ii

The dissertation of Louis-Marie Ngamassi Tchouakeu was reviewed and approved* by

the following:

Carleen Maitland

Associate Professor of Information Sciences and Technology

Dissertation Advisor

Chair of Committee

Andrea Tapia


Lynette Kvasny


Wenpin Tsai

Professor of Business Administration

Mary Beth Rosson

Professor of Information Sciences and Technology

Graduate Director for Information Sciences and Technology

*Signatures are on file in the Graduate School

iii

ABSTRACT

Massive international response to humanitarian crises such as the South Asian Tsunami

in 2004, the Hurricane Katrina in 2005 and the Haiti earthquake in 2010 highlights the

importance of humanitarian inter-organizational collaboration networks, especially in

information management and exchange. Despite more than a decade old call for more

research on the effectiveness of inter-organizational networks in the nonprofit context, to

date limited work has been done. The objective of this dissertation is to develop a theory

that provides a better understanding of organizational and network effectiveness in the

humanitarian relief field. The study deals with two broad research questions. The first

research question focuses on the relationship between network structural characteristics

and network effectiveness. The second research question concerns organizational

effectiveness and focuses on the relationship between organizational internal

characteristics (and especially the availability of information technology), ego-network

characteristics, network structural characteristics and effectiveness. To answer these

research questions, I used a multi-method research design that applies social network

analytic techniques in combination with statistical analyses (correlation and regression)

and content analysis to analyze data collected through multiple sources including a web-

based survey, semi-structured interviews, and database search. At the network level of

analysis, my findings extend a previous model for assessing network effectiveness in the

humanitarian relief field. At the organizational level of analysis, my research proposes

an integrated approach for assessing effectiveness that takes into account the

characteristics of organization but also those of the network in which the organization is

embedded. My study also highlights the catalytic role of information technology on

organizational effectiveness in humanitarian information management and

exchange. The dissertation concludes by highlighting both theoretical and practical

contributions and by suggesting directions for future research.

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TABLE OF CONTENTS

LIST OF FIGURES .............................................................................................................VII

LIST OF TABLES ............................................................................................................... IX

ACKNOWLEDGEMENTS .................................................................................................... X

1 INTRODUCTION ........................................................................................................ 1

1.1 PROBLEM DEFINITION .......................................................................................................... 2 1.2 PREVIOUS STUDIES .............................................................................................................. 3 1.3 MOTIVATION OF THE STUDY .................................................................................................. 6 1.4 RESEARCH OBJECTIVES ......................................................................................................... 7 1.5 RESEARCH DESIGN ............................................................................................................ 10 1.6 KEY FINDINGS .................................................................................................................. 12 1.7 ORGANIZATION OF DISSERTATION ........................................................................................ 13

2 CONTEXT OF THE STUDY ......................................................................................... 14

2.1 INTRODUCTION ................................................................................................................ 14 2.2 HUMANITARIAN RELIEF ...................................................................................................... 14 2.3 HUMANITARIAN INTER-ORGANIZATIONAL COORDINATION ......................................................... 15 2.4 HUMANITARIAN INFORMATION MANAGEMENT AND EXCHANGE ................................................. 18 2.5 HUMANITARIAN COLLABORATION AND COORDINATION CHALLENGES ........................................... 20

3 REVIEW OF RELEVANT LITERATURE ......................................................................... 25

3.1 INTRODUCTION ................................................................................................................ 25 3.2 ORGANIZATIONAL EFFECTIVENESS ......................................................................................... 25

3.2.1 DEFINING ORGANIZATIONAL EFFECTIVENESS .................................................................. 25 3.2.2 MODELS OF ORGANIZATIONAL EFFECTIVENESS ............................................................... 26

3.2.2.1 GOAL MODEL .......................................................................................... 26 3.2.2.2 SYSTEM RESOURCE MODEL ........................................................................ 27 3.2.2.3 INTERNAL PROCESSING MODEL ................................................................... 28 3.2.2.4 MULTIPLE CONSTITUENCIES MODEL ............................................................. 29

3.3 INTER-ORGANIZATIONAL NETWORK EFFECTIVENESS .................................................................. 31 3.3.1 DEFINING NETWORK EFFECTIVENESS ............................................................................ 31 3.3.2 MODEL OF NETWORK EFFECTIVENESS .......................................................................... 33

3.3.2.1 PERFORMANCE GAP MODEL ....................................................................... 33 3.3.2.2 PROVAN & MILWARD MODEL .............................................................................. 33

3.3.2.3 PRINCIPLES AGENTS MODEL ................................................................................. 34

3.3.2.4 STUCTURALIST PERSPECTIVE MODEL ..................................................................... 35

3.3.2.5 ADAPTIVE CAPACITY MODEL................................................................................. 36

3.3.3 PREDICTORS OF NETWORK EFFECTIVENESS IN NONPROFIT ....................................................... 37

3.4 ISSUES IDENTIFIED IN THE LITERATURE ON EFFECTIVENESS .......................................................... 40

4 THEORETICAL FRAMEWORK ................................................................................... 46

4.1 INTRODUCTION ................................................................................................................ 46 4.2 SOCIAL NETWORK THEORIES ............................................................................................... 46 4.3 RESOURCE BASED VIEW ..................................................................................................... 49

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4.4 WORKING DEFINITIONS...................................................................................................... 50 4.4.1 EFFECTIVENESS ....................................................................................................................... 50

4.4.2 NETWORK .............................................................................................................................. 50

4.4.3 LEVEL OF ANALYSIS OF NETWORK EFFECTIVENESS ................................................................... 52

4.4.3.1 NETWORK LEVEL .................................................................................................. 52

4.4.3.2 ORGANIZATIONAL LEVEL ....................................................................................... 52

4.4.3.3 BENEFICIARY LEVEL ............................................................................................... 53

4.5 RESEARCH MODELS AND HYPOTHESES .................................................................................. 54 4.5.1 NETWORK CHARACTERISTICS AND EFFECTIVENESS ................................................................... 54

4.5.1.1 CENTRALITY .......................................................................................................... 54

4.5.1.2 STRUCTURAL HOLES ............................................................................................. 55

4.5.1.3 DENSITY ............................................................................................................... 56

4.5.1.4 CLIQUES ............................................................................................................... 57

4.5.1.5 OVERLAPPING CLIQUE .......................................................................................... 57

4.5.1.6 MULTIPLEXITY ...................................................................................................... 58

4.5.2 ORGANIZATIONAL CHARACTERISTICS AND EFFECTIVENESS ........................................................ 59

4.5.2.1 ORGANIZATION SIZE ............................................................................................. 59

4.5.2.2 RANGE OF SERVICES PROVIDED ............................................................................. 60

4.5.2.3 INFORMATION TECHNOLOGY ................................................................................ 61

5 METHODOLOGY ..................................................................................................... 65

5.1 INTRODUCTION ................................................................................................................ 65 5.2 RESEARCH DESIGN ............................................................................................................ 65 5.3 RESEARCH PARTICIPANTS .................................................................................................... 67 5.4 DATA COLLECTION INSTRUMENTS ........................................................................................ 71

5.4.1 SURVEY .................................................................................................................................. 71

5.4.2 INTERVIEWS ........................................................................................................................... 73

5.4.3 DATABASE SEARCH ................................................................................................................. 74

5.5 DATA COLLECTION ............................................................................................................ 75 5.5.1 SURVEY DATA ........................................................................................................................ 75

5.5.2 INTERVIEW DATA ................................................................................................................... 77

5.5.3 DATABASE DATA ................................................................................................................... 78

5.6 DATA ANALYSIS TECHNIQUES .............................................................................................. 79 5.6.1 SOCIAL NETWORK TECHNIQUES .............................................................................................. 79

5.6.2 CONTENT ANALYSIS ................................................................................................................ 84

5.6.3 STATISTICAL ANALYSIS ............................................................................................................ 87

5.7 METHODOLOGICAL ISSUES .................................................................................................. 87 5.7.1 SOCIAL NETWORK ANALYSIS ISSUES ........................................................................................ 88

5.7.2 CONTENT ANALYSIS ISSUES ..................................................................................................... 89

5.8 SUMMARY ....................................................................................................................... 89

6 ANALYSIS ............................................................................................................... 90

6.1 INTRODUCTION ................................................................................................................ 90 6.2 QUALITATIVE DATA ANALYSIS .............................................................................................. 90

6.2.1 DEDUCTIVE CODES ................................................................................................................. 90

6.2.1.1 NETWORK BENEFIT ............................................................................................... 90

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6.2.1.2 NETWORK EFFECTIVENESS ..................................................................................... 94

6.2.1.3 COLLABORATION FACTORS .................................................................................. 100

6.2.1.4 COLLABORATION BARRIERS ................................................................................. 109

6.2.1.5 MEASURES OF EFFECTIVENESS ............................................................................. 116

6.2.2 INDUCTIVE CODES ................................................................................................................. 118

6.2.2.1 FROM ADVICE TO PROJECT COLLABORATION ........................................................ 118

6.2.2.2 NETWORK SCOPE ............................................................................................... 119

6.2.2.3 NETWORK AUDIENCE ......................................................................................... 120

6.3 EFFECTIVENESS MEASURES ...............................................................................................121 6.3.1 PERCEIVED NETWORK EFFECTIVENESS ................................................................................... 121

6.3.2 LEVEL OF ACTIVITIES AND LEVEL OF COLLABORATION............................................................. 123

6.4 NETWORK STRUCTURAL CHARACTERISTICS AND EFFECTIVENESS ................................................127 6.4.1 DENSITY ............................................................................................................................... 127

6.4.2 CLIQUE ................................................................................................................................ 128

6.4.3 CLIQUE OVERLAP ................................................................................................................. 131

6.4.4 MULTIPLEXITY ...................................................................................................................... 132

6.4.5 DISCUSSION ......................................................................................................................... 136

6.5 EGO-NET CHARACTERISTICS AND EFFECTIVENESS ...................................................................140 6.5.1 MODELS BUILDING .............................................................................................................. 141

6.5.1.1 EFFECTIVENESS MEASURED AS LEVEL OF ACTIVITIES ............................................. 142

6.5.1.2 EFFECTIVENESS MEASURED AS LEVEL OF COLLABORATION .................................... 146

6.5.2 HYPOTHESES TESTING .......................................................................................................... 152

6.5.2.1 MAIN EFFECTS .................................................................................................. 152

6.5.2.2 INFORMATION TECHNOLOGY INTERACTION EFFECTS ............................................ 156

6.5.3 DISCUSSION ......................................................................................................................... 157

7 CONCLUSIONS AND DIRECTIONS FOR FUTURE RESEARCH ...................................... 167

7.1 INTRODUCTION ..............................................................................................................167 7.2 SUMMARY OF THE LITERATURE ..........................................................................................167 7.3 KEY FINDINGS .................................................................................................................168 7.4 CONTRIBUTIONS .............................................................................................................174 7.5 LIMITATIONS AND DIRECTIONS FOR FUTURE RESEARCH ...........................................................179

8 REFERENCES ......................................................................................................... 183

APPENDIX .................................................................................................................... 199

APPENDIX A: INFORM CONSENT FORM FOR SOCIAL SCIENCE RESEARCH .............................................199 APPENDIX B: LETTER-EMAIL SENT TO POTENTIAL SURVEY PARTICIPANTS .............................................201 APPENDIX C: SURVEY QUESTIONNAIRE ........................................................................................202 APPENDIX D: INTERVIEW GUIDE .................................................................................................226

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LIST OF FIGURES

Figure 1: Level of NGOs coordination .................................................................................... 17

Figure 2. A Preliminary model of network effectiveness ...................................................... 34

Figure 3. Relationships between network effectiveness at different levels of network

analysis and influence by key stakeholders ..................................................................... 35

Figure 4: Research Model for Network Level of Analysis ..................................................... 64

Figure 5: Research Model for Organizatioanl Level of Analysis ............................................ 64

Figure 6: Global Symposium Project Collaboration Sub-Networks ....................................... 70

Figure 7: Global Symposium Advice Sub-Networks .............................................................. 70

Figure 8: United Nations Agencies Network Structure ........................................................... 77

Figure 9: Non-Governmental Organizations Network Structure............................................. 77

Figure 10: Governmental Organizations Network Structure .................................................. 77

Figure 11: Qualitative data analysis coding process (Seidel, 1998) ........................................ 84

Figure 12: Network Benefit Code’s Coverage ........................................................................ 93

Figure 13: Aggregated Benefit Cross Network ....................................................................... 93

Figure 14: Network Effectiveness Code’s Coverage .............................................................. 98

Figure 15: Network Effectiveness Code’s Loudness .............................................................. 99

Figure 16: Network Effectiveness Code’s Loudness Cross Network ..................................... 100

Figure 17. Factor’s Coverage .................................................................................................. 101

Figure 18. Factor’s Loudness ................................................................................................. 103

Figure 19: Loudness of Collaboration Factors Grouped per Category .................................... 106

Figure 20: Loudness of Collaboration Factors Cross Network ............................................... 107

Figure 21: Break Down of Structural Barriers ........................................................................ 113

Figure 22: Loudness of Barriers to Collaboration Grouped per Category .............................. 115

Figure 23: Loudness of Barriers to Collaboration Cross Network .......................................... 116

Figure 24: United Nations Agencies Clique Structure ............................................................ 134

Figure 25: Non-Governmental Organizations Clique Structure .............................................. 134

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Figure 26: Governmental Organizations Clique Structure ..................................................... 135

Figure 27:Residual Plot for Effectiveness Mesuared as the Level of Activities (Model

IVa) .................................................................................................................................. 144

Figure 28: Normal Plot for Effectiveness Mesuared as the Level of Activities (Model

IVa) .................................................................................................................................. 144

Figure 29: Residual Plot for Effectiveness Mesuared as the Level of Activities (Model

IVb) .................................................................................................................................. 145

Figure 30: Normal Plot for Effectiveness Mesuared as the Level of Activities (Model

IVb) .................................................................................................................................. 145

Figure 31: Residual Plot for Effectiveness Mesuared as the Level of Collaboration

(Model IVa) ..................................................................................................................... 148

Figure 32: Normal Plot for Effectiveness Mesuared as the Level of Collaboration (Model

IVa) .................................................................................................................................. 148

Figure 33: Residual Plot for Effectiveness Mesuared as the Level of Collaboration

(Model IVb) ..................................................................................................................... 149

Figure 34: Normal Plot for Effectiveness Mesuared as the Level of Collaboration (Model

IVb) .................................................................................................................................. 149

Figure 35: Effectiveness Models’ Explanatory Power ............................................................ 151

Figure 36: Variations in the Effectiveness Measures (Model VIa) ......................................... 159

Figure 37: Variations in the Effectiveness Measures (Model VIb) ......................................... 159

Figure 38: Inter-action effect of Technology and Degree Centrality on Effectiveness as

Measured by the Level of Activities ................................................................................ 166

Figure 39: Inter-action effect of Technology and Degree Centrality on Effectiveness as

Measured by the Level of Collaboration ......................................................................... 166

Figure 40: Inter-action effect of Technology and Network Density on Effectiveness as

Measured by the Level of Activities ................................................................................ 166

Figure 41: Inter-action effect of Technology and Network Density on Effectiveness as

Measured by the Level of Collaboration ......................................................................... 166

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LIST OF TABLES

Table 1: Principles of Humanitarian Information Management and Exchange. ..................... 20

Table 2: Summary of Inter-Organizational Coordination/Collaboration Challenges .............. 24

Table 3: Models of Organizational Effectiveness ................................................................... 30

Table 4: Inter-organizational Network Effectiveness in the Nonprofit Sector ........................ 40

Table 5: Summary of Hypotheses ........................................................................................... 64

Table 6: Surveys’ Participation .............................................................................................. 75

Table 7: Perceived Network Effectiveness Index Table ......................................................... 122

Table 8: Choosing Effectiveness Measures: Illustrative Quotes from the Interview .............. 125

Table 9: Network Effectiveness (Objective measures) ........................................................... 126

Table 10: Network Effectiveness Ranking .............................................................................. 127

Table 11: Network Density ..................................................................................................... 128

Table 12: Cliques Characteristics Project Network ................................................................. 129

Table 13: Cliques Characteristics Advice Network ................................................................ 129

Table 14: Clique Characteristics: Minimum Set Size of Five ................................................. 130

Table 15: Clique Overlap ....................................................................................................... 131

Table 16: Multidimensional Clique Overlap ........................................................................... 133

Table 17: Summary of Hypotheses Testing at Network Level of Analysis ............................ 135

Table 18: Organizational Effectiveness Variables .................................................................. 141

Table 19: Descriptive Statistics and Correlations ................................................................... 142

Table 20: Regression Analysis on Effectiveness Measured as the Level of Activities ........... 146

Table 21: Regression Analysis on Effectiveness Measured as the Level of Collaboration .... 150

Table 22: Summary of Hypotheses Testing at Organizational Level of Analysis................... 157

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ACKNOWLEDGEMENTS

I owe a debt of gratitude to the members of my dissertation committee, colleagues, friends, and

family for their guidance, support and encouragement throughout my PhD journey. My

dissertation committee members have been crucial to my training. I would like to express my

deepest appreciation to my adviser and committee chair, Dr. Carleen Maitland, for supporting this

work over time and distance and for the countless hours of patient mentoring. It has been a great

privilege to have had the opportunity to be her mentee.

I would like to thank my committee members, Dr. Andrea Tapia, for her unwavering support

throughout my PhD journey; for believing in me and constantly reminding me that a person can

always do more than he thinks he can, and then supporting me in my efforts; Dr. Lynette Kvasny,

for being available whenever I needed her assistance; for her insights in blending qualitative and

quantitative data analysis, and Dr. Wenpin Tsai, for introducing me to the world of social network

analysis; for helping me define and organize my thoughts, rounding me up when I needed focus

and for always challenging me while fully supporting me. I feel very fortunate and blessed to

have received the attention and guidance of such a team of mentors.

I have been lucky to be surrounded by wonderful friends throughout my PhD program. I would

like to thank all my fellow graduate students who have been my companions and co-learners on

this journey for their gifts of insights and their constant support. I am also honored to have been a

member of COHORT, a NSF sponsored research project. COHORT provided the financial

support to this research. COHORT also introduced me to the UNOCHA Global Symposium

community. Surveys and interviews with representatives of organization members of this

community were crucial to my research, and I am grateful to those participants for their time.

To my close and extended family, I owe thanks beyond measure. I am grateful for their prayers

and encouragement and for tolerating me through all this time of academic self-absorption when I

was not able to be the father, son, brother, uncle or friend that I would have wanted to be.

Heartiest thanks to my children Laetitia, Frank-Eric and Hermann for being so supportive and for

helping me transcribing my interviews. Above all, I want to thank my wife and best friend

Madeleine, for her love and support; for believing in me with such intensity as to make it almost

impossible for me to not believe in myself even in the most difficult moments along this journey.

1

1 INTRODUCTION

In recent years, as the number of man-made and natural disasters has risen, so has the

range of challenges faced by humanitarian organizations (Saab et al, 2008; Ngamassi et

al, 2011). One of these challenges is the management of humanitarian information.

Effective information sharing is becoming increasingly important to the humanitarian

relief sector. Humanitarian organizations need a large variety of information, such as

population displacement, relief expertise, disaster situation, availability and movement of

relief supplies, and meteorological satellite images or maps (Zhang et al., 2002). The

need for effective humanitarian information exchange is not just for supporting

emergency response operations but more importantly for enhancing the capacity of the

international community to respond to disasters before and after they occur.

Humanitarian organizations must therefore contend with the production, retrieval,

processing, validation, consumption, and distribution of humanitarian information.

Criteria for success include the information’s relevance to decision-makers, timeliness

and accuracy.

Researchers have identified numerous humanitarian information management related

problems, including the quality and timeliness of information (e.g., De Bruijn, 2006;

Fisher & Kingma, 2001), unpredictability of required information (Longstaff, 2005),

unwillingness to share (Ngamassi et al, 2011), and mismatch in location, information

overload, misinterpretation of information (Bui et al., 2000; Saab et al., 2008). Moreover,

in addition to the challenges specific to the humanitarian context humanitarian

organizations are also challenged by what are recognized as problems facing most

organizations (see Galbraith, 1977). In an attempt to mitigate these challenges,

organizations in the nonprofit sector including the humanitarian field are increasingly

forming inter-organizational networks such as coalitions, alliances, partnerships, and

coordination bodies, both within and across the sector (Guo & Acar, 2005; Stephenson,

2005; 2006; Arya & Lin, 2007). Though an accurate census of these networks does not

exist in the literature, several studies offer some insight into their growing presence (Guo

& Acar, 2005; Feiock & Andrew 2006; Jang & Feiock, 2007; Arya & Lin, 2007).

2

1.1 Problem Definition

Massive international response to humanitarian crises such as the South Asian Tsunami

in 2004, the Hurricane Katrina in 2005 and the Haiti earthquake in 2010 highlights the

importance of humanitarian inter-organizational collaboration networks, especially in

information management and exchange. Though, in recent years, humanitarian

information management has considerably improved due to significant development in

humanitarian information management principles and systems (Van de Walle et al.,

2009), humanitarian information sharing continues to challenge the international

community (Maiers et al, 2005; Wentz, 2006; Maitland et al., 2009; Bharosa et al., 2010,

Tapia et al., 2010). In the humanitarian relief field, the number of inter-organizational

networks has significantly increased with the rise in number and complexity of

humanitarian disasters of the past few decades (Stephenson, 2005; 2006; Ngamassi et al.,

2011). The effectiveness of these networks in disaster response is still to be determined.

Despite more than a decade old call for better understanding of the effectiveness of inter-

organizational networks in the nonprofit context (see O’Toole, 1997; Provan & Milward

1995), to date limited work has been done (Provan et al., 2007).

The appeal for assessing inter-organizational network effectiveness in the nonprofit

context, and especially in the humanitarian relief field, appears to stem from several

different perspectives among which, the following four seem to be the most important.

First, apart from establishing the value of networking for a member of the network,

evaluating the entire network has become increasingly important for all the stakeholders

who share an interest in systematic efforts of network (Sydow & Milward, 2003).

Second, evaluating the effectiveness of humanitarian inter-organizational networks is

critical for understanding whether networks are effective in meeting the goals of the

network as a whole, those of the individual network members and more importantly

especially in the humanitarian relief field, the extent to which the needs of the affected

people have been met. Third, establishing the level of network effectiveness is also

important for member organizations and those whose policies and funding support the

network. Ideally, an effective inter-organizational collaboration network would enhance

the quality of service provided to its clients and optimize use of resource by reducing

3

redundancies. Finally, given the high failure rates reported by network researchers in both

the for-profit and the nonprofit sectors, organizations are often overly optimistic about

the benefits of network participation (Barringer & Harrison, 2000, p. 368). A thorough

evaluation of networks could contribute to a more realistic attitude towards inter-

organizational networking (Sydow & Milward, 2003).

Assessing the effectiveness of inter-organizational networks is however a daunting task

(Alter & Hage, 1993; Provan & Milward, 1995; Sydow & Windeler, 1998; Provan et al.,

2007). There is no consensus on the criteria of measuring effectiveness among

researchers and no clear classification of the different levels of effectiveness. Assessing

effectiveness at the network level is more complex than at the organizational level due to

the involvement of multiple heterogeneous organizations in a network (Provan &

Milward, 1995; Sydow & Windeler, 1998). Moreover, networks use multiple

organizations to produce one or more pieces of a single service, thus making their

evaluation more complex than that of a single organization. Network effectiveness issues

are also problematic because networks usually have multiple stakeholders and it may be

harder to satisfy all of them (Provan & Milward, 1995; Sydow & Windeler, 1998; Sydow

& Milward, 2003).

1.2 Previous Studies

Notwithstanding the difficulties of assessing the effectiveness of inter-organizational

networks, there have been some attempts. Researchers have done some conceptual

studies on inter-organizational network effectiveness (Alter & Hage, 1993; Provan &

Milward, 2001; Sydow & Windeler, 1998; Staber & Sydow 2002). For example Alter &

Hage (1993) propose a “performance gap” model to assess inter-organizational network

effectiveness. Performance gap is defined as the difference between the current situation

and the idealized standard. According to Alter & Hage, effectiveness is usually best

measured by the expectation of what is a reasonable outcome, given the context and the

barriers to goal achievement. They argue that effectiveness is achieved when goals are

4

met within the context of technological and resource constraints, given certain levels of

internal conflict and pressure from external constituencies.

Moscovice et al., (1995) offer a network typology, a framework for assessing network

performance, and examples of measurable performance indicators. Provan & Milward

(1995) develop a model of inter-organizational network effectiveness through a

comparative study of four community mental health networks. They investigated the

relationship between the structure and context of mental health networks and their

effectiveness. In this study, effectiveness measures were linked to “enhanced client

wellbeing”. In addition to structure and context, Provan & Milward (2001) examined

network effectiveness at different levels. These three levels are (i) community, (ii)

network, and (iii) organization / participant. The paper argues that

organization/participant and network-level effectiveness criteria can be satisfied by

focusing on community-level effectiveness goals. Weech-Maldonado et al., (2003) build

upon Provan & Milward’s (2001) network effectiveness framework and Gamm’s (1996)

accountability framework to develop a “stakeholder accountability approach” in

assessing network effectiveness. The authors use this approach to evaluate the

effectiveness of community health partnerships.

Sydow & Windeler (1998) define inter-organizational network effectiveness as viable

and acceptable outcome and practices. They argue that network effectiveness form a

structurationist perspective and, is more than embedded in social interactions and

structures. It is rather social in character. Building upon Sydow & Windeler (1998),

Staber & Sydow (2002) propose the concept of adaptive capacity as an appropriate

approach to assess organizational and inter-organizational network effectiveness in

highly volatile and complex environments such as the case in the humanitarian assistance

sector. They define adaptive capacity as the ability of organizations or networks to cope

with unknown future circumstances. Organizations / networks with adaptive capacity can

reconfigure themselves quickly in changing environments.

5

These different conceptual models for assessing network effectiveness found in the

literature often borrowed from the four models traditionally used to study organizational

effectiveness. They include (i) the Goal Model (Parson 1964; Price, 1971; Cameron &

Whetten 1981), (ii) the System Resource Model (Yuchtman & Seashore, 1967; Price,

1971), (iii) the Internal Processing Model (Alter & Hage, 1993; Lee, 2006) and (iv) the

Multiple Constituencies’ Model (D’Aunno, 1992; Zammuto, 1984; Sowa et al., 2004).

All these models for assessing organizational/network effectiveness have been criticized

in the literature for their respective shortcomings.

Previous research has also identified important antecedents of inter-organizational

network effectiveness (Provan & Milward, 1995; Moscovice, et al., 1995; Wright et al.,

1995; Provan & Sebastian, 1998; Schumaker, 2003; Lemieux-Charles et al., 2005). For

example, several authors (e.g., Provan & Milward, 1995; Moscovice, et al., 1995; Wright

et al., 1995; Provan & Sebastian, 1998) highlight the importance of integration of

network members to network effectiveness. Provan & Sebastian (1998) argued that

achieving integration across an entire network of organizations is difficult. Their findings

also suggest that to be the most effective, clique integration must be intensive, involving

multiple and overlapping relationships both with and across organizations that compose

the core of a network. Similarly, the diversity of network membership is also deemed

relevant by several authors (e.g., Moscovice, et al., 1995; Schumaker, 2003). Schumaker

(2003) for example found that effectiveness is influenced by external and internal factors

that are operationalized through external control, technology, structure, and operational

process variables. Other important effectiveness predictors include the degree of

multiplexity in the network, revenue sources, and the duration of the network.

While several predictors of network effectiveness have been identified throughout the

literature, as well as conceptual models provided, limited research has used these models

or any other approach to empirically analyze the possible antecedents of network

effectiveness, particularly for humanitarian inter-organizational networks.

6

1.3 Motivation of the Study

The motivations of this study are theoretical and practical. Theoretically, my research is

motivated by the growing literature that highlights the increasing number of inter-

organizational networks in the nonprofit sector including in the humanitarian field and

stresses the need for better understanding of the effectiveness of these networks. Despite

more than a decade old call, to date limited work has been done (Provan et al., 2007). The

few existing studies have been conducted in the public health sector. In the literature,

there is virtually no study that investigates the effectiveness of inter-organizational

network in the humanitarian relief field.

The practical motivations of my research are related to three main issues including (i) the

need for better humanitarian information, (ii) the critical role of information technology

disaster response and (iii) the growing number disaster victims. First, effective

humanitarian response depends highly on the quality and timeliness of information. The

faster humanitarian organizations are able to collect, analyze and disseminate

information, the more effective the response becomes and the more lives are potentially

saved. In humanitarian relief operations, organizations deal with information that are by

nature multi-sector, multi-dimensional, multi-source, and non-standardized. Though

humanitarian information management has improved in recent years, some constraints

(such as funding, tools and technical skills) continue to handicap information from

becoming a core component of humanitarian relief operations (Wentz, 2006). More

effective inter-organizational networks would help to mitigate these constraints.

Second, information technology (IT) has also been shown to play a critical role in

mitigating the informational related issues for inter-organizational humanitarian response

(Comfort, 1990; Graves, 2004; Comfort & Kapucu, 2006; Moss & Townsend, 2006).

According to Lee & Whang (2000), the advances in information technology have

significantly facilitated inter-organizational information sharing. High use of technology

can result in the achievement of high levels of information sharing. However, the

literature also shows that information technology has hindered inter-organizational

collaboration (e.g., Bui et al., 2000; Junglas & Ives, 2007; Miller et al., 2005; Saab et al.,

7

2008). Inter-organizational collaboration issues related to technology include technical

interoperability, semantic interoperability, non-matching data formats, different

presentation forms, and heterogeneous systems. More research is needed to further

explore the solutions to these problems.

Third and lastly, the numbers of humanitarian natural disasters and the people affected by

these disasters have increased over recent years. According to the IFRC, (2005) the

average annual number of humanitarian disasters during 2000-2004 was 55% higher than

during 1995-1999. The number of people affected by humanitarian disasters has

continued to grow (ISDR, 2006). This growing trend in the number and impact of

humanitarian disasters and the high scale of international response efforts have brought

growing attention to the need for effective and efficient humanitarian disaster response

operations. More effective inter-organizational networks would help to meet this need.

1.4 Research Objectives

The objective of my research is to develop a theory that provides insight into inter-

organizational information management and exchange relationships in the field of

humanitarian relief. To this end, I combine two theoretical lenses including Social

Network and Resource Based View to assess effectiveness at two levels, organizational

and network levels. Network structural characteristics (density, centrality, clique and

clique overlap) have been found to have implications on performance/effectiveness

(Ahuja & Carley, 1999; Tsai & Ghoshal, 1998; Tsai, 2000; Nohria & Garcia-Pont, 1991;

Wasserman & Faust, 1994; Kilduff & Tsai, 2006; Provan et al., 2007). Similarly, the

embeddedness of organizations in networks of external relationships with other

organizations holds significant implications for organization performance / effectiveness

(Granovetter, 1985; Uzzi, 1996; 1997; 1999; Gulati et al., 2000). Resource Base View

(RBV) explains performance/effectiveness exclusively through internal resources

(Barney, 1991; Prahalad & Hamel, 1990; Barnett et al., 1994). As mentioned earlier,

information technology (IT) has also been shown to play a critical role in mitigating the

8

informational related issues for inter-organizational humanitarian response (Comfort,

1990; Graves, 2004; Comfort & Kapucu, 2006; Moss & Townsend, 2006).

I studied multidimensional networks of collaborative relationships among humanitarian

organizations that are members of a community of interest in humanitarian information

management. Communities of interest, as defined by Arias & Fischer (2000), are groups

from different backgrounds coming together to solve a particular problem of common

concern. According to Arias & Fischer (2000), members of communities of interest need

to learn to communicate with and learn from others who have a different perspective and

perhaps a different vocabulary for describing their ideas and establish a common ground

and a shared understanding. The goal of the community of interest studied in this

research is to foster collaboration on humanitarian information management projects and

to disseminate best practices of information exchange. This community also aims to (i)

sensitize its members on the critical aspects of humanitarian information management

preparedness, (ii) facilitate headquarter-field partnerships and (iii) advocate for more

funding from donors for humanitarian information management related projects. My

study focuses on members which actively participate in the activities in the community

and have developed collaborative relationships with other members within the

community. Also, the multidimensional networks that I investigated are not directly

involved in disaster assistance. They lay groundwork at the headquarter level, for

collaborative humanitarian disaster response.

Multidimensional networks refer to networks examined at more than one level, with more

than one set of nodes and more than one type of link (Lee, 2008). Understanding

networks in the field of humanitarian relief can be enhanced by considering the content of

relationships that exist among organizations. Katz & Anheier (2005) identify the major

types of relationships among stakeholders (e.g. nongovernmental organizations,

international nongovernmental organizations, and international governmental

organizations) in responding to humanitarian disasters. They include information

exchange, project collaboration, participation in meetings and forums, or joint

9

membership in advocacy coalitions. My study is concerned with two types of

collaborative relationships, namely projects and advice.

In the field of humanitarian relief, inter-organizational networks can be classified into

two types: those oriented to project implementation and those oriented to information-

sharing (Lee, 2008). The purpose of the implementation network is to implement

humanitarian relief projects. Implementation networks are activity-focused, project-

based networks which rely on partnerships to draw on resources such as funding, and

skills from various partners (Unwin, 2005). This type of network applies more to the field

of humanitarian relief since projects are more often implemented by numerous project

partners. Knowledge-sharing networks on the other hand are often formed through

affiliation to common events, such as global and regional committees, forums,

conferences, and publication activities (Katz & Anheier, 2005). These networks enable

organizations to be informed of their partner’s and community’s activities as a whole.

The networks investigated in this dissertation can be considered as a hybrid between

these two types of networks (implementation network and knowledge-sharing networks).

Humanitarian relief activities often involve nonprofit and for-profit organizations. There

is a vast body of literature that compares nonprofit and for-profit organizations from

different perspectives (for example, see Vladeck, 1988; Moore, 2000) including (i)

revenue sources, (ii) goals, and (iii) stakeholders. With regards to revenue sources, for-

profit organizations draw their revenue mainly from customers who pay for goods and

services while nonprofit organizations get their revenues from people and organizations

that expect no economic benefits in return (Moore, 2000). Concerning the goals, for-

profit and nonprofit organizations differ in that while for-profit organizations seek to

make profits and provide financial returns to their shareholders (Boland & Fowler, 2000)

nonprofit organizations try to achieve their social purpose and mission (Moore, 2000).

For-profit and nonprofit organizations are also distinguishable in terms of their

stakeholder characteristics. For-profit organizations have a privileged interest group that

is clearly defined and homogenous with respect to interests (Speckbacher, 2003). This

10

privileged group owns the business. On the other hand, nonprofits serve a multitude of

constituencies whose goals and needs may be heterogeneous (Speckbacher, 2003).

Most of the limited studies on nonprofit inter-organizational network effectiveness have

examined networks in which an administrative organization serves as a governing

authority (Provan & Milward, 2001). Most of these previous studies also focused on

networks of collocated organizations and explored one type of collaborative relationship.

Moreover almost all of these studies are in the domain of public health service delivery.

Studies on inter-organizational network effectiveness in the domain of humanitarian

relief are virtually nonexistent. Recent research in this domain provides a new context for

the study of inter-organizational effectiveness (Maitland & Tapia, 2007a; 2007b; 2008;

Maitland et al., 2008; 2009). These networks are formed and maintained with support

from foundations and multilateral donors (e.g. Gates Foundation, European Commission

Directorate General for Humanitarian Aid (ECHO)), including funding for meetings,

administration, report generation, and research to define the barriers to coordination.

Despite the recognized need for and support of such entities by the humanitarian relief

community there is little systematic analysis of their effectiveness, in other words the

extent to which they meet the goals the network and the donors set out to achieve. My

study is conducted in this new context.

1.5 Research Design

In this study, I use a mixed methods research design (Tashakkori & Teddlie, 2003) to

explore effectiveness of multidimensional inter-organizational networks of collaborative

relationships among humanitarian organizations, members of the Global Symposium. I

investigate how organizational characteristics and network structure properties influence

effectiveness. I explore effectiveness at two levels of analysis, organizational and

network. At the network level, I conduct a clique analysis using Provan & Sebastian’s

(1998) framework for evaluating public-sector organizational networks, to determine the

extent to which this framework explains network effectiveness in the humanitarian relief

context. At the organizational level, I use multiple regression analysis method. I combine

11

two theoretical lenses including Social Network and Resource Based View to discuss my

findings and develop my theory. Network effectiveness was assessed using three

different criteria including one subjective criteria – perceived network effectiveness and

two objectives criteria – number of organization funded projects measuring the level of

activities and number of organization funding partners measuring the level of

collaboration.

I collected data through multiple sources including surveys, interviews and online

database search. A survey instrument that also contains network-related questions was

my main data collection source. I conducted a series of three surveys during October

2007, May 2008 and July 2009. I conducted during the period of September to December

2009, 19 personal phone based semi-structured interviews with representative of

organizations members of the Global Symposium. My intent was to supplement the

quantitative survey data with a more detailed description and explanation of activities in

the Global Symposium community. My third and last data source was the ReleifWeb

Financial Tracking Service (FTS). FTS is an online database which records all reported

international humanitarian financial assistance (Office for the Coordination of

Humanitarian Affairs (OCHA), 2010). I collected data related to the amount of funding

raised, the number of funded projects and the number of funding partners of

organizations member of the Global Symposium community. I used the UCINET

software (Borgatti et al., 1999) to analyze network data.

My research questions are formulated as follows:

RQ#1: To what extent do network structural characteristics explain effectiveness

in humanitarian inter-organizational collaboration networks?

i. How accordingly does Provan & Sebastian model of network

effectiveness explain network effectiveness in the international context of

inter-organizational collaboration in the humanitarian field?

RQ#2: How accurately does a linear combination of organizational internal

attributes and network structural properties explain effectiveness at organizational

level in humanitarian inter-organizational collaboration networks?

12

i. To what extent do resources internal to organizations and especially

information technology explain effectiveness?

ii. To what extent do ego-net properties explain network effectiveness?

iii. To what extent do network level structural characteristics (e.g. density)

explain effectiveness?

iv. To what extent does the interaction of information technology and

network structural characteristics impact organizational effectiveness?

1.6 Key Findings

1.6.1 Network Level

Consistent with those of Provan & Sebastian (1998) my findings suggest that at the

network level of analysis, an inter-organizational network in the field of humanitarian

relief is more effective when it is more integrated at the subnet level (clique) and

displaying higher level of multiplexity. My study however makes one significant

additions to Provan & Sebastian model. Unlike Provan & Sebastian, in my study, I used

three different measures of network effectiveness (one subjective and two objectives).

Using these effectiveness measures allowed me to find consistent ranking pattern for each

of the six network structural characteristics I used. It is important here to note that Provan

& Sebastian’s study which is the foundation of my study, matched two out of the six

network structural characteristics. Moreover, my findings suggest that the subjective and

objective forms of network effectiveness are better explained by different network

structural attributes. Whereas subjective network effectiveness is better explained by the

number of cliques and clique membership, objective network effectiveness is better

explained by the multifaceted nature of inter-organizational relationships as measured by

clique overlap and multiplexity. These findings highlight the importance of multiple

criteria for assessing network effectiveness. In a nutshell, my research extends Provan &

Sebastian’s model.

1.6.2 Organizational Level

At the organizational level, I found that effectiveness can be accurately explained by a

linear combination of organizational internal attributes and network structural properties.

13

Regarding the resources internal to organizations, my findings suggest that information

technology was an important determinant of effectiveness. My research also highlighted

the importance of ego-net level attributes such as degree centrality and bridging structural

holes in collaborative networks of organizations in the humanitarian relief sector.

Moreover, my study also revealed that network level attributes had some implications on

organizational effectiveness. My findings suggested that the density score in

humanitarian inter-organizational networks may be detrimental for explaining

effectiveness. Overall my results were for the most part consistent with those of the two

similar studies (Zaheer & Bell, 2005; Arya & Lin, 2007). However, none of those two

previous similar studies had explored the network level attributes. My study therefore

extends the Resource Based View theory by adding network level attributes as predictor

of organizational effectiveness.

1.7 Organization of Dissertation

My dissertation begins with a background chapter which provides some information on

the humanitarian relief context in which the study is conducted. I then continue with an

overview of relevant literature on organizational and network effectiveness. This

literature review is followed by a theoretical framework chapter in which I develop my

research models and hypotheses. The theoretical framework chapter is followed by the

methods chapter which (i) provides detailed explanation on my research design; (ii)

describes the methods of data collection which consists of preliminary a series of three

surveys, semi-structured interviews, and database search; (iii) depicts the analyses that

were done on the data which includes a description of the variables used in the analyses

conducted; and (iv) provides an overview of the limitations of this research. The methods

chapter is followed by a findings chapter. This chapter begins with a discussion on my

three criteria for measuring effectiveness. The chapter then provides an analysis of

network effectiveness at two levels, organizational and network levels. Finally, a

concluding chapter is presented. This chapter discusses the implications from the

findings. Limitations of the study and future research directions are suggested.

14

2 CONTEXT OF THE STUDY

2.1 Introduction

This chapter provides some background information on the humanitarian relief context in

which the study is conducted. It focuses especially on humanitarian information

management and exchange related issues.

2.2 Humanitarian Relief

The term “humanitarian” has a wide range of different interpretations. This term is

however generally associated with actions and operations that seek to alleviate human

suffering the face of crises as diverse as armed conflicts, epidemics, famine and natural

disasters. These crises often occur in fragile environments characterized by low incomes,

sparse infrastructure and in some cases low levels of information technology skills.

Humanitarian relief efforts are complex responses to emergent situations where the facts

and challenges on the ground can change rapidly.

The international community has been increasingly putting more efforts into disaster

mitigation and humanitarian assistance (Zhang et al., 2002, UNOCHA, 2002; 2007a,

2007b). For example, a joint multi-agency exercise, combining civil, military as well as

the United Nations organizations, was carried out in the Asia/Pacific region in 2000. This

exercise aimed at establishing a forum to exchange relevant information between

humanitarian organizations and the military, delivering a coordinated response to a

population in crisis, and documenting the implementation and output of combined

activities (Zhang et al., 2002). Another example of the international community effort to

better response to disaster relief is the launch in 2002, of the Global Symposium by the

United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA, 2002;

2007a; 2007b). The goal of the Global Symposium is to foster collaboration on

humanitarian information management projects and to disseminate best practices of

information exchange.

15

The Federal Emergency Management Agency (http://www.fema.gov) defines three

phases of the disaster relief process. They include the pre-crisis phase, the crisis phase

and the post crisis phase. The pre-crisis phase is concerning which gathering and

updating disaster data as well as monitoring disaster-related information sources for early

warning purposes. The crisis phase is concerned which information management and

exchange among humanitarian organizations, disseminations of demands, and

coordination of assistance. The post-crisis phase is concerned with summarizing the

lessons learned and suggesting recommendation for better disaster preparedness.

Information needs vary in different phases of disaster relief and also vary for different

stakeholders. Humanitarian organizations engage in two broad types of activities

including relief activities and development activities. Relief activities consist of assisting

to victims of large-scale emergencies. These short-term activities focus on providing

goods and services to minimize immediate risks to human health and survival.

Development activities are longer-term assistance, focusing on community self-

sufficiency and sustainability. These activities include establishing permanent and

reliable transportation, healthcare, housing, and food.

Humanitarian organizations increasingly need to look outside their own boundaries and

engage into a significant level of inter-organizational alliances. Through partnerships,

humanitarian organizations can successfully take on issues that would be beyond the

scope of any single organization.

2.3 Humanitarian Inter-organizational Coordination

Despite the variety of academic perspectives from which research on coordination and

inter-organizational coordination is approached (e.g. Comfort & Kapucu, 2006;

Crowston, 1994; Grandori, 1997; Lewis & Talalayevsky, 2004; Mulford & Rogers, 1982;

Mulford, 1984; Thompson, 1967; Van de Ven et al., 1976; Whetten & Rogers, 1982), a

common theme across all of them is that coordination requires the sharing of information,

resources and responsibilities to achieve a common goal.

http://www.fema.gov/

16

In the particular realm of NGO coordination, initiatives are seen as a solution to

duplication of efforts in assistance projects, badly planned and implemented relief efforts,

and the lack of knowledge among humanitarian organizations on the actual situation in

which they operate. These initiatives entail developing strategies, determining

objectives, planning, sharing information, the division of roles and responsibilities, and

mobilizing resources. They are also concerned with synchronizing the mandates, roles

and activities of the various stakeholders and actors at higher organizational levels. In a

nutshell, NGO coordination is intended to ensure that priorities are clearly defined,

resources more efficiently utilized and duplication of effort minimized; the ultimate goal

being to provide coherent, effective and timely assistance to those in need (Harpviken et

al., 2001).

Coordination among NGOs, as well as between NGOs and other humanitarian actors,

takes place at different levels. Harpviken et al., (2001) identify these levels as

international, national, regional and local. At the international level, the formulation of

policy, general guiding principles and strategies are of concern. At the national level,

coordination typically revolves around program development and policy articulation. At

this level, local groups are typically less involved, while United Nations agencies,

government departments and NGOs representatives assume a central role. Coordination

at the local level usually takes place between representatives from NGOs, United Nations

agencies, and local communities. It is at the local level where humanitarian priorities can

be most readily identified and articulated. Figure 1 below depicts these different levels of

coordination, within which inter-organizational relationships may vary, depending on the

level of coordination pursued. My study focuses on coordination at the international

level.

17

Local level

National level

International level

Type of Coord.

Project/Program

coordination

Coordinated

activities

Actors:

IGOs,

NGOs,

main offices

CBs, UN

agencies,

UN

Type of Coord.

Program/ policy

coordination

(Standards)

Actors:

IGOs,

NGOs,

HQ,

Int.CBs,

UN,

Donors

Type of Coord.

Policy and norms

Actors:

IGOs,

NGOs,

CBs, UN

agencies,

UN, Donors

IGOs = Inter-governmental Organizations

CBs = Coordination Bodies

UN = United Nations

HQ = headquarters

Figure 1: Level of NGOs coordination

Source: Author adaptation from Harpviken et al., (2001)

Inter-organizational Coordination Forms: Identifying and classifying the various forms of

inter-organizational coordination has been a subject of research in both the for- and non-

profit domains. Research on for-profit organizations has identified two general structures

of coordination (Malone, 1987; Thompson et al., 1991). The first is a hierarchical

coordination structure, characterized by long-lasting relationships with fixed rules of

behavior and clear authoritative relationships. Put simply, one organization has control

over the other(s). The second is a “market” coordination structure, in which all

organizations are fully autonomous and make decisions in their own interest.

In the non-profit domain, research has similarly identified multiple structures (Donini &

Niland, 1999). The first is "coordination by command," in which the lead NGO has

authority to pursue coordination through the use of carrots or sticks and possesses strong

leadership abilities. In such a situation, a central authority has the power to define the

agenda, instigate preferences and enforce sanctions. Power can come in the form of

control of information or resources, but also the institutionalized legal means, through

which preferences might be implemented. The second form is "coordination by

18

consensus". In this form, organizations develop agreed-upon guidelines and standards to

achieve similar goals, and there is no authority to enforce compliance. The last form,

"coordination by default" describes ad-hoc coordination in which a division of labor is

generally the only exchange of information among actors. Obstacles to inter-

organizational coordination may vary depending on these various forms of coordination.

Alternatively, research on coordination structures in the humanitarian sector finds that

structure within NGOs themselves. Enjorlas (2008) argues that collectively NGOs on

their own serve as coordination structures. Due to the nature of their individual

governance structures, they reinforce the norm of reciprocity; making possible the

pooling of resources and, because of these features, thereby facilitate collective action

oriented toward public or mutual interest as well as advocacy. Moreover, this nonprofit

governance structure is also compatible with other types of coordination mechanisms,

and thus NGOs are able to operate in complex environments, mobilizing resources from

market operations, governmental subsidies, or from reciprocity (Enjorlas, 2008).

2.4 Humanitarian Information Management and Exchange

My research explored inter-organizational networks in the Global Symposium, a

community of interest in humanitarian information management and exchange

spearheaded by the United Nations Office for the Coordination of Humanitarian Affairs

(UNOCHA). UNOCHA initiated a Global Symposium in recognition of the centrality of

information management to effective and timely response to humanitarian disasters.

Timely and accurate information is recognized as integral to humanitarian action in both

natural disasters and complex emergencies. The international community's ability to

collect, analyze, disseminate, and act on key information is fundamental to an effective

response. Better information, leading to improved responses, directly benefits affected

populations. Over time, improved assessment of impacts and responses through better

data collection and management contributes to a more complete global database on

disaster impacts, leading to better risk assessment and prevention and preparedness

activities.

19

The goal of the Global Symposium is to foster collaboration on humanitarian information

management projects and to disseminate best practices of information exchange. This

community also aims to (i) sensitize its members on the critical aspects of humanitarian

information management preparedness, (ii) facilitate headquarter-field partnerships and

(iii) advocate for more funding from donors for humanitarian information management

related projects. My study focuses on members which actively participate in the activities

in the community and have developed collaborative relationships with other members

within the community.

The Global Symposium held a series of conferences and workshops, organized by

UNOCHA. The series began in 2002 as a meeting of humanitarian information

management professionals and was followed by a series of regional meetings intended to

bring humanitarian information management principles (Table 1) and best practices to a

wider range of humanitarian organizations and in particular bring together practitioners in

the field, as opposed to only headquarters staff. The second meeting of the Global

Symposium was held in October 2007 and included three days of working group

meetings, designed to update the principles and best practices and identify an agenda for

further development of humanitarian information management (HIM).

Principle Description

Accessibility Humanitarian information and data should be made accessible to all humanitarian actors by applying easy-to-use formats and by translating information into common or local languages when necessary. Information and data for humanitarian purposes should be made widely available through a variety of online and offline distribution channels including the media.

Inclusiveness

Information management and exchange should be based on a system of collaboration, partnership and sharing with a high degree of participation and ownership by multiple stakeholders, especially representatives of the affected population.

Inter-operability All sharable data and information should be made available in formats that can be easily retrieved, shared and used by humanitarian organizations.

Accountability Users must be able to evaluate the reliability and credibility of data and information by knowing its source. Information providers should be responsible to their partners and stakeholders for the content they publish and disseminate.

Verifiability Information should be accurate, consistent and based on sound methodologies, validated by external sources, and analyzed within the proper contextual framework.

20

Principle Description

Relevance Information should be practical, flexible, responsive, and driven by operational needs in support of decision-making throughout all phases of a crisis.

Objectivity Information managers should consult a variety of sources when collecting and analyzing information so as to provide varied and balanced perspectives for addressing problems and recommending solutions.

Humanity Information should never be used to distort, to mislead or to cause harm to affected or at-risk populations and should respect the dignity of victims.

Timeliness Humanitarian information should be collected, analyzed and disseminated efficiently, and must be kept current.

Sustainability Humanitarian information and data should be preserved, cataloged and archived, so that it can be retrieved for future use, such as for preparedness, analysis, lessons learned and evaluation.

Table 1: Principles of Humanitarian Information Management and Exchange.

The Geneva 2002 meeting was followed by a series of regional workshops in Bangkok

(2003), Panama (2005) and Nairobi (2006). While the issues confronting the

humanitarian community are global in scope, there are regional differences in both the

types of problems as well as the appropriate solutions. Each workshop focused on

information initiatives and tools in their regional context, each region with its different

vulnerabilities and response capacities. The goals of these workshops were to (i) bring

together regional information management professionals in order to strengthen the

professional community of practice, (ii) discuss the principles and best practices in

information management, especially those which have been developed at the regional

level, and (iii) deepen understanding of the regional issues and priorities that will help

build a plan for improving information exchange in the region. The recommendations

from these workshops reinforced the need for attention to the promotion of standards,

user requirements, quality of information, appropriate responses, tools and technology,

and strong partnerships.

2.5 Humanitarian Collaboration and Coordination Challenges

Research on barriers to inter-organizational coordination and collaboration has been

undertaken in both general organizational contexts (e.g. Burbridge & Nightingale, 1989;

Comfort, 1990; Comfort & Kapucu, 2006; Crowston, 1997; De Bruijn, 2006; Faraj &

21

Xiao, 2006; Quarantelli, 1982; Thompson, 1967), as well as among organizations in the

nonprofit context (e.g. Bennett, 1995; Bui et al., 2000; Foster-Fishman et al., 2001; Saab

et al., 2008; Uvin, 1999; Van Brabant, 1999). After an analysis of the literature,

Ngamassi et al., (2011) found a fairly consistent set of eight coordination and

collaboration barriers (Table 2). They include (i) bureaucratic and turf-protection, (ii)

divergent goals and conflicting interests, (iii) resource dependency, (iv) coordination

cost, (v) information and communication issues, (vi) assessing and planning joint

activities, (vii) competition for resources, and (viii) emergency response time.

Bureaucratic barriers and turf-protection refer to the desire to maintain autonomy and

thus avoid having individuals in other organizations interfere within one's own

organization. Burbridge & Nightingale (1989) note a common fear among organizations

is that coordination may somehow result in a take-over or a loss of decision-making

autonomy. Furthermore, the discipline of coordination can limit maneuverability, and

hence poses a major challenge (Uvin, 1999). Coordination may be perceived as

increasing bureaucracy, generating institutional resistance among bureaucratically

burdened NGOs (Van Brabant, 1999).

A common problem in inter-organizational collaboration is that divergent goals or an

over-emphasis on individual organizational goals as opposed to those of beneficiaries

may lead to conflicting interests (Bennett, 1995; Bui et al., 2000; Quarantelli, 1982; Saab

et al., 2008; Van Brabant, 1999). Goal conflicts occur when a party seeks divergent or

incompatible ends. Further, divergent goals may also lead to an exacerbation of turf

issues or other coordination problems (Bui et al., 2000).

Resource dependency is both a motivation for and barrier to coordination (Crowston,

1997; Dawes et al., 2004; Thompson, 1967). Interdependencies, whether of the pooled,

sequential or reciprocal type, require coordination (Thompson, 1967). However, at the

same time they can create problems for coordination and constrain the efficiency of task

performance (Crowston, 1997). One of these problems is the associated cost of

22

coordination, as to be effective it is time and staff intensive and the benefits must

outweigh these costs (Aldrich, 1972; Bennett, 1995; Van Brabant, 1999).

Coordination cost is yet another barrier that hampers coordination among organizations.

Inter-organizational coordination is believed to limit an organization because scarce

resources and energy have to be invested in the maintenance of relationships with other

organizations. Negotiation of resources allocation can lead to difficult bargaining among

parties engaged in coordinated activities. Usually, organizations find it difficult to

allocate scare resources (Bui et al., 2000). Aldrich (1972) argued that it is costly for

organizations to initiate and/or maintain linkages with other organizations. For example,

the costs can be seen as in term of additional staff-time necessary to attend a joint board

of directors’ meeting; or the additional funds necessary to participate in joint database.

According to Uvin (1999), the high cost in time and money that effective co-ordination

entails constitute one of the major barriers to inter-organization coordination.

Another frequently encountered barrier is related to the availability and the quality of

information. This is usually due to the inconsistency in data collection and management

across organizations and to the mismatch between the informational demands and

supplies (De Bruijn, 2006; Fisher & Kingma, 2001). According to Bui, et al., (2000),

there are varying levels of mistrust, misrepresentation of facts, and incomplete

information exchange among organizations. Further, the high level of uncertainty in

humanitarian operations likely requires greater amounts of information to be processed

among decision makers (Galbraith, 1977).

General assessment and planning of joint activities can lead to disagreement about the

means and the ends of a coordinated activity (Bui, et al., 2000). Situations tend to worsen

when organizations are unsure of their role, and act independently, without consulting or

coordinating with others. Joint activities must also confront problems of understanding,

which emanate from the fact that participants in inter-organizational relationships are

accustomed to different structures, cultures, functional capabilities, cognitive frames,

terminologies, and management styles and philosophies (Vlaar et al., 2006).

23

In addition to the resources related to coordination itself, competition for scarce resources

in general may inhibit the initiation of inter-organizational coordination generally (Uvin,

1999; Van Brabant, 1999). Given the increasing numbers of NGOs, combined with

decreasing overseas development assistance budgets, competition for funding between

organizations is heating up (Salm, 1999; Van Brabant, 1999).

Finally, response time is considered yet another obstacle to coordination among

organization. Coordination is often perceived as increasing response time especially in

case of emergency. According to Van Brabant (1999), there is the fear that the

coordination effort will cause delays in providing relief. Comfort (1990) observed that

coordination activities generated delays in response in the four events she analyzed.

Thus, inter-organizational coordination between international humanitarian NGOs will

seek to share information, resources and responsibilities that through more efficient use

of resources and minimization of duplicate activities will provide effective and timely

assistance to those in need (Harpviken et al., 2001). This coordination can occur at

multiple levels and may be carried out through one of several forms, including command,

consensus or default. Whatever the form, it must contend with a wide range of

challenges.

Barriers Issues Authors

Bureaucratic and turf protection

Desire to maintain autonomy and thus avoid having individuals in other organizations interfere within one's own organization

Burbridge and Nightingale (1989) (Uvin, 1999). (Van Brabant, 1999).

Divergent goals and Conflicting interests

Divergent goals or an over-emphasis on individual organizational goals

Bennett 1995; Bui et al, 2000; Quarantelli, 1982; Saab et al, 2008; Van Brabant, 1999.

Resource dependency Interdependencies require coordination but at the same time they can create problems for coordination and hamper performance.

Crowston, 1997; Dawes et al., 2004; Thompson 1967). Aldrich 1972; Bennett, 1995; Van Brabant 1999

24

Barriers Issues Authors

Coordination cost Scarce resources have to be invested in the maintenance of relationships with other organizations.

Bui et al, 2000; Aldrich,1972; Uvin, 1999

Information and communication issues,

Information availability and accessibility,

Information quality,

Information Sharing

Information system quality,

Standards and interoperability

Systems integration

De Bruijn, 2006; Fisher & Kingma, 2001; Bui, et al 2000; Galbraith, 1977.

Assessing and planning joint activities

Disagreement about the means and the ends of a coordinated activity

Bui, et al, 2000; Vlaar et al., 2006

Competition for resources

Competition for scarce resources may inhibit the initiation of inter-organizational coordination

Uvin, 1999; Van Brabant, 1999; Salm, 1999.

Emergency response time

Coordination is often perceived as increasing response time especially in case of emergency

Van Brabant, 1999; Comfort, 1990.

Table 2: Summary of Inter-Organizational Coordination/Collaboration Challenges

25

3 REVIEW OF RELEVANT LITERATURE

3.1 Introduction

As discussed in the introductory chapter, this study is situated in the broader context of

research on inter-organizational networks in the non-profit sector. I investigate the

organizational attributes and network structural characteristics that explain effectiveness.

In this chapter, I review the relevant literature. The chapter is made up of two sections.

The first (Section 3.2) is related to effectiveness at the organizational level of analysis

while the second (Section 3.3) is concerned with effectiveness at the network level of

analysis.

3.2 Organizational effectiveness

3.2.1 Defining Organizational Effectiveness

Although researchers have devoted considerable amount of time investigating

organizational effectiveness, the construct remains elusive. In the literature, there is a

wide range of definitions to this construct (for a review, see Goodman et. al, 1977; Cho,

2007). There is no consensus on the criteria of measuring effectiveness among

researchers (Quinn & Rohrbaugh, 1983; Scott, 1992). Moreover, debates still exit about

the primary factors that constitute organizational effectiveness (Goodman et al., 1977;

Rainey & Steinbauer, 1999) and about the validity of measuring the construct (Goodman

et al., 1983; Steers, 1975). In addition, there is no single theory of organizational

effectiveness (Goodman et al., 1983), rather each paradigm of organizational behavior

generates its own model or criterion of effectiveness (D’Aunno, 1992).

In the literature, there is a wide range of definition to the concept of organizational

effectiveness (for a review, see Goodman et Al., 1977; Cho, 2007). There is no consensus

on the criteria of measuring effectiveness among researchers and no clear classification of

the different levels of effectiveness. For Goodman et al., (1977) organizational

26

effectiveness is measured in terms of the organization's ability to satisfy constraints and

meet organizational goal. Reviewing the literature on organizational effectiveness,

Cameron (1986a; 1986b) describes the concept of effectiveness as theory-bound,

multidimensional, interest-driven, and paradoxical in nature.

3.2.2 Models of Organizational Effectiveness

A variety of different models of organizational effectiveness have been used however,

four major models dominate the literature. They include the goal model, the systems-

resource model, internal processing model, and the multiple constituencies’ model.

Below, I briefly review these different models.

3.2.2.1 Goal Model

The problem of organizational effectiveness has traditionally been studied by means of

the goal approach (Parson 1964; Price, 1971; Cameron & Whetten 1981). The

distinguishing characteristic of the goal model is that it defines effectiveness in terms of

the degree of goal achievement. The greater the degree to which an organization

achieves its goals the greater is its effectiveness. The goal model approach to

organizational effectiveness assumes that organizations are designed to achieve certain

goals, both formally specified and implicit (Perrow 1965; Sowa et al., 2004). The model

also assumes that organizations have goals that are clearly defined and easily measurable

and that data relevant to those measures can be collected, processed and applied in a

timely and appropriate manner (Herman & Renz, 2004a; 2004b). The model views

organizations as a rational set of arrangements oriented toward achieving a goal.

Yuchtman & Seashore (1967) distinguish two components of the Goal Model approach to

organizational effectiveness. The first component is the "prescribed goal approach".

According to the authors, this component focuses on the formal charter of the

organization, or in some category of its personnel as the most valid source of information

27

concerning organizational goals. The second component is the "derived goal approach".

In this component, the researcher derives the goal of the organization from his/her theory.

The Goal Model of organizational effectiveness is suitable for those organizations where

activity is shaped by a focus on output (Cameron & Whetten 1981), and organizational

effectiveness is generally operationalized in term of productivity or efficiency (Scheid &

Greenley, 1997). Organizational effectiveness in organizations with clearly defined and

easily measurable goals may be assessed using the goal model (Cameron & Whetten,

1983).

The main criticism to the Goal Model of organizational effectiveness consistently

identified in the literature especially by the adherents of the System Resource Model, has

been that its proponents have not developed measures of effectiveness which can be used

to study many types of organizations. Adherents of the System Resource approach to

organizational effectiveness make two criticisms of the goal approach (see Price, 1971).

First, they say that the goal approach has provided no means to identify organizational

goals; second, they say that the goal approach uses society, not the organization, as the

basis for the evaluation of effectiveness. The absence of general measures is serious

because it hinders the development of theory. The existence of general measures

promotes measurement standardization; measurement standardization, in turn, facilitates

comparison; and comparison, in turn, furthers the development of theory.

3.2.2.2 System Resource Model

The System Resource Model defines effectiveness, not with respect to the degree of goal-

achievement, but in terms of the ability of the organization to exploit its environment in

the acquisition of scarce and valued resources (Yuchtman & Seashore, 1967; Price,

1971). In this model, organizational effectiveness is the degree to which an organization

can preserve its internal integration, adapt to the environment and therefore survive

(Scheid & Greenley, 1997). Organizational effectiveness is positively related to the

ability of the organization to exploit its environment. According to Sowa et al., (2004),

28

in the system resource model of organizational effectiveness, the inputs into an

organization are more important than their outputs because an organization’s ability to

maintain sufficient resources for survival is the most important indicator of effectiveness.

Steers (1975) found that the most common utilized systems criteria of organizational

effectiveness were organizational adaption and flexibility. Cameron & Whetten (1981)

see systems resource models as best fitting organizations where formalization is low or

when environmental turbulence (uncertainty and complexity) is high, and, hence, system

effectiveness precedes and is a prerequisite for goal effectiveness.

The System Resource Model of organizational effectiveness has also been criticized.

Price (1971) outlined three criticisms of this approach. First, he states that the idea of

"optimization" is an important component of effectiveness as conceptualized the

proponents of the systems approach and yet, according to the author, these same scholars

show little concern for trying to measure optimization. Second, Price argues that the

systems oriented researchers have expressed the need for general measures of

effectiveness, but none have developed these general measures that they claim to be so

necessary. Finally, Price believes that the frame of reference used in the analysis process

by the system researchers is somewhat confused. According to the author, the confusion

centers around the difference between a multidimensional approach to effectiveness with

multiple measures of effectiveness, and a multidimensional approach with multiple

measures of a series of different analytical concepts.

3.2.2.3 Internal Processing Model

The Internal Processing Model conceptualizes organizational effectiveness as the absence

of internal strain and a smooth internal functioning of organizations / networks (Lee,

2006). For Alter & Hage (1993), much of the existing government and foundation

sponsored inter-organizational systems has adopted the internal processing model. They

believe that the choice of this model has been based on the assumption that the outcomes

of the system, the product or service, will be of higher quality if the system functions

29

smoothly and with a minimum of conflict. There are currently several attempts to

evaluate service delivery using the internal processing model (Lee, 2006).

3.2.2.4 Multiple Constituencies Model

The Multiple Constituencies’ Model defines organizational effectiveness as the ability of

organizations to satisfy key strategic constituencies in their environment (D’Aunno,

1992; Zammuto, 1984; Sowa et al., 2004). This approach to organizational effectiveness

began to emerge when researchers focused less on the assessment criteria of abstract

dimensions and more on the concrete expression of stakeholders’ expectation (Connolly

et al., 1980; Zammuto, 1984). The model recognizes that an organization comprises

multiple stakeholders or constituents who are likely to use different criteria to evaluate its

effectiveness (Herman & Renz, 1998). Effective organizations are viewed as those which

had accurate information about the expectation of strategically critical constituents and

adapted internal organizational activities, goals, and values to match those expectations

(Scheid & Greenley,1997). In the Multiple Constituency Model, the emphasis is on the

organizations’ ability to satisfy (or adapt to) divergent preferences. The Multiple

Constituency Model conceives of differing groups of stakeholders, such as clients or

customers, board members, staff, volunteers, and funders, as probably having different

goals and requires that researchers recognize the potential differences in their interests

(Herman & Renz, 1998).

The Multiple Constituencies’ Model of organizational effectiveness spawns a large

number of research (Whetten, 1978; Cameron, 1978; Tsui, 1990). According to Cameron

& Whetten, (1983), researchers using this approach encountered four difficult

methodological challenges including (i) When asked individual stakeholders have

difficulty explaining their personal expectations for an organization; (ii) a stakeholder’s

expectations change sometime dramatically, over the time; (iii) a variety of contradictory

expectations are almost always pursued simultaneously in an organization and (iv) The

expectations of strategic constituencies frequently are unrelated, or negatively related, to

their overall judgments of an organization’s effectiveness.

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I summarize in Table 3 below, the four model traditionally used to study organizational

effectiveness

Model Definition When Useful? Criticisms

An organization is effective to the extent that

The model is most preferred when

Goal model It accomplishes its stated goals Goals are clear, consensual, time-bound, measurable

No means to identify network goals; Absence of general measures for effectiveness.

System resource model

It acquires needed resources A clear connection exits between inputs and performance

Little concern for trying to measure optimization, a big component of effectiveness; No general measures of effectiveness; Confusion centers around the difference between a multidimensional approach to effectiveness with multiple measures of effectiveness, and a multidimensional approach with multiple measures of a series of different analytical concepts.

Internal processing model

It has an absence of internal strain with smooth internal functioning

A clear connection exits between organizational processes and performance

Strategic constituencies model

All strategic constituencies are at least minimally satisfied

Constituencies have powerful influence on the organization, and it has to respond to demands

A stakeholder’s expectations change sometime dramatically, over the time; A variety of contradictory expectations are almost always pursued simultaneously in a network Expectations of strategic constituencies frequently are unrelated, or negatively related, to their overall judgments of an organization’s effectiveness.

Table 3: Models of Organizational Effectiveness

Source: Author Adaptation from Cameron (1986)

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3.3 Inter-organizational Network effectiveness

3.3.1 Defining Network Effectiveness

The concept of inter-organizational network effectiveness is discussed at length in the

literature. Much of these discussions highlight the difficulties of defining and assessing

network effectiveness (Alter & Hage, 1993; Provan & Milward, 1995; Sydow &

Windeler, 1998). For example, Sydow & Windeler (1998) argue that since establishing

a shared understanding of effectiveness is already difficult for a single organization with

a clearly identifiable center and a rather stable boundary, it is even more likely to be

puzzling for inter-organizational networks with several centers and more blurred

boundaries. They say that what exactly counts as effective and which particular

evaluating practices are really used, depends upon these stakeholders and their diverse

interests. For Provan & Milward (1995), assessing inter-organizational network

effectiveness is more complex than organizational effectiveness due to the involvement

of multiple organizations in a network. Given that networks use multiple organizations to

produce one or more pieces of a single service, making their evaluation in order to assess

their effectiveness becomes more complex than that of a single organization.

Alter & Hage (1993), identifies two other reasons why it is difficult to conceptualize

effectiveness of network systems. First, according to the paper, inter-organizational

networks go through phases, each phase having a set of developmental tasks that must be

accomplished before the next phased can be successfully entered. Evaluation of the

network must be phase-specific, or expectations will be inappropriately high. The second

area of concern identified by Alter & Hage (1993), when assessing inter-organizational

effectiveness is the level of analysis. In network systems, even if it is possible to specify

system level goals and objectives, one is faced with deciding the level at which the data

will be collected. This is difficult because the production process is a hierarchy of cause

and effect in a cybernetic process, with change occurring at different levels, and the

outcomes at each level acting as determinants for the next set of outcomes.

32

With all these difficulties in conceptualizing inter-organizational network effectiveness,

researchers have come out with wide varieties of the concept. For Goodman et. Al.,

(1977), inter-organizational effectiveness should be conceptualized as an outcome, but

measured relative to the constraints that exit in the system. According to the authors, the

expectation of what is a reasonable outcome, given the context and the barriers to goal

achievement, is the best measure of effectiveness. Alter & Hage (1993) define

effectiveness in inter-organizational network as the perception among administrators and

workers that their collective effort is achieving what it was intended to achieve, that it

works smoothly and that it is reasonably productive. According to Sydow & Windeler

(1998), inter-organizational network effectiveness is an outcome and as a medium of

inter-organizational practices. According to the paper, “network effectiveness can be

understood as the viability and acceptability of inter-organizational practices and

outcomes in the light of system requirements and powerful stakeholders, both of which

are, of course, subject to change in the course of time”.

All these definitions highlight as I mentioned earlier, the complexity of the concept of

inter-organizational network effectiveness as it encompasses many different perspectives.

Drawing from these different perspectives and trying to be consistent with the concept of

effectiveness at the organizational level, in my study, I conceptualized network

effectiveness as a multidimensional concept measured as the level of activities and as the

level of collaboration. In this research, the most effective network or organization is the

one that displays the highest level of humanitarian activities or the highest level of

collaboration.

The daunting difficulties in determining a clear definition and the criteria for assessing

inter-organizational effectiveness may explain the limited number of studies on inter-

organizational network effectiveness in general, and especially in the nonprofit sector.

Notwithstanding these difficulties they have been attempts to investigate inter-

organizational network effectiveness. Reviewing the literature, I found five conceptual

studies on approaches to assess inter-organizational network effectiveness. In the

following subsections I first review these different approaches ; secondly I briefly

33

present the empirical work in the nonprofit sector using one of these approaches and

finally I discuss the main issues I identified in this body of literature on organizational

and network effectiveness.

3.3.2 Model of Network Effectiveness

3.3.2.1 Performance Gap Model

Alter & Hage, (1993) proposed a “performance gap” model to assess inter-organizational

network effectiveness. Performance gap is defined as the difference between the current

situation and the idealized standard. According to Alter & Hage, effectiveness is usually

best measured by the expectation of what is a reasonable outcome, given the context and

the barriers to goal achievement. They argue that effectiveness is achieved when goals

are met within the context of technological and resource constraints, given certain levels

of internal conflict and pressure from external constituencies. Alter & Hage, (1993)

identified and discussed factors associated with high level of performance gap. These

factors were grouped into the following five categories: (i) environmental controls –

vertical dependency, autonomy, involuntary status-; (ii) technological characteristics –

task scope, task uncertainty, task intensity, task duration, task volume-; (iii) structural

characteristics – centrality, size, complexity, differentiation, connectivity- (iv)

administrative decision making – impersonal methods, personal methods, groups

methods- ; and (v) task integration –sequential pattern, reciprocal pattern, team pattern-.

3.3.2.2 Provan & Milward Model

Provan & Milward (1995) developed a model of inter-organizational network

effectiveness through a comparative study of four community mental health networks.

They investigated the relationship between the structure and context of mental health

networks and their effectiveness. Findings from this research suggest that network

effectiveness could be explained by various structural and contextual factors such as

network integration, system stability and environmental resource munificence. Provan

34

& Sebastian (1998), further developed the model, focusing on clique and clique overlap

in the networks. Their findings suggest that achieving integration across an entire

network of organizations is difficult. Their theory is that most effective networks are

those that are integrated at clique or sub-network level.

Network Structure

Centralized integration

Direct, non fragmented

external control

Network Effectiveness

Network Context

System stability

High resource munificence

Figure 2. A Preliminary model of network effectiveness

Source : Provan & Milward (1995)

3.3.2.3 Principles Agents Model

Provan & Milward (2001) propose another approach to assess inter-organizational

network effectiveness based on the Principles Agents theory. In addition to structure and

context, Provan & Milward (2001) examined network effectiveness at different levels.

These three levels are (i) community, (ii) network, and (iii) organization/participant. The

paper argues that organization/participant and network-level effectiveness criteria can be

satisfied by focusing on community-level effectiveness goals. Weech-Maldonado et al.,

(2003) build upon Provan & Milward’s (2001) network effectiveness framework and

Gamm’s (1998) accountability framework to develop a “stakeholder accountability

approach” in assessing network effectiveness. The stakeholder accountability approach

posits that with each level of analysis (community, network, organizational/participant)

there are different effectiveness criteria reflecting the needs of the various stakeholders.

The authors use this approach to evaluate the effectiveness of community health

35

partnerships. Figure 3 below depicts the relationships between network effectiveness at

different levels of network analysis and influence by key stakeholders.

Network-level

effectiveness

Community-level

effectiveness

Agents

Organization/

participant-level

effectiveness

Key Stakeholders

Principals

Clients

Figure 3. Relationships between network effectiveness at different levels of network

analysis and influence by key stakeholders

Source: Provan & Milward (2001)

3.3.2.4 Stucturalist Perspective Model

Sydow & Windeler (1998) define inter-organizational network effectiveness as viable

and acceptable outcome and practices. They argue that network effectiveness form a

structurationist perspective, is more than embedded in social interactions and structures,

it is social in character. They discuss the concept of inter-organizational network

effectiveness in the light of Giddens’ (1984) duality of structure. For Sydow & Windeler

the meaning of the criteria to assess network effectiveness is not simply given, but

necessarily interpreted and ascribed (signification). Moreover, these criteria are always

interest-related and value-laden (legitimation). And finally, they are powerfully (re-)

produced by individual and collective agents (domination).

36

Sydow & Windeler (1998) identify two levels of analysis of network effectiveness

including (i) the level of the individual network firm and (ii) the level of the total inter-

organizational network. On the level of individual network organizations, they argue that

network effectiveness results from that part of the network effect which a particular

network firm is able to appropriate and eventually to represent in its accounts. In this

sense, network effectiveness contributes to organizational effectiveness. On the level of

the total inter-organizational network, network effectiveness depends upon the

effectiveness of all single network firms and upon the augmentation of resources to be

achieved by the differentiation and integration of the entire network (Sydow & Windeler,

1998). For Sydow & Windeler, network effectiveness on this level of analysis usually

evades conventional calculating and accounting practices by taking the efficacy of

network structures into account.

3.3.2.5 Adaptive Capacity Model

Building upon Sydow & Windeler (1998), Staber & Sydow (2002) propose the concept

of adaptive capacity as an appropriate approach to assess organizational and inter-

organizational network effectiveness in highly volatile and complex environments such

as the case in the humanitarian assistance sector. They define adaptive capacity as the

ability of organizations or networks to cope with unknown future circumstances.

Organizations / networks with high adaptive capacity can reconfigure themselves quickly

in changing environments and consequently are more effective. They argue that adaptive

capacity should thus be viewed in relative and dynamic terms. That is, organizations /

networks have adaptive capacity when learning takes place at a rate faster than the rate of

change in the conditions that require dismantling old routines and creating new ones.

Using Giddens’s structuration theory (Giddens, 1984), Staber & Sydow (2002) discuss

multiplexity, redundancy, and loose coupling as important structural dimensions of

adaptive capacity.

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These different conceptual models for assessing network effectiveness found in the

literature most of the time borrowed from the four models traditionally used to study

organizational effectiveness that I discussed earlier.

3.3.3 Predictors of Network Effectiveness in Nonprofit

Previous research has also identified important predictors of inter-organizational network

effectiveness (Provan & Milward, 1995; Moscovice, et al., 1995; Wright et al., 1995;

Provan & Sebastian, 1998; Schumaker, 2003; Lemieux-Charles et al., 2005). These

predictors could be grouped into two categories, structural and relational. For example,

several authors (e.g., Provan & Milward, 1995; Moscovice, et al., 1995; Wright et al.,

1995; Provan & Sebastian, 1998) highlight the importance of integration of network

members to network effectiveness.

Provan & Sebastian (1998) argued that achieving integration across an entire network of

organizations is difficult. Their findings also suggest that to be most effective, clique

integration must be intensive, involving multiple and overlapping relationships both with

and across organizations that compose the core of a network. Lerch et al., (2006)

investigated the relationships between formal cluster governance and actual networks of

relationships and between multi-dimensional network integration and innovation

activities. The paper applies a multi-level analysis that distinguishes the cluster level

from network and clique levels and accounts for the recursive interplay between

structural properties of these levels and how agents refer to them in inter-organizational

inter-actions. The paper used longitudinal data which allow for studying network

dynamics. Their results were consistent with those of Provan & Sebastian (1998). They

found that multiplex over-lapping cliques provide not only for a fair amount of network

integration, but also a social context conducive for turning complex knowledge of

research organizations into marketable products.

Lemieux-Charles et al., (2005) examined the effectiveness of four community-based,

nonprofit dementia care networks located in Ottawa, Toronto, Hamilton, and the Niagara

38

region. The research focused on the evolution, structure, and processes of the networks

and on how these networks served the needs of care recipients and caregivers who were

using community-based or ambulatory care services provided by acute-care agencies.

Though the authors studied each network as a whole, they also examined the

relationships that existed among groups of agencies within them. The types of

relationships examined were based on activities related to administrative functions and

service delivery functions. Findings of the study suggest that members perceived higher

administrative and service delivery effectiveness when network members shared multiple

ties with members of different groups within the network as opposed to the sharing of ties

across the network. The centralization of network structure was also found to be related

to the perception of service delivery effectiveness.

Morehead (2008) provides insight into the correlates of effectiveness for a type of health

network, vertically integrated rural health networks. The study uses Provan & Milward’s

(2001) framework for evaluating the effectiveness of public-sector organizational

networks to analyze the effectiveness of twenty three rural health networks. One-to-one

interviews, questionnaires, and archives were used to collect data on the networks

sampled. Findings of the study revealed a few significant predictors for the effectiveness

of vertically integrated rural health networks. Financing was found to be the most

important predictor, as it was significant at both the community and network levels. Both

cohesiveness and the number of problems in the rural environment were also found to be

significant predictors but only at the network level. No significant predictors were found

at the organizational level; however, organizational and network-level effectiveness were

found to be strongly correlated with each other. Overall, networks were found to be more

favorable about their effectiveness at the network and organizational levels.

Similarly, the diversity of network membership is also deemed relevant by several

authors (e.g., Moscovice, et al., 1995; 1996; Schumaker, 2003). Schumaker (2003) for

example found that effectiveness is influenced by external and internal factors that are

39

operationalized through external control, technology, structure, and operational process

variables. In Table 4 below, I present a summary of these studies.

Authors Issues Measures of effectiveness Findings/outcomes

Provan & Milward (1995)

Develop a theory to assess network effectiveness

Perception of solving problems Building social capital Decrease service duplication Improve coordination Goal commitment

Networks are more effective when network integration is centralized, external fiscal control by the state is non-fragmented and direct, resources are sufficient, and the overall system is secure

Moscovice et al., (1995)

Develop an approach to study vertically integrated rural health networks

Benefits and costs of health care provision to network’s clients

Questions for further research

Grusky (1995)

Assess networks effectiveness of mental health care delivery networks

Service quality Coverage Comprehensiveness Coordination

The longer key inter-organizational network agency directors have served the more likely the care system was perceived as effective. The more powerful the lead agency relative to other organizations in the network the more likely the system was perceived as effective.

Provan & Sebastian (1998)

Explore the use of clique analysis for explaining network effectiveness.

client outcomes Effectiveness was negatively related to the integration of full networks. In contrast, effectiveness was positively related to integration among small cliques of agencies when these cliques had overlapping links through both reciprocated referrals and case coordination.

Provan & Milward (2001)

Develop a framework to assess network effectiveness at three levels of analysis (community, network, and organization/participant)

Network membership growth Range of service provided Absence of service duplication Relationship strength (multiplexity) Creation and maintenance of network administrative organization (NAO) Integration/coordination of services Cost of network maintenance Member commitment to network goals

A framework with different effectiveness criteria depending on the level of analysis

Schumaker (2003)

Assess networks outcomes of rural health care delivery networks

Gap between best possible and actual practice

Effectiveness increased with network connectivity, decision making methods, and pattern of service delivery. Centrality and network size decrease together where there is little reliance on vertical sources of funds.

Weech-Maldonado et al., (2003)

Develop an approach to assess network effectiveness (stakeholder accountability approach)

Perceived benefit to the various stakeholders of the network

Use the approach to evaluate the effectiveness of community health partnerships

Lemieux-Charles et al., (2005)

Assess the effectiveness of community-based networks

Facilitate sharing Provide opportunity for share program Facilitate administrative information exchange

Perceived effectiveness increased with multiplexed ties among members of different groups within the network. Perceived effectiveness related to the centralization of network structure.

40

Authors Issues Measures of effectiveness Findings/outcomes

Lerch et al., (2006)

Study the emergence and overlap of organizational cliques in an optics/photonics cluster in Berlin-Brandenburg.

The paper applies a multi-level analysis that distinguishes the cluster level from network and clique levels and accounts for the recursive interplay between structural properties of these levels and how agents refer to them in inter-organizational inter-actions. The paper used longitudinal data which allow for studying network dynamics.

Arya & Lin (2007)

Assess the impact of organization characteristics and network structure characteristics on collaboration outcomes

Ability to obtain funding Ability to enhance reputation Ability to meet clients’ needs

High-status organizations are able to derive critical resources from network involvement

Morehead (2008)

Assess networks effectiveness of rural health care delivery networks

Perceived benefit Number or organizations added Number of service provided Existence of NAO

Financing was found to be the most important predictor, of network effectiveness

Table 4: Inter-organizational Network Effectiveness in the Nonprofit Sector

3.4 Issues Identified in the Literature on Effectiveness

After reviewing the literature on effectiveness at organizational and network level, one

general observation is that while several studies have investigated this concept, and

several other have provided conceptual models to assess effectiveness, limited research

has used these models to empirically analyze the possible antecedents of effectiveness,

particularly for humanitarian inter-organizational networks.

I also observed that each of the four models of organizational effectiveness had a

specific focused perspective of effectiveness. For instance, in the Goal Model -

effectiveness is the ability to excel at one or more output goals - the focus is on the output

of the organizations. The System Resource Model - effectiveness is the ability to acquire

scarce and valued resources from the environment -focuses on the input. Concerning the

Internal Process Approach - effectiveness is the ability to excel at internal efficiency,

coordination, motivation, and employee satisfaction- the focus is on the transformation of

input to output. However, all these different focuses had one thing in common. They all

assess effectiveness based mostly on resources internal to organizational. Not much

attention is paid to external resources.

41

Concerning the inter-organizational network effectiveness, I made the following three

observations. Firstly, in almost all of the various inter-organizational network

effectiveness models the focus was at the whole network level of analysis. Using these

models, it would be difficult to conduct organizational level of analysis. Findings from

empirical work (e.g. Stuart et al., 1999; McEvily & Zaheer, 1999; Stuart, 2000;

Rothaermel, 2001) suggest that inter-organizational relationships play a significant role in

shaping the effectiveness of an organization.

Secondly, the vast majority of studies related to inter-organizational network

effectiveness in the nonprofit field are conducted in the public health sector. Moreover, in

most of them, the level of analysis is either community or network. To my knowledge,

only two studies in the specific field of humanitarian assistance investigate humanitarian

inter-organizational network effectiveness. Those papers are Stephenson (2005) and

Stephenson (2006). Stephenson (2005) identifies some of the reasons for the problems

of inter-organizational coordination faced by humanitarian organizations and suggests

ways to address these problems in order to have more effective humanitarian inter-

organizational networks. Stephenson (2006) contributes to the debate in the humanitarian

community about how to make humanitarian assistance more effective. The author argues

that the problem of power and authority in the environment of humanitarian assistance,

best conceived as an inter-organizational social network, must reconceived.

Thirdly and more importantly, using social network theories in the study of

organizational performance, social network researchers have focused on the

organization’s ego network, which encompasses the focal organization (ego), its set of

partners (alters), and their connecting relationships (Wasserman & Faust, 1994). For

example, by counting the number of alliance partners and measuring structural

equivalence, patent counts, and relative scope, Baum et al., (2000) found that the

composition of alliance networks explains differences in organizational performance.

Ahuja (2000) examined the effects of direct ties, indirect ties, and structural holes on

innovation output. Arya & Lin (2007) found that nonprofit organizations that provide a

broad range of services enhance their effectiveness from collaboration in terms of

42

resource gains. Findings of the study also suggest that high-status organizations are able

to derive critical resources from network involvement.

Other social network studies have explored the effect of network structural characteristics

such as centrality, network density, and clique structure on network-level performance /

effectiveness in terms of outcomes (Provan & Milward, 1995; Provan & Sebastian, 1998;

Lerch et al., 2006). For example, the findings from Provan & Sebastian (1998) suggest

that the most effective networks are those that are integrated at clique or sub-network

level. Their findings also suggest that to be most effective, clique integration must be

intensive, involving multiple and overlapping relationships both with and across

organizations that compose the core of a network. Social network researchers have also

shown that strong ties differ from weak ties in terms of their effect on organizational

performance (Rowley et al., 2000).

However, many of these studies have focused on analysis only at the dyadic or the

network level. There is little research in the inter-organizational networks literature

about organizational-level characteristics that can explain whether or not organizations

can enhance their performance from their network positions. Some studies have looked at

the relationships between organizations’ network ties and these organizations’

performance (e.g., Powell et al., 1999; Stuart, 2000; Lee et al., 2001; Almeida et al.,

2003), but none of these studies have explicitly investigated the relationships between

individual organizational characteristics, ego-net characteristics and network structural

characteristics as antecedents of effectiveness. My dissertation intends to contribute to

reduce this gap in the literature.

I investigate inter-organizational network effectiveness in the humanitarian field. I study

a community of interest in humanitarian information management and exchange. Using a

mixed methods research design, I explored the relationships between the structural

properties of network and network effectiveness in humanitarian information exchange.

Network effectiveness was assessed using three different criteria including one subjective

criteria – Perceived network effectiveness and two objectives criteria – number of funded

43

projects and number of funding partners. My investigation is conducted at two different

levels of analysis, network and organizational levels.

Provan & Sebastian (1998) found that network-level effectiveness can be explained by

intensive integration through network cliques. Building upon this work, I explored

networks of international heterogeneous and geographically dispersed organizations

engaged in humanitarian assistance and disaster relief. I sought to understand the extent

to which Provan & Sebastian’s Model would explain effectiveness in this context. In

addition, unlike Provan & Sebastian who assessed network effectiveness using one

subjective criteria (Patient outcomes), in my study, I explored three effectiveness criteria

including one subjective and two objectives.

At the organizational level, I combined two theoretical lenses including Social Network

and Resource Based View. Network structural characteristics (density, centrality, clique

and clique overlap) have been found to have implications on performance/effectiveness

(Ahuja & Carley, 1999; Tsai & Ghoshal, 1998; Tsai, 2000; Nohria & Garcia-Pont, 1991;

Wasserman & Faust, 1994; Kilduff & Tsai, 2006; Provan et al., 2007). Similarly, the

embeddedness of organizations in networks of external relationships with other

organizations holds significant implications for organization performance/effectiveness

(Granovetter, 1985; Uzzi, 1996; 1997; 1999; Gulati et al., 2000). Resource Base View

(RBV) explains performance / effectiveness exclusively through internal resources.

(Barney, 1991; Prahalad & Hamel, 1990; Barnett at al., 1994). As mentioned earlier,

information technology (IT) has also been shown to play a critical role in mitigating the

informational related issues for inter-organizational humanitarian response (Comfort,

1990; Graves, 2004; Comfort & Kapucu, 2006; Moss & Townsend, 2006).

By using this combined theoretical approach, I intend to contribute to address some of the

shortcomings of the traditional models of assessing organizational effectiveness. As

discussed earlier, all these models have been criticized in the literature. For example one

main criticism to the Goal Model of organizational effectiveness has been that the model

does not provide measures of effectiveness which can be used to study many types of

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organizations. Concerning the System Resource Model, it is said that there is confusion

around the difference between a multidimensional approach to effectiveness with

multiple measures of effectiveness, and a multidimensional approach with multiple

measures of a series of different analytical concepts (Price, 1971). One of the criticisms

to the Multiple Constituencies Model is that expectations of stakeholders can change

sometime dramatically over the time. Moreover, a variety of contradictory expectations

are almost always pursued simultaneously in a network (Price, 1971).

In this study, I also intend to further extend the RVB by taking into consideration the

characteristics of the networks in which organizations are embedded. Unlike most RBV

studies that conceptualize organizations as atomistic profit-seeking entities, my study

views organizations as resource-sharing entities embedded in a web of complex inter-

organizational relationships. Reviewing the literature, I found two similar studies

including Zaheer & Bell (2005) and Arya & Lin (2007). The first was conducted in the

for-profit sector and the later in the nonprofit. None of the two studies explored network

level variables.

Zaheer & Bell (2005) investigated how innovative capabilities—both those internal to

organizations and those they access through their networks—influence the performance

of Canadian mutual fund companies. Their proposition was that organizations occupying

better network structures may be better able to exploit their internal capabilities and

therefor enhance their performance. They found that organization’s innovative

capabilities and its network structure both enhance organization performance. Their

findings also suggested that innovative organizations that bridged structural holes got a

further performance boost.

Arya & Lin (2007) extend the resource-based view perspective to a network of nonprofit

organizations by investigating the roles of organizational characteristics, partner

attributes, and network structures on organizational ability to acquire monetary and

nonmonetary resources through collaborations. The study views organizations as

resource-sharing entities that are embedded in complex network relations. The results of

this research suggested that nonprofit organizations that provided a broad range of

45

services enhanced their effectiveness from collaboration in terms of resource gains.

Findings of the study also suggested that high-status organizations were able to derive

critical resources from network involvement.

My study differs from these two on the following points: Firstly, both studies are

conducted on organizations that are geographically collocated (one country for Zaheer &

Bell (2005) and one city for Arya & Lin (2007). In my study I investigate organizations

that are geographically distant. Secondly, organizations investigated in both studies focus

on one specific type of activities. Zaheer & Bell (2005) studied mutual fund companies

and Arya & Lin (2007) examined organizations providing HIV/AIDS services. My study

explored organizations engaged in humanitarian assistance. In the humanitarian relief

field there is a wide range of needs requiring various types of services (e.g. provide

shelter, food, water, sanitation, coordination). Relief activities often involve

heterogeneous organizations including both for-profit and nonprofit.

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4 THEORETICAL FRAMEWORK

4.1 Introduction

In the humanitarian relief field, an integrated framework is required to understanding

effectiveness at organizational and network levels. I propose in this study, a framework

that incorporates three theoretical lenses including Social Network theories and Resource

Based View. In the subsection below, I provide a brief discussion of each of these four

theoretical approaches, followed by the description of my research model and

hypotheses.

4.2 Social Network Theories

Across the literature, the use of the term theory in relation to social networks varies

considerably. The phrase “social network theory” is often used interchangeably with

“social network analysis”. I thought that it was fundamental in my study to clarify

upfront, the distinction between social network theory and social network analysis. Social

network theories seek to explain the functioning of networks and the relationships that

interconnect network members. Social network analysis is the methodology used to

investigate network behavior. I elaborate more on social network analysis in the

Methodology chapter of this study.

A number of terms common to social network theories and social network analysis forms

the terminology of network research. They include:

• Network: an interconnected system

• Node/actor/social entity: “discrete individual, corporate or collective social units”

(Wasserman & Faust, 1999, p. 17)

• Ties: the relationship connection between pairs of nodes/actors/entities:

o Content: the resource shared, delivered or exchanged

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o Directed/Asymmetrical: content flows in one direction

o Reciprocal/Symmetrical: content flows in both directions

o Undirected: physically proximate but no exchange, or the exchange is not

considered relevant to the research question

o Strong: close association, based on the research context

o Weak: distant association, based on the research context

• Structural properties:

o Size: the number of logically possible relationships; the reach or extent of

a network that describes the amount of information an actor will have

access to

o Density: the extent to which members are connected to all other members

o Degree: the number of connections from an actor to others, outgoing and

incoming

o Centralization: the extent to which a set of actors are organized around a

central point(s)

o Distance: the number of connections between actors and the number of

ways available to connect two actors

o Clusters: subgroups of highly interconnected actors

o Cliques: fully interconnected clusters

• Network positions:

o Prominence: a network position of distinction that describes the position

of an individual in a network as opposed to centralization that measures

the configuration of the network as a whole

o Brokerage: those network positions that provide bridging opportunities to

other networks; where entrepreneurial opportunities exist

o Equivalence: categorizing actors who have the same profile of relations

across all other actors in the network.

The application of social network theories to the study of groups and group dynamics has

its roots in the 1930s and the formulation of sociometry (Moreno, 1934). Social network

theories investigate the patterns of relationships among network members and the

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structural network attributes (Wasserman & Faust, 1994). Social network theories suggest

that the patterns and implications of relationships demonstrate specific behavioral

principles and properties: “network theories require specification in terms of patterns of

relations, characterizing a group or social system as a whole” (Wasserman & Faust, p.

22). Understanding of these characteristics can help explain the functioning of networks.

Social network research explores form or relationships such as social groups, cliques,

social cohesion, roles, positions, dominance, social exchange, reciprocity. Important

contributions in the social network literature include the notion of structural holes (Burt,

1992); the distinction between weak and strong ties (Granovetter, 1973); the degree

centrality measures of network positions (Bonacich, 1987; Freeman, 1979; Ibarra, 1993;

Podolny, 1993); the measures of network density and social capital (Burt, 1992, 1997;

Coleman, 1988; 1990); the measures of network cohesion (Coleman et al., 1957); and the

concept of structural equivalence (Burt, 1987).

A large body of literature applies social network theories to the study of inter-

organizational networks and performance / effectiveness. Social network research

criticizes theories that seek to explain performance / effectiveness solely on the basis of

unilateral profit-seeking behavior in a resource-based or competition-oriented

environment (Granovetter, 1985; Gulati, 1995; Nohria, 1992). Instead, social network

researchers analyze inter-organizational relationship structures and examine the impact of

network-level structural and relational characteristics on organizational performance /

effectiveness.

Using social network theories in the study of organizational performance, social network

researchers have focused on the organization’s ego network, which encompasses the

focal organization (ego), its set of partners (alters), and their connecting relationships

(Wasserman & Faust, 1994). For example, by counting the number of alliance partners

and measuring structural equivalence, patent counts, and relative scope, Baum et al.,

(2000) found that the composition of alliance networks explains differences in

organizational performance. Ahuja (2000) examined the effects of direct ties, indirect

49

ties, and structural holes on innovation output. Arya & Lin (2007) found that nonprofit

organizations that provide a broad range of services enhance their effectiveness from

collaboration in terms of resource gains. Findings of the study also suggest that high-

status organizations are able to derive critical resources from network involvement.

Other social network studies have explored the effect of network structural characteristics

such as centrality, network density, and clique structure on network-level performance /

effectiveness in terms of outcomes (Provan & Milward, 1995; Provan & Sebastian, 1998;

Lerch et al., 2006). For example, the findings from Provan & Sebastian (1998) suggest

that the most effective networks are those that are integrated at clique or sub-network

level. Their findings also suggest that to be most effective, clique integration must be

intensive, involving multiple and overlapping relationships both with and across

organizations that compose the core of a network. Social network researchers have also

shown that strong ties differ from weak ties in terms of their effect on organizational

performance (Rowley et al., 2000).

4.3 Resource Based View

Resource Based View (RBV) theory conceptualizes organizations as heterogeneous

entities consisting of bundles of idiosyncratic resources (Penrose, 1959; Rumelt, 1984 ;

Wernerfelt, 1984). The Resource-Based View theory posits that organizations possess

resources, a subset of which enable them to achieve competitive advantage, and a subset

of those that lead to superior long-term performance. Barney (1991) identifies two

preconditions for competitive advantage including (i) resource heterogeneity and (ii)

imperfect mobility.

According to Barney (1991), resource heterogeneity requires that not all organizations

possess the same amount and kinds of resources. Imperfect mobility involves resources

that are non-tradable or less valuable to users other than the organization that owns them

(Peteraf, 1993). Generally, the organization is said to possess a set of resources that can

produce a positive, neutral, or negative impact on its overall competitive advantage. This

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impact depends on two characteristics of each resource: its value and its rarity (Barney,

1991). In addition, the firm’s competitive advantage is influenced by interactions,

combinations, and complementarities across internal resources of the firm (Amit &

Schoemaker, 1993). The competitive advantage of the firm can be understood as a

function of the combined value and rarity of all firm resources and resource interactions.

4.4 Working Definitions

4.4.1 Effectiveness

As discussed earlier, effectiveness is a multidimensional concept that is especially

challenging to measure in humanitarian assistance and disaster relief which often involve

a large variety of stakeholders with diverse goals and for which outputs are not easily

operationalized. In my study, effectiveness has three dimensions. In the first dimension, I

measure effectiveness in term of level of activities in humanitarian assistance. The most

effective organization/network is the most active. For this dimension, I use the number of

funded projects as measure of effectiveness. In the second dimension, I measure

effectiveness in term of level of collaboration in humanitarian assistance. The most

effective organization/network is the one the most in collaborative activities. For this

dimension, I use the number of funding partners as measure of effectiveness. The third

and last dimension is perceptual. The most effective organization/network is the one that

displays the highest perception of effectiveness. In this research, I use indifferently the

term effectiveness and performance.

4.4.2 Network

In my research, the term network is used to describe multiple-organizational relations

involving multiple nodes of interactions. A network is group of organizations which, on a

voluntary basis, exchange information and undertake joint activities and which organize

themselves in such a way that their individual autonomy remains intact. In this definition

important points are that the relationship must be voluntary, that these are mutual or

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reciprocal activities, and that belonging to the network does not affect autonomy and

independence of the members.

Multidimensional networks refer to networks examined at more than one level, with more

than one set of nodes and more than one type of link (Lee, 2008). Understanding

networks in the field of humanitarian relief can be enhanced by considering the content of

relationships that exist among organizations. Katz & Anheier (2005) identify the major

types of relationships among stakeholders (e.g. nongovernmental organizations,

international nongovernmental organizations, and international governmental

organizations) in responding to humanitarian disasters. They include information

exchange, project collaboration, participation in meetings and forums, or joint

membership in advocacy coalitions. My study will be concerned with two types of

collaborative relationships, namely projects and advice.

In the field of humanitarian relief, inter-organizational networks can be classified into

two types: those oriented to project implementation and those oriented to information-

sharing (Lee, 2008). The purpose of the implementation network is to implement

humanitarian relief projects. Implementation networks are activity-focused, project-

based networks which rely on partnerships to draw on resources such as funding, and

skills from various partners (Unwin, 2005). This type of network applies more to the field

of humanitarian relief since projects are more often implemented by numerous project

partners. Knowledge-sharing networks on the other hand are often formed through

affiliation to common events, such as global and regional committees, forums,

conferences, and publication activities (Katz & Anheier, 2005). These networks enable

organizations to be informed of their partner’s and community’s activities as a whole.

The networks investigated in this dissertation can be considered as a hybrid between

these two types of networks (implementation network and knowledge-sharing networks).

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4.4.3 Level of Analysis of Network Effectiveness

Irrespective of the purpose of a particular inter-organizational network, its effectiveness

can be conceptualized on at least three levels: the network, the individual organizational

level, and the beneficiary level. One of the main challenges to inter-organizational

network researchers is that the criteria for assessing and evaluating network effectiveness

will vary depending on the level being considered (Provan & Milward, 2001; Lemieux-

Charles et Al., 2005). I review below the criteria used for assessing network effectiveness

for the three levels in nonprofit sector.

4.4.3.1 Network Level

At the network level, network effectiveness is perceived as outcomes resulting from the

functioning of the network as a whole and whose benefits accrue to all members,

although not necessarily equally (Sydow & Windeler 1998; Provan & Milward, 2001).

Criteria for measuring network effectiveness at network level include improve

coordination, network membership growth, decrease service duplication, range of

service provided, absence of service duplication, relationship strength (multiplexity),

member commitment to network goals (Provan & Milward, 1995 ; Sydow & Windeler,

1998; Provan & Milward, 2001). The network level sees outcomes of network activity as

being beyond what each individual actor in the network can achieve alone and thus being

conditioned by variables at a higher level, beyond agency in the network, and include

structural factors.

4.4.3.2 Organizational Level

The second level of network effectiveness is organizational. It includes outcomes that

arise from the functioning of the network as a whole but generate specific benefits to

individual members. According to Sydow & Windeler (1998), at organizational level,

network effectiveness results from the part of the network effect which a particular

network member is able to appropriate and eventually to represent in it accounts.

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Whereas an overall network may be highly effective, individual members may be less so,

and benefits may be spread unevenly across the members. Examples of benefits to

individual organizations include increased opportunities for (i) building social capital,

(ii) sharing knowledge and information, (iii) obtaining resources including funding, and

(iv) better meeting clients’ needs (Provan & Milward, 2001) . Benefits that accrue to

individual organization may be determined by a host of factors, including the structure of

the network, the strength of relationship (multiplexity) and the demographics of the

individual organization.

4.4.3.3 Beneficiary Level

Network effectiveness can also be assessed from the point of view of the beneficiary of

the network activities. Criteria for assessing network effectiveness at the beneficiary level

include: service quality, perception of solving problems, range of services provided,

coverage, comprehensiveness (Provan & Milward, 2001). A highly effective network

from the perspective of its member organizations may have low levels of beneficiary

satisfaction. Hence, achieving network effectiveness at both the network and the

customer level requires communication. For example, research on networks of health

care providers found that each single network actor alone (healthcare provider in this

case) does not render the end user (the patient) the full service of “overall well-being”;

instead, this is done by a network of healthcare providers does (Provan & Milward,

1995).

In this study, the focus is on the first and second levels of network effectiveness. At the

network level, I seek to understand how network structure properties including network

density, centralization, connectedness, multiplex clique and clique overlaps impact

network effectiveness. I explore ego-net characteristics at organizational level.

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4.5 Research Models and Hypotheses

In this section, I develop two research models, one concerning the network level of

analysis and the second, related to the organizational level. I also present my hypotheses

for both research models.

4.5.1 Network Characteristics and Effectiveness

Exploring the influence of network structure on actors has long attracted the attention of

network researchers (Ahuja & Carley, 1999; Tsai & Ghoshal, 1998; Tsai, 2000; Nahapiet

& Ghoshal, 1998, McEvily & Zaheer, 1999; Nohria & Garcia-Pont, 1991; Wasserman &

Faust, 1994; Kilduff & Tsai, 2006; Provan et al., 2007). Many of the studies that have

explored the implications of network structures have focused in particular on centrality,

structural holes, density, and the existence of sub-networks or cliques. In my study, I also

investigate these characteristics.

4.5.1.1 Centrality

Researchers have studied different measures of centrality, including betweenness, degree,

and closeness. The literature on the three centrality measures (e.g. Freeman, 1979;

Wasserman & Faust, 1994; Kilduff & Tsai, 2006) suggests that each affects the network

in different ways. Degree centrality measures capture the actors with the most ties to

other actors in the network. The higher an actor’s degree centrality value the more direct

links that actor has with other actors in the network (Wasserman & Faust, 1994; Kilduff

& Tsai, 2006). The degree centrality of a network member may be seen as an indicator of

its potential communication activity (Freeman, 1979). Eigenvector centrality measures

the strength of a network member’s relationships to other members of the network and

the centrality of those other members (Faust, 1997). Flow betweenness centrality

measures the degree to which certain members of the network may be more central or

exercise greater influence due to their location on the paths between various

organizations (Wasserman & Faust, 1999; Faust, 1997). In this study, I use the degree

centrality measure.

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In the literature, centrally located network members are assumed to be important, while

peripheral network members are powerless (Knoke, 1990; Wasserman & Faust, 1994;

Stevenson & Greenberg, 2000; Kilduff & Tsai, 2006). The centrally located members are

enabled by their position to accomplish their purposes, but the peripheral members are

constrained by their position to powerlessness (Stevenson & Greenberg, 2000). Centrally

located network members, are likely to have advantages of information and resources

compared with those on the periphery since information and other resources are assumed

to flow more within the centrally located positions of a network (Knoke, 1990). Most

network researchers assume that peripheral network members are somehow

disadvantaged as compared with the centrally located members. However, peripheral

members, for some reasons, may want to stay in peripheral positions. For example,

peripheral actors have minimal obligations to others.

In the nonprofit context, findings form Galaskiewicz et al., (2006) suggest that

organizational centrality in inter-organizational networks may be more beneficial to

certain types of nonprofit organizations (public charitable nonprofits that rely on

donations), but may be less for others (public charitable nonprofits that depend on fees

and sales). It is also more likely that centrally located nonprofit organizations will be

closely monitored by partners as well as various funding agencies. This can further limit

the centrally located organization’s ability to devote all its resources to meet its goals. In

this study, I hypothesize that greater degree centrality increases organization

effectiveness measured both as the level of collaboration as well as the level of activities.

Hypothesis HO#1: Greater centrality increases organization effectiveness.

4.5.1.2 Structural Holes

Structural holes theory (Burt, 1992; Wasserman & Faust, 1994; Kilduff & Tsai, 2006)

highlights the benefits associated with making contacts that offer links to additional

resources without the costs associated with having more contacts than needed. According

to Burt (2000) an organization can obtain important performance advantages when

exploiting relationships to partners that do not maintain direct ties among one another.

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The absence of direct relationships among a organization’s partners (the presence of

structural holes) indicates that these partners are located in different parts of a network,

that they are connected to heterogeneous sources of information, and that their invitations

to interact present the focal organization with access to diverse opportunities (McEvily &

Zaheer, 1999). Network researchers have investigated the effect of structural hole on

network members. Members bridging structural holes have been frequently shown to

perform better than other members of the network (e.g., Finlay & Coverdill, 2000;

Hargadon & Sutton, 1997), whereas other studies have shown negative performance

effects of firms’ maintaining positions in open networks (e.g., Ahuja, 2000; Dyer &

Nobeoka, 2000). In this study, I hypothesize that organizations that bridge structural

holes will be well positioned to enhance their effectiveness measured both as the level of

collaboration as well as the level of activities.

Hypothesis HO#2: Bridging of structural holes increases organization

effectiveness.

4.5.1.3 Density

Density describes the overall communication links in a network and thus represents how

information flows among organizations. Kilduff & Tsai (2006) define density as the

number of links between members of the network compared to the maximum possible

number of links that could exist in the network. Researchers have used the concept of

density in a number of inter-organizational network studies and in various contexts (e.g.

Provan & Sebastian, 1998; Krackhardt, 1999; Sparrowe et al., 2001; Reagans &

Zuckerman, 2001). Findings from some of these studies were intuitive. For example,

Venkatraman & Lee (2004) found that the density of links in an inter-organizational

network tends to increase over time. Another example of intuitive result is Brown &

Ashman (1996). Findings from this study suggest that dense networks of local

organizations indicate high levels of social capital. Some other studies produced

counter-intuitive results. For instance, Provan & Sebastian (1998) study of the networks

of mental health agencies operating in three cities showed that the city with the lowest

network-wide density of ties among agencies had the highest effectiveness, whereas the

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city with the highest density of ties among its agencies had the lowest effectiveness. Here

I make the following two hypotheses:

Hypothesis HN#1: Network effectiveness increases with network density.

Hypothesis HO#3: Organization effectiveness increases with the density of the

network it which it belongs.

4.5.1.4 Cliques

A network clique consists of actors who all are interconnected but have no common links

with anyone else in the network (Wasserman & Faust, 1994; Kilduff & Tsai, 2006). In an

inter-organizational network, cliques may form on the basis of shared demographic

characteristics (Mehra et al., 1998). Cliques can also be created based on the provision of

a certain set of services (Morrissey et al., 1994; Provan & Sebastian, 1998). Studies on

cliques in inter-organizational networks have found that they can play important roles in

the creation of positive outcomes (Provan & Sebastian, 1998; Lerch et al., 2006). Provan

& Sebastian (1998) for example found that network effectiveness can be explained

through the intensive integration via network cliques. My hypotheses here are that:

Hypothesis HN#2: Network effectiveness increases with the number of cliques in

the network.

Hypothesis HN#3: Network effectiveness increases with the number of

organizations in cliques.

Hypothesis HO#4: Organization effectiveness increases with the number of

cliques to which it belongs.

4.5.1.5 Overlapping Clique

Clique overlap refers to the extent to which members of a clique interact with members

of other cliques (Kilduff & Tsai, 2006). Grouping network members into cliques is

important to understanding how the network as a whole is likely to behave. For example,

when cliques overlap it can be expected that conflict between them is less likely than

when they do not overlap. Also, when cliques overlap, resources can be mobilized and

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shared effectively across the entire network; when they do not overlap, resource sharing

may occur in one clique and not occur in others. In Provan & Sebastian (1998), clique

overlap was a measure of mental health system integration. Findings from this study

suggests that agencies involved in cliques related to sharing of information and referrals

represent stronger clique overlap relationships and these might be tied to outcomes.

Provan & Sebastian posited that to be effective, clique integration must be intensive,

involving multiple and overlapping links both within and across the organizations that

compose the core of the network. Researchers have argued that the high levels of

communication or inter-organizational activity occurring within cliques is a form of

coordination (Bolland & Wilson 1994). In this study, I hypothesize that the higher the

level of overlapping clique, the greater the network effectiveness.

Hypothesis HN#4: Network effectiveness increases with the level of overlapping

clique in the network.

4.5.1.6 Multiplexity

Two organizations have multiplex ties if they are connected in more than one type of

relationships (Scott, 1991; Provan & Milward, 2001; Kenis & Knoke, 2002; Kilduff &

Tsai, 2006). For instance, two organizations that are project partners and also maintain

advice relationship are connected with a multiplex tie. In an inter-organizational

network, multiplex ties would allow each organization to exchange several different types

of resources with any other organization in the network rather than exchange only a

single type of resource.

Multiplexity can be measured at the individual network member level and at the level of

the whole network. A high degree of multiplexity of a member indicates high

embeddedness of the member in a network and signifies less liability to disruption of

single relationships. A member with a large number of multiplex relations is expected to

have a high potential of mobilizing different resources and information through these

relations. On the other hand, such a member is subject to a high level of social control. At

the network level, the degree of multiplexity specifies the overlap between the different

relation-specific networks. For evaluating network effectiveness, multiplexity can be a

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particularly useful measure (Provan & Milward 2001). Effective networks might have a

majority of network members connected through two or more different types of

relationships. In this case, multiplexity will be high, reflecting commitments among

network members to one another through multiple activities. In this study, my hypothesis

is that Network effectiveness increases with its level of multiplexity.

Hypothesis HN#5: Network effectiveness increases with the level of multiplexity in

the network.

Hypothesis HN#6: Network effectiveness increases with the level of identical

cliques in the network.

4.5.2 Organizational Characteristics and Effectiveness

4.5.2.1 Organization Size

A general assumption in organizational theory is that organizations that are large or have

many constituents have more inter-organizational relationships compared with small

organizations (Blau & Schoenherr, 1971; Knoke & Wood, 1981). Large organizations are

apt to have more funds, larger facilities, larger and more diversified staffs, more clients,

more visibility and prestige, and better connections with community center power. These

resources make it highly likely that large organizations will be the object of large

dependency relationships initiated from smaller organizations (Lincoln & McBride,

1985). Size of an organization has also been recognized as an important determinant of

an organization's role in social service systems. According to Banaszak-Holl et al., (1998)

large organizations are often situated at the center of the delivery system. They have the

largest client populations and are responsible for referring large numbers of clients,

distributing funds to other agencies, and playing an important role in planning activities.

For Graddy & Chen (2006), large organizations are presumably better able to absorb the

costs of developing and sustaining partnerships. These transactions costs are considerable

and include the administration and coordination of the partnership function, partner

search costs, and the costs associated with negotiating and monitoring the terms of the

contract. Large organizations are expected to have sufficient budgets and staff to support

the development of effective networks (Graddy & Chen, 2006). Small organizations,

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however, may have greater need to form partnerships, as they are more likely to lack

requisite resources to meet contractual requirements. Large organizations, in contrast, are

more likely to have the internal capacity to deliver required services and thus have less

need for external collaboration. If an organization is able to provide most of their clients’

services internally, it has less incentive to form partnerships. Thus, size is associated with

differences in both the ability and the need to form partnerships. These effects may offset

each other, obscuring the role of organizational size in this relationship. I hypothesize

that the size of an organization is positively associated with its effectiveness

Hypothesis HO#5: The size of an organization is positively associated with its

effectiveness.

4.5.2.2 Range of Services Provided

The range of services a humanitarian organization provides (food, shelter, water,

sanitation, medical care, information services (media/coordination), IT infrastructure

and/or applications) is an important resource dimension on which organizations/

networks vary. Organizations delivering many different services are able to meet clients

with diverse needs. The power and autonomy of these organizations in the network are

likely to be high, for they are less dependent on the services of other organizations

(Kobrin & Klein, 1980). It is difficult to conceive of a plausible argument relating

diversity per se to homopholous interaction. This is particularly true of specialized

organizations. Those delivering the same services might find mutual support in

interaction, but equally specialized organizations performing quite different functions are

not apt to see themselves as like-minded peers. My hypothesis for this study is that the

range of service provided by an organization is positively associated with its

effectiveness.

Hypothesis HO#6: The range of service provided by an organization is positively

associated with its effectiveness.

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4.5.2.3 Information Technology

Information Technology (IT) “is a general term that describes any technology that helps

to produce, manipulate process, store, communicate, and/or disseminate information”

William & Sawyar (2005). Research has shown that information technology contributes

to the improvement of organizational performance (Mukhopadhyay et al., 1995;

Brynjolfsson & Hitt, 1996; Kohli & Devaraj 2003; Melville et al., 2004). For example,

Melville et al., (2004) found that information technology is valuable to organizations,

offering a wide range of potential benefits ranging from flexibility and quality

improvement to cost reduction and productivity enhancement. Previous research has also

revealed that the dimensions and extent of information technology value depend on a

variety of factors, such as the type of information technology, management practices, and

organizational structure (Dewan & Kraemer 2000; Cooper et al. 2000). Research has

also shown that the use information technology may have a positive impact on inter-

organizational collaboration and coordination (Malone & Crowston, 1994).

In the specific domain of humanitarian assistance, a rich body of literature points to the

critical role information technology plays in complex inter-organizational disaster

response plans (Comfort, 1993; Comfort et al., 2001; Moss & Townsend, 2006). Wentz

(2006) presents current knowledge and best practices in creating a collaborative, civil-

military, information environment to support data collection, communications,

collaboration, and information-sharing needs in disaster situations and complex

emergencies. Comfort (1993) identifies three main roles of information technology in

managing humanitarian disaster including. According to the author, information

technology enables disaster managers to create an interactive network that facilitates

communication and focuses attention on the same problem at the same time. The second

role identified by Comfort (1993) is that information technology allows the

representation of information in graphic form, thus simplifying complex data and

increasing the speed and accuracy of communication. Thirdly, information technology

enables and facilitates the development of a database for a given community which stores

relevant information about the community and its population and assists managers in

quickly formulating alternative solutions for assistance.

62

Additionally, the convergence of information and communication technologies, the

growth of the Internet including the mobile Internet, and the advent of social media also

termed social software are providing a significant contribution to the international

community ability to collaborate more efficiently in disaster relief. Suter et al., (2005)

defines social software “as a tool for augmenting human social and collaborative abilities,

as a medium for facilitating social connection and information interchange, and as an

ecology for enabling a 'system of people, practices, values, and technologies in a

particular local environment'" (p. 48). Examining the impact that these new technologies

are having on organizations, researchers on public relations have claimed that the

development of blogging and other aspects of the social media has significantly

empowered a wide variety of strategic organization stakeholders by giving them dynamic

opportunities that many are using to communicate more effectively (Wright & Hinson,

2006; 2007; 2008). Research on new social software have also highlighted the important

role that these technology play in humanitarian assistance and disaster relief (Palen et al.,

2007a; 2007b; 2007c; Sutton et al., 2008; Hughes et al., 2008; Liu et al., 2008 ; Vieweg

et al., 2008). Humanitarian organizations have been exploring these technologies as a

way of maximizing the effectiveness of their responses to emergent disasters and

enhancing the delivery of humanitarian relief to affected communities around the globe

(Zhang et al., 2002; Van de Walle et al., 2009).

Based on their functionality, social software is grouped into three major categories

including communication, collaboration and community (Clearinghouse, 2008a; 2008b;

2008c). In this study, I make the following hypotheses on the relationships between

information technology related variable and organizational effectiveness:

Hypothesis HO#7: The greater the variety of communication media available in

an organization, the higher its effectiveness.

Hypothesis HO#8: The greater the variety of collaboration social software

available in an organization, the higher its effectiveness.

Hypothesis HO#9: The greater the variety of community social software available

in an organization, the higher its effectiveness.

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The following two hypotheses are meant to assess the impact of the inter-action of

information technology and network structural characteristics on organizational

effectiveness.

Hypothesis HO#10: Organizations that possess a wide variety of communication

media will benefit more from high network degree centrality to enhance their

effectiveness than those that do not.

Hypothesis HO#10: Organizations that possess a wide variety of communication

media will benefit more from high network density to enhance their effectiveness

than those that do not.

In the table below (Table 5) I summarize the hypotheses formulated for this research.

Number Hypothesis Level of analysis

Hypothesis HO#1 Greater centrality increases organization effectiveness Organization

Hypothesis HO#2 Bridging of structural holes increases organization effectiveness Organization

Hypothesis HN#1 Network effectiveness increases with network density Network

Hypothesis HO#3 Organization effectiveness increases with the density of the network it which it belongs

Organization

Hypothesis HN#2 Network effectiveness increases with the number of cliques in the network

Network

Hypothesis HN#3 Network effectiveness increases with the number of organizations in cliques

Network

Hypothesis HO#4 Organization effectiveness increases with the number of distinct cliques to which it belongs

Organization

Hypothesis HN#4 Network effectiveness increases with the level of overlapping clique in the network

Network

Hypothesis HN#5 Network effectiveness increases with the level of multiplexity in the network

Network

Hypothesis HN#6 Network effectiveness increases with the level of identical cliques in the network

Network

Hypothesis HO#5 The size of an organization is positively associated with its effectiveness

Organization

Hypothesis HO#6 The range of service provided by an organization is positively associated with its effectiveness

Organization

Hypothesis HO#7 The greater the variety of communication media available in an organization, the higher its effectiveness

Organization

Hypothesis HO#8 The greater the variety of collaboration social software available in an organization, the higher its effectiveness

Organization

Hypothesis HO#9 The greater the variety of community social software available in an organization, the higher its effectiveness

Organization

64

Hypothesis HO#10 Organizations that possess a wide variety of communication media will benefit more from high network degree centrality to enhance their effectiveness than those that do not.

Organization

Hypothesis HO#11 Organizations that possess a wide variety of communication media will benefit more from high network density to enhance their effectiveness than those that do not.

Organization

Table 5: Summary of Hypotheses

The two figures (Figure 4 and Figure 5) below, depict my research models.

NETWORK

CHARACTERISTICS

Perceived effectiveness

Number of funded projects

Number of funding partners

EFFECTIVENESS

Network Attributes

Density (+)

Clique (+)

Org in Clique (+)

Clique overlaps (+)

Multiplexity (+)

Identical clique (+)

Figure 4: Research Model for Network Level of Analysis

INTERNAL

CHARACTERISTICS

Number of funded projects

Number of funding partners

EXTERNAL

CHARACTERISTICSEFFECTIVENESS

Organization Attributes

Size (+)

Service provided (+)

Communication media (+)

Collaboration social media (+)

Community social media (+)

Ego-net Attributes

Degree centrality (+)

Structural hole (+)

Number of clique (+)

Network Attributes

Density (+)

Figure 5: Research Model for Organizatioanl Level of Analysis

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5 METHODOLOGY

5.1 Introduction

This chapter presents the research design and method that I used in my study.

5.2 Research Design

I use a mixed methods research design (Tashakkori & Teddlie, 2003) to explore

multidimensional inter-organizational networks of collaborative relationships among

humanitarian organizations that are members of the Global Symposium. Using mixed

method allowed me to leverage on both the quantitative and the qualitative research

techniques.

Quantitative researchers use postpositivist propositions for developing knowledge, such

as hypotheses and questions, reduction to specific variables, and the test of theories.

Numerical data and statistics are their main instrument (Charles & Mertler, 2002). They

isolate variables and causally relates them to determine the magnitude and frequency of

relationships. In addition, they determine themselves which variables to investigate and

chooses instruments, which will yield highly reliable and valid scores (Ivankova et al.,

2006). Contrary to quantitative research, qualitative research is interpretive or

constructive. It is “an inquiry process of understanding” where the investigator develops

a “complex, holistic picture, analyzes words, reports detailed views of informants, and

conducts the study in a natural setting” (Creswell, 1998, p. 15). In qualitative research,

the investigator makes knowledge claims based on the constructivist (Guba & Lincoln,

1989) or advocacy/participatory (Mertens, 2003) perspectives. In this approach, data is

collected from those immersed in everyday life of the setting in which the study is framed

(Ivankova, et al., 2006). Data analysis is based on the values that these participants

perceive for their world. According to Miller (2000), qualitative research produces an

understanding of the problem based on multiple contextual factors.

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Combining both quantitative and qualitative in a mixed methods approach, the

investigators develop and build the knowledge on pragmatic grounds (Creswell, 2003;

Maxcy, 2003). They choose approaches, as well as variables and units of analysis, which

are most appropriate for finding an answer to their research question (Tashakkori &

Teddlie, 1998). A major tenet of pragmatism is that quantitative and qualitative methods

are compatible. Thus, both numerical and text data, collected sequentially or

concurrently, can help better understand the research problem (Ivankova, et al., 2006).

Mixed methods involve collecting, analyzing and combining both quantitative and

qualitative data within a single study. According to Creswell (2002), mixed methods help

to understand a research problem more completely. Another argument for mixed methods

is that neither quantitative nor qualitative methods are sufficient by themselves to capture

the details of the situation, such as a complex issue information management and

exchange among organizations engaged in disaster relief. When the quantitative and

qualitative methods are combined, they complement each other and allow for more

complete analysis (Green et al., 1989, Tashakkori & Teddlie, 1998).

While designing my research, I considered the following three important issues: priority,

implementation, and integration (Creswell et al., 2003). Priority refers to which method,

either quantitative or qualitative, is given more emphasis in the study. Implementation

refers to whether the quantitative and qualitative data collection and analysis comes in

sequence or in chronological stages, one following another, or in parallel or concurrently.

Integration refers to the phase in the research process where the mixing or connecting of

quantitative and qualitative data occurs. Quantitative method had the priority in my study.

I first collected quantitative data through a series of three surveys and then conducted

interviews to collect qualitative data. I integrated the qualitative and quantitative data in

the analysis phase. The research took place over a two-year period, encompassing

numerous interactions with various network members and feedback with the research

participants. Data analysis involved multiple levels of social network analysis, statistical

analysis and a combination of inductive and deductive content analysis techniques.

67

5.3 Research Participants

My research explored inter-organizational networks in the Global Symposium, a

community of interest in humanitarian information management and exchange

spearheaded by the United Nations Office for the Coordination of Humanitarian Affairs

(UNOCHA). The research participants were representative of organizations member of

the Global Symposium who attended to at least one of the five Global Symposium

meetings. UNOCHA provided me with the list of all the attendees of the various Global

Symposium meetings. They were almost all high ranked senior staff (e.g., CEO, CIO, IT

Director) in their organizations.

As stated earlier, the Global Symposium is a community of interest in humanitarian

information management spearheaded by UNOCHA. A community of interest as defined

by Arias & Fischer (2000), is made up of individuals from different backgrounds that

come together to solve a particular problem of common concern. The Global Symposium

began its activities in 2002 as a meeting of humanitarian information management

professionals. This community of interest is made up of about 300 information

technology (IT) and information management (IM) professionals from roughly 120

international and national organizations in the field of humanitarian assistance. The goals

of the Global Symposium include (i) to foster collaboration among members on

humanitarian information management related projects, (ii) to disseminate best practices

of information exchange, (iii) to sensitize its members on the critical aspect of

humanitarian information management preparedness and (iv) to facilitate headquarter-

field partnerships and to advocate for more funding from donors for humanitarian

information management related projects.

For both theoretical and empirical reasons, I subdivided the Global Symposium

community into different sub-networks. I identified three sub-networks including the

non-governmental organizations (NGO) subnet, the United Nations agencies (UNA)

subnet, and the governmental organization (GO) subnet. Separating members of a

network into subnets and analyzing how they overlap can be an important means for

68

understanding how the network as a whole is likely to facilitate or constrain certain

actions of these members (Giddens 1984; Sydow & Windeler 1998). Although the

members of the Global Symposium are all interested in humanitarian relief and especially

humanitarian information management and exchange, they theoretical differ on a good

number of characteristics including their missions / goals, their sources of funding and

their mode of governance. The three subnets were also identified based on UNOCHA

categories of organization in the humanitarian relief field. I briefly describe below, the

general characteristics of the organizations member of each of the three sub-networks.

a) NGO

NGOs are “private organizations that pursue activities to relieve suffering, promote the

interests of the poor, protect the environment, provide basic social services, or undertake

community development” (World Bank, 2000). One of the long-established activities of

these organizations is to provide humanitarian assistance. NGOs engage in two broad

types of activities including relief activities and development activities. Relief activities

consist of assisting to victims of natural or manmade disasters. Relief NGOS frequently

specialize in one or more of the five activities that are commonly understood to compose

the relief discipline: food distribution, shelter, water, sanitation and medical care.

Development activities are longer-term assistance, focusing on community self-

sufficiency and sustainability. These activities include establishing permanent and

reliable transportation, healthcare, housing, and food. NGOs’ resources come primarily

from private sources and major donor government contributions. NGOS are governed by

boards of directors that tend to reflect the particular culture, history and mandates of the

organizations concerned. In my study, the NGO subnet was made up of 72 organizations.

b) UNA

The United Nations (UN) plays a vital role in humanitarian assistance. For this endeavor,

the institution operates several major organizations among which five are such visible

players in most complex humanitarian emergencies that describing their functions and

mandates will describe most if not all of the operational work of the entire UN system in

relief operations. They are the World Food Program, the Office of the United Nations

69

High Commissioner for Refugees, the United Nations Children's Fund (UNICEF) and the

United Nations Development Program (UNDP) and the UN Office for the Coordination

of Humanitarian Affairs (UNOCHA). The UN World Food Program functions as the

food aid agency of the UN system, providing a central coordinating role in developing

crop production estimates, food aid requirements and logistics planning for major relief

operations. UNICEF'S special mandate is to focus on the relief and development needs of

women and children, which has made it the focal point among the UN agencies for

emergency medical interventions, mass inoculation campaigns for children, water and

sanitation programs and therapeutic feeding programs for severely malnourished children

in emergencies. UNDP technically has the mandate to manage UN emergency operations

in the field while UNOCHA is charged with the coordination and synchronization of

United Nations humanitarian efforts. Each of these UN organizations depends for funding

on the goodwill of member governments and/or the broader populations of those nations.

In my study, the UNA subnet comprised 25 agencies.

c) GO

Governmental organizations are owned by governments. Governmental organizations

work to achieve the goals set by the government. These goals are often set for political

reasons. The managers of these organizations are appointed by the government. The

government also provides the necessary resources to these organizations. In my study, the

GO subnet was made up of 53 organizations.

Subdividing the Global Symposium community in sub-networks also had an empirical

justification. Using social network block model I found that these sub-networks

presented diversified patterns of inter-organizational relationships. The level of inter-

organizational relationships (measured as network density) ranged from 0.076 to 0.193

for project collaboration dimension and from 0.025 to 0.074 for the advice dimension.

The United Nations agencies sub-network displayed was the most strongly

interconnected on both dimensions followed respectively by the non-governmental

organizations subnet and lastly the governmental organization subnet. On the project

collaboration dimension for example, approximately twenty percent (19.30%) of all the

possible project collaboration relationships between the organizations in the United

70

Nations agencies sub-network were actually found to exit. In contrast, only about eight

percent (7.6%) of all possible linkages between organizations in the governmental

organizations sub-network were found to exist. On the advice dimension, these

percentages were respectively (7.42%) for the United Nations agencies sub-network,

(2.92%) for the non-governmental organizations subnet and (2.52%) for the

governmental organizations subnet.

GO 0.071

0.193

UNA

NGO

0.0780.076

0.1

360.

128

Figure 6: Global Symposium Project Collaboration Sub-Networks

GO 0.0218

0.0742

UNA

NGO

0.02920.0252

0.0

3110.

0336

Figure 7: Global Symposium Advice Sub-Networks

71

An examination of the level of interaction cross the three subnets also shown a significant

discrepancy for both project collaboration and advice dimensions of relationships. Figure

6 and Figure 7 above depict these differences.

Understanding networks in the field of humanitarian relief can also be enhanced by

considering the different type of relationships that exist among organizations. In my

study, I investigate two types of inter-organizational relationships in the community. I

study the relationship on inter-organizational collaboration on humanitarian project

among members of the Global Symposium and the advice relationship.

5.4 Data Collection Instruments

I used multiple instruments to collect data including surveys, interviews and online database

search. Collecting data by different methods from different sources produces a wider scope of

coverage and may result in a fuller picture of the phenomena under study than would be achieved

otherwise (Bonoma, 1985). The data captures both the whole network and the individual

organizational perspectives on inter-organizational humanitarian information exchange

relationships among members of the Global Symposium community.

5.4.1 Survey

A survey instrument that also contains network-related questions was my main data

collection instrument (See Appendix B). I conducted a series of three surveys (October

2007, May 2008 and July 2009) and used two different types of survey instrument. The

first survey was paper based and the two subsequent were web based. The electronic

form of the survey instrument was designed based on the quality criteria identified in

(Wright, 2005). It had a simple layout using a straightforward navigation strategy. I kept

graphics and color to a minimum in other to minimize the downloading time. I used the

Survey Monkey software to develop the survey. I chose this software mainly because I had

earlier used it in several research projects with my adviser. I had developed extensive

experience and a high skill set in the use of that software.

72

On the survey instrument, an introduction page including an informed consent preceded the

survey questions. The purpose of the survey introduction page was multiple. First, I wanted

to create a trusting relationship with survey participants by repeating the survey purpose

already explained in the invitation letter. Second, the introduction page was intended to offer

a non-financial incentive – a report of the results, and to guarantee confidentiality and

privacy of research participants. Third, I wanted to provide a third party guarantee of the

survey’s authenticity and credibility by stating the University’s Institutional Review Board

(IRB) approval. The informed consent asked participants to give their permission for the

survey.

Following the introduction page, came the survey questions. Though they were not

completely identical cross surveys, there were significant overlaps especially with

regards to inter-organizational relationship questions. In general, the questions included

the following four categories: (i) respondent’s organization information; (ii) issues on

humanitarian information management and exchange in the Global Symposium

community; (iii) Global Symposium community collaborative benefits and effectiveness,

and (iv) the community inter-organizational networks. For questions concerning the

inter-organizational network, survey participants were provided with the list of members

of the Global Symposium community and were asked to identify (i) those with which

they had collaborated on humanitarian projects and (ii) those with which they had advice

relationships.

Most of the questions were structured using a five point Likert scale (Likert, 1932). For

every question or statement, I provided respondents with five choices representing the

degree of agreement on the question. For the network question, the survey instrument

included the list of all the organizations member of the Global Symposium. With regards

to conducting the Surveys, the paper based survey was administered during the 2007 Global

Symposium meeting. Survey questionnaires were handed to participants. They had to

compete it and turned back by the end of the conference. With regards to the electronic

surveys, the survey invitation was sent through direct email to each participant. The

invitation was a shortened version of the survey introduction page. After reading the

invitation, online community members ignored the post or self-selected to take the survey by

73

clicking on the survey URL. Two follow-up “reminder” invitations were sent approximately

one week apart to the participants. All inquiry email, whether sent as a reply to the invitation

was responded to as soon as I got the mail.

Both the electronic and paper forms of survey instrument have advantages and

drawbacks. By using a combination of the two forms, my intention was to leverage on

their advantages to limit the impact of their disadvantages on my research. As compared

to paper based surveys, electronic surveys present many advantages. They provide a way

to conduct studies when it is impractical or financially unfeasible to access certain

targeted populations (Couper, 2000; Sheehan & Hoy, 1999; Weible & Wallace, 1998)

and they are very cost effective as the costs per response decrease as sample size

increases (Watt, 1999). Electronic surveys also provide potentially quicker response time

with wider magnitude of coverage (Wright, 2005). Online surveys may also save time by

allowing researchers to collect data while they work on other tasks (Llieva et al., 2002) .

Moreover, electronic surveys are easy to edit.

One of the most significant weaknesses of electronic surveys is related to the access

issue. Electronic surveys’ population and sample are limited to those with access to

computer and online network. This weakness will not hamper my research since my

targeted population is made up of information professionals that are computer literate and

that have access to the Internet. Another important drawback of electronic survey is

related to sampling (Andrews et al., 2003; Howard et al., 2001). More often, relatively

little is known about the characteristics of people in online communities, aside from some

basic demographic variables, and even this information may be questionable (Dillman,

2000; Stanton, 1998). Once more this weakness was not an issue in my research since

my target was the whole community.

5.4.2 Interviews

Semi-structured Interview was the second data collection instrument that I used (See

Appendix C for the interview guide). My intent was to supplement the quantitative survey

data with a more detailed description and explanation of activities in the Global Symposium

74

community. Using semi-structured interviews allowed me to follow the same basic lines

of inquiry, which makes it more structured than the informal conversational interview,

but also leaves room for the interviewer to explore the topic while keeping focus on a

particular subject (Patton, 2002; Mason, 1996). Semi-structured interviews also allowed

the participants to explore the responses to both surveys and discuss the humanitarian

information exchange activity in the Global Symposium community. Maintaining

flexibility with the interview questions was important to eliciting the most useful

responses (Schensul & LeCompte, 1999).

The interviews focused various aspects related to the effectiveness of inter-organization

humanitarian information exchange networks in the Global Symposium community. The

majority of questions were taken directly from the survey projects with the intent to

maintain a semblance for comparison between the surveys and interviews. Questions

were then modified into open-ended, semi-structured format. Some questions were also

added during interview, to address areas not covered by or areas requiring further detail

than in the initial survey.

5.4.3 Database Search

Our third data source was the ReleifWeb Financial Tracking Service (FTS) . FTS is an

online database which records all reported international humanitarian financial assistance

(Office for the Coordination of Humanitarian Affairs (OCHA), 2010). I collected data

related to the amount of funding raised, the number of funded projects and the number of

funding partners of organizations member of the Global Symposium+5 community. In

the humanitarian relief literature, data from the FTS database has been used in a number

of academic work and reports to donors (e.g. Torrente, 2004; Walker et al., 2005; Amin

& Goldstein, 2008; VanDeWalle & Turoff, 2008; Tomaszewski & Czaran, 2009).

75

5.5 Data Collection

5.5.1 Survey Data

I conducted a series of three surveys (October 2007, May 2008 and July 2009) among

organizations/agencies members of Global Symposium+5. The first survey was paper

based, administered during the Geneva 2007 meeting of the Global Symposium+5. The

two subsequent surveys were web based, administered in May 2008 and July 2009 to all

those who attended either of both of the Global Symposium+5 meetings (Geneva, 2002;

Geneva, 2007) or were at one of the regional conferences (Bangkok, 2003; Panama,

2005; Nairobi, 2006). The UNOCHA provided us the list of participants to these various

meetings. The majority of respondents considered themselves to be managers, working in

the field of information management, and located at headquarters. Table 6 below

presents organizations’ participation in the various surveys.

Network

Number of

Organizations

surveyed

Number of responses

October

2007

May

2008

July

2009

Government

Organizations 37 7 11 10

Non-Governmental

Organizations 48 17 19 13

United Nations Agencies 24 12 10 10

Private sector 10 2 3 3

Total 119 38 43 36

Response rate 31.93% 36.13% 30.25%

Table 6: Surveys’ Participation

The coding of network data began by assigning unique identifiers to each organization

member of the Global Symposium community. The community is made up of

approximately one hundred and twenty (120) members. Codes ranged from OGR001 to

ORG120. An organization’s code was unique cross the three surveys. Since in my

research I was not interested in the dynamics of the networks, I merged into a single

dataset the data from the three surveys. Combining data collected at different point in

time is common practice in the literature (e.g., Hartley, 1958; Jorgenson et al., 1982;

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Bound et al., 1986; Arellano, 1992). I merged the data after conducting a Quadratic

Assignment Procedure (QAP) analysis. The QAP procedure (Krackhardt, 1988) is

principally used to test the correlation between networks. Using this procedure, I found

no statistical significant correlation among the three sets of survey data.

Organizations were then grouped into three networks including (i) governmental

organizations, (ii) nongovernmental organizations, and (iii) United Nations

organizations/agencies. These networks were identified based on the UNOCHA

categories

The next stage of data analysis began with the construction of binary adjacency matrices.

These matrices represent the linkage between organizations in the relationships

measured, with a 1 entered in a cell to indicate the presence of a tie between actors or a 0

entered if there is no tie. For example, in the case of the project collaboration

relationship, a one indicates a survey participant from organization identified another

organization as partner in a collaborative humanitarian project. The matrices were

asymmetric because one participant identifying an organization as partner does not

necessarily equal a reverse identification. The ties between organizations are directed ties

going from a source to a receiver and in the matrix, the rows representing the origin of

the directed ties, and the columns the targets (Wasserman & Faust, 1994).

As I mentioned earlier, I studied multidimensional networks. Organizations members of

the Global Symposium interact on several dimensions. My study focused on two

dimensions including (i) humanitarian information management project collaboration,

and (ii) advice seeking/receiving. I considered a project collaboration linkage to exit

between two organizations if a survey participant from either of the two organizations

reported that the two organizations had collaborated on a humanitarian information

management project. Similarly, an advice linkage exited if a survey respondent from

either of the two organizations reported that one organization had provided or had

received advice from the other. On the networks diagrams presented below (Figure 6, 7

77

and 8) the blue links represent the project collaboration relationships while the black

links represent the advice relationships.

Figure 8: United Nations Agencies Network

Structure

Figure 9: Non-Governmental

Organizations Network Structure

Figure 10: Governmental Organizations Network Structure

5.5.2 Interview Data

I started the interviews at the end of the third survey. Interviewing spanned over a period

of four months (September to December 2009). Participants to the interview were

recruited through the survey instrument. The survey included a question asking

participants if they were willing to be interviewed. I registered twenty five (25) positive

answers out of the eighty six (86) respondents of the survey. Due to certain

contingencies and scheduling constraints I ended up conducting nineteen (19) interviews.

78

The interviews were conducted over the phone. Each interview lasted between three

quarter and one and half hours. Prior to each interview, the participant consent was asked

for tape recording. All the interviewees gave their consent to be tape recorded. After the

interviews, in the privacy of my office, I transcribed manually all the recordings and

came out with document of approximately two hundred (200) pages. I reviewed each

interview, checked the spelling, adjusted the sentence structure (if needed), and printed

the interviews. I took great care not to change the intent of the participant or the integrity

of the interview. After the transcription, I sent each interview to the interviewee for

participant checking. I updated the transcripts with the feedback from the interviewees.

Because of the position of the interviewees in their organizations, the level of active

participation in humanitarian information exchange collaborative projects, responses

were accepted as credible accounts of comprehensive knowledge of organization

collaborative activities.

5.5.3 Database Data

I collected the data related to the number of funded projects and funding partners from

the ReleifWeb Financial Tracking Service, a UNOCHA web based database which

records all reported international humanitarian financial assistance (OCHA, 2010). The

the ReleifWeb Financial Tracking Service was implemented and launched in 1999. In the

humanitarian relief literature, data from the ReleifWeb database has been used in a

number of academic work and reports to donors (e.g. Torrente, 2004; Walker et al.,

2005; Amin & Goldstein, 2008; VanDeWalle & Turoff, 2008; Tomaszewski & Czaran,

2009). In order to increase the validity of data and the number of cases, I collected data

for a period of ten years (1999-2009). I realized that no data was available in this

database for organizations of the for-profit network. This network would be ranked in the

study using only the perceived network effectiveness criteria.

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5.6 Data Analysis Techniques

I used a combination of social network and statistical analyses techniques in my study.

5.6.1 Social Network Techniques

Reasons for Using Social Network Techniques

Social network analysis is appropriate for my research for the following four reasons.

First, Social network analysis is a powerful and relatively new research tool which has

developed popularity in recent years (Kilduff & Tsai, 2006; Quatman, 2008). The

network perspective offers some unique advantages to the research process. According to

Quatman (2008), network approaches allow for example for: (1) a concrete vitality for

several difficult-to-define constructs; (2) simultaneous analysis of multiple levels of

relational data thus providing some fluidity between micro-, meso-, and macro- linkages;

and (3) a unique integration of quantitative, qualitative, and graphical data producing an

intuitive, thorough, and rich analysis of phenomena .

Second, social network studies cover a wide range of research contexts. The utility and

applicability of social network analysis is very broad and has been embraced by

researchers in a number of fields (Kilduff & Tsai, 2006; Quatman, 2008). Several papers

provide quite extensive reviews and a variety of contextual examples of the uses of social

network analysis for research purposes such as: (i) Brass et al., (2004) and Parkhe et al.,

(2006) for management and organizational behavior topics; and (ii) Provan & Milward

(1995), Lemieux-Charles et Al. (2005), and Arya & Lin (2007) for health service

delivery.

Third, in a network approach, actors can be characterized by any type of entity embedded

within a larger system of entities (Granovetter, 1985; Wasserman & Faust, 1994; Kilduff

& Tsai, 2006). In the social sciences, the entities of interest are often individual people or

groups of people acting as a unit. In a network approach, researchers also have the

freedom to operationalize the relationships of interest between the actors. For example, a

80

researcher might explore friendship links between employees in an organization or

resource exchange links between organizations in a market.

Fourth, social network approaches also allow researchers to investigate several different

attributes of relational ties between actors (Wasserman & Faust, 1994; Kilduff & Tsai,

2006). Instead of simply considering whether or not a tie is present, a researcher can

examine additional implications from network configurations. For example, a network

investigation can incorporate such things as the intensity (often measured by strength or

frequency of interaction) and direction of ties (often used to represent the direction of

effect). In addition, a single set of network members can also be used to examine the

multiplexity of ties between members in the network. As discussed earlier, the

multiplexity of a tie refers to the extent to which two network members are linked

together by more than one relationship. Moreover, the attributes of the ties (for example

directionality, intensity, and multiplexity) do not have to be considered mutually

exclusively. In a nutshell, a network can be examined from any and all of these

perspectives simultaneously.

Social Network Analysis

In network analysis I first considered measurement of the basic network structural

properties. They included size, density, connectedness and centralization. This allowed

for consideration of whole network behavior as well as an understanding of individuals in

the networks. At the network level, the size of the network is an important consideration

for the potential reach or number of logically possible relationships for the number of

actors in the network. Network size at the individual level considers a number of factors

in relation to the number of adjacent actors.

As I mentioned earlier, the density of a network indicates to the number of recorded links

between network members in proportion to the number of all possible links within a

network. Density calculations illustrate the degree to which a network realizes its

potential, assuming that the optimum is a fully saturated network where everyone

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contacts everyone else. The organizational context is important in assessing the desired

density. With the humanitarian information management and exchange, high density in

project collaboration may indicate a tightly group who consult frequently to resolve

issues. On the other hand, a high density in advice may indicate many organizations

struggling with how to handle problems.

At the whole network level, another consideration is centralization. Centralization

measures the extent to which a network or group is organized around its central point

(Freeman, 1979). The arrangement of actors in the network affects how quickly and

easily information can be distributed among all the actors (Freeman, 1979; Wasserman &

Faust 1994; Haythornthwaite, 1996). Centralization is a measure of integration or

cohesion of the group. A centralized network may reflect an uneven distribution of

knowledge such that knowledge is concentrated in the focal points of the network. In

addition to matrix calculation, the sociograms illustrating these calculations are

particularly useful for viewing the different networks.

To consider network structural influence on individual actors and identify the variety of

network roles within the various networks, a number of adjacency calculations on the

direct connections from one member to another demonstrated the degree to which an

actor sends or receives information. For graph theorists, there exits four types of network

nodes including (i) isolate, (ii) transmitter, (iii) receiver and (iv) carrier. An isolate

neither sends nor receives information; a transmitter sends information; a receiver

receives information; and a carrier both sends and receives information (Wasserman &

Faust, 1994, p. 128). The outdegree calculated as the sum of connections an actor has to

others, is often used to measure an actor’s influence. In-degree links refer to the number

of actors sending information to the actor in question. Network members that receive a

lot of information may be more powerful, suffer from information overload, or hold a

position of prestige (Hanneman & Riddle, 2005, p. 43). Network members neither

sending nor receiving information either withhold information or fail to contribute to a

network. In this study, I did not take into consideration the isolates. I excluded all the

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isolated network members from my analysis. I considered the rest (those that had at least

one connection) as carriers.

The presence or absence of subgroups within the social network structures was a prime

consideration for the analysis of the inter-organizational collaboration activities among

members of the Global Symposium community. In some networks, a sub-group forms

when only two actors have a tie to each other. In others, groups of actors demonstrate

more ties to each other than to the other members of a network. This type of sub-group is

termed a clique, defined by Hanneman & Riddle (2005) as “some number of actors (more

than two, usually three is used) who have all possible ties present among themselves” (p.

80). This definition may restrict the concept’s application in many social networks. As a

result, an extension is the concept of the n-clique, where n is the maximum path length at

which members of the clique are considered connected. This extension “is much closer to

people’s everyday understanding of the word clique” (Scott, 1991, p. 115).

The impact of subsets within a social network may depend on the degree to which they

are connected. The examination of bridges, or critical ties between two actors, extends

from the consideration of small groups in a larger network. An actor who provides the

connection or critical tie to another group of actors performs “the liaison role of

connecting two otherwise disconnected cliques” (Kilduff & Tsai, 2003, p. 28). This role

becomes important in considerations of what happens if the connection drops, and the

value of maintaining or continuing to invest in the relationship.

Finally, the construction of sociograms to demonstrate visually some of the properties

across the networks assisted the analysis by highlighting numerous features for

consideration.

Unit of Analysis in Social Network

In network analysis there are four units of analysis that are frequently used. They include

dyads, triads, egocentric networks and whole networks (Wasserman & Faust, 1994). In

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my study, I am concerned with two units of analysis, the egocentric network and the

whole network. The egocentric network level has been used primarily to study network of

individuals. For example, Laumann (1973) analyzed friendship networks among urban

men; Granovetter (1974) studied how information about job is transmitted; and Minor

(1983) examined personal relationships among former heroin addicts. One problem with

the egocentric network level analysis when applied to organizational networks is that it

carries a connotation of individual relations, with a psychoanalytic orientation. For this

reason, I will refer to the unit of analysis as organizational-centered networks, implying a

focus on organizations as opposed to individuals.

This level of analysis consists of each organization along with all other organizations

with which it has a relationship. Generally, this unit of analysis is used to examine

attributes and characteristics of the relationships which exist between each organization

and all other organizations in its organizational-centered network. Each organizational-

centered network can be described by the number, magnitude, type and other

characteristics of it linkages with others in the network (Knoke & Kuklinski, 1982;

Streeter, 1989).

The network-level of analysis has also been intensively used in inter-organizational

network research. At the network-level of analysis, researchers look at the composition of

the networks (e.g., network size, network heterogeneity, mean frequency of contact) and

the structure of these networks (e.g., density of links among alters). According to

Wellman & Frank (2000), such analyses seek to understand how the properties of

networks affect what happens in them and to them. Provan et al., (2007) provide an

extensive and comprehensive review of inter-organizational network research conducted

at the network level of analysis. According to this review, research at network level has

mainly been conceptual, anecdotal, or based on single, descriptive case studies performed

at one point in time. Also most of the research reviewed by Provan et al., (2007) was

done in the health sector.

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5.6.2 Content Analysis

Content analysis “is a well-established set of techniques for making inferences from text

about source, content, or receivers of information” (Schamber, 2000, p. 735). Organizing

and properly coding data is critical to content analysis. Coding is the process of combing

the data for themes, ideas and categories and then marking similar passages of text with a

code label. Coding the data makes it easier to search the data, to make comparisons and

to identify any patterns that require further investigation. The process of coding is an

iterative and cyclical process of constant discovery. Seidel (1998) developed a model

(figure 9) to explain the basic process of qualitative data analysis. The model consists of

three parts: Noticing, Collecting, and Thinking about interesting things. These parts are

interlinked and cyclical. For example while thinking about things you notice further

things and collect them. Noticing interesting things in the data and assigning ‘codes’ to

them, based on topic or theme, potentially breaks the data into fragments. Codes which

have been applied to the data then act as sorting and collection devices.

Figure 11: Qualitative data analysis coding process (Seidel, 1998)

In my research, I coded the transcribed interviews both deductively and inductively

(Epstein & Martin, 2004). In the deductive coding approach, the codes are developed

before data collection. I developed my set of codes based on my research questions.

Usually the deductive approach is used when researchers may be seeking to test existing

theories or, as it is the case in my research, expand on them.

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During the coding process I also let some inductive codes emerge from the data. The

inductive approach reflects frequently reported patterns used in qualitative data analysis.

Inductive coding begins with close readings of text and consideration of the multiple

meanings that are inherent in the text. The researcher then identifies text segments that

contain meaning units, and creates a label for a new category into which the text segment

is assigned. Additional text segments are added to the category where they are relevant.

At some stage the researcher may develop an initial description of meaning of category

and by the writing of a memo about the category (e.g., associations, links and

implications). The category may also be linked to other categories in various

relationships such as: a network, a hierarchy of categories or a causal sequence. Coding

inductively, researchers are likely to create new codes, they therefore need to go back and

check the units of data they coded previous to creating this code. This is to check if there

is any more data that should be coded at the newly created node. The diagram below

shows how I applied new codes to previously coded data.

While organizing and coding qualitative data, it is important to carefully read and

recognize data prior to the coding process. Mason (1996) suggests there are three main

epistemological reading schemes; literal, interpretive and reflexive. In literal reading, the

researcher is interested in the literal form of the data, whether it is the content, structure,

style, and layout. Researchers do not make interpretation of the data. They look at the

data as it is presented. Most qualitative researchers argue that a literal version of reading

data could not yield desirable results for this kind of data organization as it might direct

our attention from the whole to details and style. In interpretive reading, researchers look

beyond literal form of the data, and try to get to the underlined or implied meanings.

Interpretive reading brings the researcher's own opinions into play. In reflexive readings,

the researcher will look at him/herself as part of the data they generated, and will seek to

explore his or her role and perspective in data generation and data interpretation. The

interpretive and reflexive reading puts emphasis on construction and documentation of

the meaning of data rather than the literal structure of it. In my research, I used the

interpretative reading scheme.

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Mason (1996) also highlights two different methods for coding qualitative data including

(i) cross-sectional or categorical coding and non-cross-sectional or contextual coding.

Cross-sectional coding consists to consistently code the whole data set according to some

sets of common principles in a very systematic way. The simplest form of cross-sectional

coding is serial coding, which is to insert subheadings at relevant points in the text data.

Some of the advantages of this method are that (i) it makes sorting and retrieval easier (ii)

it gives a holistic view or understanding of the data set (iii) it can index the locations of

interpretive, conceptual and theoretic themes within the data, and (iv) it can provide

analytic handles of different parts of the data set for cross-comparison. According to

Mason (1996), there are three main limitations to the cross-sectional coding approach. Its

categories might be too broad to be very useful. Second, a section of text is likely to be

related to more than one concept, thus serial coding might be inappropriate. Third, serial

coding is unlikely to work well if the data is not of a uniform layout. Cross-sectional

indexing can be done very easily, if the data is mostly textual information. For instance it

might not cater to some relevant comparisons across categories. In addition, it tends to be

less useful for interview transcripts, particularly when the interview is either semi-

structured or unstructured.

Non-cross-sectional coding, on the other hand, relates to a totally different idea. The

researcher using this method reads over the data set and constructs a different lens for

each document by examining the documents individually. The principal advantage

behind this approach is that it involves the evaluation of each document in its entirety

inclusive of the context of the data generation. Since this approach helps the researcher

build a case out of each examination, it is also referred to as the case study method of

organizing data. According to Mason (1996), one of the driving reasons to perform non-

cross-sectional data organization is related to the fact that the researcher can identify and

analyze deeply the ideas inherent in each document and how these ideas are interwoven.

In my research, I used the cross-sectional coding method.

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5.6.3 Statistical Analysis

In my study, I used correlation and multiple regression for statistical analysis. Multiple

regression is a statistical technique that allows to predict the value of a dependent

variable on the basis of the values of several independent variables. Multiple regressions

and multiple correlations deals with the relationship of one variable compared with a

number of other variables. In this research, the multiple regression and multiple

correlation was used to compare different predictors of organizational effectiveness in

humanitarian information management networks.

5.7 Methodological Issues

The issues of external validity involve the degree to which the results of the research

study apply to other communities. These issues can occur at different levels including the

level of theories and methods used as well as the level of the findings. At the level of

theories at methods, threats to external validity occur if inappropriate concepts,

instruments, or methods are applied to a research study. At the level of findings, external

validity concerns the extent to which the results of the study hold true for similar

populations. My literature review, the concepts and methods that I used were appropriate

and indicate no threat by the external validity concerns to my study.

External reliability relates to the ability of other independent researchers to “discover the

same phenomena or generate the same constructs as an original researcher if they did

studies in the same or similar settings” (Schensul, Schensul, & LeCompte, p. 275).

Clearly, identifying the research steps and detailing the analysis process and

interpretation of results elevates external reliability. I believe that it is not possible to

duplicate the research setting and results of my study. However, other researchers can

duplicate the research process.

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5.7.1 Social Network Analysis Issues

Validity

According to Wasserman & Faust (1994), construct validity is the extent to which the

questions really assess what they purport to measure. Wasserman & Faust state that “very

little research on the construct validity of measures of network concepts has been

conducted” (p. 58). However, they do acknowledge that “the construct validity of social

network measures can be studied by examining how these measure behave in a range of

theoretical propositions” (p. 58). The construction of questions (both survey and

interview) relied on validated questions I had used on numerous occasions (Cross &

Parker, 2004) in the humanitarian field, to “uncover important network relationships” (p.

147) of information exchange and collaboration.

Measurement Errors

A discrepancy between what is measured and the “true” value of a concept constitutes

measurement error (Wasserman & Faust, 2004, p. 59). In my research, the response rate

of the three surveys that I conducted had an impact on the measurement of network

structural properties, reciprocity in particular. Higher response rate would have been

better. One way to counter threats to internal validity and measurement error is to use

member checking. Presenting the data results to the members of the Global Symposium

community improved the degree to which the responses obtained reflected their

perception of network interactions. The data acquired through interviews provided

additional confirmation.

Reliability

Assessing reliability of measures of sociometric data relates to the success of achieving

the same estimates from repeated measurements. In the words of Wasserman & Faust

(1994) “this assumption is likely to be inappropriate for social network properties, since

social phenomena cannot be assumed to remain in stasis over any but the shortest spans

of time” (p. 58). In this study, sociometric data collection relied on three datasets

collected at three different points in time. Admittedly, this introduces an element of error.

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5.7.2 Content Analysis Issues

Validity

Validity concerns the extent to which instruments are accurate and dependable, and the

degrees to which the research results make sense to the people studied and generalize to

other similar populations (Schensul & LeCompte). In my research, content validity

reflects the high correlation between what users described in their own words and

examples of those expressions in the research literature. The frequency and redundancy

of these descriptions supports the degree to which the research results apply to the people

studied.

Reliability

The issue of reliability concerns whether another researcher using the same methods can

replicate the research results. Internal reliability for content analysis relates to the match

between the constructs identified and the data sets that generated the constructs. One way

to increase the reliability of these matches is for at least one other researcher to review

the data sets and the constructs to see if there is agreement on the matches generated. To

accomplish this, one colleague with content analysis experience reviewed a sample of

three interview transcripts. Krippendorff’s alpha (Krippendorff, 2004) was the basis of

the test for inter-coder reliability. In this case, the observed disagreement divided by the

expected disagreement produced a calculation of desired agreement, with 84% agreement

meeting the minimum goal of 80% acknowledged as appropriate for exploratory studies

(p. 242). Confirming the coding reliability increased the confidence in applying the

results.

5.8 Summary

In this study, I used a mixed methods research design to investigate the relationships

between structural properties of inter-organizational networks and network effectiveness

among organizations members of a community of interest in humanitarian information

management and exchange. Data collection included surveys, individual semi-structured

interviews, and database search. The process of triangulation encompassed in this

research design provided the means to corroborate findings and extend the results beyond

this research setting. The next chapter presents the research results.

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6 ANALYSIS

6.1 Introduction

In this chapter I present the findings of my investigations. The chapter contains four main

sections. In the first section, I analyze the qualitative data collected through interviews. In

section two, I present my three criteria for assessing effectiveness. As discussed in the

research model, they are my dependent variables. The third section presents my findings

related to the network level of analysis. In this third section, I present my analysis of the

relationships between the structural properties (density, clique, clique overlap and

multiplexity) and network effectiveness. Finally in the fourth and last section, I present

my findings related to organizational level effectiveness.

6.2 Qualitative Data Analysis

As discussed in the method section, in my research, I collected data through multiple

sources. In this section, I analyze the qualitative data gathered through interviews. I

conducted a total of nineteen semi-structured interviews among members of the Global

Symposium. I coded the data using deductive and inductive methods. The five deductive

code categories that I used were guided by my research questions and the literature. They

included (i) network benefit, (ii) network effectiveness, (iii) collaboration factors, (iv)

barriers to collaboration and (v) measures of network effectiveness. Three inductive code

categories emerged from the data including (i) from advice to collaboration, (ii) network

scope and (iii) network audience.

6.2.1 Deductive codes

6.2.1.1 Network benefit

One of the objectives of the interview was to assess the benefit of the Global Symposium

to individual organizations as well as to the community as a whole. It is important to

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recall that membership to the Global Symposium network is voluntary. If members do

not benefit from being part of the network, they will more likely cease to participate in

the activities of the network, and if the feeling is widespread, the network will cease to

function. Recognizing the benefits that members receive from network membership is

therefore a crucial tool to assess how well the network is functioning. Based on the data

that I collected, numerous benefits were perceived to be associated with the Global

Symposium inter-organizational network.

Benefit to individual organization

I asked to the interview participants, what benefits their organization had gained from

being a member of the Global Symposium. About eighty five percent (84.21%) of the

interviewees answered this question. Some of the most commonly cited benefits of the

network to individual organization members included: increased access to humanitarian

information; expertise and financial resources; solidarity and support; and increased

networking. Another important perceived benefit reported was increased credibility of the

Global Symposium members. One subject expressed this benefit in the following term:

Subject#18: The greatest as I said, was meeting with various people from all over, networking and then of course it was informal relations but strangely enough you could think that this networking will be closer with the working group which I was part, but it was not. At the time I made those networks it was at the closing session in fact.

Another subject said:

Subject#1: There are people and entities we met that we are now discussing with and sharing information with sharing ideas with and you know just keep in touch at an informal level. I think that is very good for us.

Benefit to the whole community

The second aspect of the question on benefits was related to the contributions of the

Global Symposium to the community. I asked the interview participants about how the

Global Symposium benefited the humanitarian community as a whole. Approximately

fifty eight percent (57.89%) of the interviewees answered this question. Among those that

responded, the vast majority expressed highly positive opinions on the Global

Symposium’s contributions to the community. Especially, they believed the Global

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Symposium benefited the community in two major aspects including (i) promoting the

use of humanitarian information management principles and dissemination of best

practices; (ii) fostering collaboration on humanitarian information management projects. I

provide below, some illustrative quotes from interview data.

Subject#8: I think the development of the principles was useful. I think that just the working groups to address certain issues was useful although I would be the first to admit there has been not any real organized follow up. But I think documenting the information management principles and the actual document itself that came out of the symposium I think was useful in terms of the issues of humanitarian information management. Subject#17: It was a very very important networking opportunity, and you know in some respect it was very cutting edge. The only thing I find very disappointing was the lack of invitation to some key players and so, the one in particular. Subject#5: I think one of the great benefits is actually making like minded people and you realize that we are all confronting the same problems, so I think that firstly is one big positive aspect. I think the second, is that there was a lot of networking going on that was actually quite crucial.

However, for some interviewees, the benefit of the Global Symposium was limited to

certain linguistic regions. They said the English speaking members of the community

were those that benefited the most.

The impact of the Global Symposium for this region [Spanish] is very very low, I would say or almost invisible. I am not sure if we would benefit from the Global Symposium in that sense. No unfortunately not. Subject#16

Across the interviews, the discussion on the benefits of the Global Symposium to

individual organizations on the one hand and to the whole community on the other hand

was done with almost the same intensity (Figure 12). The former represented

approximately fifty eight percent (57.81%) of occurrences of benefits discussed in all the

interviews combined while the later represented about forty two eight percent (42.19%).

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Figure 12: Network Benefit Code’s Coverage

Figure 13: Aggregated Benefit Cross Network

Aggregated data per network (Figure 13) shows that cross networks, the Global

Symposium was perceived to benefit more to individual organizations than to the

community as a whole. In the network of NGOs the proportion is almost seventy percent

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(70.00%) and thirty percent (30.00%). In the two other networks, the proportion is

approximately sixty percent (60.00%) and forty percent (40.00%).

6.2.1.2 Network effectiveness

Another objective of the interview was to assess the perception of the participants about

the effectiveness of the Global Symposium as a network of organizations engaged in

humanitarian information management and exchange. Though I often used open

questions during the interviews, in this case, I recalled some of the main objectives of the

Global Symposium to the interviewees. I then asked them to comment on whether or not

the Global Symposium was effective in the pursuit of these objectives. I coded the data

collected using four categories. They included (i) resource availability, (ii) internal

processing characteristics, (iii) goal achievement, and (iv) multiple constituencies’

satisfaction. These categories came from the literature on organizational and network

effectiveness (Parson 1964; Yuchtman and Seashore, 1967; Price, 1971; Cameron and

Whetten 1981; Conlon, D’Aunno, 1992; Zammuto, 1984; Sowa et al., 2004). Figure 14

depicts the coverage of these codes.

Resource availability

In this code category, effectiveness was defined in terms of the ability of the organization

/ network to acquire resources necessary to it survival. The greater the ability of the

organization / network to acquire needed resources, the greater its effectiveness. Only

about thirty two percent (31.58%) of the interviewees discussed the effectiveness of the

Global Symposium in term of resource availability. The majority of these subjects had a

negative opinion about the ability of the Global Symposium to make resources more

available to its members. For example, Subject#2 talked about the “unrealistic”

objectives of the Global Symposium.

Subject#2: How could you possibly do that? I mean these objectives are ummm let us be realistic. How are we going to help organizations get more resources? Subject#5: I think a lot of the organization including ours, have simply not got the time or context to seek the necessary funding and resources.

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Internal processing characteristics

In this category, effectiveness was conceptualized as the absence of internal strain and a

smooth internal functioning of organizations / networks. Approximately fifty eight

percent (57.89%) of the interviewees discussed the effectiveness of the Global

Symposium with regards to its internal processing characteristics. Once more, the

majority of these subjects expressed a negative opinion. The two majors grievances most

frequently reported included the relatively big size of the Global Symposium and the lack

of a clear and concise definition of the objectives of the event. I provide below, some

illustrative quotes from interview data.

Subject#1: you know you can only coordinate it if people, institutes want to be coordinated. And to do that you need a certain trust, it has to be a two way things, you cannot just come waving the coordination flag and expect everyone to lineup nicely. Subject#12: we spent so long figuring out what we were supposed to be talking about that we never got to the details. Subject#14: Well I think that we need to look at what is the purpose? What are you trying to achieve? and I would certainly not go in any kind of precooked formula which will reflects the earlier symposiums, on come up with a list of those and answer these questions. Subject#2: any type of meetings and workshops where you are gathering various organizations and numerous people I think it’s important to clarify terms and terminologies and I am not sure if this happened there. Subject#2: It’s always very difficult globally to bring people together. So I would say, first try to do it within a country or a region instead of trying to do it globally.

Another negative view expressed on the internal processing characteristics of the Global

Symposium was related to the lack of follow-up activities. A good number of interview

participants noted that the fact that there was no rigorous planning of follow-up activities

significantly compromised the effectiveness of the Global Symposium.

Subject#1: I don’t see any specific follow up or activities. Subject#11: It would have been good to have even virtually, not necessary another event, but if there was some follow up, it would have been good.

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Subject#9: But I think we need follow up. Follow up in particular at the regional level; follow up at the country level. How they helped? And what are the achievements at the regional level.

Goal achievement

In the goal achievement category, effectiveness was defined as the extent to which

organization / network’s goal was achieved. The greater the degree to which an

organization / network achieves its goals the greater its effectiveness. During the

interviews, I recalled to the participants some of the main goals of the Global

Symposium. They included: (i) promote the use of humanitarian information

management principles; (ii) disseminate best practices of humanitarian information

management; (iii) improve the community’s preparedness in humanitarian information

management; (iv) help organizations/agencies acquire resources; (v) improve the level of

professionalization in the field of humanitarian information management (vi) foster

collaboration on humanitarian information management projects (vii) facilitate sharing

of expertise among organizations/agencies; (viii) promote humanitarian information

sharing; (ix) strengthen relationships between organizations/agencies; (x) increase

awareness of humanitarian information systems and (xi) improve humanitarian

information quality.

Approximately eight five percent (84.21%) of the interviewees discussed the

effectiveness of the Global Symposium with regards to achieving its goals. For most of

these respondents, the Global Symposium was effective in the pursuit of its goals. More

specifically, as illustrated in the following quotes from the interview data, the Global

Symposium was reported to be very effective in strengthening relationships between

organizations/agencies, in promoting the use of humanitarian information management

principles and in promoting humanitarian information sharing.

Subject#11: So I would say the Global Symposium was one of other events which promoted interactions among different partners. Subject#15: I think the event was mostly successful in coming to agreement among the various actors on certain standards for use of information in humanitarian response. So I

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think the report was useful and the input that all the groups provided into that report was useful. Subject#19: the global symposium has actually been very important to us, as group of organizations that has been working on this effort. Because originally, this working group wasn’t on the agenda, but we requested that it be put on the agenda, to recognize the various information that affect the population. Subject#2: I mean I am talking specifically about one the positive things that came out with which is the ability to meet others in the field and in particular in bringing together the private and NGOs the UN communities which was good. Subject#8: I would say that there have been more information related projects and initiatives in the last two years and so I mean I think it encouraged information related projects.

However, for some interviewees, the impact of the Global Symposium was limited to a

single event (e.g Subject#15:). According to these participants, this was mainly due to

the lack of follow-up activities.

Subject#15: I think they disseminated [information on humanitarian information management best practices] but mostly to the conference participants. I don’t know how much this was disseminated beyond the conference.

Constituencies’ satisfaction

For constituencies’ satisfaction, effectiveness was defined the ability of network to satisfy

key multiple stakeholders. In the case of the Global Symposium, some of the main

stakeholders include the individual organizations, the governments, the United Nations,

the victims of humanitarian disasters and the international community. My study included

only members of the Global Symposium. They could be grouped into the following four

categories: NGOs, governmental organizations, private organizations and United Nations

organizations. I did not for example interview any victim of the humanitarian disaster.

Approximately eight five percent (84.21%) of the interviewees discussed the

effectiveness of the Global Symposium with regards to constituencies’ satisfaction.

Almost all of them expressed a mixed feeling about the effectiveness of the Global

Symposium in satisfying its multiple stakeholders.

Subject#8: I think my organization has used the symposium to a high degree. I would say the broader community has lessen so. I mean at a scale of 1 to 10, I think that the HIU

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has used and benefited from the symposium probably of a 7 or 8, and the entire humanitarian community at a 4 or 5. Subject#15: Well I think the event was mostly successful in coming to agreement among the various actors on certain standards for use of information in humanitarian response. So I think the report was useful and the input that all the groups provided into that report was useful.

Figure 14: Network Effectiveness Code’s Coverage

Across the interviews, the intensity of the discussion on network effectiveness varied

significantly depending on the code category (Figure 15).

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Figure 15: Network Effectiveness Code’s Loudness

Exploring my data cross networks (Figure 16), I found almost a similar pattern in the

ranking of the loudness of the different categories of network effectiveness. In all the

networks, the category “goal achieved” was ranked first followed respectively by

“internal processing”, “constituencies’ satisfaction” and then “resource availability”. The

pattern of the percentage of discussion of the different categories was also similar. They

were over fifty percent (50.00%) for the “goal achieved” category, approximately twenty

two percent (22.00%) for the “internal processing” category; approximately fifteen

percent (15.00%) for the “constituencies’ satisfaction” category; lest than ten percent

(10.00%) for the “resource availability” category.

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Figure 16: Network Effectiveness Code’s Loudness Cross Network

6.2.1.3 Collaboration factors

While conducting the interviews, I was also interested in assessing the participants’

opinion about factors for collaboration among members of the Global Symposium. I

present below the major factors that emerged from the interviews. A total of seven factors

were found including mandate/goals, skills, trust/reputation, funding, size, geographical

proximity/language and processes. The identification of these factors was guided by the

literature.

Mandate /Goals

One of the most frequently cited factors for inter-organizational collaboration on

humanitarian information management and exchange was related to the similarity in the

mandate and goals of the organizations wishing to work together. About eighty five

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(84.21%) of the interviewees discussed the similarity of mandate / goals as being an

important motive for their organization to engage into collaboration with another

organization. I provide below, some illustrative quotes from the interview, expressing this

point of view:

Subject#9: We have to have a common work plan in order to work together. Subject#17: What it takes for us to collaborate is just a kind of share objective.

The issue of mandate and goals as driving factor for inter-organizational collaboration

was not only cited by the greatest percentage of participants, this factor was also among

those that were the most intensively discussed. It represented approximately twenty nine

percent (28.38%) of occurrences of collaboration factors discussed in all the interviews

combined (Figure 19).

Figure 17. Factor’s Coverage

Skills

The skills set of the potential collaboration partners was the second most reported factor

for inter-organizational collaboration among the members of the Global Symposium.

Approximately seven four percent (73.68%) of the participants reported that in deciding

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to engage into collaboration, their organization would highly consider the type of skills of

the potential partners (Figure 17). The issue of skills as factor for collaboration was

discussed in two different perspectives. The majority of participants, who mentioned this

issue, discussed it in term of availability of complementarity set of skills in the potential

partner organizations. Their organization would collaborate with another organization if

the later possessed a set of skills that they lacked. For example, participant number two

(Subject#2) said:

Subject#2: Both have to be able to bring to the table their competitive advantage. You can’t have two organizations that do the same thing. So you need different skills set from any of the organizations.

Other participants discussed the issue of skills in term of high quality and competency. In

deciding to engage into collaboration their organizations would consider the quality and

competency of the skills available to the potential partner organizations.

Subject#7: We think about the quality of what that agency does and the quality of what that agency is known to do. Subject#17: We are trying to be a service provider to those organizations. So I guess we are trying to provide a competency. But we also have interest in the ability of these other organizations to develop new competencies.

Similarly to the issue of mandate and goals, the discussion of skills as factor of

collaboration was also very intense. This factor also represented approximately thirty

percent (29.73%) of occurrences of collaboration factors in all the interviews combined

(Figure 19).

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Figure 18. Factor’s Loudness

Trust / Reputation

The third most discussed factor for inter-organizational collaboration among the members

of the Global Symposium was trust and reputation. Approximately forty eight percent

(47.37%) of the interview participants mentioned that they will more likely not get into

collaborative activities with an organization that they do not trust or an organization that

has a poor reputation (Figure 17). Below, I illustrate this point of view with quotes from

subject number five and subject number eight.

Subject#5: I thing on the one hand getting the quality information which is credible and I think the emphasis has to be on the word credible because there is no good having information which is bad because people see through that very very quickly and you can lose your credibility very quickly. Subject#8: we are looking for partners that have a good reputation that provide value added to what we can provide.

This third most reported factor of collaboration was also very intensely discussed. It

represented roughly seventeen percent (16.22%) of occurrences of collaboration factors

in all the interviews combined (Figure 18).

Geographical Proximity / Language

Geographical proximity was the fourth most reported factor for inter-organizational

collaboration among the members of the Global Symposium. Approximately thirty seven

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percent (36.84%) of the interview participants mentioned proximity and especially the

language as an important factor that drives collaboration (Figure 17).

Subject#16: I think we are gaining popularity among humanitarian here because of the Spanish.[….] Spanish is a really an important and imperative thing if you want to enter here. English is a secondary one and really a secondary one. So that is why I think we are gaining popularity among humanitarian here because of the Spanish. Subject#18: if people are approaching us, I would say that first that because we are French and people that approach us usually are also French NGOs.

The intensity of discussion around the geographical proximity and language as factor for

collaboration was relatively low as to compare with others factors. This factor accounted

only for roughly eleven percent (10.81%) of occurrences of collaboration factors in all

the interviews combined (Figure 18).

Size

The fifth most discussed factor for inter-organizational collaboration among the members

of the Global Symposium was the size of the potential partners. Approximately sixteen

percent (15.79%) of the interview participants reported that they consider the size of the

organizations that approach them to seek for collaboration (Figure 17). Participant

number one for example reported that:

Subject#1: We tend to work I guess it is natural, we tend to work better with the smaller entity that seem to be more flexible, more users oriented than big entities, be they national entity or the private companies or of course UN entities.

The issue of size was also among the factors the least intensively discussed. It accounted

only for roughly seven percent (6.76%) of occurrences of collaboration factors in all the

interviews combined (Figure 18).

Funding

Funding was ranked sixth most reported factor for inter-organizational collaboration

among the members of the Global Symposium. Approximately sixteen percent (15.79%)

of the interview participants reported that they look at the funding possibilities available

at the potential partners before deciding to engage into collaboration (Figure 17).

Subject#8: we all provide any funding for you know we contribute to our work, and they contribute to their work. So you know they need to be sort of self-sufficient.

Surprisingly, the issue of funding as factor of collaboration was among the factors the

least intensively discussed. This factor represented only approximately eight percent

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(5.41%) of occurrences of collaboration factors in all the interviews combined (Figure

18).

Processes

The seventh and last reported factor for inter-organizational collaboration among the

members of the Global Symposium was the processes. Nearly eleven percent (10.53%) of

the interview participants reported that they engage into collaboration with partners that

follow clearly predefined processes (Figure 17). This was especially important for

collaborating with donors organizations.

Subject#14: We have very well defined process for people, partners contacting us.

The issue of processes the least intensively discussed. It accounted for less than three

percent (2.7%) of occurrences of collaboration factors in all the interviews combined

(Figure 18).

Borrowing from the framework developed by Ngamassi et al., (2011) to analyze factors

that hinder inter-organization coordination and collaboration among humanitarian

organizations, the seven factors identified in this study could be grouped into the

following three categories: organizational, structural and behavioral (Figure 19). The

organizational category would include factors related to the mandate/ goals and the

processes. The factors in the structural category would be skills, funding, size and

geographical proximity. The last category, behavioral, would include factor related to

trust and reputation. This categorization allows to have another perspective of the

influential drivers of inter-organizational collaboration in the humanitarian relief field.

Figure 19 below, depicts the aggregated loudness of factor per category.

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Figure 19: Loudness of Collaboration Factors Grouped per Category

It is important to note that nearly half of the reasons to collaborate are structural. This is

important because these structural factors are the ones most likely to be supported and

affected by the use of information technologies. The use of this framework helps to

highlight the fact information technologies have important potential to influence inter-

organizational collaboration relationships among humanitarian organizations.

Analyzing cross networks (Figure 20), the loudness of the different collaboration factors

that emerged from the interviews I made the following two observations. First, there was

a similar pattern in the ranking of the different factors of collaboration based on the

number of their occurrences. The structural factors were the most reported cross networks

followed respectively by organizational and lastly the behavioral factors. Second, I found

a wide discrepancy in the loudness of the different factors, cross networks. For example,

in the network of the United Nations agencies the discussion around the structural factors

represented approximately sixty seven percent (66.67) of the discussion related on

collaboration factor in that network. This proportion was fifty (50%) for the network of

Governmental organizations and just about forty two percent (41.67) for the network of

non-governmental organizations. Another important discrepancy was observed on the

collaboration factors grouped in the behavioral category. Discussion around this category

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represented less than ten percent (9.52%) in the network of the United Nations agencies

and approximately twenty percent in the two other networks.

Figure 20: Loudness of Collaboration Factors Cross Network

Information Technologies

During the interviews, I also asked participants to give their opinion specifically on the

implications of information technologies on inter-organizational collaboration among

members of the Global Symposium. Approximately half (47.37%) of the interviewees

shared their opinion on this issue. I registered a wide range of diverse point of views.

Some participants, roughly thirty one percent (30.77%) of those that answered the

question, had a very positive opinion about the implications and especially the catalytic

role that information technologies play in fostering humanitarian inter-organizational

collaboration. The vast majority (69.23%) however, expressed mixed feelings.

For the participants that had positive opinions, information technologies served as an

important catalyst for inter-organizational collaboration in the Global Symposium

community. They argued that if without information technologies, effective simple

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communication is difficult, collaboration would be even harder. Participant number five

for example reported that:

Subject#5: I think that information technology is extremely important because we basically need to communicate to all these different communities in as many different ways as possible.

They also believed that the use of information technologies is instrumental in quickly

gather analyze and disseminate humanitarian information leading to effective disaster

response. Below, I illustrate this point of view with quotes from three participants,

number six, seven and eleven.

Subject#6: You cannot do it without information technology. Gathering information, managing information, analyzing information, distributing information, really you cannot do all this without information technology. So I think the question is kind of obvious. Subject#7: Information technology essentially supports what we do. It helps in sharing information, mainly transporting information around, maintaining our communication. Subject#11: I think the information technology is key of cause, because without proper systems in place, you will not be able to do that.

The participants who expressed mixed feelings about the role of information technologies

as catalyst in inter-organizational collaboration believed that taken alone, information

technologies would not lead to better / more collaboration. They gave a number of

reasons that could be grouped into two main categories. The first category of reasons was

related to the information technologies infrastructure. Participants argued that more often,

organizations in the field do not have the necessary technology tools either because they

were destroyed by the disaster or because they did not even exist in the first place. They

also talked about the discrepancy in term of infrastructure between organizations based in

developed countries and those in the developing countries. They argued that people in

developed countries often enjoy latest technologies but the realty in developing countries,

scenes of most humanitarian disasters is quite different. Participant number twelve for

example reported that:

Subject#12: when you get out on the fields you see that the most basic important tool is paper map and a pencil. And I think we have got to really recognize that fact. […]You know we do this information technology that we love where they follow the latest systems and the fastest processor and stuff like that and we really like to paddle

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ourselves on the back on what we are able to do here in Washington DC. And then you get out on the fields and everyone is using paper maps and a pencil.

Finally they talked about the fast pace of change in technology which makes it difficult

for organizations to have and especially keep the technical staff that possesses adequate

knowledge to make use of these new technologies.

Subject#6: as the technology changes, it is hard to find the people that have skills that are up to date.

The second category of reasons concerned the management of information. Participants

believed that without proper standard for humanitarian information exchange, the

technology will be of no effective use.

Subject#5: I think yes, continue to explore all the new technologies that are available but at the same time realize that in the end what it really comes down to is quality information and information that is based on facts and that’s credible but people actually belief in. So I think we should not be allowed to be measured by technology if the content is not there. Subject#14: One is developing some basic standards, and some basic platforms for information exchange.

They also believed that the humanitarian field needs better processes and well trained

staff in order to make good use of the technology.

Subject#8: I think there are certain organizations who think that technology can solve all the problems, so they don’t have a proper appreciation and understanding of the information management challenges and obstacles, but at the same time there is probably some information, people who are very skeptical about technology and do not sort of realize the value that it has.

6.2.1.4 Collaboration barriers

The interview included questions about barriers to inter-organizational collaboration. I

asked the interview participants to identify the major barriers to inter-organizational

collaboration among members of the Global Symposium on humanitarian related

projects. The lack of leadership, extensive bureaucracy, the lack humanitarian

information standard, the lack sharing spirit the lack of skills and the lack of resources are

some of the most frequently reported barriers to inter-organizational collaboration among

members of the Global Symposium. I coded these barriers using three categories

including (i) structural, (ii) behavioral and (iii) mandated. These categories were once

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more borrowed from the framework developed by Ngamassi et al., (2011) to analyze

factors that hinder inter-organization coordination and collaboration among humanitarian

organizations. As discussed by Ngamassi et al., (2011), this higher order analytical

structure that organizes these collaboration barriers into three larger categories is

appropriate to both the context of humanitarian relief as well as to collaboration among

organizations engaged in humanitarian information management and exchanged. I

present below, these barriers in order of their intensity as discussed by interview

participants.

Structural

Collaboration barriers in the structural category included barriers such as extensive

bureaucracy, lack of humanitarian information standard, problems of communications /

language, size of organization, lack of tools (IT/IM) for collaboration, geographical

distance, lack of technical skills, lack of resources and lack of leadership. This category

represented the most frequently reported barriers to collaboration. All of the participants

to the interview identified at least one collaboration barrier that fell into the structural

category. In other to refine my investigation and pay more attention to IT and IM related

barriers, I distinguished the following three subcategories of structural barriers.

Information Management (IM) related

Approximately eighty five percent (84.21%) of the interview participants talked about

challenges to inter-organizational collaboration related to information management.

Issues such as information quality, information standards and information security were

frequently reported.

Subject#5: I thing on the one hand getting the quality information which is credible and I think the emphasis has to be on the word credible because there is no good having information which is bad because people see through that very very quickly and you can lose your credibility very quickly. Subject#14: For instance, information security, you know, that is becoming more and more of a concern. It used to be that you could pretty much share information freely, but now it is not more the case. [….] Subject#7: there are things like not sharing security information because you think it is so important to you.

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Another major barrier to inter-organizational collaboration related to information

management that emerged from the data concerns the language.

Subject#16: Spanish is a really an important and imperative thing if you want to enter here. English is a secondary one and really a secondary one. So that is why I think we are gaining popularity among humanitarian here because of the Spanish. But unfortunately we are not gaining worldwide reach or even in headquarters because we do not have too many Spanish readers so they do not see this importance. Subject#18: I would say that first that because we are French and people that approach us usually are also French NGOs […] as I have mentioned in your second survey who have establish relationship with the Groupe URD and that is because we are French.

Information management related barriers to inter-organizational collaboration were also

the most intensively discussed. It represented approximately forty percent (40%) of

occurrences of collaboration barriers discussed in all the interviews combined.

Information Technology (IT) related

Approximately seventy four percent (73.68%) of the interview participants discussed

challenges to inter-organizational collaboration related to information technology. They

talked for example about some technology tools that are not wide spread and are used

only specific organizations.

Subject#2: And in the same way these organizations are all doing information management and a lot of these organizations have these tools which they only know about within that organization. And so we talk about the community but in reality there isn’t much of a community.

They also talked about lack of IT skills

Subject#8: I think there are certain organizations who think that technology can solve all the problems, so they don’t have a proper appreciation and understanding of the information management challenges and obstacles, but at the same time there is probably some information, people who are very skeptical about technology and do not realize the value that it has.

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Barriers to inter-organizational collaboration related to information technology

represented approximately thirty three (33%) of occurrences of collaboration barriers

discussed in all the interviews combined.

Other structural barriers

I grouped in this category all other structural barriers that were identified and that were

neither IT nor IM related. Approximately fifty eight percent (57.89%) of the interview

participants discussed challenges to inter-organizational collaboration that fell into this

category. The most frequently reported barrier in this category was the lack of

humanitarian dedicated staff and also the competition for funding.

Subject#18: In fact the main challenge is human resources that are dedicated and that have time to do the work. Because why? Most of the people that were there were note really specialist or were not fully dedicated to the job of information management. […] you cannot ask someone to share information if it is not his job. You cannot ask someone to produce a map with the right standard with the right quality if it is not his job. Subject#5: Unfortunately what is happening now is that there are too many organizations running after the money, running after they think what others want, and not running after the real needs. Subject#16: I would say that the most difficult part ummmm. I use a word here “humanware”. [….] In my opinion, and considering my experience in Africa, in Asia this is where most of the information management systems are struggling to survive or to go ahead, to move forward to achieve their objectives. Subject#11: Very often we have a situation that the information in available and everything but who is able then to present it, to analyze it, to prioritize it, and all of these, that is for me the role in information management, or information management for doing that, I think for me one of the key issues.

These other structural barriers represented approximately twenty seven percent (27%) of

occurrences of collaboration barriers discussed in all the interviews combined (Figure

21).

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Figure 21: Break Down of Structural Barriers

Behavioral

Inter-organizational collaboration barriers in the behavioral category included barriers

such as lack of sharing spirit, lack of trust, lack of incentives. They were the second most

frequently reported barriers to collaboration. Approximately forty seven percent

(47.37%) of the interview participants identified at least one collaboration barrier that fell

into the behavioral category (Figure 21). I present below some illustrative quotes drawn

from the interview data.

Subject#12: There is a big problem with information sharing. But that you know that’s the problem of the world. I do not know if that is a very specific problem with these organizations. Subject#13: I think the main challenge here is that the idea of sharing formation has always been said in many areas. It is usually always said yeah it is good to share but you do not sometime see concrete platforms or formalities on how to share this information. It is not formalize. It is always thought as an objective but never formalize.

Behavioral barriers represented approximately sixteen percent (16.10%) of occurrences

of collaboration barriers discussed in all the interviews combined.

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Mandated

Collaboration barriers in the mandated category included barriers such as conflict of goal,

conflict of interest and lack of leadership. This category of barriers was the third and last

most frequently reported barriers to collaboration. Approximately twenty one percent

(21.05%) of the interview participants identified at least one collaboration barrier that fell

into the mandated category (Figure 21). I present below some illustrative quotes drawn

from the interview data.

Subject#9: I mean the different administrative work of organization is very difficult and it varies from organization to organization. There is no commonality among organizations. Subject#5: I think the problem is getting the decision making of all the organizations to actually understand what the issues are and have to understand that they have the responsibility. Subject#9: I think there is the issue of contingency plan. Contingency plan is very important, contingency fund. Because by the time funding happens it might be already too late. I know that in some countries they already have it at the governmental level but where we work where the government is very weak or even nonexistent you do not have contingency plan. So it depends on international actors to provide that type contingency fund that can be utilized during emergency.

Inter-organization collaboration barriers the mandated category represented

approximately five percent (5.08%) of occurrences of collaboration barriers discussed in

all the interviews combined.

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Figure 22: Loudness of Barriers to Collaboration Grouped per Category

Summing up, the most significant barriers to inter-organizational collaboration among

organizations in the humanitarian relief field are structural (Figure 22 above).

Exploring the intensity of the different collaboration barriers cross networks, I made the

following observation (Figure 23): Behavioral barriers to inter-organizational

collaboration were less discussed in the network of non-governmental organizations

(approximately ten percent – 10.26%) than in the two other networks where this

percentage was almost double (21.88% for the network of Governmental organizations

and 17.39% for the network of the United Nations agencies).

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Figure 23: Loudness of Barriers to Collaboration Cross Network

6.2.1.5 Measures of effectiveness

Effectiveness is a multidimensional concept that is especially challenging to measure in

humanitarian assistance and disaster relief which often involve a large variety of

stakeholders with diverse goals and for which outputs are not easily operationalized. One

other objective of my interview was to get the opinion of the member of the Global

Symposium of what would make an appropriate metric for measuring network

effectiveness in their community. About sixty nine percent (68.42%) of the interviewees

answered this question. Guided by the literature, I coded the data in the following four

categories, range of activities, level of coordination, level of collaboration and

availability of resource including funding. The intensity of discussion was almost evenly

distributed cross these categories. I provide below some illustrative quotes for each of the

categories.

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Range of activities

Subject#1: I think if we were to be an effective symposium, one thing would be add new activities that could be implemented within a reasonable timeframe. [….]Research activity, research projects; or even operation activities that could be implemented within a reasonable short time following direct results you know, or direct resources or direct resolution from the symposium. Subject#18: I think for me, I would look at the number of implemented projects.

Level of coordination

Subject#17: I think it would definitely be knowledge exchange. I think at this point, I would want to be able to read, learn about things that I didn’t know anything about or features about things that I have just heard about, and then and those things that I would assume would be facilitate and lead to collaboration. […] So the first step of that would be sufficient information sharing. An then one of things that I would expect to see is the consolidation of this and a lot less competition and a lot more synergy. So one of the eventual outcomes is that we will just become a lot more collaborative and a lot more of these systems talk to each other and a lot more links to one another, and we don’t do as much replication of efforts. Subject#4: organization inter-operate although they are competing for donors whom they appeals, I would seek to analyze the attitude of all those organizations, to see to what extent they compete in the market and to what extent they understand what synergy mean.

Level of collaboration

Subject#10: I think you need to look at the level of coordination and funding. How much of funding have organizations successfully secured to work in this area? The extent to which there are working with other partners or coordinating. Subject#17: I think it would definitely be knowledge exchange. I think at this point, I would want to be able to read, learn about things that I didn’t know anything about or features about things that I have just heard about, and then and those things that I would assume would be facilitate and lead to collaboration. […] So the first step of that would be sufficient information sharing. An then one of things that I would expect to see is the consolidation of this and a lot less competition and a lot more synergy. So one of the eventual outcomes is that we will just become a lot more collaborative and a lot more of these systems talk to each other and a lot more links to one another, and we don’t do as much replication of efforts. Subject#4: organization inter-operate although they are competing for donors whom they appeals, I would seek to analyze the attitude of all those organizations, to see to what extent they compete in the market and to what extent they understand what synergy mean.

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Funding and other resources

Subject#10: I think you need to look at the level of coordination and funding. How much of funding have organizations successfully secured to work in this area? The extent to which there are working with other partners or coordinating. Subject#11:it is probably easier to use the money which has been given because that will at least express a certain level of satisfaction of what we are doing. Because we are funded by volunteering contribution from donors. So at least I would say that if we get a lot of money for one of the other projects that at least indicate the level of satisfaction from our stakeholders. So maybe that is a better one. Subject#3: I think another way which is being relatively successful is the way you have the agencies to have high number of technical people in them. Especially when they come from a professional background where you can do humanitarian practices across agencies where people really know how to improve the competency or work.

6.2.2 Inductive codes

The inductive coding process of my interview data yielded three set of codes that I

believe would help to shed more light in inter-organizational collaboration in the Global

Symposium community and consequently to better understand the effectiveness of this

community in providing disaster assistance. These three code categories included (i)

from advice to collaboration, (ii) the scope of the Global Symposium community and (iii)

the audience / stakeholders.

6.2.2.1 From advice to project collaboration

The first inductive code was related to the connection between advice and project

collaboration relationships. My data highlighted the fact that in the Global Symposium

community, organizations that are linked through advice relationship would in a long run

collaborate in humanitarian relief project.

Subject#1: yes I would say so, I mean it is not humongous impact, but it is an important element as well. There are people and entities we met that we are now discussing with and sharing information with sharing ideas with and you know just keep in touch at an informal level. I think that is very good for us. Also because the UN being UN some

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institutions are more difficult to approach officially or let say institutionally. While if you have this more ad hock loose network where you could exchange without it become very formal that is very useful. Subject#8: I mean you know, I have been in this community for a long time and I have a lot of informal contacts with people asking for advice or asking for you know and you it sort of this informal, this sort of one on one type of thing.

6.2.2.2 Network Scope

The second inductive code that emerged from the data concerned the scope of the Global

Symposium. Some interview participants reported that the large size of the Global

Symposium would more likely negatively impact its effectiveness. Other participants also

highlighted the fact that the “ambitious objectives” of the Global Symposium would

undermine its effectiveness.

Subject#2: I would say, first try to do it within a country or a region instead of trying to do it globally. Because then you have a smaller community and those community are much more important, the regional community or the national community. Subject#2: Tried to strive for much less ambitious objectives and discuss some of the core issues within each of those sub sectors if you will or the sectors. Subject#5: there should be smaller groups that held very very specifically with mixed of media communication people and these organizations perhaps have small groups that meets for one day but in a highly intensive manner, and really look at the issues maybe to review what happened in the last symposium, but reviewed this in a very very pragmatic manner and a very outspoken critical manner as well. Subject#11: May be a smaller group, because it was rather a large event, so maybe if you could do it regionally, let say one in Latin America, and another one in Africa or central Africa, west Africa, maybe that would be more effective, because you would have fewer participants. On the other hand it would not necessary always get the global perspective, so then you would not have everybody in one place western countries, eastern countries, developing, developed countries and whatever. May be a mixed you know a regional approach is not too bad. It can also be as an advantage. Subject#12: I think it needs to be longer basically. […] We spent so long figuring out what we were supposed to be talking about that we never got to the details. And I remember I think we were only in that room for like you know a day or less than a day or something like that. […] And these are big difficult issues and I think you need to spend a lot longer on them rather of assuming that you are going to come up with answers in a few hours especially when you have so many different organizations at the table.

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Subject#12: I think it would be a very very very good idea for just you knows the UN organizations themselves to get together. And not try and bring on all those non-governmental NGO types. Because you know the UN needs to kind of figure out its own game and how it wants to interact with the rest of us. Subject#16: In this region, it seems that this type of global agreement or these international events are not having strong impact this is my perception at this point.

6.2.2.3 Network Audience

The third and last inductive code that emerged from the data was related to the audience

of the Global Symposium. Interview participants would have liked to have a more

diversified audience especially people from the field of disaster reduction.

Subject#1: For the other external partners or external participants, it would be nice to see their contributions are really recognized, but for them it would be less important but for us it would be a major boost because it would be an overall recognition of our work and what we have been doing since 2001, but there were some forces in OCHA that for personal reasons did not want to see that. Subject#5: One of the things that we were suggesting, is that when you have a symposium like this, it doesn’t help just to have media or information people talking among themselves. We need to mainstream. We need to bring in the heads, in fact the very senior people within these organizations; whether it is the UN agencies such as FAO or UNHCR. They should not be communication people, they should be operation people. Subject#5: I think that what need to be done is that the major organizations and the senior operation people needs to be invited and included, and also may even see in them. They should say, look unless you are serious about his, then you shouldn’t be in the business. But if you are serious about helping refugees, displaced people, you name it, then you have another responsibility to communicate with these people. Subject#10: As I said these thing needs to be mainstream at all levels of the disaster response rather than be treated as a separate area of expertise or data collection. Subject#17: I thought the symposium would have been extremely relevant to national platforms for disaster prevention or disaster reduction what even they are calling them. And I know that ISDR was part of the planning process, but I think that, as far as I know, the part of ISDR that was most internally involved in the planning process was the Information Management Division and they did not involve the rest of ISDR and so nobody realized that this was a tremendous opportunity to bring some very specifically qualified people from national disaster reduction platforms. And you know there is probably a hundred of those. And you know rather than… usually it’s important to

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participate in things and we never get to actually get the people together who do the information systems work. That would have been an incredible addition to the audience. Subject#12: I think it would be a very very very good idea for just you know the UN organizations themselves to get together. And not try and bring on all those non-governmental NGO types. Because you know the UN needs to kind of figure out its own game and how it wants to interact with the rest of us.

6.3 Effectiveness Measures

As I stated in the previous chapter, the measurement of effectiveness has always been a

nagging and unsolved problem for inter-organizational network researchers. There is no

consensus on the criteria of measuring effectiveness among researchers. Prior research

has used wide varieties of measures (see Table 4). These measures include the perception

of solving problems, decreased service duplication, improved coordination (Provan &

Milward,1995); service quality (Grusky, 1995); and perceived benefit to various

stakeholders of the network (Weech-Maldonado et al., 2003). In my research, I use three

different measures of network effectiveness including one subjective (perceived network

effectiveness) and two objectives measures (number of funded projects and number of

funding partners). The number of funded projects measures effectiveness in term of level

of activities in humanitarian assistance while the number of funding partners measures

effectiveness in term of level of collaboration.

6.3.1 Perceived Network Effectiveness

I used as one criteria of effectiveness, the perception of the members of the Global

Symposium community about the effectiveness of their humanitarian information

management and exchange network. As discussed earlier, subjective measures of

effectiveness have been widely used in previous research. I also chose this measure in

order to take into account the context of my study. My survey instrument included a five

point Likert scales question that asked respondents about their perception of the

effectiveness of the network on the following items: (i) dissemination of best practices

and humanitarian information principles; (ii) accessibility to resources; (iii) community

development; and (iv) knowledge and information exchange. These items were drawn

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from my prior research and also from the literature. Network effectiveness ranged from 1

(strongly disagree) to 5 (strongly agree). Cronbach’s alpha coefficient for this four-item

scale was .83.

For each of the three networks investigated, I computed the mean score of the responses

on each of the items. The results are presented in Table 7 below. I also conducted an

independent-samples t test to evaluate the differences among the three networks on these

items. I found that there was not a statistically significant difference.

Perceived Effectiveness

Network

Governmental

Organizations

(n= 12)

Non-Governmental

Organizations

(n= 17)

United Nations

Agencies

(n= 11)

Dissemination of best practices

and information principles

M

(SD)

2.14

0.98

2.27

0.64

2.45

1.00

Accessibility to resources M

(SD)

3.08

1.44

2.82

1.29

2.18

0.60

Community development M

(SD)

2.30

1.09

2.59

0.80

2.06

0.49

Knowledge and information

exchange

M

(SD)

2.17

0.98

2.40

0.87

1.93

0.69

Mean score 2.42 2.52 2.16

Table 7: Perceived Network Effectiveness Index Table

My first general observation was that the overall patterns of results within each of the

three networks (governmental organizations/agencies – GO; non-governmental

organizations – NGO; and United Nations agencies – UNA) were similar. The main

contrasting difference observed concerned UNA. This network registered the lowest

score on the following three items: accessibility to resources (UNA score: 2.18; mean

score: 2.70), community development (UNA score: 2.06; mean score: 2.35), and

knowledge and information exchange (UNA score: 1.93; mean score: 2.21). Conversely,

perceptions of respondents of UNA revealed this network is more effective in the

dissemination of best practices and humanitarian information management principles.

UNA displayed the highest score on this (UNA score: 2.45; mean score: 2.27). When

considering all the different survey items on which network effectiveness was measured,

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accessibility to resources came first in two out of the three networks including GO (AR

score: 3.08; mean score: 2.42) and NGO (AR score: 2.82; mean score: 2.25).

In order to reflect the overall network level of perceived effectiveness, I developed an

index by averaged the four factor scores generated for each network to produce a single

mean item score for that network. Based on this mean score, NGO displayed the highest

level of network effectiveness follow respectively by GO, and UNA in the last position.

6.3.2 Level of Activities and Level of Collaboration

The two objective measures for assessing effectiveness that I used were respectively the

number of funded projects and the number of funding partners in humanitarian relief. I

used the number of funded projects as proxi measure for the level of activities while the

number of funding partners was the measure of the level of collaboration. These indices

are important performance factors in humanitarian disaster assistance. Both measures are

related to the concept of social capital. Social capital refers broadly to characteristics of

social structure that function as a resource for individuals and groups. Putman (1993)

defines social capital as the “features of social organization, such as trust, norms and

networks that can improve the efficiency of the society by facilitating coordinated

actions” (P. 167). Social capital can be interpreted as combining a structural component

consisting of involvement in voluntary associations and a cultural component consisting

of norms, values and trust. In my study, I used this interpretation of social capital. The

structural component of social capital was measured by the level of collaboration while

the cultural component was measured by the level of activities. As I discussed earlier, I

considered that greater level of activities and or greater level of collaboration was

associated with higher level of effectiveness. One outcome of voluntary interaction

among members in a community is the development of social trust that facilitates

collective social action toward achieving common social goals. The level of collaboration

in a community is therefore a function of interaction among members via their social

networks. As the level of collaboration increases, so does the effectiveness of the

community in achieving its goals. Thus communities with vibrant communication

networks are likely to display higher level of effectiveness.

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Moreover, I considered the opinion of research participants to choose these two measures

for my study. For instance, during interviews, I asked participants about what in their

opinion would be an appropriate measure that could be used to assess network

effectiveness in their community. More than half of the interviewees (53%) answered

this question. Not surprisingly, I registered a diversified range of responses.

Summarizing, approximately nine (9) different criteria for assessing network

effectiveness emerged from the interviews. They included (i) the range of activities

provided by the network, (ii) the level of preparedness in of the Global Symposium

community in responding to humanitarian disaster especially with regards to information

management and exchange, (iii) the level of coordination in the network, (iv) the level of

collaboration among members (v) the availability and access to funding (vi) the timely

response to crises especially with regards to information sharing (vii) the level of use of

best practices (viii) the availability and access to resources especially technology tools

and technical staff, and finally (ix) the level of attendance to Global Symposium events.

I present in the table below (Table 8) some illustrative quotes from the interview data

concerning these effectiveness measures.

Effectiveness measure

Illustrative quotes

Range of activities

I think if we were to be an effective symposium, one thing would be add new activities that could be implemented within a reasonable timeframe. [….]Research activity, research projects; or even operation activities that could be implemented within a reasonable short time following direct results you know, or direct resources or direct resolution from the symposium. (Subject#1)

I think for me, I would look at the number of implemented projects (Subject#18)

Level of Coordination

I think it would definitely be knowledge exchange. I think at this point, I would want to be able to read, learn about things that I didn’t know anything about or features about things that I have just heard about, and then and those things that I would assume would be facilitate and lead to collaboration. […] So the first step of that would be sufficient information sharing. An then one of things that I would expect to see is the consolidation of this and a lot less competition and a lot more synergy. So one of the eventual outcomes is that we will just become a lot more collaborative and a lot more of these systems talk to each other and a lot more links to one another, and we don’t do as much replication of efforts. (Subject#17)

organization inter-operate although they are competing for donors whom they appeals, I would seek to analyze the attitude of all those organizations, to see to what extent they compete in the market and to what extent they understand what synergy mean. (Subject#4)

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Effectiveness measure

Illustrative quotes

Level of collaboration

I think you need to look at the level of coordination and funding. How much of funding have organizations successfully secured to work in this area? The extent to which there are working with other partners or coordinating. ( Subject#10)

I think it would definitely be knowledge exchange. I think at this point, I would want to be able to read, learn about things that I didn’t know anything about or features about things that I have just heard about, and then and those things that I would assume would be facilitate and lead to collaboration. […] So the first step of that would be sufficient information sharing. An then one of things that I would expect to see is the consolidation of this and a lot less competition and a lot more synergy. So one of the eventual outcomes is that we will just become a lot more collaborative and a lot more of these systems talk to each other and a lot more links to one another, and we don’t do as much replication of efforts. (Subject#17)

organization inter-operate although they are competing for donors whom they appeals, I would seek to analyze the attitude of all those organizations, to see to what extent they compete in the market and to what extent they understand what synergy mean. Subject#4

Funding and other resources

I think you need to look at the level of coordination and funding. How much of funding have organizations successfully secured to work in this area? The extent to which there are working with other partners or coordinating. (Subject#10)

[…]it is probably easier to use the money which has been given because that will at least express a certain level of satisfaction of what we are doing. Because we are funded by volunteering contribution from donors. So at least I would say that if we get a lot of money for one of the other projects that at least indicate the level of satisfaction from our stakeholders. So maybe that is a better one. (Subject#11) I think another way which is being relatively successful is the way you have the agencies to have high number of technical people in them. Especially when they come from a professional background where you can do humanitarian practices across agencies where people really know how to improve the competency or work.(Subject#3)

Table 8: Choosing Effectiveness Measures: Illustrative Quotes from the Interview

It is by analyzing and trying to synthesize this wide range of diverse opinions on

effectiveness measures and by considering the findings of my previous research in this

community (Ngamassi et al, 2010) that I chose to use the number of funding partners and

the numbers of funding projects. As mentioned earlier, I collected the data related to the

number of funded projects and funding partners from the ReleifWeb Financial Tracking

Service, a UNOCHA web based database which records all reported international

humanitarian financial assistance. The the ReleifWeb Financial Tracking Service was

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implemented and launched in 1999. In the humanitarian relief literature, data from the

ReleifWeb database has been used in a number of academic work and reports to donors

(e.g. Torrente, 2004; Walker et al., 2005; Amin & Goldstein, 2008; VanDeWalle &

Turoff, 2008; Tomaszewski & Czaran, 2009). In order to increase the validity of data and

the number of cases, I collected data for a period of ten years (1999-2009).

I started the analysis of this data by conducting a paired-sampled t test to evaluate the

differences between the numbers of funded projects and funding partners in the whole

community. The results indicated that the mean of the number of funded projects (M =

253.02, SD = 523.98) was significantly greater than the mean of the number of funding

partners (M = 20.13, SD = 37.10), t(55) = -3.50, p < .001. The 95% confidence interval

for the mean difference between the two numbers rating was -366.20 to -99.59.

I continued the analysis by computing for each of the three networks, the mean score of

the number of funded projects and funding partners. The results (Table 9 below) will be

used to compare and rank the networks. I then conducted an independent-samples t test

to assess the differences among the three networks on these two items. Concerning the

number of funded projects, I found that there was a statistically significant difference

between the GO and UNA [t(25) = -1.874, p = .07] on the one hand, and on the other

between NGO and UNA [t(39) = -2.239, p < .05]. The difference between GO and NGO

was not statistically significant [t(42) = -.231, p = .818]. With regards to the number of

funding partners, there was a significant difference only between NGO and UNA [t(39)

= -2.470, p < .05].

Effectiveness Measure

Networks

Governmental Organizations

(n= 15)

Non-Governmental Organizations

(n= 29)

United Nations

Agencies (n= 12)

Total

(n=56)

Number of funded projects (from 1999 to 2009)

M (SD)

141.47 346.82

166.48 337.48

601.58 872.07

253.02 523.98

Number of funding partners (from 1999 to 2009)

M (SD)

19.20 60.15

14.62 20.82

34.58 29.37

20.13 37.10

Table 9: Network Effectiveness (Objective measures)

Note: (i) Data collected form ReliefWeb Financial Tracking Service; (ii) data were not available for for-profit organizations.

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Notwithstanding the fact that not all the networks presented a statistically significant

difference on both effectiveness measures, I used the two criteria for ranking the

networks. When using the number of funded projects as measure for effectiveness, I

found that the most effective network was UNA followed respectively by NGO and GO.

UNA was also found to be the most effective network when using the number of funding

partners as measure for effectiveness. With this measure, GO came second and NGO last.

A preliminary observation of these results (see Table 10) is that, as one could anticipate,

that network effectiveness varies depending of the effectiveness measure. For instance,

based on the mean score of perceived network effectiveness, the United Nations agencies

is the least effective network of the three networks in our study. This same network is the

most effective when network effectiveness is measured either by the number of funded

projects or by the number of funding partners.

Network

Criteria

GO Governmental Organizations

NGO Non-Governmental

Organizations

UNA United Nations

Agencies

Perceived effectiveness 2nd 1st 3rd

Number of funded projects 3rd 2nd 1st

Number of funding partners 2nd 3rd 1st

Table 10: Network Effectiveness Ranking

6.4 Network Structural Characteristics and Effectiveness

I present in this section the structural characteristics of the three multi-dimensional inter-

organizational collaboration networks (GO, NGO, and UNA) that I studied.

6.4.1 Density

I started the analysis of network structural characteristics by computing the overall level

of network integration in all three networks. I used the density score to measure the level

of integration of the network. This practice is common in inter-organizational network

research (Provan et al., 2007; Arya & Lin, 2007). I used UCINET (1991) to compute the

density scores. In overall, the level of integration was low in all the three networks I

studied. Density scores ranged from roughly six percent (6.27%) to approximately

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nineteen percent (18.33%) (Table 4). GO displayed the lowest network wide integration

(6.27%). This means that GO had less organizations involved in inter-organizational

relationships (project collaboration and advice) than the other networks. GO was

followed respectively by NGO (6.77%), and UNA (18.33%).

Network

Relationship



Organizations

UNA United Nations

Agencies

Project collaboration 0.0762 0.0779 0.1933

Advice 0.0493 0.0575 0.1433

Multidimensional 0.0627 0.0677 0.1833

Ranking 3rd 2nd 1st

Table 11: Network Density

With this ranking of the three networks, my hypothesis (HN#1) that network

effectiveness increases with network density found support when effectiveness was

measured as the of level of activities (number of funded projects).

6.4.2 Clique

My first step in clique and clique overlap analyses was to determine the minimum set size of a

clique. Apart from the study done by Provan & Sebastian (1998), there is no research in the

literature that reports clique overlap analysis. Given this lack of information, I set the minimum

clique size based on the data I had. Similarly to the Provan & Sebastian (1998) study, I assumed

in my study that that greater and more intensive integration within and across cliques would mean

higher level of effectiveness. In a first step I determined the clique size in all two dimensions that

could be compared across the different networks. I began by generating lists of three, four, and so

on actor cliques in all three networks. Tables 11 and 12 below present the general

characteristics of cliques in the three networks and for the two dimensions I investigated.

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Project Network GO Governmental Organizations


Organizations

UNA United Nations

Agencies

Number of cliques 40 72 8

Min Size 3 3 3

Max Size 5 5 8

Average 3.825 4.014 5.375

Stddev 0.958 0.702 1.847

Clique members 35/53 55/72 14/25

Table 12: Cliques Characteristics Project Network

Advice Network GO

Governmental Organizations


Organizations

UNA United Nations

Agencies

Number of cliques 20 57 8

Min Size 3 3 3

Max Size 5 5 7

Average 3.4006 3.6667 5.1250

Stddev 0.6806 0.6362 1.6421

Clique members 23/53 40/72 12/25

Table 13: Cliques Characteristics Advice Network

I also noted that the six actor cliques did not yield the possibility to compare cliques and

clique overlap in all dimensions, in the rest of the three networks. Consequently, I set the

minimum clique set size at five, even though there are larger cliques present in the

networks (especially in UNA). Table 7 below presents the number of cliques and the

number of organizations in cliques for each of the networks. These results were obtained

by calculating the number of cliques with five or more organizations. I then calculated

the total number of organizations in each network involved in one or more of these

cliques. The number of cliques on the multidimensional row was generated for each

network by summing the results of the two dimensions (projects collaboration and

advice) and subtracting the total number of identical cliques. I used the same method to

calculate the number of organizations on the multidimensional row.

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Clique Characteristics: Minimum Set Size of Five

Clique Characteristics



Organizations

UNA United Nations

Agencies

Number of Cliques

Project collaboration 15 18 5

Advice 2 5 5

Multidimensional 17 22 8

Ranking 2nd

1st

3rd

Number of agencies in clique

Project collaboration 14 24 11

Advice 6 9 10

Multidimensional 15 26 11

Ranking 2nd

1st

3rd

Table 14: Clique Characteristics: Minimum Set Size of Five

When analyzing these results, I made two observations. First, I found that that across the

different networks investigated, NGO was the most integrated as measured by the

number of cliques and the number of organizations in cliques. This finding may indicate

a sort of cluster environment in which organizations knew each another and interacted

frequently through collaborative projects and/or advice. The second observation was that,

at the multidimensional level, there was a similar ranking pattern of the networks using

the number of cliques or the number of organizations in cliques. For both ranking criteria,

NGO was first (22 cliques and 26 organizations in cliques), followed respectively by GO

(17 cliques and 19 organizations in cliques) and finally UNA (8 cliques and 12

organizations in cliques). Exploring individual dimensions, the same ranking pattern held

for projects collaboration relationships. The ranking was different with regards to the

advice relationships. On this dimension, UNA was first both in term of number of cliques

and the number of organizations in cliques followed respectively by NGO and GO.

With these findings, my two propositions, HN#2 - network effectiveness increases with

the number of cliques in the network and HN#3 - network effectiveness increases with

the number of organizations in cliques found support when I used perceived effectiveness

as measured network effectiveness. These propositions were not supported when using

the other measures of network effectiveness.

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6.4.3 Clique Overlap

One-dimensional clique overlap analysis explores one single type of inter-organizational

relationship at a time. I calculated clique overlap in several ways using Provan &

Sebastian’s (1998) procedure. I counted the number of times organizations in a particular

relational type of clique appeared in at least n (n being a cut off number) cliques of that

type and divided the result by divided by the total number of organizations in cliques.

The few previous studies in the literature that used this procedure (e.g. Provan &

Sebastian, 1998; Lemieux-Charles et Al., 2005) set the cut number at 50%. Unlike these

studies, I explored four different levels (low medium and high) of clique overlap using

respectively 25%, 40%, 50% and 75% as cut off number (See table 8). Low overlap

would indicate that the members of these cliques interact intensively among themselves

but only little across different cliques. In the contrast, in a network with high clique

overlap, many clique members would also belong to other cliques. This would lead to a

highly integrated core of organizations spanning multiple cliques.

Clique Characteristics: One-dimensional Clique Overlap

Clique Characteristics



Organizations

UNA United Nations

Agencies

75%

Project collaboration 3/14 = 21.43% 2/24 = 8.33% 3/11 = 27.27%

Advice 4/6 = 66.66% 3/9 = 33.33% 4/10 = 40.00%

50%


Advice 6/6 = 100% 4/9 = 44.44% 6/10 = 60.00%

40%


Advice 6/6 = 100% 7/9 = 77.77% 9/10 = 90.00%

25%

Project collaboration 8/14 = 55.14% 5/24 = 20.83% 11/11 = 100%

Advice 6/6 = 100% 9/9 = 100% 10/10 = 100%

Ranking 2nd 3rd 1st

Table 15: Clique Overlap

After a preliminary analysis of the results, I chose to use the lower level (25%) of cliques

overlap in this study. The reason for my choice was threefold. First, the lower level

(25%) of cliques overlap presented in overall, the highest percentage of cliques overlap

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across networks and across the two dimensions of inter-organizational relationships

investigated. Second, I observed that the different networks maintained the same ranking

irrespective of the level of clique overlap. On the projects collaboration dimension, UNA

came first followed by GO and then NGO. On the advice dimension the order was GO,

UNA and NGO for three levels of overlap (40%, 50% and 75%). At 25% clique overlap

was similar (100%) in all the networks. This meant that working at this level the

differences in clique overlap among the networks at multidimensional level could be

assessed just using the projects collaboration relationships. Analyzing these results the

projects collaboration relationships results, I observed a very high discrepancy in cliques

overlap scores cross networks. These scores ranged from roughly twenty one percent

(20.83%) to hundred percent (100.00%). UNA displayed the highest score (100.00%)

followed by GO (55.14%) and finally NGO (20.83%). With this ranking of the three

networks, my hypothesis (HN#4) that network effectiveness increases with the level of

overlapping clique in the network found support when effectiveness was measured as the

of level of collaboration (number of funding partners).

6.4.4 Multiplexity

As discussed earlier, in this study, multiplexity indicates the level overlap between the

two different dimensions of networks. I measured multiplexity as the extent to which

organizations belonged to cliques in more than one relational dimension. I computed

multiplexity as the percentage of organizations that were members in cliques in both

advice and projects collaboration dimensions. I also explored clique identical overlap. I

calculated the degree of identical overlap as the percentage of cliques in the advice

dimensions exactly matching (or completely embedded in) cliques in the projects

collaboration dimension.

Table 9 below presents the results of my investigations. I found a high discrepancy in

both the multiplexity and the identical clique overlap scores. With regards to

multiplexity, scores ranged from approximately seventeen percent (66.66%) to ninety

percent (100.00%). UNA displayed the highest multiplexity scores (100.00%) followed

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by NGO (77.77%) and finally GO (66.66%). Concerning identical cliques overlap, UNA

was ranked first with a score of forty percent (40.00%) followed by NGO (20.00%) and

GO (0%).

Clique Characteristics: Multidimensional clique overlap

Relationship overlap



Organizations

UNA United Nations

Agencies

Multiplexity 4/6=66.66% 7/9 = 77.77% 10/10 = 100.00%

Identical 0/2 = 0% 1/5 = 20.00% 2/5 = 40.00%

Table 16: Multidimensional Clique Overlap

With this ranking of the three networks, my two propositions HN#5 (network

effectiveness increases with the level of multiplexity in the network) and HN#6 ( network

effectiveness increases with the level of identical cliques in the network in the network)

found support when effectiveness was measured as the of level of activities.

As a final way of exploring my finding regarding clique structure and overlap, I

generated a graphical representation of the clique structure for each of the three networks.

Using the NetDraw function of UCINET (1991), I developed graphics of all clique

members for each network. Figures 24, 25, and 26 present these graphics. I used three

different types of line each representing one the type of inter-organizational relationship.

Organizations that were member of a clique in the Project Collaboration relationship

were linked by dotted lines. Members of a clique in the Advice relationship were linked

by dashed lines. Clique overlap, in which both types of relationships occur among clique

members, was represented by a thick solid line. This line linked each pair of

organizations for which overlap existed. Examining these graphics, it was clear that there

were important differences in the overlap structures of cliques for each network.

Link overlap among clique members in UNA (Figure 24) was substantial. All of the of

the eleven clique-member organizations, maintained at least one multiplex relationship

(both project collaboration and advice) with another clique member. More than thirty five

percent (36.33%) of organizations were connected exclusively through multiplex ties.

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Examining the effectiveness of these organizations, I found that they were among those

that displayed the highest number of funded projects. For example, Org188 had 2777

funded projects as compared to 601, the average number in the network. NGO (Figure 25

had many more organizations (twenty six) in cliques than the UNA. Similarly to UNA

organizations, organizations in NGO also maintained at least one multiplex relationship

with another organization. In this network, only less than four percent (3.83%) of the

organizations had exclusively a multiplex relation. It was also found that these

organizations were among the most effective in the network in term of number of funded

projects. An examination of the last graphic (GO) depicted in Figure 26, also revealed

that many organizations were involved in cliques. In this network however, the level of

link overlap among clique member was lower than in the two previous networks.

Approximately seven percent (6.66%) of the organizations maintained only one type of

relationship with other organizations.

Figure 24: United Nations Agencies Clique

Structure

Figure 25: Non-Governmental

Organizations Clique Structure

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Figure 26: Governmental Organizations Clique Structure

Summing up, I found that overlap in inter-organizational relationships across cliques

appeared to be important for explaining network effectiveness. Moreover, the specific

composition of these overlapping cliques was also important, particularly when the

cliques involved organizations, like the leading humanitarian organizations (e.g. org188),

that may be critical to overall network success.

In Table 17 below, I summarize the network level hypotheses. For each hypothesis, I

indicate the measure of effectiveness for which I found support.

Number Hypothesis Supported

Perceived Effectiveness

Level of Activities

Level of Collaboration

HN# 1 Network effectiveness increases with the density

Yes

HN# 2 Network effectiveness increases with the number of cliques in the network

Yes

HN# 3 Network effectiveness increases with the number of organizations in cliques

Yes

HN# 4 Network effectiveness increases with the level of overlapping clique in the network

Yes

HN# 5 Network effectiveness increases with the level of multiplexity in the network

Yes

HN# 6 Network effectiveness increases with the level of identical cliques in the network

Yes

Table 17: Summary of Hypotheses Testing at Network Level of Analysis

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6.4.5 Discussion

I studied the relationship between network structural characteristics and network

effectiveness in multidimensional networks of collaborative relationships among

humanitarian organizations. I used three different measures for network effectiveness

including one subjective criteria – perceived network effectiveness and two objective

criteria – number of funded projects measuring the level of activities and number of

funding partners measuring the level of collaboration. It is important here to note that in

Provan & Sebastian (1998) study which forms the foundation of this work, the authors

used only one measure for network effectiveness.

Based on the perceived network effectiveness index, NGO displayed the highest level of

network effectiveness followed respectively by GO and UNA in the last position. When

using this measure, findings from my study suggested that network effectiveness was

driven by two network structural properties including the number of network cliques and

the number of members in cliques. This result meant that in a network, inter-

organizational interactions among humanitarian organizations through multiple

relationships would be more effective when only a small number of closely connected

sub-groups of organizations are involved. This finding is consistent with the one of some

previous studies on inter-organizational network effectiveness (e.g. Provan & Sebastian,

1998; Lemieux-Charles et Al., 2005). These studies found that clique structures played

important role in the creation of positive network outcomes.

When using the level of activities as effectiveness measure, my findings suggest that

network effectiveness is driven by two other network structural characteristics including

network density and the level of multidimensional identical clique overlap in the

network. As discussed earlier, the density of a network is the number of links between

members of the network compared to the maximum possible number of links that could

exist in the network (Kilduff & Tsai, 2006). Multidimensional identical overlap degree,

in my study, is the percentage of advice cliques exactly matching project collaboration

cliques. The ranking of the three networks studied based on the level of activities

matched their ranking based on the density and on the degree of multidimensional

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identical clique overlap. UNA was found to be the most effective network. UNA was

followed by NGO and then GO.

Finally, when using the level of collaboration as effectiveness measure, I found that

network effectiveness is driven by the level of one dimensional and multidimensional

(multiplexity) clique overlap in the network. Based on the level of collaboration, the

ranking of the networks investigated matched their ranking based on the level of one

dimensional and multidimensional clique overlap. UNA was found to be the most

effective network. UNA was followed by GO and then NGO.

One first general observation of these findings is that, as one could anticipate, network

effectiveness varies depending of the effectiveness measure. For instance, based on the

mean score of perceived network effectiveness, the United Nations agencies is the least

effective network of the three networks in our study. This same network is the most

effective when network effectiveness is measured either by the level of activities (number

of funded projects) or by the level of collaboration (number of funding partners). These

findings corroborate with the literature on inter-organizational network effectiveness

which highlights the existence of a wide range of definitions and criteria for network

effectiveness (Alter & Hage, 1993; Provan & Milward, 1995; Sydow & Windeler, 1998;

Provan et al., 2007).

Though it would be risky to generalize about research results from a sample of only three

networks in a single area of humanitarian information exchange, my study contributes to

the literature on inter-organizational humanitarian networks in a number of ways. Firstly,

building on Provan & Sebastian (1998), my study further highlights the need to consider

network analyses in smaller substructures than what has been done previously. Large

scale integration across an entire network of organizations is difficult to achieve and is

probably not a very efficient way of organizing (Provan & Sebastian, 1998). For

instance, in the field of humanitarian relief field, disaster response often involves

heterogeneous organizations, both for-profit and nonprofit, with a wide range of different

characteristics. In this field, achieving effective inter-organizational collaboration is more

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challenging especially with regards to information management and exchange (Ngamassi

et al, 2011; Maitland et al., 2009). As the findings of my research suggest, it is more

appropriate to assess network effectiveness in smaller substructures such as subnets or

cliques. These findings derived from quantitative analysis corroborated with results from

qualitative data. For instance, some interview participants reported that the large size of

the Global Symposium would more likely negatively impact its effectiveness. I provide

below, illustrative quotes from Subjects #2, 5 and 11.

Subject#2: I would say, first try to do it within a country or a region instead of trying to do it globally. Because then you have a smaller community and those community are much more important, the regional community or the national community. Subject#5: there should be smaller groups that held very very specifically with mixed of media communication people and these organizations perhaps have small groups that meets for one day but in a highly intensive manner, and really look at the issues maybe to review what happened in the last symposium, but reviewed this in a very very pragmatic manner and a very outspoken critical manner as well. Subject#11: May be a smaller group, because it was rather a large event, so maybe if you could do it regionally, let say one in Latin America, and another one in Africa or central Africa, west Africa, maybe that would be more effective, because you would have fewer participants.

Secondly, my research extends Provan & Sebastian’s model in the humanitarian relief

field. My research offers some evidence that similarly to the public health service

delivery sector, network effectiveness can be explained by intensive integration and

network cliques in the humanitarian relief field. My data supported the idea that

differences in effectiveness across networks could be better understood by focusing on

cliques and the overlap among cliques of multiple relationships among humanitarian

organizations. My study would help to do the clique analysis or to search for closely

connected and cohesive subgroups. Additionally, my work can help to design efficient

inter-organizational network structures in the humanitarian relief sector. For example, by

increasing the level of clique overlap (one dimensional or multidimensional) in inter-

organizational humanitarian networks, network designers should expect a higher level of

inter-organizational collaboration.

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Thirdly, by empirically testing Provan & Sebastian (1998) conceptual framework for

assessing network effectiveness, my study contributes to further research in inter-

organizational collaboration in the humanitarian relief field. During my investigations, I

realized the importance of understanding the different type of relationships that exist

among humanitarian organizations. I found out that the relationships were significantly

complex especially when considering their motives. As mentioned earlier, disaster

response often involves heterogeneous organizations with a wide range of different goal

and need which render collaboration very challenging. In my study for example, when

asked about their reasons for getting into a relationship, my study subjects provided a

wide range of different reasons. Network designers need to examine more closely the

nature of relationships in which humanitarian organizations are engaged and the self-

reinforcing dynamic of overlapping groups.

Fourthly, my research also highlights the need to explore network effectiveness using a

set of different measures. The majority of existing work on network effectiveness,

including that of Provan and Sebastian (1998) was conducted using one measure. As

mentioned earlier, Provan & Sebastian used client outcomes, a subjective measure, to

assess network effectiveness. Moreover, in most cases, the effectiveness measure was not

selected with input from the various network members. In my study, I used input from

network members to determine the three measures of effectiveness. Using a set of three

different measures for network effectiveness allowed me to find consistent ranking

pattern for each of the six network structural characteristics studied. Moreover, my

findings suggest that the subjective and objective forms of network effectiveness are

better explained by different network structural attributes. Whereas subjective network

effectiveness is better explained by the number of cliques and clique membership,

objective network effectiveness is better explained by the multifaceted nature of inter-

organizational relationships as measured by clique overlap and multiplexity. My study

serves as an example of effectiveness being measured with multiple criteria. In a nut

shell, my work extends in the humanitarian relief field, Provan & Sebastian (1998)’s

model of inter-organizational network effectiveness.

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Lastly, my research also has implications for network theories. For many organization

theorists, the study of both inter-organizational and intra-organizational networks has

primarily been an exercise in analysis and methods (Salancik, 1995). Building upon

Provan & Sebastian (1998), my study further develops an alternative method for network

analysis and contributes to building network theories by examining and explaining how

network structural properties including network density, cliques and overlapping cliques,

might promote the interests of network members and that of the community as a whole.

6.5 Ego-Net Characteristics and Effectiveness

I used the multiple linear regression method to investigate effectiveness at organizational

level. The independent variables, nine in total, were grouped into the following three

categories: organization, ego-network and network. These independents variables are

described in Table 18 below. I also used two interaction variables with the purpose to

assess the combined impact of technology and network characteristics on organizational

effectiveness. I conducted the regression analysis on two different measures of

organizational effectiveness, the dependent variable. The first measure was the level of

activities, measured as the number of funded projects and the second was the level of

collaboration, measured as the number of funding partners. In order to examine

separately the influence of each category of the independent variables on the dependent

variable, I developed four models as described below.

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Variable level Variable Definition

Organization

Size

Size of the organization

Service Range of services provided by the organization

Com_Med Varieties of Communication media (e.g. Internet, Website)

Coll_SM Varieties of Collaboration social software (e.g. wiki, shared

db)

Cty_SM Varieties of Community social software (e.g. Facebook)

Ego-network

Centrality Degree centrality of the organization in the network

Bridge Structural hole value of the organization in the network

cliques Number of distinct cliques to which organization is a member

Network

Density Density of the network to which organization is a member

Interaction

Com_Med x Density Interaction between communication media and density

Com_Med x Centrality Interaction between communication media and centrality

Table 18: Organizational Effectiveness Variables

6.5.1 Models Building

As a first step in the model building process, I examined my data to check for consistency

and eventual errors of data due to data manipulation. This examination led to the

identification of one outlier in the data. Before proceeding to the next step of the analysis

I removed the outlier.

The next step in the model building process was to compute some basic statistics and to

check the correlation between the variables. Table 19 reports the descriptive statistics and

correlations between the variables. One preliminary observation was that all the

correlations between the independent variables and the dependent variables were

positive. This was an indication that organizations that have higher number on these

variables would tend to display higher level of effectiveness.

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M SD 1 2 3 4 5 6 7 8 9 1 11 12

Size Service .483** Com_Med .106 .176 Coll_SM -.0.56 -.016 .674** Cty_SM .142 .183 .691** .753** Clique .221 .088 .384** .261* .312* Centrality .200 .057 .46** .365** .384** .875** Bridge .108 -.016 .383** .328** .262* .407** .601** Density .283 -.081 -.022 -.047 -.122 .256 .214 .094 Com_Medxcentrlity .179 .079 .565** .396** .406** .888** .958** .524** .215 Com_Medxdensity .231 .022 .668** .427** .376** .526** .524** .379** .636** .613** Funding_Parners .211 .030 .259* .152 .272** .350** .646** .253 .203 .608** .351** Funded_Projects .264* .107 .393** .209 .274** .627** .699** .336* .351 .770** .643** .724**

Note: N = 56

** correlation is significant at the 0.01 level (2-tailed)

* correlation is significant at the 0.05 level (2-tailed)

Table 19: Descriptive Statistics and Correlations

As mentioned earlier, I built four models to investigate the independent and interaction

effects of the organization and network level variables on organizational effectiveness. I

built these various models using a linear combination of the dependents variables. I had

checked the non-linear approach and found no improvement in fit of the models. In

Model I, my baseline model, I modeled effectiveness as a function of the variables of the

organization category. This model shows the regression results for the effects of

organizational characteristics only, on effectiveness. In Model II, I added the independent

variables of the ego-net category. Model III included the independent variables of the

network category. Finally, in Model IV (the full model) I used all the independent

variables including the interaction terms. I build separately a full model for each of the

two interaction terms. I examined all the models for collinearity issues. I used the

variation inflation factor (VIF) for this endeavor. VIF is a technique commonly used for

collinearity diagnostic. A general rule is that the VIF should not exceed 10 (Belsey,

1991). The VIF values for the variables in all the four models were not higher than 8. I

concluded therefore that collinearity was not an important issue. I present below the four

models that I developed for each of the two measures of organizational effectiveness.

6.5.1.1 Effectiveness measured as Level of Activities

I developed the first set of multiple linear regression models, using the level of activities

as dependent variable. As mentioned earlier, the number of funded projects of an

organization was used as a proxy measure for the level of its activities.

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Model I

In the first model, I used only the organizational internal characteristics as independent

variables to predict effectiveness. The linear combination of these variables was

significantly related to effectiveness, F (5, 50) = 2.7, p < .05. The multiple correlation

coefficient was .461, and the adjusted R2 was 0.134. This value for R

2 was an indication

that approximately 13% of the variance in organizational effectiveness can be accounted

for by the linear combination of organizational internal characteristics. In this model, two

predictors were found to significantly contribute in explaining organizational

effectiveness. They included the size of the organization (β = 0.259; p < .05) and the

communication media available in the organization (β = 0.418; p < .05).

Model II

To build this model, I added in Model I the independent variables of the ego-net

category. The multiple regression model of the eight predictors also yielded a significant

linear relation with the effectiveness, F(8, 47) = 6.75, p < .001. The multiple correlation

coefficient was .731 and the adjusted R2 was 0.456. This value for R

2 was an indication


for by the linear combination of organizational internal resources and the organization

ego net characteristics. In this model, only one predictor – the organization centrality

degree – was found to significantly contribute in the model (β = 0.768; p < .001).

Model III

Model III shown the regression results for the effects of organizational internal

characteristics, organizational ego-net properties and organizational network structural

properties on effectiveness. This model had nine independents variables. The linear

combination of these predictors was also significantly related to effectiveness, F (9, 46) =

6.83, p < .001. The multiple correlation coefficient of this integrated model was .756. The

adjusted R2 for this model indicated that the model explained approximately 49% of the

variance in the organizational effectiveness. In this model, two predictors were found to

significantly contribute in the model. They included the organization centrality degree (β

= 0.763; p < .005) and the network density (β = 0.219; p < .1).

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Model IV

As mentioned earlier, for each of the two interaction terms, I built a separate full model.

The interaction term in the first full model (Model IVa) was the combination of

communication media and degree centrality (ego-net category variable). In the second,

Model IVb the interaction term was the combination of communication media and

network density (network category variable).

Model IVa

The linear combination of all the predictors was significantly related to the effectiveness,

F(10, 45) = 10.71, p < .001. The multiple correlation coefficient of this integrated model

was .839. The adjusted R2 for this full model indicated that the model explained

approximately 64% of the variance in the organizational effectiveness. In this model,

three predictors were found to significantly contribute in the model. They included the

number of cliques (β = -0.414; p < .05) the network density (β = 0.196; p < .05) and the

interaction term (β = 1.736; p < .001). Figure 27 and Figure 28 below depict the normal

probability plot and the plot of residuals. They provide an indication of the normal

distribution of the data and the residuals produced by the model.

Figure 27:Residual Plot for Effectiveness Mesuared

as the Level of Activities (Model IVa)

Figure 28: Normal Plot for Effectiveness Mesuared

as the Level of Activities (Model IVa)

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Model IVb


F (10, 45) = 9.85, p < .001. The multiple correlation coefficient of this integrated model

was .828. The adjusted R2 for this full model indicated that the model explained

approximately 62% of the variance in the organizational effectiveness. In this model, five

predictors were found to significantly contribute in the model. They included the

communication media available in the organization (β = -0.462; p < .05), the degree

centrality (β = 0.869; p < .001) the network density (β = -0.403; p < .05), bridging

structural hole (β = -0.207; p < .05) and the interaction term (β = 1.013; p < .001).

Figure 29 and Figure 30 below depict the normal probability plot and the plot of

residuals. They provide an indication of the normal distribution of the data and the

residuals produced by the model.

Figure 29: Residual Plot for Effectiveness Mesuared

as the Level of Activities (Model IVb)

Figure 30: Normal Plot for Effectiveness Mesuared

as the Level of Activities (Model IVb)

In Table 20 below I provide a summary of the four models. A brief analysis of the table

shows that, cross model, none of the independent variables was consistently found to be

an important predictor of effectiveness.

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Variable Model I Model II Model III Model IV

A (Centrality) B (Density)

β t β t β t β t β t Organization Size .259 1.78† .128 1.08 .041 0.33 .089 0.86 .059 0.56 Service -.097 -0.66 -.029 -0.24 .023 0.19 .011 0.11 .063 0.62 Com. Media .418 2.25* .213 1.38 .209 1.40 -.185 -1.20 -.462 -2.20* Coll. Media -.081 -0.38 -.117 -0.69 -.151 -0.91 -.146 -1.05 -.153 -1.07 Cty. Media .027 0.13 -.037 -0.22 .037 0.22 .172 1.18 .075 0.51 Ego-network Centrality .768 3.04** .763 3.11** -.584 -1.61 .869 4.07** Bridge -.155 -1.16 -.147 -1.14 -.009 -0.08 -.207 -1.83* Clique -.047 -0.22 -.100 -0.47 -.414 -2.16* -.303 -1.59 Network Density .219 2.00† .196 2.12* -.403 -2.23* Interaction ComMxCentrality 1.736 4.49** ComMxDensity 1.013 4.05**

N 56 56 56 56 56 P 0.031 0.000 0.000 0.000 0.000 Adjusted R2 .134 .456 .488 .638 .617

† p < .1

* p < .05

** p < .01

Standardized coefficient and t statistics are reported.

Table 20: Regression Analysis on Effectiveness Measured as the Level of Activities

6.5.1.2 Effectiveness measured as Level of Collaboration

Building this second set of models, I proceeded the same way as for the previous set. The

only difference here was that I used the level of collaboration as the dependent variable.

Model I

The linear combination of independent variables was somewhat significantly related to

effectiveness, F (5, 50) = 1.605, p = .176. The multiple correlation coefficient was .372,

and the adjusted R2 was 0.052. This value for R

2 was an indication that only about 5% of

the variance in organizational effectiveness can be accounted for by the linear

combination of organizational internal characteristics. In this model, none of the

predictors was found to significantly contribute in explaining effectiveness.

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Model II

The multiple regression model of the eight predictors also yielded a significant linear

relation with the effectiveness, F(8, 47) = 19.06, p < .001. The multiple correlation

coefficient was .874 and the adjusted R2 was 0.724. This value for R

2 was an indication


for by the linear combination of organizational internal characteristics and the

organization ego net characteristics. In this model, four predictors were found to

significantly contribute in the model including one in the organizational category

(collaboration social media) and all the three ego-net category variables (degree

centrality, clique-count and bridging structural hole). The statistical parameters of these

predictors in the model are as follow: collaboration social software (β = -0.257; p < .05);

degree centrality (β = 1.913; p < .001); clique-count (β = -1.183; p < .001); bridging

structural hole (β = -0.410; p < .001). The community social software was fond to

contribute somewhat significantly (β = 0.176; p = .155)

Model III

The linear combination of these predictors was also significantly related to effectiveness,


was .882. The adjusted R2 for this model (0.734) indicated that the model explained

approximately 73% of the variance in the organizational effectiveness. The five

predictors that were found to significantly contribute in the previous model (Model II)

also significantly contribute to this model. The statistical parameters of these predictors

in the model are as follow: collaboration social software (β = -0.276; p < .05);

community social software (β = 0.219; p < .1); degree centrality (β = 1.910; p < .001);

clique-count (β = -1.214; p < .001); bridging structural hole (β = -0.406; p < .001). The

network density was found to contribute somewhat significantly (β = 0.129; p = .122).

Model IVa


F (10, 45) = 17.63, p < .001. The multiple correlation coefficient of this integrated model

148

was .893. The adjusted R2 for this full model (0.751) indicated that the model explained

approximately 75% of the variance in the organizational effectiveness. The five

predictors that were found to significantly contribute in the previous model (Model III)

also significantly contribute to this model. The statistical parameters of these predictors

in the model are as follow: collaboration social software (β = -0.274; p < .05);


clique-count (β = -1.335; p < .001); bridging structural hole (β = -0.352; p < .001) and

the interaction term (β = 0.667; p < .05). I also found in this model that the network

density contribute somewhat significantly (β = 0.129; p = .122).

Figure 31 and Figure 32 below depict the normal probability plot and the plot of

residuals. They provide an indication of the normal distribution of the data and the

residuals produced by the model.


as the Level of Collaboration (Model IVa)

Figure 32: Normal Plot for Effectiveness

Mesuared as the Level of Collaboration (Model

IVa)

Model IVb



was .895. The adjusted R2 for this full model (0.757) indicated that the model explained

approximately 76% of the variance in the organizational effectiveness. In addition to the

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interaction term, the five predictors that were found to significantly contribute in Model

III also significantly contribute to this model. The statistical parameters of these

predictors in the model are as follow: collaboration social software (β = -0.277; p < .05);


clique-count (β = -1.307; p < .001); bridging structural hole (β = -0.433; p < .001) and

the interaction term (β = 0.665; p < .05).


as the Level of Collaboration (Model IVb)

Figure 34: Normal Plot for Effectiveness

Mesuared as the Level of Collaboration (Model

IVb)

In Table 21 below I provide a summary of the four models. Unlike in the previous set of

models where none of the independent variables was consistently found cross model to

be an important predictor of effectiveness, in this set, I found six variables. They include

two variables of the organization category (collaboration social software and community

social media) all the three variables of the ego-net category (degree centrality, bridging

structural hole, number of cliques) and the only network category variable (network

density). The relationship between these variables and effectiveness was also

consistently either positive or negative across models. The two interaction terms were

also found to significantly contribute to explain effectiveness.

150

Variable Model I Model II Model III Model IV

A (Centrality) B (Density)

β t β t β t β t β t Organization Size .223 1.46 .133 1.58 .082 0.92 .101 1.17 .091 1.07 Service -.162 -1.05 -.089 -1.06 -.059 -0.69 -.063 -0.77 -.040 -0.50 Com. Media .195 1.01 .042 0.39 .040 0.37 -.111 -0.87 -.268 -1.60 Coll. Media -.165 -0.74 -.257 -2.12* -.276 -2.31* -.274 -2.37* -.277 -2.43* Cty. Media .259 1.16 .176 1.45† .219 1.79† .271 2.24* .237 2.02* Ego-network Centrality 1.913 10.64** 1.910 10.81** 1.392 4.61** 1.959 11.52** Bridge -.410 -4.31** -.406 -4.34** -.352 -3.75** -.433 -4.81** Clique -1.183 -7.62** -1.214 -7.89** -1.335 -8.37** -.1307 -8.59** Network Density .129 1.62† .120 1.56† -.157 -1.09 Interaction ComMxCentrality .667 2.08* ComMxDensity .465 2.34*

N 56 56 56 56 56 P 0.176 0.000 0.000 0.000 0.000 Adjusted R2 0.052 0724 0.734 0.751 0.757

† p < .1

* p < .05

** p < .01

Standardized coefficient and t statistics are reported.

Table 21: Regression Analysis on Effectiveness Measured as the Level of Collaboration

Comparing the two sets of models, I made a number of observations. First, when

examining the models within each of the two effectiveness measures, I found that the

linear combination of the independent variables used in the model was significantly

related to effectiveness. When using the level of collaboration as effectiveness measure,

the explanatory power (Adjusted R2) of the models gradually increased ranging from

approximately 5% for the baseline model to nearly 76% for the full model. In the full

model, the variables in the organization category accounted for approximately 5.2% of

the variance while those of the ego-net category explained 67.2%. The network category

variable accounted for 1% of the variance while the interaction term explained 1.7%

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(ModelIVa) and 2.3% (ModelIVb). Similarly, when using the level of activities as

effectiveness measure, the explanatory power of the models also gradually increased. Its

value ranged from approximately 13% for the baseline model to nearly 64% for the full

model. In the full model, the variables in the organization category accounted for

approximately 13.4% of the variance while those of the ego-net category explained

32.2%. The network category variable accounted for 3.2% of the variance while the

interaction term explained 15% (ModelIVa) and 12.9% (ModelIVb). Figure 35 below

highlights the contribution of each category of variables in explaining effectiveness.

Figure 35: Effectiveness Models’ Explanatory Power

Second, when examining the models between the two effectiveness measures, I found a

discrepancy in the explanatory power of the full model. When using the level of

collaboration as effectiveness measure, the linear combination of my independent

variables in the full model, explained more than 75% of the variances (75.1% for

ModelIVa and 75.7% ModelIVb). This value was less than 64% when effectiveness was

measured as the level of activities. In both cases, ego-net category variables made the

highest contribution, 67.2% and 32.2% respectively.

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6.5.2 Hypotheses Testing

6.5.2.1 Main Effects

Hypothesis HO#1

I argued in hypothesis HO#1 that greater centrality increases organizational effectiveness

both in terms of level activities as well as the level of collaboration. I found support to

this hypothesis in almost all the models in which centrality score was used as a predictor

and for both measures of organizational effectiveness. When using the level of activities

as effectiveness measure, I obtained the following statistics in the full model (Model IVb)

β = 0.869; p < .005. When using the level of collaboration as effectiveness measure, the

statistics were the following: Model IVa (β = 1.392; p < .005); Model IVb (β = 1.959; p <

.005). The level of significance was relatively high with this measure of effectiveness

than with the previous one. This finding is consistent with most previous research that

explored the influence of network position and especially the degree centrality on

outcome such as performance and effectiveness (Knoke, 1990; Wasserman & Faust,

1994; Stevenson & Greenberg, 2000; Kilduff & Tsai, 2006).

Hypothesis HO#2

Hypothesis HO#2 concerned bridging structural hole in a network. My proposition was

that organizations will enhance their effectiveness by bridging structural holes both in

terms of level activities as well as the level of collaboration. When using the level of

activities as effectiveness measure, I found that bridging structural hole was an important

predictor of effectiveness only in Model IVb (β = -0.207; p < .05). This variable showed

no significance in in Model IVa. Bridging structural hole was found to be an important

predictor of effectiveness when using the level of collaboration as effectiveness measure.

I obtained the following statistics: Model IVa (β = -0.352; p < .005); Model IVb (β = -

0.433; p < .005). But contrary to my proposition, my findings rather showed a negative

relationship between bridging structural hole and effectiveness. This result is not an

isolated case. For instance, while several previous studies have shown that organizations

improve their performance as a result of bridging structural holes (e.g., Hargadon &

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Sutton, 1997; Finlay & Coverdill, 2000), other studies have shown negative performance

effects of bridging structural holes (e.g., Ahuja, 2000; Dyer & Nobeoka, 2000). This

result may be due to the high heterogeneity of the humanitarian organizations concerned

in this study.

Hypothesis HO#3

My Hypothesis HO#3 was related to the importance of the density of the network in

predicting network effectiveness. I proposed that organization will benefit from high-

density networks to enhance their effectiveness both in terms of level activities as well as

the level of collaboration. When using the level of activities as effectiveness measure, I

found that network density was an important determinant of effectiveness. Both Model

IVa (β = 0.196; p < .05) and Model IVb (β = -0.403; p < .05) yielded statistically

significant evidence that supported this hypothesis. The negative sign on the β coefficient

in Model IVb results from adding the interaction term (communication media X network

density) in the model. This hypothesis was somewhat supported in Model IVa (β =

0.120; p < .1) when using the level of collaboration as effectiveness measure. The main

effect of network density was not statistically significant in Model IVb.

Hypothesis HO#4

My proposition in Hypothesis HO#4 was that the effectiveness of an organization will

increase both in terms of level activities as well as the level of collaboration, with the

number of the distinct cliques to which it belongs. This hypothesis was not supported by

my findings. In both measures of effectiveness the number of cliques was found to be an

important predictor of effectiveness, but contrary to my proposition, the final models

rather showed a negative relationship. When using the level of activities as effectiveness

measure, I obtained the following statistics: Model IVa (β = -0.414; p < .05); Model IVb

(β = -0.303; p = .119). When using the level of collaboration as effectiveness measure,

the statistics were as follow: Model IVa (β = -1.335; p < .005); Model IVb (β = -0.1307;

p < .005).

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Hypothesis HO#5

I argued in Hypothesis HO#5 that the size of an organization will be positively associated

with its effectiveness both in terms of level activities as well as the level of collaboration.

I found no support to this hypothesis. When using the level of activities as effectiveness

measure, I obtained the following statistics: Model IVa (β = 0.089; p = .395); Model IVb

(β = 0.059; p = .579). When using the level of collaboration as effectiveness measure,

the statistics were the following: Model IVa (β = 0.101; p = .249); Model IVb (β =

0.091; p = .291).

Hypothesis HO#6

In Hypothesis HO#6, I proposed that the range of service provided by an organization

will be positively associated with its effectiveness both in terms of level activities as well

as the level of collaboration. This hypothesis was also not supported in any of the models.

The range of service provided by an organization was not found to be an important

deterrent of organizational effectiveness. When using the level of activities as

effectiveness measure, I obtained the following statistics: Model IVa (β = 0.011; p =

.913); Model IVb (β = 0.063; p = .540). When using the level of collaboration as

effectiveness measure, the statistics were the following: Model IVa (β = -0.063; p =

.442); Model IVb (β = -0.040; p = .622). This result may suggest that in inter-

organizational network for humanitarian information management, organizational

effectiveness measured both in terms of level activities as well as the level of

collaboration is driven by other factors regardless of the number of services an

organization provides.

Hypothesis HO#7

Hypothesis HO#7 was related to my proposition that the greater the variety of

communication media available in an organization the higher its effectiveness both in

terms of level activities as well as the level of collaboration. When using the level of

activities as effectiveness measure, Model I yielded statistically significant evidence that

supported this hypothesis (β = 0.418; p < .05). Communication media was also found to

significantly contribute to explain effectiveness in one of the full models (Model IVb).

155

When using the level of collaboration as effectiveness measure, none of the models

produced statistically significant evidence and thus failed to support the hypothesis.

Hypothesis HO#8

I proposed in hypothesis HO#8 that the greater the variety of collaboration social

software available in an organization the higher its effectiveness both in terms of level

activities as well as the level of collaboration. When using the level of activities as

effectiveness measure, none of the models produced statistically significant evidence and

thus failed to support the hypothesis. This hypothesis was supported when using the level

of collaboration as effectiveness measure. The full models showed that the range of

collaboration social software available in an organization was an important predictor for

effectiveness. I obtained the following statistics: Model IVa, (β = -0.274; p < .05);

Model IVb, (β = -0.277; p < .05). But contrary to my proposition, the final model rather

showed a negative relationship between the range of collaboration social software and

effectiveness.

Hypothesis HO#9

I hypothesize in HO#9 that the greater the variety of community social software available

in an organization the higher its effectiveness both in terms of level activities as well as

the level of collaboration. When using the level of activities as effectiveness measure,

similarly to the previous hypothesis, none of the models produced statistically significant

evidence and thus failed to support the hypothesis. When using the level of collaboration

as effectiveness measure, I found support to the hypothesis. The full models yielded

statistically significant evidence that the range of community social software was an

important predictor of organizational effectiveness (Model IVa, β = 0.271; p < .05; and

Model IVb, β = 0.237; p < .05).

156

6.5.2.2 Information Technology Interaction Effects

Hypothesis HO#10

In hypothesis HO10, I proposed that organizations that possess a wide variety of

communication media will benefit more from high network degree centrality to enhance

their effectiveness than those that do not. I found support to this hypothesis for both

measures of organizational effectiveness. When using the level of activities as

effectiveness measure, I obtained the following statistics: Model IV (β = 1.736; p <

.005). When using the level of collaboration as effectiveness measure, the statistics were

the following: Model IV (β = 0.667; p < .05).

Hypothesis HO#11

In hypothesis HO11, I proposed that Organizations that possess wide varieties of

communication media will benefit more from high network density to enhance their

effectiveness than those that do not. I found support to this hypothesis for both measures

of organizational effectiveness. When using the level of activities as effectiveness

measure, I obtained the following statistics: Model IV (β = 1.013; p < .005). When

using the level of collaboration as effectiveness measure, the statistics were the

following: Model IV (β = 0.465; p < .05).

In Table 22 below, I summarize the results of the hypotheses testing for both measures of

effectiveness. For each hypothesis, I indicate (with ‘S’) whether the independent variable

in the hypothesis was found to be an important predictor of effectiveness. I also indicate

if the hypothesis was support (with ‘SS’).

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Number Hypothesis Significant & Supported

Level of Activities

Level of Collaboration

HO# 1 Greater centrality increases organization effectiveness SS SS

HO# 2 Greater bridging of structural holes increases effectiveness S

HO# 3 Organization effectiveness increases with the density of the network it which it belongs

SS SS

HO# 4 Organization effectiveness increases with the number of distinct cliques to which it belongs

S S

HO# 5 The size of an organization is positively associated with its effectiveness

HO# 6 The range of service provided by an organization is positively associated with its effectiveness

HO# 7 The greater the varieties of communication media available in an organization, the higher its effectiveness

S

HO# 8 The greater the varieties of collaboration social software available in an organization, the higher its effectiveness

S

HO# 9 The greater the varieties of community social software available in an organization, the higher its effectiveness

SS

HO# 10 Organizations that possess wide varieties of communication media will benefit more from high network degree centrality to enhance their effectiveness than those that do not.

SS SS

HO# 11 Organizations that possess wide varieties of communication media will benefit more from high network density to enhance their effectiveness than those that do not.

SS SS

Table 22: Summary of Hypotheses Testing at Organizational Level of Analysis

6.5.3 Discussion

I begin this discussion section by restating that previous studies that used the theoretical

lenses of Resource Based View primarily focused on characteristics internal to the

organizations to predict effectiveness and performance. Most of these studies were

conducted in the for-profit sector, conceptualizing organizations as atomistic profit-

seeking entities (Arya & Lin, 2007). Subsequent research on organization performance

and effectiveness highlighted the importance to view organizations as embedded in a web

of inter-organizational relationships which may serve both as resources themselves and as

mediums for accessing external resources (Granovolta, 1985; Baum & Dutton, 1996;

Portes, 1998; Gulati et al., 2000; Shipilov, 2006). On the other hand, most studies that

apply social network approach to explore effectiveness focused on the characteristics of

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network structure, without paying much attention to the attributes of the organizations

that comprise the network. In my study, I draw on both the RBV and the social network

theories to investigate how organizations’ attributes combined with network structural

characteristics influence organizational effectiveness. I especially explore the influence of

information technology on effectiveness. I discuss below the findings of my

investigations with regards to (i) the measures of organizational effectiveness (ii) the

relationship between organization internal characteristics and organizational

effectiveness (ii) the relationship between ego-network characteristics and

organizational effectiveness (iii) the relationship between network structural

characteristics and organizational effectiveness and (iv) the Catalytic role of Information

Technology on organizational effectiveness.

Measure of organizational effectiveness

The findings from my investigations suggest that in networks of organizations engaged in

humanitarian information management, organizational effectiveness would be better

assess using the level of collaboration. When using the level of collaboration as

dependent variable in a regression model, the linear combination of the independent

variables explained almost 76% of the variances. This proportion was less than 64%

when effectiveness was measured as the level of activities. Figures 36 and 37 below

depict these variations. Figure 36 presents the variation for the case where the interaction

term is combination of communication media and degree centrality; while for Figure 37

the interaction term is communication media and network density.

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Figure 36: Variations in the Effectiveness Measures (Model VIa)

Figure 37: Variations in the Effectiveness Measures (Model VIb)

Organization internal characteristics and organizational effectiveness

My research also showed the importance of considering the characteristics internal to

organizations when explaining effectiveness. As discussed in the analysis section, when

using only the organizational internal resources as independent variables to predict

effectiveness, the regression model showed that the linear combination of these variables

was significantly related to effectiveness. Taken alone, organizations internal

characteristics explained only approximately 5% of the variances in organizational

effectiveness when using the level of collaboration but this percentage was much higher

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(over 13%) when using the level of activities. This finding was consistent with previous

studies that used the Resource Based View to assess organizational effectiveness (e.g.

Zaheer & Bell, 2005; Arya & Lin, 2007).

Moreover, the findings of my investigation suggest that among humanitarian

organizations engaged in information management and exchange, information technology

would be one of the most important internal characteristics that would more accurately

predict effectiveness. Previous research has shown an increase in the adoption and use of

information technology in general, for disaster relief among humanitarian organizations

(Comfort, 1993; Quarantelli, 1997). For these organizations, information technology

plays a vital role in disasters relief. Research has also shown that the use information

technology may have a positive impact on inter-organizational collaboration and

coordination (Malone & Crowston, 1994). Studies have also highlighted the importance

of the use of social software in humanitarian disaster relief and crisis management (Palen

et al., 2007a; Palen et al., 2007b; Palen et al., 2007c; Sutton et al., 2008; Vieweg et al.,

2008; Hughes et al., 2008; Lui et al., 2008). Although most of these studies investigated

the use of social software at the individual user level of analysis, the positive impact of

these tools for disaster relief could easily be extrapolated at other levels of analysis

including the organizational level and the network level.

In my study, all the three information technology related variables were in some ways

found to significantly contribute to explain organizational effectiveness. However, not all

these information technology related variables were found to be positively related to

organizational effectiveness as I hypothesized. For instance, while wide varieties of

community social software were found to be positively associated with effectiveness, my

findings rather suggest a negative relationship between collaboration social software (e.g.

wiki, shared database) and effectiveness.

These contrasting results obtained from my statistical analysis concerning the importance

of information technology to humanitarian organizations were somewhat similar to those

obtained from the qualitative data gathered through interviews. As mentioned earlier,

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interview participants were asked to give their opinion specifically on the implications of

information technologies on inter-organizational collaboration among members of the

Global Symposium and the contribution of these technologies in helping to meet the

organizational goal. Approximately seventy percent (68.42%) of the interviewees shared

their opinion on this issue. I registered a wide range of diverse point of views. Some

participants, roughly thirty one percent (30.77%) of those that answered the question, had

a very positive opinion about the implications and especially the catalytic role that

information technologies play in fostering humanitarian inter-organizational

collaboration. The vast majority (69.23%) however, expressed mixed feelings.

For the participants that had positive opinions, information technologies served as an

important catalyst for inter-organizational collaboration in the Global Symposium

community. They argued that if without information technologies effective simple

communication is difficult, collaboration would be even harder. Participant number five

for example reported that:

Subject#5: I think that information technology is extremely important because we basically need to communicate to all these different communities in as many different ways as possible.

They also believed that the use of information technologies is instrumental in quickly

gather analyze and disseminate humanitarian information leading to effective disaster

response. Below, we illustrate this point of view with quotes from three participants,

number six, seven and eleven.

Subject#6: You cannot do it without information technology. Gathering information, managing information, analyzing information, distributing information, really you cannot do all this without information technology. So I think the question is kind of obvious. Subject#7: Information technology essentially supports what we do. It helps in sharing information, mainly transporting information around, maintaining our communication. Subject#11: I think the information technology is key of cause, because without proper systems in place, you will not be able to do that.

Most of these participants who had a very positive opinion about the important

implications of information technologies in fostering collaboration among humanitarian

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organizations also believed that information technologies were instrumental for their

organizations in meeting their goals.

The participants who expressed mixed feelings about the role of information technologies

in inter-organizational collaboration gave a number of reasons that could be grouped into

two main categories. The first category of reasons was related to the information

technologies infrastructure. Participants argued that more often, organizations in the field

do not have the necessary technology tools either because they were destroyed by the

disaster or because they did not even exist in the first place. They also talked about the

discrepancy in term of infrastructure between organizations based in developed countries

and those in the developing countries. They argued that people in developed countries

often enjoy latest technologies but the realty in developing countries, scenes of most

humanitarian disasters is quite different. Participant number twelve for example reported

that:

Subject#12: when you get out on the fields you see that the most basic important tool is paper map and a pencil. And I think we have got to really recognize that fact. […]You know we do this information technology that we love where they follow the latest systems and the fastest processor and stuff like that and we really like to paddle ourselves on the back on what we are able to do here in Washington DC. And then you get out on the fields and everyone is using paper maps and a pencil.

Finally they talked about the fast pace of change in technology which makes it difficult

for organizations to have and especially keep the technical staff that possesses adequate

knowledge to make use of these new technologies.

Subject#6: as the technology changes, it is hard to find the people that have skills that are up to date.

The second category of reasons concerned the management of information. Participants

believed that without proper standard for humanitarian information exchange, the

technology will be of no effective use.

Subject#5: I think yes, continue to explore all the new technologies that are available but at the same time realize that in the end what it really comes down to is quality information and information that is based on facts and that’s credible but people actually belief in. So I think we should not be allowed to be measured by technology if the content is not there.

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Subject#14: One is developing some basic standards, and some basic platforms for information exchange.

They also believed that the humanitarian field needs better processes and well trained

staff in order to make good use of the technology.

Subject#8: I think there are certain organizations who think that technology can solve all the problems, so they don’t have a proper appreciation and understanding of the information management challenges and obstacles, but at the same time there is probably some information, people who are very skeptical about technology and do not sort of realize the value that it has.

Ego-network characteristics and organizational effectiveness

My investigations highlighted the significant impact that ego-network characteristics

have on organizational effectiveness. I explored how humanitarian organizations would

benefit from better ego-network characteristics (e.g. degree centrality, and bridging

structural holes, numbers of cliques). When using the level of collaboration as

effectiveness measure, ego-network related variables accounted for approximately 67%

of the explanatory power of the full regression model. This proportion was about 32%

when using the level of activities as effectiveness measure. These findings corroborated

with the view of organizations as embeddedneed in a web of relationships that provide

opportunities and values (Gulati, 1999). Network characteristics can be understood as

external resources embedded in organizations’ networks. According to Gulati et al.,

(2000), the embeddedness of organizations in networks holds significant implications for

organization performance.

The degree centrality was found to be the most important predictor of effectiveness. This

variable was consistently found cross models and cross effectiveness measures to be

significantly and positively related to organizational effectiveness. As mentioned earlier,

this finding is consistent with most previous research that explored the influence of

network position and especially the degree centrality on outcome such as performance

and effectiveness (Knoke, 1990; Wasserman & Faust, 1994; Stevenson & Greenberg,

2000; Kilduff & Tsai, 2006).

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Bridging network structural hole was another ego-network category variable found to be

an important determinant of organizational effectiveness. This finding supported the

direct effect of network structure, in the form of access to structural holes, on

effectiveness. My results thus confirmed prior research by Burt (1992), McEvily &

Zaheer (1999), and others that access to structural holes influences organizational

performance. However, contrary to my proposition that organizations will enhance their

effectiveness by bridging structural my findings rather suggested a negative relationship

between bridging structural hole and effectiveness. This finding was not consistent with

those of Zaheer & Bell (2005) and Arya & Lin (2007). Both similar studies found a

positive effect of bridging structural holes on organizational effectiveness. As mentioned

earlier, one explanation of this result may be due to the high heterogeneity of the

organizations that I investigated. In the humanitarian relief field and especially in

humanitarian information management and exchange, maintaining non redundant may be

very costly to humanitarian organizations. Some previous studies have also shown

negative performance effects of bridging structural holes (e.g., Ahuja, 2000; Dyer &

Nobeoka, 2000).

In my study, the number of cliques another ego-network category variable explored. This

variable was also found to be significantly and negatively related to organizational

effectiveness measured both as the level of collaboration as well as the level of

collaboration. This finding was one of the most surprising of my investigations. In my

knowledge, no previous study had explored the relationship between the number of

cliques and organizational effectiveness. However, given some previous research on

effectiveness conducted at network level (e.g. Provan & Sebastian, 1998) and theoretical

reasoning, this was an unexpected result. I was expecting at the organizational level, a

similar the positive relationship that exists between the number of cliques and network

effectiveness. One possible reason for this finding may the fact that there may have been

a high level of overlap in the cliques. Using distinct cliques may have probably yielded

more meaningful results.

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Whole network characteristics and organizational effectiveness

The density of the network was the only network category attribute that was in the study.

This variable was found to be an important predictor of effectiveness. I found that high-

density networks benefited more to organizations than low density networks. This

evidence makes a significant contribution to similar previous studies such as Zaheer &

Bell (2005) and Arya & Lin (2007) that emphasized an integrative approach in exploring

organizational effectiveness. None of these two previous similar studies had examined

the impact of network category variable on organizational effectiveness. Moreover, by

showing that organizations perform better when they occupy a better network position,

my study contribute to demonstrate the value of including external resources, or the

ability of an organization to exploit a favorable network structural position (Gnyawali &

Madhavan, 2001; Gulati, 1999).

Catalytic role of Information Technology on organizational effectiveness

The most important contribution of my study is related to the catalytic role of information

technology on organizational effectiveness in humanitarian inter-organizational networks.

For instance, my findings suggest that organizations that possess a wide variety of

different types of communication media (e.g. internet - available to the majority of staff-,

website – regularly updated-, blogs, etc…) will benefit more from high network degree

centrality to enhance their effectiveness than those that do not. These organizations will

benefit more from high network density than those that do not possess these technologies.

These findings are illustrated by the interaction plots presented in Figure 38-41 below. To

generate these plots I first grouped the organizations investigated in two categories based

on the number of different types of commination media. This number ranged from 1 to 5.

Organizations grouped in the first category had 1 or 2 different types of commination

media. This category is represented on the interaction plots as “low variety of

communication media”. Organizations grouped in the second category were those that

had more than 2 different types of commination media. This category is represented on

the interaction plots as “high variety of communication media”. Secondly, for each

category, I generated a scattered plot (with the fitted line option checked) using

respectively each of the two effectiveness measures (level of activities and level of

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collaboration) as dependent variable and the degree centrality then network density as

independent variable.

Degree centralityLow

Low

High

High

Lev

el o

f act

ivit

ies Wide variety of communication media

(y= 11.72x -6.4)

Low variety of communication media

(y= 8.75x + 26.30)

Degree centralityLow

Low

High

High

Lev

el o

f co

lla

bo

rati

on

Wide variety of communication media

(y= 0.795x + 1.74)


(y= 0.345x + 7.90)

Figure 38: Inter-action effect of Technology and

Degree Centrality on Effectiveness as Measured by

the Level of Activities


Degree Centrality on Effectiveness as Measured by

the Level of Collaboration

Network DensityLow

Low

High

High

Lev

el o

f act

ivit

ies


(y= 7871x - 279.2)


(y= 997.4x + 19.17)

Network DensityLow

Low

High

High

Lev

el o

f co

lla

bo

rati

on


(y= 249.9x + 8.26)


(y= 102.8x + 1.67)


Network Density on Effectiveness as Measured by

the Level of Activities


Network Density on Effectiveness as Measured by

the Level of Collaboration

An examination of these interaction plots highlights the significant boost of

communication media on the effectiveness of organizations that are centrally located in

the humanitarian information management networks. As mentioned earlier, these plots

also show that organizations that possess a wide variety of different types of

communication media will benefit more when they belong to high network density than

when they are member of loosely connected networks.

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7 CONCLUSIONS AND DIRECTIONS FOR FUTURE RESEARCH

7.1 Introduction

In this chapter, I provide a summary of the key findings of my research. I also briefly

discuss the contributions of my work to the literature on organizational and inter-

organizational network effectiveness. This discussion is followed by some limitations of

the research and the future directions that I envisioned.

7.2 Summary of the Literature

Previous research on organizational and inter-organizational network effectiveness has

provided some empirical and conceptual approaches for assessing effectiveness.

However, little research has explored the antecedents of effectiveness for humanitarian

inter-organizational networks. The review of the literature on organizational effectiveness

highlights the difficulty in assessing effectiveness. There is no consensus on the criteria

of measuring effectiveness among researchers and no clear classification of the different

levels of effectiveness. Moreover, there are various approaches to view effectiveness

such as the Goal Model (ii) the System Resource Model (iii) the Internal Processing

Model and (iv) the Multiple Constituencies’ Model. Concerning inter-organizational

networks in the nonprofit sector, they have been more than a decade long clarion call for

a better understanding of their effectiveness (O’Toole, 1997; Provan and Milward 1995).

To date limited work has been done (Provan et al., 2007). Additionally, the few studies

that have investigated the effectiveness inter-organizational networks in the nonprofit

sector have also used a wide range of effectiveness measures and have almost all been

conducted in the public health delivery services. Furthermore, for some authors (e.g.

Stephenson, 2005; 2006; Van de Walle et al., 2009) not much is known about network

effectiveness for humanitarian inter-organizational networks and especially with regards

to humanitarian information management and exchange (Van de Walle et al., 2009). My

research thus intended to provide answers to the following research questions:

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RQ#1(Network level of analysis): To what extent do network structural characteristics

explain effectiveness in humanitarian inter-organizational collaboration networks? More

specifically, how does Provan & Sebastian model of network effectiveness explain

network effectiveness in the international context of inter-organizational collaboration in

the humanitarian field?

RQ#2(organizational level of analysis): How accurately does a linear combination of

organizational internal attributes and network structural properties explain effectiveness

at organizational level in humanitarian inter-organizational collaboration networks? (i)

To what extent do resources internal to organizations and especially information

technology explain effectiveness? (ii) To what extent do ego-net properties explain

network effectiveness? (iii) To what extent do network level structural characteristics

(e.g. density) explain effectiveness? (iv)To what extent does the interaction of

information technology and network structural characteristics impact organizational

effectiveness?

7.3 Key findings

In this dissertation, I investigated how organizational characteristics and network

structural properties influence effectiveness in humanitarian inter-organizational

networks. I explored effectiveness at two levels of analysis, organizational and network.

I used three different criteria to assess effectiveness including (i) perceived network

effectiveness (ii) level of activities and (iii) level of collaboration. To answer my

research questions, I used a multi-method approach that applies social network analytic

techniques in combination with statistical analyses (correlation and regression) and

content analysis to analyze data collected through multiple sources including a web-based

survey and semi-structured interviews and database search.

RQ#1: Relationship between network structural characteristics and network effectiveness

To answer this question, I conducted a clique analysis using Provan & Sebastian’s (1998)

framework. One first general observation of my findings was that network effectiveness

varies depending of the effectiveness measure. These findings corroborate those of most

research on inter-organizational network effectiveness which highlight the existence of a

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wide range of definitions and criteria for network effectiveness (Alter & Hage, 1993;

Provan & Milward, 1995; Sydow & Windeler, 1998; Provan et al., 2007). Consistent

with those of Provan & Sebastian my findings suggest that at the network level of

analysis, an inter-organizational network in the field of humanitarian relief is more

effective when it is more integrated at the subnet level (clique) and displays higher level

of multiplexity. My study however makes one significant addition to Provan & Sebastian

model. Unlike Provan & Sebastian, in my study, I used three different measures of

network effectiveness (one subjective and two objectives). Using these effectiveness

measures allowed me to find consistent ranking pattern for each of the six network

structural characteristics used in my work. It is important to note that Provan &

Sebastian’s study which forms the foundation of my study, matched two out of the six

network structural characteristics. This study found a match in ranking only among

multiplexity and identical clique overlap and effectiveness.

Moreover, my findings suggest that the subjective and objective forms of network

effectiveness are better explained by different network structural attributes. Whereas

subjective network effectiveness is better explained by the number of cliques and clique

membership, objective network effectiveness is better explained by the multifaceted

nature of inter-organizational relationships as measured by clique overlap and

multiplexity. These findings highlight the importance of multiple criteria for assessing

network effectiveness. Finally, comparing the three measures of effectiveness that I used

in my study, my findings suggest that the level of activities is the best. This measure

matched three out of the six network structural characteristics investigated.

At the network level, the findings of my investigations could be summarized as follow:

Finding #1: In inter-organizational humanitarian information management networks,

network effectiveness will be better explained by network structural characteristics when

assessed at subnet levels.

Finding #2: In inter-organizational humanitarian information management networks, the

level of effectiveness will likely be higher in networks that are more dense and cohesive

at subnet levels.

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Finding #3: Given the high heterogeneity and differentiation among humanitarian

organizations, network effectiveness in humanitarian information management, will more

accurately be explained by multiplexity and clique overlap.

RQ#2: Relationship between organization internal characteristics, network structural

properties and organizational effectiveness.

In addition to investigating effectiveness at the network level, in this work, I also studied

organizational effectiveness. I took several steps to explore organizational level

effectiveness. I used two measures of effectiveness including the level of activities

measured as the number of funded project and the level of collaboration measured as the

number of funding partners. For each of these effectiveness measures, I built a set of

four consecutive multiple linear regression models. In the baseline model, I modeled

effectiveness as a function of the variables of the organization category. I then gradually

added variables from ego-network category (Model II), network category (Model III) and

two inter-action terms (Model IVa and Model IVb). Overall, my findings suggest that in

humanitarian inter-organizational networks, organizational effectiveness can be

accurately explained by a linear combination of organizational internal attributes and

network structural properties.

RQ#2a: Relationship between organization internal characteristics and especially

information technology and organizational effectiveness.

My research also showed the importance of considering the characteristics internal to

organizations when explaining effectiveness. Taken alone, organizational internal

characteristics accounted for over 13% of the variances in organizational effectiveness

when I used the level of activities as effectiveness measure. The regression model

showed that the linear combination of organizational internal characteristics was

significantly related to effectiveness for both effectiveness measures. Moreover, my

findings suggested that among humanitarian organizations engaged in information

management and exchange, information technology would be one of the most important

determinants of effectiveness. In my study, all the three information technology related

variables that I used, were found to significantly contribute to explain organizational

171

effectiveness. However, not all these variables were found to be positively related to

organizational effectiveness as I hypothesized. For instance, while the availability of a

wide variety of community social software was found to be positively associated with

effectiveness, my findings rather suggest a negative relationship between collaboration

social software (e.g. wiki, shared database) and effectiveness. These contrasting results

obtained from my statistical analysis concerning the importance of information

technology to humanitarian organizations are somewhat similar to those obtained from

the qualitative data gathered through interviews.

RQ#2b: Relationship between ego-network characteristics and organizational

effectiveness.

Exploring the relationship between ego-network characteristics and organizational

effectiveness I also got some interesting results. My findings suggested that ego-network

characteristics have a significant impact on organizational effectiveness. Taken alone,

ego-network variables accounted for approximately 67% of the variances in

organizational effectiveness when I used the level of collaboration as effectiveness

measure. This proportion was about 32% when using the level of activities as

effectiveness measure. Among the ego-network variables, the degree centrality was

found to be the most important predictor of effectiveness. This variable was consistently

found cross models and cross effectiveness measures to be significantly and positively

related to organizational effectiveness. Bridging network structural hole was another ego-

network category variable found to be an important determinant of organizational

effectiveness. However, contrary to my proposition that organizations will enhance their

effectiveness by bridging structural my findings rather suggested a negative relationship

between bridging structural hole and effectiveness. This result may be due to the high

heterogeneity of the organizations that I investigated. In the humanitarian relief field and

especially in humanitarian information management and exchange, maintaining non

redundant may be very costly to humanitarian organizations. The number of cliques

another ego-network category variable explored was also found to be significantly and

negatively related to organizational effectiveness measured both as the level of

collaboration as well as the level of collaboration. This finding was one of the most

172

surprising of my investigations. In my knowledge, no previous study had explored the

relationship between the number of cliques and organizational effectiveness. However,

given some previous research on effectiveness conducted at network level (e.g. Provan &

Sebastian, 1998) and theoretical reasoning, this was an unexpected result. One possible

reason for this finding may the fact that there may have been a high level of overlap in

the cliques. Using distinct cliques may have probably yielded more meaningful results.

RQ#2c: Relationship between network characteristics and organizational effectiveness.

Investigating the relationship between network structural characteristics and

organizational effectiveness was one of the peculiarities of this study. None of the two

previous similar studies (Zaheer & Bell, 2005; Arya & Lin, 2007) had examined the

impact of network category variable on organizational effectiveness. The density of the

network, the only network category variable that was in the study was found to be an

important predictor of effectiveness. Taken alone, network density accounted for

approximately 3.2% of the variances in organizational effectiveness when I used the level

of activities as effectiveness measure. For both measures of effectiveness, my findings

suggested that high-density networks benefited more to organizations than low density

networks.

RQ#2d: Impact of inter-action between information technology and network structural

characteristics on organizational effectiveness.

Examining the impact on organizational effectiveness of the inter-action between

information technology and network structural characteristics in humanitarian

information management networks was another important peculiarity of my research.

My findings suggested that organizations that possess a wide variety of communication

media (e.g. internet - available to the majority of staff-, website – regularly updated-,

blogs, etc…) will benefit more from high network degree centrality to enhance their

effectiveness than those that do not. These organizations will benefit more from high

network density than those that do not possess these technologies.

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At the organizational level of analysis, the findings of my investigations could be

summarized as follows:

Finding #1: In inter-organizational networks for humanitarian information

management, centrally located organizations will more likely display higher level

of effectiveness than those situated at the peripheral.

Finding #2: Organizations that are member of dense and cohesive humanitarian

information management networks will more likely display higher level of

effectiveness than loosely connected networks.

Finding #3: In humanitarian information management networks, organizations

that possess wide varieties of communication media will benefit more from high

network degree centrality to enhance their effectiveness than those that do not.

Finding #4: Other things being equal, in humanitarian information management

networks, organizations that possess wide varieties of communication media will

benefit more from high network density to enhance their effectiveness than those

that do not.

Summing up, my investigations confirmed the proposition that organizational

effectiveness is affected by different organizational and network attributes in

humanitarian information management networks. More broadly, my findings on the one

hand pointed to a need in inter-organizational social network studies to go beyond a

structuralist view and take into consideration the characteristics of individual

organizations, as predicted by the RBV, in assessing effectiveness. On the other hand, my

study highlighted the fact that organizational level network studies that tend to overlook

resources internal to organizations may be missing a large source of variance in

effectiveness. Finally my study highlighted the important role of communication media

in organizational effectiveness among humanitarian organizations.

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7.4 Contributions

Network Effectiveness

My research extents to the humanitarian relief field, Milward & Provan’s (1998)

framework for evaluating public-sector organizational networks. My research contributes

to the literature on network effectiveness in a number of ways. First, my findings

confirmed some of the results of previous research and especially those of Provan &

Sebastian (1998) which showed that most effective networks are those that are integrated

at clique level. Specifically, my findings confirmed the importance of network structural

characteristics such as integration and cohesion to network effectiveness measure.

Moreover, building on Provan & Sebastian (1998), my study further highlighted the need

to consider network effectiveness analyses in smaller substructures instead the whole

network as has usually been the case.

Secondly, my research highlighted the need to explore network effectiveness using a set

of different measures. The majority of existing work on network effectiveness, including

that of Provan and Sebastian (1998) was conducted using one measure. Moreover, in

most cases, the effectiveness measure was not selected with input from the various

network members. In my study, I used input from network members to determine the

three measures of effectiveness. Using a set of three different measures for network

effectiveness allowed me to find consistent ranking pattern for each of the six network

structural characteristics. Moreover, my findings suggested that the subjective and

objective forms of network effectiveness are better explained by different network

structural attributes. Whereas subjective network effectiveness is better explained by the

number of cliques and clique membership, objective network effectiveness is better

explained by the multifaceted nature of inter-organizational relationships as measured by

clique overlap and multiplexity. My study serves as an example of effectiveness being

measured with multiple criteria. In a nut shell, my work builds on various models of

effectiveness already present within the literature on inter-organizational effectiveness to

provide a multidimensional model for evaluation effectiveness in the nonprofit

humanitarian field.

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Lastly, my research also has implications for social network theories. For many

organization theorists, the study of both inter-organizational and intra-organizational

networks has primarily been an exercise in analysis and methods (Salancik, 1995).

Building upon Provan & Sebastian (1998), my study further develops an alternative

method for network analysis and contributes to building network theories by examining

and explaining how network structural properties including network density, cliques and

overlapping cliques, might promote the interests of network members and that of the

community as a whole.

Characteristics of successful inter-organizational networks

My investigations have helped to identify the following four main characteristics that

seem to be common among organization members of successful networks. These

characteristics include (i) their ability to share, (ii) their ability to contribute, (iii) their

commitment to networking and (iv) the level of their embeddedness through multiplex

ties in the network.

1. Sharing spirit: Organization members of a network must “dare to share” (ICCO

2004). They need to be open, willing and able to learn from each other. In my research,

the lack of sharing spirit was consistently reported as one of the biggest problems that

undermines network effectiveness.

Subject#13: I think the main challenge here is that the idea of sharing formation has always been said in many areas. It is usually always said yeah it is good to share but you do not sometime see concrete platforms or formalities on how to share this information. It is not formalize. It is always thought as an objective but never formalize.

Network members must feel confident enough about what they do and the information

they possess that they are willing to share with others. There must therefore exist an

atmosphere of openness among members and potential members which allows them to

admit mistakes and to learn from them. Networks cannot flourish without this trust. A

network can help to develop sharing spirit among its members by creating an open

environment in which people are willing to analyze and learn from both their successes

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and their mistakes. Networks and partnerships are more likely to become effective when

they are founded by members that share a history of working together, that know each

other and have relationships characterized by mutual trust. This suggests that networks

may have a longer incubation and startup period before they can reach the stage of

maximum effectiveness.

2. Capacity to contribute: Organization members of a network must have the capacity

to contribute especially in terms of skills, access and time/money available. In my study,

the ability of a potential network partner to contribute was reported to be one of the main

collaboration factors.

Subject#2: Both [organizations] have to be able to bring to the table their competitive advantage. You can’t have two organizations that do the same thing. So you need different skills set from any of the organizations. Subject#7: We think about the quality of what that agency does and the quality of what that agency is known to do.

In order to foster inter-organizational coordination/collaboration there must be space for

learning, reflection and interaction. Also, it is paramount that senior leaders of

organization provide support to network by emphasizing the importance of networking.

They must also encourage the involvement of staff in the activities of the network.

Moreover, all network members must have equal access to any technology that the

network uses so that certain groups are not marginalized.

3. Commitment: Organization members of a network must be committed to the

networking activities. They must consider the priorities of the network their own. They

must also be motivated by self-interest because networking is a potential added-value to

their daily work. Commitment will be strong if members see the network as adding value

to their work, and if the priorities of the network match their own. According to ICCO

(2004) incentive grants are of little value in enticing members. I agree with this author in

his contention that funding should not be the reason that a NGO joins a network. For

instance, he suggests that a golden rule for success may be to let a network start from its

own resources with the idea that initial self-reliance builds commitment (ICCO 2004).

The author also mentions, however, that this does not mean networks do not need funding

177

for the activities they would like to undertake. Networks need funding for example to

help support a facilitator, coordinator, or staff of some sort that is able to spend the time

required to nurture relationships and in order to keep the group together. It is important

that careful attention is given to these aspects when funding is initially proposed.

Another important condition is that the initiators of networks are enough committed to

overcome the organizational and establishment phase, which takes a lot of effort, while

often working for not immediately seen results with little money.

4. Multiplexity: Organization members of a network must strive to keep multiple type of

connection with other members. Multiplexity can be measured at the individual network

member level and at the level of the whole network. A high degree of multiplexity of a

member indicates high embeddedness of the member in a network and signifies less

liability to disruption of single relationships. A member with a large number of multiplex

relations is expected to have a high potential of mobilizing different resources and

information through these relations. On the other hand, such a member is subject to a

high level of social control. At the network level, the degree of multiplexity specifies the

overlap between the different relation-specific networks. For evaluating network

effectiveness, multiplexity can be a particularly useful measure (Provan & Milward

2001). Effective networks might have a majority of network members connected through

two or more different types of relationships. In this case, multiplexity will be high,

reflecting commitments among network members to one another through multiple

activities.

Organizational Effectiveness

Concerning the literature on organizational effectiveness, my study illustrates the

importance of both internal organizational characteristics as well characteristics external

to organizations, for effectiveness. My findings confirm the extended Resource Based

View perspective of organizational effectiveness. More specifically, my analysis of the

relationships of the various determinants of effectiveness illustrated that variables from

all the three categories (organization, ego-network and whole network) are found to be

178

important predictors for organizational effectiveness. One of the most significant

contributions of my research to organizational effectiveness literature and especially to

the resource based view perspective concerns including whole network category variables

in assessing organizational effectiveness. None of the two previous similar studies

(Zaheer & Bell, 2005; Arya & Lin, 2007) had examined the impact of network category

variable on organizational effectiveness. By showing that organizations enhance their

effectiveness when they occupy a better network position, my research contributes to

demonstrate the value of including external resources, or the ability of an organization to

exploit a favorable network structural position (Gnyawali & Madhavan, 2001; Gulati,

1999). Another important contribution of my study is that it extends the Resource based

view perspective in the nonprofit sector and especially in the humanitarian relief field.

Most of the previous studies that draw on the RBV examine effectiveness in for-profit

network contexts. By applying the RBV to a collaborative nonprofit context as opposed

to a competitive for-profit context, my research shows that internal and external resources

allow some organizations to enhance their capabilities by collaborating with others.

Humanitarian Inter-organizational Network Effectiveness

With regards to humanitarian inter-organizational network effectiveness, my research

offers some evidence that similarly to the public health service delivery sector; network

effectiveness can be explained by intensive integration and network cliques. My data

supports the idea that differences in effectiveness across networks could be better

understood by focusing on cliques and the overlap among cliques of multiple

relationships among humanitarian organizations. My study would help to do the clique

analysis or to search for closely connected and cohesive subgroups. Additionally, my

work can help to design efficient inter-organizational network structures in the

humanitarian relief sector. For example, by increasing the level of clique overlap (one

dimensional or multidimensional) in inter-organizational humanitarian networks, network

designers should expect a higher level of inter-organizational collaboration.

179

Information Technology and Humanitarian Organizational Effectiveness

The most important contribution of my study is related to the catalytic role of information

technology on organizational effectiveness in humanitarian inter-organizational networks.

For instance, my findings suggest that organizations that possess a wide variety of

communication media will benefit more from better network positions (e.g. high network

degree centrality, high network density) to enhance their effectiveness than those that do

not possess these technologies. The Resource Based View perspective of organizational

effectiveness tends to focus more on organization’s internal resources, while

downplaying those available to the organization from external sources. On the other

hand, network researchers tend to focus attention on the value of the network structure,

without considering the capabilities of the organizations tied together by the network. My

study highlights the importance of fusing these two streams of research, and considering

simultaneously the inner capabilities of the organizations in a network together with

capabilities they derive from the structure of the network that binds them together.

7.5 Limitations and Directions for Future Research

My study has some theoretical, methodological and practical limitations that suggest a

number of directions for future research.

Theoretical Limitations

Theoretically, the implications of relating organization characteristics, ego-network

characteristics and network structure, to organizational and network effectiveness need

further investigation. Provan & Sebastian’s (1998) study on the relationship between

network structure and network effectiveness applied to inter-organizational networks of

mental health delivery. Their research provides suggestions for application to other fields.

Building upon Provan & Sebastian, this study investigated networks in the humanitarian

relief field. These networks were not defined based on sound theoretical perspectives but

just on the categories provided by UNOCHA. Studies on better defined networks would

help to further understand network effectiveness in the humanitarian relief field.

180

Moreover, my study further illustrated the need to consider network effectiveness

analyses in smaller substructures instead the whole network as has usually been the case.

It would be interesting however, to investigate what network configuration enables

humanitarian organizations to operate at optimum and effectiveness while meeting their

individual goals. This may include factoring in the impact of organizations internal

resources and especially the information technology over time as new organizations join

the network and/or new information technology tools are used in humanitarian

information management and exchange. In this way, the predictive nature of network

structure may complement assessment of organizational effectiveness and extend to ways

to support humanitarian information management and exchange.

Finally, to investigate further and identify better measures of network effectiveness,

future research should consider exploring similar network operations in other non-profit

sector activities.

Methodological Limitations

Methodologically, the first limitation is related to the source of information. Much of the

organizational and network data that I analyzed was provided by individuals. The

position of these individuals in their organization may not allow them to always have the

complete and accurate information about the organization especially with regards to

inter-organizational relationships. For instance, network structures were generated based

on information provided by these individuals. They meant to demonstrate the existence

of a relationship based on the data collected. They represent neither the totality nor the

absence of relationships. Future research may envision a much better source of data that

is less subjective.

Secondly, the most obvious and probably the most serious shortcoming of the research at

its network level of analysis is the small number of networks. My study involved only

three networks. At the network level of analysis, this can create an important problem

with regards to generalizing the research findings. Moreover, it is certainly possible that

181

the network effectiveness measures used, which were tied to individual organizations, did

not accurately reflect network effectiveness at each network. Future research should

consider a much bigger network sample size.

Thirdly, while in my research design at the organizational level of analysis I included a

number of variables that have been neglected in previous research (e.g. whole network

category variables), there are still others that could be added. These clearly include tie

multiplexity, but perhaps also a better understanding of the processes through which

effectiveness comes about, such as communication flows across the ties and the content

of ties. Future research could consider examining closely communication and other flows

passing through the network (Gnyawali & Madhavan, 2001).

Fourthly, another limitation of my study is related to alter characteristics. Much previous

research has highlighted the important impact that alter characteristics have on

organizational effectiveness (Arya & Lin, 2007). In my research design, I did not include

any of these characteristics. Future research in the humanitarian relief field may consider

building models that would help to assess the effect of alter capabilities organizational

effectiveness. For example, current literature suggests that the exchange of humanitarian

information between organizations is highly challenging. Understanding how and why

beneficial network structure captures alter organization capabilities may help to better

understand the inter-organizational humanitarian information management and exchange.

Lastly, research on information and knowledge exchange has illustrated the importance

of proximity and co-location (Almeida & Kogut, 1997; Tallman et al., 2004) as an

enabler in the transmission of tacit knowledge. Future research may consider including

geographical propinquity as a further element that could affect the efficacy of

humanitarian information exchange and influence effectiveness.

Practical Limitations

The opportunity to apply social network tools to explore networks of humanitarian

organizations engaged in information management and exchange provides a practical

182

benefit to the international community in considering this approach to encourage

collaboration in responding to humanitarian disasters. The network diagrams depicting

project collaboration and advice relationships among humanitarian organizations

demonstrate the reach accomplished in humanitarian information management and

exchange. Linking these network structures as well as organizational characteristics to

effectiveness highlighted the determinants of effectiveness among organizations in the

humanitarian relief field. This provides the opportunity for humanitarian organizations

and especially UNOCHA to consider weaknesses and strengths of the Global Symposium

to increase network effectiveness in managing and exchanging humanitarian information.

Identifying the information technology tools that the Global Symposium community

needs for better humanitarian information exchanged would be an extension of this study

worthy of consideration.

183

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APPENDIX

Appendix A: Inform Consent Form for Social Science Research


Title of Project:

Inter-organizational decision making and organization design for improved ICT

coordination in disaster relief

Principal Investigators: Dr. Carleen Maitland


102 J IST Bldg.

University Park, PA 16802

(814) 863-0460; [email protected]

Dr. Andrea Tapia


329 G IST Bldg.

University Park, PA 16802

(814) 865-1524; [email protected]

1. Purpose of the Study: We are conducting research concerning the flow of

information and information sharing among relief organizations. In particular, we

seek to understand the ways in organizational characteristics (e.g. organizational

structure, size, goals, and resources) and inter-organizational network structural

and relational properties influence the ability of humanitarian organizations to

make decisions that will enable them to effectively collaborate during disaster

relief as well as development.

2. Procedures to be followed: You will be asked to participate in an online survey.

3. Duration: The survey is likely to take between 30 and 40 minutes to be

completed.

4. Benefits: There are no direct benefits to you as a participant in this study.

However, with the data collected from this study we hope to improve information

flows and sharing among relief organizations and eventually get help to those that

need it more efficiently.

5. Statement of Confidentiality: Your participation in this research is confidential.

Only Drs. Maitland and Tapia and their assistants, will know your identity. Each

participant will be assigned a number. The data will be stored and secured in a

secure computer located in Dr Maitland’s office (Room 102J, IST Building) on

the Penn State University Campus. Your confidentiality will be kept to the degree

200

permitted by the technology used. No guarantees can be made regarding the

interception of data sent via the Internet by any third parties. The data will be

destroyed in December 2012 (3 years following the end of the project). In the

event of a publication or presentation resulting from the research, no personally

identifiable information will be shared.

6. Right to Ask Questions: Please contact Dr. Carleen Maitland or Dr. Andrea

Tapia at 814-865-1524 or 814-863-0460 with questions or concerns about this

study.

7. Voluntary Participation: Your decision to be in this research is voluntary. You

can stop at any time. You do not have to answer any questions you do not want to

answer. Refusal to take part in or withdrawing from this study will involve no

penalty or loss of benefits you would receive otherwise.

8. You must be 18 years of age or older to take part in this research study. If

you agree to take part in this research study and the information outlined above,

please sign your name and indicate the date below.

9. Completion and submission of the survey is considered your implied consent

to participate in this study. Please print this form for your records.

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Appendix B: Letter-Email sent to potential Survey participants

Hello ____,

We are two professors from the Pennsylvania State University, USA. We are studying

how organizations which provide humanitarian relief coordinate around information

management projects. In particular, we seek to understand the ways in organizational

characteristics and inter-organizational network structural and relational properties

influence the ability of humanitarian organizations to make decisions that will enable

them to effectively collaborate during disaster relief as well as development.

In October 2007 the Global Symposium +5 was held in Geneva. We are writing to you

because we believe you also attended this event or previous related symposia. We would

like to ask for your help in participating in our short online survey.

The survey is likely to take between 30 to 40 minutes. Everything you say will be kept

confidential and Drs. Maitland and Tapia will not share your information or response

with anyone or store it with any personally identifying information. You decision to

participate in the survey is voluntary. You do not have to participate or answer any

questions you do not want to answer. You must be 18 years of age or older to participate.

More information about this survey can be found in our research consent form.

If you agree to participate, please visit our online survey at www.

Thank you!

Drs. Carleen Maitland and Andrea Tapia



University Park, PA, USA

http://www.abcd.edu/

202

Appendix C: Survey Questionnaire

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Appendix D: Interview Guide

Interview appointment mail

Dear ,

Thank you again for responding to our follow up survey on the Global Symposium+5 and

for accepting to take part in a phone interview. The main purpose of this interview is to

share some of your personal experiences and perspectives on the Global Symposium+5.

I am writing to set up an appointment for this interview which I anticipate will take no

more than one hour. Please email me a time slot you will be available for the interview.

Interviewing can be as early as next Monday October 25, 2009.

Best regards,

Louis-Marie

Interview Guide

Greetings

Informed consent (to get the verbal consent to participate to the interview)

Do you agree to be interviewed?

Do you mind if we record you for reference in our research? In reference we will

maintain anonymity.

Your organization

o We would like to hear about your specific experience at the Global

Symposium (GS).

o Did your organization participate in any working groups?

o What do you see as the goals of the GS and the extent to which the GS is

effectively meeting these goals?

o Some more general goals of the GS were

Policy making

Agenda setting

Networking

What do you feel the GS was effective at, for your organization?

o In your opinion, what are the major barriers to collaboration in the Global

Symposium community?

o What do you see as the most important challenges to your organization to

fully participate in the Global Symposium community activities?

o Can you tell us about some contacts you made at the GS and if (and how)

that led to project partnerships?

o Can you tell us if the project you realized with the people you met at the

symposium were already in your to do list?

227

o How do you think your organization size impact your ability to work with

other people? Do you feel like other organizations want to work with you

because you are small or they do not want to work with you because you

are small? (Similar question for: mission, region of focus, Head quarter

location, IT infrastructure).

o What is the most important benefit that your organization has gained from

being member of the Global Symposium?

o What factors (organizational, project) do you consider in selecting a

partner for collaboration

o Overall, how successful are collaborative projects resulting from your

participation to the GS?

o What relationships would say exist between IT and IM?

o In your organization is IT used just as a tool or is it part of your

Organization competency?

o Talking about IT, where would you say your organization is a Consumer,

a Producer or an Implementer?

o What role if any, play Information Technology in fostering collaboration

among organization?

o What type of social media is available in your organization?

o In your opinion, how useful are these new social media in humanitarian

information management and exchange?

Effectiveness of the Global Symposium Community

o How would you measure the effectiveness of the Global Symposium

Community? (Easy access to humanitarian information, extend of

information sharing, number of collaborative projects, easy access to

funding, level of satisfaction of different stakeholders)?

o Based on these measures, in your opinion, how effective has been the

Global Symposium?

o How do you think organizational characteristics (e.g. size, age, missions

etc..) of the Global Symposium members impact the effectiveness of

collaboration in the community?

o Does the advice/communication relationship with another org help to

establish project collaborations?

o Does higher frequency of interaction reflect stronger relationship among

organizations?

Project collaboration formation

o What triggers collaboration partnerships?

o Do larger organizations often have more projects on their agenda/to-do

lists?

o Will your organization's evaluation of a potential collaborative project be

influenced by your acquaintance?

228

Vita

Louis-Marie Ngamassi Tchouakeu is a doctoral student in the College of Information

Sciences and Technology at Penn State University. He holds a Master’s degree in

Computer Information Systems from Pace University – New York and B.S. in Economics

from the University of Yaoundé - Cameroon. His research interests include information

systems (IS) and information technology (IT) development in the international context.

Fundamentally, he is interested in the use of information and communication

technologies in coordination and collaboration between organizations, which provide

humanitarian relief, and development services. Prior to joining Penn State University he

worked as a program staff at the UN Economic Commission for Africa (UNECA) in

Addis Ababa, Ethiopia and has over a decade of experience in IT administration at the

University of Dschang - Cameroon.

Louis-Marie’s work has appeared in the following journals: International Journal of

Intelligent Control and Systems (IJICS); International Journal of Society Systems Science

(IJSSS); International Journal of Information Systems and Social Change (IJISSC); He

has also presented his work at different conferences including : the iConference, the

Americas Conference on Information Systems (AMCIS), the Biennial Conference of the

International Telecommunications Society (ITS); the International Information Systems

for Crisis Response and Management (ISCRAM) Conference; the World Congress on

Social Simulation (WCSS) and the Research Conference on Communication, Information

and Internet Policy (TPRC).

Louis-Marie is a former Fulbright and a former United Nations Fulbright Fellow. He is

also recipient of numerous scholarships and awards from organizations such as USAID

and the French Agency for Technical and Cultural Cooperation (ACCT).

humanitarian information management network effectiveness

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