is always a problem. I have worked in organisations like this for pretty close on 30 years and the mindsets are quite different so it is quite difficult to get them to understand each other and for the policymakers to use good science, for the scientists are not very
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good at explaining what they do (male, State-based, policy-maker, Interviewee No. 4, 2006).
There are all sorts of cultural differences that make it quite difficult. I think we don’t speak each other’s language very well (male, State-based, scientist, Interviewee No. 15, 2007).
However, rather than focus on information attributes of content or timeliness, there is
increasing emphasis on the social connections between researchers and policy-makers,
as pointed out by Hanney et al. (2003: 14):
increasing attention is focusing on the concept of interfaces between researchers and the users of research. This incorporates the idea that there are likely to be different values and interests between the two communities, with their different time-frames, and that research is less likely to be utilised in a significant way unless networks and mechanisms are established at the interfaces.
In line with this view, Huberman (1990) and Landry et al. (2001a, 2003) suggested that
research use may be increased by emphasising linkages between researchers and
decision-makers. Interactions with researchers were also reported by Belkhodja et al.
(2007) to have an important influence on the use of research by managers and
professionals in health service organisations. Results of the Online Questionnaire were
aligned with this finding; policy-makers who were more actively engaged with
scientists were more likely to use science for policy. This may be because, as
highlighted in Chapter 2, the growth of close personal relationships between scientists
and policy-makers can form the basis of trust to share and learn knowledge.
7.4.3 Comparing male and female groups
Chi square and t-tests were conducted to explore how gender influenced responses, with
significant gender differences in bold type (Tables 7.8 and 7.9). Male policy-makers
were more likely to use science than female policy-makers (χ2 = 21.4, df = 1, n = 80,
p<0.05) (Table 7.8).
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Table 7.8: Use of science by gender
Use of science
Low High
Male 36.0 64.0
Gender
Female 70.0 30.0
There were significant gender differences in the use of science scores for males ( =
23.52, σ = 4.32) and females ( = 20.67, σ = 6.31; t (45) = 2.1, p = .03 (two tailed)
(Table 7.8). The magnitude of the differences in the mean (mean difference = 2.85, 95%
CI: .22 to 5.48) was moderate (eta squared = .05). However, there were no significant
gender differences in research experience and in formal and informal linkage scores.
The quantity of social capital did appear to be important for science use, since females
tended to accumulate as much bridging social capital as men but were less likely to use
science. There are similarities here with studies of personal networks that found women
and men may have networks of similar size (e.g. see Agarwal, 2000; Moore, 1990).
Moreover, these studies also pointed to large gender differences in network
composition. Women have more informal and kinship related networks than men, who
include more co-workers and powerful players in their network. The gender difference
in use of science, thus, may be related to men’s social networks being better positioned
to capitalise on returns from social relationships, in terms of information resources that
may accrue from social ties (e.g. see Agarwal, 2000; Molyneux, 2002, Gidengil et al.,
2003.). Hence, the female policy-makers may have had similar amounts of bridging
social capital but less effective social capital for information benefits (i.e. less
knowledge about information services and less social opportunities to influence the
sourcing, shaping or use of information).
Furthermore, as Newman and Dale (2005: r2) emphasised, ‘a dynamic balance between
bonding and bridging links is needed’. Bridging links extend outside the community and
provide access to a diverse set of information resources, whereas bonding links within
the organisation may also be needed to absorb the benefits of the resources (Newman
and Dale, 2005). Thus, women and men may have the same amount of bridging social
capital with similar wide access to information resources, but women may have less
bonding social capital within organisations to capitalise on information returns in an
organisational context.
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Table 7.9: Mean values of science use and dimensions of use by gender
Q19 Your gender? n Mean Std. Deviation Sig
(2-tailed)
male 50 23.52* 4.320 Use of research index female 30 20.67 6.310
.034
male 50 14.64 4.060 Informal Linkage Index female 30 13.13 4.431
.125
male 50 18.98 6.232 Policy role index female 30 16.90 6.211
.152
male 50 21.14* 4.664 Intensity of Use Index female 30 18.77 4.710
.031
male 50 7.10 1.555 Information relevance index female 30 6.37 1.884
.063
male 50 23.06 4.649 Formal Linkage Index female 30 23.93 6.264
.478
male 50 36.42 5.504 Adaptation Index
female 30 36.63 5.487 .867
male 50 7.76 3.159 Content index
female 30 9.40* 3.024 .025
male 50 12.86* 3.301 Organisational Culture Index female 29 10.97 2.958
.013
male 50 4.32 2.035 Research experience index female 30 4.43 2.144
.814
In terms of a supportive organisational culture for science use (as measured by two
dimensions in this study: an Organisational Culture Index and an Intensity of Use Index,
see Section 7.5), males reported more frequent use of external information sources,
including informal networks and joint science-policy teams (males ( = 21.14, σ =
4.66) and females ( = 18.77, σ = 4.71; t (78) = 2.19, p = .03 (two tailed)), and
perceived a more supportive organisational culture for research use in policy-making
(males ( = 12.82, σ = 3.30) and females ( = 10.97, σ = 2.95, t (77) = 2.55, p = .01
(two tailed)) than females. The magnitude of the differences in the mean scores of
Organisational Culture (mean difference = -1.64, 95% CI: -3.07 to -.221) was moderate
(eta squared = .08). The magnitude of the differences in the mean scores of Intensity of
Use (mean difference = 2.38, 95% CI: .22 to 4.52) was moderate (eta squared = .06)
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(Table 7.9). The differences in frequency of use may be due to men being more
accepting of science and technology, more likely to view risks as smaller and less
problematic than women, and more likely to have greater levels of trust in formal
institutions, particularly science, technology and government (Miller et al., 2007).
Consequently, men generally may seek and use new scientific findings more often than
women. The difference in organisational culture scores for males and females, with
females holding a less positive view that research constituted a preferred source of
information for policy, lends weight to the importance of including gender in analyses
of organisational context (e.g. see Mills, 1988). Females may view their organisational
culture as being less supportive for research use because of weaker bonding links in
NRM organisations (alluded to above) that limit their opportunity (and the incentives
offered to them) to be involved in joint science-policy activities organised by
organisations. This in turn may translate into their lower use of science compared with
males.
There were also significant difference in scores of the importance of organisational
interests for males ( = 7.76, σ = 3.16) and females ( = 9.40, σ = 3.02; t (78) = -2.28,
p = .03 (two tailed). The magnitude of the differences in the mean (mean difference = -
1.64, 95% CI: -3.07 to -.221) was moderate (eta squared = .06). This may be related to
greater consideration given by women about managing potential sources of conflict, as
evidenced by the greater capacity in NRM groups to manage conflict when women are
present (Westermann et al., 2005). Consequently, women may be less likely to use
science if a conflict with organisational interests is perceived.
From the findings of the Online Questionnaire it would appear that one of the ways that
science use can be excluded from policy-making is associated with gender. Women
tended to use science less and this could not be explained by gender differences in
educational attainment nor research experience. Neither could this gender gap be
explained by differences in amounts of social capital. Women policy-makers seemed to
accumulate as much social capital as men. Rather than quantity, it appeared to be the
quality of social capital that contributed to this gendered pattern of science use. While
social capital can have a positive effect on knowledge sharing and use, Bresnen et al.
(2004a) pointed out that it can also be exclusionary. Women may generate just as many
contacts as men but the scope and diversity of their social networks may be limited to
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less powerful and knowledgeable players (e.g. see Agarwal, 2000; Molyneux, 2002,
Gidengil et al., 2003.). Thus, while women may accumulate as much bridging social
capital as men, the social capital that women accumulate may be less effective for
facilitating science use (i.e. less knowledge about information services and less social
opportunities to influence the sourcing, shaping or use of information). The gender gap
raises questions about the extent to which the construction of scientific rationale for
policy is exclusionary. By recognising a science use activity that is strongly informed by
a masculine hegemony, attention is also focused on the asymmetry of power defined in
terms of gender. The associations of masculinity with science use may also illustrate
what Jackson (1991) has called ‘dominant masculinist ideals’, including intellectual
rationality.
7.5 Empirical Relationships: Correlation Analyses
As outlined in Section 7.2, a range of variables were drawn from the literature to
potentially explain research use. In this regard, the relationships among the use of
science and these variables (Table 7.1) were explored using a Pearson’s r correlation
analysis. The operational definitions (i.e. specific dimensions that make up each index)
of the variables are presented in Appendix E. To recap, the variables were indicative of
the following: the importance of links between research suppliers and users (i.e.
Informal and Formal Linkage Index); the importance of information relevance (i.e.
Information Relevance Index); the significance of supporting organisational interests
(i.e. Content Index); the intensity with which research sources are used (i.e. Intensity of
Use Index); the preference for research as source of information (i.e. Organisational
Culture Index); the value of involvement in policy-making activities (i.e. Policy Role
Index); the importance of the user’s experience in research (i.e. Research Experience
Index) and the effort to adapt research products to user (i.e. Adaptation Efforts Index).
Table 7.9 shows large correlations among the use of science, intensity of use index and
informal linkage index variables, indicating quite strong relationships between them.
Interactions between policy-makers and researchers appear important in transferring
research to policy-makers. This fits especially well with the ‘interaction’ model of
utilisation (Section 2.3). The model suggests that the more sustained and intense the
interaction between researchers and users, the more likely there will be use. The policy
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role index, information relevance index, formal linkage index, organisational culture
index and research experience index were also identified as having statistically
significant relationships with science use. Drawing a link between science-policy and
the research experience of researchers, Hanney et al. (2003) pointed out that in
countries where the research and policy connections are strongest, the relationship has
been enhanced by the fact that some of the senior administrators have had research
experience or interests as part of their prior education.
The importance of efforts needed to adapt research (Adaptation Index - the importance
attached to jargon free, clearly presented research) was not significantly associated with
any of the variables. As most of the sample (almost 78 per cent) had gained a natural
science degree and thus a significant level of technical competence and comprehension,
it may be expected that they would not regard information factors such as presentation
in non-technical language or access to in-house ‘information interpreters’ important
enough to affect their use of science. Belkhodja et al. (2007) also found that research
use was not sensitive to research adaptation efforts but did not provide an explanation
for the finding.
7.6 Empirical Relationships: Regression Analyses
To more deeply explore the relationships between the independent variables and to
better understand how much of the variance in the use of science can be explained by
these variables, a multiple regression analysis was conducted. As the sample size (n =
80) was limited a reduced version of the conceptual ‘interaction’ model outlined in
Chapter 2 was used. The size of the multiple coefficient of determination (r2) was used
to evaluate the conceptual model. Changes in variance were also assessed as the model
was applied across the three organisation types studied. The Pearson’s r between each
pair of independent variables was calculated and found not to exceed 0.80 (Table 7.9) so
they were not highly correlated. Thus, it would not be expected that their contribution to
variance depends on the order in which they are entered into an equation. The
dependent and independent variables and the analysis process are described below.
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7.6.1 Dependent variable
The dependent variable for the regression analysis is the use of science.
Use of science
By using a modified version of the Knott and Wildavsky scale (Chapter 2.3), the use of
science variable was captured as a stage-based process. The scale included the eight
stages: seeking, reception, cognition, reference, adaptation, effort, influence and
application described in Table 7.5. Respondents were asked to indicate, on a 0 to 4 scale
(0 = doesn’t apply or don’t know; 1 = never and 4 = very often), the frequency that best
described their use of science in the past year).
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Table 7.10: Relationships among variables
Variables Use of
Research index
Informal Linkage
Index Policy Role
Index Intensity of Use Index
Information Relevance
Index
Formal Linkage
Index Adaptation
Index Content
index Organisational Culture Index
Research Experience
Index
Use of Research Index 1 .581** a .486** a .616** a .367** a .321** .057 .033 .413** a .342** a
Informal Linkage Index 1 .432** .602** .372** .488** .168 -.008 .379** .246*
Policy role index 1 .603** .179 .286* .084 -.147 .346** .314**
Intensity of Use Index 1 .266* .431** .067 -.208 .429** .284*
Information relevance index 1 .316** .107 .024 .271* .212
Formal Linkage Index 1 .102 .063 .242* .315**
Adaptation Index 1 .409** -.003 -.153
Content Index 1 .160 -.171
Organisational Culture Index 1 .131
Research Experience Index
1
*p<0.05, Pearson Product-Moment Correlations, **p<0.01, Pearson Product-Moment Correlations a used in regression model
- 239 -
7.6.2 Explanatory variables
The regression analyses were limited to five independent, continuous variables for a
reliable equation with 80 cases (Pallant, 2001). Selection of these variables was based
on the highest correlation strengths (Table 7.10), and hence, strongest relationships with
the Use of Science dependent variable. The statistical model, thus, was not completely
specified, raising issues of internal validity which were addressed in part by
supplementary statistical approaches (e.g. factor analysis). These variables, as explained
above, were: Intensity of Use Index, Informal Linkage Index, Policy Role Index,
Organisational Culture Index, and Information Relevance Index.
Multicollinearity is a potential problem of linear regression and means that the
regression coefficients may be unstable and vary considerably from sample to sample
(Bryman and Cramer, 2005). This was not found to be a problem since the Pearson’s r
between each pair of independent variables was calculated and found not to exceed 0.80
(Table 7.11). Descriptive statistics for the 80 cases included in the analyses are shown in
Table 7.12.
Table 7.12 recaps the different categories of variables that potentially affect science use
among NRM policy-makers. The following dimensions are presented: Informal Linkage
Index, Policy Role Index, Organisational Culture Index, Intensity of Use Index and
Information Relevance Index. The potential effect of the control variables is not
identified.
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Table 7.11: Correlation coefficients for continuous independent variables
Model Explanatory variables Use of Science
Content Index .033
Information Relevance Index .367** a
Intensity of Use Index .616** a
Organisational Culture Index .413** a
Policy Role Index .486** a
Demand pull
Research Experience Index .342**
Dissemination Adaptation Index .057
Formal Linkage Index .321** Interaction (combines all above plus linkage mechanisms) Informal Linkage Index .581** a
**p<0.01, Pearson Product-Moment Correlations a used in regression model
Table 7.12: Descriptive statistics for variables used in regression analyses
Variable Units Mean Std. Deviation Min Max
Use of Science index 22.45 5.3 8 32
Information relevance index 6.83 1.71 2 11
Informal Linkage index 14.08 4.23 5 26
Intensity of Use index 20.25 4.79 10 32
Organisational Culture index 12.16 3.29 2 20
Policy Role index 36.00 6.26 6 36
Table 7.13: Predictions regarding the impact of the independent variables on science use.
Explanatory variables Expected effects on Dependent Variable
(Use of Science)
Information Relevance Index +
Intensity of Use Index +
Organisational Culture Index +
Policy Role Index +
Informal Linkage Index +
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7.6.3 Regression analysis process
The research use of NRM policy-makers was analysed with the explanatory variables
shown in Table 7.13. The dependent variable was based on the different utilisation
stages defined in the utilisation scale developed by Knott and Wildavsky (1980). To
study the impact of these explanatory variables on the quantitative dependent variable,
the following simple regression model was developed:
Use of Science = β0 + β 1Informal Linkage + β 2Policy Role
+ β 3Organisational Culture + β 4Intensity of Use
+ β 5Information Relevance
where β i (i = 0….5) are the coefficients.
This model was estimated for all the NRM organisations taken a whole (Table 7.14). A
Reduced Model (reduced because of limited number of cases) based on the General
Model was estimated for all the NRM organisations and likewise estimated for each
type of organisation taken separately (Chapter 7.6.4). These two different estimations
were based on the hypothesis that factors to explain research use vary between different
types of organisations.
Table 7.14: Contributions of each independent variable (Standardised coefficients - Beta values)
General Model
Independent Variables β t
Information Relevance Index .135 .980
Intensity of Use Index .301* 1.941
Organisational Culture Index .108 .339
Policy Role Index .133 .406
Informal Linkage Index .247* 1.873
n 78
F 12.68**
Adjusted r2 .428
*p<0.05, **p<0.01
Standardised beta coefficients are used as they are preferred when the variables are not
scaled in conceptually meaningful units and are not very meaningful in a comparative
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context with other independent variables. However standard partal beta coefficients are
more sample dependent because of variation in standard deviations between samples.
(Schwab, 2005)
The General Model (information relevance, intensity of use, organisational culture,
policy role and informal linkage variables) presented in Table 7.14 explains 42.8 per
cent of the variance in use of science. Of these five variables the intensity of use (β =
.301) and informal linkage (β = .247) were statistically significant, with a positive effect
on science use. Information relevance, organisational culture and policy role variables
were not significant. The following section explores the influence of these explanatory
variables in the different types of NRM policy-making organisations.
7.6.4 Reduced regression model
The limited number of cases for each organisation type restricted the number of
independent variables that could be included in the regression equation. Thus the
stepwise regression method based on a variable omission level of p = 0.5, was used to
omit very low significance variables and generate the Reduced Model results referred to
below. This meant independent variables were selected on the basis of statistical
criteria (Bryman and Cramer, 2005). Step-wise regression gives the best model with two
variables that is, without Information Relevance Index, Organisational Culture Index
and Policy Role Index. To explore the inter-relationships among variables, and to
corroborate the findings of the reduced model a factor analysis was also conducted.
Table 7.15 shows the reduced regression results for each type of NRM organisation.
The ability of different variables to explain research use in the different types of
organisations was examined. Table 7.15 shows that the Reduced Model consisted of
Informal Linkages and Intensity of Use variables and explained 40.7 per cent of the
variance in use of science. Of these variables Informal Linkages made the largest
unique contribution (β = .134) although Intensity of Use also made a statistically
significant contribution (β = .119). The predictive strength of the Reduced Model
(Adjusted r2 = 40.7 per cent) was not much less than the General Model (Adjusted r2 =
42.8 per cent) indicating that the Informal Linkages and Intensity of Use variables were
dominant effects on the use of science. The Federal Model explained 65.3 per cent of
the variance in use of science in federal organisations, the State Model explained 31.4
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per cent of the variance in use of science in State organisations and the Catchment
Management Model explained 48.7 per cent of the variance in catchment management
organisations.
Table 7.15: Contributions of each independent variable (Standardised coefficients - Beta values) by organisation type General Model
Reduced Modela
Federal Model
State Model
Catchment Model
Independent Variables β β β β β
Information Relevance Index .135
Intensity of Use Index .301* .119* .666** .306* .740*
Organisational Culture Index .108
Policy Role Index .133
Informal Linkage Index .247* .134* .317 .379* -.009
n 79 79 14 40 22
F 12.68** 27.74** 20.77** 9.92* 10.96**
Adjusted r2 .428 .407 .653 .314 .487
a Reduced using step-wise regression at p = 0.5 level.
*p<0.05, **p<0.01
Table 7.16: Correlation coefficients for dimensions of the Intensity of Use Index Use of Science
Intensity of Use 616** · universities and co-operative research centres .412**
· State government science or research agencies .570**
· federal government science or research agencies (e.g. CSIRO, LWA)
.380**
· scientific journals and papers .596**
· world wide web via the internet and online discussion groups/bulletin boards .247*
· popular media (newspapers, TV) .133
· informal networks .297**
· joint science-policy teams or think-tanks .427**
· research products generated by internal experts .332**
· commissioned research products .472**
*p<0.05, **p<0.01 Spearman’s Rho
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Overall, the intensity of use variable was significant in the regression models with a
positive effect on research use, as indicated by the regression model results of the
General Model (Table 7.14), the Reduced Model (Table 7.15). There were significant
correlations between dimensions of the Intensity of Use Index and Use of Science
variable (Table 7.16), except for ‘popular media’. However, it is noteworthy that the
role of the popular media can be significant in the overall context of science use. By
framing a problem ‘as novel and important’, Irwin (2001b: 21) pointed out that media
attention can play an important role in influencing science production for policy. For
example, the media drew the attention of the public and government to the salinity
problem in rural Australia stimulating funding for research into the problem (Beresford
et al., 2004) (see Section 4.3.1). .
The Informal Linkage Index was measured by the intensity of the informal person-to-
person links between researcher suppliers and users. The informal linkage variable
explained research use in State organisations but it did not explain use in the federal or
catchment management organisations.
The interaction and relationships between researchers and users are key attributes of the
interactive perspective and interaction model of science use introduced in Chapter 2. In
addition to integrating the variables of previous science-push and science-pull models
(e.g information and dissemination attributes), the interaction model introduced
variables to measure the linkages between researchers and users (e.g. informal contacts,
participation in committees) (Landry et al., 2001; 2003; Nutley et al., 2007). As Landry
et al. (2007: 539) recently pointed out ‘the transfer of research also depends on bridges
linking the push and pull factors’.
The linkage variables offered a measure of the level of social capital that the policy-
maker had with researchers. Social capital has been widely recognised in organisational
and knowledge management studies as an important ingredient for knowledge
development in and between organisations (e.g. Cohen and Prusak, 2001; Ingpen and
Tsang, 2005). Social capital, Fischer et al. (2004) observed, may generate better
knowledge sharing and transfer within a group, due to the established trust, shared
language and goals and informal ties.
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The regression model results showed that the informal linkages variable was significant
in the general model with a positive effect on research use. This accords with the
findings of Landry et al. (2001a, 2003, 2007) and Belkhodja et al. (2007) who reported
that ‘linkage variables’ were positively related to the uptake of research. In a context of
knowledge asymmetry (see Section 2.4.1), Landry et al. (2007: 569) argued that
the transfer of information is unlikely if researchers and research users do not have frequent interactions Forging linkages between researchers and research users can bridge this information asymmetry and facilitate the utilisation of the opportunities provided by research.
However, Oh (1997a) found that the interaction between researchers and decision-
makers had little effect on the impact of information use in policy. He suggested that
communication between researchers and decision-makers may be important for
boosting uptake of information but is not significant in actual decision-making. Actual
policy-making, Chapter 1 noted, is a political process and ultimately the decisions made
depend more on the collective values of the policy-makers than on scientific evidence
(Allen and Gould, 1986; Mercer, 2000; Sarewitz, 2004; Pielke, 2007). NRM policy-
making typically involves multiple stakeholders, multiple objectives and considerable
uncertainty so the resulting scientific conflicts and political disputes may limit the
impact of science in ‘wicked’ policy contexts (see Chapter 2). In highly political
conditions, thus, the interactions between researchers and decision-makers may have
minimal influence on the impact of information use.
The results of the Reduced Model showed that informal linkage dimensions were
significant to explain the use of science by policy-makers in State organisations but not
those in federal and regional organisations. This was consistent with the findings of Oh
and Rich (1996) that the effects of interaction between researchers and decision-makers
varied across policy areas, and of Landry et al. (2003) that the same variables do not
account equally for knowledge use across different policy domains. In a similar vein,
Landry et al. (2007) noted that knowledge transfer cannot be reduced to the building of
better linkages between researchers and research users. As Landry et al. (2007: 586)
pointed out: ‘Knowledge transfer is also influenced by contingent factors that are
difficult to integrate into general theories on knowledge transfer. More theoretical and
empirical work is needed to shed light on these contingent factors’.
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Clearly, paying more attention to linkage mechanisms may increase the uptake of
research in State agencies but not in federal and regional organisations. One explanation
for this difference may be that State agencies traditionally have a strong internal
scientific capacity (e.g. State agricultural departments - see Chapter 4). With both a
critical mass of scientists and their co-location with policy-makers, there would be
greater opportunity for more effective person-to-person contact and information transfer
in State organisations. An alternative explanation may be that policy-making at federal
level was informed largely by ‘business modes of thought’ (see Section 6.2.2) and at
regional level by political considerations (see Section 5.2.5), that perhaps negated the
effect of interactions between science ‘suppliers’ and policy ‘customers’ on use, as
viewed in the context of a science-policy ‘supply chain’ (See Chapter 5). From this
perspective it may be surmised that whilst the interactions between researchers and
policy-makers may have resulted in information that was scientifically valid and
reliable, the information may not have met the political and organisational needs of the
policy-making ‘customers’ and was thus, considered less useful for policy. Accordingly,
Oh and Rich (1996) emphasised that the importance of any factors to all policy areas or
cases cannot be overgeneralised.
The regression model results also showed that the intensity of use variable was
significant in the general model with a positive effect on research use. The Intensity of
Use Index was measured by how frequently NRM policy-makers used different
information sources for their daily policy activities (e.g. universities, State and federal
government agencies, scientific journals, the internet, popular media, joint science-
policy teams). According to Belkhodja et al. (2007), the intensity with which research
sources are used reflects the individual and organisational commitment to promote
research culture within an organisation. From this perspective it may be understood that
there was an individual and organisational commitment to promote research culture
across federal, State and regional organisations.
7.7 Factor Analysis
A factor analysis was also conducted to identify patterns of factors influencing use of
science by NRM policy-makers and to corroborate the findings of the regression model.
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A factor analysis is concerned with whether the covariances or correlations between a
set of observed variables can be explained in terms of smaller number of factors
representing common dimensions in the larger set. It is an approach to summarising and
uncovering patterns in sets of multivariate data, essentially by reducing the complexity
of the data. This analysis seeks to further test the validity of the evidence presented so
far that suggests the intensity of use and informal linkages variables explain a
considerable amount of the variance in the use of science, attesting to their importance
in explaining its use.
In SPSS Principal Component Analysis (PCA) is classed as a form of factor analysis.
The nine explanatory variables of science use were subjected to a PCA analysis using
SPSS. Before performing PCA the suitability of the data for factor analysis was
assessed. A correlation matrix of the data revealed the presence of many coefficients of
0.3 and above (Table 7.10). The Kaiser-Meyer-Olin value was 0.725, exceeding the
recommended value of 0.6 (Kaiser, 1970, cited Pallant, 2001) and the Bartlett’s Test of
Sphericity (Bartlett, 1954, cited Pallant, 2001) reached statistical significance,
supporting the factorability of the correlation matrix.
PCA revealed the presence of two components with eigenvalues exceeding 1,
explaining 34.7 and 17.3 per cent of the variance respectively. Using Catell’s (1996,
cited Pallant, 2001) scree test, it was decided to retain the two components for further
investigation. To aid in the interpretation of the two components, Varimax rotation was
performed. The rotated solution (Table 7.17) revealed the presence of simple structure
with both components showing a number of strong loadings, and all variables loading
substantially on only one component, except for Research Experience. The rotated
solution accounted for 52.0 per cent of the variance. The largest rotated factor loadings
are reported in Table 7.16.
By using the scores of the two components to display the science use data in a scatter
plot (Figure 7.1), the patterns of influence of the two components on science could be
more easily visualised. For ease of interpretation, the ‘use of science’ continuous
variable was also recoded into an ordinal variable; split into two groups based on
whether use was above (‘high’ use), or below (‘low’ use), the mean ‘use of science’
index value for the sample (mean = 22.45). This was not considered an unreasonable
assignation of values since an index value greater than or equal to 24 can only be
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attained if the policy-maker used science at least ‘often’ for all stages of the ‘use of
science’ index (eight stages multiplied by index value of three (‘often’)) or if the policy-
maker used science ‘very often’ in at least six stages (six stages multiplied by index
value of four (‘very often’)).
Table 7.17: Organisational and information dimensions of science use: Rotated Factor Loadings*
Variable Component 1 Component 2 Intensity of use .809 Informal Linkage .799 Policy Role .694 Formal Linkage .671 Organisational Culture .599 Information Relevance .539 Research Experience .461 -.420 Content .837 Adaptation Efforts .758 Per Cent Variance Explained 34.6 17.4
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. * only loadings above .3 are displayed
Component 1 accounted for 34.6 per cent of the variance. It had high component
loadings for intensity of use, informal linkage, policy role, formal linkage,
organisational culture, and information relevance. The variables intensity of use and
informal linkage loaded strongly only on component 1. This was consistent with their
significant, positive relationships with science use and major role in explaining variance
in science use shown in the regression and correlation analyses (Sections 7.5 and 7.6).
Thus, the y-axis of Figure 7.1 indicated the importance of variables that reflected an
organisations’ culture and openness to research, as well as its’ social capital (see Section
2.4.2).
The second component had high loadings for content and adaptation efforts. It was also
negatively correlated with research experience. Component 2 accounted for 17.4 per
cent of the variance. The scores for Component 2 on Figure 7.1 may thus, reflect the
levels of effort needed for adapting the science to meet policy needs, including adapting
the presentation of the information or matching the findings to the organisational intent.
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Figure 7.1 helped to visualise the science use patterns of the NRM policy-makers.
Scores on the y-axis indicated the perceived level of social capacity (see Section 2.4.2),
while scores on the x-axis indicated the perceived level of ‘innovative capacity’ - or the
ability of a policy-maker to adapt the research and produce ‘science-informed’ policy.
-3.00000 -2.00000 -1.00000 0.00000 1.00000 2.00000 3.00000
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nt 1
Quadrant 1 Quadrant 2
Quadrant 3 Quadrant 4
Principal Component 2
Figure 7.1: Scatter plot of the two principal components showing their influence on use of science by policy-makers (High, Low).
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Policy-makers who reported high levels of use generally had more interactions with
researchers, sourced information more and worked in an organisational context that was
positive about science use (Quadrant 1, Figure 7.1). Policy-makers who reported lower
levels of use generally had fewer interactions with researchers, less research experience
(see Figure 7.2) and an organisational context that did not encourage science use.
(Quadrant 4, Figure 7.1). The likelihood of low levels of use, thus, seemed to be
associated with a reduced potential of policy-makers to be innovative in adapting
science to inform policy.
A matrix based on Figures 7.1 and 7.2 provided a framework to distinguish between
alternative causes of the differences in science use (Table 7.18). Whereas, previous
conceptual models of science use focused on discrete sets of limited variables (see
Chapter 2), this model identified the interdependencies between characteristics of the
policy-maker and aspects of the organisational context. Although the model does not
imply that these variables are the final or complete listing, they may be sufficient to
demonstrate the conditions under which science may play a role in shaping policy.
Overall, this framework models science use as being influenced by two components. It
suggested that the level of science use by NRM policy-makers depended on the degree
to which:
1. policy-makers accessed external sources of information and were engaged in
information exchange with researchers – integrated in the notion of ‘social
capacity’; and
2. the potential for policy innovation was constrained by organisational interests –
integrated in the idea of ‘conflicts of interests’.
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Figure 7.2: Distribution of the research experience of
policy-makers (High, Moderate, Low) in regard to the
two principal components. Not all the labels for the
data are loaded as some data elements overlap.
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Principal Component 2
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Relational
power -
social
capital
HIGH
LOW
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LOW
The results suggested that social capital played an important role in shaping science use.
Beyond simply increasing the level of interactions between policy-makers and
researchers, the increased opportunities for information exchange could have improved
researcher understanding of the information needs of policy-makers. As pointed out by
Caplan (1979) and Dunn (1980), policy-makers need to communicate with researchers
about the type of information needed or its purpose in order to increase the impact of
information. However, whilst social capital may be important for information uptake,
the results also indicated that the impact of information may be limited by social/values
conflicts. Low levels of research experience also seemed to limit the influence of
science; being less science-‘savvy’ may restrict the ability of policy-makers to
innovatively adapt science for policy.
The simple typology of the ‘Use of Science in Policy Model’ stressed policy-making
processes and contexts, rather than products. It distinguishes policy-making contexts
according to first the level of science-policy interactions, and second the degree of
Table 7.18: Use of science in policy model
QUADRANT 1 - ‘Interactive’
High chance of science use
· Many and frequent science-policy links.
· High access of external sources
· High research experience.
· Organisational interests not very
important
QUADRANT 2 - ‘Innovation’
Moderate chance of science use
· Many and frequent science-policy links.
· Moderate to high research experience
· Organisational interests very important
QUADRANT 3 - ‘Inertia’
Low chance of science use
· Few and infrequent science-policy links
· Low access of external sources
· Moderate to high research experience
· Organisational interests not very
important
QUADRANT 4 - ‘Political’
Low chance of science use
· Few and infrequent science-policy links
· Low access of external sources
· Low research experience
· Organisational interests very important
HIGHPolitical power - conflicts of interests
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social/value consensus (Table 7.18). Four distinctive contexts emerged from the data.
When strong science-policy linkages were combined with social consensus then the
policy-maker could act with a degree of independence to integrate science into policy –
a context termed ‘interactive’. When there were weak interactions between policy-
makers and researchers then knowledge exchange was poor and the notion of ‘inertia’
was used to describe the reduced uptake of science. When there were strong value
conflicts and strong science-policy links combined, the chance of science use was
moderate and the idea of an ‘innovation’ context was adopted. A ‘political’ context
described the situation of a low likelihood of science use when knowledge exchange
between researchers and policy-makers was poor and there were strong value conflicts.
The matrix opened a new, and perhaps more holistic way of understanding science use,
one that is not bounded by a focus in isolation of specific characteristics of the policy-
maker, the organisation or the information product, but by considering how they interact
in relation to each other.
7.8 Discussion
Science use has an asymmetrical nature
To a large extent, the early literature on science use in policy had adopted the two
communities’ theory (Section 2.3.2). Early focus, thus, was on dissemination strategies
to ‘bridge the gap’ between the two communities of science and policy. The assumption
was that the process was a linear, mechanical transfer of information; science had an
instrumental role and if appropriately packaged (i.e. relevant, timely and
comprehensible), then it would be used for policy (see Section 2.3.2). By drawing
analytical attention to a stage-based, process of science use, the findings reported in this
Chapter indicated that science use by NRM policy-makers can be conceptualised as the
gradual accumulation and processing of information.
The results suggested that science was not produced and transferred directly into a
policy application in a linear, uninterrupted fashion. A large proportion of research that
was read and understood by NRM policy-makers seemed to have negligible impact on
policy-making. Only about half of received research results were progressed through the
reference, adaptation, effort, influence and application stages. Research knowledge
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seemed to be filtered or adapted before its use in policy. There appeared to be a shared
perception that the knowledge received was far more than the knowledge eventually
applied in policy. This suggested that the nature of the process of translating science
into policy tended to be asymmetrical; a notion that is consistent with:
· the research use in policy model of ‘enlightenment’ - science provides a broad
base for decisions and its use is perceived as a gradual assimilation of research.
Some filtering, thus, of the received science would be expected before it could
be applied – thus, the science received would be more than the science applied
(See Section 2.3.1);
· studies that suggested individuals would actively filter information due to value
systems and cultural norms shaped by scientific conflicts and political disputes
(e.g. see Allen and Gould, 1986; Caldwell, 1993; Dovers, 1995; Martin, 1998;
Sarewitz et al., 2000).
Science use is socially dependent
The analysis of research use by NRM policy-makers presented in Section 7.4.2
indicated that policy-makers who tended to be more actively engaged with scientists
were more likely to use science for policy-making. In line with this view, Huberman
(1990) and Landry et al. (2001a, 2003) suggested that interpersonal influence through
social networks is a dominant mechanism for research use. Interactions with researchers
were also reported by Belkhodja et al. (2007) to have a major influence on the use of
research by managers and professionals in health service organisations.
Perhaps, as highlighted in Chapter 2, the growth of close personal relationships between
scientists and policy-makers can form the basis of trust to share and learn knowledge.
As communication-related studies pointed out (see Caplan, 1979; Dunn, 1980), policy-
makers need to communicate with researchers about the type of information needed or
what its intended use is. As elaborated on in Chapter 5, of the 80 respondents to the
Online Questionnaire (Section 5.2.2), a total of 53 (66 per cent) used informal networks
as an information source ‘often’ to ‘very often’. The development of trust has the
potential to be an element not only in enhancing shared learning in supply chains (see
Section 5.2.2) but also in the development of social capital (e.g. structural, relational
and cognitive bonds – see Section 2.4.2). The production and maintenance of social
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capital appeared important for research transfer in the science-policy supply chain.
Notably, there is a ‘darker’ side to social capital, and while it can have a positive effect
on knowledge sharing and use, it can also have an exclusionary effect (e.g. female
sources of knowledge that fall outside the existing social network may be neglected -
see Section 2.4.2).
Science use is context-dependent
McNie (2007: 32) suggested that, ‘context – the importance of temporal, spatial,
political, scientific and cultural factors – plays a significant role in the relative success
or failure in the production of useful information for decision-makers’. The uptake and
application of research by policy-makers is also a process that occurs in a complex
context (e.g. political, social and cultural factors in the policy-making space). The
analysis of differences in science use between organisational types suggested that
science use by federal NRM organisations was less likely compared with State and
regional organisations (Section 7.4.1). The perceived dominance of business modes of
thinking (Section 6.2.2), the trend to outsourcing (Section 6.4.1) and political power
dynamics (Section 6.3) in a federal NRM organisation may have contributed to a
subordinated view of science for policy. The analysis of differences in science use
between policy-makers indicated that organisational context played a significant role in
the distribution of different levels of science use. Therefore, not only is science
production dependent on context, but the use of science can be dependent on context.
Science use is gender-dependent
The complexity of policy systems that the gender analysis opened up was enlightening.
By drawing analytical attention to the relational aspects of power in organisations,
social capital offered an interpretation of gender differences, linking them to differences
in the power inherent in the interactions between individuals. Accordingly, Pelling and
High (2005: 314) noted
Social capital opens opportunities for exploring power as enacted through relational and networked space….Situated analyses of social capital oblige one to reject linear causation and recognise the complexity of social systems.
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The gender analysis suggested that men were more likely to use science than women but
this could not be explained by differences in bridging social capital. Men, it was
suggested, may tend to have higher levels of intra-organisational bonds with more
influential or powerful actors. The gender difference in use of science, thus, may be
related to men’s social networks being better positioned to capitalise on returns from
social relationships, in terms of information resources that may accrue from social ties
(e.g. see Agarwal, 2000; Molyneux, 2002, Gidengil et al., 2003.). This is the ‘darker
side’ of social capital - the potential for it to have an exclusionary effect (see Section
2.4.2).
Hence, the female policy-makers may have had similar amounts of bridging social
capital but less effective social capital for information benefits (i.e. less knowledge
about information services and less social opportunities to influence the sourcing,
shaping or use of information). As pointed out by Gidengil et al. (2003: 1), ‘men and
women with equivalent amounts of social capital can have very different stocks of
political information.’
Science use is power-dependent
A multidimensional framework for understanding the use of science by policy-makers
(Table 7.18) was suggested by the empirical evidence from the Online Questionnaire
presented in Section 7.7. The proposed ‘relational power-political power’ framework
may be used as a lens to examine important tensions between social structures
influencing science use; tensions between the power ‘enacted through relational and
networked space’ (Pelling and High, 2005: 314) (i.e. social capital) and the power
enacted via social structures (i.e. rules and resources) as echoed in Gidden’s theory of
structuration (Section 6.3).
The heterogenous framework considered the relation between the nature of social
connectivity and the socio-political context in which policy was being developed. The
findings suggested that perceptions of science-policy linkages, social capital, political
power and organisational interests were important in the relative likelihood of science
use in policy. This indicated that the use of research cannot be separated from its social
context and was consistent with the social construction of information use, particularly
in terms of structuration theory that involved assessing the power dynamic that
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underpinned information use (Chapter 6). In line with this interpretation, Jasanoff
(1987: 198) observed that, ‘Policy-makers have an overriding interest in responding
flexibly to changing political currents’, emphasising a variable demand for science that
is responsive to the political conditions of the policy-making ‘customer’.
The findings are in line with the argument of Rich and Oh (2000: 202) that ‘the use of
information is a complex political activity within an organisation rather than individual
rational behaviour.’ Policy-makers, thus, tend to use science within a political process,
turning to information that is considered trustworthy and supports policy positions or
organisational interests (see also Knott and Wildavsky, 1980; Rich, 1991). The results
are consistent with the findings of Rich and Oh (2000) that pointed to the linkage factor
(similar to the interactive perspective) and the political factor (similar to the
organisational interest perspective) having the greatest effect in explaining the use of
research in policy.
7.9 Conclusion
The analysis of the extent of research use by NRM policy-makers presented in this
chapter suggested that science was not produced and transferred directly into a policy
application in a linear, uninterrupted fashion. A large proportion of research that was
read and understood by NRM policy-makers seemed to have negligible impact on
policy-making. Thus, it cannot be assumed that the use of science in policy can be
represented by a linear, one-way model of information flow direct into a policy
application.
Science use by NRM policy-makers varied between federal, State and regional
organisations. The results presented in this chapter showed that science use by federal
NRM organisations was less likely compared with State and regional organisations
(Section 7.4.1). The perceived dominance of business modes of thinking (Section 6.2.2),
the trend to outsourcing (Section 6.4.1) and political power dynamics (Section 6.3) in a
federal NRM organisation may have contributed to a subordinated view of science for
policy. Thus, the local organisational conditions in which policy is made can influence
the use of science.
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Science use also depended on gender. The gender analysis suggested that men were
more likely to use science than women but this could not be explained by differences in
bridging social capital. Men, it was suggested, may tend to have higher levels of intra-
organisational bonds with more influential or powerful actors. Therefore, the gender
difference was attributed to men’s social networks being better positioned to capitalise
on returns from social relationships, in terms of information resources that may accrue
from social ties.
The analysis of science use by NRM policy-makers presented in this chapter called
attention to social capital in the development of trust. Policy-makers who were frequent
users generally interacted more with researchers, were more interconnected through
personal networks and usually were less exposed to socio-political conflicts. In
particular, policy-makers who perceived their relationships with scientists were based
on limited discussion and information flow were less likely to use science than those
who perceived their relationships with scientists were based on adequate discussion and
information flow (Section 7.4.2).
The results suggested that the use of science by NRM policy-makers will be more likely
in conditions of high relational power (i.e. interactions between scientists and policy-
makers) and low political power (i.e. conflicts of values and interests). Interactions with
researchers were also reported by Landry et al. (2001a, 2003) and Belkhodja et al.
(2007) to have a significant positive influence on the use of research by decision-
makers. The analysis presented in this chapter highlighted the need for future studies to
consider how social capital can be developed and maintained to support a viable
science-policy interface.
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CHAPTER 8: CONCLUSION
8.1 Introduction
Salinity, erosion, acidity, water logging, and the loss of biodiversity and soil structure,
resulting in land and water degradation are symptomatic of failings in NRM policy-
making and planning in Australia (e.g. Hamblin, 2009). One of the most significant
contributors to policy shortcomings is the difficulty in translating research findings into
improved decisions and concrete policy actions – the difficulty of bridging the science-
policy interface (e.g. Committee for the National Institute for the Environment, 1994;
Kendy, 2004; van den Hove, 2007).
To date, science has not been always been successfully used in environmental policy-
making, even though it has been touted by some scientists and policy-makers as
essential to determine policy and practice (Section 2.2). Finding a conceptualisation of
the interface will be an important focus to build collective understanding and develop
more effective relations between the realms of science and policy (e.g. Landry et al.,
2001a, 2003; Cummings et al., 2007; Belkhodja et al., 2007; Sarewitz and Pielke,
2007). This thesis has addressed the social constitution of the interface to gain insight
into the socio-political dynamics of the exchange and co-production of knowledge for
NRM policy-making.
The approach taken in this thesis is to use the data collected from participant-
observation, semi-structured interviews, contextual analyses and online questionnaires
to reflect on the dynamics of science use across micro-organisational, organisational and
system levels (see Chapter 3). Special reference is made to three approaches drawn from
different research streams to provide three pillars of an analytical framework: the supply
chain approach (i.e. system level), the interaction model of knowledge use (i.e.
organisational level) and structuration theory (i.e. individual level) (see Section 2.4). It
is an innovative study in that it is one of the first comprehensive analyses of the science-
policy interface in the Australian NRM sector.
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The thesis had four objectives. First, it endeavoured to examine the science-policy
interface. Second, the thesis aimed to explore the context of resource and environmental
policy-making as played out in Australia. Third, it aimed to elaborate an analytical
framework to explore and reflect on the socio-political dynamics of science use. Fourth,
the thesis aimed to examine the influence of a range of factors on science use by NRM
policy-makers. This chapter presents a synthesis of the research findings, and provides
insights towards the improvement of the practical consequences of the use of knowledge
in NRM planning processes.
8.2 Research Summary
8.2.1 Science use and NRM policy-making
There is a growing rhetoric about the use of science in the natural resource policy
agenda. Policy-makers and scientists have included scientific findings in recent policy
statements and debates on major resource and environmental issues, such as climate
change (e.g. Stern, 2006), water use and management in Australia (e.g. The Wentworth
Group, 2006), and the health of natural ecosystems (e.g. Australian State of
Environment Committee, 2006). At an international level, scientific information is
considered vital to policy-making for sustainable development (IAASTD, 2008). Some
policy-makers and scientists strongly believe that environmental policy-making is
dependent on science. But others are less of this view. Innes (1998) argued that research
on practice demonstrates how experts and policy-makers tend to disagree and how,
generally, little formal information and analysis influence decisions.
As Chapter 2 pointed out, there is rising scepticism about the underlying assumptions of
rationality and instrumental use that have permeated studies about research use in policy
(e.g. Lachapelle et al., 2003; Sarewitz, 2004). Its use was challenged by arguments
highlighting the insight of local knowledge, the importance of politics and values in
societal decisions, the inability of any one discipline to fully address problems, the
uncertainty in conclusions and the inherent subjective value interpretations (e.g.
methodological assumptions) that leave science unable to answer resource policy
questions definitively (Allen and Gould, 1986; Collingridge and Reeve, 1986; Cullen,
1990; Cullen, 1990; Cortner, 2000). There are tensions between the often idealised
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assumption of rationality that suggests science feeds directly into policy, and the wicked
space that policy-making realistically inhabits (Lachapelle et al., 2003). Research on
practice demonstrates how experts and policy-makers tend to disagree and how,
generally, formal information and analysis have little influence over decisions (Innes,
1998: 55). Furthermore, science is asserted by some to be socially constructed, not
impartial and often used to serve the interests of those in positions of power (Fischer,
2005).
As the policy-making process is rarely rational and the policy-making context typically
involves multiple stakeholders, multiple objectives and considerable uncertainty, then
the resulting scientific conflicts and political disputes pose limitations to the usefulness
and use of science for policy-making in wicked contexts (Allen and Gould, 1986;
Caldwell, 1993; Dovers, 1995; Martin, 1998; Sarewitz et al., 2000). The use of science
for policy-making is clearly dependent on the political conditions, but for a deeper
understanding of science use, also sector (e.g. State or regional level) specific
conditions should be taken into account. Processes of science use that occur at different
levels and relate to different contexts cannot be captured within a single dimension.
Therefore, this thesis examined the broader structural changes in NRM research and
policy-making organisations in Australia in an attempt to position factors influencing
science use in the local, policy-making context which is transient. The focus of this
thesis centred on the social dimensions of research use by policy-makers in
Commonwealth, State and regional organisational contexts.
8.2.2 Science use and the Australian NRM policy context
Information on the general characteristics and capability profile of Australian
Commonwealth, State and regional NRM organisations is an important step towards an
understanding of the information use behaviour of policy-makers and the factors
affecting their science use patterns. Over the past decade, a rapid area of institutional
development has been the regional delivery of NRM. This has been associated with an
increase in community environmental organisations (Section 4.2). Australian State and
Commonwealth governments have made significant investments in broad NRM policies
and programs via regional delivery to mobilise 57 regional communities in NRM
planning processes. Although the Productivity Commission (Productivity Commission,
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2004) has argued that regional bodies are an important means of achieving resource
management objectives, the capacity of regional organisations to respond, however, has
proved less than optimal. In particular, poor availability and access to science by
regional organisations have been cited as a hindrance in the progress of quality NRM
plans (Commonwealth of Australia, 2004a; 2008a). Asymmetries of power and
knowledge between regions have also emerged, reflected by inequalities in power and
funding and difficulty in accessing relevant information and skills (see Ewing, 2003;
Hajkowicz, 2009). Delivering science informed policy through this policy-making
environment is very difficult. Rapid institutional and structural change in the Australian
Commonwealth and State public service over the past twenty years has also been
associated with potentially, negative consequences for science use in NRM policy
(Section 4.3.2).
The rise of managerialist approaches in the Australian public sector has tended to
contribute to a diminished capacity of government agencies to assess and transfer
scientific information. Increasingly the Australian public service works through
contractors, charges users and run agencies as commercial business operations. There is
increased ministerial control of the policy agenda, decision making and senior
appointments. Outsourcing and contractual arrangements have become the norm (Davis
and Rhodes, 2000; Teicher et al., 2006). By not directly engaging in the analysis of
policy information, the capability of agency (i.e. members of the Department) to store
and transfer knowledge is weakened, the agency becomes more dependent on the
consultant and less capable to act (e.g. assess quality of commissioned studies) and
knowledge relations become more asymmetric. Hence, the capacity of the Australian
public sector to engage with scientific knowledge; to assess the soundness and value of
scientific knowledge for transfer into NRM policy-making may have been greatly
weakened by the move to managerialism (see Section 4.3.2 and Chapter 6).
8.2.3 An analytical framework for science use
A thriving area of scholarship has not only abandoned the notion of natural resource
planning as a representation of the rational-comprehensive model, but also the concept
of a science-policy interface as a representation of the linear model of science use.
Instead, this body of thought has chosen to favour the notion of an interactive model of
science use and a closer inquiry into the social processes by which scientific findings
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are translated into policy (see Chapter 2). As a first step it was important to establish a
method to assess these social processes under different scales and conditions. Rather
than a ‘model’ a flexible, analytical framework was developed that promotes thinking
about what comprises a comprehensive process of science use in policy and that can be
used for descriptive, analytical or prescriptive purposes. An analytical approach
incorporating three elements demonstrated that:
1. at micro-organisational level – structuration theory may be used as an analytical
approach to reflect on socio-political dynamics of information use within
organisations (Chapter 6). This conceptual structure brings to the fore the issue
of power embedded in the management of information use by social institutions
of communication norms and resource control. It is the power over the formation
of rules of behaviour and power over the flows of material resources, Giddens
(1984) argued, that is manifested as the ‘structures’ that people live and work
within. By treating human action and social structure as a duality, the theory
contributed to an understanding of how individual use of knowledge can
contribute to and interact with the institutional context.
2. at organisational level - an interaction model introduced an analytical focus on
the influence of social dimensions of whole organisations on use (Chapter 7).
Previous research had found that the quality of research, its timeliness and
relevance were all important but rarely sufficient preconditions for science use.
The process of use was considered more complex, and involved a more
fundamental relationship between researchers and policy-makers (Chapter 2).
Applying the interaction model showed that policy-makers who frequently used
science, generally interacted more with researchers than less frequent users,
were more interconnected through personal networks and usually were less
exposed to socio-political conflicts (Chapter 7).
3. at system level - a supply chain framework introduced an analytical focus on
dimensions of organisational networks, to examine how whole populations of
organisations and society may interact and influence research use (Chapter 5).
By exploring supply chain processes that spanned science suppliers’ suppliers
(e.g. funding bodies) to policy customers’ customers (e.g. lobby groups),
insights were gained into how external factors influence use. In particular, the
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analysis suggested that information products would be used when the integration
of findings and connectedness of people in the research-policy network is well
supported.
The application of three approaches across different scales yielded important insights
for understanding science use in policy. Whilst each framework interpreted the social
context in different ways (e.g. structuration theory viewed this in terms of a duality of
agency and structure and highlighted institutional norms) the social constructs of power
and trust emerged as common themes.
The application of the analytical framework emphasised the importance of trust in the
social context of science use. The system perspective, that used a supply chain
framework pointed to the sense of trust and confidence in the information source as an
important factor in the decision by policy-makers to use science. The supply chain
analysis also indicated that technical jargon and the lack of transparency of the policy-
making process makes it difficult for information to be shared, trust to be built and for
policy-makers and scientists to learn from each other. This in turn, makes it difficult for
science to be responsive to end user needs and consequently for science to be useful for
policy (see Section 5.2.3). Similarly, the organisational perspective demonstrated the
importance of close relationships between researchers and policy-makers (i.e. social
capital) for science use (Chapter 7), suggesting that the established trust, shared
language and goals and informal ties influences use (see Section 2.4). By applying
structuration theory to the social construction of information use by NRM policy-
makers, the concept of the duality of structure suggested that by their actions policy-
makers could reproduce social structures that limit social connectivity and thereby deny
opportunities for trust and direct links forming between policy groups (i.e. reducing the
formation of bridging social capacity), and hence, also for more effective and direct
sharing of information (Chapter 5).
The application of the analytical framework also emphasised the importance of power in
the social context of science use. The notion of power is a fundamental element in
structuration theory. Central to structuration theory, is the idea that power in
organisations is mediated via the organisational resources that members mobilise within
the interaction. Hence, information is a resource that functions as a medium of power
and members in different government Departments with access to more or better quality
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information (i.e. information asymmetry) will be able to exercise power more. Hence
knowledge asymmetries contribute to the importance of power in the social context of
science use. Failures in the science ‘supply chain’ to ensure transparency and
integration of regional customers in the process, pointed to issues of knowledge
asymmetry that denied power to demand pull elements (i.e. end-users) (Section 5.2.3).
Tensions in structures of power also emerged from the organisational level analysis;
tensions between the power ‘enacted through relational and networked space’ (Pelling
and High, 2005: 314) (i.e. social capital) and the power enacted via social structures (i.e.
rules and resources) as echoed in Gidden’s theory of structuration (Section 6.3).
8.2.4 Socio-political dynamics of science use
Science-policy interfaces are defined as social processes that include relations between
scientists and other actors in the policy process (van den Hove, 2007). The results
presented in this thesis showed that the socio-political dynamics in the policy-making
environment were important determinants of use. Policy-makers who were frequent
users generally interacted more with researchers than less frequent users, were more
interconnected through personal networks and generally were less exposed to socio-
political conflicts (Chapter 7). Those who perceived their relationships with scientists
were based on limited discussion and information flow were less likely to use science
than those who perceived their relationships with scientists were based on adequate
discussion and information flow (Section 7.4.2). Interactions with researchers were also
reported by Landry et al., 2001a(2001, 2003) and Belkhodja et al., 2007 (2007) to have
a significant influence on the use of research by decision-makers.
However, social networks can also have an exclusionary effect on science use. In this
thesis, men were found to be more frequent users of science and the gender difference
was attributed to men’s social networks being better positioned to capitalise on returns
from social relationships, in terms of information resources that may accrue from social
ties (e.g. see Agarwal, 2000; Molyneux, 2002; Gidengil et al., 2003.). This is the ‘darker
side’ of social capital - the potential for it to have an exclusionary effect (see Section
2.4.2). Nevertheless, social interaction provides an opportunity for the growth of close
personal relationships that can form the basis of trust to share and learn knowledge
(Chapter 2).
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According to Fischer et al. (2004: 359), due to the established trust, shared language
and goals and informal ties such connections may generate better knowledge sharing
and transfer within a group. The development of trust has the potential to be an element
not only in enhancing shared learning in supply chains (see Section 5.2.2) but also in the
development of social capital (e.g. structural, relational and cognitive bonds – see
Section 2.4.2). This is consistent with the view of Davis and Howden-Chapman (1996:
871): ‘Increasingly, research evidence points to the importance of trust and on-going
commitment between parties when research is successfully translated into action.’
The social construct of power also emerged as a common theme in the analyses
presented in this thesis. A micro-organisational perspective that used structuration
theory as an analytical approach brought to the fore the issue of institutional power and
its effect on science use through norms of information control, policies affecting
organisational communications, access to resources and conflicts of values and interests
(Chapter 6). The findings indicated that research use was significantly influenced by a
tension between relational power (i.e. social capital) and political power (i.e. differences
in values or interests) in policy-making spaces (Chapter 7).
The results of the thesis also demonstrated that the shifts in the Australian
Commonwealth public service over the past thirty years to a managerial focus, business
skilled workforce and downsizing have contributed to the loss of specialist skills,
emergence of new responsibilities and increased degree of consensus (Chapter 4).
Interpreted through the lens of structuration theory the information structures that
emerged from these changes were less resilient and less open to science; dominated by
business modes of thought and more adverse to risk - lacking the expertise to judge and
manage information to inform policy and limiting the entry of new ideas and
information (Chapter 6). These could be considered symptomatic of the ‘pathology of
natural resource management’ defined as ‘defined as the ‘loss of system resilience when
the range of natural variation in the system is reduced’ (Holling and Meffe, 1996: 331).
Holling and Meffe (1996) warned that institutions that are dominated by cultures of
control, resistance to new ideas, and unwilling to change contribute to on-going
degradation of ecosystems.
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8.3 Relevance and Conclusion
Although various criticisms have been levelled against using science as the foundation
of policy decisions, science is generally viewed as important for improved policy
outcomes and as a political counterbalance in NRM policy-making (Chapter 1).
However, the successful translation of research findings into improved decisions and
concrete policy actions is difficult. This thesis is of particular relevance to this problem
– the difficulty of bridging the science-policy interface – as it contributes to a broader
understanding of factors that influence information exchange across the interface.
This thesis is of particular relevance to the practice of policy-making as it calls attention
to the value of an often ‘hidden’ capital - social capital in the development of resources
for the exchange and co-production of knowledge in policy-making. Physical capital is
usually obvious to external onlookers whereas social capital is less so. The neglect of
the importance of human and social capital (i.e. the planning of resources for gaining
and sharing knowledge) to economic growth and development is common according to
Ostrom (1994: 527):
The importance of physical capital to economic growth and development is generally accepted. The importance of social capital, particularly local institutions has not generally been recognised.
In line with this view the importance of focusing on the construction of physical capital
(i.e. the planning of information resources) gained early and wide acceptance in the area
of science use in policy. However, these early science-push and demand-pull models
were found lacking and attention has since turned to an interactive model that
emphasises social connectivity for research use (Chapter 2). There has been a general
lack of scholarly attention to the social processes of science use in environmental
policy-making in Australia (Section 3.1). Therefore, this thesis explored the socio-
political dynamics of science use by NRM policy-makers in Australia.
It is clear that those concerned with increasing the influence of research in NRM policy
face a difficult task. Improving the supply, or even the quality and awareness of
research is a necessary but not sufficient means of getting research to inform policy.
Even if there is scientific consensus and the research is relevant and credible, the
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information may be filtered, processed or even ignored in policy practice, depending on
the policy-making process, availability of resources and political conditions. Assuming
the desirability of improving the research base and increasing its use in policy-making
some recommendations emerged from the findings reported in this thesis that could
foster a stronger science-policy interface:
· A need to better understand how social capital is formed and maintained in
the context of the NRM science-policy interface;
· Recognition that a ‘one size’ fit all strategy to strengthen bridging of the
science-policy interface may not achieve the desired result since some
groups may be excluded (e.g. on basis of gender, access to resources).
The results presented in this thesis showed that the socio-political dynamics of the
science-policy interface can influence the use of science by NRM policy-makers. Those
who were frequent users generally interacted more with researchers than less frequent
users, were more interconnected through personal networks and generally were less
exposed to socio-political conflicts. Social interaction provides an opportunity for the
growth of close personal relationships that can form the basis of trust to share and learn
knowledge. By applying an analytical framework to reflect on the socio-political
dynamics of science use across micro-organisational, organisational and system levels,
the social constructs of trust and power emerged as common elements.
To summarise, the thesis found that the use of science by NRM policy-makers will be
more likely in conditions of high relational power (i.e. interactions between scientists
and policy-makers) and low political power (i.e. conflicts of values and interests). This
is consistent with previous studies of the use of science in policy-making, but also calls
attention to further research on the importance and shaping of social dimensions (e.g.
trust, power and networks) in bridging the science-policy interface.
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APPENDIX A: KEY ATTRIBUTES OF INTERVIEWEES Interviewee Identifier
Gender Affiliation Sector Interviewed
ID 1 male State policy August 06
ID 2 male State science September 06
ID 3 male State science September 06
ID 4 male State policy September 06
ID 5 male State policy September 06
ID 6 male State science September 06
ID 7 male State policy September 06
ID 8 male Regional policy March 07
ID 9 female Regional policy October 06
ID 10 female Regional policy December 06
ID 11 male State policy March 07
ID 12 male State science March 07
ID 13 male Regional policy March 07
ID 14 male Regional policy March 07
ID 15 male State science March 07
ID 16 male Regional policy April 07
ID 17 male State policy April 07
ID 18 male Regional policy April 07
ID 19 male Regional policy April 07
ID 20 female Federal policy April 07
ID 21 male Federal policy April 07
ID 22 male Federal science April 07
ID 23 male Federal science April 07
ID 24 male Federal policy April 07
ID 25 female Federal policy March 07
ID 26 male State policy March 07
ID 27 male Federal policy April 07
ID 28 female State policy May 07
ID 29 female Regional policy March 07
ID 30 male State policy August 06
ID 31 male State policy September 06
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APPENDIX B: GUIDE QUESTIONS USED IN THE SEMI-STRUCTURED INTERVIEWS
For scientists:
Background questions
- How did you get involved in research?
- Area of responsibility in current organisation?
- How do you engage with policy-makers/policy-making in your job?
- Which policy-makers do you interact with?
- What do you understand by the terms NRM policy and NRM science?
- What do you think affects policy-makers use of science?
Views on importance and influence of science
- How important is science to NRM policy-making?
- How much influence does science have in the development of NRM policy?
Views on use of science in policy development and implementation
- How have you or your organisation been involved in policy development and
implementation? (NAPSWQ as an example)
- What was the experience of using science?
Views on the importance of interaction between NRM scientists and policy-makers
- How important is the interaction?
- What might influence the interaction?
Views on the demand for science by policy-makers
- How important is the demand?
- What might influence the demand?
Views on the importance of quality of the relationship between policy-makers and
scientists
- How important is the quality of the relationship?
- What might influence the quality of the relationship?
Other comments.
- Level of comfort with questions?
- Other sources of information?
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For policy-makers:
Background questions
- How did you get involved in NRM policy-making?
- Area of responsibility in current organisation?
- How do you engage with science/scientists in your job?
- Which scientists do you interact with?
- What do you understand by the terms NRM policy and NRM science?
- What do you think affects policy-makers use of science?
Views on importance and influence of science
- How important is science to NRM policy-making?
- How much influence does science have in the development of NRM policy?
Views on use of science in policy development and implementation
- How have you or your organisation been involved in policy development and
implementation? (e.g. NAPSWQ as an example)?
- What was the experience of using science?
Views on the importance of interaction between NRM scientists and policy-makers
- How important is the interaction?
- What might influence the interaction?
Views on the demand for science by policy-makers
- How important is the demand?
- What might influence the demand?
Views on the importance of quality of the relationship between policy-makers and
scientists
- How important is the quality of the relationship?
- What might influence the quality of the relationship?
Other comments
- Level of comfort with questions?
- Other sources of information?
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APPENDIX C: ONLINE QUESTIONNAIRE
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APPENDIX D: INTERNAL RELIABILITY COEFFICIENTS (CRONBACH’S ALPHA) FOR VARIABLES INCLUDING MULTIPLE ITEM SCALES
Name of variables Number of Cases
Number of Items in Scale
µ
Use of science 72 8 0.90 Experience in research 80 7 0.75 Role in policy 79 6 0.84 Relevance of the science for the user 72 3 0.58 Intensity of use of science 74 13 0.82 Adaptation efforts 71 10 0.75 Formal linkages 71 7 0.68 Informal linkages 76 5 0.70 Organisational culture 59 6 0.65
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APPENDIX E:
a) EXPLANATION OF INDEPENDENT VARIABLES
Variable
Explanation
Information Relevance Index
The degree of research relevance as perceived by the user measures the NRM policy-maker’s openness to scientific information and is indicative of their capacity to learn from newly acquired knowledge (Belkhodja et al., 2007). To this can be added that the degree of research relevance concomitantly also measures the extent to which scientific information received appears to meet policy-making needs (i.e. scientific information is available and relevant, scientific information meets needs and expectations, scientific studies and reports arrive at just the right time to be used).
Content Index
The organisational interest perspective assumes that organisational norms, rules and procedures are essential for understanding information use, and information is more likely to be used when it is not counterintuitive and when it supports organisational interests (Caplan, 1979 cited Oh, 1996). In this study the Content Index is based on that developed by Oh (1996). The Content Index measures the importance of organisational interest factors on use of the information (e.g. findings support the limited options of the organisation or challenge existing institutional arrangements or are in line with micro-politics or assumptions of your organisation).
Intensity of Use Index The Intensity of Use Index is measured by how frequently NRM policy-makers use external information sources for their daily policy activities (e.g. universities and co-operative research centres, State and federal government science or research agencies, scientific journals and papers, the internet, popular media (newspapers, TV), informal networks, joint science-policy teams or think-tanks, commissioned research products). Research suggests that decision-makers do not always rely on internal information sources and are not biased towards any specific sources (Belkhodja et al., 2007). This is contrary to the assumptions of the organisational interest perspective which holds that:
· information is essential to maintaining bureaucratic power; · the desire to protect organisational interests affects the information
bureaucracies produce; · bureaucracies tend to search for information from limited sources;
and · the ‘credibility’ of the information source is essential for use, often to
confirm a policy position or interest already held by a policy-maker (Oh, 1996).
Organisational Culture Index
Organisational culture is a specific set of standards, values, attitudes, beliefs, traditions, language and ways of doing things that are particular to a given organisation. Belkhodja et al. (2007) suggests organisational culture is a potentially explanatory variable of research use and can be measured through indicators that reflect the individual and organisational commitment to promote research culture within an organisation. Belkhodja et al. (2007) measured organisational culture through two dimensions to define the individual and organisational contexts in which knowledge use occurs:
· research constitutes a preferred source of information, reflecting whether an organisation is open to research coming from external sources or closed and inward directed; and
· the intensity with which research sources are used, reflecting the individual and organisational commitment to promote research culture within an organisation.
Based on these indicators this study measures organisational culture by two dimensions: An Organisational Culture Index which combines the first dimension suggestion of
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Belkhodja et al. (2007), with those suggested by Oh (1996) (e.g. incentive/reward systems) and Landry et al. (2003) (e.g. acquisition efforts), to better capture the totality of a broad concept like organisational culture.(i.e. researchers are located near policy-makers, research is a preferred source of information for policy-making, people have paid time to do NRM research for policy-making, joint science-policy activities are organised to integrate science with policy-making, interaction with researchers and use of research information is encouraged by way of professional rewards or incentives, sufficient resources are reserved to obtain research information for policy making.); and an Intensity of Use Index which is described below.
Policy Role Index
The role of the policy-maker measured by the frequency of engagement in policy-making activities is a potential determinant of the use of science. For each activity in the policy-making cycle there is an associated need for research and information, for example, in the policy analysis phase cost-benefit analyses may be undertaken and in the consultation phase there is a need for data on public opinion (Bridgman and Davis, 2004). This is important because according to Sabatier (1978) the degree of involvement in policy-making affects their need for information. To the extent that the policy-maker is oriented towards policy-making they will be more reliant on expertise. Therefore any assessment of the use of science needs to take into account the extent the policy-maker is engaged in policy-making activities (i.e. defining NRM problems and issues (e.g. problem identification and agenda setting); generating and evaluating NRM policy options and making recommendations (e.g. policy research and analysis); consulting stakeholders and negotiating policy (e.g. organising and conducting workshops, seminars, meetings); choosing option(s) and making policy decisions (e.g. policy approval for creation, succession or termination); implementing policy (e.g. programme design and execution, action planning, resource and change management); seeking feedback and evaluating outcomes (e.g. monitoring, surveys, impact analyses)).
Research Experience Index Belkhodja et al. (2007) points to the role of organisational learning in the use of research and suggests this can be measured by indicators that reflect the capacity of individuals to learn (e.g. training activities, the higher educational attainment and research experience of policy-makers, the degree of research relevance). In this study the research experience of NRM policy-makers is indicative of a support for learning. The experience reflects the ease with which research results are learned and retained, and influences the use of the acquired knowledge. The Research Experience Index measures their previous involvement in professional research activities (e.g. been principal investigator or co-investigator, collected data, contributed data from their organisation for a research study, been involved in outcomes evaluation or quality assurance projects, carried out an in depth review of the literature, been involved in a research study that combined academic and non-academic investigators).
Adaptation Index
The Adaptation Index measures the importance given by the policy-maker to the effort of adapting research products. Adaptation efforts are a determinant of the use of research results that is well established in the literature (Landry et al., 2003; Belkhodja et al., 2007) and in this study are measured as an index composed of ten dimensions (i.e. access to in-house advisors or experts to interpret this information, confidence in the reliability and validity of the methodology employed, presentation of results in non-technical language, examples or demonstrations of how to use research results, relevance of the information for policy use, credibility or prestige of the source, capacity to verify the quality of research results, capacity to control exclusivity of research results use, appeal of reports and products (graphics, colour, packaging), strength of working relationships with researchers).
Formal Linkage Index
Formal linkage mechanisms are a determinant of the use of research that is well established in the literature (Belkhodja et al., 2007). It is a measure of the importance of the formal linkages between NRM policy-maker users and research suppliers (e.g. active involvement by policy-makers in research projects; participation in professional conferences and workshops involving researchers; membership on expert panels, and committees involving researchers; newsletters and research delivered directly to you; research reports sent to you by email; science available on World Wide Web via the internet; science delivered through professional associations and other venues).
Informal Linkage Index
Informal linkage mechanisms are a determinant of the use of research results that is well established in the literature (Belkhodja et al., 2007). It is a measure of the intensity of the informal person-to-person links between research suppliers (i.e.
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universities and cooperative research centres, State and federal government agencies, private firms and non-government organisations) and NRM policy-maker users. The lack of interaction between researchers and their potential audiences is widely considered as the main problem in under utilising research findings (Huberman, 1987; Oh and Rich, 1996). The more sustained and intense the interaction between researchers and users, the more likely use is expected to occur.
b) DEFINITIONS OF INDICES Subitems Method Method
(range) Dependent Variable Use of science index
During the past year I have: · Seeking: sought science information relevant to
areas of your responsibility · Reception: received science information relevant
to areas of your responsibility · Cognition: read and understand science
information received · Reference: cite science reports to colleagues,
managers or politicians as a reference in your work
· Adaptation: adapt the format of science reports to provide information useful to decision makers
· Effort (Adoption): make efforts to promote the adoption of science information
· Influence: use science information to make professional choices and decisions that you would not have made otherwise
· Application: use science information that has led, or will lead to concrete changes in the programs or services provided by your organisation
Measured as an index on a Likert-type scale of frequency ranging from 0 to 4, where 0 = does not apply, 1=never to 4 = very often regarding 8 dimensions
Sum (0-32)
Independent variables Informal Linkage Index How often in the past year did you have person-to-
person contact with researchers in each of these areas? · Universities and Co-operative research centres
(CRCs) · State government agencies (E.g. environmental and
agricultural departments) · Federal government agencies (e.g. CSIRO) · Private firms · non government organisations (NGOs) or
community groups
Measured as an index on a Likert-type scale of frequency ranging from 0 to 6, where 0 = does not apply, 1 = never to 6 = more than once a week, regarding 5 dimensions.
Sum (0-30)
Formal Linkage Index In terms of your professional satisfaction, what is the importance of.: · your active involvement in research projects? · your participation in professional conferences and
workshops involving researchers? · your membership on expert panels, and committees
involving researchers? · newsletters and research delivered directly to you? · research reports sent to you by email? · science available on World Wide Web via the
internet? · science delivered through professional associations
and other venues
Measured as an index on a Likert type scale of importance ranging from 0 to 5, where 0 = no opinion, 1= not important at all, to 5 = extremely important, regarding 7 dimensions.
Sum (0-35)
Policy Role Index For each of the following policy-making activities how often during the past year did you participate in: · defining NRM problems and issues (e.g. problem
identification and agenda setting) · generating and evaluating NRM policy options and
making recommendations (e.g. policy research and analysis)
· consulting stakeholders and negotiating policy (e.g. organising and conducting workshops, seminars, meetings)
· choosing option(s) and making policy decisions (e.g. policy approval for creation, succession or termination)
· implementing policy (e.g. programme design and
Likert type scale of frequency ranging from 0 to 6, where 0 = does not apply, 1 = never to 6 = more than once a week, regarding 6 dimensions.
Sum (0-36)
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execution, action planning, rsource and change management
· seeking feedback and evaluating outcomes (e.g. monitoring, surveys, impact analyses)
Research Experience Index
During your professional life have you... · been principal investigator for a research study? · been a co-investigator for a research study? · collected data for a research study? · contributed data from your organisation for a
research study? · been involved in outcomes evaluation or quality
assurance projects? · carried out an in depth review of the literature? · been involved in a research study that combined
academic and non-academic investigators?
Measured as a variety index indicating the number of the different categories of research activities (Yes = 1, No = 0) in which the respondent was involved in their professional life
Sum (0-7)
Intensity of Use Index How often did you use each of the following information sources for your daily policy activities in the past year? · universities and co-operative research centres · State government science or research agencies · Federal government science or research agencies
(e.g. CSIRO, LWA) · scientific journals and papers · world wide web via the internet and online
discussion groups/bulletin boards · popular media (newspapers, TV) · informal networks · joint science-policy teams or think-tanks · commissioned research products
Measured as an index on a Likert-type scale of frequency ranging from 0 to 4, where 0 = does not apply, 1=never to 4 = very often regarding 9 dimensions
Sum (0-36)
Organisational Culture Index
Generally in my organisation... · research is a preferred source of information for
policy-making · people have paid time to do NRM research for
policy-making · researchers are located near policy-makers · joint science-policy activities are organised to
integrate science with policy-making · interaction with researchers and use of research
information is encouraged by way of professional rewards or incentives
· sufficient resources are reserved to obtain research information for policy making.
Measured as an index on a Likert-type scale of frequency ranging from 0 to 4, where 0 = don’t know, 1=never to 4 = very often regarding 6 dimensions
Sum (0-24)
Information relevance Index
How often do these statements apply to your policy-making activities ? · scientific information is available and relevant for
my professional practice · scientific information meets my needs and
expectations · scientific studies and reports have reached me at
just the right time to be used
Measured as an index on a Likert-type scale of frequency ranging from 0 to 4, where 0 = does not apply, 1=never to 4 = very often regarding 3 dimensions
Sum (0-12)
Content Index Suppose you had received a scientific or technical report. Please rate how important the following factors would be to you when deciding to use this information: · findings support the limited options of the
organisation · findings challenge existing institutional
arrangements? · findings are in line with micro-politics or
assumptions of your organisation
Measured as an index on a Likert type scale of importance ranging from 0 to 5, where 0 = no opinion, 1=not important at all, to 5=Extremely important, regarding 3 dimensions.
Sum (0-15)
Adaptation Index Suppose you had received a scientific or technical report. Please rate how important the following factors would be to you when deciding to use this information. · access to in-house advisors or experts to interpret
this information · confidence in the reliability and validity of the
methodology employed? · presentation of results in non-technical language? · examples or demonstrations of how to use research
results? · relevance of the information for policy use? · credibility or prestige of the source? · capacity to verify the quality of research results? · capacity to control exclusivity of research results
use? · appeal of reports and products (graphics, colour,
packaging)? · strength of working relationships with researchers?
Measured as an index on a Likert type scale of importance ranging from 0 to 5, where 0 = no opinion, 1=not important at all, to 5=Extremely important, regarding 10 dimensions.
Sum (0-50)
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APPENDIX F: SELECTED INTERVIEW ABSTRACTS
Per cent of interviews consistent
Themes Description Selected representative interview extracts
Policy-
makers
(n = 21)
Scientists
(n = 10)
Total =
31
ORGANISATIONAL (CHAPTER 7)
Formal linkages the importance of the formal linkages between NRM
policy-maker users and research suppliers (Belkhodja
et al., 2007)
‘Some policy areas have had restructured working groups, you know, often inter-agency
working groups as well and I think that’s often the best way to go, is to have a structured
working group approach so that you know where the input’s coming from, you get some
continuity through the life of the development of a policy, otherwise if it is ad hoc well then
you get bits of information coming in and not necessarily filling the gaps. Because if you’ve
got some continuity and a definite contact person, well then there’s a stronger responsibility
to say we need to make sure that all the holes are plugged’. (Interviewee No. 12, male, State,
science)
‘I think you need forums where they really do have a good discussion and understand it. But
I think there’s nothing like partnerships. And embedding people from the catchment
organisation into research projects right from the beginning, and right through and resource
them. So that they are generally engaged right from the beginning. And likewise it’s
probably useful to go the other way as well if the resources are there. That’s in my advice if
people would want a project to work. So I said, well you really need to have someone in the
policy house or the delivery house sitting with you integrated into the project right from the
start.’ (Interviewee No. 16, male, State, policy)
‘Well ‘no I don’t believe the research, don’t believe outcomes because I haven’t been
involved personally involved in it,’ is a fundamental one. And it really is, both in the
farming system sense and also in a catchment wide sense. So we still prefer to pilot in any
research program.’ (Interviewee No. 14, male, regional, policy)
100 88 90.6
313
Informal linkages the intensity of the informal person-to-person links
between research suppliers (i.e. universities and
cooperative research centres, State and federal
government agencies, private firms and non-government
organisations) and NRM policy-maker users (Belkhodja
et al., 2007)
‘Policy makers who don’t have weekly contact with scientists are in grave danger of
losing touch with what their policies are supposed to be doing. I have made it my
business to have regular contact with our scientists and similarly I have exactly the same
view with policymakers and operational staff. Policymakers who don’t stay in touch with
operational staff run the same risk of putting in place policies which are unachievable, it
happens all the time.’ (Interviewee No. 31, male, State, policy)
‘This is the matter with science of trying to deal with the huge amount of information
that’s coming through, trying to pull out the pertinence, knowing whom you can rely on
for advice, going to the odd inspirational thing. Go to good conferences, networking is an
important thing in this area, its amazing what gets done over a casual conversation at the
conference or at a Christmas do or something like this.’ (Interviewee No. 10, female,
regional, policy)
‘But in fact it’s about relationship management. I develop and maintain relationships with
the key policy makers, or the key staffers of policy makers in most of the jurisdictions in
Australia and the federal jurisdiction. I have to leave this interview in half an hour to go
brief the chief of staff of X. You’re just engaged all the time and you’re listening all the
time for where research and development can help the nation.’ (Interviewee No. 22, male,
federal science)
96 75 87.5
Policy role the frequency of engagement in the policy-making cycle
(Bridgman and Davis, 2004)
‘The science informs in a substantial way the early development of the policy. Then we
set the framework. Then it’s critical again in the implementation.’ (Interviewee No. 21,
male, federal, policy)
‘I was as chair of the X I was pretty regularly around the table with those people
observing and directing as much without necessarily being involved because my place, is
to ensure that the direction of science is relevant that it’s actually not science for science
sake, its science for an end purpose that’s useful, that it doesn’t just continue to sit on the
shelf’ (Interviewee No. 9, female, regional, policy)
‘So when you’re back in your planning stage, you are looking around for information
about the relative importance for different things. So planning all about the future. So any
science that enables us to predict where a system might go with or without an
intervention. Any science that might give us an idea about what are the options about
intervention, what it’s going to cost…’ (Interviewee No. 11, male, State, policy)
43 38 40.6
314
Organisational
culture
the individual and organisational commitment to promote
research culture within an organisation (Oh, 1996; Landry
et al.; 2003; Belkhodja et al., 2007;)
‘We certainly found that the policies that have been developed recently, you know, just
with my experience here, there’s been a lot of back up from science...our policy people
are very reluctant to put forward proposals to Ministers and so on without a good backing
from science.’ (Interviewee No. 12, male, State science)
‘You can do as much steering from above as you like if it doesn’t gel at officer level, then
you’ll always have a problem so the challenge for all of us senior management is to how
to build the teams and make sure that we build those teams multi-disciplinary teams that
come together on particular projects and that’s what we do….Our policy and science
people are working together on revising those projects, reviewing and then revising the
plan so that includes policy people thinking of new and different initiatives, interacting
with the science to check the feasibility of those things and working together to come up
with a structured response in a sense.’ (Interviewee No. 15, male, State, science)
‘that sort of regional surveillance type of resource condition monitoring is not something
that the regional groups are mainly community-based recognize as a priority. Many of the
community people that are in the decision-making role at regional level with those funds
see that as a State agency responsibility so they tend not to want to put a lot of money into
it however the salvation for these may well be that there is a strategic reserve at State
level’ (Interviewee No. 7, male, State, policy)
83 63 81.2
Intensity of Use the frequency of sourcing external information sources by
policy-makers for their daily policy activities (Belkhodja
et al., 2007)
‘so if you’re a policymaker you have to keep your eye on the cutting edge stuff, obviously
you can’t read everything that CSIRO brings out… I don’t get much from the Internet
normally I get a bit but I prefer paper.’ (Interviewee No. 10, female, regional policy-
maker)
‘we have sort of quite strong connections with parts of CSIRO, with places like BRS,
ABARE and LWA, with State Government scientific providers if you like, a whole range.
Some of it’s about, like we know a lot of people involved so if we know there’s a
particular set of experts on a particular thing, we will often go over and talk to them about
what do they think and what’s the latest situation with that.’ (Interviewee No. 20, female,
federal, policy)
‘I would like to have X really in front when he sits down in Cabinet. He looks really good
in Cabinet because he’s got a best brief from CSIRO on this stuff. And it’s good and it’s a
pager’. (Interviewee No. 26, male, State, policy)
70 63 65.6
315
Information
Relevance
the extent to which scientific information received
appears to meet policy-making needs and the NRM
policy-maker’s openness to scientific information
(Belkhodja et al., 2007)
‘one of the things that you’ve got to have accept, that when you are going to use science
for policy making, it comes back to this, you won’t have, sometimes you won’t have the
preferable solution because the research may not have been in your area. But you may
have the second best solution, which is, that research was done on, in a similar climate,
similar soils somewhere, but at least you’ve got enough then to go forward.’ (Interviewee
No. 14 male, regional, policy)
‘it’s quite interesting to look at the way some of the regions use science, and those regions
that are approximate to cities that have significant university capacity or CSIRO base or
whatever will often get the benefit of a whole heap of research that’s actually being done
on their turf.’ (Interviewee No. 21, male, federal, policy)
‘I’ve got a very immediate and up-to-date understanding of what the policy concerns of
the Department and how they might be translated into scientific advice. It’s very close.
So it’s very … it gives us the capacity to provide very policy relevant scientific advice.
As opposed to let’s say, the other end of the spectrum, which might be in a university,
where research is often driven by the curiosity of the researcher, and as a result of that,
may or may not be all that relevant to the day-to-day policy concerns of the Department of
State.’ (Interviewee No. 23, male, federal, science)
83 75 78.1
Research
experience
their previous involvement in professional research
activities (Oh, 1996; Landry et al.; 2003; Belkhodja et al.,
2007)
‘quite a few of our policy people have come through a practical background or a scientific
background of sorts or a regulatory background. So they’ve been in an operational role
perhaps before, not everyone, but quite a few so that the policy people understand the
implications and understand the practicalities of the policies they are developing so that
makes the relationship with the scientists much more amicable because they can talk on
the same wavelength, you know they can both stretch a bit to each others fields, whereas
if you have a specialised policy group that’s dealing today with natural resources, and
tomorrow with crime and the next day with health which is what often happens in groups
like the Cabinet offices or somewhere you know, where policy people are working on
government policy but not necessarily specialising in a field. Its much more difficult for
science to connect with them. So having policy people that understand the operational
side of the policy is really good.’ (Interviewee No. 12, male, State, science)
‘we have a lot of people in the Natural Resource Management area who have come
through some form of science training at university. So what we often find is that people,
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316
bureaucrats who spend a fair bit of time in the NRM Division have that sort of
background…As we get to a more senior level, people tend to move around the Public
Service from, across Department’s and so on and they don’t automatically come with a
science base and in some sense it’s almost a bit less relevant at times because the skills
that people need is to be able to sort of have the strategic vision and approach and to be
able to show the leadership and make it all happen and in doing that they need to know,
we‘ve got access to science or we’ve got to access expertise here but they don’t
necessarily have to have qualifications in that sense.’ (Interviewee No. 21, male, federal,
policy)
‘my strong R&D background was a real asset because I was able to do only talk about it in
a superficial high level but I knew what was going on’, (Interviewee No. 30, male, State,
policy)
Content the importance of organisational interest factors on use of
the information
‘X have not done the modelling or sums. They are in a situation about their survival and
they believe what they want to believe. They are totally lacking in any science skills… X
is being reviewed at the moment as to whether they continue to exist.. So faced with that
people running X don’t want to believe in the science because it is not in their interest it is
about their survival.’ (Interviewee No. 1 male, State policy)
‘there was the technicians in the organisations and they were getting people in. And they
said, “well, if you had to invest, blah, blah, blah, what’s the science telling us, you know,
where should we go”. You know and that’s why we went specifically to sub catchment
approaches. Now that created a political problem for us then. Because it meant that we
were withdrawing public dollars from some areas that historically had them. Now we’ve
had to with, and we have, we’ve sort of stood our ground and said, ‘sorry’, you know, this
is a scientific approach, this is what it is telling us. And we’ve got to meet an outcome.
And that hasn’t been easy, you know. Because it comes from all sorts of directions.’
(Interviewee No. 14 male, regional policy)
‘There was a real push to get the money out of the door. There were funding cycles. The
money had to go out of the door and be used within that funding cycle…. It was an
enormous waste of money, a huge waste of money. I know it is. I know it is. Which is
crazy stuff. It’s not good policy because it’s sending the wrong messages. It’s sending
messages that government is actually much more interested in getting the money out of
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317
the door and on ticking the boxes and on having photo opportunities than actually
delivering good outcomes. You’ve got to have that … you’ve got to be able to link the
policy to the science that enables you to know where to target your outcomes.’
(Interviewee No. 25 female, federal, policy)
Adaptation the importance given by the policy-maker to the effort of
adapting research products.
‘Accessibility of science, availability, how user friendly it is are the key determining
factors and the credibility.’ (Interviewee No. 1 male, State, policy)
‘To come, to distil within to something that helps us as far as policy making goes, is a
huge task. That’s because it involves multiple individuals often working independently of
each other or they might be talking about their own networks. But how do you draw that
all together?’ (Interviewee No. 20 female, federal, policy)
‘A piece of science in isolation without having an analysis to then provide knowledge
products is of very little value to the practitioners, and some of those pieces of science
have never been thought through at the very beginning to say, ok, we do this bit of
science, what knowledge products can then be developed. Now they might need not
develop them, but can it be presented in that form and then for the practitioners to use?’
(Interviewee No. 17 male, State, policy)
91 88 87.5
‘SUPPLY AND DEMAND’ OF SCIENCE (CHAPTER 5)
Order fulfilment
– readability and
interpretation
needed
This process requires effective integration of the
production and delivery plans.
‘Scientists and policymakers don’t talk the same language and I guess that is always a
problem. I have worked in organisations like this for pretty close on 30 years and the
mindsets are quite different so it is quite difficult to get them to understand each other and
for the policymakers to use good science, for the scientists are not very good at explaining
what they do.’ ((Interviewee No. 4, male, State, science)
‘I think there’s a real disconnect and that’s always been that the scientific process is an
ugly process because it relies on people disagreeing with each other, thats how it works.
Policy doesn’t accommodate that very well. They want to hear what the answer is, they
don’t want to hear people disagreeing and saying its wrong.’ (Interviewee No. 13, male,
regional, policy)
‘There are all sorts of cultural differences that make it quite difficult. I think we don’t
speak each other’s language very well.’ ((Interviewee No. 15, male, State, science)
40 40 39
Order fulfillment
– trust and
This process requires effective integration of the
production and delivery plans.
‘we actually will get to a tool kit and it is still about building confidences and trust
between agencies, and between agencies and community, and between governments. I
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318
credibility needed
don’t think there’s a great deal of it yet in at any of those levels, they’ve all got little
components, so the policy development is going to take some time, and it will be through
trust and believing that the other partner has as much to contribute as they have’
(Interviewee No. 28, female, regional, policy)
‘Number one is trust. if they [policy-makers] dont trust us to give them a) the information
of the quality we claim it to be. b) If they dont trust us to manage that information through
the process with discretion. They trust [research organisation] not to fling a dead cat on
the table.’ (Interviewee No. 22, male, federal, scientist)
‘I would be relying on a summary of scientific advice or evidence and in addition I would
always want to know where it has come from and whose looked at it because I would also
do relying on the fact of the level of trust and the bond I have established with the person
who had transmitted that knowledge to me’ (Interviewee No. 28, female, State, policy)
Manufacturing
flow management
– conflict and
uncertainty
This process includes all activities necessary for
managing the product flow through the production
facility.
‘You know, for instance this drought, there’s been a whole range of policies had to be
made or decisions have to be made, perhaps in the absence of good information, which is
obviously using the best available information, but perhaps in the absence of perfect
information, so I think policy makers want perfect information but they’re not always
going to get it.’ (Interviewee No. 12, male, State, science)
‘unfortunately, unless you’ve got that understanding of the role of science and what it
does, you always think, and I see it all the time, you know, the science says this and you
could rightly say, “well that’s today’s opinion and that’s our current knowledge, but
there’s five other research programs that’s suggesting that there’s something flawed in
that outcome there.’ (Interviewee No. 14, male, regional, policy)
‘And from the policy point of view. And indeed, as you know, you can get two or three
opinions on any item in science. We have it all the time. We have it on water, the quality
of water, the recycling benefits or otherwise of water, the politics or the policies
associated with climate change, the belief systems associated with climate based on
science … so I mean it’s a contestable marketplace and you’ve got to therefore win the
logic of the debate.’ (Interviewee No. 27, male, federal, science)
28 50 35
Demand
management -
‘customer’ values
The demand management process needs to balance the
customers’ requirements with the supply capabilities (e.g.
forecast demand) in order to keep up with competitors.
‘I felt that perhaps the science may have disappeared a little bit out of some of it and it got
down to my assets is more important than your assets, rather than necessarily keeping it
purely scientific.’ (Interviewee No. 9, female, regional, policy)
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319
‘…and then you overlay a political process, where once you get to a political process you
might see that decisions aren’t made based necessarily on science.’ (Interviewee No. 13,
male, regional, policy)
‘I perceived this a promise of funding was made direct from the politician to a group of
catchment farmers and some people were instructed to retrofit policy and science to
justify spending the money on this.’ (Interviewee No. 31, male, State, policy)
AUSTRALIAN NRM CONTEXT (CHAPTER 4)
Public Sector
Management
(organisational
restructuring)
‘that particular agency is absolutely strapped for resources and has to go through
restructuring. They’ve lost a lot of their staff, personnel who give us this kind of science
support that we need. But we need it. We need it for us to be able to provide reports
when they talk about having a catchment health report. For us to be able to do that we
have to have support so it’s absolutely essential that it’s there. One of the missing pieces
for us is we’re not even able to have the conversation with the science department to say
well what kind of support is going to be there, so that we can actually … even if it’s not
all there, to know how much is going to be there, so that we can say well we’re going to
be able to have this data to report against that target.’ (Interviewee No. 29, female,
regional, policy)
‘Quite often your adaptive managements fail because you get change in personnel that
haven’t seen the mistakes before’. (Interviewee No. 18, male, regional, policy)
‘we use Government agencies where they’re appropriate.. Quite often though the skills
sets in the agencies are not strong enough to answer these questions. And this is one of
the big problems that science policy is a problem in the sense that the agencies have lost
some of the capacity in science. So we are depending more and more on Universities and
so on.’ (Interviewee No. 26, male, State, policy)
37 20 36
Prevailing
Ideologies
‘And we’ve gone for years and years and couldn’t get any traction. The actual science
wasn’t changing it was slowly accreting, slowly getting more, then we got the drought,
then we had not enough water for cities to drink and suddenly all this policy work went
off like a rocket. And now everyone everywhere was trying to do things with this climate
change, yeah. So you could do all the good science but to actually get it to the stage
where politicians take notice of it, needs some of these social drivers. You’ve got to
convince society first.’ (Interviewee No. 11, male, State, policy)
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‘generally there is a recognition in Australia that the decade of Landcare was really good
it was a bit light and what was good at was raising awareness empowering communities
getting communities working together understanding NRM issues what it also did was
generally spread the investment fairly thinly’ (Interviewee No. 7, male, State, policy)
‘NSW has created new NRM system with catchment management authorities, fairly new
construct in NSW and the National Resources Commission, report directly to the Premier.
The NRC has created the standards targets framework. What is happening at the moment
is that we’re trying to operationalise those in a sense and create, under the Monitoring
Evaluation Reporting Strategy, create the monitoring and reporting programs that are
going to be needed to have a look at how well we’re moving towards the targets. And
again, those are things that are anchored in both science and policy’(Interviewee No. 15,
male, State, scientist)
Public Sector
management
(outsourcing)
‘so we got in an independent person to drive the review of what was needed and so on. It
also gives a bit more credibility to the government policy if you can say well its not just
come up through within the department, we’ve actually got independent experts to make
sure that the science is valid and the rationale for the policy is correct.’ (Interviewee No.
12, male, State, science)
‘And so there are specific evaluations that start to look at the outcomes of biodiversity or
the outcome of the salinity outcomes of those programs. And so certainly going through
those evaluations we use science in those. And so out of an evaluation like that we start to
get recommendations that we need to improve the quality of the science that goes into the
decision making for salinity investments and all those sorts of things….They’re done by a
range of people. Usually we often use consultants to do them and they are being managed
by a Steering Committee of our people and often State Territory people and others.’
(Interviewee No. 21, male, federal, policy)
‘we currently have a $20 million project that we’re doing …Yes, well we do it. Well we
contract it. But we design it, we contract it out, we’re the accountable party.’ (Interviewee
No. 27, male, federal, science)
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