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Developing a Model for Competitive Advantage through
Integration of Data Mining within a Strategic Knowledge
Management Framework: A Deep Case Study of a Global Mining
and Manufacturing Company
Sanaz Moayer
This thesis is presented for the degree of Doctor of Philosophy of Murdoch University
Principal Supervisor: Dr. Scott Gardner
Associate Supervisor: Dr. Amy Huang
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DECLARATION
I declare that this thesis is my own account of my research and contains as its main content
work which has not been previously submitted for a degree at any tertiary education institution.
Sanaz Moayer
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ABSTRACT
The study explores the benefits, limitations and opportunities arising from the application of
an integrated (hard and soft systems) Knowledge Management (KM) model within a global
mining and manufacturing company. It employs a mixed method (interview and survey
approach) to explore related Knowledge Management (KM), Organisational Learning (OL),
and Data Mining (DM) processes (as a proxy for broader data management tools and practices).
It employs the Resource Based View (RBV) and Knowledge Based View (KBV) of strategy
to explore how the case company has built unique human knowledge capability based on a
Continuous Improvement (CI) culture supported by Global Virtual Teams (GVTs) and
Communities of Best Practice (CoBP). It is argued and statistically proven that this unique
capability has supported the Competitive Advantage and possibly survival of the case company
during a period of challenging market conditions.
The study also explores to role of Data Mining and related Business Intelligence (BI) and ICT
platforms to leverage knowledge embedded across the firm’s global networks. By exploring
the gaps and synergies between hard (technological) and soft (human) systems, this deep case
study of a multinational, mining, processing, and manufacturing firm addresses one of some of
the key questions still to be resolved in organisational and information system studies.
These questions are examined through detailed interviews with ten senior managers and their
reports, (115 survey respondents, identifying as technical specialists, departmental and
operational managers/ senior supervisors, working in nine1 operations across five continents).
The practices spanning the global operations of the case organisation are compared with a
conceptual model of Strategic Knowledge Management (SKM) and Resource based
Competitive Advantage (RCA) derived from the relevant academic literature. The study aims
to contribute to the body of knowledge exploring tacit and explicit knowledge, Organisational
Learning (OL), and Data Mining (management) practices as a strategic resource and basis for
competitive advantage. It also aims to inform current knowledge and data management
practices employed by Global Virtual Teams (GVT) and Communities of Best Practice (CoBP)
spanning the case organisation’s mining, refining and manufacturing operations.
The study uses NVivo to analyse the qualitative data on the relationships between KM practices
in the case organisation (involving knowledge creation; knowledge storage; knowledge
transfer; and knowledge application), Data Mining processes, (extracting, transforming, and
1 The number of refining operations for the case company were reduced from 9 to 6 in 2016
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loading transaction data; storing and managing data; providing access; analysing and
presenting) and the sustainable competitive advantage for the case organisation. The
relationships identified in Stage 1 qualitative findings are supported by the Stage 2 survey
results based on the PLS structural equation modelling (SEM) analysis.
The empirical evidence generated from the mixed method approach indicates that KM practices
positively affected Data Mining processes in the case organisation and that soft KM systems
and practices focused on the creation, configuration, and practical application of tacit
knowledge were crucial to the organisation’s Competitive Advantage (CA).
The Competitive Advantage (CA) impact of these soft system elements far outweighed hard
systems despite the technical production orientation of the business. The company’s current
Data Mining activities did not have a significant mediating effect on the relationship between
Knowledge Management and the organisation’s Competitive Advantage. These results suggest
that the Data Mining systems (as an important part of the organisation’s hard KM systems)
have not been effectively integrated with the soft knowledge creation, transfer and application
systems in the organisation. This is highlighted in the study implications as a major opportunity
for the case organisation which faced with lean market conditions over the past decade has
been very successful in generating and applying a scalable, portfolio of useful knowledge via
Global Virtual Teams. Based on these findings, the study concludes with recommendations on
how hard knowledge and data management systems can augment the value of the soft KM
practices, and generate Competitive Advantage for global mining and manufacturing
companies in the knowledge age.
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TABLE OF CONTENTS DECLARATION ....................................................................................................................... ii
ABSTRACT ............................................................................................................................. iii
TABLE OF CONTENTS ........................................................................................................... v
TABLE OF FIGURES ............................................................................................................... x
TABLE OF TABLES ............................................................................................................... xi
ACKNOWLEDGEMENTS ..................................................................................................... xii
GLOSSARY .......................................................................................................................... xiii
CHAPTER ONE ...................................................................................................................... 1
1 INTRODUCTION .............................................................................................................. 1
1.1. Research Background .................................................................................................. 1
1.1.1. The Shift from Tangible to Intangible Assets ...................................................... 5
1.1.2. Developments in Knowledge Management and ICT since the Late 1990s ......... 5
1.1.3. The Development of the Global Knowledge Economy ....................................... 7
1.1.4. Web 2.0 as Collaboration and Knowledge Sharing Enabler ................................ 7
1.2. Study Rationale ........................................................................................................... 8
1.2.1. Business Intelligence (BI), Data Mining (DM) and Knowledge Management
(KM)…………. .................................................................................................................. 9
1.3. The Australian Mining Industry ................................................................................ 12
1.3.1. Application of Knowledge Management in the Australian Mining Industry .... 14
1.3.2. Application of Data Mining in the Australian Mining Industry ........................ 15
1.4. Research Objectives .................................................................................................. 16
1.5. The Research Question and Sub-Questions .............................................................. 17
1.6. Thesis Outline ........................................................................................................... 17
CHAPTER TWO ................................................................................................................... 21
2. LITERATURE REVIEW ................................................................................................. 21
2.1. Introduction ............................................................................................................... 21
2.2. Strategy and Strategic Management .......................................................................... 24
2.2.1. What is Strategy? ............................................................................................... 24
2.2.2. Different ‘Views’ or Perspectives on Strategy .................................................. 24
2.3. Competitive Advantage ............................................................................................. 28
2.3.1. Five Forces Model and Sustained Competitive Advantage Based on MBV ..... 29
2.3.2. Firm Resources and Sustained Competitive Advantage Based on RBV ........... 30
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2.3.3. Competitive Advantage with VRIN(E) Model .................................................. 33
2.4. Definition of Knowledge ........................................................................................... 35
2.4.1. What is Knowledge? .......................................................................................... 35
2.4.2. Tacit and Explicit Knowledge ........................................................................... 36
2.4.3. Alternative Perspectives on Knowledge ............................................................ 38
2.5. Knowledge Management........................................................................................... 38
2.5.1. Knowledge Management in Practice ................................................................. 38
2.5.2. Benefits of Knowledge Management................................................................. 39
2.5.3. Knowledge Management, Quality Management, Continuous Improvement and
Best Practices .................................................................................................................... 40
2.5.4. Knowledge Management and Communities of Practice (CoPs) ........................ 42
2.5.5. Knowledge Management and Virtual Teams (VT) ........................................... 44
2.5.6. Knowledge Management Systems ..................................................................... 44
2.6. Knowledge Management Defining Characteristics and Processes ........................... 45
2.6.1. Knowledge Creation .......................................................................................... 47
2.6.2. Knowledge Storage ............................................................................................ 53
2.6.3. Knowledge Transfer........................................................................................... 54
2.6.4. Knowledge Application ..................................................................................... 54
2.7. Knowledge Management Models from the Literature .............................................. 55
2.8. Data Mining Concepts, Processes, and Major Elements ........................................... 59
2.8.1. What is Data Mining? ........................................................................................ 59
2.8.2. Importance of Data Mining ................................................................................ 60
2.8.3. Data Mining Objectives ..................................................................................... 61
2.8.4. Data Mining Benefits ......................................................................................... 62
2.8.5. Major Elements and Tasks of Data Mining Processes ....................................... 64
2.8.6. Advantages and Disadvantages of Data Mining ................................................ 65
2.9. The Role of Data Mining and Business Intelligence in Strategic Knowledge
Management ......................................................................................................................... 66
2.10. Strategic Knowledge Management (SKM) ........................................................... 67
2.11. SKM Model and Study Hypotheses ...................................................................... 68
2.12. Chapter Conclusion ............................................................................................... 72
CHAPTER THREE ............................................................................................................... 74
3. METHODOLOGY ........................................................................................................... 74
3.1. Introduction ............................................................................................................... 74
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3.2. Research Paradigms Relevant to the Research Question .......................................... 76
3.2.1. Research Philosophy and Central Paradigms .................................................... 77
3.2.2. Research Approaches ......................................................................................... 78
3.2.3. A Deep Case Study Analysis ............................................................................. 80
3.3. Research Design ........................................................................................................ 81
3.3.1. Phase 1: Qualitative Exploratory Study ............................................................. 82
3.3.2. Phase 2: Survey Questionnaire .......................................................................... 85
3.3.3. Ethical Issues ..................................................................................................... 97
3.4. Chapter Conclusion ................................................................................................... 98
CHAPTER FOUR .................................................................................................................. 99
4. QUALITATIVE DATA ANALYSIS AND FINDINGS ................................................. 99
4.1. Introduction ............................................................................................................... 99
4.2. Interviewee Demographic Background and Roles (Interviewees 1-10) ................. 100
4.3. Key Findings ........................................................................................................... 103
4.3.1. Knowledge Management Key Points and Discussion ..................................... 104
4.3.2. Data Mining Key Points and Discussion ......................................................... 115
4.3.3. Resource Based Competitive Advantage (“Valuable, Rare, Inimitable, and Non-
substitutable”) Key Points .............................................................................................. 120
4.4. Chapter Conclusion ................................................................................................. 122
CHAPTER FIVE ................................................................................................................. 126
5. QUANTITATIVE DATA ANALYSIS AND HYPOTHESES TESTING .................... 126
5.1. Introduction ............................................................................................................. 126
5.2. Profile of Respondents ............................................................................................ 128
5.3. Preliminary Analysis ............................................................................................... 130
5.3.1. Data Analysis Procedure .................................................................................. 130
5.3.2. Missing Values and Unengaged Responses ..................................................... 130
5.4. Reflective-Reflective Hierarchical Component Model ........................................... 132
5.5. Evaluating Model Fit (Reliability and Validity) ..................................................... 136
5.5.1. Assessment of Reliability and Validity of the Lower-Order Components
(LOCs)/ First-Order Measurement Model ...................................................................... 137
5.5.2. Assessment of the Higher-Order Component (HOC)/Second-Order Model ... 145
5.5.3. Assessing and Testing the Structural Model .................................................... 146
5.5.4. Global Goodness-Of-Fit (GOF) ....................................................................... 151
5.6. Hypothesis Testing (Test of Direct Effects) ............................................................ 152
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5.7. Additional Tests of the Mediation Effect ................................................................ 152
5.8. Chapter Conclusion ................................................................................................. 154
CHAPTER SIX .................................................................................................................... 155
6. DISCUSSION, CONCLUSIONS, AND RECOMMENDATIONS FOR FUTURE
RESEARCH ........................................................................................................................... 155
6.1. Introduction ............................................................................................................. 155
6.2. Discussion Regarding Identified Aspects of the Constructs and Key Findings ...... 156
6.2.1. Knowledge Management Definitions and Constructs ..................................... 156
6.2.2. Data Mining Construct ..................................................................................... 167
6.2.3. Resource based Competitive Advantage (Valuable, Rare, Inimitable, and Non-
substitutable Resource) ................................................................................................... 170
6.3. Key Research Themes and Conclusions ................................................................. 173
6.3.1. The Relationship between Knowledge Management and Data Mining in the
Case Company ................................................................................................................ 174
6.3.2. The Effect of Knowledge Management on Resource Based Competitive
Advantage in the Case Company.................................................................................... 178
6.3.3. The Effect of Data Mining on Resource Based Competitive Advantage in the
Case Company ................................................................................................................ 182
6.3.4. The Indirect Effect of Knowledge Management on the Resource Based
Competitive Advantage through Its Effect on Data Mining (DM) Processes in the Case
Company ......................................................................................................................... 182
6.3.5. The Effect of Integration of Data Mining Within a Strategic Knowledge
Management Framework on Resource Based Competitive Advantage ......................... 183
6.4. Research Contribution and Implications ................................................................. 185
6.4.1. Theoretical Implications .................................................................................. 185
6.4.2. Managerial and Practical Implications for the Global Minerals and Metals
Industry and Case Company ........................................................................................... 187
6.4.3. Implications and Recommendations for Future KM Practice within the Case
Organisation.................................................................................................................... 191
6.5. Limitations of Research and Recommendations for Future Research .................... 194
6.6. Chapter Conclusion ................................................................................................. 196
REFERENCES ...................................................................................................................... 201
APPENDICES ....................................................................................................................... 216
APPENDIX A: INTERVIEW SCHEDULES ....................................................................... 216
APPENDIX B: CONSENT FORM INTERVIEW ................................................................ 219
APPENDIX C: QUESTIONNAIRE SURVEY ..................................................................... 220
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PENDIX D: STATISTIC RESULTS..................................................................................... 227
APPENDIX E: ATLASSIAN SOFTWARE COLLABORATION ...................................... 253
APPENDIX F: KNOWLEDGE MANAGEMENT MODELS & STRATEGIES ................ 254
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TABLE OF FIGURES
Figure 1.1: Number of Articles including “Knowledge Management” and “Data Mining”
Themes in the Title, Abstract, Keyword, or Body of Articles ................................................. 10
Figure 1.2: Chapter Themes ..................................................................................................... 18
Figure 1.3: Detailed Chapter Thesis Outline ........................................................................... 19
Figure 2.1: Overview of Chapter Two ..................................................................................... 23
Figure 2.2: Porter Five Competitive Forces Model ................................................................. 29
Figure 2.3: VRINE Model ....................................................................................................... 34
Figure 2.4: Data, Information, Knowledge and Purposeful Action ......................................... 36
Figure 2.5: Philosophy of Gilbert Ryle and Michael Polanyi .................................................. 37
Figure 2.6: Knowledge Management Processes ...................................................................... 47
Figure 2.7: Three Layers of the Knowledge-Creation Process ................................................ 50
Figure 2.8: Combination of Components of Layers of Knowledge Creation .......................... 50
Figure 2.9: Twentieth- Century Systems Theory: Epistemological and Ontological
Grounding…………………………………………………………………………………….52
Figure 2.10: Five Key Elements of the Data Mining Process.................................................. 65
Figure 2.11: SKM Model: Creating Competitive Advantage through Integration of Data
Mining and Strategic Knowledge Management ...................................................................... 69
Figure 3.1: Overview of the Methodology Chapter ................................................................. 74
Figure 3.2: Details of the Research Design ............................................................................. 82
Figure 3.3: An Illustration of the Snowball Sampling Process ................................................ 88
Figure 3.4: Theoretical Framework ......................................................................................... 91
Figure 3.5: Four Types of Latent Variable Models ................................................................. 96
Figure 3.6: Hierarchical Components and Dimensions ........................................................... 97
Figure 4.1: Overview of Qualitative Data Analysis Chapter ................................................... 99
Figure 5.1: Pictorial Presentation of the Quantitative Data Analysis Chapter ...................... 127
Figure 5.2: Correlation Tests Between Indicators in the First-order Measurement Model ... 134
Figure 5.3: Conceptual Presentation of the Hierarchical Component Model for KM ........... 135
Figure 5.4: Conceptual Presentation of the Hierarchical Component Model for DM ........... 136
Figure 5.5: Conceptual Presentation of the Hierarchical Component Model for RCM ........ 136
Figure 5.6: Results of the Structural Model ........................................................................... 149
Figure 6.1: Pictorial Representation of Discussion and Conclusion Chapter ........................ 155
Figure 6.2: The Relationship between KM and DM in the Case Company .......................... 177
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TABLE OF TABLES
Table 1.1: Trade Performance Index (Mineral Sector): Australia (2011, 2012, 2013, And
2014) ........................................................................................................................................ 13
Table 2.1: Overview of Widely Cited Knowledge Management Models ............................... 58
Table 2.2: Summary of SKM variables investigated. .............................................................. 72
Table 3.1: The Structure of the Questionnaire - Allocating Questions in Questionnaire to the
Components of the Conceptual Model .................................................................................... 90
Table 3.2: Summary of the Measures of Constructs ................................................................ 93
Table 4.1: Personal Background Information ........................................................................ 100
Table 4.2: Case Company, Identified versus Potential Sources of Competitive Advantage . 125
Table 5.1: Profile of Respondents .......................................................................................... 129
Table 5.2: Descriptive Statistics of Variables ........................................................................ 132
Table 5.3: First-order Loadings ............................................................................................ 139
Table 5.4: The Reliability and Validity Assessment of the Reflective Measurement Model 143
Table 5.5: The Discriminant Validity Assessment of the Reflective Measurement Model .. 144
Table 5.6: Correlations between Second-order Constructs, and the Discriminant Validity of
the Higher-order Component (HOC)/Second-order Model ................................................... 145
Table 5.7: Significance of the Structural Model Path Coefficients ....................................... 147
Table 5.8: The Coefficients of Determination R2 .................................................................. 148
Table 5.9: Effect Sizes ƒ² ....................................................................................................... 150
Table 5.10: Predictive Relevance Q2 ..................................................................................... 151
Table 5.11: Hypotheses Testing Results ................................................................................ 152
Table 5.12: Test of the Mediation Effect of DM ................................................................... 154
Table 6.1: Summary of Key Findings from the Study and Practical Implications for the Case
Company ................................................................................................................................ 198
Table 6.2: Summary of Key Findings from the Study and Implementation Recommendations
for the Case Company ............................................................................................................ 200
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ACKNOWLEDGEMENTS
This research has been a long journey and I am deeply indebted to all those that helped me
along the way. I would never have managed it the way I did without the help of the following
people, to whom I am highly indebted:
My Supervisors
First and foremost, I would like to sincerely thank my principal supervisor Dr Scott Gardner,
for his support, and guidance. His insights, suggestions, and coordination made the whole
progress of my study much smoother. This thesis would not have been possible without you! I
thank you from the bottom of my heart.
I would also like to express my sincere gratitude to my supervisor Dr Amy Huang, for the great
suggestions, recommendations, and feedback. Big thanks to her for all support during the
research.
My Parents
Without my parents, none of this would have been possible. Mom and Dad, thank you for your
endless love, support, and encouragement.
My lovely husband (Pirooz) and my beloved daughter (Rozhin)
I am extremely delighted to express my love to both of you for your encouragement and support
through my life’s journey. Thanks for your patience.
I am also thankful to Professor Leland Entrekin, Mr James Grey and Dr Mohammad Reza
Tabibi for their suggestions and recommendations during my study.
Finally, big thanks to all my family and friends for their support and friendship
I am indebted to all of you!
xiii
GLOSSARY
ABARE Australian Bureau of Agriculture and Resource Economics
ATC The case organisation’s Global Technical Centre
BI Business Intelligence
BP Best Practice
BSC Balance Scorecard
CA Competitive Advantage
COE Common Operating Environment
CoP Community of Practice
CoBP Community of Best Practice
DM Data Mining
ETL Extract, Transform, and Load
GDD Geological Data Design
GVT Global Virtual Team
IC Intellectual Capital
ICAS Intelligent Complex Adaptive Systems
ICT Information and Communication Technology
JORC Joint Ore Reserves Committee
KBV Knowledge-Base view
KM Knowledge Management
LO Learning Organisation
MBV Market-Base View
MTS Mining Technology Service
OL Organisational Learning
PDCA Plan, Do, Check, and Act
QA Quality Assurance
QUASAR Quality Automation Solutions in Alumina Refining
RBV Resource-Base View
SBV Stakeholder-Base View
SCM Supply Chain Management
SECI Socialisation, Externalisation, Combination, and Internalisation
SKM Strategic Knowledge Management
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SOE Standard Operating Environment
SWOT Strengths-Weaknesses-Opportunities-Threats”
TDG Technology Delivery Group
TQM Total Quality Management
VRIN Value, Rare, Imperfect Imitability, and Non-Substitutability
VRINE model Value, Rarity, Inimitability, non-substitutability and exploitability
VT Virtual Team
ROI Return on Investment
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CHAPTER ONE
1 INTRODUCTION
In today’s globally interconnected economy, knowledge is recognised as a valuable intangible
asset and source of Competitive Advantage for firms operating in both established and
emerging industries. The organisation’s success depends on how it can manage and organise
its corporate intangible assets and Intellectual Capital. Within these contexts, Knowledge
Management (KM) becomes manifest as a set of organising principles which form management
routines, structures, technologies and cultures within organisations. If KM is employed as a
part of business strategy, it can blend and develop the expertise and capability which is
embedded in human and technological networks. This may add value to products, services, and
reputation. Despite the growing treatment of knowledge as an asset or capability within high
technology service industries, there have been limited arguments about business strategy linked
to Knowledge Management in traditional capital intensive industries such as mining. Within
this industry IT-centric Knowledge Management Systems (KMS) (Moayer & Gardner, 2012)
have dominated, with varying degrees of success as business analysis, process improvement
and cost reduction tools.
This study aims to explore the opportunities and benefits arising from the testing, refinement
and application of an integrated Knowledge Management, Information and Communication
Technology ICT and Data Mining (DM) framework within a global mining and manufacturing
company.
This chapter commences with the background of the research, study rationale, and a profile of
the mining industry in WA; after that it describes the research objectives, questions and
hypothesis questions; and finally, it explains the structure of this thesis and outlines key points
in each chapter.
1.1. Research Background
The root of modern business and systems improvement programs originated in the 1800s. It
was deployed in several companies where management encouraged employee-driven
improvement and set up systems for rewarding employees who brought positive changes to the
2
organisation (Schroeder & Robinson, 1991). During the late 1800s and early 1900s, larger
industrial enterprises adopted work flow studies, scientific management and methods for
analysing and solving problems using systematic analysis and arguably scientific methods
(Bhuiyan & Baghel, 2005). During the Second World War the US government developed a
“Training within Industry” service for enhancing the industrial output (Bhuiyan & Baghel,
2005, p. 762). Job method training, programs for educating supervisors, and the techniques for
quality insurance and work flow efficiency were embedded in this service (Bhuiyan & Baghel,
2005). Later, similar programs were introduced in Japan by management experts such as
Deming, Juran, and Gilbreth (Bhuiyan & Baghel, 2005, p. 762). Eventually, the Japanese
advanced their thinking on quality control into management tools for ongoing improvement
through the whole organisation (Imai, 1986). Initially Continuous Improvement (CI) focused
on various principles for improving work processes and practices predominately in
manufacturing. By the late1980s and 1990s, typically CI had extended to service industries and
the public sector. It was applied with related methodologies such as Lean Manufacturing, Six
Sigma, Balanced Scorecard, Best practices, Benchmarking, and lean Six Sigma.
Supply Chain Management (SCM) also became a major area of focus for increased efficiencies,
cost and risk reduction for large scale manufacturing, logistics and other global industries. The
success of the Toyota group through the 1990s bore testimony to the effectiveness of
combining various CI methods within a supply chain constellation (Dyer & Nobeoka, 2000).
Whilst the relationship between KM and SCM has been explored by a number of authors with
particular regard to effective knowledge sharing, and risk reduction within client and supplier
networks, this is not a primary concern of this study, but is further discussed in the Managerial
and Practical Implication for the case organisation in section 6.4.2 (Cantor et al., 2014; Tang,
2006).
Continuous Improvement (CI) programs originally derived from Quality Assurance (QA),
statistical and industrial engineering techniques applied in the West in the 1940s, which were
subsequently developed and translated into Kaizan in Japanese industry during in the 1960s.
This shift to CI thinking and tools formed the basis for global Competitive Advantage in the
Japanese automotive electrical and electronics manufacturing from the mid-1970s to late
1990s. CI was also popularly associated with the Total Quality Management (TQM)
movement, which also obtained leverage in Japan (Bhuiyan & Baghel, 2005) but achieved
limited results when adopted for culture change purposes in the West. (Barclay & Murray,
2000, p. 6).
3
Whilst acknowledging the continued influence and importance of QA and CI models and
thinking for industrial progress in Japan and the West, (and the case study organisation), this
study takes a more contemporary view of the basis of Competitive Advantage in the minerals
and metals mining industry. This considers the rapid development of ICT applications in
organisations and opportunities presented for differentiation, value adding and Competitive
Advantage, when combined with 21st-century organisational design principles and Knowledge
Management principles. Arguably, this presents a powerful alternative to the often failed Total
Quality Management (TQM) and Business Process Re-engineering (BPR) initiatives
implemented by international consulting firms for large client organisations in the 1990s and
beyond (Barclay & Murray, 2000, p. 6).
In the 1980s and 1990s, the dominant approaches to change management included TQM and
various forms of IT strategies and interventions including ERP and e-commerce systems which
aimed to simultaneously reconfigure the structure, processes, technology, and human skills.
However, planned change had a poor track record throughout the 1990s, with TQM and IT
failures resulting in massive financial and human resource deployment costs, with limited
returns to the client organisation (Gardner & Ash, 2003). Despite the growing power of
computing and potential of ICT to support integration of knowledge across organisational
boundaries, Enterprise Resource Planning ERP systems in the 1990s often achieved little return
on investments of millions of dollars. Often the failure of ERP implementations was not caused
by ERP software itself, but by compounding, often unforeseen, changes ERP causes across
multiple interfaces in the organisation (Scott & Vessey, 2000; Helo, Anussornnitisarn, &
Phusavat, 2008; Maditinos, Chatzoudes, & Tsairidis, 2012; Seo, 2013).
A number of management theorists such as Peter Drucker, Paul Strassmann, and Peter Senge
in the United States, have played an important role in the development of Knowledge
Management and the allied systems thinking and management principles reflected in
Organisational Learning (OL) and the Learning Organisation (LO) (Easterby-Smith & Araujo,
1999; Senge, 2014). Drucker and Strassmann have focused on the growing importance of
information and explicit knowledge as organisational resources, whilst Senge, following his
learned colleague at MIT Edger Schein, has emphasised the Learning Organisations (LO) and
cultural dimension of managing knowledge (Schein, 2010; Senge, 2014). Peter Drucker was
one of the first writers to create a vision of management that placed human resources as the
most important asset of organisations in an emerging knowledge age which he foresaw in the
late 1950s and elaborated in his book Post Capitalist Society in 1993 (Katsoulakos & Zevgolis,
2004; Drucker, 1993). Drucker heralded the coming of the knowledge age, following visionary
4
sociologists such as Daniel Bell (Bell, 1962). Various facets of managing knowledge and
learning, as an organisational and institutional asset, have been examined by other theorists
such as Chris Argyris, Christoper Bartlett, and Dorothy Leonard-Barton of Harvard Business
School (Barclay & Murray, 2000; Katsoulakos & Zevgolis, 2004). More recent contributions
to the field of Knowledge Management and the allied thinking on Organisational Learning
(OL) include Ijuro Nonaka (Nonaka I. , 1994), Peter Senge (Senge, 2014), Garvin, Edmonson,
and Gino (Garvin, Edmondson, & Gino, 2008), and Otto Scharmer (Scharmer, 2009). The work
of these authors and other leading thinkers in the field of Knowledge Management (KM),
Organisation Learning (OL), Data Mining (DM) and broader ICT information and knowledge
platforms will be explored in the literature review and throughout the thesis. In the 1980s the
literature on information technology and systems started to focus on information assets and a
broader systems view of the organisation (Katsoulakos & Zevgolis, 2004). The importance of
knowledge as a competitive asset was manifested in this decade with the parallel development
of expert systems and artificial intelligence for managing knowledge (Barclay & Murray,
2000).
In keeping with the developing popularity of Knowledge Management across business
organisations, from the beginning of the 1990’s, some firms worked with Peter Senge’s theories
on learning enterprises. The application of these ideas met with mixed results throughout the
mid to late 1990s. Senge’s intuitively appealing but hard to implement model of the Learning
Organisation (LO) was subsequently critiqued and adapted for easier translation into
management operations by his colleagues David Garvin and Amy Edmonson (Garvin,
Edmondson, & Gino, 2008). However, despite various critiques of the Learning Organisation
(LO) and seminal KM models such as Nonaka (Nonaka, Toyama, & Konno, 2000), the true
potential of ICT and the knowledge age was emerging by the mid-2000s. A second wave of
Knowledge Management and Organisational Learning (OL) had arrived supported by massive
improvements in ICT platforms, information sharing and collaboration software. The
Competitive Advantage of the firm was now directly linked to the ability of its leaders and
managers to develop and mobilise a dynamic portfolio of tacit and explicit knowledge (Moayer
& Gardner, 2012). This dynamic, and how best to manage the interface between hard and soft
systems, is the main concern of this thesis. The study is an investigation into Knowledge
Management (KM), ICT and Data Mining (DM) systems and practices in a multinational
mining, processing and manufacturing firm.
5
1.1.1. The Shift from Tangible to Intangible Assets
In the early 1980’s, tangible assets such as: plant and equipment; accounts payable and
receivables; inventory; and formalised processes represented more than 60 percent of a firm’s
market value. In 2005 this was estimated at less than 20 percent (Aughton & Barton, 2005).
In fact, return on intangibles has been identified as a new human resource - Return on
Investment (ROI) (Ulrich & Smallwood, 2005). Intangible assets can present big opportunities
for human resources in companies (Aughton & Barton, 2005). Intangibles are not new to a
company’s market value, but they become a significant portion of market capitalisation (Ulrich
& Smallwood, 2005). Leveraging intangible assets such as competencies, customer
relationships and innovations for success, or managing knowledge, became a mainstream
business objective for market leaders (Moshari, 2013). In the 21st century companies are
increasingly focused on managing big data, information and knowledge to remain
internationally competitive, and to ensure they meet and exceed the requirements of customers.
As far back as the mid-1990’s, advanced companies in America and Europe recognised the
need to manage knowledge in a systematic way for decades (Wiig K. M., 1997). How best to
manage knowledge is a major commercial and societal concern and the global Knowledge
Management community has developed a broad scope of applications and technologies for
practical use and academic research (Liao, 2003).
With regard to the measurement and impact assessment of Knowledge Management, Firestone
(2001, p.116) noted: “It is clear that a thoroughgoing KM benefit assessment would: explicitly
postulate and measure goals, objectives and progress toward them, gauge the impact of KM
introduction on business processes and their success in attaining goals and objectives, and
finally interpret these descriptive analyses of KM impact or projected impact on goals in terms
of corporate benefit. Descriptions of impact (should not be) confused with measurements of
actual benefit” (Firestone, 2001, p. 116).
1.1.2. Developments in Knowledge Management and ICT since the Late 1990s
Knowledge Management in this global context is a set of practises for managing knowledge
which enhances performance in the organisation (Wang & Wang, 2008). True Knowledge
Management is related to human subjective knowledge, not data or objective information
(Seeley & Davenport, 2006). (This contrasts to the typical IT and Enterprise systems vendor
view of KM systems in a box).The tacit and explicit knowledge framework which is used in
Knowledge Management models focus on a dynamic human process of justifying personal
6
belief toward revealing truths which are typically non-technology dependent (Nonaka &
Takeuchi, 1995; Wang & Wang, 2008). Therefore, Knowledge Management is concerned with
unstructured information and tacit knowledge (Wang & Wang, 2008), and using ICT to
optimise the balance between tacit and explicit sides of the equation. ICT (and Data Mining)
only affects explicit knowledge and codification (Hendriks, 2001).
Although Knowledge Management falls in the domain of management, not in computer science
(Tiwana, 2002), many authors and researchers have stressed the benefits of ICT as a platform
for Knowledge Management applications. Some authors have considered information
technology as a catalyst for Knowledge Management which cannot deliver it directly. This
important distinction between hard information technology systems and soft human knowledge
systems is elaborated throughout the thesis. (Hendriks, 2001). In the 1990s, over 25 years ago,
Wiig (1997, p8) stated “Over the next few years we can expect drastic changes in our reliance
on Information Technology (IT)”. He predicted increasing use of IT for support of Knowledge
Management in the form of passive infrastructure functions such as Local Area Networks
(LANs), use of intranet and the World-Wide-Web (WWW), e-mail, rudimentary groupware
applications, and corporate memory data bases (Wiig K. M., 1997, p. 9). In order to identify
the potential role of ICT in Knowledge Management, some researchers referred to work flow
tools for knowledge dissemination, databases for knowledge storage, and search engines for
knowledge interpretation (Hendriks, 2001, p. 58). Junnarkar and Brown (1997) stressed that,
for Knowledge Management to become effective, it requires symbiosis between people,
information and information technology (Junnarkar & Brown, 1997). The advent of Web2.0
greatly increased the power of supporting electronic platforms (see section 1.1.4).
Knowledge Management technologies enable Continuous Improvement (CI) of business
processes and also, they contain communication, collaboration and networking functions for
supporting knowledge capture, storage, structure, and distribution (Nyame-Asiamah, 2009).
Therefore, technologies can play an important role in facilitating the process of representation
and exchange of knowledge (Nath, Iyer, & Singh, 2011). Whilst many technologies are now
available to support Knowledge Management, only a few of them are suitable for cognitive
mapping and promotion of higher level individual and Organisational Learning (OL). The deep
investigation of Knowledge Management (KM), Data Mining (DM) and allied ICT practices
undertaken in this case study organisation focuses on understanding the interplay of hard and
soft systems as a basis for Competitive Advantage (CA) of the firm. As the study reveals, other
companies seeking to use knowledge assets as capability and a source of CA, would be well
advised to focus on Knowledge Management technologies that capture and support the creation
7
of useful ideas, management insights and other forms of tacit knowledge for value adding or
problem solving (Nyame-Asiamah, 2009).
1.1.3. The Development of the Global Knowledge Economy
The idea of the knowledge economy and knowledge workers was first discussed by sociologist
Daniel Bell (1962) in his book- “The coming of Post- Industrial Society”, and around the same
time by Peter Drucker, who later fully defined and developed these concepts in his 1994 book
“Post- Capitalist Society”. Subsequent authors linked knowledge capitalism to an organisation
ability to develop and translate intangible learning, collaboration, and innovation process into
tangible value (Dovey & Fenech, 2007, p. 575). The fundamental component of a knowledge
economy is a greater reliance on the intellectual capabilities (of an organisation), rather than
on physical inputs or natural resources (Powell & Snellman, 2004).
1.1.4. Web 2.0 as Collaboration and Knowledge Sharing Enabler
From the mid-2000’s, organisations began to utilise a new generation of internet-based
collaborative tools that have grown up as part of the Web 2.0, ICT revolution (Kane &
Fichman, 2009). Web 2.0 is at once a universal library, a global market, and a public square
for communication among people (Martorell & Canet, 2013)Web 2.0 has the capacity to
aggregate and direct human potential and collaborative value creation across business
networks. It can create dynamic services, and deliver peer-to-peer interactions among users
(Nath, Iyer, & Singh, 2011). Nath, Iyer, & Singh (2011) stated “Web 2.0 technologies include
Wikis, Blog, RSS, Aggregation, Mash ups, Audio blogging and Podcasting, Tagging and social
bookmarking, Multimedia sharing, and Social networking” (Nath, Iyer, & Singh, 2011, p. 1).
These tools and technologies extend into groupware engineering science, text mining,
document management, retrieval technology, and enterprise knowledge portals (Muhammad
et al, 2014, p.29). Other Knowledge Management tools, such as expert systems, enable the
capture of explicit knowledge from a single source or network of expertise for providing
diagnostics, and answers to problems or search queries. These systems enable knowledge
sharing of a practical and experimental nature, so in this way individuals and groups are able
to arrive at their own conclusions (Nath, Iyer, & Singh, 2011).
8
1.2. Study Rationale
Early Knowledge Management approaches have focused on capturing, describing, and
transferring explicit knowledge in databases; newer approaches focus on communicative
aspects and take a knowledge-in-action perspective treating ICT as a platform to support human
communication networks (Riemer, Scifleet, & Reddig, 2012). With respect to balancing tacit
and explicit knowledge stocks and flows in the organisation, ICT plays a significant role in
fulfilling Knowledge Management objectives such as process, product or service innovation,
retention of corporate memory and problem solving. The ultimate expression of Strategic KM
systems usefully combines ICT with Knowledge Management, Organisational Learning (OL),
expert systems, data repositories, corporate memory, information sharing, and collaborative
decision support and embeds these integrated activities into the organizational coda and culture.
(Liao, 2003).
The rapid advancement in ICT and growing capabilities for generating and collecting data has
created a quest for new techniques and tools that can transform data to valuable information
and knowledge for effective decision making (Khan & Quadri, 2012). In this regard, Business
Intelligence (BI) can help organisations to best utilise information to support tactical, strategic
and operational decision making (Muhammad et al, 2014). The majority of organisational
knowledge is in the employees’ mind in the unstructured form; Knowledge Management
encompasses both tacit and explicit knowledge to increase the organisations performance by
using collaborative tools for learning, creating, and sharing knowledge in organisations.
Business Intelligence focuses primarily on explicit or codified knowledge (Khan & Quadri,
2012).
For codifying knowledge IT support is critical. Knowledge can be codified and stored in
databases. Codification strategies allow people to retrieve codified knowledge without having
to contact the person who originally developed it. By contrast personalisation strategies invest
in building networks of people. Knowledge can be shared not only face-to-face but also over
the phone by e-mail or using state of the art collaborative platforms. Companies for managing
knowledge need to use both codification and personalisation strategies. In codification strategy
the reuse of knowledge saves work and reduces communication costs. The personalisation
strategy relies on the wisdom of expert networks, offering access to expertise and rich tacit
knowledge. Both strategies are required for companies- personalisation strategies, in which
knowledge is shared person-to-person, and codification strategies, in which networked
computers are used to codify and store knowledge (Hansen, Nohria, & Tierney, 1999).
9
1.2.1. Business Intelligence (BI), Data Mining (DM) and Knowledge Management
(KM)
Integrating Business Intelligence (BI) in Knowledge Management (KM) frameworks and
practices is imperative for organisations (Khan & Quadri, 2012). According to the previous
studies of Knowledge Management, Data Management, and Business Intelligence, there are
more than 400 articles (1970-2015)2 regarding “Knowledge Management” and “Data
Management”, and more than 1067 articles (1996-2015)3 about “Knowledge Management”
and “Business Intelligence”. It is a very broad area, so this study narrows down the field to
provide a working definition and practical view of the relationship between human and IT
components of Knowledge Management.
Business Intelligence and Big Data are important sources of codified knowledge which provide
a competitive edge for companies employing these technologies (Wang & Wang, 2008; Wu et
al., 2014). Data Mining can be a valuable component of the Big Data analytics process, as it
allows staff who are not professionals in statistics to manage and extract knowledge from data
and information (Baicoianu & Dumitrescu, 2010).
Business Intelligence (BI) plays an important role for extracting information and discovering
the hidden patterns in sources of data, so the purpose of Business Intelligence (BI) is to discover
knowledge and information that helps managers to make effective decisions pursuant to
organisational goals (Khan & Quadri, 2012). Nowadays, many organisations are using Data
Mining (DM) as a Business Intelligence (BI) tool (Wang & Wang, 2008). DM is a technology
for knowledge discovery in databases, so it provides various methodologies for analysis,
problem solving, decision making, integration, learning and innovation (Liao, 2003). Wang
(2008) stressed the process of Data Mining (DM) could be viewed as a Knowledge
Management (KM) process because it involves human knowledge and extends it. In this view,
Data Mining (DM) is able to connect hard systems, such as Business Intelligence (BI), with
Knowledge Management (KM) as a soft system (Wang & Wang, 2008, p. 623). Just how to
achieve these synergies between hard (technological) and soft (human) systems remains one of
the central questions yet to be addressed in organisational studies. The design principles and
management routines and culture that support this integration process are highlighted in this
2 Electronic searches were performed in ProQuest databases (1970-2015) with filtering “Knowledge
Management” and “Data Management” 3 Electronic searches were performed in ProQuest databases (1996-2015) with filtering “Knowledge
Management” and “Business Intelligence”
10
study as key determinants of industry sector Competitive Advantage for the case organisation.
Data Mining (DM) technology as an important tool of Business Intelligence has been chosen
as a proxy for broader ICT and BI applications because it is a narrow area with clear steps and
practical applications. The author’s review of previous studies of “Knowledge Management”
and “Data Mining” revealed around 539 articles (1997-2015)4 in this area. Most of the articles
are in 2000 (43 articles), 2001 (43 articles), 2008 (50 articles), and 2012 (42 articles). The total
number of publications by year is shown in Figure 1.1:
Figure 1.1: Number of Articles including “Knowledge Management” and “Data Mining”
Themes in the Title, Abstract, Keyword, or Body of Articles
Silwattananusarn & Tuamsuk (2012) also conducted a review of Data Mining (DM)
applications in the process of Knowledge Management from 2007 to 2012. They chose 10
articles which related to the application of Data Mining (DM) in Knowledge Management
(KM). They divided knowledge resources into eight groups in which knowledge objects were
stored and manipulated in Knowledge Management (KM) processes with considerations of
how Data Mining (DM) aids this in different organisational contexts. These eight contexts or
groups are: Health Care Organisation, Retailing, Financial/Banking, Small and Middle
Businesses, Entrepreneurial Science, Business, Collaboration and Teamwork, and
Construction Industry (Silwattananusarn & Tuamsuk, 2012, pp. 18-20). Given the central
importance of the resource industry in Australia, adding this sector to the research on Data
4 Electronic searches were performed in ProQuest databases (1997-2015) with filtering “Knowledge
Management” and “Data Mining”
0
10
20
30
40
50
60
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Nu
mb
er o
f A
rtic
les
Year of Publication
Series1
11
Mining (DM) technique within Knowledge Management (KM) frameworks may well be of
theoretical and practical value. Therefore in the mining and resource industry a conceptual
framework for integrating Data Mining (technology or hard system) in the Strategic
Knowledge Management (SKM) (people or soft system) within complex organisations would
be needed.
With respect to the multiple definitions presented in the literature review, the key operational
terms used in this study are as follow:
- Knowledge Management (KM): Knowledge Management is an integration strategy for
getting the right knowledge to the right people at the right time. It is improving organisational
performance by sharing and putting information into action (Halawi, Anderson, & McCarthy,
2005).
- Business Intelligence (BI): Business Intelligence (BI) is a wide category of applications
and technologies of gathering, accessing, and analysing massive data for making effective
business decisions in organisation (Wang & Wang, 2008).
- Data Mining (DM): According to Giudici (2003, p.2) Data Mining is the process of
selection, exploration, and modeling of large quantities of data to discover regularities or
relations that are at first unknown with the aim of obtaining clear and useful results for the
owner of the database. Data Mining finds patterns in the data which have information about
internal hidden relationships and involves discovering human understandable patterns
(Seddawy, Khedr, & Sultan, 2012, p. 5; Silwattananusarn & Tuamsuk, 2012).
- Strategic Knowledge Management (SKM): Strategic Knowledge Management tries to
incentivise knowledge creation and knowledge transfers when formulating strategy and making
strategic decisions (López-Nicolás & Meroño-Cerdán, 2011). According to Moayer and
Gardner (2012, p.67) SKM improves organisational performance by realisation of human and
technological capability embedded in organisational networks. It acknowledges this
complexity while outlining key elements and broad interrelationships, which subject to further
empirical investigation may advance KM and DM practice and it is a platform for building
competitive advantage (Moayer & Gardner, 2012).
- Organisational Learning (OL): According to Easterby (2008, p.239) Organisational
Learning is a dynamic process of strategic renewal, involving a tension between creating new
knowledge (exploration) and using existing knowledge (exploitation). It as a method of
decision making with learning processes represents an opportunity to unify the insights from
both dynamic capabilities and knowledge management (Easterby‐Smith & Prieto, 2008).
12
- Resource based Competitive Advantage (RCA): Resource based Competitive
Advantage is a function of industry analysis, organisational governance and firm effects in the
form of internal resource advantages and strategies (Mahoney & Pandian, 1992, p. 375). The
Resource Based View (RBV) analyses internal resources of organisations and emphasises the
use of these in formulating strategy for achieving sustainable Competitive Advantage
(Madhani, 2010).
1.3. The Australian Mining Industry
Wiig (1997) stressed, during the 18th and 19th centuries and throughout the industrial
revolution, people began to use technology to produce high quality goods and services at a low
price and in this way created market advantages for their enterprises (Wiig K. M., 1997, p. 4).
The modern mining industry, which was established in Australia in the late 1800s, pioneered
industrial engineering and improvement methods and made a significant contribution to
economic development of the nation (Kemmis, 2013). While the primary techniques for coal
and copper mining were brought in by British and German immigrants from the early 1800’s,
gold mining began in the mid 1800’s using Californian technology (Kemmis, 2013). To support
this industry, schools of mines were formed in the early 1870s and the Australian Institute of
Mining and Metallurgy was established in 1893. In this regard, the University of Melbourne
(1874) and the University of Sydney (1892) introduced formal mining and metallurgy courses
(Kemmis, 2013). In 1959 the Australian Mineral Industry Research Association (AMIRA) was
formed for organising the research activities and investments in minerals and metals mining
industry (Kemmis, 2013). In the last decade, due to high demand for mineral commodities from
China and other emerging economies, the Australian mining industries investments in Research
and Development (R&D) has increased (Kemmis, 2013).
Major mining companies in Australia have consolidated their position among the business
investors in R&D with mining R&D expenditure of $3.8 billion in 2010-11 (21.4% of total
business R&D expenditure), the second largest industry share behind manufacturing (Kemmis,
2013, p. 14). Mining companies are usually interested in R&D collaboration with potential
suppliers for improving problem solving (Kemmis, 2013). Suppliers also play key roles in the
mining industry. With the advent of enhanced stakeholder awareness of the environmental and
social impacts of mining, relationships with contractors and suppliers are of increasing
importance for developing innovative processes and a broader network of intellectual and
social capital for large miners important (Richards, 2009). Suppliers can drive organisations to
13
produce new services in different ways (Richards, 2009), so knowledge-based suppliers are
key sources to increase performance and play significant roles ensuring that the Australian
mining industry is globally competitive (Urzúa, 2011).
One third of the word’s mineral resources are produced in Australia (Nimmagadda & Dreher,
2009), which is a global centre for mining products (Kemmis, 2013). A large amount of high
quality mineral reserves are close to the surface. This makes mining in Australia relatively
price-competitive on a global scale (IBISWorld, 2014). The Australian mining sector generated
revenue of about $138.8 billion in 2006-7, growing to a projected $205 billion in 2011-12
(IBISWorld, 2012).
The Australian minerals industry generated 8% of Australia’s GDP (Gross Domestic Product)
in 2006-7. Direct employment was about 127,500 people and indirect employment was up to
200,000 people in the same period (Fernandez, 2010). In 2012, the total employment figures
were estimated at 265,000 people (Kemmis, 2013).
With regard to Trade Competitiveness, Australia ranked number two in 2012, number three in
2011 and 2014, and number four in 2013 (Internatioanl Trade Centre, 2014).
Table 1.1: Trade Performance Index (Mineral Sector): Australia (2011, 2012, 2013, And
2014)
Australia is the one of the largest producers of metals and minerals in the world, and the mining
industry plays an important role in national revenue growth. This study is premised on the idea
that finding new ways to identify and activate the knowledge capabilities embedded in human
Indicator's
Description
Minerals
(Value)
In 2011
Minerals
(Rank)
In 2011
Minerals
(Value)
In 2012
Minerals
(Rank)
2012
Minerals
(Value)
In 2013
Minerals
(Rank)
2013
Minerals
(Value)
In 2014
Minerals
(Rank)
2014
General
Profile
Share in
national
exports (%)
60%
58% 60% 59%
Position for
Current
Index
Net exports
(in thousand
US$)
121,745,308 3 106,466,041 7 110,705,740 3 106,527,258 4
Position for
Current
Index
Share in world
market (%)
4.37% 4 3.93% 5 4.44% 4 4.29% 5
Average
Index:
Current
Index
3 2 4 3
14
resources and technological networks, will result in increased efficiency, productivity and
Competitive Advantage within the minerals, mining and resource industry.
1.3.1. Application of Knowledge Management in the Australian Mining Industry
For increasing Competitive Advantage in this sector, skilled professionals in science,
engineering and technology qualifications are deemed essential. The mineral industries have
built a national infrastructure all over Australia and Australian’s minerals have created Mining
Technology Services (MTS) companies. This sector comprises companies, institutes and other
organisations that receive a significant part of their revenue from mining companies for
providing goods and services which are based on technology, Intellectual Property (IP) and
knowledge (Fernandez, 2010; Fisher & Schnittger, 2012).
Mining sites in Australia are usually located in remote locations. These areas are rich in
minerals, so they allow for long-term exploitation. All contractors connected to mine sites have
a significant impact on other services and businesses operating within the broader network of
providers. Mining sites have the potential to be knowledge-intensive hubs and innovation
environments. MTS professionals provide essential knowledge transfer from mine to mine or
project to project. MTS sectors also produce training materials for mining sites, these occur on
informal networks (Fernandez, 2010).
Retaining core knowledge in-house has economic benefits for mining companies. In this way
the company does not repeat mistakes and incur additional expenses. These firms try to access
the knowledge and expertise which has been accumulated over the years (Fernandez, 2010).
Loss of knowledge and experience, as a result of downturns in the industry and turnover of
personnel, presents a significant threat to the future competitiveness of mining and resource
firms (Kridan & Goulding, 2006).
With respect to a combination of both internal and external expertise as Intellectual Capital
(IC) for accumulating knowledge in a company, some MTS companies put a special emphasis
on working as an “open book” in different parts of the company. For instance, conversations
with clients are recorded to formalise a job as a source of knowledge. This recorded knowledge
is shared throughout the company (Fernandez, 2010). Other MTS companies mix and match
knowledge and expertise by linking up project managers working within different locations.
They transfer relevant experiences and insights to project groups in the form of reports
(Fernandez, 2010).
15
Larger mining companies use the MTS knowledge network to find better solutions for their
specific problems. MTS companies are able to implement suitable solutions within mining
companies. As a result, the whole mining industry can benefit from increasing technological
knowledge flowing through a knowledge-based techno-economic network (Fernandez, 2010;
Fisher & Schnittger, 2012). Whilst this precedent for Knowledge Management exists within
the Australian mining industry, there are few detailed cases available on Best Practice (BP),
globally benchmarked, Knowledge Management Systems (KMS), operating within Australian
mining companies. This highlights the need for a deep and detailed case study of an
organisation which is operating a mature Knowledge Management System with supporting IT
infrastructure, and management practices within the Australian minerals and metals mining
sector. This is the principal source of evidence for this study which aims to inform both theory
and practice in the fields of Strategy, Knowledge Management, and Data Mining. The evidence
from the case is interpreted using a Strategic Knowledge Management (SKM) framework and
a model examining the Valuable, Rare, Inimitable, and Non-substitutable (VRIN) aspect of
Knowledge Management Systems (KMS) routines and practices within the case study
organisation (see sections 2.3.3 and 2.10).
This rationale is consistent with the Australian government current focus on improving
innovative capacity in mining (primarily through the development and testing of new
technologies and equipment). According to the Committee for Economic Development of
Australia (CEDA), the government should give greater weight to transition activities in funding
universities. This is intended to encourage innovative procurement policies, and improve the
funding arrangements for industry research (Osman, 2016). This will build innovation and
efficiencies across Australia’s economy in the post mining boom era.
1.3.2. Application of Data Mining in the Australian Mining Industry
In the resources industry, various types of data are stored and processed in large databases.
Typically this includes exploration, drilling and production data. This provides a basis for
specialist management decisions and analytics using universal servers (Nimmagadda &
Dreher, 2009).
Resource companies, which require specific analytics, create data marts. This supports a range
of business units, functions and specialist activities including: exploration, drilling, production
and marketing (Nimmagadda & Dreher, 2009).
16
Data Mining techniques can be used to discover patterns of data for creating new information
(Lee M.-C. , 2009).
Managers in the resources industry, who understand Data Mining applications and techniques,
can improve their business processes in highly competitive environments and in doing so create
flow on effects to improve Knowledge Management practice and give them an edge over their
competitors.
Due to the similarities in management information systems, operational and management tools
used by large mining multinationals on a global basis, Competitive Advantage and superior
decision making is not derived from the technology alone. This is demonstrated in a deep case
analysis of Data Mining and Knowledge Management across the global operations of a major
mining processing and manufacturing company. We argue that this company was able to
combine information, obtained using standard technologies with unique organising principles
to produce strategically unique and valuable knowledge.
1.4. Research Objectives
The main purpose of this research is to develop a new model for creating Competitive
Advantage within a mining and resources industry context. This is derived from a mixed
methods study of Knowledge Management and Data Mining practices within a global mining
and manufacturing firm, which has significant operations in Australia. The findings from the
study are interpreted using an original Strategic Knowledge Management (SKM) framework
and VRIN model generated from the extant literature on Strategy, Knowledge Management
(KM), and Organisational Learning (OL). Relevant literature on Information and
Communication Technology (ICT) and Business Intelligence (BI) as they relate to KM and
Data Mining is also referenced.
This study investigates the effect of combining Knowledge Management activities and Data
Mining processes on Competitive Advantage in the Australian mining and resources industry.
At an ontological level, the study also attempts to identify the maturity of Knowledge
Management and Data Mining practices within the organisational design, management
thinking, and deeper cultural assumptions of the case company.
17
1.5. The Research Question and Sub-Questions
The main research question is:
“How can the relationship between Strategic Knowledge Management and Data Mining be
effective in creating Competitive Advantage for a large organisation in the global minerals
and metals mining industry?”
To address this, three sub-questions must be explored:
1- How do Knowledge Management processes affect five defined elements of Data
Mining processes (ETL (Extract, Transform, and Load transaction data), Store and Manage
data, Provide data access, Analyse data, and Present data) in mining and resource
organisations?
2- How do these elements affect Resource based Competitive Advantage in mining and
resource organisations?
3- How do Knowledge Management processes affect (directly and indirectly) Resource
based Competitive Advantage indicators in mining and resource organisations?
These questions were addressed using hypothesis testing, quantitative and qualitative analysis
described in Chapter Three and further elaborated in Chapters Four and Five.
1.6. Thesis Outline
This thesis includes six thematic chapters as shown in Figure 1.2. Figure 1.3 provides a more
detailed outline of these chapters.
18
Figure 1.2: Chapter Themes
Chapter SixDiscussion, Conclusions and Recommendations for
Future Research
Chapter Five
Quantitative Data Analysis and Hypotheses Testing
Chapter Four
Qualitative Data Analysis and Findings
Chapter Three
Methodology
Chapter Two
Literature Review
Chapter One
Introduction
19
Figure 1.3: Detailed Chapter Thesis Outline
Introduction
•Research Background
•Study Rationale
•The Australian Mining Industry
•Research Objectives
•The Research Questions and Sub-Questions
•Thesis Outline
Literature Review
• Introduction
•Strategy and Strategic Management
•Competitive Advantage
•Definition of Knowledge
•Knowledge Management
•Knowledge Management Defining Characteristics and Processes
•Knowledge Management Models from the Literature
•Data Mining Concepts, Processes and Major Elements
•The Role of Data Mining and Business Intelligence in Strategic Knowledge Management
•Strategic Knowledge Management (SKM)
•SKM Model and Study Hypotheses
•Chapter Conclusion
Methodology
• Introduction
•Research Paradigms Relevant to the Research Question
•Research Design
•Chapter Conclusion
Qualitative Data Analysis and
Findings
• Introduction
• Interviewee Demographics Background and Roles (Interviewees 1-10)
•Key Findings
•Chapter Conclusion
Quantitative Data Analysis and Hypothesis
• Introduction
•Profile of Respondents
•Preliminary Analysis
•Reflective-Reflective Hierarchical Component Model
•Evaluating Model Fit (Reliability and Validity)
•Hypothesis Testing (Test of Direct Effect)
•Additional Tests of the Mediation Effect
•Chapter Conclusion
Discussion, Conclusions and Recommendation
for Future
• Introduction
•Discussion Regarding Identified of Aspects of the Constructs and Key Findings
•Key Research Themes and Conclusions
•Research Contribution and Implications
•Limitations of Research and Recommendations for Future Research
•Chapter Conclusion
20
Chapter Two is the literature review. This chapter explores the relationship between the
relevant literature on Strategic Management, Knowledge Management, Data Mining, and
theories of Competitive Advantage linked to the Resourced Based and Knowledge Based
Views of the firm. It also explores the relationship between hard and soft systems, ICT, BI,
Data Mining, and Knowledge Management. At the end of Chapter Two, a new model of
Strategic Knowledge Management (SKM) is introduced.
Chapter Three introduces the two research paradigms (positivist and interpretivist) that have
informed the design of study. The methodology and mixed method approach employed for the
deep top down and bottom up investigation of Knowledge Management practices in the case
company are also justified and further elaborated in Chapter Four and Five.
Chapter Four reports the findings of the exploratory qualitative component of the study. Ten
respondents selected from the global senior management team were interviewed, either face to
face or remotely, using the semi-structured questionnaire format. NVivo software was used for
processing and analysing the data. The result of this first stage of the empirical component of
the study was used to inform the design of the survey of reporting managers and specialists for
stage two.
Chapter Five describes the quantitative component of study which measured the effects of
five designated elements of Data Mining processes, and Knowledge Management processes on
the capability and Competitive Advantage of the firm. These relationships and relevant
hypothesis were tested using Structural Equation Modelling supported by PLS-SEM software.
Chapter Six synthesizes the findings from the qualitative and quantitative components of the
study. These findings are discussed with reference to the SKM framework, VRIN model and
related concepts covered in Chapter Two. Finally, the limitations, implications, and
recommendations of the study for theory and practice are presented.
21
CHAPTER TWO
2. LITERATURE REVIEW
2.1. Introduction
This literature review chapter presents an overview and discussion of the academic literature
models and concepts in the four main thematic areas informing the core objectives and
concerns of the study. These areas are- “Strategic Management” and related theories of
“Competitive Advantage”, “Knowledge Management” and “Data Mining”.
Contemporary thinking on Strategic Management models and theories will be explored with
operational definitions of Strategic Management and Competitive Advantage (CA). The idea
of Knowledge Management as a process for generating Intellectual Capital (IC) assets, strategic
differentiation and competitive capability will be investigated.
The concept of Competitive Advantage is defined in section 2.3. In this study, Competitive
Advantage (CA) is interpreted through the lenses of the Market-Based View (MBV), Resource-
Based View (RBV) and Knowledge Based View (KBV) of strategy. The Stakeholder Based
View (SBV) of strategy is also considered central to understanding the link between a firms’
strategy, Competitive Advantage and broader networks of human relationships with embedded
social and intellectual capital (section 2.3.1 and 2.3.2). The link to Competitive Advantage is
further elaborated with reference to the VRIN(E) model (Value, Rarity, Inimitability, Non-
substitutability and Exploitability) (section 2.3.3). (It should be noted that in the tested
Strategic Knowledge Management (SKM) model and supporting VRIN framework,
‘Exploitability’ was assumed as a condition for conversion of Value, Rarity, Inimitability, Non-
substitutability into Competitive Advantage. This assumption was also built into the questions
used to test the hypothesis and the SKM and VRIN models).
Using Knowledge Management models and principles, as a vehicle to harness the firms’
resources for creating and sustaining Competitive Advantage (CA), is the major concern of this
review of the extant literature. The concepts of knowledge and Knowledge Management (KM)
are defined (section 2.4 and section 2.5). In these sections, key operational definitions of
knowledge (section 2.4.1), knowledge types (section 2.4.2), and alternative perspectives on
knowledge creation and application (section 2.4.3) will be identified. Knowledge Management
22
(KM) (section 2.5.1), and its potential benefits will be explored along with the (section 2.5.2),
link between Knowledge Management and Quality Management (section 2.5.3). This will be
followed by a consideration of the rationale for adoption and potential applications of
Knowledge Management models, systems and practices in organisations. Knowledge
Management processes, and some of the most prominent models from the contemporary KM
literature (section 2.6) will be investigated and compared (section 2.6.1 to section 2.6.4).
Knowledge Management models and related strategies are further elaborated in (section 2.7).
The concept and operational definition of Data Mining and related Business Intelligence (BI)
processes will be identified and elaborated in section 2.8. An operational definition of Data
Mining (DM) is established in section 2.8.1. The importance of Data Mining within the SKM
process is explained in section 2.8.2. Also, through section 2.8.3 and 2.8.4 the Data Mining
objectives and benefits are discussed in detail. Major elements and tasks of Data Mining
processes are identified in section 2.8.5. The advantages and disadvantages of Data Mining
will be investigated (section 2.8.6). Integration of Data Mining (DM) within Knowledge
Management (KM) systems and practices as a central concern of in this study, is discussed in
section 2.9. The conception of Strategic Knowledge Management (SKM) is also identified in
section 2.10 with the full SKM framework applied for this study. A pictorial overview of the
elements of Chapter Two is represented below.
23
Figure 2.1: Overview of Chapter Two
SKM
Model
Conclusion
(2.2) Strategy and Strategic Management
(2.2.1)What is Strategy?
(2.2.2) Different ‘Views’ or Perspectives on Strategy
Resource
Based View
(RBV)
Firm
Resources for
Competitive
Advantage
(2.3) Competitive Advantage
(2.3.1) Five Forces model and Sustained Competitive Advantage Based
on MBV
(2.3.2) Firm resources and Sustained Competitive Advantage Based on
RBV
(2.3.3) Competitive Advantage with VRINE model
(2.5) Knowledge Management
(2.5.1)Knowledge Management in Practice
(2.5.2)Benefits of Knowledge Management
(2.5.3)Knowledge Management, Quality Management, Continuous
Improvement, and Best Practices
(2.5.4)Knowledge management and Communities of Practice (Cops)
(2.5.5)Knowledge Management and Virtual Teams (VT)
(2.5.6)Knowledge Management Systems
(2.4) Definition of Knowledge
(2.4.1)What is knowledge?
(2.4.2) Tacit and Explicit Knowledge
(2.4.3) Alternative perspectives on knowledge
(2.7) Knowledge Management Models From the Literature
(2.6) Knowledge Management Defining Characteristics and Processes
(2.6.1)Knowledge Creation
(2.6.2)Knowledge Storage
(2.6.3)Knowledge Transfer
(2.6.4)Knowledge Application
T
Four key
Knowledge
Management
Processes
(2.8)Data Mining Concept, Processes, and Major Elements
(2.8.1) What is Data Mining?
(2.8.2) Importance of Data Mining
(2.8.3) Data mining Objectives
(2.8.4) Data mining Benefits
(2.8.5) Major Elements and Tasks of Data Mining Processes
(2.8.6) Advantages and Disadvantages of Data Mining
Five Major
Elements of
Data Mining
Processes
(2.9) The Role of Data Mining and Business Intelligence in Strategic
Knowledge Management
(2.10) Strategic Knowledge Management
(2.1) Introduction
24
2.2. Strategy and Strategic Management
2.2.1. What is Strategy?
The word of strategy comes from strategos which means “army” and “lead”. The Greek verb
strategos means to “plan the destruction of one's enemies through effective use of resources”
(Bracker, 1980, p. 219). The concept of strategy has featured in military and political thinking
through classical and modern history. After a period of policy driven fiscal growth in the
Western post war economies, rapidly changing social norms and consumer expectations in the
1960s led to a more competitive market-led growth environment. This promoted a more
detailed exploration of the concept and application of strategy in business (Bracker, 1980).
From the 1960s through to the mid-1990s, Strategic Management could be characterized as
“the art and science of formulating, implementing and evaluating cross-functional decisions
that enable an organisation to achieve its goals and objectives” (David, 2011, p. 6). In the
context of large firms, Strategic Management was mainly concerned with the integration of
activities across functions such as - human resources, marketing, finance, accounting,
production, research and development, IT and information systems. David (2011) following
Porters five forces thinking noted: “The purpose of Strategic Management is to exploit and
create new and different opportunities for tomorrow” (David, 2011, p. 6). Hence external
opportunities, threats, internal strengths, and weaknesses are monitored and evaluated.
Traditional perspectives on Strategic Management see it operating at four levels: Corporate
Strategy, Business Strategy, Functional Strategy, and finally Operating Strategy (Thompson &
Strickland, 2003, p. 52).
2.2.2. Different ‘Views’ or Perspectives on Strategy
Contemporary literature goes beyond strategy as planned, linear and market focused and
identifies a spectrum of different, (but not mutually exclusive), views of strategy. These include
the Market-Based View, Stakeholder-Based View, Resource-Based View, and Knowledge-
Based View, which are elaborated below:
- Market-Based View of strategy:
The Market-Based View (MBV), with its focus on achieving an attractive position in a market
or industry sector, can help the organisation to define and select competitive dimensions. In the
25
Market-Based View, senior managers analyse the industry structure for risks and barriers to
entry, competitor activity, and opportunities for effective positioning of goods or services in
specific markets. The MBV approach focuses on the positioning of the organisation, its brand
and products in an industry environment informed by feedback from scanning and analysing
the environment in which the company operates. The Market-Based View has a strong external
orientation using customer, supplier, and external stakeholder feedback to develop an effective
and suitable strategy. The Market Based View of strategy is helpful for strategic and marketing
planning in organisations. (Caves & Porter, 1977; Caves & Porter, 1978; Caves & Porter, 1980;
Makhija, 2003). Since this earlier work Porter has gone onto focus on supply chain networks,
stakeholder relationships and shared value (Porter & Kramer, 2011).
- Stakeholder-Based View of strategy
This view employs stakeholder management and engagement to incorporate social and political
complexity, environmental turbulence and encourage change into a more dynamic, fluid
strategy process (Freeman & McVea, 2001).
The Stakeholder-Based View (SBV) helps managers to incorporate personal values and
orientations, political considerations, and emerging issues into the formulation and
implementation of strategic plans. A Stakeholder orientated approach to Strategic Management
encourages managers to pay attention to the needs of salient stakeholders, who “can affect or
are affected by”, the firms’ goals, issues, programs or activities at a given point in time. This
involves formulating and implementing “…processes that satisfy people who have a stake in
the business” (Freeman & McVea, 2001, p. 10). Managers pay attention to the market but also
surrounding networks of social capital. These hold the embedded knowledge, relationships and
interests of employees, shareholders, managers, customers, suppliers and other groups who can
shape the tactics and longer-term success of the organisation. Thus, the stakeholder approach
emphasises active management for promoting shared interests (Freeman & McVea, 2001).
- The Resource-Based View of strategy:
The Resource-Based View (RBV) of the organisation focuses on building unique internal
capabilities to differentiate organisations seeking to achieve sustainable Competitive
Advantage (Halawi, Anderson, & McCarthy, 2005). The RBV takes an inside-out view, or
firm-specific, perspective for investigating why firms succeed or fail in the market place
(Madhani, 2010; Barney, 1995).
Applying the RBV approach, internal resources are more important than external resources for
achieving Competitive Advantage (David, 2011).Using RBV thinking managers focus on
26
developing human capital, unique knowledge assets and technology into a portfolio of
capabilities focused on future performance. In the RBV approach, firms accept that attributes
relating to past experiences, organisational culture, and competence are critical for the future
success of the firms (Madhani, 2010).
According to David (2011), the RBV also emphasises that the organisational performance will
be supported by internal resources which are categorised in three groups- “physical resources,
human resources, and organisational resources” (David, 2011, p. 96). Physical resources
include- “all equipment, technology, raw materials and machines; human resources include all
employees, experience, intelligence, knowledge, skills and abilities; and organisational
resources include organisation structure, planning process, information systems, patent,
copyright, trademarks and databases” (David, 2011, p. 96). Choosing the right combination of
resources to deal with market and external conditions can help an organisation utilise
opportunities and defuse threats (David, 2011). The attributes of a firm’s physical, human, and
organisational capital enable a firm to realise and implement strategies for improving its
efficiency and effectiveness (Barney, 1991), and by extension “…strategic HR practices may
be viewed as the key to achieving Competitive Advantage.” (Barratt-Pugh, Bahn, & Gakere,
2013b, p. 750). RBV helps managers to understand how they can use the competencies as
important firm assets for improving business performance (Madhani, 2010).
RBV became popular with consultancies and firms concerned with building future facing
portfolios of competence and capability through strategic HRM practices. Hamel and
Prahalad’s (1994) book “Competing for the future” made RBV thinking widely available to
HR managers in the USA and internationally. Whilst Hamel (cited in Scharmer (2009), has
recently qualified the universal application of RBV thinking in the socially complex emergent
environment of firms in the 21st century, this perspective remains influential amongst
academics and practitioners.
Recently, scholars of the Dynamic-Capability View (DCV) have extended the RBV to examine
the influences of dynamic markets. (Lin & Wu, 2014). The dynamic capability concept
enhances RBV theory by identifying the need for managers to make sense of an emergent and
morphing external environment in their efforts to obtain Competitive Advantage (Ferlie et al,
2015, P.129). DCV studies investigate the “attributes, origination, process, and contribution of
the dynamic capabilities”. Most significantly for this study and the SKM and VRIN models
presented in section 2.11 and 2.3.2, Lin and Wu (2014, P.411) claim that dynamic capabilities
significantly mediate Valuable, Rare, Inimitable, and Non-substitutable (VRIN) (see section
2.3.2) resources to improve firm performance.
27
- The Knowledge-Based View of strategy:
Knowledge-Based View (KBV) is integral to current conceptions of the Resource-Based View
(Jashapara, 2011). In the context of post 2009, knowledge and the Web 2.0 information age
RBV puts a knowledge based perspective on strategy centre stage (Ferlie et al, 2015). In
organisations where knowledge is treated as an important intangible resource, this view plays
a significant role for achieving Competitive Advantage.
The Knowledge-Based View revisits. “many tenets of individual knowledge, Organisational
Learning (OL), conversion of knowledge from one form to another and organisational routines
as the potential sources of Competitive Advantage”(Jashapara, 2011, p. 101). Knowledge-
based resources are usually socially complex and hard to copy and imitate, so the KBV of the
firm may support long-term Competitive Advantage in these contexts (Alavi & Leidner, 2001).
Harris and Moffat stated (2013, p348): Both the Resource Based and Knowledge Based View
are concerned with how resources and capabilities are created and deployed through effective
management of human and codified knowledge.
In the Knowledge Based View, knowledge sharing is an essential factor. Establishing systems
and underlying organising principles for collaboration and sharing of tacit knowledge, is a key
challenge to be addressed by senior decision makers in the pursuit of Competitive Advantage
for their organisation. In keeping with other leading scholars and consultants in the field of
KM, Jashapara posits that a major role of the firm (in the 21st century), is to integrate the
existing knowledge into products and services (Jashapara, 2011).
The Knowledge-Based View cultivates systems and practices focused on knowledge creation
and exploitation. Knowledge Management, supported by Organisational Learning (OL)
principles and allied management practices, has been identified by Easterby-Smith & Prieto as
a key factor for sustained Competitive Advantage (Easterby‐Smith & Prieto, 2008) (Bogner
& Bansal, 2007). In this sense Organisational Learning (OL) translates high level Knowledge
Management thinking into day to day management practice (Turner & Makhija, 2006;
Easterby‐Smith & Prieto, 2008).
In a recent commentary Villar et al. (2014) noted that both the Resource-Based View and
Knowledge-Based View have been used to explain the basis of success for businesses. RBV
attempts to describe why one organisation can perform better than another, whereas KBV
focuses on how to develop stocks of explicit and tacit knowledge into an internal portfolio plus
capability to support organisational performance in the short, medium, and long term (Villar,
Alegre, & Pla-Barb, 2014, p. 39).
28
2.3. Competitive Advantage
Competitive Advantage distinguishes firms from their competitors in the minds of customers
and stakeholders (Amadeo, 2012). Firms are able to gain advantage over competitors by
offering customers goods and services that represent value for money, through process,
procurement, and supply chain efficiencies (Attiany, 2014). In the web enabled global
knowledge economy definitions of Competitive Advantage are extending into new ways of
configuring intangibles such as human expertise, brands, and reputation within social and
business networks to gain advantage at key hubs or nodal points within networks and virtual
market places.
In the last two decades, achieving and sustaining Competitive Advantage has been a major
concern of the literature in the field of Strategic Management (Asad, 2012). According to Porter
(2004) Competitive Advantage is at the heart of a firm’s performance (section 2.3.1).
The Strategic Management literature focuses on models and determining factors for
Competitive Advantage seeking to provide case study and empirical evidence to explain why
some firms perform better than others (Carpenter et al., 2010). Carpenter et al. (2010) reframe
the relationship between MBV and RBV to accommodate the more dynamic and fluid market
and environment conditions in the post global financial crisis (2009) period. They offer three
primary perspectives which directly address the creation and application of knowledge as a
capability for the firm. The first one is the internal perspective -which focuses on management
of internal resources as a source of capability. The second one is the external perspective that
refers to the structure of industry sectors. The third one is the dynamic perspective which
incorporates the previous two approaches and focuses management thinking on how best to
exploit emerging opportunities and mitigate threats on a day to day basis (Carpenter et al.,
2010, p. 17-19).
The idea of Competitive Advantage is supported by two distinct perspectives on the nature and
application of strategy. The first perspective is derived from Michael Porters seminal work on
the industry structure, five forces model of strategy, which provided a centerpiece for the
positioning school in 1980. This thinking was increasingly challenged by academics who
emphasised the importance of developing the core capabilities of the organisation, to anticipate
and address future competitive conditions. This was achieved by combining traditional factors
of production with a strategic focus on sourcing, recruiting, developing and retaining key
people or human resources. The development of the approaches is elaborated below:
29
2.3.1. Five Forces Model and Sustained Competitive Advantage Based on MBV
The Five forces model describes the firm’s strategy in relation to its product and market
positioning. According to Porter (2004), the rules of competition are embodied in five forces
(Porter, 2004, pp. 4-5):
1- The entry of new competitors
2- The treat of substitutes
3- The bargaining power of buyers
4- The bargaining power of suppliers
5- The rivalry among the existing competitors
Figure 2.2: Porter Five Competitive Forces Model
Reprinted from (Porter, 2004, p. 5)
Porter’s work sets out the basis for the Market Based View (Delfmann, 2005). In this view, the
sources of value for the firm are embedded in competitive conditions of different industries
and market (Makhija, 2003). The MBV builds on this external orientation, arguably in Porter’s
and his Positioning School followers pre-2000 work, at the expense of internal resource
management as a prerequisite for competitive success. However, this dominant view helped
Potential
Entrance
Suppliers Buyers
Substitutes
Industry
Competitors
Rivalry Amongst
Existing Firms
Bargaining
Power of
Suppliers
Threat of New
Entrance
Threat of substitute
Products or Services
Bargaining
Power of
Buyers
30
senior decision makers to understand industry dynamics and predict the impact of external
factors on the firm’s operating environment for the two decades following the original
development and popularisation of Porter’s five forces model (Gratzer & Winiwarter, 2003).
2.3.2. Firm Resources and Sustained Competitive Advantage Based on RBV
A further conception of Competitive Advantage, is the ability to earn return on investment
above the average for the industry (Halawi, Anderson, & McCarthy, 2005). According to
Mahoney and Pandian (1992) Competitive Advantage is a function of industry analysis,
organisational governance and firm effects in the form of resource advantages and strategies
(Mahoney & Pandian, 1992, p. 375). The RBV analyses internal resources of organisations and
emphasises the use of these in formulating strategy for achieving sustainable Competitive
Advantage (Madhani, 2010). By using these resources, firms are able to develop, manufacture,
and deliver products or services to meet - or exceed - their customer requirements (Barney,
1995). Financial resources include debt, equity, and retained earnings. Physical resources
consist of the machines, manufacturing facilities, and buildings, which firms use for their
operations. Human resources include the knowledge, experience, judgment, and wisdom of
individuals associated with a firm. Human capital assets refer to acquired individual attributes,
skills, knowledge, and other characteristics, which have productive value in workplaces
(Molloy & Barney, 2015). Finally organisational resources include the history, trust, and
organisational cultures that are attributes of groups of individuals related with a firm (Barney,
1995, p. 50).
RBV theory claims that organisations can achieve sustained Competitive Advantage if they
pursue a unique or differentiated strategy not used by any other organisation. For this purpose,
organisations should exploit valuable resources (David, 2011). As originally stated by Barney
(1991) firms seeking Competitive Advantage must deploy resources demonstrating key
characteristics notably Value, Rare, Inimitable, and Non-substitutable (VRIN). This VRIN
framework, (Figure 2.3), was later elaborated by other authors. Notably- Halawi (2005) who
stated organisational knowledge when deployed for competitive purposes should have four
properties, these being- Valuable, Rare, Imperfectly imitable, and Non-substitutable (Halawi,
Anderson, & McCarthy, 2005, p. 80). Madhani (2010) notes that “Competitive Advantage
occurs when there is a situation of resource heterogeneity and resource immobility” (Madhani,
2010, p. 3). In effect resources should not be a freely available and mobile. Collis and
Montgomery (1995) previously developed a similar argument suggesting that valuable
31
resources, which can support superior firm performance, must be hard to replicate and durable
in the sense that the value added does not rapidly depreciate as soon as competitors launch a
counter strategy (Collis & Montgomery, 1995). According to Lee, Tsai, & Amjadi (2012)
managers can build their organisational strategies based on resources which pass this test (Lee,
Tsai, & Amjadi, 2012).
Returning to Barney’s (1991) original conception of VRIN in order to gain Competitive
Advantage the firm’s internal resources must have four attributes pursuant to competitive
superiority: (a) This must be valuable, (b) rare in the market or amongst firms, (c) imperfectly
imitable, and (d) and cannot be replaced by strategically equivalent substitutes (Barney, 1991,
pp. 105-6).
(a) Valuable Resources
Firms using valuable resources are able to implement new strategies for improving efficiency
and effectiveness, improving customer satisfaction, and reducing cost (Madhani, 2010). For
evaluating the competitiveness of the firm’s resources, managers should answer the question:
‘Do the firm’s resources add value by enabling it to exploit opportunities and/or neutralise
threats?’ (Barney, 1995, p. 50). Valuable resources are able to help firms in exploiting market
opportunities or reducing market threats (Madhani, 2010). According to Newbert (2008) if
resources or capacities enable a firm to respond to environmental opportunities and threats and
reduce costs, they are valuable. Firms may also able to improve their performance using the
“Strengths-Weaknesses-Opportunities-Threats” (SWOT) analysis framework, but only when
their strategies exploit opportunities or neutralise threats (Barney, 1991). However, a complete
understanding of internal resources capabilities requires analysis of the internal strengths and
weaknesses of the firm (Barney, 1995, p. 49).
(b) Rare Resources
If the valuable firm resource is being implemented by large numbers of other firms at the same
time, it gives no one firm a Competitive Advantage (Barney, 1991). Resources must be difficult
to find amongst competing firms (Madhani, 2010).
Valuable firm resources can be used to conceive and implement strategies, which require a
particular mix of physical, human, and organisational capital resources (Barney, 1991). If
resources are possessed by several firms in the market place, the other competing firms will be
32
able to implement the strategies, so these resources cannot provide Competitive Advantage
(Madhani, 2010).
(c) Imperfectly Imitable Resources
Imperfect Imitability means it is not easy for competitors to imitate configurations of tacit and
codified intellectual assets that can be converted into tangible process, product, or service
improvements, or innovations (Madhani, 2010). Difficulties in obtaining resources, ambiguous
relationships between capability and Competitive Advantage, or complexity of resources may
contribute to the inimitability of resources (Madhani, 2010).
According to Barney (1991, p 107) the unique configuration of a firm’s resource base is
inimitable subject to: (a) The ability to acquire a resource is dependent upon unique historical
conditions, (b) The link between the resources possessed by a firm and a firm’s sustained
Competitive Advantage being causally ambiguous, or (c) The resource mix generating a firm’s
advantage is socially complex (Barney, 1991, p. 107).
Barney (1991) suggests that the performance of a firm does not depend only on an industry
structure which exists at a point in time, but also on the path a firm follows through history to
arrive at this point in time. Hence, Competitive Advantage is partially determined by historical
patterns of transactions communications and relationships within a dynamic market landscape
and stakeholder network.
(d) Non-substitutable Resource
Non-substitutability (Halawi, Anderson, & McCarthy, 2005; Madhani, 2010) is the last
requirement for configuring a competitive portfolio of internal resources.
Firms with valuable, rare, and imperfectly imitable resources will be able to conceive and
implement effective strategies. If there are no strategically equivalent resources being deployed
by competitors, sustained Competitive Advantage is possible. On the other hand, if there are
strategically equivalent firm resources, the competing firms can implement the same strategies
in a different way using different resources. Subsequently these strategies will not create a
sustained Competitive Advantage (Barney, 1991).
In summary, when firms have Valuable, Rare, Inimitable and Non-substitutable resources, they
are able to develop value-enhancing strategies which are not easily copied by other competing
firms. However, dynamic and disrupted conditions in global markets dictate that firms must
constantly renew and rebuild their strategic capabilities and competencies.
33
2.3.3. Competitive Advantage with VRIN(E) Model
The VRINE model (Value, Rarity, Inimitability, Non-substitutability and Exploitability)
developed by Carpenter et al. (2010), posits that internal resources contribute to Competitive
Advantage to the extent that they satisfy the five competitive requirements of the model. The
VRINE model is an analytical framework designed to help managers determine how to
configure the portfolio of Valuable, Rare, Inimitable, Non-substitutable, and Exploitable
resources are able to gain Competitive Advantage. Managers, or researchers working with this
model, can test the importance of particular resources and the desirability of acquiring new
resources and capabilities (Carpenter et al., 2010). The flowchart (Figure 2.3) illustrates these
relationships (Carpenter et.al, 2010, p. 105-106):
34
Figure 2.3: VRINE Model
Reprinted from (Carpenter et al., 2010, p. 106)
No
Yes
Is it valuable?
Does the resource allow the company to meet
market demand or protect the company from
competitive threats?
Yes
The Company is able to compete in an industry but value by itself does not
directly convey an advantage.
Is it rare?
Is the resource scarce relative to demand or is
it widely possessed by competitors?
No
Valuable resources that are also rare contribute to a Competitive
Advantage, but it is a temporary advantage.
Is it inimitable and/or non-substitutable?
Is it difficult for competitors to imitate or
substitute other resources and capabilities
that yield similar benefits?
No
Valuable and rare resources are also difficult to imitate or substitute and
can contribute to sustainable Competitive Advantages.
Is it exploitable?
Does the company exploit the resources?
No
Yes
The first four VRINE criteria must be exploited to obtain Competitive
Advantage. (In the tested version of this model this assumption was built
into the questions which focused on the first four VRIN elements)
Resource Capability Meets VRINE Requirements for Competitive Advantage
Res
ourc
e C
apab
ilit
y d
oes
not
Mee
ts V
RIN
E R
equir
emen
ts f
or
Co
mpet
itiv
e A
dv
anta
ge
Yes
35
2.4. Definition of Knowledge
2.4.1. What is Knowledge?
The terms- Data, Information, and Knowledge are used synonymously in the literature, while
there are fundamental differences between them should be reviewed. Data is known facts used
as a basis of inference. Data acquired from external environment, as a form of business
intelligence, becomes internal facts (Jashapara, 2011, p. 16). Data can be quantitative or
qualitative. Quantitative data requires an association with something else and it is meaningless
when is taken out of context. Qualitative data is tougher, because it depends on the perceptions
of the receivers. For instance the participants in a meeting may provide many different
descriptions depending on their unique perspectives. (Jashapara, 2011, p. 17). Information is
known as a “systematically organised data” (Meadows (2001) cited in Jashapara (2001, p.17).
The data should be organised through some form of taxonomy or classification scheme to create
a framework for thinking (Jashapara, 2011, pp. 17-18). Information is data that is meaningful
and purposeful. It provides a new point of view for interpreting events or objects (Nonaka &
Takeuchi, 1995). Information may have a subjective meaning (And not necessarily scientific
meaning) given by the receiver of data. Giving meaning to data often occurs through forms of
association with other data. Knowledge is arguably information in action (Kucza, 2001) which
allows people make better decisions and address problems and challenges in organisations
(Jashapara, 2011, p. 18). Knowledge has the active nature represented by terms such as ‘belief’
that is rooted in individual’s value system (Nonaka, Toyama, & Konno, 2000), so it is generated
in human’s mind, so it is very complex (Kucza, 2001). Knowledge is related to human action
and emotion (Nonaka & Takeuchi, 1995) and allows people act more effectively than
information or data and give more ability to predict future outcomes. It occurs by providing
information at the right place, at the right time and in the appropriate format (Tiwana (2000)
cited in Jashapara (2011, p.18)). Data and information are both essential, but knowledge can be
applied and experiences and skills that are used should make the difference between a good
decision and a bad decision (Tiwana, 2002). Drongelen (1996, p214) emphasised knowledge is
information internalised by means of research, study or experience, that has value for the
organisation. In addition, more wisdom and truth are shown to have higher qualities than
knowledge. Wisdom is the ability to act critically or practically in specific situation and based
36
on ethical judgment related to personal beliefs (Dalkir, 2005). A conception of the hierarchy of
data, information, and knowledge is shown in Figure 2.4 below.
Figure 2.4: Data, Information, Knowledge and Purposeful Action
Reprinted from (Jashapara, 2011, p. 19)
2.4.2. Tacit and Explicit Knowledge
The notions of tacit and explicit knowledge are important concepts in the Knowledge
Management literature. According to Jashapara (2011) the contemporary philosopher’s
Gilbert Ryle and Michael Polanyi claim different positions concerning the nature of tacit and
explicit knowledge. Ryle refers to the difference between ‘knowing how’ and ‘knowing that’.
Intelligence is associated with the ability of a person to perform tasks. But ‘knowing that’ is
holding knowledge in person’s brain. Therefore, ‘knowing how’ cannot be defined in terms
of ‘knowing that’ (Jashapara, 2011, pp. 42-43). Michael Polanyi claims tacit knowledge
comes from a number of experiments. As Polanyi (cited in Jashapara (2011, p.42)) established,
‘We can know more than we can tell’. Indeed, tacit knowledge is embedded and should be
passed between people. However it takes a long time to acquire knowledge through learning
by doing (Harris & Moffat, 2013, p. 349).
Truth
Wisdom
Knowledge
Information
Data
Truth
Wisdom
Knowledge
Information
Data
37
Figure 2.5: Philosophy of Gilbert Ryle and Michael Polanyi
Reprinted from (Jashapara, 2011, p. 43)
Explicit knowledge can be shared in a variety forms of data and expressed in formal and
systematic language easily (Nonaka, Toyama, & Byosiere, 2001). It can be codified and
transferred in a large group easily (Dyer & Nobeoka, 2000), expressed in formal and
systematic language, and shared in the form of data (Nonaka, Toyama, & Konno, 2000).
Therefore explicit knowledge is defined as verbalised and articulated. On the other hand, tacit
knowledge is highly individual and hard to formalise. It is difficult to communicate to others
(Nonaka, Toyama, & Byosiere, 2001). Tacit knowledge may be transferred only in a small
group based in the specific location where it is used (Dyer & Nobeoka, 2000). Consequently,
tacit knowledge is rooted in action, routines, emotion, ideals, and commitment (Nonaka,
Toyama, & Konno, 2000).
Westerners tend to favor explicit knowledge, whereas the Japanese tend to view knowledge as
tacit. Both types of knowledge are complementary and vital to knowledge creation. If
organisations focus on explicit knowledge, this can lead to paralysis by analysis. On the other
hand an extreme focus on tacit knowledge places too much confidence in past successes
(Nonaka, Toyama, & Byosiere, 2001). By analysing experiences, one understands meaning
which can transform to the next experience. In this way, tacit knowledge and explicit knowledge
interchange with each other (Nonaka, Toyama, & Byosiere, 2001).
In the Nonaka et al widely cited SECI knowledge creation and conversion model here four
modes of interaction and conversion between tacit and explicit knowledge notably-
Socialisation, Externalisation, Combination, and Internalisation (Nonaka, Toyama, & Konno,
SECI, Ba and Leadership: a Unified Model of Dynamic Knowledge Creation, 2000; Nonaka,
Toyama, & Byosiere, 2001). (See section 2.6.1.1 for detailed discussion of SECI Model)
Continuum
Knowing How
Intelligence
Activity orientation Ability
to perform task
Knowing That
Possessing knowledge
container metaphor
being
Tacit Knowledge
(doing) Explicit Knowledge
(being)
RY
LE
P
OL
AN
YI
38
Knowledge is created through a dynamic interaction between tacit and explicit knowledge.
(Nonaka, Toyama, & Byosiere, 2001). The transforming processes are socialisation like
everyday friendship, externalisation such as formalising knowledge, combination existing
codified and tacit knowledge, and internalisation arguably translating theory into practice
(McAdam & McCreedy, 1999). The four modes of knowledge creation allow us to
conceptualise the actualisation of knowledge with social institutions through a series of process
(Lee M. C., 2010).
2.4.3. Alternative Perspectives on Knowledge
Knowledge can be investigated from five perspectives (Alavi & Leidner, 2001, pp. 109-110).
(1) A state of mind, (2) an object, (3) a process, (4) a condition of having access to information,
or (5) a capability.
State of mind focuses and enables an individual’s knowledge and applies it to the organisation’s
needs. The second view of knowledge is as an object where knowledge can be viewed as a thing
to be stored and manipulated. The process perspective focuses on the applying of expertise as
a third party. A fourth view of knowledge is conditional to access of information. It focuses on
organising and facilitating access to key sources of information and intelligence. Finally, using
the last perspective, knowledge is viewed as a capability with the potential for influencing
future action (Alavi & Leidner, 2001).
Effective knowledge processes, supported by sharing of tacit and explicit knowledge,
appropriate technology and cultural environments, served to enhance an organisation’s
Intellectual Capital (IC) and improve organisational performance (Jashapara, 2011, p. 14). In
this way knowledge can be a source of Competitive Advantage.
These various perspectives of knowledge lead to different perceptions of Knowledge
Management which will be discussed below.
2.5. Knowledge Management
2.5.1. Knowledge Management in Practice
Knowledge Management is defined by Jashapara (2011, p.14): as “the effective knowledge
processes associated with exploration, exploitation and sharing of human knowledge (tacit and
39
explicit) that use appropriate technology and cultural environments to enhance an
organisation’s Intellectual Capital and performance”. Skyrme (2001) defined Knowledge
Management as “the explicit and systematic management of vital knowledge – and its
associated processes of creation, organisation, diffusion, use and exploitation in pursuit of
organizational objectives” (Skyrme, 2001, p. 6).
The Knowledge Management process helps the organisation define, select, organise, distribute,
and transfer information, knowledge and expertise which remained in the organisation’s
memory in an unstructured manner (Turban & Volonino, 2010, p. 392). Knowledge
Management is able to increase useful knowledge within an organisation by offering
opportunities to learn and promote the sharing of suitable knowledge (Silwattananusarn &
Tuamsuk, 2012). The major goal of Knowledge Management is to provide opportunistic
application of fragmented knowledge through integration (Tiwana, 2002).
According to Tiwana (2002) Knowledge Management falls in the domain of information
systems and management (of people), not in computer science (Tiwana, 2002). It is about
process, not digital networks. Knowledge Management includes development and innovation
of business processes, that are able to produce efficiencies and effective performance
outcomes in many different types of organisations (Tiwana, 2002). Knowledge Management
is a conscious orchestration and integration strategy for getting the right knowledge to the
right people at the right time and improving organisational performance by sharing and putting
information into action (Halawi, Anderson, & McCarthy, 2005).
Effective Knowledge Management improves operational efficiency, enhances products and
services and creates customer satisfaction (Lee M.-C. , 2009). Knowledge Management using
an organisation’s intellectual assets, is (contingent on other Competitive Advantage (CA)
conditions highlighted in this study), a significant leverage point for sustaining CA (Halawi,
Anderson, & McCarthy, 2005).
2.5.2. Benefits of Knowledge Management
Knowledge Management has many potential benefits. Some of the most important of
are listed below (Skyrme, 2001, p. 3):
- “Faster access to knowledge
- Better knowledge sharing
- Cost saving
- Cost avoidance
40
- Increased profitability
- Less down-time for maintenance and refurbishment
- Shorter time-to-market
- Improved customer relationships
- Faster revenue growth
- New business opportunities”.
Two types of companies may be interested in using Knowledge Management. The first type
is one that needs to remain in the competitive market place and the second refers to
organisations that use knowledge to keep ahead, not just viably compete (Tiwana, 2002).
2.5.3. Knowledge Management, Quality Management, Continuous Improvement and
Best Practices
Ribière and Khorramshahgol (2004, p40) believe that KM can play an important role in
improving quality and customer satisfaction. Allied change methods such Total Quality
Management (TQM) strive to achieve sustainable organisational success by encouraging
employee feedback, satisfying customer expectations, respecting societal values, and obeying
governmental statutes (Ribière & Khorramshahgol, 2004, p. 43).
TQM relies and focuses on quality improvement in all functional and operational areas at all
levels of organisation for achieving customer satisfaction, while KM focuses on knowledge as
a source of Competitive Advantage (Zhao & Bryar, 2001; Loke et al., 2011). Both KM and
TQM are useful for organisations. The aim of both is improving the work processes of the firm
to better serve customers (Loke et al., 2011). The literature indicates that Knowledge
Management and some conceptions of TQM have some common goals although as revealed in
the case study there is a clearer complementarity between KM and Continuous Improvement
(CI) principles and processes. It can be argued that organisations, which have incorporated KM
concepts into their management routines or organisational processes, are likely to demonstrate
a more mature approach to Quality Management and Continuous Improvement (CI) will be
achieved (Zhao & Bryar, 2001; Moballeghi & Galyani Moghaddam, 2008). With knowledge
based TQM, Continuous Improvement (CI) and learning will be facilitated (Moballeghi &
Galyani Moghaddam, 2008). In simple terms, Continuous Improvement (CI) consists of
improvement initiatives for enhancing success and reducing failures (Bhuiyan & Baghel,
2005). Costin (1999) defines Continuous Improvement (CI) as “The integration of
41
organisational philosophy, techniques, and structure to achieve sustained performance
improvements in all activities on an uninterrupted basis” (Costin, 1999, p. 48).
According to Schiuma & Lerro (2012) to improve organisational performance, an organisation
needs to continuously improve its effectiveness and efficiency. Competences which are rooted
in organisational knowledge assets can contribute to the Intellectual Capital (IC) base of the
organisation (Schiuma & Lerro, 2008, pp. 3-4). Intellectual Capital management should
therefore play an important role in organisational process improvement (Schiuma & Lerro,
2008, p. 8). Whilst these and other authors of various conceptual studies provide a useful
overview of the potentially virtuous relationship between TQM, CI, and KM, this is not widely
supported by empirical evidence. The relationship between TQM, CI, and KM is pertinent, but
not central to our case study which provides analysis and supporting empirical evidence of the
contribution of appropriate designed and configured Knowledge Management Systems (KMS)
to the Competitive Advantage of the case company (see section 6.3.2).
One of the most popular tools for Continuous Improvement (CI) is benchmarking. It is a
method for identifying new ideas and ways of improving processes. The ultimate objective of
benchmarking is a process improvement that meets the customer requirements, needs, and
expectations. Also benchmarking can find and implement Best Practices in the business
(Elmuti & Kathawala, 1997). According to Barczac and Khan (2012) practitioners are aware
of Best Practice prescriptions made by benchmarking (Barczak & Kahn, 2012, p. 304). Best
Practices refer to the replication of an internal practice which is performed in superior way in
some part of firm (Szulanski, 1996, p. 28). During the late half of the 1990s the identification
and transfer of Best Practices was widely recognised as a key concern for managers in global
or large national manufacturing and services firms (Szulanski, 1996). Experience shows that
transferring capabilities between a firms, business units or operations can be very complicated.
With this in view Strategic Management research has examined obstacles to the transfer of
Best Practices (Szulanski, 1996). Internal benchmarking and transfer of Best Practices play
important roles in the expression of Knowledge Management (O'Dell & Grayson, 1998).
Identifying and transferring Best Practices can be very time consuming. Szulanski (1996),
found that a Best Practice might be unrecognised for years and even after recognition could
take took more than two years before other sites started trying to adopt this practice (O'Dell &
Grayson, 1998). Benchmarking is a crucial tool for evaluating performance. Internal and
external benchmarking is a critical step in recognising performance gaps which leads to
breakthrough performance improvements (Welborn & Kimball, 2013). In external
benchmarking prominent practices are identified, understood, and adapted from others (O'Dell
42
& Grayson, 1998). However in the broader context of Knowledge Management and innovation
in ICT enabled organisation networks, best practices and benchmarking can be seen to limit
KM and innovation by focusing on improvements to existing systems with associated
underlying assumptions and worldviews. This limitation is elaborated in the discussion of
Double Loop Learning, Triple Loop Learning, K2 versus K3 knowledge. (See section 2.6.1.4
and 2.6.2 for the detailed discussion)
Before transferring Best Practices, defining and finding them is necessary. Some organisations
have specific mechanisms informed by R&D experts for identifying and spreading practices.
Unfortunately they do not always work.
2.5.4. Knowledge Management and Communities of Practice (CoPs)
Etienne Wenger whose work formalised and popularised the use of Communities of Practice
in organisations around the world, observed that CoP and KM both require a receptive
organisational culture and context to coordinate knowledge stocks and flows and integrate
them into business processes (Wenger, 2004). He argued that organisations need to involve
specialists and practitioners actively in the process for managing knowledge assets. Also
practitioners need to share their individual expertise and knowledge in fields too complex for
single individuals to cover (Wenger, 2004). This is where Communities of Practice play an
important role. Communities of Practice, which are known under various names such as
learning networks, thematic groups, or ‘tech clubs’, are groups of people who share a passion,
idea, insight, and concern for something they do and learn how to do it better as they interact
regularly (Wenger, 2011).
The Community of Practice (CoP) is not only a network of connections between people, it is
defined by a shared domain of interest building relationships that enable people to learn from
each other. Through participation in Communities of Practice, people develop innovative
practices and exemplar cases to inform future practices.
As Wenger (2004) established “…communities of practice are the cornerstones of Knowledge
Management.” (Wenger, 2004, p. 2)
Communities of Practice; previous research in the case company:
According to Gupta (2012), who conducted a previous study exploring dynamic capabilities,
virtual teams and Communities of Global Best Practice in the case organisation (Chapters
Three to Six), CoP meetings are one of the major platforms for managing knowledge activities
43
across the researched company (Gupta A. , 2012, p. 90). According to his research, some
communities have identified their long term goals and objectives across sites and continue to
work towards those objectives (Gupta A. , 2012, p. 112). Indicators and attributes of
community performance included implementation of new practices and ideas, effective
problem solving, and timely conflict- resolution (Gupta A. , 2012, p. 92). CoP were also seen
to contribute to operational performance as an outcome of their dynamic knowledge creation
capability. Each CoP generates new operational Best Practices. These have to pass a rigorous
assessment (‘sanctioning’) by the company’s senior technical group. Confirmed operational
Best Practices were deemed suitable for implementation across the company’s operation
(Gupta A. , 2012, p. 99).
In the case company, CoP members learn as individuals and as part of groups. Members of the
community may also undertake coaching to improve their interpersonal and communication
skills (Gupta A. , 2012, p. 108). Members of these communities are encouraged to undertake
their work-related travel to other national and international locations, meet people, and visit
other plants of company. Also they may attend company conferences, workshops, and seminars
as the opportunities arise. All these activities would be helpful for developing their learning
and accumulation of knowledge (Gupta A. , 2012, p. 109).
According to Gupta (2012) collective knowledge sharing and learning between CoP members
is undertaken via a number of channels including teleconferences, videoconferences, Net
Meetings, WebEx, and the discussion board on their community web-portal is used (Gupta A. ,
2012, pp. 110-112). A typical conference meeting runs for about 60-90 minutes once a month.
The leader of the community facilitates discussion among the members in concert with the
Chair of the meeting. Through this process staff can join in conversations from various
locations across five continents. Minutes of the meeting are noted and preserved on the
community portal in most communities. Sometimes representatives of external stakeholders
like customers, and suppliers, experts within and outside of the company, are invited as guests,
to provide presentations around specific topic. Gupta also emphasised the communities are
open to outside views and knowledge inputs.
Members of COP ensure that generated best practises are piloted at their respective sites. They
occasionally observe the implementation of practices generated, by being physically present at
the work-site. Then they discuss and review the implementation process and results in
community meetings, so they can update their documents if required. These activities help
them to improve their respective understanding of targeted projects and operations and promote
knowledge internalisation (Gupta A. , 2012, pp. 116-7).
44
2.5.5. Knowledge Management and Virtual Teams (VT)
In some research studies the concept of a virtual Team (VT) often overlaps with concepts such
as virtual network organisations, virtual workplace, or virtual communities (Kimble, Li, &
Barlow, 2000). According to Kimble and his colleagues (2000, p.3) a Virtual Team is a micro-
level form of work organisation in which a group of geographically dispersed workers
undertake a specific organisational task or problem solving exercise supported by ICT.
According to Wipawayangkool (2009, P.327) members in virtual teams share a common
purpose, but they might be separated by distance, time, and organisational boundaries. Hence
they may have few physical interactions with collaborative activities undertaken in a virtual
space. Wipawayangkool (2009, P.325) claims that virtual teams enable to significantly enhance
the value of Knowledge Management Systems and practices. Virtual Teams perform well in
both knowledge creation and overall team effectiveness. (Wipawayangkool, 2009, pp. 325-7).
Global Virtual Teams (GVTs) in the case company:
In the case organisation, GVT have defined goals such as: Best Practice implementation, Best
Practice documentation, training, and knowledge stewarding. GVT report regularly to senior
management on scalable benefits achieved against agreed strategic and operational goals. In
the case company GVTs act as meta-knowledge managers consulting or seconding members
from different Communities of Practice to deal with specific problems and situations (Grey,
2015).
In the case of both CoP and GVT culture is the glue that holds together Knowledge
Management activities. Culture, values and allied rewards ultimately shape GVT and CoP
members’ knowledge sharing behavior, and influences how they learn (Wiewiora et al, 2013).
Developing a supportive culture within the organisation is essential to cultivate collaboration
and successfully grow the knowledge base of organisation (Arshad & Scott-Ladd, 2010, p.
102).
2.5.6. Knowledge Management Systems
Knowledge Management Systems (KMS) refer to a class of information systems for managing
organisational knowledge. KMS can serve as a repositories and dissemination centres for the
collated knowledge (Barratt-Pugh, Kennett, & Bahn, 2013, p. 23). These IT-based systems
45
support and enhance the organisational processes of knowledge creation, storage/retrieval,
transfer, and application, so many Knowledge Management initiatives rely on IT as a
significant enabler (Alavi & Leidner, 2001).
Information technologies are able to play an important role in enacting and realising the
Knowledge Based View of the organisation (Alavi & Leidner, 2001). Information technology
is able to facilitate Organisational Learning (OL) and Knowledge Management (KM) (Issa &
Haddad, 2008). Advanced information technologies such as internet, intranets, extranets,
browsers, data warehouses, and Data Mining technologies can systemise and enhance
Knowledge Management performance (Alavi & Leidner, 2001; Issa & Haddad, 2008).
The role of IT is to provide a link among sources of knowledge to create depth of knowledge
flows.
2.6. Knowledge Management Defining Characteristics and Processes
Knowledge Management (KM) is the central part in the learning process, which consists of
acquisition and exploitation of knowledge, so it is essentially the creation and application of
knowledge as a resource (Villar, Alegre, & Pla-Barb, 2014). Knowledge Management is the
process of identifying, capturing, organising and disseminating the intellectual assets in the
organisation (Debowski, 2006). Many definitions of Knowledge Management refer to creating
intangible assets for organisations. Other definitions focus on sharing and distributing
knowledge within organisations.
Holzner and Marx (1979) as early researchers in the field divided the Knowledge Management
process into four steps: consciousness, extension, transformation, and implementation (Holzner
& Marx, 1979).
Pentland (1995) proposed five stages for the Knowledge Management process: construction,
organising, storing, distributing, and applying (Pentland, 1995).
Nonaka and Takeuchi (1995) divided the process of Knowledge Management to four stages:
creation, access, dissemination, and application (Nonaka & Takeuchi, 1995).
Ahmed, Lim and Zairi referred to four stages in the Knowledge Management process: Plan,
Do, Check, and Act (PDCA). The ‘plan’ stage refers to the capture and creation of knowledge.
In second stage ‘do’ with using communicational tools sharing knowledge is done. In next
stage ‘check’ is the measurement of the effects. The learning and improving is related to ‘act’
in the PDCA cycle (Ahmed, Lim, & Zairi, 1999).
46
Alavi & Leidner (2001) mentioned there are four Knowledge Management processes such as:
knowledge creation, knowledge storage/retrieval, knowledge transfer, and knowledge
application. This view of organisations as knowledge systems represents both social nature of
organisational knowledge and the individual’s cognition of knowledge (Alavi & Leidner, 2001,
p. 115)
Darroch (2003) divided Knowledge Management processes in three parts: acquisition,
dissemination, and the use or responsiveness to knowledge. Acquisition refers to the
development process and creating insights. In the dissemination stage, sharing acquired
knowledge is done. The use of knowledge is regarded as the capacity of the organisation in
applying knowledge generated (Darroch, 2003).
Chen (2005) defined the four processes of Knowledge Management: knowledge creation,
knowledge conversion, knowledge circulation and knowledge completion. Knowledge creation
is related to add intangible assets. Knowledge conversion refers to individual and
organisational memory. Knowledge circulation focuses on exchanging knowledge between
source and receiver. And finally the knowledge completion is that the source of Competitive
Advantage resides in the knowledge itself (Chen & Chen, 2005).
Lee (2005) noted the knowledge circulation process in five stages: knowledge accumulation,
knowledge sharing, knowledge utilisation, and knowledge internalisation (Lee, Lee, & Kang,
2005).
Following a detailed review of Knowledge Management processes and models from the
literature a number of similar steps were identified. However a number of different terms were
used by the various authors to characterise KM processes and characteristics. See Appendix F
for more details on prominent KM models in the literature and Figure 2.6 below:
47
Figure 2.6: Knowledge Management Processes
Four basic Knowledge Management processes (Alavi & Leidner, 2001, p. 115), which feature
in the other Knowledge Management (tacit and explicit) hard and soft KM models reviewed in
this chapter. Typically these processes included a series of interdependent activities bundled
around-: “Knowledge Creation”, “Knowledge Storage”, “Knowledge Transfer”, and
“Knowledge Application”.
2.6.1. Knowledge Creation
Organisational knowledge creation focuses on developing new content or replacing existing
content through the organisation’s tacit and explicit knowledge (Alavi & Leidner, 2001). It
According to Nonaka et al (2000) who are commonly considered to have developed the most
influencial knowledge creation model, to be successful the process requires a continuous
dynamic interaction between tacit and explicit knowledge through each of the four modes of
knowledge conversion (Socialisation, Externalisation, Combination, and Internalisation)
(Nonaka, Toyama, & Konno, 2000).This view refers to continual interplay between the tacit
and explicit knowledge (see section 2.4.2). It also involves a growing spiral flow as knowledge
moves through individual, group, and organisational levels (Alavi & Leidner, 2001).
To understand how organisations create knowledge dynamically, four layers of knowledge
creation are explained below. These three layers are (1) SECI model, (2) ba the platform for
knowledge creation, and (3) knowledge assets (or the inputs, outputs and moderators of the
knowledge creation process), and (4) New thinking in KM (Scharmer’s K1 to K3) (Nonaka,
Toyama, & Byosiere, 2001; Nonaka, Toyama, & Konno, 2000; Scharmer, 2009).
Knowledge Creation
Knowledge Storage
Knowledge Transfer
Knowledge Application
Holzner & Marks (1979) Consciousness Extension Transformation Implementation
Distribution Application Pentland (1995) Construction Organising - Storage
Alavi & Leidner (2001) Knowledge Creation Knowledge Storage Knowledge Transfer Knowledge Application
Darroch (2003) Acquisition ----- Dissemination Use knowledge
Chen (2005) Knowledge Creation Knowledge Conversion Knowledge Circulation Knowledge completion
Construction Distribution Application Nonaka & Takeuchi
(1995) Organising - Storage
Creation Accumulation Sharing Utilisation &
Internalisation
Lee (2005)
48
2.6.1.1. The SECI Processes: Four Modes of Knowledge Conversion
According to Nonaka, Toyama, & Konno (2000) there are four modes of knowledge conversion
outlined below (Nonaka, Toyama, & Konno, 2000, pp. 9-10):
Socialisation:
Socialisation is the process of creating tacit knowledge through share common experience
(Lee M. C., 2010). Tacit knowledge is difficult to formalise but through shared experience,
such as spending time together or living in the same environment, tacit knowledge can be
acquired (Nonaka, Toyama, & Konno, 2000). In this process experiential knowledge assets
are created by sharing tacit knowledge (Nonaka, Toyama, & Byosiere, 2001).
Externalisation:
Externalisation is the process of converting and articulating of tacit knowledge into explicit
knowledge (Nonaka, Toyama, & Konno, 2000). In this process, new explicit knowledge is
created by sharing tacit knowledge and it is the key to knowledge creation (Nonaka, Toyama,
& Byosiere, 2001). Metaphors, analogies, and models support the successful conversion of
tacit knowledge into explicit knowledge (Lee M. C., 2010). When explicit knowledge is
articulated, conceptual knowledge assets are created (Nonaka, Toyama, & Byosiere, 2001).
Conceptual knowledge assets, which are easier than experiential knowledge assets, are built
through a process of externalisation (Nonaka, Toyama, & Byosiere, 2001).
Combination
Combination is the process of converting elements of explicit knowledge into more complex
sets of explicit knowledge (Nonaka, Toyama, & Konno, 2000). Nonaka et al. (2001) establish
combination includes three processes. First, explicit knowledge is collected from inside or
outside the organisation and combined with each other. Second, the new created explicit
knowledge is diffused through members of organisation. Third, the created explicit knowledge
is processed in organisation for making it more usable (Nonaka, Toyama, & Byosiere, 2001,
p. 497).
Internalisation
Internalisation is a process of converting explicit knowledge to tacit knowledge. When
knowledge is internalized to become a part of human’s tacit knowledge bases in the form of
technical know-how, so it becomes a valuable asset (Nonaka, Toyama, & Konno, 2000). This
tacit knowledge accumulated at the individual level and shared with other individuals in
49
socialisation process (Nonaka & Takeuchi, 1995), as set off a new spiral of knowledge
creation.
2.6.1.2. Ba: Shared Context in Motion for Knowledge Creation
For enhancing organisational knowledge creation understanding the characteristics of ba and
the relationship with the modes of knowledge creation is important (Alavi & Leidner, 2001).
ba is defined as a shared context in which knowledge is created and utilised. Ba provides the
place to perform the individual conversions to moving along knowledge spiral and is a place
(not just a physical space, but specific time and space) for interpreting information to become
knowledge (Nonaka, Toyama, & Konno, 2000). Ba sets an open boundary for interactions
between individuals and now it is an open place where participants can share their own
contexts. Also ba allows individuals share time and space which are important in knowledge
creation, especially in Socialisation and Externalisation (Nonaka, Toyama, & Konno, 2000).
According to Nonaka and his colleagues (2000, p16-17) established there are four types of ba
such as: “Originating ba” which is defined by individual and face to face interaction and shared
experiences; “Dialoguing ba” is defined by collective and face to face interactions, where ba
serves as a virtual or physical place where individuals share their mental models; “Systemising
ba” which is defined by collective and virtual interactions, so in this stage explicit knowledge
can be easily transmitted to a people in written form (IT can offer virtual collaborative
environment for the creation of systemising ba). “Exercising ba” is defined by individual and
virtual interactions, so individuals embody explicit knowledge which is communicated through
virtual Medias (Nonaka, Toyama, & Konno, 2000).
2.6.1.3. Knowledge Assets
Knowledge assets are the base of knowledge creating processes. Nonaka and his colleague
(2000) categorised knowledge assets into four major types: “Experiential knowledge assets”
which include of the shared tacit knowledge which is built through shared experience among
individuals. “Conceptual knowledge assets” consist of explicit knowledge which is articulated
through images and languages. “Systematic knowledge assets” include systemised explicit
knowledge like manuals and documents. “Routine knowledge assets” consist of the tacit
knowledge which is routinised in the actions and practices of the organisation (Nonaka,
Toyama, & Konno, 2000, pp. 21-22).
50
Figure 2.7: Three Layers of the Knowledge-Creation Process
Reprinted from (Nonaka, Toyama, & Byosiere, 2001, p. 493)
Socialisation
Originating Ba
Experimental knowledge assets
Externalisation
Interacting Ba
Conceptual knowledge assets
Internalisation
Exercising Ba
Routine knowledge assets
Combination
Cyber Ba
Systematic knowledge assets
Figure 2.8: Combination of Components of Layers of Knowledge Creation
The use of modern IT can be helpful for enhancing efficiency of the combination mode of
knowledge creation (Alavi & Leidner, 2001). Computer-mediated collaboration can enhance
the quality of knowledge creation by sharing belief and new ideas. An intranet can be used to
present online organisational information both horizontally and vertically. It can support
personal learning in conversion of explicit knowledge to tacit knowledge. Data warehousing
and Data Mining, documents repositories may be of great value in cyber ba (Alavi & Leidner,
2001).
2.6.1.4. New thinking on Knowledge Creation: Scharmer’s S1/K1 to S3/K3 Model
Otto Scharmer’s (2009) Theory U model encapsulates new thinking in the field of KM. This
model emphasises personal transformation of the individual manager or leader, as a
prerequisite for effective KM, organisational or societal change. His radical conception of the
new generation of KM thinking calls for a fundamental shift in the worldviews of
organizational community and political leaders attuned to the unfolding crisis in the world
financial markets, climate and eco systems. From an ontological perspective this thinking goes
beyond the assumptions and underlying paradigms of the KM models reviewed below. As
SECI
Knowledge assets
Ba (platforms for
knowledge creation)
Moderate
In Out
51
illustrated in Figure 2.9, Scharmer identifies the need for increasingly sophisticated leadership
thinking and Knowledge Management processes as the complexity of the social environment
escalates. Scharmer refers to S1 to S3 levels of social complexity which require K1 to K3
leadership thinking, knowledge assimilation and often radical departures from past thinking
and practices to ensure short and long term survival in an increasingly disrupted context.
Scharmer ’s model also differs from SKM in that it operates on the periphery of organisations
and institutions whereas SKM operates at the core as a framework to integrate operational,
strategic and meta-knowledge. Scharmer’s (2009) thinking was influenced by Nonaka and his
co-developers of SECI and the knowledge creating company (Scharmer, 2009, p. 70). Nonaka
and Scharmer’s views on the process and situated nature of knowledge creation display
similarities, particularly in relation to ‘ba’ as a philosophical, physical, and /or virtual safe
space for knowledge creation and dissemination and the idea of different worldviews fostering
different level of knowledge (K1 toK3). Scharmer’s (2009) K1/S1 to K3/S3 framework
emphasizes that when confronted with increasing degrees of social complexity (S1-S3), leaders
of commercial organisations, institutions and communities need to adopt a deep
‘presencing,’and sense-making approach (Scharmer, 2009, p. 227). This helps leaders to
anticipate emergent knowledge and integrate it across functional divisions, and complex
stakeholder networks. The shift from K1 to K3 is achieved when leaders and their key
managers, decision makers or followers hold a shared worldviews. These are developed by
individuals through personal reflection and insight into their own thoughts, behaviors, and the
complex ecosystems, which they inhabit and affect (this is similar to the change in focus and
assumptions which occur with a shift from Double Loop Learning to Triple Loop Learning
(see section 2.6.2)). Whilst the SKM model and deep case research undertaken for this study
is based on less ontologically ambitious grounds than S3/K3, both Scharmer and Nonaka’s
advanced views of social complexity and knowledge as a living thing that cannot be managed,
are acknowledged as possible future influences on knowledge leadership and collaboration for
organizational and societal advancement, in the implications and recommendations of this
study (Scharmer, 2009, pp. 106-8).
52
K1
Explicit Knowledge:
Independent of
Context
K2
Tacit Embodied
knowledge:
Situated in context
K3
Self-transcending
“primary knowing”:
Not Yet Embodied
S1
Linear systems
Simple systems
“Old mainstream”:
Conventional
systems theory
Situated action: all
knowing happens in
a context
Blind spot:
Source of knowing
S2
Nonlinear, dynamic
systems
Autopoietic systems
Nonlinear, dynamic
systems theory:
Accounts for the
phenomenon of
emergence
“New mainstream”:
Accounts for both
emergence and being
situated in context
S3
Source of deep
emergence Self-
transcending
systems
Blind spot: source of emergence
Figure 2.9: Twentieth- Century Systems Theory: Epistemological and Ontological Grounding
Reprinted from (Scharmer, 2009, p. 107)
In the left-hand corner of the model, there is the old mainstream systems theory (S1) grounded
in linear systems and explicit knowledge (K1). There is a progression in two directions: from
S1 (linear systems) to S2 (non-linear systems) accounting for the phenomenon of emergence;
and from K1 (explicit knowledge) to K2 (tacit knowledge), accounting for the fact that all
knowledge is situated and embedded in context. The model highlights a shift in in social
systems theory from (S1, K1) to (S2, K2) which accounts for both emergence and being
situated in context. This is characterised as the “New mainstream” (Scharmer, 2009, pp. 106-
7). The implications of this model and K3 perspectives for the case company and the practice
of KM are discussed in Chapter Six. .In terms of the implications of S3/K3 for global
processing and manufacturing Scharmer (2009), argues there is a need for a radical shift from
Midstream to Upstream thinking and modes of operating as a response to emerging complexity
in the environment. This shift is characterised by “a collapse of boundaries between functions”
and a need for more effective integration of knowledge across different operations and
divisions. To facilitate this shift, the case company needs to cultivate different management
53
skills and mind sets to support knowledge creation and “resilience, profound, renewal, and
change” (Scharmer, 2009, pp. 65-6).
2.6.2. Knowledge Storage
Empirical research shows after creating knowledge, the storage of knowledge is necessary. The
storage and retrieval of organisational knowledge also referred to as organisational memory
incorporates different elements like structured information stored in electronic databases,
written documentation, codified human knowledge stored in expert systems and documented
tacit knowledge, is shared and recorded individuals (Alavi & Leidner, 2001).
According to Alavi and Leidner (2001) there are two kinds of memory such as individual
memory and organisational memory. Individual memory focuses on experiences, observations,
and actions. On the other hand collective or organisational memory focuses on person’s
experiences, and contextually situated activities (Stein & Zwass, 1995). Organisational
memory includes- “Organisational culture, transformations work procedure and production
process organisational, structure organisational roles, internal and external information
archives, and physical work settings” (Alavi & Leidner, 2001, p. 118).
Memory may have some positive and negative sides. Solutions and operational or project
experiences can be captured and in some cases related to standards to avoid replicating work.
Equally organizational or cultural memories can also have a negative effect on individual or
organisational performance. Individual level memory and embedded assumption, may create
decision- making bias or reinforce legacy routines through single loop learning (Argyris,
Smith, & Hitt, 2005).
Single Loop Learning (SLL) and Double Loop Learning (DLL) have acquired significant
resonance in cognitive science, reflective practice, and Organisational Learning (OL)
(Reynolds, 2014). In the first stage (SLL) the problems are structured and single-loop learning
is experienced. In second stage (DLL) the problems are seen as a moderately structured. In the
third stage of Triple Loop Learning (TLL) the problems are completely unstructured. These
problems are seen more as an ideological, and systemic requiring complex learning loops in
the context of mutual distrust (Gupta J. , 2016). TLL derives cybernetic (Reynolds, 2014). It
does not come easy, it requires a transition from a situation of low trust to a situation of high
trust (Gupta J. , 2016). TLL address the political dimensions behind learning. It goes beyond
looking at “what is the right thing”. Reynolds (2014, P382) noted: “This third loop of learning
suggests coercive relations of power associated with either domination of decision making
54
“might” – a relationship of “power-over” – or conversely knowledge-based sense of what’s
“right” – a relationship of “power-with””. The TLL domain represents wisdom and
corresponding with learning in cybernetics and reflection on boundary judgments (Reynolds,
2014).
Computer based information system can play significant role for enhancing organisational
memory (Pentland, 1995). Advanced computer storage technology such as query languages,
multimedia databases, and data base management systems can be effective for increasing the
speed at which organisational memory can be accessed (Alavi & Leidner, 2001). With
document management technology organisations can access to past organisational knowledge
which is distributed among variety of facilities (Alavi & Leidner, 2001). Knowledge can be
stored in data bases, lessons learnt documents, and project reports as contextual facts
(Wiewiora et al, 2013).
2.6.3. Knowledge Transfer
Knowledge transfer is an important process in Knowledge Management. A significant issue in
any organisation is distributing and transferring knowledge to place which is most needed to
improve operational and strategic performance (Pentland, 1995). There are various levels in
the knowledge transfer process: transfer of knowledge between individuals, from individuals
to groups, between groups, across groups, and from the group to the organisation (Alavi &
Leidner, 2001, p. 119). For achieving successful knowledge transfer, organisations need to
encourage and reward employees who use the latest ICT platforms and other channels to
collaborate and transfer knowledge effectively. This overcomes old thinking and hoarding of
individual knowledge as power (Issa & Haddad, 2008).
Timely, targeted and orchestrated knowledge interchange is an important process of
Knowledge Management for getting knowledge to locations where it is needed (Alavi &
Leidner, 2001).
2.6.4. Knowledge Application
Knowledge application is an important aspect of Knowledge Management processes and
efficient KM practices deals with the application of knowledge (Villar, Alegre, & Pla-Barb,
2014). If knowledge is not applied purposefully based on a shared understanding of situated
55
work practices, processes, or problem contexts it does not create value or organisational
performance improvement (Pentland, 1995).
Grant (1996) identified three mechanisms for the integration of knowledge to create
organisational capability (by extension CI) Directives, organisational routines, and self-
contained task teams. Directives refer to specific set of rules, standards, procedures, and
instructions developed through the conversion of tacit to explicit knowledge (Alavi & Leidner,
2001, p. 108). Organisational routines imply development of task performance and
coordination patterns, interaction protocols, and process specifications that allow individuals
to apply their knowledge without the need to communicate what they know to others (Alavi &
Leidner, 2001, p. 122). In situations of task uncertainly and complexity, using the
organisational directives and routines may be impossible, so the creation of self-contained task
teams will be helpful. In this situation teams of individuals using their specific knowledge are
able to customised solutions for problem (Alavi & Leidner, 2001).
IT can have a positive influence on knowledge application process. IT might facilitate efficient
handling of routine and increase knowledge integration by facilitating the capture and updating
of organisational directives. IT can also codify and automate organisational routines, so the
speed of knowledge integration is increased (Alavi & Leidner, 2001, p. 122).
2.7. Knowledge Management Models from the Literature
The KM models outlined below (Table 2.1) contribute to different perspectives or positions
relating to the nature, form, design and application of Strategic Knowledge Management
(SKM) framework. Key epistemological and practitioner considerations include- The nature of
knowledge itself; Conceptualising and converting knowledge as an asset or as a capability;
Design of knowledge enabling structures, cultures, and supporting leadership and management
practices (Easterby‐Smith & Prieto, 2008). Perhaps the most widely known and applied value
adding KM model is represented by Nonaka and Takeuchi’s knowledge spiral (Nonaka &
Takeuchi, 1995) and Hedlund and Nonaka’s KM framework (Hedlund & Nonaka, 1993). Both
focus on surfacing, combining and actioning tacit knowledge (based on human cognition) and
explicit knowledge (based on repositories of data and information) to add value to
organisations. This is achieved through the SECI (Socialisation, Externalisation, Combination
and Internalisation) knowledge conversion process. The SECI process is in turn enabled by Ba
- Nonaka and Takeuchi’s concept (Nonaka & Takeuchi, 1995) of a safe space (or cyberspace).
This supports conversion of knowledge assets into value added products, processes or services,
56
enabled by Information and Communication Technology (ICT) infrastructure, management
and teamwork practices (Nonaka & Takeuchi, 1995). The converted knowledge assets are
simultaneously carried up through the organisational structure in a dynamic spiral to inform
senior management decision making and support the strategy process. This model is most
applicable within project based industries which typically rely on matrixes superimposed on
functional structures to align staff expertise and capacity with business requirements.
Model Elements
The Boisot knowledge category Model (Boisot, 1987)
Codified-
Undiffused
Propriety knowledge
Uncodified
-
Undiffused
Personal knowledge
Codified-
Diffused
Public knowledge
Uncodified
- Diffused
Common sense
Kogut and Zander’s Knowledge Management Model
(Kogut & Zander, 1992)
Knowledge Creation
Knowledge Transfer
Process & Transformation Of Knowledge
Knowledge capabilities
Individual “Unsocial sociality”
Hedlund and Nonaka’s Knowledge Management Model
(Hedlund & Nonaka, 1993)
Articulated knowledge- Individual (Knowing
calculus)
Tacit knowledge- Individual (Cross-cultural
Negotiation Skills)
Articulated knowledge- Group (Quality Circle’s
documented analysis of its performance)
Tacit knowledge- Group (Team coordination in
complex work)
Articulated knowledge- Organisation
(Organisation chart)
Tacit knowledge- Organisation (Corporate
Culture)
Articulated knowledge- Inter- Organisational
Domain (Supplier’s patents and documented practices)
Tacit knowledge- Inter- Organisational Domain (Customer’s attitudes to products and
expectations)
The Wiig Model for Building and Using Knowledge (Wiig,
1993)
Public Knowledge
Shared experience
Personal knowledge
Individual knowledge
57
The von Krogh and Roos Model of organisational
Epistemology (Von Krogh & Roos, 1995)
Social knowledge
The Nonaka and Takeuchi Knowledge Spiral Model
(Nonaka & Takeuchi, 1995)
Knowledge creation
Knowledge
conversion
Socialisation
Externalisation
Combination
Internalisation
Skandia Intellectual Capital Model of Knowledge
Management (Chase, 1997); (Roos & Roos, 1997)
Equity
Human Capital
Customer Capital
Customer Base
Customer Relationships
Customer Potential
Innovation Capital
Process Capital
Demerest’s Knowledge Management Model
(Demerest, 1997)
Knowledge construction
Knowledge embodiment
Knowledge dissemination
Use
The Choo Sense-making KM Model (Choo, 1998)
Sense making
Knowledge creation
Decision making
Boisot I-Space KM Model (Boisot M. H., 1998) Codified-Uncodified
Abstract-Concrete
Diffused-Undiffused
Stankosky and Baldanza’s Knowledge Management
Framework (Stankosky & Baldanza, 2001)
Learning
Leadership
Organisation, structure & culture
Technology
Frid’s Knowledge Management Model (Frid, 2003) Knowledge Chaotic
58
Knowledge Aware
Knowledge Focused
Knowledge Managed
Knowledge Centric
Complex Adaptive System Model of KM (Bennet &
Bennet, 2004)
Creating new ideas
Solving problems
Making decisions
Taking actions to achieve desired results
The Inukshuk: A Canadian Knowledge Management
Model (Girard, 2005)
Measurement
Process
Leadership
Technology
Culture
Orzano’s Knowledge Management Model: Implications
for Enhancing Quality in Health Care (Orzano, 2008)
Finding Knowledge
Sharing Knowledge
Developing Knowledge
Decision-Making
Organisational Learning
Organisational Performance
Integrated socio-technical Knowledge Management model
(Handzic, 2011)
Knowledge stocks
Knowledge processes
Socio-technical knowledge enablers
Knowledge Management model of community business:
Thai OTOP (‘‘One Tambon One Product’’) Champion
(Tuamsuk, Phabu, & Vongprasert, 2013)
Knowledge identification
Knowledge creation
Knowledge storage
Knowledge distribution
Knowledge application
Knowledge validation
Table 2.1: Overview of Widely Cited Knowledge Management Models
59
Some other models focus on the process of creating, sharing and distributing knowledge within
organisations. Alavi & Leidner mentioned there are four knowledge management stages:
Knowledge creation, knowledge storage/retrieval, knowledge transfer, and knowledge
application (Alavi & Leidner, 2001, p. 115). This view of organisations as knowledge systems
represents both the social nature of organisational knowledge and the individual’s cognition of
knowledge (Alavi & Leidner, 2001). Darroch (2003) divided a typical Knowledge Management
process into three parts: Acquisition, dissemination, and the use or responsiveness to
knowledge. Acquisition refers to the knowledge capture process and allied insights. In the
dissemination stage the acquired knowledge is widely shared within the organisation. The use
of knowledge is regarded as the capacity of the organisation to apply the knowledge generated
for useful purposes (Darroch, 2003, p. 42). Chen and Chen (2005) defined a four-stage process
of knowledge management: Knowledge creation, knowledge conversion, knowledge
circulation and knowledge completion. Knowledge creation generates intangible assets.
Knowledge conversion capacity depends on individual and organisational memory. Knowledge
circulation focuses on exchanging knowledge between the source and receiver. And finally
through knowledge completion the source of Competitive Advantage resides in the newly
generated and circulated knowledge (Chen & Chen, 2005, p. 391). Lee et al (2005) identified
the knowledge circulation process in five stages: Knowledge creation, accumulation, sharing,
utilisation, and internalisation (Lee, Lee, & Kang, 2005, p. 470). For a more detailed over view
of industry models of KM see Appendix F.
2.8. Data Mining Concepts, Processes, and Major Elements
2.8.1. What is Data Mining?
Data Mining is the process of finding meaningful patterns through huge databases (Yu et al.,
2009). It is also the technique for identifying relationships between data in the large database
(Lee M.-C. , 2009) which were not apparent before. Data Mining is defined as non-iterative
process of extracting implicit and unknown useful information from data (Brusilovsky &
Brusilovskiy, 2008).
A more complete definition of Data Mining is proposed by Giudici (2003, p.2): as “The
process of selection, exploration, and modeling of large quantities of data to discover
60
regularities or relations that are at first unknown with the aim of obtaining clear and useful
results for the owner of the database”.
Data Mining is the process of using machine learning techniques and artificial intelligence for
identifying helpful information and knowledge from database (Nemati, 2001). It has deep roots
in statistics, artificial intelligence, and machine learning (Shetty & Achary, 2008).
Methodologies used in Data Mining processes come from two main branches of research such
as “machine learning community” and the “statistical community” (Giudici, 2003, p. 5).
Machine learning is related to computer science and artificial intelligence. Statistics is
generating models for analysing data. As regards the possibility of using computers to do it,
statisticians are interested in using machine learning methods as well (Giudici, 2003). Data
Mining is use of computer science and statistical technologies for supporting company
marketing and decisions.
According to Giudici (2003), statistics create methods for analysing data. These methods are
developed in relation to the data being analysed in a conceptual reference paradigm. Statistical
analysis concerns analysing primary data which is collected to check research hypotheses. In
Data Mining also the data can be produce experimental and observational data. Statisticians
adapt themselves quickly to the new methodologies arising from new information technology.
Using Data Mining can support and formalise statistical thinking and methods to solve
problems and identify useful patterns and opportunities for innovation (Giudici, 2003, pp. 5-
6).
Data Mining combined with statistical and machine learning techniques will extract useful
information from large databases. Data Mining techniques are usually predictive or descriptive.
Predictive Data Mining infers something about future events with using historical data and
predicts unknown values. Descriptive Data Mining finds patterns in the data which have
information about internal hidden relationships and involves discovering human
understandable patterns (Seddawy, Khedr, & Sultan, 2012, p. 5; Silwattananusarn & Tuamsuk,
2012). Data Mining by using variety of data analysis tools can discover knowledge, patterns
and relationships in data which may be used to make valid predictions (Jindal & Bhambri,
2011, p. 94).
2.8.2. Importance of Data Mining
Arguably the most important challenge of contemporary corporations is to explore the large
volumes of “Big Data” and extract useful information and knowledge for future decision
61
making and actions (Wu et al., 2014). The world of business and science faces many problems
relating to data analysis and processing using traditional methods.
Therefore generating a new technology such as Data Mining with intelligent and automatic
capabilities for transforming and processing data to useful information and knowledge is
deemed imperative (Bal, Bal, & Demirhanc, 2011, p. 2). Another reason for using these new
technologies, instead of human analysis, is the insufficiency of the human brain when searching
for complex multifactor dependencies and the lack of objectiveness of human processed
analysis (Baicoianu & Dumitrescu, 2010).
A comprehensive Data Mining process can replace with the work of professional statisticians.
This means staff that are not professionals in data analysis, statistics or programming will easily
manage to extract knowledge from data. Data Mining is very flexible and provides very useful
methods to recognise efficient economic analysis that classical methods cannot provide
(Baicoianu & Dumitrescu, 2010).
2.8.3. Data Mining Objectives
Given the rapid growth of data used and stored in the organisations, discovering valuable
information and meaningful patterns is one of the biggest challenges facing organisations
today.
Data Mining aims to discover unknown patterns, hidden knowledge and new rules from large
data base that are useful for making critical decisions in organisations (Baicoianu &
Dumitrescu, 2010, p. 187). Useful patterns are achieved by analysing set of given data or
information (Jindal & Bhambri, 2011, p. 94). Data Mining assists the organisations to look for
hidden patterns and find new relationships in data (Chopra, Bhambri, & Krishan, 2011).
Managers use Data Mining techniques and relational databases to analyse, address or resolve
operational problems within a broader and strategic decision making and KM context.
Data Mining as a decision support tool generates not always obvious yet potentially useful
structured and unstructured information for decision makers using very large data warehouses
(Lee M. C., 2010). Browning & Mundy (2001) described data warehouses as (2001): as a
means to “Support business decisions by collecting, consolidating, and organising data for
reporting and analysis with tools such as online analytical processing OLAP and Data Mining”.
Data warehousing is a process for centralising, maintaining, and retrieving data. Jambhekar
(2011): defines “Data Warehouses (DW) as a subject-oriented, integrated, time variant, non-
62
volatile collection of data in support of management's decision making process” (Jambhekar,
2011, p. 67).
Data warehouses can bring in data from various data sources such as personal computers,
minicomputers, mainframe computers. (Chopra, Bhambri, & Krishan, 2011). Data
warehousing can also support Business Intelligence (BI) operations and with this specific aim
in mind (Giudici, 2003).
If a data warehouse is not available, data can be mined from some transactional and operational
databases or data marts (Jackson, 2002). A Data Mart is a subset of an organisational data store
and smaller than a data warehouse. It is designed to focus on specific functions for a particular
communities in organisations (Lee M.-C. , 2009). Data Marts focus on business units that have
specific data analysis needs so they can focus on their own required data for achieve specific
purposes.
In summary Data Mining techniques are used to find out meaningful patterns and relationships
in a data warehousing environment. Data Mining and data warehouses are critical technologies
to enable knowledge creation to support strategic decision making (Lee M. C., 2010).
2.8.4. Data Mining Benefits
Data Mining has many benefits for the business environment. Bal and Bal and Demirhan (2011)
have categorised these benefits to three levels: (a) business, (b) individuals, and (c) society
which are explained below (Bal, Bal, & Demirhanc, 2011, p. 8):
(a) Benefits for business:
The benefits for business are as follows:
Recognise services and products which are important to customers
Suggest appropriate offerings to particular needs of customer
Discover what customers will be interested in new services and products
Recognise customers with a high rate for purchasing particular products
Determine new market opportunities
Customise marketing plans to particular markets
Support better customer relationship management
Find, attract and retain the top customer
Analyse delivery channels
63
Increase productivity
Reduce risk
Save time and cost
(b) Benefits for individuals:
The benefits for individuals can be described as:
Rapid access to integrated information rapidly
Fast response to customer requirements
Give better services and facilities to customers
Serve more customised services and products
Perceive the requirements of consumers better
Have better customer relationships
Achieve results that go beyond simple analysis of human interactions
(c) Benefits for society:
Also the benefits of society are described as:
Provide useful intelligence
Identify criminal activities
With respect to customer acquisition, Data Mining becomes a significant tool for profiling good
customers and improving the results of direct-marketing campaigns (Chopra, Bhambri, &
Krishan, 2011). Data Mining can provide relevant data which can be used by the organisation
for acquiring new territories and customers.
Data Mining also helps organisations to address business problems by discovering patterns,
associations and correlations that are hidden in their business information (Chopra, Bhambri,
& Krishan, 2011, p. 883). The business problems can be categorised as structured or
unstructured. Statistical analysis is useful for overcoming structured problems. But Data
Mining can also deal with unstructured problems. Potential sources of Competitive Advantage
may reside in these unstructured problems because competitors are not familiar with these
kinds of problems (See VRIN elements in section 2.3.2). Therefore managers can potentially
gain Competitive Advantage using Data Mining for solving business unstructured problems
(Brusilovsky & Brusilovskiy, 2008).
64
Data Mining comes to the heart of Competitive Advantage with providing more relevant and
useful information about business and its markets (Baicoianu & Dumitrescu, 2010). Business
information received from Data Mining and data analysis is a significant factor for
organisations for maximising Competitive Advantage (Bal, Bal, & Demirhanc, 2011).
2.8.5. Major Elements and Tasks of Data Mining Processes
Data Mining is an interactive and iterative process (Zhu & Li, 2006) for finding patterns from
large relational databases. It has involved numerous steps. A review of the work of prominent
authors in the field of Data Mining and information management revealed five key elements of
Data Mining processes. These elements are identified in the work of the following authors,
between 2006 and 2012: Bill Palace (1996); Xlinlianf Zhu and Jianzhang Li (2006); Surendra
Shetty and K.K Achary (2008); Tie-Li Yang (2008); Prof. S.K. Tyagi (2010); Navin
D.Jambhekar (2011); Deepika Jindal and Vivek Bhambri (2011); Seddawy and Khedr and
Sultan (2012).
The five major elements are as follows:
1. Extract, transform, and load transaction data onto the data warehouse system
2. Store and Manage the data in a multidimensional database system
3. Provide data access to business analysts and information technology professionals.
4. Analyse the data by application software.
5. Present the data in a useful format, such as a graph or table.
The Extract, Transform, and Load (ETL) is a widely used term in the IT profession. The tool
extracts data from underlying data sources and provides a facility to transform and load it to
data warehouse (Hellerstein, Stonebraker, & Caccia, 1999, p. 45). Data is extracted from
multiple operational databases and external sources. ETL extracts events and actions from the
operational database and loads them into the enterprise data warehouse (Dayal et al., 2009). In
typical Data Mining algorithms all data should be loaded into the main memory (like data
warehouse) (Wu et al., 2014). Extracted and integrated data should be stored in
multidimensional database. It can then be optimised for data warehouse and online analytical
processing applications. Multidimensional databases are generated using input from relational
databases. For business analysts and information technology professionals the ability to
capture, analyse, and easily access relevant data is crucial to effective business operations.
65
These new methods and technologies provide categorised and integrated data which is stored
in databases and analysed from different perspectives (Rouse, 2005).
Figure 2.10: Five Key Elements of the Data Mining Process
2.8.6. Advantages and Disadvantages of Data Mining
Data Mining techniques are used to predict future trends and behaviours in markets. Data
Mining can be used with a forward facing perspective, to propose better ways to make profits,
save cost, produce higher quality services and products, and increase customer satisfaction
(Baicoianu & Dumitrescu, 2010).
Baicoianu and Dumitrescu (2010) established three major advantages of Data Mining below:
“Provides relevant information about business process, customer and market
behaviours”,
“Takes advantage of data which is available in operational data collections, data marts
or data warehouse
“Discovers patterns of behaviour from data to predict future events” (Baicoianu &
Dumitrescu, 2010, p. 186)
Stored unstructured data in data warehouses is analysed and transformed into useful
information by Data Mining activities which add value to a data warehouse (Chen, Sakaguchi,
& Frolick, 2000). Data Mining is able to organise and analyse a large amount of data quickly,
Extract
Transform
Load data
Store and Manage
data
Provide data
access
Analyse data
Present data
66
so the operating efficiency is increased compared to traditional methods (Chen, Sakaguchi, &
Frolick, 2000). Data Mining users are able to control and pull data which is needed, so it
provides flexibility in using data (Chen, Sakaguchi, & Frolick, 2000). Modern Data Mining
with using highly complicated hardware and software components can analyse huge massive
data with reduced operating costs (Chen, Sakaguchi, & Frolick, 2000, p. 6).
Through contributions to priority areas of for Best Practice or enterprise excellence such as
products, customers, and operations, Data Mining can add value, increase revenue, reduce
costs, and improved market access. Hence by providing actionable results and supporting KPIs
and other measurable areas of strategic performance Data Mining can be seen as a valuable
competitive weapon (Bal, Bal, & Demirhanc, 2011).
Although Data Mining has many advantages, disadvantages can include- high costs, complex
and lengthy project times, and a high assumed knowledge requirement on the part of data
analysts, system design and support specialists and end users such as managers. Understanding
these disadvantages helps managers to have a realistic expectation and prepare for potentially
undesirable results at the adoption stage. (Chen, Sakaguchi, & Frolick, 2000).
Finally it should be noted Data Mining has become a major component of (often complex
multi-interface) enterprise decision support systems (Jashapara, 2011, p. 204). It is often
employed to deal with unstructured problems and this capability to interpret problem
characteristics and dimensions makes DM potentially compatible with the human cognitive
processes required to generate useful context specific, knowledge and address complex
problems. This is consistent with the logic of gaining Competitive Advantage through unique
processes, products and services that are hard to replicate. As noted by (Brusilovsky &
Brusilovskiy, 2008, p. 31) the strategic strength of DM resides in the ability to deal with
unstructured problems because competitors are not familiar with the characteristics of, or
solutions to, these kinds of problems.
2.9. The Role of Data Mining and Business Intelligence in Strategic
Knowledge Management
Knowledge Management (KM) is a set of processes using knowledge for enhancing
organisation performance (Marakas, 1999). On the other hand Business Intelligence (BI) is a
wider category of applications and technologies for gathering, accessing, and analysing
massive amounts of data, to inform more effective business decisions. The central base of BI
67
is to utilise large amount data to help organisations for achieving Competitive Advantage
(Wang & Wang, 2008).
Therefore both KM and BI improve the use of information and knowledge available to the
organisations. However KM is concerned with human subjective knowledge, while the BI deals
with data and objective information. On the other hand KM focuses on unstructured
information and tacit knowledge which BI is unable to address (Wang & Wang, 2008).
Strategic application of Knowledge Management is necessary as a key strategic factor for
achieving Competitive Advantage. This is based on leadership style and philosophy, organising
principles, systems, processes and reward mechanisms which support a superior capacity to
capture and configure portfolios of knowledge that will add value to products, services, brands
and reputation before organisation’s competitors. Organisations also need to explore and
combine data from diverse sources with the tacit knowledge embedded in human networks.
This process is supported by the latest generation of collaborative technologies. This kind of
data is normally heterogeneous in nature, so the appropriate Data Mining techniques may be
useful (Jindal & Bhambri, 2011, p. 94). Integrating Data Mining in a broader Strategic
Knowledge Management (SKM) framework can enhance Knowledge Management processes
and systems (Silwattananusarn & Tuamsuk, 2012). However based on a broad review of the
KM, DM, and BI literature, it is proposed that for DM can become a truly effective contributor
to the larger realm of Business Intelligence (BI) tools in turn contribute to the strategic
performance of the organization. Collaborative DM and BI motivations and activities must
embedded into a broader KM rubric and culture.
2.10. Strategic Knowledge Management (SKM)
As Industrialised economies move from exploiting natural resources and proprietary
technologies to creating value from intellectual assets, the design and application of smarter
SKM systems becomes an increasing imperative for Western multinationals. Many global
companies are seeking to survive global disruption of their markets and at best maintain some
kind of edge over global competitors. In digitally disrupted knowledge economies Strategic
Management requires recognition of the potential strategic value of the organisation’s stock of
knowledge. Strategic Knowledge Management must incentivize knowledge creation and
knowledge transfers when formulating strategy and making strategic decisions (López-Nicolás
& Meroño-Cerdán, 2011). In these organisations knowledge is used in different strategic
contexts with different conceptions of how to realise value from portfolios of intangible assets.
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In the post 2015 period the management of organisational knowledge and learning must be
viewed through a strategic lens and engaged within a Meta process of cross silo integration,
innovation and value creation.
As noted by Jashapara the firm’s Knowledge Management strategy should be aligned with the
business strategy. Organisations are never static and they are moving in direction towards
efficiency or innovation according to specific market conditions (Jashapara, 2011, p. 104). An
effective Strategic Knowledge Management (SKM) model supports the sometimes mutually
exclusive requirements of efficiency and innovation. SKM can overcoming the practical
limitations of converting intangible stock and flows of human knowledge into increased
product, service or brand value or differentiation, by establishing clearly articulated organising
principles and KM architectures. These in turn align Organisational Learning (OL) activities,
leadership thinking, management routines and narratives, and people/ technology interactions,
with dynamic changes in the competitive environment.
This fluid notion of SKM as a strategic thinking process is consistent with the notion of
dynamic complexity and Scharmer (2009) contention that today’s leaders have to deal with:
dynamic, social, and emerging complexity (Scharmer, 2009, pp. 59-62).
For Scharmer (2009) Dynamic complexity means that there is a systematic distance between
cause and effect in space. If the dynamic complexity is low, it can be dealt with piece by piece.
For higher levels of dynamic complexity the “whole-system approach” must be engaged and
leaders must pay increased attention to cross-system interdependencies (Scharmer, 2009, p.
59).
The second type of complexity is the social complexity. Scharmer views this as a product of
diverse interests and worldviews among stakeholders. With lower levels of social complexity,
experts can guide decision making, but in the greater the social complexity, the more important
is that “multi-stakeholder voices are invoked.” to real problem solving.
Emerging complexity exists when problem definitions have not been fully formulated and the
solution is not clear (Scharmer, 2009, p. 59). (See discussion of K1-K3 knowledge in section
2.6.1.4 above).
2.11. SKM Model and Study Hypotheses
This chapter provides a detailed review of key concepts, models and arguments from relevant
academic literature on Strategic Management incorporating different perspectives on strategy
69
and the VRINE model of factors leading to Competitive Advantage (CA), Knowledge
Management (KM), Data Mining (DM), Continuous Improvement (CI), and Best Practices
(BP). The well-known SECI model developed by Nonaka et al. (2001) and other key
Knowledge Management models have been reviewed. The SKM model and research
hypothesis below have been developed from this review undertaken prior to empirical testing
through quantitative analysis of KM activities and behaviours across nine international
operations in the company.
The SKM model relating key views of Strategy, Knowledge Management processes, and Data
Mining elements as potential sources of Competitive Advantage for the case study and other
‘firms’ in the global Minerals and Metals, Mining sector is presented in Figure 2.11 below:
Figure 2.11: SKM Model: Creating Competitive Advantage through Integration of Data
Mining and Strategic Knowledge Management
This model (incorporating the VRINE factors outlined in Figure 2.3), was presented for
empirical testing through the mixed method, deep case research approach described in Chapter
Three. Qualitative and quantitative findings are reported in Chapters Four and Five.
The relevant research hypothesis investigated in the study are represented below:
H1: Knowledge Management processes are positively related to Data Mining processes in the
global mining and manufacturing company.
Strategic knowledge
management
process1
process2
process3
process4
...
Market based view
Resource based view
Data mining
element 1
element 2
element 3
...
Knowledge based view
Stakeholder based view
Competitive Advantage
Inte
gratio
n
70
H2: Data Mining processes are positively related to the Resource based Competitive
Advantage in the global mining and manufacturing company.
H3: Knowledge Management processes are positively related to the Resource based
Competitive Advantage in the global mining and manufacturing company.
These questions relate to the detailed breakdown of the composite elements of the SKM
model investigated in the study. See detailed breakdown in Table 2.2 below
Variable Indicator Description References
Knowledge
Management
Knowledge Creation
- Employees are interested in
to share their ideas, beliefs
and insights with other
colleagues. (For example
walking around inside the
company and talk to other
employees about their ideas.)
- Employees are involved in
the articulation of their
knowledge through dialogue
and the use of figurative
language, metaphors,
narratives and images. - Knowledge gathering,
transferring, defusing, and
editing exists in the
company?
- Employees are interested in
learning and acquiring new
knowledge through action
and practice.
(Alavi & Leidner,
2001)
(Nonaka, Toyama,
& Byosiere, 2001)
(Gottschalk, 2005)
(Gupta &
Govindarajan,
2000)
Knowledge Storage
- Recording knowledge is
routine in a company.
- When a team complete the
task, the details are
documented for reusing.
Knowledge Transfer
- Sharing knowledge is routine
in a company?
- Individuals are visibly
rewarded for knowledge
sharing and reuse
- Formal networks exist to
facilitate to transfer
knowledge?
Knowledge Application
- Employees are co-operative
and helpful when ask for
some information or advice.
- In the day to day work, it is
easy to find the right
information.
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- Some Knowledge
Management behaviour such
as creating new knowledge,
reusing existing knowledge,
sharing knowledge, and
transferring knowledge is
promoted on a day to day
basis.
Data Mining
- Extract,
Transform and
load
- The data from various
sources (such as MS office
documents, legacy systems,
files, and archive) is collected
in the company and required
data is provided.
- The company pays special
attention to extract
information in electronic and
physical formats.
- Collected information is
converted to specific format
(according to the required
format by the company)
- Converted information is
loaded into relevant database.
(Nimmagadda &
Dreher, 2009);
(Palace, 1996)
(Zhu & Li, 2006)
(Shetty & Achary,
2008)
(Yang, Gong, &
Bai, 2008)
(Tyagi & Sharma,
2011)
(Jambhekar, 2011)
(Jindal & Bhambri,
2011)
(Seddawy, Khedr,
& Sultan, 2012)
(Viljoen, 2010)
- Store and Manage
data
- Specific departments (for
example finance department)
need to store and manage the
extracted data for that
department to use.
- Provide data
access
- IT and technical employees
can access the required data
easily.
- Analyse data
- Employees are able to
analyse unstructured data
easily in a short time.
Present data
- Employees can present the
data in a useful format (such
as a graph or table) at the
right time.
- There is one or more user
friendly system(s) for
preparing appropriate reports
in the company.
Resource
based
Competitive
Advantage
Valuable resource
- Some of the employees have
specialised skills or
technological expertise.
- The company gives
importance to understanding
of customers’ needs.
- The company tends towards
long term contracts with
customers.
(Barney, 1991)
(Carpenter et al.,
2010, p. 105-106)
(Halawi, Anderson,
& McCarthy, 2005)
(Madhani, 2010)
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- The company gives attention
to “cost-effective labour” for
reducing costs.
Rare resource
- The company has special
patents, trade secret, or
branding.
- The company has Intellectual
Property rights.
Inimitable resource
- Transfer of special skills and
technologies, which are
employed in the company,
takes a long time.
Non-substitutable - The company has ability to
innovate.
Table 2.2: Summary of SKM variables investigated.
2.12. Chapter Conclusion
This chapter began with operational definitions of strategy and Strategic Management. The
connection between Knowledge Management (KM) and strategy was established with
reference to some of the most widely cited KM the Strategic Knowledge Management (SKM)
framework elaborated throughout the study.
The key elements of Data Mining systems and practices were also investigated as components
of the SKM model. The major operational elements of Data Mining practice within the case
organisation were also investigated. The major challenges of integrating Data Mining into a
Knowledge Management framework were identified, along with a potential benefits of
combining BI and DM practices within a broader SKM framework.
This model combining concepts and principles from the Strategic Management, Knowledge
Management and Data Mining literature as an analytical tool for understanding how knowledge
can be used as a strategic capability within multinational resource based organisations. The
VRIN(E) model of elements supporting Competitive Advantage in the firm was integrated into
the broader SKM model. Three hypothesis were presented for testing the relationship between
Strategic Management, Knowledge Management, and Data Mining systems and practices and
the case organisations ability to survive and surpass the performance of the competitors, within
a global market for mining, refining and manufacturing bauxite and aluminium product. These
hypothesis were tested and empirically validated using PLS. This software was used to
investigate the nature and strength of relationships between KM, Strategic Management, and
Data Mining within the organisation and broader industry context. PLS was employed to
73
establish any direct or indirect relationships between the key elements described above and
Resource based Competitive Advantage of the firm.
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CHAPTER THREE
3. METHODOLOGY
3.1. Introduction
This chapter identifies the research methodology and design for the study. Through this chapter
the paradigm and approach of the study are chosen and justified. The research design is also
specified. The organisation of this chapter is illustrated as below:
Figure 3.1: Overview of the Methodology Chapter
3.1. Introduction
3.2. Research Paradigms Relevant to the Research Question
3.3. Research Design
Phase 1:
- Collect data through interviews
- Qualitative analysis
- Finding key themes of the
exploratory interviews
- Designing the survey
questionnaire using global and
local industry terminology and
company cultural conventions
-
Phase 2:
- Collect data through
questionnaire
- Quantitative analysis (SEM)
- Testing relationships among
different variables in the model
Discussion, Conclusion and Recommendations for Future Research
3.4. Chapter Conclusion
75
The review of the relevant literature in Chapter Two concludes with an overview of the SKM
and VRIN models and the potential relationships between their defining elements and firm
Competitive Advantage (CA). In Chapter Three, the focus is on how (qualitative and
quantitative) methods are used to explore and test the key relationships between SKM, VRIN
and CA across nine global operations within the case organisation. Relevant ontological and
epistemological perspectives, considerations and underlying assumptions informing the design
of the research are also considered (Mason, 2006), This is an important step in any exploratory,
interpretive and empirical research investigation, to justify the method (how), clarify the
rationale for the study (why), identify the key actors (who), and the broader context (where and
when) of the relationships, activities and interactions investigated. (Mason, 2006).
In this study the main research question is:
“How can the relationship between Strategic Knowledge Management and Data Mining be
effective in creating Competitive Advantage for a large organisation in the global minerals
and metals mining industry?”
In order to systematically investigate the phenomenon relating to the principal research
question, and the translation of relationships outlined in the SKM model into day to day
operational practices in the global operations of the case organisation, the following sub-
questions were developed: ‘How do Data Mining systems and practises relate to Knowledge
Management processes designed to achieve Competitive Advantage in the mineral and metals
mining industry?’; ‘How do Knowledge Management processes affect Data Mining processes
within the global mineral and metals mining industry?’; ‘How do Data Mining systems and
practices impact the Resource based Competitive Advantage of the firm?’; How do Knowledge
Management processes(specifically) affect the Resource based Competitive Advantage of the
case study organisation?’; And finally- ‘To what extent can integrating Data Mining and
Strategic Knowledge Management thinking and practices support the achievement of
Competitive Advantage?’
In order to address the research questions, two phases are defined in this study. In the first
phase a conceptual model is designed and used to create a series of semi-structured interviews
with ten senior managers working throughout nine of the firm’s global operations. Common
terms and relevant management and technical practices are established. Findings from
interviews are used to fine-tune the model. These also informed the design of the survey
questions for the second (quantitative) phase 2 of the study.
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The survey targeted management, supervisory, technical and specialist respondents, reporting
to or referred by the global senior managers interviewed for phase 1 on the basis of being users
of, or contributors to KM systems and practices in the organisation. The survey yielded 115
valid and complete responses, out of an initial list of 300 designated potential respondents. The
survey instrument incorporated 24 structured questions focusing on particular aspects of
Knowledge Management, Data Management and Data Mining practices across nine global
operations. Data generated in this second phase is analysed using PLS software to test the
effects of specific KM and DM systems, processes, routines and practices on the Competitive
Advantage of the firm.
In view of the social-technical complexity of the research environment and potential
commercial in confidence considerations relating to interview and survey data:
1) The research was undertaken under a non-disclosure agreement regarding any items
that may have to be removed post examination prior to the research being published or
released into the public domain;
2) The research design and communication process was facilitated by a senior, long serving,
highly trusted internal KM expert (the Global Knowledge Manager). At the outset of the
four year study, the student researcher, principal research supervisor and senior
representatives of the case company agreed that this was a necessary step to expedite
privileged access to global KM systems stakeholders in order to produce rigorous
theoretical and relevant practical outcomes. Working closely with this facilitator ensured
that interview and survey respondents understood the research purpose, relevance to their
roles (why they were chosen) and potential value of the finding to the company. The global
technical manager was also consulted to advise on in-house technical terms and the wording
of questions to ensure that the questions were clearly framed using contextually sensitive
language. All interview and survey data was collected and collated face to face by the
researcher or via email or a survey web link, to ensure that responses could not be altered
by a third party.
3.2. Research Paradigms Relevant to the Research Question
The research study design begins with selection of a paradigm. Paradigms represent essential
worldviews or frameworks of beliefs, values and methods in research. Guba and Lincoln (1994,
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p108) stated: “Paradigms define for inquirers what it is they are about, and what falls within
and outside the limits of legitimate inquiry.” Also they believed all paradigms can be
summarised based on three fundamental questions (Guba & Lincoln, 1994, p. 108) - The
Ontological question: “What is the form and nature of reality?” And “What can be known about
it”, with the focus on “How things really are?” and “How things really work?”; The
Epistemological question: “What is the nature of relationship between the knower and what
can be known?”; and the Methodological question: “How can the knower go about realising
whatever he or she believes can be known?“ These three questions serve as the major focal
points for the analysis of paradigms. In summary, ontology concerns the nature of reality,
epistemology focuses on gaining knowledge of that reality, and methodology refers to
particular ways of knowing that reality (Sale, Lohfeld, & Brazil, 2002).
3.2.1. Research Philosophy and Central Paradigms
The mixed method research approach, described earlier, incorporates thinking from the two
central paradigms in social research. These are known as the ‘positivist’ and the “interpretive’
approach (Ticehurst & Veal, 1999, p. 19). The interpretivist approach is adopted for the first
phase of the study. This is a flexible approach to data collection usually involving qualitative
methods (Ticehurst & Veal, 1999, p. 20). Qualitative researchers tend to favour an interpretive
perspective rather than a positivist view of reality (Silverman, 2010, p. 104; Sale, Lohfeld, &
Brazil, 2002). Under this paradigm, the world is socially constructed and subjective, and there
is no reality outside of people’s perceptions; researchers attempt to discover meanings and
understandings of the broad interrelationships in their situation. They also try to get inside the
minds of their subjects and see the world from their point of view (Ticehurst & Veal, 1999, p.
20). In this sense researchers not only interact with their environment, but also seek to make
sense of it through their interpretation of events (Saunders, Lewis, & Thornhill, 2003, p. 84).
This position has many alternative names such as hermeneutic, qualitative, phenomenological,
interpretive, reflective, inductive, ethnographic, and action research (Ticehurst & Veal, 1999,
p. 20). This paradigm fits the first phase of the study where the main purpose is to get an in-
depth and thorough understanding of management and technical practices relating to KM and
DM, in the case organisation, through semi-structured interviews with ten senior managers so
that common understanding and practices can be identified.
On the other hand, under the positivist paradigm, the world is external and objective for the
researchers, similar to the position adopted in the natural sciences (Ticehurst & Veal, 1999, p.
78
19). It also refers to scientific, experimental, empiricist, quantitative or deductive research
(Ticehurst & Veal, 1999, p. 20). The positivist paradigm fits the second phase of the research.
This approach focuses on description, explanation and discovering facts and behaviours are
explained on the basis of the facts (Ticehurst & Veal, 1999, pp. 19-20), so researchers prefer
to work with an observable social reality. This highly structured methodology is able to
investigate and quantify observations gained from the first phase in this research for statistical
analysis (Saunders, Lewis, & Thornhill, 2003, p. 83).
3.2.2. Research Approaches
In business as an area of enquiry, the researcher’s main activities involve the collection,
analysis and presentation of data. The data may be quantitative or qualitative in nature
(Ticehurst & Veal, 1999, p. 20). The approaches to the collection, analysis and presentation of
quantitative and qualitative data are distinctive but they have some similarities and overlaps,
so they can be used together in various ways (Punch, 1999, p. 29).
For phase 1, a qualitative approach is used because this phase aims ‘to discover’, ‘seeks to
understand’, ‘explores the processes’, and ‘describes the participant’s experiences’ through the
interviews with senior managers (Punch, 1999.p19). This phase of qualitative research is based
on the purpose of gaining a full understanding of the organisational and individual experiences
and contexts in the case organisation (Ticehurst & Veal, 1999, p. 21). It generates insights and
perceptions rather than quantifiable measurements (Krishna, Maithreyi, & Surapaneni, 2010).
Typically, the qualitative phase of a mixed methods study tends to be exploratory using semi-
structured interview questions or open ended listening and observing devices, such as focus
groups or search conferences. (Ghauri & Gronhaug, 2002, p. 196). During the qualitative phase,
the researcher is a part of the research process and seeks to uncover meanings of the themes
arising from the interviews (Ticehurst & Veal, 1999, p. 94). The interpretive ontological
position discussed earlier is the underlying rationale for this particular qualitative approach. It
is premised on multiple realities and truths based on one’s construction of reality. The
researchers’ main role is to get inside the minds of their subjects and see the world from their
point of view to discover meanings and obtain understandings of the broad interrelationships
in the context of the case organisation (Sale, Lohfeld, & Brazil, 2002).
The techniques usually used in qualitative research include: informal and in-depth interview;
group interview; participant observation; and ethnography (Ticehurst & Veal, 1999, p. 21 &
97; Sale, Lohfeld, & Brazil, 2002). In the first phase, ten senior global managers and directors
79
are chosen for the in-depth interviews. Qualitative research gathers deep information about
small numbers of people or organisations (Ticehurst & Veal, 1999, p. 21). In this study the
sample does not represent a large population as a small purposeful sample is able to provide
important information (Sale, Lohfeld, & Brazil, 2002), The ten carefully chosen senior
managers interviewed work throughout the firm’s global operations and were able to provide
huge amounts of relevant information on the research questions. In qualitative research, the
gathered data is not presentable in numerical form and it is not concerned with statistical
analysis (Ticehurst & Veal, 1999, p. 21), therefore the interview findings are presented
thematically in a narrative form rather than a statistical form (Ticehurst & Veal, 1999, p. 95).
In the second phase, the relationships between constructs in the conceptual model, established
through a detailed review of the literature on strategy, RBV, KM, DM, CA, and other areas
salient to the case company such as Continuous Improvement (CI), Best Practice (BP), and
Business Intelligence (BI) are broken down into defined elements. These survey elements were
defined using feedback from the Global Knowledge Manager and senior management
interview respondents from the first phase, and quantitatively measured through the survey of
115 reporting positions. Quantitative research relies on numerical data and uses statistical
analysis for drawing conclusions and testing hypotheses (Ticehurst & Veal, 1999, pp. 20-21).
Quantitative studies also require instruments and methods for measurement (Krishna,
Maithreyi, & Surapaneni, 2010). The positivist ontological position, discussed earlier is the
underlying rationale for this quantitative phase, believes there is only one truth or an objective
reality that exists independent of individual conception, so the researchers and subjects are not
dependent entities. Researchers should be able to investigate phenomenon without affecting it
or being affected by it (Sale, Lohfeld, & Brazil, 2002). This approach involves numerical data
that could be quantified to answer the research question(s) (Saunders, Lewis, & Thornhill,
2003, p. 327). To ensure the reliability of the results, a relatively larger sample of people or
organisations and the use of computer to analyse data is necessary. In this phase, a survey
questionnaire was designed to collect the quantitative data from 300 reporting management,
supervisory, technical and specialist respondents. The causal (cause-and-effect) relationships
within the quantitative data are analysed based on the 115 responses.
Mixing methods presents great potential for discovering new dimensions of experiences and
skills in social life (Mason, 2006). Each method, qualitative or quantitative, has its strengths
and its weaknesses when answering the research questions (Punch, 1999, p. 241), so the
combination of these two methods is regarded as the best approach for this study. The
qualitative data gathered, from the insiders’ perspective with “experts” in the small sample, has
80
a high degree of holism and richness (Punch, 1999, p. 243). The quantitative data gathered in
the larger sample could be useful for measuring the relationships between key constructs and
investigating whether the qualitative findings from the first phase can be generalised to the
entire case organisation. Therefore, by combining the two methods, the scope, depth, and
power of the research are increased (Punch, 1999, p. 243). According to Mason (2006, p13),
“It [mixing methods] can encourage researchers to see differently or think ‘outside the box’ ”.
Sale, Lohfeld, and Brazil (2002, p46) also pointed out combining the two research methods
will be very useful when the complexity of phenomena requires data from various perspectives.
Based on these arguments, the mixed methods research design is highly suitable for this study
given the complex nature of the research questions. Similar to most mixed-method designs,
this research starts with exploratory qualitative research followed by quantitative research to
validate the findings from the qualitative phase (Sale, Lohfeld, & Brazil, 2002).
3.2.3. A Deep Case Study Analysis
A case study is used to drive in-depth understanding of a single or more cases set in their real-
world context. Case study research should not be limited to a single source of data. A sound
case study should collect multiple sources of evidence such as direct observation, interviews,
archival records, documents, participant-observation, and physical artefacts (Yin, 2012, p. 10).
Through the use of multiple sources, the robustness of the results would be increased and the
findings could be strengthened (Gable, 1994). In this study a mixed method research design,
as explained above, (i.e. a top down perspective obtained via - interviews with global senior
managers and a bottom up view – derived from a survey of reporting managers and specialists)
was employed in the case organisation context. The deep case study analysis used in this study
is different from a conventional case study. It does not follow the standard formula for case
study development involving triangulation of evidence. The Global Knowledge Manager was
engaged to facilitate access to Knowledge Management system users and other internal
stakeholders. This included ten senior managers based in nine different global locations and
300 potential survey respondents (typically engineers, business managers, IT and technical
specialists), referred by, and reporting to senior management, (115 usable survey responses
were received). This allowed the researcher to gain privileged access to a key personnel
concern with the development and day to day operation of the firm’s global Knowledge
Management Systems (KMS) and practices. Working directly with the research facilitator
ensured that the context, purpose, relevance, and potential benefits of the study were clearly
81
understood by respondents. This approach arguably improved both the quality and quantum of
responses, (although directly comparable material was hard to source). In summary, this
deliberate approach ensured a high quality, committed, situated and detailed response by the
10 senior managers participating in phase one interviews. The expertise of these participants
was then used to inform the selection of a carefully targeted (referred) sample, contextually
appropriate questions and instrument design for the survey phase. This context rich and highly
relevant approach to the study, was initiated and formally agreed (via the terms of the NDA
agreement) following extensive preliminary discussions between the principal supervisor,
researcher and Global Knowledge manager.
3.3. Research Design
The research involves an extensive review of the contemporary academic and practitioner
literature on Knowledge Management, Data Mining and ICT networks, senior management
interviews and a staff survey in the case organisation. The main focus of the interview, and
survey questions, is on how Data Mining (DM) and ICT infrastructure (hard systems) can be
combined with the firm’s expertise and human capital (soft Knowledge Management Systems)
to improve the strategic performance of mining or similar resources based organisations.
Research design provides a plan and framework for data collection and analysis (Ghauri &
Gronhaug, 2002, p. 54). It situates researchers in the experimental world (Punch, 1999, p. 66)
and shows how the research questions can be linked to the data (Punch, 1999, p. 67). The
research design is the logic that connects the collected data to initial questions of the study
(Yin, 1994, p. 18).
Design of this study follows two phases as illustrated below:
82
Figure 3.2: Details of the Research Design
3.3.1. Phase 1: Qualitative Exploratory Study
The relevant literature on Strategic Management, Knowledge Management and Data Mining
has been reviewed and a preliminary conceptual model is designed in Chapter Two. After the
first phase was completed, the conceptual model was fine-tuned by incorporating key points
from the exploratory interviews. The purpose of the first phase is to achieve familiarity and
Phase 1
Semi-Structured Senior Management Interviews
Research Design
Analyse Qualitative Data
- Identify key points and themes from the exploratory
interviews
- Design questionnaire
Phase 2
Questionnaire Survey
Analyse Quantitative Data
With Structural Equation
Modelling (SEM)
Discover relationship
between defined
variables
Discussion of quantitative and qualitative findings in
relation to SKM and VRIN models.
Conclusions and recommendations for future
research
83
understanding of how the Knowledge Management processes and Data Mining elements, and
processes outlined in the SKM model, translate in context of the case company and its global
operations within the minerals and metals mining industry. The semi-structured interviews
conducted with senior managers were also designed to elicit clues regarding firm wide and
local technical language and cultural conventions. Subsequent informal discussions with the
research facilitator and the respondents were used to sensitise the survey design and questions
to global and local industry contexts.
3.3.1.1. Qualitative Research Sample and Case Company Selection
The global mining and manufacturing multinational company chosen for the case study is a
producer of aluminium and has been a pioneer in the aluminium industry for over 125 years.
The 10 multinational directors and managers who participated in the interview phase were
voluntarily recruited from a pool of KM and Data Mining systems users, designers or
contributors. These interviewees held expertise relevant to the effective and efficient
operations of the company’s plant based KM and DM operations and broader data and
knowledge integration activities. They were initially identified through the consultation with
the Global Knowledge Manager of the company. Permission to gain privileged access to this
pool of respondents was obtained from senior management. Whilst the study focused on KM
and DM systems and practices, and not the data itself, a non-disclosure agreement was
developed to allow for removal of commercially sensitive information, prior to the research
findings being released into the public domain. All interviews were recorded and transcribed.
This facilitated detailed thematic analysis using NVivo, one of the most widely used software
packages for qualitative data analysis (Silverman, 2013, p. 266).
3.3.1.2. Development of Interview Questions
A list of open-ended questions, covering the three key constructs Strategic Management,
Knowledge Management and Data Mining were developed. Before the start of each interview,
the meaning of the terms- “Knowledge Management” and “Data Mining”, were clarified within
the work context of each of the ten senior management interviewees. The interviews
commenced with general questions about the company and the interviewee’s department, e.g.
“Please briefly describe your department responsibility in this company”. Then the interviewee
is asked specific questions such as “How would you define knowledge and/or information
84
management?”, “What kind of Knowledge Management practices are employed across the
organisation?”, “Does your company have a Data Mining system?”, and “To what extent do
you think such Knowledge Management practices and Data Mining systems, can support the
Competitive Advantage of your organisation?”. The questions investigate the general
Knowledge Management practices, existing Data Mining systems, and the sources of
Competitive Advantage in the case organisation from the senior management and specialists’
perspective. The complete list of interview questions is in Appendix A.
3.3.1.3. Qualitative Data Collection
Interviews are used as one of the main data collection tools in qualitative research (Punch,
1999, pp. 174-5). An interview can be categorised as structured, semi-structured, and
unstructured (Saunders, Lewis, & Thornhill, 2003, p. 246; Punch, 1999, p. 175). The semi-
structured interview was used in this phase, given advantages such as flexibility in range and
contextual depth of responses and the ability to address specific issues. In semi-structured
interviews, the researchers have a list of questions, some of which might be omitted in
particular interviews or new questions might be added to help further explore the research
questions (Saunders, Lewis, & Thornhill, 2003, pp. 246-7). It is more flexible than a structured
interview where a series of pre-established and standardised questions are asked (Saunders,
Lewis, & Thornhill, 2003, p. 246; Punch, 1999, p. 176). It is also more formal and organised
than an unstructured interview where there is no predetermined list of questions (Saunders,
Lewis, & Thornhill, 2003, p. 247).
Before each interview, consent forms and information letters, approved by the University,
(Murdoch) ethics committee, were signed by the interviewee. In the information letter the
purpose and benefits of the study, and privacy and confidentiality issues, were clearly defined
(see Appendix B). Each interview was recorded and the transcript is provided to interviewees
to review after the interview.
3.3.1.4. Qualitative Data Analysis
The purpose of data analysis is to gain insights from the collected data and bring structure and
meaning to the mass of collected data (Ghauri & Gronhaug, 2002, p. 199). The key to
qualitative data analysis is breaking down a complex whole into its essential parts (Ghauri &
Gronhaug, 2002, p. 199).
85
Ghauri and Gronhaug (2010) identified important analytical activities such as categorisation,
abstraction, comparison, dimensionalisation, integration, iteration, and refutation in qualitative
data analysis: categorisation - categorises data with codes. Abstraction - identifies the patterns
in the data; comparison - discovers similarities and differences within the data and provides
guidelines for collecting additional data; dimensionalisation- defines the properties of
categories which is important for theory construction; integration - maps relationships between
conceptual elements; iteration - goes through data collection and analysis in such a way that
previous operations shape subsequent ones as sometimes researchers use a negative case or
negative incident to disconfirm the emerging analysis; and refutation - disconfirms these
phenomena (Ghauri & Gronhaug, 2002, pp. 200-203). Largely based on this guideline,
activities in the first phase of this study included: Categorising and coding the collected data;
Identifying patterns between classified data; Comparing the identified patterns with the
preliminary conceptual model, designed from the relevant literature; Discovering similarities
and differences and finally customising the conceptual model by integrating the findings from
the analysed data. In view of the large amounts and diversity of data, generated from this phase
the researcher employed NVivo, to organise, categorise and analyse the data.
3.3.1.5. Validity and Reliability
Qualitative researchers use the views of people who conduct, participate in, and review the
study, so they do not focus on scores and instruments (Creswell & Miller, 2000). The quality
of qualitative research can be assessed by methodological trustworthiness, rigor and
generalisability (Healy & Perry, 2000). In this research, a rigorous data collection and analysis
process, as described above, is used to ensure the accuracy and reliability of the results.
3.3.2. Phase 2: Survey Questionnaire
3.3.2.1. Quantitative Research Sample Selection
The population sample for the second phase covered nine global operations of the entire case
organisation. The target population consisted of directors, global managers, technical
managers, operational managers, team leaders and supervisors, research scientists, engineers,
and other staff from various departments. These included: accounting and finance; marketing
and sales; customer relationship and stakeholder management; operational planning; technical
86
support; business unit operations; business systems; IT; human resources development and
organisation development; and research and development (R&D). The requisite sample size
targeted for phase 2 was between 100 to 150 survey respondents
In order to make sure the quantitative results are valid and accurate, the sample should be a
good representation of the population (Cooper & Schindler, 2008, p. 377). The sampling also
determines the generalisability of the findings from the sample to the population of interest
(Cooksey & McDonald, 2011, p. 449). The sampling plan can be categorised into two groups:
Probability and Nonprobability (Cooper & Schindler, 2008, p.378; Zikmund et al, 2013,
p.392). For phase 2 the target survey respondents are identified as technical specialists,
departmental and operational managers/senior supervisors familiar with the company’s
objectives, vision, strategies, business processes, Knowledge Management initiatives, and Data
Mining systems and practices in the organisation, Given this referral based sampling approach
designed to maximise informed or expert responses by targeting KM relevant roles in the
organisation-nonprobability sampling was used. It was deemed suitable, as not everyone in the
organisation had an equal probability to be selected. Nonprobability sampling is frequently
used in business research given pragmatic considerations (Zikmund et al, 2013, p.392).
However, nonprobability sampling also has some limitations such as it being conceived as
arbitrary and subjective as participants are not randomly selected (Cooper & Schindler, 2008,
p. 379), but selected based on researchers personal judgement (Zikmund et al, 2013, p.392).
The common techniques of nonprobability sampling include: Convenience sampling,
Judgment sampling, Quota sampling, and Snowball sampling (Zikmund et al, 2013, p. 392-
395). Convenience and Snowball sampling were used in phase 2. With the convenience
sampling researchers access people or units that are most conveniently available (Cooksey &
McDonald, 2011, p. 461). Snowball sampling, based on the initial convenience sampling, is
subsequently used to locate more potential participants who might also be interested in, and
suitable for, the research project by the referrals (initial responders) (Zikmund et al, 2013,
p.395).
The snowball method has some weaknesses basically related to the general disadvantages of
nonprobability sampling, such as there is no guarantee that all individual units in the population
have an equal chance to be selected to the sample and the probabilities of being selected are
unknown. Therefore the final sample obtained by the researcher may only represent a small
subgroup of the entire population (Voicu & Babonea, 2007), the real distribution of the
population is not accurately represented.
87
Despite these weaknesses, convenience sampling combined with snowball sampling was still
regarded as the most suitable sampling approach for this research because in this way
researchers are able to obtain large amount responses quickly and economically (Zikmund et
al, 2013, p. 393), and have the freedom to choose whomever they find (Cooper & Schindler,
2008, p. 397). The snowball sampling process adopted in the study is shown in Figure 3.3
below:
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Figure 3.3: An Illustration of the Snowball Sampling Process
Edited from (Cooksey & McDonald, 2011, p. 462))
Researcher
……
..…
Operational
Manager 1
Operational
Staff n
Global
Director
Mining
Operation
Technical
Manager
Engineer
n
……
..…
Engineer1
Director
Research and
Development
Global Refining
Research
Scientist n
……
..…
Research
Scientist 1 ……
..…
Global
Technical
Manager
Technical
Manager 1
Engineer
n
……
..…
Director of
AWA
Manufacturing
Excellence
Technical
Manager 1
Engineer n
Regional
Technical
Manager
……
..…Engineer n
Engineer1
89
3.3.2.2. Survey Questionnaire Design
The main criteria for a good survey questionnaire are relevance and accuracy. The
questionnaire should collect the most relevant information that addresses the research questions
(Zikmund et al, 2013, p. 334-335). An accurate questionnaire provides reliable and valid
information. In this study, the preliminary questionnaire is designed based on characteristics
drawn from the key Strategic Management, Knowledge Management, and Data Mining
literature. It is then modified based on the phase 1 findings, to make sure it is suitable for the
industry context. For example, the terms of the key constructs commonly used in the industry
are identified from phase 1 and then used to replace the more opaque academic terms so that
they could be easier to understand to participants. The questionnaire uses simple,
understandable, unbiased, and non-irritating words. All of these practices would ensure the
accuracy of the questionnaire design.
The first part of the questionnaire includes a brief introduction to explain the importance, nature
and purpose, and potential benefits of the study. The introduction also specifies that the results
of this study will be shared with their organisation after data analysis is completed and they
may access this information on request. All of these practices are used to keep respondents
interested and engaged. The respondents are more likely to be cooperative when they are
interested in the subject and purpose of the research (Zikmund et al, 2013, p. 334-335). In the
second part of the questionnaire personal details, which are needed for descriptive statistics,
are collected. The last part, as a main part of the questionnaire, includes 24 questions about
Strategic Knowledge Management, IT and Data Mining activities, and Resource based
Competitive Advantage. There are another four questions designed as open-ended questions.
Respondents need to explain and provide some examples to answer the open-ended questions.
A copy of the questionnaire can be found in Appendix C. The Table 3.1 below briefly shows
how the questionnaire is structured.
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Construct Sub-Construct Questions (variables)
References
Knowledge
Management
Knowledge Creation
Q1-Q6
(Kaplan & Norton, 1996, pp. 25-29);
(Alavi & Leidner, 2001);
(Nonaka, Toyama, & Byosiere, 2001)
(Gottschalk, 2005);
(Gupta & Govindarajan, 2000);
(Edmondson, Garvin, & Gino, 2008)
Knowledge Storage
Q7- Q8
Knowledge Transfer
Q9-Q10
Knowledge Application Q11-
Q12
Data Mining
Extract, transform and load
(ETL) transaction data
Q13 (Nimmagadda & Dreher, 2009);
(Palace, 1996)
(Zhu & Li, 2006)
(Shetty & Achary, 2008)
(Yang, Gong, & Bai, 2008)
(Tyagi & Sharma, 2011)
(Jambhekar, 2011)
(Jindal & Bhambri, 2011)
(Seddawy, Khedr, & Sultan, 2012)
Store and Manage data and
Provide data access
Q14
Analyse data
Q15-
Q16
Present data Q17
Resource
based
Competitive
Advantage
Valuable Resource Q18-
Q21 (Barney, 1991)
(Kaplan & Norton, 1996, pp. 25-29)
Rare Resource Q22-
Q23
Inimitable and Non-
substitutable Resource Q24
Table 3.1: The Structure of the Questionnaire - Allocating Questions in Questionnaire to the
Components of the Conceptual Model
3.3.2.3. Validity and Reliability
The validity and reliability are also the two main criteria for a quantitative study (Yin, 1994, p.
33). Lincoln and Guba (1985, p290) defined the internal validity “as the extent to which
variations in an outcome (dependent) variable can be attributed to controlled variation in an
independent variable”, this is largely determined by how accurate the measures are to capture
the constructs of interest. The questionnaire is designed based on the relevant and key Strategic
Management, Knowledge Management, and Data Mining literature reviewed in Chapter Two
which can largely ensure the validity of the measures as it uses the measures that have been
widely used in published research. The details of the measurement will be discussed in the next
91
section 3.3.2.4. In addition, in a quantitative study, there are also some statistical indictors
which can be used to test the validity and reliability of the measures, for example, Cronbach’s
alpha used as an estimate of the reliability of constructs. Further details of the validity and
reliability tests for phase 2 will be provided in Chapter Five.
3.3.2.4. Measurement of Constructs
The theoretical framework of the study (Figure 3.4) includes three key constructs: Knowledge
Management, Data Mining, and Resource based Competitive Advantage. They are all latent
variables composed of sub-constructs/dimensions which are measured by survey questions. All
the questions are on a seven-point Likert scale from strongly disagree (1) through Neutral (4)
to strongly agree (7). Each construct and their dimensions are explained below and Table 3.2
summarises the literature based on which the measures are identified. The full survey
questionnaire can be found in Appendix C.
Figure 3.4: Theoretical Framework
1- Knowledge Management (KM) Construct
In this study KM construct is measured through its main dimensions: Knowledge Creation,
Knowledge Storage, Knowledge Transfer, and Knowledge Application, which is validated by
Alavi & Leidner (2001, p115). This construct includes twelve items (survey questions)
reflecting these four main dimensions (i.e. four questions for Knowledge Creation, two
questions for Knowledge Storage, two questions for Knowledge Transfer, and four questions
for Knowledge Application).
2- Data Mining (DM) Construct
The DM construct is measured through its main dimensions: ETL, Store and Manage data and
Provide data access, Analyse data, and Present data, which is validated in prior research by
Palace (1996); Xlinlianf Zhu and Jianzhang Li (2006); Surendra shetty and K.K Achary (2008);
Tie-Li Yang (2008); Prof. S.K. Tyagi (2010); Navin D.Jambhekar (2011); Deepika Jindal and
Knowledge Management
Data MiningResource based
Competitive Advantages
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Vivek Bhambri (2011) and Seddawy and Khedr and Sultan (2012). In this study, “Store and
Manage data” and “Provide data access” elements are merged to one dimension (due to the
close interrelationship between these). This construct includes five items (survey questions)
reflecting these four dimensions (one question for “ETL”, one question for “Store and Manage
data” and “Provide data access”, two questions for Analyse data, and one question for Present
data).
3- Resource based Competitive Advantage (RCA) Construct
In this study RCA construct is measured through four attributes: Valuable Resource, Rare
Resource, Inimitable Resource, and Non-substitutable Resource which is validated by Pankaj
and Madhani (2010) and Barney (1991). In this study, the two attributes of RCA Inimitability
and Non-substitutability are merged into one attribute due to similar characteristic and
influence on Competitive Advantage. This construct includes seven items (survey questions)
reflecting these three attributes (four questions for Valuable Resource, two questions for Rare
Resource, and one question for Inimitability and Non-substitutability Resource).
Construct Variables Items References
KM
Knowledge Creation 4
Alavi & Leidner (2001) Knowledge Storage 2
knowledge Transfer 2
Knowledge Application 4
DM
ETL Data 1 Palace (1996), Xlinlianf Zhu and
Jianzhang Li (2006), Surendra
shetty and K.K Achary (2008),
Tie-Li Yang (2008), Prof. S.K.
Tyagi (2010), Navin
D.Jambhekar (2011), Deepika
Jindal and Vivek Bhambri (2011)
and Seddawy and Khedr and
Sultan (2012)
Store and Manage data and
Provide data access
1
Analyse Data 2
Present Data 1
RCA Value resource 4
Rare resource 2
93
Inimitability and Non-
substitutability
1 Pankaj Madhani (2010) and
(Barney, 1991)
Total 24
Table 3.2: Summary of the Measures of Constructs
3.3.2.5. Quantitative Data Collection
The convenience sample for the questionnaire survey was obtained though the ten senior
managers and directors interviewed in phase1. They provided referrals to relevant reporting
positions. The sample for the survey phase included managers, operations supervisors,
engineers, IT specialists, and other KM system contributors or users. A web based
questionnaire was sent with an email to the relevant employees via the Global Knowledge
Manager of the company. The Global Knowledge Manager also acted as the research facilitator
to carefully communicate the context, objectives and potential benefits of the survey to the
respondents. Reminder emails were also sent though him to increase the response rate. The
questionnaires were distributed to 300 employees across ten departments in the company. 115
questionnaires were completed and returned and all of these responses were valid (with no
missing values). Using this facilitated approach, informed responses to the questions were
obtained – adding quality and strength to the data and a solid response rate of nearly 40% of
the targeted population.
3.3.2.6. Quantitative/Statistical Analysis Technique
According to the hypotheses put forward earlier, there are two sets of causal relationships to
test, 1) Resource based Competitive Advantage as the Dependent construct and Knowledge
Management and Data Mining as the two Independent constructs; 2) Data Mining as the
Dependent construct and Knowledge Management as the Independent construct. All these
constructs are broad constructs, which cannot be directly measured. They are composed of
multiple explanatory sub-constructs which then can be captured by observed
variables/indicators (questions in the questionnaire). In order to measure the complex causal
relationships between latent constructs, Structural Equation Modelling (SEM) is the most
suitable statistical analysis tool for this phase (Joreskog & Sorbom, 1986, p. 1). According to
Joreskog and Sorbom (1986, p3-6), the SEM model includes two parts: Structural Equation
Model which tests the causal relationship between latent variables or constructs. It is used to
94
demonstrate the casual effects and the amount of unexplained variance; and Measurement
Model: which measures the latent variables by observed variables. It is used to explain the
validities and reliabilities of the observed variables.
3.3.2.7. Why PLS-SEM?
There are two methods of SEM estimate: Covariance-Based SEM (CB-SEM) and variance-
based Partial Least Squares SEM (PLS-SEM) which are different but complementary statistical
methods (Hair et al., 2012). The use of PLS-SEM modelling approach was adopted for the
study, as it has fewer restrictive assumptions regarding measurement scales, sample size,
incorporation single-item measures, distribution and normality of data, in comparison with the
Covariance-Based technique (Hair et al., 2012; Hulland, 1999; Chin W. W., 1998). It is
important to acknowledge the limitations of PLS-SEM such as problems of multicollinearity if
not handled well, and no capacity to model undirected correlations, since the arrows are always
single headed (Wong, 2013, p. 3). However, these potential issues are not applicable to this
research. PLS was chosen for this study because of its main advantages to deal with the
relatively small sample size -115 in this study, and the non- normality of the data (for details
of the normality test please see Chapter Five). SmartPLS package version 2.0 is employed to
conduct the PLS-SEM modelling. There is a typical two-step approach to the PLS-SEM
analysis as recommended by Anderson and Gerbing (1988), Chin (1998), and Wilden and his
colleagues (2013): (1) assessment of reliability and validity of the measurement model (or outer
model) and (2) testing of the structural model (inner model). This study follows these two steps
to analyse the qualitative data collected from the survey (full details can be found in Chapter
Five).
3.3.2.8. Hierarchical Component Model
In this research, a higher-order/hierarchical component model is employed which contains two
layers of constructs because the three main constructs of “Knowledge Management”, “Data
Mining” and “Resource based Competitive Advantage” can be defined at different levels of
abstraction. A first-order construct has directly observed variables/indicators (survey
questions). Second-order constructs use unobserved constructs/first-order constructs as their
indicators.
In this study, Knowledge Management, Data Mining, and Resource based Competitive
Advantage are second-order constructs. Knowledge Creation, Knowledge Storage, Knowledge
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Transfer, and Knowledge Application are the dimensions (first-order constructs) of Knowledge
Management; ETL, Store and Manage data and Provide data access, Analyse data, and
Present data are the dimensions (first-order constructs) of Data Mining; Valuable Resource,
Rare Resource, Inimitable, and Non-substitutable Resource are the dimensions (first-order
constructs) of Resource based Competitive Advantage (see Figure 3.6). More explanations and
details are given in Chapter Five.
3.3.2.9. Reflective Versus Formative Indicators
It is very important to identify the structural relationships between latent, unobserved
constructs and their observed variables/indicators in a structural equation model (Coltman et
al., 2008). There are two types of relationship: reflective and formative. In a formative
relationship, indicators form a construct (Coltman et al., 2008), while in a reflective
relationship indicators are a reflection of the theoretical construct (Chin W. W., 1998; Hulland,
1999). The formative and reflective relationship are assessed differently – the formative model
is assessed by indicator weights, significance of weights (standard errors, significance levels,
t-values/ p-values for indicators weights) and multicollinearity (variance inflation factor or
VIF, tolerance, condition index (Hair et al., 2012); while the reflective model is assessed by
indicator reliability (squared standardized outer loadings), internal consistency reliability
(composite reliability, Cronbach’s alpha), convergent validity (average variance extracted or
AVE), and discriminant validity (Fornell-Larcker criterion, cross-loadings) (Hair et al., 2012).
In a hierarchical component model, the relationship between the first-order and second-order
constructs can be also classified into formative or reflective. According to Becker et al. (2012),
there are four types of relationships in a hierarchical component model as illustrated below
(Becker, Klein, & Wetzels, 2012, p. 263). Based on the theoretical relationships between the
constructs in the conceptual model, this study (quantitative part) falls in the First Type which
is based on a Reflective-Reflective Hierarchical Component Model. Details are discussed in
Chapter Five.
96
Figure 3.5: Four Types of Latent Variable Models
Reprinted From (Becker, Klein, & Wetzels, 2012, p. 363)
97
Figure 3.6: Hierarchical Components and Dimensions
3.3.3. Ethical Issues
This research has been approved by Murdoch University Ethics Committee in July 2013. All
research involving human participation needs to get ethics clearance from the committee before
data collection, in this research both interview and questionnaire survey were approved by the
ethics committee.
The consent letter specified that all interviewees and survey respondents are ensured their
anonymity is guaranteed by the researcher. They can withdraw at any time without needing to
give a reason (for details see the consent letter in Appendix B).
Resource
based
Competitive
Advantage
Valuable
Resource
Rare
Resource
Inimitable &
Non-substitutable
Resource
Knowledge
Creation
Knowledge
Storage
Knowledge
Transfer
Knowledge
Application
Knowledge
Management
Data Mining
ETL
Store and
Manage data and
Provide data
access
Analyse
data
Present
data
98
3.4. Chapter Conclusion
The study uses a mixed methods design: top down (phase 1 interviews with ten global senior
managers) and bottom up (phase 2 survey of 115 reporting managers and specialists), to
investigate the Knowledge Management and Data Mining systems and practices in the context
of the case organisation. This fits with the blended paradigm approach using interpretivist
methods to set up the interviews, then using feedback from the respondents to design the survey
which employs a positivist approach. Carefully facilitated, coordinated communication
between the researcher and the interviewees/ respondents meant the context, objectives and
potential benefits of the research are well understood by those volunteering to participate in the
research. This type of facilitated approach can also ensure informed responses to the questions
and high quality data are obtained.
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CHAPTER FOUR
4. QUALITATIVE DATA ANALYSIS AND FINDINGS
4.1. Introduction
This chapter provides an analysis of the data collected in the interviews (phase 1) and
explained in the previous chapter. It is structured as follows:
Figure 4.1: Overview of Qualitative Data Analysis Chapter
The nature of Knowledge Management activities and Data Mining practices, in the resources
industry, are investigated through in-depth interviews combining structured and open-ended
questions. This section presents the summarised findings from interviews incorporating
responses from ten directors and global senior managers of an international company,
conducted from September 2013 to January 2014. The demographic backgrounds of the
interviewees are presented, then the key findings from the qualitative data are provided,
through summarised responses, which have been organised, analysed and synthesised using
NVivo software. Interview data from Stage 1 and feedback from the Global Knowledge
Manager, who acted as the internal research facilitator within the company, was used to inform
the design of the structured survey questionnaire for Stage 2 of the research.
4.1. Introduction
4.2. Interviewee Demographic Background and Roles
4.3. Key Findings
4.4. Chapter Conclusion
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4.2. Interviewee Demographic Background and Roles (Interviewees 1-10)
During the interviews, the ten global senior managers presented their perspectives on
organisational culture, management systems and practices and related Knowledge
Management and Data Mining activities in their organisation. The demographic profile of the
interview respondents, and their role within the global operations of the company, is shown in
Table 4.1 as follows:
Interviewee
Number
Interviewee
code Current position
1 Interviewee 1 Technical Manager
2 Interviewee 2 Global technical Manager
3 Interviewee 3 Director Research and Development Global
Refining
4 Interviewee 4 Global director mining operations
5 Interviewee 5 IT Manager
6 Interviewee 6 Technical Manager
7 Interviewee 7 Technical Manager
8 Interviewee 8 Director of AWA Manufacturing Excellence
9 Interviewee 9 Regional Technical Manager
10 Interviewee
10 Global Technical Managers
Table 4.1: Personal Background Information
Interviewee 1 is a Technical Manager within West Australia operations of the company. His
role focuses on reviewing the performance of plants within Western Australia, and improving
the planning budgeting plus day to day operational and production processes. His department
ensures that each plant meets government requirements relating to green gas, energy efficiency,
and other mandated standards and processes.
Interviewee 2 is a Global Technical Manager. In addition to his technical leadership role for
West Australian operations, he has a global role as a Community of Global Best Practice leader
and is a specialist in the area of precipitation. (This is a key component in the alumina
production process). Essentially he is a representative for nine refineries. He also conducts
operational reviews of refineries on a monthly basis examining performance and cost. He
investigates the variations against plans so as to explain and provide feedback on how the
refineries can be supported to improve their monthly operations. He also uses data received
from the nine refineries to support a month-to-month review of key operations. This data
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includes automated outputs and reports which are then interpreted for patterns and trends which
in turn inform process improvement and target setting. There is a ten-year plan for production
and allied activities. The role also requires contribution to the development of five year
scenarios combined with strategic planning and three year rolling operational plans which
provide more granularity on production profiles and priorities. The primary focus of his team
is setting measurable five year targets cascaded down into three year plans. He is also a
representative for the Technology Delivery Group (TDG). TDG is a technical department that
transfers information internally regarding the use, application and performance of specific
technologies. This activity is related to a series of focus events and workshops centred on
knowledge sharing between different divisions and operations. This is linked to production and
productivity improvements across the system, and helps refineries to participate in sharing
information and assimilating knowledge on advanced production practices across the
company’s operations.
Interviewee 3 is responsible for Research and Development (R&D) for the global refining
operations of the organisation. R&D develops new technologies, as required by the technology
strategy, and delivers them to nine refineries around the world. The role involves the integration
of assets across the different areas. His department is broadly considered to be a technology
leader, generating significant global income for the company, and drives that technical
capability of the company’s refineries and all systems. The respondent emphasised that the role
historically has added tangible value for the company as indicated in publically available
annual reports with a particular focus on productively benefits.
Interviewee 4 is a director of global mining operations. He has 33 years of working experience,
a background in agriculture and environment, and he is also a director of a global Mining
Centre of Excellence. The global Mining Centre of Excellence is a relatively new sector of the
company. It can be accessed from all international locations to inform all aspects of running
local business operations, developing talent and improving mining processes. Interviewee 4
also has been with the company for many years, having worked with mining operation teams
in Western Australia for 50 years and has seen the growth, key technological developments
and emerging operating practices of the company. Members of the environmental mining group
within the centre of excellence have had Best Practices acknowledged by the United Nation’s
environment program.
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Interviewee 5 is responsible for the IT function across the company’s global operations. The
role is responsible for IT commercial systems, infrastructure, applications, and development of
application support for all business units -this includes Enterprise Resource Planning (ERP)
systems and collaboration systems. The role is also responsible for the manufacturing and
process control systems for the company’s global primary products group, working across the
areas of mining, refineries, smelting, and power generation. The respondent also leads a team,
which includes six members, responsible for developing IT strategies for the company. He also
manages the IT group that provides the systems and data required for capacity management,
and network performance.
Interviewee 6 is a Technical and IT Manager for the refining operations within a major US site.
His responsibility is to lead a team of fifteen process engineers, six information technology
specialists, process control employees, and a laboratory staff of twenty people. The role is
responsible for providing process control optimisation for the refinery and for setting most of
the operating strategies for how the refinery should run on a technical basis. From the
laboratory point of view his responsibility is to analyse process streams from around the
refinery. This information informs decisions on setting process parameters. The information
technology component of the role is responsible for ensuring that all of the information
management systems, which provide operational data on how the refinery is performing, is
provided in an efficient and continuous way back to the end user. The role also maintains the
document control systems and ensures that all the IT solutions around the refinery are kept up
to date and operational.
Interviewee 7 is a Technical Manager. In his role he oversees a technical group in providing
support for the operations of a major Caribbean based refinery and ensuring production,
efficiency and quality goals are achieved both in the short and long term. Additionally, he
ensures that all aspects of the company’s values and EHS (Environment, Health and Safety)
systems are respected, and deals with any hard (technical) or soft (human) systems obstacles
to improvements and development.
Interviewee 8 is a director for the Manufacturing Excellence Global Support group. The
purpose of this group is to share Best Practices across the global operations of the entire
company, and access the knowledge embedded in an international network of expertise
pursuant to operational excellence. The respondent defined ‘global support’ as helping
103
operations with problems to get back to a stable condition. Her team applies Best Practice to a
short term pipeline of work focused on helping plants meet their operating plans for the year.
Additionally, they do deep reviews of the plants on a longer term basis. She and her team look
at the overall vision for specific business units and translate this into concrete steps that
personnel at each of the locations can take to make that happen.
Interviewee 9 is a Global Technical Manager with Latin American and Caribbean regional
focuses. He looks after four refineries and monitors their performance. The role encompasses
the identification of specific operational problems and broader analysis of how the individual
refineries are running. This leads to technical improvements which are used to advance the
business. The respondent is a high priority person for the refineries to call on as he has access
to the necessary resources and expertise from around the world. He is responsible for ensuring
the refineries have best access to the information required to make good business decisions.
Interviewee 10 is a Global Manager, with specific accountability for manufacturing excellence.
Her core responsibility is linked with improving costing and she tries to accelerate this by Best
Practices. She also gets involved in looking at operational issues, and identifying opportunities
to transfer Best Practice, or knowledge, between refineries to benefit a refinery that might be
struggling with a particular issue. She stressed that another core responsibility is going to a
particular location and dealing with - or assisting on location with - problem solving, execution,
of solutions and developing a strategy or a roadmap. Other areas of responsibility include
operational reviews, for which she provides technical support. This involves going through and
identifying the major opportunities for improvement, developing the scope of the review and
determining the potential, or the dollar impact, of the proposed review. Additionally, she also
looks at quality, setting up systems and identifying potential issues. She and her team also try
to identify and provide the resources necessary to resolve these issues before the customer is
affected by them.
4.3. Key Findings
This section summarises the key findings from the exploratory interviews, which are presented
in their entirety available on request from the principal supervisor (see contact details in
Appendix B). Three parts that follow the three main theoretical constructs of the research
104
framework form the structure of this section. These three parts are Knowledge Management,
Data Mining, and Competitive Advantage. Each of the key findings has emerged based on
interviewees’ perceptions regarding that particular theoretical construct.
4.3.1. Knowledge Management Key Points and Discussion
4.3.1.1. General Concept and Key Points
Broad feedback from the ten respondents pointed towards a working definition of Knowledge
Management (KM) as the storage of data, information, reports, and also the verbal sharing of
corporate knowledge on a face to face basis. Or as observed by one of the respondents,
Knowledge Management processes can be characterised as arrangements where people are
given the tools to visualise interactions between disparate data sources. In a mining operation,
having access to raw plant data, and identifying information that can be converted into useful
knowledge, is a critical issue. Given the massive amount of data generated across the
organisation’s global operations, turning this into useful information is a big challenge.
Obtaining and interpreting a broad spectrum of well-defined data, derived from a wide range
of function and activities within the organisation, is crucial to effective decision making and
performance across all operations. Sound strategic and operational decision making for the
global organisation and its respective operations incorporates data derived from: resource and
reserve planning: financial and cost analysis activities: plant performance measurement:
technical, operational and process concerns. These include- instrumentation and equipment
status, engineering and laboratory data. These activities form part of a broader process of data,
information, and knowledge sharing, generated through formal and informal interactions
between people. Both hard data and human expertise are required to identify and realise the
potential benefits of developing knowledge and associated processes, systems and practices
that are ahead of the competition. The respondents agreed that operational efficiencies and
required levels of performance required day to day manipulation and interpretation of data in
order to understand and solve problems. Established benchmarking practices combined with
KMS as an integration mechanism enabled senior managers to determine the comparable status
and performance of any one global operation, now and in the past. One respondent noted that
relevant staff working in all operations have access to practical limit data, and the best historical
data when setting operational targets and people goals. This information is organised,
understood and put into an operating plan for each business unit within the global portfolio.
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Therefore, there are probably over a hundred thousand different data points and variables in a
single refinery (Interviewee 6). The historical movement of some of those data points needs to
be looked at so that the changes in one area can be understood as well as the impact of that
change upstream and downstream from other process units. All of this information also needs
to be looked at together so that informed decisions can be made. Most of the interview
respondents made reference to KM processes as data converted to information and was used as
a basis for informed decision-making. Within the mining operation, KM practices and
procedures are well established. Formal data and information management systems were
viewed as essential in the mining activities of the organisation due to the complexity of
extensive underground operations, where multiple engineering factors need to be considered.
Regular data collection and analysis were also viewed as essential to control all environmental
conditions in terms of safety and stability of slopes. This data was also required to understand
and track productivity and performance, against a whole range of measures. It was also
essential to support legal reporting and compliance requirements and related third party and
internal audits, licensing, and global corporate standards related to safety and environmental
management.
Respondents from both mining and refinery operations reported extensive information, and
Knowledge Management related activities as a basis for identifying potential problems,
opportunities and the right information when it is needed. Knowledge Management is identified
as a structured set of routines and practices for developing data and information while adding
value to the organisation’s processes and products. By extension, Strategic Knowledge
Management (SKM) within this context requires systems that combine the most useful
knowledge and an organisational level of understanding and sharing. It involves deploying
technical practices for solving and reducing problems, or preventing errors. This can be as
simple as providing up-to-date troubleshooting guides or implementing predictive control
systems. The respondents had a strong view on the importance of KM as a basis for preserving
and utilising corporate memory. More specifically, that Knowledge Management can help them
to store, identify, and retrieve the company’s past organisational knowledge to support
effective planning strategic and operational decision making.
More specific applications for KM and data management included development and
maintenance of storage systems through to advanced process control applications in the
refineries. Within the refineries, the core KM competency was identified as a modelling
capability, supporting the development of global scenarios for these operations. As technicians
in the refineries have access to the same central models, KM thinking and practices help to
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support information management processes and knowledge transfer. Nominees from the
technical functions in the refineries are appointed to the global COE (Common Operating
Environment) which provides access to ‘chat room’ type functions for open discussion of
problems and improvement opportunities with effective networking across the plants.
Standard procedures for Knowledge Management are in place throughout the company’s
operations. This standardisation is very important for consistency of approach but as one
respondent (Interviewee 7) noted- “Too much standardisation can cause a loss of autonomy
and ability which are the reasons that other improvements are made in the process”.
4.3.1.2. “Knowledge Creation” Key Points
Six out of the ten interviewees believe that Knowledge Management Systems (KMS) enable
unstructured human knowledge, which has been gained through the experience of working
teams, to be captured - so Knowledge Management is what makes this knowledge available. It
should be noted that the average length of service for the case company is over 15 years (see
section 6.2.1). From a collective corporate memory perspective this makes available a vast
repository of tacit knowledge embedded in the firm’s formal structure and broader stakeholder
networks. The director of the company (Interviewee 4) mentioned that they are trying to
develop a mechanism to capture standard procedures to better facilitate accessing the valuable
knowledge that employees have. He also referred to the organisation’s Mining Centre of
Excellence having set up knowledge hubs. These enable access to knowledge on different
themes from different parts of the business, which is a unique way of leading and improving
operational systems and practices on a global basis.
For Knowledge Creation the company tries to identify the gaps between knowledge from
available resources, and knowledge the community brings together from other sites. This
enables Global Virtual Teams (GVTs) to make better comparisons between the sites. In this
way knowledge can be discovered from the expertise of other sites, so that new knowledge is
in a sense ‘created’.
Knowledge is created in response to business requirements, as especially technical knowledge
is valuable for the company. One of the managers (Interviewee 10) emphasised that their
Kaizen events, which seek to involve particular people who are familiar with operations, are
another key platform for obtaining knowledge from across the global operations of the
company. Interviewee 10 noted:
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At “… Kaizen events we tend to bring in the experts from the group from the CEO down within
the organisation. We also bring in persons from operations. It's really important”
One of the interviewees referred to the application of operational standards (especially those
relating to maintenance, safety and environment) as an effective vehicle for capturing
knowledge associated with priority areas focused on the improving use of resources and
outcomes. Therefore, by looking at a standard and identifying the gaps, relevant knowledge
can be captured.
Another interviewee referred to ATC (the case organisation’s Global Technical Centre) the
organisation’s corporate technology development group, as having some knowledge capture
and Knowledge Management processes in place. In addition, there are also processes in place
for Knowledge Management capture, deployment and transformation, which support a broader
framework of Best Practice.
4.3.1.3. “Knowledge Storage” Key Points
Most of the interviewees mentioned that they have several senior people with long-standing
experience working for them. When skilled and experienced people leave or retire from the
company, there is invariably a wealth of history and knowledge that is lost. Added to this, as
they employ (as engineers) a substantial number of young people who come straight from
university, it is a time consuming process for them to become skilled. This issue could ideally
be solved through the documentation of processes and procedures as a successful business
relies on having information being readily available to new employees joining the company. In
this regard the interviewees believe Knowledge Management is helpful for storing knowledge.
In the company all staff try to maintain their history, because knowing the plant’s background
they are then able to find the correct way of doing things as well as recognising patterns that
indicate whether something is right or wrong. Additionally, in the Global Virtual Teams
(GVTs) there are experienced, retired engineers who, having worked more than 40 years in the
company, are able to provide useful information such as historical data regarding the plant
details. This enables less experienced staff to make sense of what has happened in the past and
with this knowledge, or corporate memory, they are able to avoid making the same errors.
One of the directors (Interviewee 4) considered that some of the knowledge and experiences of
employees could not all be documented because it is hard to quantify, document, and write
everything down. In order to solve this challenge they are trying to define what the Best
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Practice is for the operations relating to strategic planning, short-term planning and asset
utilisation. Therefore, they try to document the most important experience and knowledge,
which would be defined as Best Practices. (The company is well known for its Best Practices
in the hydrometallurgical field and processes concerned with the mining and processing of
bauxite and manufacturing of specialised aluminium products many of which are covered by
Intellectual Property (IP) provisions). They also attempt to codify the information in
accordance with Best Practice principles, and share this with people working in relevant
environments. SharePoint sites are employed to store and share knowledge with standardised
and identified Best Practice documentation collected for almost every process. One director
(Interviewee 8) working outside of Australia mentioned that the SharePoint sites are “Not
slick,” and it is not easy to get information out of them. Also Global Technical Manager of the
company (Interviewee 2) noted:
“we’ve got a lots of information stored on Share point sites, but you can’t always find it, to
regularly to access the easy stuff, you know if you are using this information frequently it was
easier you know to pick up. I don’t think it’s easy to access for the end user.”
For instance, after a global focus plant meeting everything discussed or tabled during that event
would be stored in the SharePoint system as a corporate resource. However, after the event it
would not always be easy to find specific information via well controlled documentation in
different locations; the information (knowledge) regarding major projects is stored and indexed
on the intranet system. (Concentrating resources on business planning and operational
improvements within specific focus plants is a widely used method for developing, modelling
and transferring Best Practices and useful knowledge).
One manager referred to the importance of knowledge maintenance as an essential component
of the KM system. He observed that despite the existence of a number of processes (such as
active engagement of retired specialists in training and project design) there is more room to
maintain organisational history, incorporating expert subject matter, obtained from people
doing knowledge development and preservation work (Interviewee 9).
4.3.1.4. “Knowledge Transfer” Key Points
To ensure a successful organisation, it is necessary to combine the experience of senior staff
with the energy and ideas of younger staff. Some interviewees have referred to different
channels of communication and various other conditions being necessary for providing
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knowledge and sharing it among the different groups. One IT Manager (Interviewee 5)
mentioned that since 1994 the company has been developing a strategy of information sharing
and they need to complete this in a timely and efficient manner. In this company, the
transferring of information occurs from the lowest level up, so there can be a number of blocks
to information and knowledge sharing as this flows to progressively higher levels. Knowledge
sharing is related to areas such as safety, efficiency, and environmental standards in the
company. In the company’s West Australian operations, the staff come in on rostered days off
just to attend forums specifically for knowledge sharing. Knowledge sharing is thus a major
focus of the organisation, and the primary concern of the Global Virtual Teams (GVTs)
embedded within an international Community of Global Best Practice. These provide an ideal
platform for sharing knowledge. One of the directors (Interviewee 8) mentioned that they
present awards every year for the Best Practices to incentives and conspicuously reward the
transfer of knowledge for strategic and operational performance improvement. The
Community of Global Best Practice provides an opportunity to share knowledge about how to
set up plant and equipment; select the best mode of operation of the best equipment for a plant,
as well as the best way to use and control agreed operating parameters. In this regard
Interviewee 1 noted:
“For knowledge sharing the best platform is the community of global best practice. We try
structuring our operations to reach the point where something is a best practice, but it can be
challenging because you’ve got the differences in the plants that are not identified.”
In these Communities of Practice, there are face to face activities that allow many people to
look at similar problems and share their knowledge and understanding in relation to specific
contexts. In addition, regular face to face meetings are held that focus on planned events. One
of the techniques used for sharing and leveraging knowledge across the organisation is to
identify those people in the company who, through specialised training, past practice, insight
and experience are best placed to advice on the investigation and resolution of issues and
problems. There is a lot of local (contextual) knowledge that can be circulated very rapidly
within a group, for example this can occur when people go and use the coffee machine and
people from different areas in the refinery chat about their job. Thus the sharing of knowledge
can also occur in informal ways, which are nonetheless as effective as a corporate Intellectual
Capital asset generated from different locations. One of the directors interviewed (Interviewee
4) believed that face to face communication between employees is really good, but also
remarked that sometimes this cannot be achieved easily given physical and geographical
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barriers. To fill this gap, the organisation uses Global Virtual Teams (GVT) as an integrating
mechanism to enable information and knowledge sharing in virtual space with flow on effects
for improved performance. By using the GVT they can maintain good contact with each other,
and are able to discuss and solve problems and get ideas on a regular basis. Therefore GVT is
an integral feature of Knowledge Management practices within the organisation enabling the
transfer of knowledge.
Most of the interviewees referred to SharePoint sites as a platform for all staff to work with
standardised and identified Best Practices when sharing documents. Skills typically specified
by respondents for effective engagement through SharePoint include: identifying
improvements occurring at one location; transferring the know-how derived from these to other
locations; and providing insight into core knowledge that should be shared across all the
locations to support whole of organisation knowledge sharing, problem solving and value
creation. This presents an important, potential, and considerable advantage over their
competitors. Additionally, they are about to migrate to the next version of SharePoint and
employ Yammer as an additional source of Business Intelligence. One director (interviewee 3)
believes that the relationships existing between the R&D functions, TDG (Technology
Delivery Group), and the QUASAR (Quality Automation Solutions in Alumina Refining)
group, who do advanced applications out at the refineries, ensure that knowledge transfer works
effectively at an advanced level. Also in place is the organisation’s “Technology Advantage,”
process for sharing and transferring R&D knowledge. This provides an effective platform for
developing new knowledge and codifying it into the operating system in the refineries. It
provides a vehicle for knowledge transfer out of R&D function into operational and project
environments. As the director of Research and Development Global Refining (Interviewee 3)
observed:
“We have this technology advantage process and it’s designed to share R&D knowledge; as
opposed to (purely) technical knowledge across the business unit and the corporate R&D
function. It is (also) about how do you value projects?; How do you manage projects?; And
(How do) you do strategy?. So stuff like that- knowledge that is now shared corporately,
whereas it wasn’t five years ago.”
Staff in the Technology Delivery Group (TDG) have a good understanding of the requirements
for the effective transfer of information, relating to the performance of specific technologies,
and how to employ the broader ICT infrastructure for transferring and sharing knowledge on a
local (plant) and global (whole of organisation) basis. This provides a good opportunity to put
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into practice the knowledge that has been transferred from different locations across the
organisation.
TDG staff also focus on how to optimise learning and implementation relating to knowledge
transferred from different areas within the organisation, – as a major department. The company
also operates an online training system for staff development and knowledge transfer. It
supports the development of internal (employee and management) capabilities, and provides a
platform for dealing with technical, operational and broader systems questions that arise from
day to day. The online training system is also used to administer tests which assure that the
skills and knowledge of employees in different global locations is at the required level for
promotion or progression of responsibilities. Compulsory online training is provided for senior
managers and technical specialists throughout the organisation by external companies. This
covers themes such as: ‘the team leader’, ‘dealing with government’, company regulations’,
and ‘ethical conduct.’ According to interviewee 3, this training system is relatively effective
on a global level but leaves some gaps in the knowledge transfer. A The company has
commenced the use of screen capture and video systems for training and problem solving
through discussions with expert staff and retired specialists across the organisation’s global
network. Video interviews are used to effectively address various operational, safety, and
governance training requirements. This medium was noted as being particularly effective at a
Corporate (whole of organisation) level for knowledge transfer and retention. Respondent 3
noted that the system had a number of limitations and required upgrading to increase the
organisations capability to deliver interactive, real time learning.
Globally the organisation undertakes an operational review to identify gaps in learning within
particular locations, so that intensive knowledge transfer forums (presentations and
discussions) can be organised to bring staff at all locations up to the required level. Respondent
10 noted that, at these forums, a significant amount of knowledge is transferred. In addition,
technical managers from all across the plants come together once a year for an annual meeting.
In this meeting they share and develop discussions on problems and potential process
improvements and innovations for which they want to receive the input from their colleagues
and counterparts from different locations. In addition, there are several forums throughout the
year which allow technical managers to meet with each other to share information on their
recent projects. Additionally, there are also monthly meetings scheduled to discuss various
operational activities and issues.
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Whilst corporate knowledge sharing also extended to customers and suppliers, it was noted by
respondent 4 that this process could be further developed as a source of Competitive
Advantage.
4.3.1.5. “Knowledge Application” Key Points
With regard to the application of knowledge across the organisations global operations, specific
Best Practice provisions are in place. One of the interviewees - a Technical Manager
(Interviewee 1) - mentioned that the key to effective Knowledge Management should be
aligning staff roles and activities to the question “What do (we) mean by Best Practice?”
Therefore, by defining Best Practices, the scope and focus of Knowledge Management
activities could be specified. The organisation’s global Mining Centre of Excellence helps to
identify and communicate Knowledge Management and other Best Practices, across all the
organisation’s mining operations. This interviewee also conceded that management and staff
in these operations faced many challenges in their efforts to achieve and maintain Best
Practices. One of the major challenges to global Best Practice is the differences in core
characteristics of each plant, notably: different people, chemical processes, physical layouts,
and equipment used across the organisation; what occurs in one plant cannot always be directly
applied to others. However, the same broad principles, or standards, that are used in one plant
can also be used and interpreted to meet the requirements of another. This means that standards
are useful reference points for ensuring that comparable and consistent Best Practice and
Knowledge Management activities are undertaken across various plants and operations leading
to required outcomes. Therefore, rather than trying to apply the same approach to Best Practice
in different locations, managers and staff must consider what can successfully be applied to the
unique problems and challenges within their work context, so that in this way knowledge
relating to Best Practice can be transferred from one location to another. Respondent 7, a
Technical Manager, considered that the principles of Best Practice are very well aligned with
the goals and objectives of the company. He believes that if Best Practices are used and
documented as part of day to day activities, then their whole organisation would be able to
improve and achieve its goals, especially since Best Practices are translatable. Every year one
particular plant acts as the global benchmark, to guide Continuous Improvement (CI) activities.
In this way the Best Practices are understood and they can then also be used, developed, and
transferred. A number of interviewees also referred to ‘franchised standards’ instead of Best
Practice. Franchise standards broadly specify the way they must implement changes or set up
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systems, but Best Practices are still optional. As the director of manufacturing (Interviewee 8)
noted:
“We always used the word Best Practice as to describe what we’re doing. The new
management talks about franchised standards instead of Best Practice. The reason they use
that new language is they don’t want people to think the practices are optional. If you say Best
Practice it sounds like it’s a bit option. Franchise standard says that’s the way we do it. So I
think that’s an example of trying to even more bedding it in. And in our group we have a set of
practices that are mandatory Best Practices that we have to do.”
There are several Communities of Practice (CoPs), within the global organisation, which focus
on identifying and communicating Best Practices throughout the organisation. Recently, the
focus has been on using current performance, and the live performance data, to identify specific
sites requiring development consistent with the standards and Best Practice principles. In the
Communities of Practice they can get feedback from across the organisation using
teleconferences, teleconference calls, and internet meetings to discuss common focus plants
with people all around the world. These focus meetings are run on a regular basis every six
weeks. Participating in this community, staff and managers are linked to production or
productivity improvements across the system, helping mining, refining and production
operations to share with others, take knowledge from other advanced areas and share that
knowledge for creating improved productivity outcomes. Focus plants facilitated by CoP
meetings encompass discussions relating to technical and operational standards and bring
together technical and maintenance specialists and decision makers at the top strategic level.
People refer to standards and identify gaps, so in this way they try to capture relevant
knowledge, associate with that particular topic, and focus on the outcome. One director
(Interviewee 3) referred to the importance of the Global Virtual Team (GVT), which is
responsible for the global manufacturing technology across the system, saying it was also a
more sophisticated version of Communities of Best Practice. In this GVT meet on a routine
basis to exchange ideas through regular interactions between members of management teams
based across the organisation global operations. One interviewee, a Technical Manager
(Interviewee 7) observed that, GVTs provide a mechanism to draw on external support from
various groups within the organisation to solve technical and operational problems quickly.
These groups are also able to provide support for units dealing with issues or crisis.
Another Technical Manager (Interviewee 10) mentioned that there are several systems
introduced over the years for aggregating knowledge which is used in various applications,
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cultural processes, Best Practice transfer, and target setting. Another manager (Interviewee 5)
noted that although the organisation does not necessarily have a global Knowledge
Management System (KMS), it does have a mechanism to collect, share and interpret
knowledge in various forms across the global refinery network. Some of the managers have
access to multiple knowledge points and supporting resources across the refinery system and
this network based system supports various business locations as one united refinery. This
provides an advantage over their competitors, through the systematic and orchestrated use of
collaboration mechanisms such as intranet (including SharePoint), social media, live meetings,
and web collaboration tools.
As identified in a previous study of learning mechanism within the case organisation (Gupta
2012) (Section 2.5.4), SharePoint is widely used as part of global community web-portal and
repository for many documents crucial to inform both operational and strategic decision
making. This web-portal is accessed by Community of Best Practice, and Global Virtual Team
(GVT) members, along with other authorised personnel, to support operational improvements,
modelling and control engineering processes in focus plant designated for Continuous
Improvement (CI). The shared web-portal is also used by the Research and Development
(R&D) teams based at the corporate headquarters, and various sites across the globe, to share
knowledge and identify patterns and connections in the data derived from activity in various
plants. The organisation also operates a Continuous Improvement (CI) system called
Connections. This is supported by standard work instructions and troubleshooting guides.
According to a number of respondents, unstructured knowledge sharing through the social
networking process was used more by IT staff than technical specialists across the
organisations operations.
The organisation’s managers at different locations tended to focus on day to day problems and
on regional issues. All of the interviewees mentioned that whilst most managers deployed
Knowledge Management practices as a part of their daily work routine, these needed further
encouragement and incentives. The managers used Global Virtual Teams (GVTs) as a resource
to solve problems, particularly when concentrating on the development of business cases and
Continuous Improvement (CI) strategies for focus plants. There is evidence from the interviews
to suggest both managers and staff, working at various points across the organisation, employed
Knowledge Management when: participating in transfer of Best Practices; working on specific
focus plant issues; Continuous Improvement (CI) and Kaizen activities; and through
participation in: Communities Of Practices (CoPs); Global Virtual Teams (GVTs); and when
addressing specific focus plant issues.
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One respondent, a Global Technical Manager (Interviewee 2), noted that due to the global
financial crisis there were insufficient funds available to spend on implementing technological
programs. Therefore, they have to make more use of the intellectual capability that they already
possess rather than employ capital solutions. This encompasses different pillars of Knowledge
Management strategy including an Intellectual Property (IP) portfolio which informs some of
the organisations Best Practice, policies and procedures including learning management
processes. In order to increase the efficiency, effectiveness and productivity of its operational
functions, the company must tap into the expertise of current and former staff (Alumni) across
their global network. All categories of staff - including new graduates wishing to learn from
experienced practitioners - are encouraged to participate in a broader Knowledge Management
ecosystem of the organisation. One example cited of combining local specialist knowledge
with global corporate knowledge is that of an instrument technician based at a specific location,
who is involved in engineering problem solving across the organisation. This is linked to a
broader training regime focused on both, staff development, cultivating Best Practice and
combining useful local insights with global performance improvement activities.
4.3.2. Data Mining Key Points and Discussion
4.3.2.1. General Concept and Key Points
Data management systems exist throughout all refineries which are data driven. One Global
Manager believes that the existing data sources are well used by engineers and middle
management; historically they practice good information management which is used for
improving plant operations. Two director level respondents based in Western Australia
(Interviewee 3 and 4) offered their broad operational definitions of information management,
(incorporating Data Mining). One director (Interviewee 3) stressed that information
management is how to ‘sort of’ collect information, store it, and make it accessible to people.
The second director (Interview 4) emphasised that information management is how they can
plan, organise, process, evaluate, distribute, and control data and information from one or more
sources. An IT Manager (Interviewee 5) offered his perspective on information management
as a system that incorporated the electronic or traditional hard copy format, (including all sorts
of information from ad hoc talk, email systems, internal social networking and collaboration).
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The feedback from these three interview respondents suggested giving it a very broad working
definition for information management practices within the organisation’s Australian
operations. Across the company’s international operations, the IT strategy focuses on getting
more value out of all information sources to support better decision making. The organisation
has a particular database technology for financial and commercial planning, Business
Intelligence tools, and corporate data warehouses focused on financial transactions and
commercial functions. One of the respondent directors (Interviewee 4) emphasised that,
although Data Mining is not explicitly mentioned in his working definition of information
management, the company CEO wants to start looking at opportunities related to using Data
Mining as one of the most important tools of Business Intelligence. This would more usefully
accommodate the outputs from a vast array of complex processes in the nine global refining
operations. These represent a huge quantity of variables to be tracked and measured, and data
which can be integrated into a global portfolio of useful knowledge. Systematic Data Mining
is viewed as an important means to integrate the expertise and human Intellectual Capital with
data embedded in a global communication network.
In response to the question - “Do you have Data Mining systems in the company?” all
interviewees reported that they were not aware of specific Data Mining tools and algorithms
employed by the company. They do not use some of the advanced procedures available for
Data Mining, but are pulling data together and looking for correlations that sometimes may not
be obvious. This information is then used in order to understand current conditions and to
recognise which were the ‘Best Practices’ achieved in the past, as a guide to maintaining or
improving standards for future performance.
Certain methods are also used to find information patterns relating to what essential data
collection and analysis has, or has not, been undertaken across the information and
communication technology network. In day to day activities, managers and authorised staff
access information on SharePoint sites containing corporate knowledge and information drawn
from standards, procedures, projects and specific repositories across global operations. Whilst
the Global IT Manager (Interviewee 5) had a clear idea about current status and future strategic
application of dedicated Data Mining systems within the company, other staff held different
views on what constituted Data Mining systems and practices. One interviewee referred to
search engine software currently employed within the company as a Data Mining system.
Another one referred to ‘Wallpaper’ as part of a Data Mining system. Wallpaper is data
captured by a monthly report that collects the technical information from each site. These
reports tend to run to many pages, hence the term Wallpaper. In summary, feedback from
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interview respondents with expert insights into the architecture and application of information
systems in the company indicated that they are at a very early stage of using Data Mining in
relation to broader Strategic Knowledge Management (SKM). They identified some
opportunities and potential for dedicated Data Mining activities but existing systems and
practices are not referred to as Data Mining. While current systems and practices used across
global operations does not constitute advanced Data Mining systems, such as incorporating
artificial intelligence, current data capture and analysis practices were still viewed by
respondents as relatively sophisticated for their industry.
4.3.2.2. “Extract, Transform, and Load Data (ETL)” Key Points
In order to identify and address key operational problems and Continuous Improvement (CI)
opportunities, data must be extracted from existing systems transferred and/or loaded to
centralised or decentralised systems to provide access to managers and staff at various locations
through the network of company operations. If localised problems are encountered within any
plant, managers or staff can request support via web based internal information and knowledge
systems. Data can be accessed from different locations for benchmarking and Best Practice
purposes plus the system provides access to specialists who can analyse the relevant data and
help to interpret in context. In effect, this represents a translation of information into explicit
knowledge, then tacit knowledge consistent with Nonaka’s Socialisation, Externalisation,
Combination, and Internalisation (SECI) Model (Amadeo, 2012; Nonaka, Toyama, &
Byosiere, 2001). This approach is used to resolve complex technical problems often involving
chemistry or metallurgy by tapping into data and expertise embedded in the broader knowledge
network of the organisation.
This Knowledge Management and Continuous Improvement (CI) process is particularly
powerful when applied within the company’s focus plant framework. This involves a selection
of one plant from the company’s global operations for particular attention in a specific year.
Priority operational problems are identified by local managers, staff, and technical specialists.
The expertise of their counterparts in different locations is then leveraged via teleconferencing
and face to face meetings. The focus plant initiatives are accessible globally. In this way they
can look at spend reduction and all monthly Data Mining activity. Focus Plants are face to face
events that measure a section of a refinery against a suite of Best Practices to determine the
improvements required for the location to reach Best Practice. GVT’s contribute heavily
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through producing the Best Practices, having members attend and assisting with follow up and
the provision of knowledge.
The Global IT Manager of the organisation (Interviewee 5) observed that they have a good
globally integrated computer system that allows them to drive technical information from a
network of structure drivers. However, while they have access to valuable integrated data from
various data sources in complex systems, some of the unstructured data needs to be better
managed. Therefore, in recent years, the IT strategy has focused on controlling unstructured
data and building connections using collaborative software. There is of course a particularly
large amount of data in the refinery, which is scanned every second in a control system and
then aggregated for comparison to other plants. Also, according to the Global IT Manager
(Interviewee 5), data systems in place collecting information from thousands of data points
every minute and the engineers understand interactions between the different components of
their process and how to use that data.
Director of Research and Development Global Refining (Interviewee 3) noted that the
company uses a system for the collection, storage and analysis of data across all the refineries.
This system supports analysis, historical data and provides a direct link to global research and
development projects and activities.
4.3.2.3. “Store and Manage Data and Provide Data Access” Key Points
According to a range of interviewees, there are several mechanisms for storing data throughout
the organisation. They intend to build a small data mart in each business unit that will focus in
particular on procurement analysis. There is also a global data warehouse which runs as part
of a broader Enterprise Resource Planning (ERP) system. Other collaborative information is
stored on SharePoint sites and informs Knowledge Management processes, decision making
and target setting activities for each business.
The Wallpaper system is also used for data storage and providing summarised data and
information required for high level decision making across the organisation. The Wallpaper
system forms a basis for identifying synergies and opportunities for performance improvement
across the various refineries.
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4.3.2.4. “Analyse Data” Key Points
The Global IT Manager (Interviewee 5) highlighted the importance of being able to analyse
and present large amounts of data in a simple format through visualisation, summarisation, and
predictive data pattern recognition - applicable to all types of work within the organisation. He
believed that the technology platforms in place across the organisation had a capability to
provide the analytics and visualisations to support effective Knowledge Management across
the organisation.
Structured laboratory data is fed into online management systems for analysis to maintain and
improve processes across the organisation. Director of Research and Development Global
Refining (Interviewee 3) noted that the company had commenced working with third party
specialists who provide advice on advanced analytics. These parties will look primarily at the
refining system which has very sophisticated controls and collects and stores online up to
50000 data points every minute. Historians, engineers, and operational employees come
together to analyse real time aggregated data with advanced tools, control and analyse a wide
range of data including time series, and build knowledge sharing relationships for a more
detailed understanding of what is happening locally, and across the global refining network.
Many of the business units also have data marts, to enable systematic integration of company
wide data and visualisation of related data profiles.
Day to day and systemic problems are addressed by networks of operational staff, using
Continuous Improvement (CI) methods and specialists from the company’s Technical Centre,
who supplied complex statistical analysis when required. However, despite this Continuous
Improvement (CI) activity and specialist support, the director of Manufacturing Excellence
(Interviewee 4) noted that the organisation required developing the infrastructure and expertise
for the level of 21st century big data analytics found in comparable multinational businesses.
4.3.2.5. “Present Data” Key Points
As mentioned above, in order to solve problems data has to be collected and analysed. Directors
and top managers track plant performance to ensure that it is meeting efficiency and
effectiveness criteria, and metrics specified by the company. They recognise that analysing and
presenting large amounts of data in a clear and understandable format is mission critical for the
organisation. Consequently, they generate numerous reports highlighting performance
achievements and shortfalls across the business. They also employ multiple reporting tools
linked to commercial and manufacturing systems which produce reports and visualisations in
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various formats. The aim of these systems is to provide a facility for analysing and
communicating key company performance data in a timely, simple and understandable fashion
to all levels of the workforce. For example, standard query reporting tools are used which
source data from a series of platforms which range from excel to oracle tools. The Global IT
Manager (Interviewee 5) noted:
“We’ve had standard query reporting tools, arranging everything from Excel, to Oracle tools.
We are looking at statistical analysis packages and more recently some of the more advanced
tools that Microsoft now have, with Business Intelligence analytical services.”
There are also some data warehouses and relevant data marts which support some of the local
query reporting. The IT Manager (Interviewee 5) of the company mentioned that some
reporting tools are more flexible and hopefully some new tools might provide better capability
to support the balance between flexibility and capacity.
Interviewee 1 commented on the extensive use of dashboards to provide critical base
information along with morning, daily and monthly financial reports and summaries.
Summarised data is available from the refinery operating systems in terms of reports. Each
refinery provides monthly tactical reports on how they have performed. These reports,
combined with Kaizen Continuous Improvement (CI) documents, can be aggregated into a
broader strategic performance profile for the business as a whole.
An outsourced system is used for collecting data, so any authorised party in the refineries can
source graphs from different sensors and generate reports. Technical Manager (Interviewee 7)
referred to the Manufacturing Execution System (MES) that enables easy configuration of
summary data and summary reports. It also incorporates all the Wallpaper or historical data for
each plant. The business generates paper reports and graphs which are posted as references
points in close proximity to production areas providing at a glance updates on production
statistics and performance against standard measures.
4.3.3. Resource Based Competitive Advantage (“Valuable, Rare, Inimitable, and Non-
substitutable”) Key Points
The organisation has technology which, combined with human expertise, represents a potential
source of Competitive Advantage. For example, Continuous Improvement (CI) over fifteen
years, focused on the organisation’s mining activities, has resulted in numerous process
innovations and efficiencies in the transport systems and logistics associated with the supply
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of millions of tonnes of bauxite to nine refineries at different location across the globe. Other
technological and process improvement innovations are built into mining activities, including
advanced maintenance practices for managing and planning, and upkeep of equipment in mines
and refineries. These improvements and innovations contribute to the broader KMS, operating
routines, and culture across all global operations. When responding to questions on possible
sources of Competitive Advantage for the company, a US based Technical Manager
(Interviewee 6) observed that expenditure on Research and Development was on average,
greater than their competitors and the resulting data, information, knowledge and insights
translate into Competitive Advantage across all global operations.
On the information management side of the equation, real time data and data warehouses are
used to convert relevant data and information into useful knowledge applied specific plants or
business units.
Several of the senior managers interviewed emphasised that to remain globally competitive
required ongoing internal and external benchmarking plus constant review and reflection on
current practices. This was necessary to support systems performance and achievement of long
and short term targets. One interviewee noted that their competitors shared internal
benchmarking information on the performance of equipment but with less emphasis on broader
process innovation and Knowledge Management. This was one of the distinguishing features
from which the company derived cost efficiencies and maintained its competitive position
during both favourable (and currently depressed) market conditions for bauxite, alumina and
manufactured products.
In this way, knowledge is converted into a tangible asset for improving company processes on
broader performance outcomes. The senior managers interviewed shared the broad belief that
the company had well developed systems to translate and aggregate embedded knowledge from
across the company’s network of operations. This involved the day to day combination of
human expertise, technical information and data in anticipation of, and a response to, the
company business challenges and requirements. The technical managers identified the
harnessing of deep technical knowledge and experience of the refining process (with allied
chemistry and engineering processes) built up over many years, as a particular competitive
strength of the company. This capability was further enhanced by expert knowledge of global
commodity reserves and how best to explain them. The Manufacturing Execution System
(MES) also provides a platform for knowledge creation and sharing through data generation,
dynamic help chains and the generation of summary reports covering all aspects of operation
and production. A dynamic help chain is a document that guides operators in troubleshooting
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a process when problems arise. It goes beyond a standard troubleshooting guide by ensuring
the operator tries to solve the problem in the same way an engineer would. As the steps they
go through are the same as the engineer would, it thereby enshrines the engineer’s knowledge
in the operator’s work.
The Director of Manufacturing Excellence (Interviewee 4) noted:
“Right, it’s going to be great. Our team is going to be working with the very first product of
that in order to be best being able to use it. That’s another thing that we really didn’t talk
about when you asked me what kind of knowledge we think is really valuable. I talked a lot
about practices. Another kind of knowledge that will be valuable is … we call them help chains
or dynamic help change. But it’s if you have a problem, how to solve that, so a troubleshooting
guide”
The average length of service profiles for staff across the company’s operations (15 years), was
also identified as a contributor to the useful knowledge pool and competitive strength to the
company. This was captured and shared via Knowledge Management processes, practices and
supporting technology platforms, reinforced by a broader Knowledge Management culture
within the organisation. The company was also in a unique position to combine data from
sophisticated control systems with Research and Development (R&D) activities designed to
maintain operational superiority relative to competitors. The Global IT Manager (Interviewee
5) qualified this claim by noting that, whilst the company did not have access to totally unique
technologies, the Knowledge Management Systems (KMS), culture and management practices
adopted throughout supported unique applications for existing Data Mining and technological
platforms.
4.4. Chapter Conclusion
Knowledge Management practices and Data Mining activities were identified by most
respondents as key components of Competitive Advantage for the company. They were able
to demonstrate the value adding that resulted from a Knowledge Management and Data Mining
system that combined, and activated, Intellectual Capital (IC) in the form of human expertise
and technical data. Over the past 15 years, the company's Knowledge Management Systems
(KMS) had consistently generated process innovations and efficiencies resulting in
Competitive Advantage in both favourable and exacting market conditions. The company’s
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success was built on a longer tradition (30 years) of Continuous Improvement (CI). The design,
development and application of Knowledge Management Systems (KMS) and practices was
informed and supported by key Continuous Improvement (CI) and Best Practice principles
applied and fine-tuned across the company’s global operations. Over the past 30 years the
company has established a strong reputation in the industry for identifying, adopting, and
rapidly executing Best Practices. This tradition is reinforced by the Communities of Global
Best Practice active across the organisation.
This extension of thought, from the Japanese tradition of Kaizen, (which underpinned the
dominant position of their automotive and electronics manufacturing industries through the
1990’s), to a model of Knowledge Management based collaboration, is well illustrated in the
work of Nonaka et al, (1995) (see Appendix F-6).
The Communities of Global Best Practice bring value to the organisation by providing regular
forums (both face to face and virtual) to guide quality improvement and lower costs across the
organisation, as part of a broader knowledge based approach to gaining Competitive
Advantage. These communities spread throughout the global organisational network, identify,
share, and develop Best Practices consistent with Japanese Kaizen routines and Nonaka’s SECI
model of knowledge creation and sharing. This in turn is supported by a broad culture of
Knowledge Management which has been consciously developed by an overarching Knowledge
Management integration team - headed up by a Global Knowledge Manager. These elements
are combined into a formal Knowledge Management framework for sharing and creating
knowledge, transfer of Best Practices and value adding, primarily through process innovation.
The Intellectual Capital (IC) generated within this framework contributes both to measureable
performance improvement (which has to be demonstrated against agreed metrics on a regular
basis) and the Intellectual Property portfolio of the organisation. Intellectual Capital (IC)
becomes Intellectual Property (IP) to be licensed or patented.
The organisation values the knowledge of their former employees as an integral part of
corporate memory and a broader portfolio of Intellectual Capital (IC). A network of Alumni
(retired staff) is called upon to maintain important elements and facets of corporate memory
and facilitate the development of young engineers and scientists. Retired technical staff also
support expert groups focused on the resolution of challenges and problems as they arise in
different operational contexts.
What is commonly termed “knack or know-how” covers a multitude of contingencies where
expertise (skills, knowledge and reflection on experience) is applicable. This is also a useful
risk reduction process focusing on elimination of preventable errors or repeating past mistakes.
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This is a big issue for a lot of companies, and reinforces the value of these extended networks
of expertise which connect corporate memory and risk.
This type of Knowledge Management practice can be combined with sophisticated Data
Mining and analysis methodologies to reinforce the Competitive Advantage of the firm.
The global refining business, in particular, has a strong record of applying these approaches of
techniques to get more out of its assets over extended time periods without outlaying big capital
investment. The CEO of the organisation strongly supports the idea of being a smart
manufacturer, based on generating and applying quality information, and knowledge and use
of strong predictive analytics for accurate pattern recognition and more effective decision
making.
The organisation’s Information and Knowledge Management Systems (KMS) can also be used
to demonstrate compliance with governance, environmental management and quality insurance
standards and improved performance post-audit. The global Knowledge Management culture
organisational principles, allied systems and management practices also strongly encourage
narratives around the future performance requirements of the organisation. Arguably, this has
allowed the company to remain competitive and viable through extended periods of constrained
trading and depressed prices in the market, for both commodities and manufacture product.
Based on the qualitative findings of this study, key elements of Resource based Competitive
Advantage are listed in table below. Chapter Six (section 6.3.5) discusses the current sources
of Competitive Advantage for the case company identified in the global senior management
interviews. It also considers the potential application of upgraded Data Mining infrastructure
with new generation Knowledge Management thinking, to access and interpret valuable
intelligence embedded in customer, supplier, and broader external stakeholder relationships.
This provides an external and internal focus for Strategic Knowledge Management within and
across the global boundaries of the organisation.
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Identified sources of Competitive
Advantage for the Case Company
Potential role of Data Mining and Strategic
Knowledge Management
Unique and scalable combinations
of Human Assets, Intellectual
Capital (IC) and Intellectual
Property(IP) derived from
operations across the company’s
global knowledge network
Using SKM and DM to release the potential of
structured and unstructured knowledge
embedded in human networks spanning all
operations within the global boundaries of the
case organisation. (It should be noted that
questions concerning supplier and other
external stakeholders were included in the
senior management questionnaire and survey.
Very limited feedback was provided by
respondents on the current or potential social
or Intellectual Capital embedded in these
relationships. Using current generation Data
Mining tools and predictive analytics, these
relationships could yield useful knowledge and
commercial intelligence supportive to future
Competitive Advantage)
Deep Technical Knowledge and
Best Practices including active
engagement of an extended Alumni
network (retired specialists)
Using SKM thinking and latest generation
Data Mining, Business Intelligence and
collaborative tools to support and complement
the identification and application of Best
Practices across case organisation networks
R&D Output combined with KM
processes
Using DM and analytical tools to support and
integrate R&D practices and findings within a
broader Strategic Knowledge Management
approach
Table 4.2: Case Company, Identified versus Potential Sources of Competitive Advantage
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CHAPTER FIVE
5. QUANTITATIVE DATA ANALYSIS AND HYPOTHESES
TESTING
5.1. Introduction
In this chapter, the relationships between Knowledge Management activities, Data Mining
elements, and Resource based Competitive Advantage will be examined. The proposed
research hypotheses will be tested by Structural Equation Modelling (SEM). This chapter
includes three main sections: profile of respondents, model development and hypotheses
testing, followed by findings and conclusion. The structure of this chapter is illustrated as
below:
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Figure 5.1: Pictorial Presentation of the Quantitative Data Analysis Chapter
5.1. Introduction
5.2. Profile of Respondents
5.3. Preliminary Analysis
Measurement Model:
- Reliability
- Convergent Validity
- Discriminate Validity CONTINUUM
Structural Model:
- Collinearity assessment (VIF)
- Significance of coefficients (p-values)
- R2
- Effect size f2
- Stone-Geisser criterion (Q2)
5.5. Evaluating Model Fit (Reliability and Validity)
General Model:
Global Goodness-Of-Fit (GOF)
5.4. Reflective-Reflective Hierarchical Component Model
5.6. Hypotheses Testing (Test of Direct Effects)
5.8. Chapter Conclusion
5.7. Additional Tests of the Mediation Effect
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5.2. Profile of Respondents
The profile of respondents is presented in Table 5.1 (details see Appendix D-1). This includes
the respondents’ gender, department, working years in the company, position, and educational
qualification.
As reported in Table 5.1, as for their educational background, 52.2% of the respondents held a
bachelor degree and 27.8% held a master’s degree. As for the job position, 45.2% of
respondents were senior managers of the company such as: global manager, technical manager,
operational manager, and team leader or supervisor; 32.2% of respondents were engineers. In
terms of working experience, the majority of respondents (87%) had more than five years’
experience in the company. 56.5% of respondents were working in the Technical Support and
Business Unit Operations department of the company.
129
Characteristic n Percent Characteristic n Percent
Respondent’s gender Respondent’s educational
qualification
Male 92 80 High School 6 5.2
Female 23 20 College Diploma 6 5.2
Bachelor Degree 60 52.2
Master Degree 32 27.8
Doctoral Degree 11 9.6
Total 115 100.0 Total 115 100.0
Characteristic n Percent Characteristic n Percent
Respondent’s department Respondent’s position
Accounting and
Finance 1 9 Director 1 0.9
Marketing and Sales 0 0 Global Manager 3 2.6
Customer Relationship 0 0 Technical Manager 13 11.3
Operational Planning 7 6.1 Operational Manager 13 11.3
Technical Support 48 41.7 Team Leader/ Supervisor 23 20.0
Business Unit
Operations 17 14.8 Research Scientist 5 4.3
Business Systems 2 1.7 Engineer 37 32.2
IT Department 5 4.3 Staff 12 10.4
Human Resources 2 1.7 Other 8 7.4
Research and
Development (R&D) 12 10.4
Other 21 18.3
Total 115 100.0 Total 115 100.0
Characteristic
n Percent Mean
Std.
Deviation
Respondent’s working years in the company 4.08 1.13
Less than 1 year 1 0.9
1-4 years 14 12.1
5-9 years 21 18.3
10-14 years 18 15.7
15 years above 61 53
Total 115 100.0
Table 5.1: Profile of Respondents
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5.3. Preliminary Analysis
5.3.1. Data Analysis Procedure
As discussed earlier, the focus of this research is on 1) postulating and verifying the
relationships between the three key underlying latent constructs (i.e. Knowledge Management,
Data Mining, and Resources based Competitive Advantage) and their respective observed
variables; and 2) examining the relationships between independent and dependent constructs
(as hypothesised in the Chapter Two) by SEM (Structural Equation Modelling).
PLS-SEM is particularly suitable when the sample size is small; and when the collected data is
non-normal (Hair et al., 2012). In this study, SPSS 19 is used for testing the assumption of
normality. The One-Sample Kolmogorov-Smirnov Test showed the Asymp.sig values of all
variables (Q1-Q24) are less than 0.05 (see Appendix D-2), so the distribution of data is “non-
normal”. Given the relatively small sample size of this study (115), and the non-normal
distribution of the data, PLS-SEM is employed. The PLS-SEM is deployed by two models -
(1) measurement model and (2) structural model (Tenenhaus et al., 2005): the measurement
model relates the observed (manifest) variables to their respective latent variables; and the
structural model relates the endogenous (dependent) latent variable to other predictor
(independent) latent variables indicating the casual relations between them (Tenenhaus et al.,
2005).
The statistical tools used to analyse the data include SPSS Statistics version 19 and SmartPLS
package version 2.0. For the preliminary analysis, i.e. the analysis of missing values,
unengaged responses, means and standard deviations, SPSS and Microsoft Excel are used; for
the structural equation modelling, SmartPLS is used.
5.3.2. Missing Values and Unengaged Responses
There is no missing value in the survey questionnaire because the “Forced Response” function
(i.e. requiring a question response before allowing the respondent to continue) has been set in
the online survey mechanism Qualtrics. The descriptive statistics of all variables (i.e. mean,
standard deviation, minimum, and maximum) are presented in Table 5.2 (see Appendix D-3
for details).
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Items
(questions) Mean S.D Minimum Maximum
Q1 5.42 1.32 2.00 7.00
Q2 4.99 1.38 1.00 7.00
Q3 5.40 1.02 2.00 7.00
Q4 5.23 1.34 2.00 7.00
Q5 4.97 1.35 1.00 7.00
Q6 4.88 1.43 1.00 7.00
Q7 4.66 1.34 1.00 7.00
Q8 4.52 1.54 1.00 7.00
Q9 5.50 1.16 2.00 7.00
Q10 4.33 1.69 1.00 7.00
Q11 5.12 1.36 1.00 7.00
Q12 4.61 1.44 1.00 7.00
Q13 5.03 1.30 1.00 7.00
Q14 4.70 1.57 1.00 7.00
Q15 5.02 1.37 1.00 7.00
Q16 5.08 1.21 1.00 7.00
Q17 5.32 1.19 1.00 7.00
Q18 5.94 1.16 2.00 7.00
Q19 5.15 1.26 1.00 7.00
Q20 6.04 0.99 2.00 7.00
Q21 5.43 1.21 1.00 7.00
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Table 5.2: Descriptive Statistics of Variables
5.4. Reflective-Reflective Hierarchical Component Model
In this research, a higher-order/hierarchical component model is employed. This model
contains two layers of constructs because the three main constructs of “Knowledge
Management”, “Data Mining” and “Resource based Competitive Advantage” can be defined
at different levels of abstraction. A hierarchical component model can be established following
a bottom-up or top-down approach (Hair et al., 2014), the top-down approach is used here
because each of the three general constructs of “Knowledge Management”, “Data Mining” and
“Resource based Competitive Advantage” consists of several sub-dimensions based on the
literature reviewed earlier. The hierarchical component model is made up of higher-order/first-
order components (HOCs) - “Knowledge Management”, “Data Mining” and “Resource based
Competitive Advantage” and the lower-order/second order components (LOCs) – “Knowledge
Creation, Knowledge Storage, Knowledge Transfer, and Knowledge Application”; “ETL,
Store and Manage data and Provide data access, Analyse data, and Present data”; and “Valuable
Resource, Rare Resource, and Inimitable & Non-substitutable Resource” which respectively
capture the sub-dimensions of the three abstract themes - “Knowledge Management”, “Data
Mining” and “Resource based Competitive Advantage”.
As mentioned earlier in the Methodology Chapter, there are two types of relationship in SEM
- formative and reflective. The decision of whether to measure a construct reflectively or
formatively is not clear-cut and there is not a definite answer to this decision (Hair et al., 2014,
p.46). Most of the research shows the decision is largely dependent on how the researcher
conceptualises the construct relative to the indicators (Hair et al., 2014; Chin W. W., 1998). In
a formative model, indicators create the construct directly while in a reflective model,
indicators are the reflection of the construct (Chin W. W., 1998; Hulland, 1999). It means in a
typical reflective measurement model, measures should represent the effects of an underlying
construct (the direction of causality flows from construct to the indicators) and the construct is
a trait explaining the indicators (Hair et al., 2014). Based on this criteria, the measurement
Q22 5.61 1.06 2.00 7.00
Q23 4.42 1.71 1.00 7.00
Q24 5.11 1.25 2.00 7.00
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model in this research is reflective because the indicators (i.e. survey questions which are
detailed in the Methodology Chapter) all respectively reflect the particular first-order
constructs (i.e. Knowledge Creation, Knowledge Storage, Knowledge Transfer, and
Knowledge Application; ETL, Store and Manage data and Provide data access, Analyse data,
and Present data; and Valuable Resource, Rare Resource, and Inimitable & Non-substitutable
Resource). The indicators (i.e. survey questions) represent the effects of the first-order
constructs rather than act as drivers that “ultimately lead” to these constructs (Hair et al., 2014,
p. 46).
Research also suggests that some statistical tests could be used to supplement the theoretical
considerations on the formative or reflective decision. Since all indicator items of a reflective
measurement model are reflected by the same particular construct, the indicators associated
with the same construct should be highly correlated with each other (Hair et al., 2014;
MacKenzie and Podsakoff, 2005), while in a formative measurement model, “indicators need
not be correlated nor have high internal consistency such as Cronbach’s alpha” (Chin W. W.,
1998b, p. ix). This research tested bivariate correlations between every two indicators related
to the same construct in the measurement model (this test was not necessary for the constructs
with a single-item indicator), all correlations are significant at the 0.01 level and range from
0.323 to 0. 689 (see Figure 5.2 and Appendix D-4), except the correlations associated with Q5
and Q6 which will be deleted in next section (i.e. Section 5.5.1). The Cronbach’s alpha values
were also tested, and all values of the first-order constructs are high (see Table 5.4), most above
the widely accepted level of 0.7 (Hair, Ringle, & Sarstedt, 2013, p. 7). These test results can
fully support that the nature of the first-order model is reflective.
134
Figure 5.2: Correlation Tests Between Indicators in the First-order Measurement Model
When deciding the relationship between the LOCs/first-order constructs and HOC/second-
order constructs, the same process/principles should be followed (Chin W. W., 1998b), which
means the theoretical/conceptual reasoning behind a reflective second-order model should be:
all first-order constructs must reflect rather than lead to the same underlying second-order
constructs. “If the higher-order construct is reflective, the general concept is manifested by
several specific dimensions themselves being latent (unobserved).” (Becker, Klein, & Wetzels,
All Spearman’s correlation coefficients are significant at 0.01 level (2-tailed) except the correlation coefficient
between Q6 and Q2, and between Q6 and Q3 which are significant at 0.05 level (2-tailed)
0.342 Knowledge
Transfer
Eman
cipati
Q10
0.589 Knowledge
Storage
Q7
Q 8
0.630
Q15
Q 16
Analyse
Data 0.414
Q22
Q 23
Rare
Resource
0.323
0.593
0.390
0.413
0.407 0.334
Valuable
Resource
Q18
Q19
Q20
Q21
Q5
Q6
Knowledge
Creation
Q1
Q2
Q3
Q4 0.219
0.320
0.548
0.279
0.479
0.429
0.370
0.553
0.689
0.461
0.273
0.318
0.292
0.221
0.338
Knowledge
Application
Q11
Q12
0.554
135
2012, pp. 362-3). Based on this criteria, the second-order model in this research is also
reflective, because all the second-order latent variables are constructed by relating them to the
underlying first-order dimensions, (i.e. Knowledge Management) (KM) as a second-order
construct, is operationalised with four reflective dimensions - Knowledge Creation, Knowledge
Storage, Knowledge Transfer, and Knowledge Application; Data Mining (DM) as a second-
order construct, is operationalised with four reflective dimensions - ETL, Store and Manage
data and Provide data access, Analyse data, and Present data. The Resource based Competitive
Advantage (RCA) as a second-order construct, is operationalised with three reflective
dimensions - Valuable Resource, Rare Resource, and Inimitable & Non-substitutable
Resource. These dimensions were all established, based on relevant theory reviewed in Chapter
Two, and this approach is also consistent with the argument that the reflective model is more
appropriate when a researcher wants to test theories with respect to the construct (Hair et al.,
2014, p.45). Cronbach’s alpha tests also showed high internal consistency (i.e. all values of the
second-order constructs are greater than 0.8, see Table 5.4), which also fully supports the
reflective nature of the second-order model.
Therefore, the SEM mode employed in this research is essentially a reflective-reflective type
of a hierarchical component model which indicates a reflective relationship between the
HOC/second-order constructs and the LOCs/first-order constructs, and each of the first-order
constructs is measured by reflective indicators. The graphical presentations of the Reflective-
Reflective model are shown as below:
Figure 5.3: Conceptual Presentation of the Hierarchical Component Model for KM
All loadings and weights are significant at 0.001 (2-tailed)
136
Figure 5.4: Conceptual Presentation of the Hierarchical Component Model for DM
Figure 5.5: Conceptual Presentation of the Hierarchical Component Model for RCM
5.5. Evaluating Model Fit (Reliability and Validity)
A PLS-SEM model must be analysed through two stages: (1) the assessment of reliability and
validity of the measurement model and (2) the assessment of the structural model (Hulland,
All loadings and weights are significant at 0.001 (2-tailed)
All loadings and weights are significant at 0.001 (2-tailed)
137
1999, pp. 198-200). As explained earlier, a hierarchical component model (containing two
layers of constructs – first-order and second-order constructs) is employed in this research, so
these steps are extended to three steps with the first two steps respectively testing the lower-
order components (LOCs) and higher-order components (HOCs).
5.5.1. Assessment of Reliability and Validity of the Lower-Order Components (LOCs)/
First-Order Measurement Model
According to Hulland, (1999, p. 198-200), the reliability and validity of the measurement
model can be assessed in terms of the individual item reliability, the convergent validity of the
measures associated with individual constructs, and the discriminant validity.
Hair and his colleagues (2012, p. 328) also suggest the validity and reliability of the reflective
and formative measurement model should be assessed in different ways: the reflective
measurement model is usually evaluated through internal consistency (Cronbach’s Alpha and
Composite Reliability (CR)), which is not applied to the formative measurement model. The
assessment of a reflective measurement model is typically by means of indicator reliability
(outer loadings), internal consistency reliability (Cronbach’s Alpha, CR), convergent validity
(average variance extracted (AVE), and discriminant validity (Fornell-Larcker criterion, cross-
loadings) (Hair et al., 2012, p328-9).
As mentioned earlier, as all constructs and sub-constructs in the model are reflective in nature,
in this study the indicator reliability, in terms of the outer loadings of the measures, and the
internal consistency reliability in terms of the Cronbach’s Alpha and Composite Reliability
(CR) will be first examined.
Table 5.3 shows all first-order loadings (between first-order constructs and indicators/survey
questions). All loadings (rounded to the 1 decimal place) are greater than 0.7, except the Q5’s
and Q6’s (also see details in Appendix D-5). Loadings of 0.7 or more is the widely
recommended level, because it means there is more shared variance between the construct and
its measures than error variance, plus more than 50% of the variance in the observed variable
is due to the construct (Hulland, 1999). Based on this criteria, Q5 and Q6 will be removed from
the measurement model.
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Construct Items
(questions) Loadings
Second-Order construct - Knowledge Management (KM)
First Order construct - Knowledge Creation (Reflective)
Q1 0.8
Q2 0.8
Q3 0.8
Q4 0.7
Q5 0.6
Q6 0.5
First Order construct - Knowledge Storage (Reflective)
Q7 0.9
Q8 0.9
First Order construct - Knowledge Transfer (Reflective)
Q9 0.9
Q10 0.8
First Order construct - Knowledge Application (Reflective)
Q11 0.9
Q12 0.9
Second-Order construct - Data Mining (DM)
First Order construct - ETL(Reflective)
Q13 1.0
First Order construct – Store and Manage data and Provide data access
(Reflective)
Q14 1.0
First Order construct – Analyse data (Reflective)
Q15 0.9
Q16 0.9
First Order construct - Present data (Reflective)
Q17 1.0
Second-Order construct - Resource based Competitive Advantage
First Order construct - Valuable resource
(Reflective)
139
Q18 0.7
Q19 0.7
Q20 0.8
Q21 0.8
First Order construct - Rare resource (Reflective)
Q22 0.9
Q23 0.8
First Order construct - Inimitable & Non-substitutable resource
(Reflective)
Q24 1.0
Table 5.3: First-order Loadings
As mentioned earlier (Hair 2012, p328), the main indicators of internal consistency reliability
include Cronbach’s Alpha and Composite Reliability (CR), and the widely accepted level of
both indicators are 0.7 and above (Hair, Ringle, & Sarstedt, 2013, p. 7). All values of
Composite Reliability (CR) are greater than 0.7; and all Cronbach’s Alpha values are above
0.7 - except “Knowledge Transfer” (α=0.54), and “Rare Resource” (α= 0.60) (see Table 5.4 or
Appendix D-6). As the constructs of “Knowledge Transfer” and “Rare Resource” just have two
questions, and their CR values are very high (CR=0.81 for “Knowledge Transfer” and CR=0.83
for “Rare Resource”), removing their indicators (survey question items) is not recommended.
Overall the reliability of the measurement model is within the acceptable level.
The convergent validity can be assessed by examining the Average Variance Extracted (AVE),
which indicates the amount of variance a construct captures from its items in relation to the
amount of the variance due to the measurement error (Fornell & Larcker, 1981). According to
Fornell and Larcker (1981), Hair et al. (2013) and Penga and Lai (2012), the acceptable level
of AVE is 0.5 and above (Fornell & Larcker, 1981, p. 46; Hair, Ringle, & Sarstedt, 2013, p. 7;
Penga & Lai, 2012, p. 471). In this study, all AVE values of first-order constructs are greater
than 0.5 (see Table 5.4 or Appendix D-6), so the convergent validity of the measurement model
is within the acceptable level.
Assessing discriminant validity is the traditional approach that complements the convergent
validity test (Hulland, 1999). Discriminant validity can show the extent to which measures of
a construct differ from measures of other constructs in the same model (Hulland, 1999). It is
evaluated by comparing the square root of AVE with (i.e. ensuring it is larger than) the
140
correlations between the associated construct and all other constructs (Penga & Lai, 2012). In
this study, the square root of each AVE of (all variables) is greater than the related correlations
in the correlation matrix (see Table 5.5 or Appendix D-7), so the discriminant validity of the
measurement model is within the acceptable level.
141
Constructs
Indicators
(survey
questions)
Mean
S.D
Second-order
loadings
(between second-order
constructs and first-
order constructs)
First-order
loadings
(between first-
order constructs
and indicators)
Secondary
loadings
(between second-
order constructs
and indicators)
C.R α AVE
√AVE>
Largest
Correlation
Second-Order construct Knowledge Management (KM) 0.89 0.86 0.63
First-Order construct: Knowledge Creation (Reflective) 0.89 0.87 0.81 0.63 0.795>0.697
Q1 5.42 1.32 0.82 0.77
Q2 4.99 1.38 0.82 0.74
Q3 5.40 1.02 0.80 0.65
Q4 5.23 1.34 0.75 0.66
Q5 (deleted) 4.97 1.35
Q6 (deleted) 4.88 1.43
First-Order construct: Knowledge Storage (Reflective) 0.61 0.89 0.74 0.80 0.892>0.495
Q7 4.66 1.34 0.91 0.58
Q8 4.52 1.54 0.87 0.50
First-Order construct: Knowledge Transfer (Reflective) 0.81 0.81 0.54 0.68 0.825>0.646
Q9 5.50 1.16 0.89 0.75
Q10 4.33 1.69 0.76 0.52
First-Order construct: Knowledge Application (Reflective) 0.84 0.87 0.70 0.77 0.878>0.685
Q11 5.12 1.36 0.88 0.71
142
Q12 4.61 1.44 0.88 0.74
Second-Order construct Data Mining (DM) 0.87 0.81 0.61
First-Order construct: ETL (Reflective) 0.80 1.00 1.00 1.00 1.000>0.618
Q13 5.03 1.30 1.00 0.81
First-Order construct: Store and Manage data and Provide data
access (Reflective)
0.76 1.00 1.00 1.00 1.000>0.615
Q14 4.70 1.58 1.00 0.77
First-Order construct: Analyse data (Reflective) 0.78 0.90 0.77 0.81 0.903>0.610
Q15 5.02 1.37 0.91 0.72
Q16 5.08 1.21 0.90 0.67
First-Order construct: Present data(Reflective) 0.79 1.00 1.00 1.00 1.000>0.618
Q17 5.32 1.19 1.00 0.79
Second-Order construct Resource based Competitive Advantage (RCA) 0.87 0.83 0.72
First-Order construct: Valuable resource
(Reflective)
0.92 0.83 0.72 0.55 0.740>0.697
Q18 5.94 1.16 0.67 0.67
Q19 5.15 1.26 0.68 0.58
Q20 6.04 0.99 0.81 0.71
Q21 5.43 1.21 0.79 0.72
First-Order construct: Rare resource (Reflective) 0.85 0.83 0.60 0.72 0.846>0.630
Q22 5.61 1.06 0.85 0.74
143
Q23 4.42 1.71 0.84 0.70
First-Order construct: Inimitable & Non-substitutable resource
(Reflective)
0.76 1.00 1.00 1.00 1.000>0.604
Q24 5.11 1.25 1.00 0.77
Table 5.4: The Reliability and Validity Assessment of the Reflective Measurement Model
(Note: All loadings are significant at 0.001 level (2-tailed). C.R = Composite reliability. α = Cronbach’s Alpha)
Knowledge
Creation
Knowledge
Storage
Knowledge
Transfer
Knowledge
Application ETL
Store and Manage
Data and Provide
Data Access
Analyse Data Present Data Valuable
Resource
Rare
Resource
Inimitable,
Non-
substitutable
Resource
Knowledge
Creation _ 0.333 0.646 0.683 0.428 0.431 0.610 0.513 0.697 0.608 0.495
Knowledge
Storage 0.333 _ 0.373 0.484 0.495 0.439 0.344 0.382 0.346 0.264 0.402
Knowledge
Transfer 0.646 0.373 _ 0.569 0.418 0.416 0.361 0.449 0.575 0.531 0.410
Knowledge
Application 0.683 0.484 0.569 _ 0414 0.465 0.536 0.350 0.658 0.577 0.519
ETL 0.428 0.495 0.418 0414 _ 0.615 0.387 0.618 0.440 0.303 0.258
Store and Manage
Data and Provide
Data Access
0.431 0.439 0.416 0.465 0.615 _ 0.411 0.470 0.391 0.430 0.371
144
Analyse Data 0.610 0.344 0.361 0.536 0.387 0.411 _ 0.452 0.539 0.363 0.385
Present Data 0.513 0.382 0.449 0.350 0.618 0.470 0.452 _ 0.541 0.421 0.336
Valuable
Resource 0.697 0.346 0.575 0.658 0.440 0.391 0.539 0.541 _ 0.630 0.558
Rare Resource 0.608 0.264 0.531 0.577 0.303 0.430 0.363 0.421 0.630 _ 0.604
Inimitable, Non-
substitutable
Resource
0.495 0.402 0.410 0.519 0.258 0.371 0.385 0.336 0.558 0.604 _
√AVE 0.795 0.892 0.825 0.878 1.000 1.000 0.903 1.000 0.740 0.846 1.000
Table 5.5: The Discriminant Validity Assessment of the Reflective Measurement Model
(Note: The greatest correlation value between variables in each column is highlighted in grey)
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5.5.2. Assessment of the Higher-Order Component (HOC)/Second-Order Model
The assessment of the validity and reliability of HOC/second-order model is explained as
below following the same procedure for the first-order measurement model:
As presented in Table 5.4, firstly all second-order constructs display Cronbach’s Alpha above
0.7 - KM (0.86), DM (0.81), and RCA (0.83); and the Composite Reliability above 0.7 - KM
(0.89), DM (0.87), and RCA (0.87), which means the second-order model has achieved the
internal consistency reliability. Secondly, all the second-order loadings are greater than 0.7
(except Knowledge Storage which is a little lower but still above 0.6) and significant at the
0.001 level. Thirdly, in term of the convergent validity, AVE for second-order constructs could
be calculated by averaging the squared second-order factor loadings (Wilden et al., 2013)
which are shown as below. It can be seen they are all above the threshold of 0.5.
AVE (Knowledge Management) =(0.89)2+(0.61)2+(0.81)2+(0.84)2
4 = 0.63
AVE (Data Mining) =(0.80)2+(0.76)2+(0.78)2+(0.79)2
4 = 0.61
AVE (Resource based Competitve Advantage) =(0.92)2+(0.86)2+(0.76)2
3 = 0.72
Fourthly, the discriminate validity requirement has also met the criteria, as the square root of
each second-order construct’s (namely KM, DM, and RCA) AVE is larger than its correlations
with other main constructs as shown in Table 5.6.
Knowledge
Management
Data
Mining
Resource
Competitive
Advantage
Knowledge Management _ 0.715 0.782
Data Mining 0.715 _ 0.625
Resource based Competitive Advantage 0.782 0.625 _
√AVE 0.794 0.781 0.849
Table 5.6: Correlations between Second-order Constructs, and the Discriminant Validity of
the Higher-order Component (HOC)/Second-order Model
146
Based on all of these tests, it can be summarised that the higher-order component
(HOC)/second-order model has also met all the reliability and validity requirements.
5.5.3. Assessing and Testing the Structural Model
After confirming the validity and reliability of the construct measures, the next step is to
evaluate the structural model. As stated earlier, the structural model relates the endogenous
(dependent) latent variables to predictor (independent) variables indicating the causal relations
between them (Tenenhaus et al., 2005). Based on the three hypotheses developed in the
Literature Review Chapter, there are two structural paths in the structural model. For H1:
Knowledge Management processes are positively related to Data Mining processes in the
global mining and manufacturing company, Data Mining (DM) is the dependent variable, and
Knowledge Management (KM) is the Independent variable (i.e. KM as a predictor of DM,
hence the structural path KM → DM). For H2: Data Mining practices are positively related
to the Resource based Competitive Advantage in the global mining and manufacturing
company, and H3: Knowledge Management processes are positively related to the Resource
based Competitive Advantage in the global mining and manufacturing company, Resource
based Competitive Advantage (RCA) is the dependent variable, and Data Mining (DM) and
Knowledge Management (KM) are the independent variables (i.e. KM and DM as predictors
of RCA, hence the structural path DM, KM → RCA).
According to Hair et al. (2014), the assessment of the structural model includes five steps: (1)
collinearity between the constructs, (2) significance of the structural model relationships, (3)
coefficient of determination (R2 value), (4) effect size f 2, and (5) predictive relevance Q2 and
blindfolding. The details of each step are discussed as below (Hair et al., 2014, p.169-184):
Step1: Collinearity assessment between the constructs
This step assesses the levels of collinearity between each set of predictor/independent
constructs. If the level of collinearity is very high (Variance Inflation Factor (VIF) value is 5
or higher), the methods such as eliminating constructs, merging predictors into a single
construct, or creating higher-order constructs should be used to solve collinearity problems
(Hair, Ringle, & Sarstedt, 2011). SPSS 19 is used in this study for the collinearity diagnosis.
This study assesses the following set of constructs: Knowledge Management (KM) and Data
Mining (DM) as predictors of Resource based Competitive Advantage (RCA) (in the first
147
structural path, KM is the single predictor of Data Mining DM, so the collinearity assessment
between predictor/independent constructs is not necessary). The VIF value is 1.48 (Appendix
D-9), which is much lower than 5, so collinearity among the constructs is not an issue in the
structural model.
Step2: Assessing significance of the structural model relationships (path coefficients)
Path coefficients represent the relationships between the constructs, and their significance
dependency on the p-value. Estimated path coefficients close to +1 show a strong positive
relationship, and close to -1 represent a strong negative relationship. The estimated path
coefficients close to 0 indicate a weak relationship (Hair et al., 2014, p.171). Estimated path
coefficients can be obtained by running the PLS-SEM algorithm for every relationship in the
structural model (details see Appendix D-8).
To assess the path coefficients’ significance levels, standard bootstrapping algorithm was
applied. According to Hair et al. (2011), “The minimum number of bootstrap sample is 5,000
and the number of cases should be equal to the number of observations in original sample”
(Hair, Ringle, & Sarstedt, 2011, p. 145). In this study, a resampling bootstrap method with
5,000, along with each bootstrap sample containing the same number of survey respondents
(115 cases), was used and the results are shown in Table 5.7 (see Appendix D-10).
Structural path Path
coefficient
t-value Significance
level
KM → DM 0.731 13.957 ***
KM → RCA 0.689 9.296 ***
DM → RCA 0.122 1.664 +
Table 5.7: Significance of the Structural Model Path Coefficients
Note: + p < .10; *p < .05; **p < .01; ***p < .001 (
+│t│>= 1.65; *│t│>= 1.96; **│t│>= 2.58; ***│t│>= 3.29)
The results show the coefficient between KM and DM is 0.731 (and significant at the 0.001
level) which indicates a strong positive relationship between the two latent variables. Path
coefficient between KM and RCA is 0.689 (and significant at the 0.001 level) which also
presents a strong positive relationship between them. However, the path coefficient between
148
DM and RCA is 0.122 (significant only at the 0.1 level but not at the 0.05 level), which
indicates a positive but not strong relationship between these two constructs.
Step3: Coefficient of determination (R2 value)
The coefficient of determination (the value of R2) is one of the frequently used measures to
assess the structural model in PLS-SEM. R2 values are within the range between 0 and 1
(namely 0 < R2 <1) with higher levels indicating higher levels of predictive accuracy (Hair et
al., 2014, p.175). According to Chin (1998), the R2 value > 0.67 is “substantial”, 0.33 is
“moderate”, and less than 0.19 is “weak” (Chin W. W., 1998, p. 323). The R2 values of the
structural model on the dependent variables DM and RCA are 0.534 and 0.613 respectively,
indicating a moderate level of predictive accuracy (Table 5.8).
For the endogenous/dependent construct R2 values Threshold
DM 0.534 >0.33 (moderate)
RCA 0.613 >0.33 (moderate)
Table 5.8: The Coefficients of Determination R2
Note: The values of R2 - 0.19, 0.33, 0.67 for weak, moderate, substantial thresholds respectively.
The Figure 5.6 below summarises the main test results of the structural model.
149
Figure 5.6: Results of the Structural Model
0.689
0.731
0.122
0.0798 0.762 0.784
0.788
ETL
R2= 0.636
Analyse data
R2= 0.615
Store and Manage & Provide data
access
R2= 0.581
Present data
R2= 0.618
Data Mining
R2= 0.534
Knowledge
Creation
R2=0.793
Knowledge
Storage
R2= 0.369
Knowledge
Transfer
R2= 0.652
Knowledge
Application
R2= 0.706
0.891
0.607
0.807
0.840
Knowledge
Management
0.919
0.863
Inimitable & Non-
substitutable
R2= 0.582
Valuable
Resource
R2= 0.844
Rare Resource
R2= 0.727
Resource Competitive
Advantage
R2= 0.613
0.763
150
Step4: Effect size ƒ²
The ƒ² effect size is a commonly used measure for assessing the relative impact of an
exogenous/independent latent construct on an endogenous/dependent construct. The ƒ² effect
size measures the change in the R2 value when an independent construct is omitted from the
model. It is used to evaluate whether the omitted construct has a substantive influence on the
R2 values of the dependent constructs (Hair et al., 2014, P.177-178).
The ƒ² effect size of an exogenous/independent construct can be calculated as:
ƒ²=
Where R2included and R2
exluded are the R2 values of the model on the endogenous/dependent
variable, when a selected exogenous/independent latent variable is included in or excluded
from the model.
According to Cohen (1988), Chin (1998, p. 316-7) and (Hair et al., 2014, p.178), the ƒ² value
of 0.02 indicates small effect size, 0.15 indicates medium effect size, and 0.35 indicates large
effect size, however a small ƒ² does not necessarily imply an unimportant effect (Cohen, 1988;
Chin W. W., 1998, pp. 316-7; Hair et al., 2014, p. 178). The SmartPLS is not able to provide
the ƒ² values, so it was manually computed. Table 5.9 shows all ƒ² values (details see Appendix
D-11).
ƒ² (KM→RCA) =
ƒ² (DM→RCA) =
Structural path R2included
R2excluded
Effect size (ƒ²) Degree
KM → RCA 0.613 0.391 0.57 large
DM → RCA 0.613 0.605 0.02 small
Table 5.9: Effect Sizes ƒ²
Note: The values of f²; 0.02, 0.15, 0.35 for weak, medium, large effects thresholds respectively
R2RCA (KM included) - R2
RCA (KM excluded)
1 - R2RCA (KM included)
R2RCA (DM included) - R2
RCA (DM excluded)
1 - R2RCA (DM included)
R2included - R
2excluded
1 - R2included
151
Step5: Predictive relevance Stone- Geisser’s Q2
Stone- Geisser’s Q2 is used to evaluate the structural model’s predictive relevance, namely the
model’s capability to predict (Henseler, Ringle, & Sinkovics, 2009). The Q2 value is a measure
of predictive relevance of an independent construct on the endogenous/dependent construct
based on the Blindfolding technique in PLS-SEM (Hair et al., 2014). The Q2 value of 0.35,
0.15, and 0.02 indicates the independent construct has a respectively large, medium, and small
predictive relevance for a certain endogenous/dependent construct (Hair et al., 2014, p.184).
In a SEM model, Q2 values of zero or below indicate a lack of predictive relevance. Q2 values
were calculated by running the blindfolding function in SmartPLS. Table 5.10 shows the Q2
values of the models on the two endogenous/dependent constructs (also see Appendix D-12).
For the endogenous/dependent constructs Q2 Degree
DM 0.312 Moderate
RCA 0.310 Moderate
Table 5.10: Predictive Relevance Q2
Note: The values of Q2: 0.02, 0.15, 0.35 indicate weak, moderate, strong degree of predictive relevance
5.5.4. Global Goodness-Of-Fit (GOF)
A global Goodness-Of-Fit (GOF) criterion has been suggested by Tenenhaus et al. (2005) for
PLS path modelling. It can be regarded as an index for globally validating the PLS model
(Tenenhaus et al., 2005). GOF is defined as the geometric mean of the average communality
and the average R2 for endogenous/dependent constructs (Wetzels et al., 2009).
According to Wetzels (2009), the GOF (0 < GOF < 1) value less than 0.1 is small, less than
0.25 is medium, and above 0.36 is high (Wetzels, Odekerken-Schröder, & Van Oppen, 2009,
p. 187).
In this study, the average of communality is 0.814 (the average of 1.000, 0.815, 1.000, 0.770,
0.631, 0.795, 0.681, 1.000, 0.716, 1.000, 0.547) (see Appendix D-13), and the average of R2 is
0.636 (the average of 0.793, 0.369, 0.652, 0.706, 0.636, 0.581, 0.615, 0.618, 0.844, 0.727,
0.582, 0.534, 0.613 which are presented in Figure 5.6); so the GOF value is 0.720 which
indicates a high degree of global goodness-of-fit of the model.
GOF = Communality × R2
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5.6. Hypothesis Testing (Test of Direct Effects)
Three basic hypotheses of this study for the quantitative research section have been put forward
in Chapter Two (Literature Review).
H1: Knowledge Management processes are positively related to Data Mining processes in the
global mining and manufacturing company.
H2: Data Mining processes are positively related to the Resource based Competitive
Advantage in the global mining and manufacturing company.
H3: Knowledge Management processes are positively related to the Resource based
Competitive Advantage in the global mining and manufacturing company.
In Table 5.11 all the path coefficients, the t-values and their significance levels, are presented.
Structural path Path
coefficient
t-value Significance
level
Conclusion
KM → DM 0.731 13.957 *** H1 is supported
KM → RCA 0.689 9.295 *** H3 is supported
DM → RCA 0.122 1.664 + H2 is supported
Table 5.11: Hypotheses Testing Results
Note: + p < .10; *p < .05; **p < .01; ***p < .001 (
+│t│>= 1.65; *│t│>= 1.96; **│t│>= 2.58; ***│t│>= 3.29)
According to Table 5.11, H1 and H3 are fully supported at the 0.001 significance level and H2
is supported at the 0.1 significance level. It can be concluded that a Knowledge Management
process has a strong effect on Data Mining processes (H1: Path coefficient=0.731, p < .001)
which has a strong effect on the company’s Resource based Competitive Advantage (H3: Path
coefficient=0.689, p < .001). In comparison Data Mining has a comparatively weaker
contribution to the company’s Resource based Competitive Advantage (H2: Path
coefficient=0.122, p < .10).
5.7. Additional Tests of the Mediation Effect
Some additional tests have also been conducted to explore whether the relationship between
Knowledge Management (KM) practices and the Resource based Competitive Advantage
153
(RCA) is indirect, namely whether the effect of Knowledge Management (KM) on RCA is not
direct, but through DM as a mediator:
H4: Knowledge Management (KM) positively affects the Resource based Competitive
Advantage (RCA) through its effect on Data Mining (DM) processes (namely DM mediates
the effect of KM on RCA) in the global mining and manufacturing company.
The mediation effect indicates a situation where the mediator variable, to some extent, absorbs
the effect of an exogenous/independent variable on an endogenous/dependent variable in PLS
path models (Hair et al., 2014). The mediation effect could be full or partial. A variable could
be defined as a full mediator when the entry of the mediator variable drops the relationship
between the independent variable and the dependent variable to almost zero. Partial mediation
is indicated by the situation where the mediator variable accounts for some, but not all, of the
relationship between the independent and dependent variable (Hair et al., 2014).
Sobel test is one of the useful approaches to the test of the statistical significance of any indirect
effect of an independent variable on a dependent variable through a mediator variable (Preacher
& Hayes, 2004). However the Sobel test is less suitable for a small sample size (Preacher &
Hayes, 2004). In this study, given the relatively small sample size (115 survey respondents), a
non-parametric bootstrapping method is adopted (Hair et al., 2014; Preacher & Hayes, 2004).
To test the mediating effect of DM with the bootstrapping approach, the PLS algorithm
function is used to first estimate the direct path coefficient, and t-value excluding the mediator
variable DM.
Next, the Variance Account for (VAF) is computed as the ratio of indirect effect to total effect-
the indirect effect is calculated by multiplying the path coefficient between independent
variable and mediator variable by the path coefficient between mediator variable and
dependent variable; and total effect is the total of the indirect effect and the path coefficient
factor between independent variable and dependent variable (Sarstedt et al., 2014, P. 8).
VAF =
A VAF >80% shows a full mediation effect. If a VAF is larger than 20% and smaller than 80%,
it indicates a partial mediation effect while VAF <20% suggests no mediation effect (Hair et
al., 2014, P. 224-225). In this study the direct effect of KM on RCA is 0.689, while the indirect
effect of KM on RCA is 0.089 (0.731×0.122). Thus the total effect is 0.778 (0.689+0.089).
Indirect effect
Total effect
154
Based on the equation above, the VAF value is only 0.114, so there is no mediation effect of
the DM (DM is not a mediator) on the relationships between Knowledge Management
processes and Resource based Competitive Advantage. This hypothesis testing result suggests
that the Data Mining activities (as an important part of the organisation’s hard KM systems)
haven’t been effectively integrated with the soft knowledge creation, transfer and/or
application system in the case organisation.
Only 11% of the total effect on the Competitive Advantage of Knowledge Management relates
to Data Mining.
Effect of Direct
effect
Indirect
effect
Total
effect
VAF
(%)
Mediation
Level
Conclusion
KM → DM
→ RCA
0.689 0.089 0.778 0.114
(11%)
No mediation H4 is not
supported
Table 5.12: Test of the Mediation Effect of DM
5.8. Chapter Conclusion
This chapter presents the statistical analysis process based on the measurement model and
structural model. The models of this study show a high level of reliability and validity. Based
on the hypotheses testing results, the hypothesis 1 and 3 are fully supported at the 0.1%
significance level while hypothesis 2 is supported at the 10% significance level, and Data
Mining (DM) does not mediate the relationship between Knowledge Management (KM)
practices and the Resource based Competitive Advantage (RCA) in the case company. The
implications of these results will be further discussed in the Discussion and Conclusion
Chapter.
155
CHAPTER SIX
6. DISCUSSION, CONCLUSIONS, AND
RECOMMENDATIONS FOR FUTURE RESEARCH
6.1. Introduction
In this chapter the research results and key findings are interpreted to: 1. Present the study
conclusions in relation to the research questions and hypothesis; 2. Discuss the contributions
of the research and highlight the implications for future theory and practice; 3. Outline the
limitations of the research and recommendations for future research. The following Figure
illustrates the structure of the chapter:
Figure 6.1: Pictorial Representation of Discussion and Conclusion Chapter
6.1. Introduction
6.2. Discussion Regarding Identified Aspects of the
Constructs and Key Findings
6.3. Key Research Themes and Conclusions
6.4. Research Contribution and Implications
6.5. Limitations of Research and Recommendations for Future
6.6. Chapter Conclusion
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6.2. Discussion Regarding Identified Aspects of the Constructs and Key
Findings
In this section, the relevant literature, which is provided in Chapter Two, will be compared
with the key findings of qualitative data analysis of the study. This subsection shows how the
findings fit into the body of relevant literature. All three constructs (Knowledge Management,
Data Mining, and Resource based Competitive Advantage) cited in the conceptual model (see
section 2.11) are discussed below:
6.2.1. Knowledge Management Definitions and Constructs
According to the relevant academic literature provides various definitions of Knowledge
Management. They believe Knowledge Management is the effective knowledge processes that
help an organisation define, select, organise, distribute, and transfer knowledge and expertise
which exists in the organisation’s memory in an unstructured manner (Jashapara, 2011; Turban
& Leidner, 2008). Hence, Knowledge Management is a strategy for providing the right
knowledge to the right people at the right time, to improve organisational performance and
operational efficiency, enhance products and services, and create customers satisfaction
through sharing and putting information into action (Halawi, Anderson, & McCarthy, 2005;
Lee, 2009).
The mixed methods research revealed that the company has a culture which encourages staff
to use Knowledge Management Systems (KMS) in their routine daily work. The company uses
information (explicit) and human (tacit) knowledge for problem solving and making informed
decisions. Well-defined data, which is derived from functions and activities within the
organisation, is crucial to effective decision making. From this point of view, Knowledge
Management is one of the best structured ways for adding value to data and information and in
converting them to knowledge, Knowledge Management is identified as a structured practice
for developing data and adding value to organisational processes. The research revealed that
rather than working with common academic definitions of Knowledge Management, the
interviewees (global senior managers) and survey respondents (reporting managers and
technical specialists), used more informal working definitions of Knowledge Management
consistent with Argyris’ concept of ‘theories in use’ (Argyris, Smith, & Hitt, 2005). However,
the day to day management practices and working culture of the organisation did reflect
operational and Strategic KM applications. These included: A dynamic and collaborative
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system of GVTs and a supporting culture of knowledge sharing and Continuous Improvement
(CI) across the company’s global operations; Retention and active engagement of staff and
alumni expertise to inform training practices, project work and operations, throughout the
company network; CI related management systems and practices which combine tacit and
explicit knowledge as a resource to enhance plant specific and company-wide processes and
systems, the broader operational capabilities and ultimately- Competitive Advantage of the
organisation (Alavi & Leidner, 2001; Halawi, Anderson, & McCarthy, 2005; Davenport &
Prusak, 1998; Silwattananusarn & Tuamsuk, 2012; Jashapara, 2011).
Some participants identified Knowledge Management as an important tool for informed
decision making. Key managerial and operational decision making processes were supported
by systems that enabled the identification storage, sharing, and collective interpretation of
company wide data and information. This system combined access to real time data sets to
inform timely and effective decision making within specific businesses, or operations. The
expertise of retired specialists, along with repositories of key data and historical records were
also treated as important and interrelated components of corporate memory. The company’s
SKM framework supports the use of globally scalable KM to provide a measurable return on
investment for the global business and component units. This meta- knowledge creation
process was coordinated via the companies GVTs and dedicated SKM group headed up by the
Global Knowledge Manager. This group and the GVTs worked annually with local ‘focus
plants,’ and year-round with other targeted operations or business units, or the global senior
management team- to co-create product and process innovations and improvements. This
systematic approach to Strategic Knowledge Management (SKM), built on a Continuous
Improvement (CI) culture, proved to be a key differentiator and survival mechanism over an
extended period of falling commodity prices and reduced demand for raw materials and
processed products. The KM system and culture supported informed choices at all levels in
their operations through coordinated investigation of issues and problems, and use of hard and
soft systems data, information and context specific knowledge. This is consistent with
Knowledge Management decision making frameworks in the literature including: Wiig’s
Intelligent Model for Building and Using Knowledge (1993); Choo’s Sense-making KM
Model (1998); and the use of a Complex Adaptive Systems model of KM (ICAS) (2004) to
support different decision making methods (Dalkir, 2005) (see sections 2.7 and Appendix F,
for further elaboration of the relationship between Complex Adaptive Systems, KM and
decision making).
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In next subsection, the findings on Knowledge Management processes (Knowledge Creation,
Knowledge Storage, Knowledge Transfer, and Knowledge Application) are compared with the
relevant literature.
6.2.1.1. Knowledge Creation
The findings from senior managers revealed the researched company has been in the mining
industry for 50 years and some employees have up to 40 years’ experience. These employees
have a vast amount of valuable knowledge which has been generated through these
experiences. The director of the case organisation’s Mining Centre of Excellence (Interviewee
4) noted how the Centre supported a set of knowledge hubs and a platform for integrating
different knowledge themes from across the business. The knowledge identified, captured and
shared, with reference to these whole of organisation themes, often incorporated Best Practices
aligned with the strategic priorities and corporate goals of the company.
Several other participants referred to Global Virtual Teams (GVTs) as playing an important
roles for discovering knowledge and making comparisons between the sites. According to Grey
(2015), GVTs enable members from different communities of practice to collaboratively define
and address whole of organisation problems and challenges.
Kaizen events were also viewed as a well-established approaches to continuously improving
existing processes and obtaining new knowledge and insights. This supported more effective
hard (technical) and soft (people) systems design across the global operations of the company.
Some participants referred to ATC (the case organisation’s Global Technical Centre) and the
organisation’s corporate technology development group, as significant contributors to
knowledge capture and creation. (See section 4.3.1.2)
The Knowledge Creation process combines internal and external sources like printed
documents, computer databases, and interactions among people (Holden, 2001) (See section
5.7 regarding to direct effect of Knowledge Management processes (soft systems) on Resource
based Competitive Advantage). Mobilising tacit knowledge is the most significant factor in the
Knowledge Creation process (Nonaka I. , 1994). Nonaka refers to three layers of Knowledge
Creation: 1) The SECI process (Socialisation, Externalisation, Combination, and
Internalisation). 2) The ba platform (Originating ba, Interacting or Dialoguing ba, Systemising
ba, Exercising ba) which refers to a mental and physical space for knowledge generation and
sharing; 3) Knowledge assets can take three forms: Experiential- for converting tacit to more
developed tacit knowledge; Conceptual- for converting tacit to explicit knowledge; Systematic-
for converting explicit to new explicit knowledge; And Routine- for converting explicit to new
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tacit knowledge. These knowledge assets are inputs to the SECI process which is driven by the
collaborative action and spirit of the participants in ba or cyber ba, if ICT platforms are being
used (Nonaka, Toyama, & Byosiere, 2001; Nonaka, Toyama, & Konno, 2000).
In the company when employees walk around inside, chat near the coffee machine about their
work, and attend face to face/ informal meetings, they share their beliefs, feelings, and
emotions in informal ways. These informal interactions generate corporate Intellectual Capital
assets, drawing on shared experience from different locations. In this way Socialisation,
Originating ba, and Experiential knowledge asset come together, so knowledge is created
through direct experience. Managers embody their experience in soft systems and connect
corporate strategy to ideas generated from daily social interactions (Nonaka, Toyama, &
Konno, 2000).
Externalisation, and Dialoguing (interacting) ba, produces valuable knowledge assets when
employees articulate their tacit knowledge into new layers or generations of explicit
knowledge. Tacit knowledge converts to explicit knowledge through dialogue when using
figurative language, metaphors, and images. The Knowledge Management culture within the
company broadly supports Nonaka’s model. At every monthly, or annual meeting, or
Community of Best Practice gathering, employees are able to share their mental models,
understandings, and insights into current organisational rules and routines. In this regard,
computer-mediated information sharing can increase the quality of Knowledge Creation by
enabling real time sharing of data, new ideas, practical insights and personal assumptions and
beliefs. Information systems can support collaboration and communication processes within a
safe space or high trust context similar to Nonaka’s conception of ba or cyber ba. In the case
company, the GVT and ATC have the potential to elevate these processes to a higher level
through careful attention to trust and collaborative culture building. Even in highly capital
intensive industries, such as minerals and metals mining processing and manufacture,
Knowledge Creation is fundamental to the survival of the business. Mobilising tacit knowledge
is a significant factor in Knowledge Creation process (Nonaka I. , 1994; Wipawayangkool,
2009). Virtual teams can perform well in Knowledge Creation and increase the value of
Knowledge Management significantly (Wipawayangkool, 2009).
Combination, Systemizing (Cyber) Ba, and Systematic knowledge assets relies on conversion
of explicit to more explicit knowledge- capturing, gathering, and storing new explicit
knowledge. In the case organisation this process incorporates, documentation of knowledge
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through databases including data warehouses and data marts supported by a broader ICT
network.
Finally, Internalisation, Exercising ba, and Routine knowledge assets involves conversion of
explicit to tacit knowledge through shared learning and training. In the case organisation, a
number of interview participants noted that this conversion is constrained by basic online
training systems with limited bandwidth. These were reported to limit real time interactive
learning and does not interface well with other data, information and knowledge sharing
platforms such as SharePoint. The research revealed that Knowledge Creation within the
company was well supported through day to day routines but could be improved by the
introduction of cutting edge, ICT supported training systems.
6.2.1.2. Knowledge Storage
Most of the participants believed Knowledge Management can be helpful for storing
knowledge and capturing experience as intellectual assets for the company. The organisation
has successfully retained the skills and expertise of technical, scientific and engineering
specialists. (The average length of service for staff and management in this organisation is
approximately 15 years). When staff leave or retire, there is a risk that critical operational and
strategic knowledge will be lost. (See section 2.6.2 for discussion of organisational memory).
To some extent this kind of issue can be addressed through improved documentation,
information storage and access, but this does not account for the body of collective,
contextually-dependent knowledge gained over many years of working in different operational
and project environments. This tacit knowledge and experience is hard to quantify and measure.
However, at an aggregated level the Global Knowledge Manager and members of GVTs are
able to demonstrate significant direct savings and value add for the company as a whole. This
suggests that Knowledge Management activities are of the most value when scalable across
multiple operations.
Within the company, the risk of organisational memory loss is mitigated through the
application of Best Practices and related standards for collection, storage, access and
dissemination of relevant knowledge and information. SharePoint sites were identified as an
effective means for storing knowledge and documenting and sharing Best Practices. However
one interview respondent mentioned that the SharePoint site is “not slick” or easy to search, or
extract information from. For example, the data and information required to support annual
global Focus Plant meetings should be easily available from the SharePoint system as a
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corporate resource. However, in the experience of interviewee participants, this was not always
the case. On occasions it was not easy to search for, and retrieve, information required to inform
improved systems and operations in specific Focus Plant locations. So from this point of view,
SharePoint is seen as a potential weakness within broader Knowledge Management Systems
(KMS).
The company employs a significant number of young graduate engineers, who take some time
to be fully conversant with company systems and processes and to develop the deep knowledge
and insights required for them to make informed decisions and become skilled problem solvers.
In a successful organisation, it is necessary to combine the experience of senior staff with the
energies and fresh ideas of younger staff. With this in mind, membership of GVTs, comprising
inexperienced graduates with long serving staff and retired specialists, provide a dynamic
platform for knowledge retention, sharing, and value adding. Through this immersion young
employees, who are able to understand the past history of various plants and gain thorough
access to corporate memory, were able to avoid repeating errors of the past. (In the case
organisation, GVTs undertake a strategic role in facilitating knowledge sharing and leveraging
the value of this knowledge across all global operations. Communities of Best Practise support
this globally scalable aggregation and sharing of knowledge by coordinating and contributing
more localised data and insights.)
In section 2.6.2 in the literature review there is a discussion of individual memory and links to
organisational memory. Individual memory focuses on personal experiences, and actions, but
organisational memory focuses on personal experiences and how this influences organisational
activities (Alavi & Leidner, 2001). The evidence shows in the case organisation, knowledge
maintenance is an essential component of the Knowledge Management System (KMS). The
company also attempts to identify and store Best Practices. These are embedded in
organisational or collective memory generated by the aggregated individual memories of
members of a group (Olick, 1999). Organisational memory in this case refers to human
expertise combined with organisational archives of annual reports or decision outcomes made
under specific circumstances decisions (Alavi & Leidner, 2001).
In the Literature Review Chapter (section 2.6.2), memory is identified as having both a positive
or negative influence on organisational performance. On the positive side, memory helps with
storage of Best Practises to provide practical solutions for management and operational
problems. Typically, the focus of these solutions is to avoid resource wastage and replicating
of previous work (Alavi & Leidner, 2001). On the negative side, memory and ingrained
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routines may lead to correction of errors without changing behaviours, assumptions, and the
underlying or master program (single-loop learning) (Argyris, Smith, & Hitt, 2005). Precedent
or fixed routines can also result in decision making bias at the individual level (Alavi &
Leidner, 2001). This can produce inertia in terms of the Knowledge Creation and innovation
activities of staff.
6.2.1.3. Knowledge Transfer
Knowledge transfer is one of the most important processes of Knowledge Management in an
organisation for transferring knowledge to locations where it is needed (Alavi & Leidner,
2001). Gupta and Govindarajan (2000) have the conceptualised knowledge transfer in terms of
the five elements below (Gupta & Govindarajan, 2000, pp. 475-6):
“Perceived value of the knowledge in the source unit” is the first element of the transmission
process. The research findings revealed the case company makes wide use of Best Practices as
a valuable knowledge source for operational improvement, strategic planning, and effective
asset utilisation. This approach is best illustrated through regular face to face, or electronically
facilitated, meetings between managers, engineers, and other technical specialists representing
all the major global operations of the firm. This regular update on project and operational
lessons and insights ensures ongoing knowledge transfer within the broader organisation
ecosystem. Knowledge transfer, with an emphasis on Continuous Improvement (CI) and
application of Best Practices, at a single site is undertaken through annual Focus Plant events.
This brings together the composite expertise of people who, through specialised training, past
practices, insight and experience, are best placed to advice on investigation and resolution of
issues and problems.
According to the interview participants, Communities of Best Practice (CoBP) in the case
organisation provide a mechanism for face to face activities that allow employees to get
feedback from other plants, look at similar problems and share knowledge about how to
maintain and set up plant and equipment. Transfer of information occurs from the lowest level
up (see Nonaka knowledge spiral (see section 2.6.1 and Appendix F-6), so that information and
knowledge is shared horizontally (across plants) and vertically at progressively higher levels
throughout the company structure. The interviews also highlighted the role of QUASAR
(Quality Automation Solutions in Alumina Refining) specialists (see section 4.3.1.4). These
personnel undertake advanced applications in the refineries; ensure that highly technical
knowledge, information, and data transfer is used to optimise all nine refining operations across
the company global mining refinery and manufacturing network. Therefore, Best Practices in
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source units are perceived to be very valuable for improving and achieving company’s goals
and objectives.
“Motivation to transfer knowledge in the source unit” is the second element. In order to support
effective knowledge transfer, organisations should encourage employees to get rid of
individualistic and localised thinking about protecting what they know and embrace a
knowledge sharing culture supported by aligned rewards. The company presents awards every
year for Best Practices to incentivise rewards for the transfer of knowledge from local
operations to support whole of organisation performance improvement. As an illustration of
this commitment to collaborative knowledge sharing and transfer, when interviewed, the
Global Knowledge Manager reflected on how several staff from the West Australian operations
regularly attend knowledge sharing meetings on days off. This is an indicator of a mature
knowledge sharing culture in the organisation. The ability to capture and apply human insight
and experience is a crucial part of the KMS value proposition and the use of human capital and
intangibles as a basis for Competitive Advantage.
“Existence and richness of transmission channels” is the third element for transferring
knowledge in the organisation. In the case company, the Communities of Best Practice were
identified as a primary mechanism for sharing knowledge and Best Practices across global
locations. In addition, some participants referred to various informal channels of
communication for sharing knowledge among different groups. For instance people from
different areas in the main West Australian refinery chat about their job when they go and use
the coffee machine or lunch facilities. These personalised knowledge transfer strategies relate
to soft systems, or interactions between people, as opposed to hard systems focused on the
generation of data and information for storage and transfer through electronic interfaces. Use
of internal communication software (such as Yammer), and social media platforms, creates and
transfers unstructured information and knowledge from human interaction and conversations.
Trying to employ this unstructured knowledge as part of a broader Knowledge Management
strategy is one of the major challenges facing the case organisation and companies around the
world.
A number of new collaboration software products claim to manage the interface between
human and electronic systems for intuitive knowledge transfer and capture. This process has
significant potential for value adding to processes, services and brands and reputation. This
aspect of the Knowledge Management and transfer process involves the conversion of
intangibles into bottom line value and strategic outcomes. Whilst the sharing of corporate
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knowledge through verbal and face to face communication between employees is well
supported in the culture and management processes of the organisation, knowledge transfer is
constrained by physical and geographical barriers. Global Virtual Teams (GVTs) attempt to
address this issue by acting as knowledge integration mechanisms enabling knowledge transfer
and collaboration across the whole organisation, through teleconferencing and video
conferencing, site visits, and global forums. GVT have representation from various
Communities of Best Practice, external experts, and includes core members and the sponsor.
Also using Yammer, Wikis, and SharePoint sites, authorised staff are able to transfer
knowledge, and interpret it with reference to standardised Best Practices. The Director of
Research and Development Global Refining (Interviewee 3) mentioned the “Technology
Advantage” process, as a means to share and transfer R&D knowledge. This provides an
effective platform for developing new knowledge and codifying it into the operating system.
Also, the company’s TDG (Technology Delivery Group) supports the effective transfer of
information relating to the performance of specific technologies. Knowledge transfer is also
supported by the online training system used for staff development throughout the firm’s
operations. This system also provides a platform for dealing with broader systems questions
that arise from day to day. The company has commenced the use of video systems for training
through discussions which bring together trainees with skilled staff and specialists across the
organisation’s global network. Most of the senior managers interviewed believed people would
learn better in a more advanced, ICT enabled learning environment. The organisation regularly
undertakes operational reviews to identify learning gaps in particular locations. Knowledge
transfer events, like presentations and team discussions, are regularly organised to bring staff
at all locations up to the required level of training. Throughout the year there are several forums
for sharing information on knowledge and insights arising from recent project work. The
company also schedules monthly meetings to discuss various operational activities and issues.
The “motivational disposition of the target unit” and “the absorptive capacity of the target
unit” are the fourth and fifth elements which support a dynamic and proactive knowledge
transmission, and learning process, super imposed across the formal company structure
divisions, business units, and projects. The research revealed that an opportunity exists for
these two knowledge transfer elements to be explored in more detail, as a basis for value adding
and strengthening the existing culture of Continuous Improvement (CI), Best Practices (BP),
and Knowledge Management (KM) (See discussion in section 6.4.3).
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6.2.1.4. Knowledge Application
Whilst most of the interview participants and reporting managers, technical specialists, and
operational staff deployed Knowledge Management practices as a part of their daily work, this
required constant encouragement. Managers and staff across the various sites, divisions, and
operations used Knowledge Management thinking and practices in conjunction with the
transfer of Best Practices (BP) or working on specific Focus Plant issues or a broader range of
routine Kaizen and Continuous Improvement (CI) activities. The case organisation operates a
Continuous Improvement (CI) system called “Connections” (section 4.3.1.5) which is
supported by standard work instructions. Moballeghi & Galiyani Moghaddam (2008) stressed
Continuous Improvement (CI) is facilitated by knowledge based TQM. Both KM and TQM
are useful for organisations seeking quality improvement in processes and innovation of
products and services. The aim of both is improving the work processes of the firm to better
serve the customers (Loke et al, 2011). Therefore, according to these authors, KM and TQM
are complementary. Continuous Improvement (CI) consists of improvement initiatives for
enhancing successes and reducing failures (Bhuiyan & Baghel, 2005). Also benchmarking as
the most popular method of Continuous Improvement (CI), enables management and staff to
identify and action new ideas and ways of improving process.
From the perspective of the interview respondents, management and staff in the operation units
face significant challenges in their efforts to achieve and maintain Best Practices which reflect
differences in core characteristics of each plant. Hence managers and staff should consider
what can successfully be applied to the unique problems and challenges within their work
context, rather than trying to literally use the same approach to Best Practice in different
locations. Therefore, in this way knowledge relating to Best Practices can be transferred and
translated from one location to another. This process for connecting global Best Practices to
local activity and associated knowledge transfer is part of the company’s unique approach to
Strategic Knowledge Management (SKM). If Best Practices (especially translatable Best
Practice) are used and documented as a part of day to day activities, then the whole of
organisation is better positioned to achieve its goals. As discussed in section 4.3.1.5, every year
one particular “Focus Plant” acts as a global benchmark to guide Continuous Improvement
(CI) activities and better understanding of using, developing, and transferring Best Practices.
Continuous Improvement (CI) and benchmarking of Best Practices are used to improve
existing systems within a positivist paradigm; Knowledge Management could potentially be
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employed to design new systems, based on double loop or triple loop learning and a shift to an
interpretivist paradigm.
Given the constraints of a long term depressed price for the metals which the company mines,
refines, and turns into manufactured products, other factors arising from the global financial
crisis, means limited funds are available to spend on implementing technology given programs.
The case organisation has responded by establishing mechanisms to leverage the expert
knowledge of staff in each area. They try to make a use of embedded Intellectual Capital (IC)
rather than employ capital solutions. Intellectual Capital and significant Intellectual Property
(IP) portfolios incorporating Best Practices, policies and procedures to support knowledge
transfer and learning add value to the company’s activities provide a basis for differentiation
from competitors in the same industry sectors. The company constantly engages the knowledge
of all managers and staff with regular program discussions which combine training,
brainstorming, and related knowledge creation and transfer activities. This approach is
consistent with Grant’s (1996) view on the role of directives and organisational routines for
integration of knowledge as a basis for creating organisational capability (Grant, 1996; Nilsson
et al., 2012). These mechanisms and specific rules and procedures are defined, so individuals
are able to apply their knowledge without the need to directly communicate (Alavi & Leidner,
2001). However, when there are uncertain and complex tasks, self-contained task teams will
help individuals to use their specific knowledge to develop customised solutions for problem
solving (Alavi & Leidner, 2001).
In the case company, the GVT groups acted as a high level, self-contained, task team, that can
play an important role in a virtual space for solving no-routine problems. The Global Virtual
Team (GVT), which is a more developed version of supporting Communities of Best Practices,
is responsible for the global manufacturing technology across the entire system, and provides
a mechanism to draw on external support from various groups within the organisation to solve
operational problems quickly. The GVT investigate particular problems as required or
undertake specific projects referred by CoPs. The Global Virtual Team (GVT) also acts as a
resource to enable resolution of problems, particularly when focusing on the development of
business cases and Continuous Improvement (CI) strategies for Focus Plants. The Focus Plant
approach collects experts from different plants together in one location so they can solve the
common problems. This is the primary vehicle for developing and transferring Best Practices,
expertise and knowledge across the firm’s global structure. (See section 4.3.1.5 and 4.3.2.2)
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6.2.2. Data Mining Construct
The research revealed the case company would pay special attention to opportunities related to
using Data Mining as a significant tool of Business Intelligence (BI) (Wang & Wang, 2008).
There are complex processes with a huge quantity of variables to be measured and data which
can be integrated into a global portfolio of useful knowledge. The research shows the case
company does not use advanced procedures for Data Mining. They do undertake basic Data
Mining activities, but also need to focus on identifying less obvious operationally significant
correlations. In summary, feedback from the interview respondents indicates that the
company’s Data Mining practices were relatively immature in terms of contribution to
Strategic Knowledge Management (SKM) and Competitive Advantage. However, the current
CEO recognises this as a priority and is looking at opportunities to use Data Mining as an
important tool for strengthening Business Intelligence (BI).
Traditional methods of data analysis within the organisation will not deal with the high volume
data generated across the global network of operations. Therefore Data Mining, with intelligent
capabilities for transforming and processing data to useful information and knowledge, is
imperative to support ongoing Competitive Advantage for the firm (Bal, Bal, & Demirhanc,
2011). Data Mining, using a variety of data analysis tools, enables discovery of embedded
information and knowledge, and identification of meaningful patterns and relationships to
support valid predictions. A flexible Data Mining tool with Business Intelligence capability
supports efficient economic analysis that classical methods cannot provide (Jindal & Bhambri,
2011; Baicoianu & Dumitrescu, 2010).
Some participants referred to search engine software and wallpaper as Data Mining systems.
This suggested that they were not fully aware of the benefits and applications of the latest Data
Mining systems for operational and strategic purposes. One of the most important objectives
of Data Mining is to discover useful hidden patterns and relationships for increasing decision
making capabilities and reducing implementation time. As discussed, in section 2.8.4 of the
literature review, potential Data Mining benefits for business include increased productivity,
reduced risk, and time and cost savings. Also there are benefits for individual managers and
specialists such as access to integrated dashboard information and improved results based on
optimizing human and ICT systems interaction (Bal, Bal, & Demirhanc, 2011).
In subsections below the findings relating to Data Mining elements: Extract, Transform, and
Load transaction data; Store and Manage Data (including Provide data access to business
analysts and information technology professionals); analyse the data by application software;
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and Present data in a useful format; are discussed with reference to the relevant literature review
in Chapter Two.
6.2.2.1. Extract, Transform, and Load (ETL) Transaction Data
The findings, from senior management interviewees, revealed that company managers and staff
can access a web-based internal information and knowledge system to address localised
problems in their plant. However, a number of issues were identified with the existing system.
These included the need to request relevant information which is not available online and
difficulties experienced by end users in gaining timely access to important operational
information. Furthermore, data that should be routinely accessed for benchmarking analysis by
specialist and Best Practice (BP) implementation is not always available.
Extract, Transform, and Load (ETL) tools extract data from underlying data sources and
provide a facility to transform and load it to a data warehouse. The data is cleaned, transformed
and integrated before loading to the main memory or data warehouse (Hellerstein, Stonebraker,
& Caccia, 1999; Dayal et al., 2009; Wu et al., 2014). One of the participants referred to a good
globally integrated computer system that allows them to drive technical information from a
network of drivers. They have an outsourced system for collection, storage and analysis of data
across all the refineries. This system is provided by a specialist process control systems
manufacturer, data processing and analysis organisation. This company claims to provide a
world-class environment for optimal process improvement solutions and decision making. This
system supports effective operational decision making based on the analysis of real time data
from integrated databases.
6.2.2.2. Store and Manage Data and Provide Data Access
The interviewees revealed that information for collaborative purposes is stored on SharePoint
sites. The wallpaper system is also used for storing data and providing summarised data and
information required for high level decision making across the organisation.
The interviewee participants identified several other mechanisms for storing data throughout
the global operations of the organisation. The IT manager mentioned a new strategic plan for
building a small data mart, in each business unit, that will focus on procurements analysis and
is connected to a global data warehouse. Data warehouses bring in data from various sources
such as personal computers, minicomputers, and mainframe computers. A data warehouse is
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similar to a container for all data required to carry out Business Intelligence operations.
Building a large data warehouse can be a huge task, taking a lot of time, and costing millions
of dollars (Jackson, 2002). Some essential data can be mined from transactional and operational
databases like data marts (Jackson, 2002) smaller than a data warehouse, and designed to focus
on specific functions in organisation (Lee M.-C. , 2009).
6.2.2.3. Analyse the Data by Application Software
According to a number of respondents, the global refining system has sophisticated controls
and collects and stores up to 50000 data points every minute. Engineers and operational staff
come together to analyse real time aggregated data for a more detailed understanding of what
is happening locally and across the global refining network. Systematic problems are addressed
by operational staff using Continuous Improvement (CI) methods and specialists from the
company’s Technical Centre (ATC) who supplied complex statistical analysis when required.
However, although this Continuous Improvement (CI) activity and specialist support exists,
the Director of Manufacturing Excellence (Interviewee 4) highlighted that developing the
infrastructure and expertise for a 21st century level of Big Data analytics is required for the
company.
Data Mining would be helpful, as part of the big data analytics process, as it allows staff who
are not professionals in statistics to manage and extract knowledge from data and information
(Baicoianu & Dumitrescu, 2010). One of the participants noted that the third party organisation
providing advanced data analytics would be integral as a partner for developing a Big Data
strategy for the global operations of the company. (See section 2.8.2 for discussion of Big Data
and Data Mining).
6.2.2.4. Present the Data in a Useful Format Aspect
The company employs multiple reporting tools linked to commercial and manufacturing
systems which produce reports and visualisations in various formats. For instance, there are
local query reporting systems which are supported by data warehouses and relevant data marts.
Each refinery provides monthly tactical reports which are combined with Kaizen Continuous
Improvement (CI) documents for the business as a whole. Also, the outsourced third party
system can be used by any authorised party in refineries to source graphs from different sensors
and generate reports. This system provides productivity tools for retrieving, displaying,
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analysing, and reporting process data. The company also employs the Manufacturing Execution
System (MES) system to configure summary reports.
Findings from senior manager interviewees showed that analysing and presenting large
amounts of data in a comprehensible format is critical for the organisation. They have
automated systems to mine data and generate numerous reports highlighting performance
achievements across the business. In this way, they are able to present data in a timely, simple,
and understandable format to all levels of the workforce. However, as previously observed,
whilst web reports contain significant amounts of useful information the methods for extracting
this are still quite basic. Other management systems provide reports which are highly structured
and only specialists can quiz and analyse them. Existing Data Mining technology, involving
pattern-based queries, is able to provide custom outputs in various formats.
6.2.3. Resource based Competitive Advantage (Valuable, Rare, Inimitable, and Non-
substitutable Resource)
This case study focuses on the resources of the firm and how these are organised into unique
systems, management routines, and operational practices pursuant to achieving sustained
advantage over competitors in similar industry sectors. The Resource Based View (RBV) and
Knowledge Based View (KBV) of strategy (see section 2.2.2), are concerned with building
unique capabilities consistent with emerging market conditions. In recent literature, the
thinking behind these views of strategy has been extended by the models of dynamic capability
and of Scharmer’s (2009) K1 to K3 new generation knowledge matrix underlying, discussed
in sections 2.6.1.4.
However, the main focus and design of the thesis is based on RBV, KBV and other more
conventional approaches to Strategic Management thinking and practice. These have featured
prominently in the Strategy, Knowledge Management, and hard and soft systems literature for
the last two decades. So while emergent aspects of strategic thinking and management are
acknowledged in the Literature Review (Chapter Two), Conclusions and Recommendations
(Chapter Six), they are not the primary concern of the study. This study focuses on the VRIN(E)
model of Competitive Advantage. It seeks to provide qualitative and quantitative evidence to
demonstrate the relative strength or weakness of factors which support Competitive Advantage
within the case organisation. The VRIN model is based on the assumption that to achieve
sustainable Competitive Advantage within a defined market sector, the firm should employ
unique combinations or portfolios of human knowledge assets and technological resources that
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are not easily replicated by, or accessible to, competitors. Some of the most important elements
of Competitive Advantage that were identified within the case company were as follows:
Intellectual Capital (IC) and Intellectual Property (IP)
With respect to management of tacit and explicit knowledge resources within the case
company, a number of senior managers acknowledged the importance of creating both
Intellectual Capital (IC) and Intellectual Property (IP) as valuable knowledge assets and
sources of process innovation. With respect to Intellectual Capital in the form of intangible
stocks and flows of human knowledge Schiuma & Lerro (2008, p5) noted that organisations
with an IC strategy can create new business models and successfully pursue existing objectives.
In some cases, Intellectual Capital (IC) can be licensed and registered as Intellectual Property
(IP). This represents market value created through brand and share value enhancement and
sales of related products or technologies. These elements combine to support Competitive
Advantage. IP also represents the creations of the mind, so that ideas and unique creative
processes which exist in every business are crucial for long term financial success. This is
consistent with Barney’s (1991) suggestion that firms with valuable and rare resources obtained
in unique paths throughout history, are able to implement value creating strategies that cannot
be duplicated by competing firms.
R&D Output
The interview findings showed the case company is in a unique position to combine data from
sophisticated control systems with R&D activities to maintain operational superiority relative
to competitors. The company has integrated the R&D knowledge obtained from systematic
investigation, testing, and operating systems data. This unique knowledge informs hard and
soft system design which is hard for competitors to replicate. A number of respondents
suggested that the company spent more money on Research and Development than their
competitors. (This claim was not verified with actual budget figures for commercial confidence
reasons). In this case, R&D knowledge constitutes part of a broader set of capabilities and
competitive assets that could be viewed as valuable and rare resources (Madhani, 2010).
Best Practices and Deep Technical Knowledge
The interviews also revealed that a collaborative focus, using real time data and data
warehouses to support the operational systems, also contributed to the production of useful
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knowledge and identification Best Practices for operational and broader strategic purposes.
Working through Global Virtual Teams, Focal Plant meetings, and regular video conferencing
between managers and staff from across the global network operations, the company is able to
aggregate and orchestrate embedded knowledge for process efficiency, Continuous
Improvement (CI), Benchmarking, and innovation. This combination of Meta thinking and
Best Practices (BP) has maintained the company’s competitive position despite a steady decline
in the price and demand for relevant commodities and processed ore.
In addition, a number of interview respondents suggested that deep technical knowledge of the
refining process, built up by managers and technical staff, is a particular competitive strength
of the company. In addition, the company’s Manufacturing Execution System (MES) provides
an important platform for knowledge creation and sharing through data generation, dynamic
help chains, and generation of summary reports. These activities contribute to the development
and application of inimitable and non-substitutable resources (Madhani, 2010).
Human assets and emergent thinking
Going back to Barney’s original conception of RBV (1995, p.50) the company’s resources
include financial, physical, human, and organisational assets. The human assets include the
knowledge, experience, judgment, and wisdom of individuals associated with a firm.
According to Molloy & Barney (2015), human capital (assets) incorporate acquired individual
knowledge and expertise for market relevant and productive working practices and cultural
values in workplaces (Molloy & Barney, 2015). The findings of research showed there is a
substantial amount of valuable knowledge embodied in employees, CoPs, and GVTs. This deep
knowledge is created through contextually rich, collaborative experiences, which translates
into rare and inimitable resource.
Otto Scharmer arguably presents a more progressive view of how knowledge is created and
applied for innovative purposes, with reference to his new model of post-industrial emergent
systems and knowledge creation (see section 2.6.1.4). According to Scharmer’s (2009), schema
which references leading strategy, Knowledge Management, and systems theorists including
Gary Hamel, Nonaka, and Takeuchi, organisational survival now depends on the ability of
organisational leaders and managers to anticipate and adapt to global drivers including
disruption of traditional markets, climate change, growing inequality between rich and poor,
and accelerated eco system degradation emerging future (Scharmer, 2009, pp. 67-70). This
encompasses a shift from linear systems (S1) and explicit knowledge (K1) to non-linear
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systems (S2) and tacit embodied knowledge (K2) situated in specific context (see section
2.6.1.4). Evidence from the quantitative and qualitative components of this study highlight the
important link between management of tacit embodied knowledge and Competitive Advantage
and the lesser contribution of Data Mining and explicit knowledge. This suggests that the case
company, and other global organisations, must focus on creating safe and productive spaces
for Knowledge Management focused on innovation for survival and sustainable organisational
transformation versus more traditional notions of Competitive Advantage.
Whilst this type of transformational thinking was not manifested through the interview and
survey process, respondents did express concerns about major shifts in their industry and the
firm’s ability to remain globally competitive in the face of high input cost and falling
commodity prices. Their current conventional but effective KM thinking and modus operandi
was to focus on internal and external benchmarking and analysis to support the achievement
of long term targets. Whilst benchmarking internally and externally has proven historically
successful to maintain a hard systems and technological edge, it falls short of the paradigm
shift in Knowledge Management practice highlighted in the recent literature. However, the
company has maintained a strong collaborative CI and KM culture over the past twenty years
and may have the capability to absorb S2K2 thinking and practices (see section 2.6.1.4). In
keeping with current authors Barney (1995) and Molloy & Barney (2015) emphasised the
importance of history trust and organisational culture as competitve capabilities of the firm.
6.3. Key Research Themes and Conclusions
Consistent with the dynamic relationships depicted in the SKM conceptual model tested in this
thesis- “Creating Competitive Advantage through integration of Data Mining and Strategic
Knowledge Management” (Figure 2.11), the main research question of the study is “How can
the relationship between Strategic Knowledge Management and Data Mining be effective in
creating Competitive Advantage for a large organisation in the global minerals and metals
mining industry?” The qualitative and quantitative findings, and discussion above, provide a
detailed response to this question by addressing four sub themes- The effects of Knowledge
Management on the five major elements of Data Mining; The effects of Knowledge
Management processes on Resource based Competitive Advantage; The effects of Data Mining
processes on Resource based Competitive Advantage; And finally, The effects of Knowledge
Management processes with integrated Data Mining processes on Resource based Competitive
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Advantage in a global mining and resource organisation. In this regards the following
conclusions and recommendations can be drawn from the study:
6.3.1. The Relationship between Knowledge Management and Data Mining in the Case
Company
Whilst the quantitative component of the study was concerned with measuring direct effects,
within the broader complex relationship, between Knowledge Management and Data Mining
the mixed method approach drew in contextually rich qualitative data to account for this
complexity. The quantitative findings supported that Knowledge Management processes
(Knowledge Creation, Knowledge Storage, Knowledge Transfer, and Knowledge Application)
had a direct effect on Data Mining elements (Extract, Transform, and Load transaction data
(ETL), Store and Manage data, Provide data access, Analyse data, and Present data). Statistical
results presented in Chapter Five, show that, for the case company, Knowledge Management
processes have a strong and significant effect on elements of the Data Mining processes (path
coefficient=0.731, t=14.239) (see Table 5.11). The qualitative findings also highlighted clear
connections and dynamic relationships between of KM and DM in the case company. This
connection was reinforced in the discussion of key KM models and DM processes and elements
in the literature review. From these sources it can be concluded that, for the case company,
Knowledge Management supports value adding and informed decision making. This rich
process of combining and interpreting data and information, with reference to specific
problems or opportunity contexts, is supported through Data Mining practices using a diversity
of data analysis tools to discover useful information and knowledge across the company’s
global networks. The Data Mining systems and practices, within the company, have the
potential to identify significant hidden patterns among vast amounts of data, enhance decision
making capabilities and shorten decision making and implementation cycles (Lee M. C., 2010;
Bal, Bal, & Demirhanc, 2011; Paddock & Lemoine, n.d.). According to Galliers and Newell
(2003), in the past, traditional methods of IT based data analysis worked in isolation and could
not produce these results. However, new generation Data Mining processes, if strategically
deployed, are able to strengthen Knowledge Management processes and support knowledge
workers and decision making relating to real world problems (Galliers & Newell, 2003).
Data Mining systems which effectively combine five major elements (Extract, Transform, and
Load transaction data (ETL), Store and Manage data, Provide data access, Analyse data, and
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Present data), with statistical, mathematical, and artificial intelligence enable the extraction and
identification of useful knowledge (Nemati, 2001; Silwattananusarn & Tuamsuk, 2012) from
operational or multidimensional databases. The findings from Chapter Two (Literature
Review), Chapter Four (Qualitative data analysis) , and relevant hypothesis testing in Chapter
Five (Quantitative data analysis) (section 5.6) established a highly significant relationship
between the employment of strategic approach to Data Mining embedded in Knowledge
Management processes (Knowledge Creation, Knowledge Storage, Knowledge Transfer, and
Knowledge Application) within the case company.
The ETL element of the Data Mining process extracts data from underlying data sources and
transfers it to a relevant data warehouse. This process is performed by an outsourced system
for the case company (the provider cannot be identified for commercial in confidence
purposes). To address key operational problems and resolve technical problems (often
involving chemistry or metallurgy) data must be extracted from existing systems, transferred
and loaded to a centralised system for providing access to specialists who can analyse the
relevant data. Also, the case company has several mechanisms for storing data such as building
small data marts in each business unit and global data warehouses run as part of a broader ERP
system. Other collaborative information is stored on SharePoint sites and Wallpaper. The
‘Wallpaper’ system provides summarised data for high level decision making across the
company. Therefore, through the second and third major elements of the Data Mining process
Store and Manage data and Provide data access, all extracted and loaded data will be stored
and prepared for access by business analysts and information technology professionals. With
Analyse data as the fourth element of Data Mining process, the stored data is analysed through
analytical processing applications (Rouse, 2005). This element of Data Mining is important for
the managers and specialists within the case company when addressing and solving non-routine
problems, which often requires analysis of structured and unstructured data in real time.
Historians, engineers, and operational employees come together to analyse aggregated data
with advanced tools. Using Data Mining tools, with structured and unstructured data, enables
the organisation to get breakthroughs and identify Best Practises (O'Dell & Grayson, 1998;
Welborn & Kimball, 2013). Therefore through these processes, Data Mining might strengthen
Knowledge Creation and Knowledge Application practices. Also the results of data analysis
would be present in an understandable format.
Present data in a simple format is one of the critical missions for the case company. Reporting
systems in the case company provide summary reports in various formats, however most of
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them are manual and highly structured. Customised output reports can be generated by
applying Data Mining tools which involve pattern-based queries. The comprehensive
presentation of data provides some opportunities for new discussion, so through this
communication Knowledge Creation and Knowledge Transfer occur. Also the meaningful
outcomes would potentially add value and inform decision making. This is also useful for
Knowledge Application practice. In this way, through management of captured data and
information in context, it is possible to create meaningful knowledge for the business and store
explicit knowledge in a data warehouse.
More data warehousing would enhance operational decision making using analysis of real time
data. It would also provide infrastructure to process Big Data (Wu et al., 2014). This is
consistent with important business trends relating to state of the art Knowledge Creation and
decision support systems and practices (Khan, Ganguly, & Gupta, 2011). Data warehousing
and documents may also contribute to cyber ba with the result of enhanced efficiency of the
combination mode of Knowledge Creation. Increasing the capacity of new generation data
warehousing, within the company, may also improve the retention and intuitive search ability
of organisational memory (Alavi & Leidner, 2001). As noted by Wiewiora et al. (2013)
Knowledge, in the form of contextual facts, can be stored in data warehouses which contain
relational data bases (Wiewiora et al, 2013). Therefore data warehousing technologies, if
integrated into a Strategic Knowledge Management (SKM) framework, can potentially
improve Knowledge Creation and Knowledge Storage practices in the company.
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Figure 6.2: The Relationship between KM and DM in the Case Company
Knowledge Management Processes in the Case Company
ETL
• Extracts data from
underlying data
source
• Transform and load
data to data
warehouse/
Centralised system
Store and Manage Data
• Data Mart
• Data Warehouse
• SharePoint site
• Wallpaper
Stored knowledge in
databases
Provide Data Access
Summarised data for
high level decision
making by
Wallpaper systems
Analyse Data
Analyse aggregated data by:
Historians, engineers, and
operational employees with
advanced tools
Output: Best Practices
Present Data
Present identified Best
Practices in
understandable format
Output: Understandable
Reports
Knowledge
Creation
Knowledge
Storage
Knowledge
Transfer
Knowledge
Application
Elements of Data Mining Process in the Case Company
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6.3.2. The Effect of Knowledge Management on Resource Based Competitive
Advantage in the Case Company
Human assets, Best Practices and Deep Technical Knowledge, R&D output, and Intellectual
Capital/ Intellectual Property proved to be key internal resources for Competitive Advantage
in the case company. In this section we outline how Knowledge Management processes work
with these elements to underpin Competitive Advantage for the firm.
Human asset utilisation and KM processes in the case company
As highlighted in section 6.2.1.1 the case company has been mining in Australia for 50 years.
They also have a long term average years of service profile (15 years) with some employees
registering up to 40 years’ service. There is a huge amount of valuable tacit knowledge
embodied in these staff members who individually and collectively represent important human
assets. According to López-Nicolás & Mero˜no-Cerdán (2011) tacit knowledge is unique,
imperfectly mobile, inimitable and non-substitutable, so if guided by appropriate organising
principals and Knowledge Management models (see SKM framework Chapter Two) it would
represent a source of advantage for the organisation.
The Knowledge Creation process, when linked with the externalisation stage of the SECI
model (interacting ba), can support the conversion of embodied tacit knowledge to explicit
knowledge. The combination stage that follows (Cyber ba) allows this explicit knowledge to
be converted back into codified explicit knowledge. This can be accessed and shared via
databases and new generation collaboration software (see Appendix E). Whilst the technology
provides a powerful platform for aggregating and interrogating data, it does not in itself create
knowledge. According to Nonaka et al. (2000) useful knowledge is created in context via
dynamic conversations in a safe physical and virtual space (Exercising ba). Therefore product,
process and service differentiation as a basis for Competitive Advantage is only possible
through the combination of tacit and explicit knowledge enabled by appropriate organisational
design, management systems and practices (Nonaka, Toyama, & Konno, 2000).
Knowledge Storage as a Knowledge Management process enables the company to support the
documentation of explicit knowledge and making it available for a combination of tacit
knowledge to form organisational memory and Intellectual Capital (IC).
Knowledge Transfer processes support the transformation of knowledge throughout the
company when enabled by a culture of knowledge sharing and rewards aligned to collaborative
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goals (Gupta & Govindarajan, 2000, pp. 475-476). Therefore, through this process the valuable
knowledge of experienced employees is retained in the company.
(The findings of the deep case study undertaken for the thesis, when disseminated by the
researcher to senior decision makers and technical support staff with reference to the SKM
framework outline in Chapter Two, serve as an example of knowledge transfer within a specific
operational, strategic and cultural context).
Knowledge is transferred as a dynamic Intellectual Capital (IC) asset. This approach to
knowledge transfer encourages systematic combination of tacit and explicit knowledge. This
is linked to a broader knowledge portfolio incorporating Intellectual Property (IP) and IC
highlighting the value of aggregated tacit knowledge as a competitive asset. This encourages
further consideration of Polanyi’s fundamental question on the nature of cumulative tacit
knowledge within the organisation “How do we know what we know?” (Gilbert Ryle and
Michael Polanyi cited in Jashapara (2011, P.43)) (See section 2.4.2).
KM processes, Deep Technical Knowledge, and Best Practices in the case company
By creating a platform which provides broader awareness, understanding and accessibility of
knowledge reserves and resources and how to exploit them, the case company has set up
conditions conducive to improved strategic performance. As one long serving director
(Interviewee 4) observed (with reference to the example of mine planning processes), staff –
“At some locations within the company are very clear on available reserves of information and
knowledge and may be able to help others access this asset through face to face meetings, video
conferencing, participation in GVTs or a virtual space” (akin to Nonaka et al. Cyber ba
(Nonaka, Toyama, & Konno, 2000). Through GVT and available ICT and collaborative
platforms, they can integrate and share knowledge and insights based on project, operational
and technical expertise located at different nodes in the organisational network. This combines
planned information management, and structured interrogation of data with thematic and
emergent approaches, to knowledge generation and creation. This represents a unique and rare
competitive resource. By combining existing Knowledge Management processes and
supportive management practices with SKM framework, the key decision makers within the
case company can progress their conscious strategy for delivering the’ right knowledge, to the
,right people’ at the ‘right time’. In this way, the organisations performance will be improved
by sharing (and actioning) information in specific contexts (Halawi, Anderson, & McCarthy,
2005).
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The interview findings also indicated that the case company practices extensive internal and
external benchmarking. Benchmarking enables them to identify and implement Best Practices
across the global operations of the business (Elmuti & Kathawala, 1997; Barczak & Kahn,
2012). Best Practices are employed systematically as a valuable, rare and inimitable knowledge
asset for the company and are aligned with its broader strategic goals and objectives. The
interview findings indicated that Best Practices were widely employed to raise production
standards, avoid repeating mistakes, and re-inventing the wheel. The Communities of Best
Practice within the organisation represented the primary platform for sharing and applying this
knowledge across all global locations. These communities support a process comparable to
Nonaka’s knowledge spiral with a big emphasis on socialising and originating ba. Knowledge
Creation can then be engaged to support process innovation and improvement linked to global
Best Practices. The case company provides awards every year for projects, collaborations, and
operational improvements relating to sharing and application of Best Practices.
The case company executive team encourages employees to share their Best Practices, so
Knowledge Transfer process occurs. Also storage and documentation of the Best Practices is
an important challenge for the company which the Knowledge Storage process might be able
to support. Through SharePoint sites all collected and identified Best Practices documents are
stored and shared. Using ICT tools such as SharePoint, GVT groups (representing the most
mature and sophisticated Communities of Best Practice from the across the organisation), enter
the virtual safe space (akin to cyber Nonaka’s ba) to solve problems, generate ideas insights,
and new knowledge. The Meta knowledge generation activities of the GVTs support
integration of tacit and explicit knowledge across functional boundaries to develop new and
innovative processes informed by both Best Practices and interactive Data Mining and
Knowledge Management. In this way (consistent with Nonaka’s SECI process) Knowledge
Transfer and Application in particular problem solving, or project contexts, is supported.
R&D Output and KM processes in the case company
The research also revealed the case company treats Research and Development (R&D) as a
valuable knowledge asset. This includes unique IP and sophisticated systems for control and
integration of R&D output into operating systems. This R&D output represents a rare, unique,
and inimitable resource as it is hard to reproduce the embedded scientific knowledge,
conversations and patterns of interaction between centrally located specialists, departmental
managers, project and operational staff. In this way the R&D process supports accumulation,
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dissemination, and application of useful knowledge amongst networks of agents. These people
have a shared understanding of the meta- context in which they are operating, so in this regard
Knowledge Management can influence knowledge accumulation and dissemination
(Drongelen et al., 1996). As discussed in section 4.3.1.5, the Research and Development
(R&D) teams based at the corporate headquarters, and various sites across the globe, employ
shared web-portal to identify and share patterns and connections to data derived from activity
in various plants.
IC/IP and KM processes in the case company
In response to the tight market and budgetary conditions that followed the global financial
crisis, the case company has had to make good use of Intellectual Capital (IC) as a valuable
and inimitable resource. Also they have different pillars of Knowledge Management including
an Intellectual Property (IP) portfolio to form some of the Best Practices and procedures to
learning management (see section 4.3.1.5 and 4.4). In this regard Knowledge Management
includes Intellectual Capital (IC) and Intellectual Property (IP) which informs organisational
policies and procedures. The contents of a portfolio might also be licensed, patented, or
incorporated into a flow of intangibles. These activities in turn add value through systematic
data, information and deep contextual insights to support effective problem solving or process
innovation. This follows Schiuma & Lerro (2008) in their broader investigation of the
relationship between Intellectual Capital management and company performance, where they
observed that IC management plays a significant role in driving process improvement
(Schiuma & Lerro, 2008). This is consistent with the evidence presented on the case company
and its basis for Competitive Advantage.
This study is not directly concerned with the relationship between CI and KM. However as
discussed earlier the case company developed its KM capacity and infrastructure to
complement well established CI processes. The qualitative and quantitative evidence presented
in Chapters Four and Five support the claim made in this study that Knowledge Management
(including CI and BP) processes have a direct and positive effect on Resource based
Competitive Advantage. The statistics presented in Chapter Five (see section 5.6) support a
strong, significant and direct effect on Resource based Competitive Advantage (path
coefficient=0.689, t=9.108) (see Table 5.11).
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6.3.3. The Effect of Data Mining on Resource Based Competitive Advantage in the
Case Company
According to the qualitative evidence presented in Chapter Four, the global management team
had limited knowledge of Data Mining systems or perceived the company to be in a relatively
early stage of implementing advanced Data Mining technology. They employ Data Mining
processes in their day to day operations but not within integrated system and advanced
procedure. They use outsourced system for collecting data and graphical evidence to support
site specific and global reporting for the company. They also employ a Manufacturing
Execution System (MES) to provide summary reports of all operation activity. Several
participants observed that the company has too many, and varied, systems for accessing and
analysing information. It was also observed that some elements of these systems were not easy
to use for end users and required some specialists who understand the relevant data and analysis
procedures. The company Technical Centre (ATC) employs a dedicated team of statisticians,
but was reported by one global senior manager (Interviewee 9) to currently lack the
infrastructure for big data analytics. In a similar vein, what was perceived by the representatives
of the global management teams to comprise the broader Data Mining systems accessed by
various divisions and operations across the company network, did not have a significant effect
on Resource based Competitive Advantage for the firm. This finding was supported by the
statistical evidence (path coefficient=0.122, t=1.664) (see table 5.11).
This represents an important shortfall in the firms competitive capacity as a Data Mining
system fully integrated into a Strategic Knowledge Management (SKM) framework has
significant potential to provide actionable results and according to Bal et al. (2011) and
Baicoianu & Dumitrescu (2010) would furnish competitive success through cost reduction,
increase turnover and profitability.
6.3.4. The Indirect Effect of Knowledge Management on the Resource Based
Competitive Advantage through Its Effect on Data Mining (DM) Processes in the Case
Company
In the Quantitative Chapter (section 5.7) the effect of Knowledge Management (KM) on
Resource based Competitive advantage (RCA) through DM as a mediator was tested. The
result shows only 11% of total effect of Knowledge Management processes on Resource based
Competitive Advantage is related to Data Mining processes. As reported in section 4.3.2.1 and
6.3.3, the ten interview respondents from the global senior management team indicated limited
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knowledge of Data Mining practices in the company. Advanced Data Mining tools and
algorithms were not widely used across the operational network. The case findings revealed
that while company information is stored in a centralised database, it is not incorporated into
fully integrated Data Mining systems and software to support comprehensive data analysis and
decision support.
With respect to their role in Competitive Advantage- advanced Data Mining technologies
working with the human cognitive processes are able to support analysis on of unstructured
problems. This capability is important as responses to complex unstructured problems and
opportunities in unfamiliar context are hard for competitors to understand and emulate
(Brusilovsky & Brusilovskiy, 2008). The next section illustrates how integration of an
appropriate Data Mining system within a Strategic Knowledge Management (SKM)
framework may support future Competitive Advantage for the case company.
6.3.5. The Effect of Integration of Data Mining Within a Strategic Knowledge
Management Framework on Resource Based Competitive Advantage
In this section we show how Data Mining technology is able to strengthen Knowledge
Management processes which support and create the Resource based Competitive Advantage.
Using SKM and DM to release the potential of human assets within the case organisation
As mentioned in section 6.2.3, knowledge which is stored in the mind of employees, represents
a significant, unique and often definitive resource pursuant to the Competitive Advantage of
the firm. In the case company, Knowledge Management practices blending tacit and explicit
knowledge were able to support a sequence of Knowledge Management activities. These
include- Identification, Storing, Sharing, Retrieving, Aggregation, and Interpretation of data
and information in relation to specific project, operational and company-wide contexts. This
approach is consistent with the argument presented by Wang & Wang (2008) that Data Mining
becomes a powerful Business Intelligence tool when it incorporates discovery of new
knowledge based on accurate, accessible and contextualised data and information by informed
managers or trained specialist. Data Mining technologies can also strengthen Knowledge
Management and related practices (section 6.3.1). For instance, when the individuals share
their understanding of a situation and convert their abstract tacit knowledge into explicit
knowledge, this valuable explicit knowledge can then be converted into more concrete,
codified and accessible forms. This supports more informed and insightful decisions by
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managers and stronger shared understanding of the situation by relevant parties or stakeholders.
Data Mining, when used with data warehousing technologies, can support tacit to explicit to
tacit conversion through Systemising/Cyber ba and deploying multi-dimensional databases and
groupware tools. Also, according to (Rouse, 2005) Data Mining processes deploying multi-
dimensional databases and data warehousing technologies enable more effective management
storage, access, and sharing of this valuable codified knowledge. The sharing capability of
these systems has been greatly improved since the development of sophisticated groupware
and collaborative systems, (post 2010). These new platforms provide a competitive edge for
organisations by enabling simultaneous access to important data and market intelligence for a
wide range of specialists, staff, developers and interest groups spread across the organisations
stakeholder and client networks. The composite effect of real time accessibility to data, when
simultaneously explored by diverse groups, can reveal value in hidden patterns and lead to
product and service innovations. In the corporate sector, collaboration platforms such as
Yammer or SharePoint are widely used to disseminate knowledge and generate ideas and
innovations from threads of conversation between staff with diverse background and expertise
(Riemer, Scifleet, & Reddig, 2012). (The application of collaborative software within a
Strategic Knowledge Management (SKM) framework are discussed in section 6.4.2)
Using SKM and DM to support the identification and application of Best Practices within the
case organisation
As discussed in section 6.2.1, Knowledge Management Systems (KMS) and processes in the
case company supported identification, storing and sharing of Best Practices as a potentially
rare and valuable organisational asset. Best Practices are analysed in accordance with specific
performance criteria and Data Mining technologies support the benchmarking process with
relevant statistical data (Giudici, 2003). In the case company Best Practices are identified
through benchmarking processes which involve analysing operational activities and work
patterns at different sites to identify why one plant performs better than others. Data Mining
supports this process by providing statistical data and enabling the discovery of hidden patterns
relating to existing operational processes, management and working routines. Once structured
and unstructured data is identified it can be discussed and analysed by range of managerial and
specialist staff as part of local and global benchmarking and Continuous Improvement (CI)
exercise. In this way Knowledge Management Systems (KMS) in the case company
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complement or incorporate benchmarking, Best Practices (BP), and Continuous Improvement
(CI) activities.
R&D Output can be supported by integrated DM
As discussed (section 6.3.2), within the case company Knowledge Management processes also
incorporate R&D activities as part of a broader portfolio of value adding knowledge, pursuant
to Competitive Advantage. Data Mining technologies can act as significant tools to support the
broader Knowledge Management process in the firm through discovery, extraction, creation,
and dissemination (Wang & Wang, 2008; Jambhekar, 2011).
6.4. Research Contribution and Implications
6.4.1. Theoretical Implications
This study provided several major theoretical contributions to the academic and professional
literature on Knowledge Management. This was achieved by integrating four theoretical
perspectives developed from the substantive literature on Strategy and Strategic Management,
the nature and definition of Competitive Advantage, Knowledge Management concepts and
processes, and Data Mining concepts, elements, and applications. (See sections 2.11 and 2.3.3
incorporating SKM framework and VRIN model for detailed explanation of how concepts from
the literature are combined and used to support the interpretation and analysis of deep, mixed
method case study findings. See Chapters Four and Five and section 6.4.2)
This study focused on the Resource Based View (RBV) and Competitive Advantage. It focused
on capability building, using organising principles, which optimised the combination of human
and technological resources within the global mining and manufacturing (case study)
organisation. Based on a review of the extant academic literature and current industry and
vendor sources), there are a range of conceptually focused and practical (often proprietary)
Knowledge Management models. (See Appendix F for leading industry adopted KM models).
The SKM model and VRIN framework that form the conceptual basis for the study incorporate
thinking on four basic processes of Knowledge Management (Knowledge Creation,
Knowledge Storage, Knowledge Transfer, and Knowledge Application).
The Data Mining component of the study focused on five major elements identified in the
literature as common to most Data Mining systems and processes (ETL -Extract, Transform,
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and load transaction data, Store and Manage data, Provide data access, Analyse data, and
Present data). Through this approach, a high level conceptual framework for integrating hard,
ICT and soft, human systems was developed. This framework represents the explicit and tacit
knowledge embedded within broader networks of mining and resource company activity. This
illustrates the combination of human and technological aspects of Knowledge Management
and is reflected in the Strategic Knowledge Management (SKM) model used to interpret the
research findings from a deep case study of a global mining and manufacturing company. The
mixed method case study approach was used to develop a top down, bottom up, multi
perspective, understanding of Knowledge Management and allied Data Mining and Continuous
Improvement (CI) activities. This laid the basis for adding value to processes, products and
services. It also supported improved decision making, and by extension advantage over global
competitors in the same industry.
Many researchers (López-Nicolás & Meroño-Cerdán, 2011; Zack, McKeen, & Singh, 2009)
have emphasised Knowledge Management as a key factor for achieving Competitive
Advantage. Other authors have asserted the power of Business Intelligence and Big Data as
important sources of codified knowledge which provides a competitive edge for companies
employing these technologies (Wang & Wang, 2008; Wu et al., 2014). Data Mining techniques
are powerful tools of Business Intelligence for knowledge discovery and tapping into networks
and repositories of expertise. Data Mining was identified in the literature as a link between a
broader field Business Intelligence and ICT (hard systems) and human interactions (soft
system).
In the literature review, operational definitions and related constructs of Knowledge
Management, Data Mining, and Competitive Advantage were established. The relationship
between these constructs was tested. Hypothesised relationships (direct and indirect) between
the relevant constructs were tested empirically in the context of a global mining and
manufacturing company (in the case study organisation).
Based on the findings of the research, the Knowledge Management construct was found to have
a direct positive effect on Data Mining and Resource based Competitive Advantage in the case
company. Knowledge Management has a strong effect on Data Mining processes with path
Coefficient=0.731 and t-value= 13.9575 and also has a strong effect on Resource based
Competitive Advantage with Path Coefficient= 0.689 and t-value= 9.295.
These relationships when combined with the practical insights into KM practice of ten highly
experienced global managers, the quantitative relationship established supports the conceptual
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design of the SKM model and how optimal combination of these elements can translate into
firm Competitive Advantage within with in particular context. The validated SKM model can
be used inform to the configuration of hard and soft systems aligned to both operational
efficiencies and strategies performance outcomes. This represents an important contribution to
both theory and practice in the broad field of Knowledge Management.
6.4.2. Managerial and Practical Implications for the Global Minerals and Metals
Industry and Case Company
In the minerals and metals mining industry there is a finite supply of resources which can be
extracted from the earth. Mineral supplies are naturally reduced as the world develops. These
factors lead large mining companies to constantly seek reductions in the cost of extracting and
processing these resources with a major focus on technical fixes. Due to difficulties in
measurement, the cost benefit analysis from using advanced ICT solutions (like Data Mining)
incorporated within Knowledge Management Systems (KMS) is not completely clear. As a
result, many companies in the minerals and metal mining industry in Australia continue to use
more traditional methods and technologies instead of actively engaging with the emerging
future. However, if companies pay attention beyond licence to operate factors like safety, and
consider the intangible value of knowledge embedded within their internal and external
stakeholder networks, the case for adopting a more strategic approach to Knowledge
Management and Data Mining is clearer. For organisations (such as the case company) with
multiple operations spread through the world, the returns on investment for their Knowledge
Management System (KMS) are scalable. With this in mind, the case company and other
multinational organisations with similar operations and infrastructure stand to gain
significantly from investment in Business Intelligence and Data Mining tools. However, this
success is contingent on good design and sufficient incentives for staff and other relevant
stakeholders such as suppliers and contractors to collaborate and contribute to the shared
knowledge base. As evidenced by the findings of this study, new generation Data Mining can
effectively support (but does not in itself represent) a Strategic Knowledge Management
System (KMS). ICT enabled SKM has been proven in the qualitative and qualitative findings
of this study to enable or directly support Competitive Advantage for the case organisation. In
the literature review and Appendix F there are other examples of large firms employing a
strategic approach to Knowledge Management and information systems. They use these
systems to improve performance, or gain an edge over competitors, through process and system
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efficiencies and innovation (López-Nicolás & Meroño-Cerdán, 2011). According to
Wickramasinghe and Gururajan (2015) Business Intelligence infrastructure incorporates
feedback from customers, suppliers, and other partners in an integrated system. This type of
system helps customers see their purchasing habits and suppliers find the demand patterns for
offering volume discounts. In this way BI, and associated Data Mining activities, support
decision making and interpretation of complex data in specific contexts. Competitive
Advantage results from easy access to rich data resulting in informed decisions and human
interactions (Wickramasinghe & Gururajan, 2015, pp. 200-201).
The qualitative findings reported for the case company identified a strong tradition and culture
of process improvement. This resulted in a more process-based view of Knowledge
Management supported by benchmarking and Best Practice in the case company. According to
Scharmer (2009) companies that adopt process based philosophies such as: TQM, Activity-
Based Costing, more traditional KM and Organisational Learning (OL) sit in the ‘Midstream’
of his management functions model (Scharmer, 2009, pp. 64-65). This focus began during the
1990s and does not pay sufficient attention to new sources of innovation and value creation
beyond optimising processes. Following Scharmer (2009), there is a need for a radical shift
from Midstream to Upstream thinking and modes of operating as a response to emerging
complexity in the environment. This shift is characterized by “a collapse of boundaries between
functions” and a need for more effective integration of knowledge across different operations
and divisions To facilitate this shift, the case company needs to cultivate different management
skills and mind sets to support knowledge creation and “resilience, profound, renewal, and
change” (Scharmer, 2009, pp. 65-66).
The Midstream stage in Scharmer’s model deals with the issue of generating third order
knowledge from emerging complexity and cybernetic feedback from the environment
Companies operating in the Upstream zone are able to dynamically tap knowledge embedded
in complex market networks and business equal systems. Through this navigation and sense
making approach these companies are able to assimilate and apply K3 third generation thinking
and innovative ideas via technologically enabled networks of collaboration (see section 2.10).
According to Scharmer (2009) some companies can successfully create a culture of innovation
(typically high technology and web-based businesses). However, companies that are more
process oriented and less innovation-centered will find it harder to embrace K3 thinking. Those
companies have built success by focusing on organisation efficiency, making innovation a big
challenge (Scharmer, 2009, p. 432). Working from Scharmer’s discourse and the qualitative
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findings of the study thinking in the case organisation corresponds more closely with K2. This
thinking focuses on core competencies in strategy, lean supply chain in manufacturing, and
Communities of Practice as a main platform for Knowledge Management. The case company
situation is analogous to sailing a boat with well-tuned rigging and a hull structure designed
for relatively predictable conditions (1990s industrial environment). The captain and crew are
experienced river sailors but have not sailed on the open ocean with its powerful swell and
unpredictable weather conditions (the turbulent environment of the post 2000 period). This K2
configuration of boat design and expertise does not anticipate disruptive storm conditions and
may not be able to adapt and avoid serious damage. The executive teams and senior managers
of the case company now need to think about what a means to move to K3. This shift in thinking
is consistent with a revolutionary and dynamic approach to strategy as a means to build creative
capacity and innovation into the DNA of the business. The lean supply chain focus on K2
moves to a totally integrated constellation of ICT and human capability. Communities of
practice are replaced by cyber ba as the main platform for sharing tacit knowledge and
combining with codified knowledge. According to Nonaka, cyber ba represents a safe ICT
enabled physical and philosophical space where deep knowledge can be shared with a common
worldview. The mounting global pressures within the minerals and metal mining sector dictate
that the case company must adopt a fundamental shift in thinking, system design and human
patterns and interaction to assure survival as a prerequisite for Competitive Advantage. K3
thinking is not mutually exclusive with K2 thinking. The company can implement elements of
K3 without abandoning the more successful systems and practices within the organisation. The
strategic thinking has to rapidly evolve to accommodate emergent change and market
disruption.
Additionally, scholars consider R&D output as an important source of innovation. R&D
functions affect different innovation outputs, such as patents, product and process innovations
(Barge-Gil & Lo´pez, 2015). In this regard Knowledge Management processes and Data
Mining technologies can make a positive influence to the extraction, creation, accumulation,
and dissemination of knowledge for supporting R&D processes (Jambhekar, 2011; Wang &
Wang, 2008; Drongelen et al, 1996).
There is huge amount of valuable knowledge embedded in employee networks which
organisations risk losing. Loss of human assets presents a significant threat to organisation in
the knowledge-based economy. As identified for the case company, the ability to absorb and
capture existing knowledge and experience from global operations and projects was crucial to
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the return on investment from the dynamic network of GVT, CoP, DM, and KM activities.
Scalable and integrated SKM incorporating these activities could then translate into process
innovation and efficiencies for local operations and ultimately contribute to Competitive
Advantage and superior firm performance. With this in mind the case company has to attract
and motivate the best people, with appropriate cultural values and mindsets, then develop the
firm’s capacity to deal with emerging conditions. This can be achieved by rewarding,
collaborative behaviors, and effective integration of human patterns of interaction and multiple
ICT and Data Mining interfaces. According to Wickramasinghe, & Gururajan (2015) human
assets represent special skills and expertise which have effectively combined can elicit
significant value for the business and create organisational Competitive Advantage
(Wickramasinghe & Gururajan, 2015, p. 200). With regard to collaboration supported by ICT
platforms, the research findings (section 4.3.1) identified Yammer software as a social intranet
system in the case company. Yammer assumes the role of a feeder into a knowledge work and
conversation space for building social connections (Riemer, Scifleet, & Reddig, 2012).
According to Riemer et al. (2012) Yammer has become an information-sharing channel, a
space for crowdsourcing ideas, a place for finding expertise and solving problems, and a
conversation medium for context and relationship building (Riemer, Scifleet, & Reddig, 2012,
p. 15). This social network service becomes more feasible, as a Knowledge Management tool,
when combined with unstructured data analysis technology like advanced Data Mining
systems. Staff relationships can be analysed for identifying who is having the greatest impact
and engaging collaboratively with other staff members. In this way, leaders, group champions,
idea generators, and problem solvers can be surfaced.
Also, the internal Knowledge Creation process enables conversion of existing explicit
knowledge to tacit knowledge through Internalisation or Exercising ba (see SECI processes
section 2.6.1), supported by appropriate training in Knowledge Management practices and use
of supporting technologies. Based on the feedback from several senior managers, the company
needs to invest in online technologies to support increased collaboration and interactive
learning. They identified the limited bandwidth of existing training systems as a limitation on
the user’s ability to get information easily from SharePoint sites which are used to support
virtual teams. A SharePoint site provides a collaboration and communication tool for
employees and a channel for sharing Best Practices.
The Mining Centre of Excellence within the company is also responsible for setting up
knowledge hubs and identifying, and disseminating, Best Practices that are aligned with the
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company’s goals and objectives. Achieving Best Practices within their global operations is a
major KM and learning challenge for the case company supported by existing collaborative
platforms GVT an internal benchmarking process (Elmuti & Kathawala, 1997; Barczak &
Kahn, 2012).
The global Community of Best Practice, within the case company, is one of the best platforms
for sharing Best Practices and knowledge. The Global Virtual Team (GVT) represents a more
developed, overarching version of the Communities of Best Practice (CoBP) operating across
the global network. The GVT is a self-contained task team, with a remit to identify and share
specific knowledge for customising solutions designed to solve non-routine problems (Alavi
& Leidner, 2001). This approach has consistently added value for the case company in the post
global financial crisis (2009) period when limited funds have been available to spend on
upgrading the companies ICT infrastructure. In this regard, the case company, actively makes
use of the Intellectual Capital (IC) as a valuable resource. By drawing on different pillars of CI
and Knowledge Management (including an Intellectual Property (IP), policies, procedures,
benchmarking and Best Practices, the case company has developed a learning culture.
6.4.3. Implications and Recommendations for Future KM Practice within the Case
Organisation
Over the past decade the case company has successfully developed standard procedures for
Knowledge Management. However, one of the senior management interview respondents
(Interviewee 7) suggested that this approach can also limit autonomy and creativity. The
adoption of more integrated and systematic approaches to Knowledge Management, as
illustrated in the SKM model, is proposed to increase firm wide creativity and to leverage
supporting ICT and Data Mining capability (Gabberty & Thomas, 2007).
Additionally, the findings show the researched company values and regularly engages
organisational memory systematically mapped from aggregated individual memories. This
avoids re-inventing the wheel and associated waste of organisational resources. On the other
hand, organisational memory may revert to outdated assumptions and reinforce legacy
management practices without adapting to the emerging environment (Argyris, Smith, & Hitt,
2005). This approach is consistent with single-loop learning by detecting and correcting errors
in the same traditional ways and patterns without changing the governing values of the master
program. The use of double-loop learning to support Knowledge Management activities in the
company is already evidenced through the GVT and Community of Practice collaborative
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network. The evidence presented on Global pressures currently acting on the case organisation
points to adoption of Triple Loop Learning (learning how to learn) or K3 thinking to stimulate
innovation and drive fundamental rethinking of the firm’s current strategic imperatives,
management practices and systems within the organisation (Argyris, Smith, & Hitt, 2005). This
may also increase scope for detecting master program errors, deep within specific project,
operational and strategic knowledge creation contexts. By interpreting data and information,
germane to specific situational contexts, and sharing these insights through Communities of
Practice (CoP) and GVT, a shift from conventional KM is possible. This philosophical shift
may also enabling changes are made to governance, standards, policies and objectives. This
approach also helps to contextualise and disseminate new ideas and reduce resistance to
change. According to Villar et al. (2014) embracing double-loop-learning alone, enables
(managers and staff) to explore existing behaviours, play with new ideas, and discover new
solution. Therefore, double-loop or explorative learning can become an important internal
resource, as new knowledge arises from learning processes inside the firm (Villar, Alegre, &
Pla-Barb, 2014). According to Gupta (2016) Triple Loop Learning (TLL) represents the
possibly of exponential, progression on the Organisational Learning (OL) spectrum. This
concept has been developed by various authors in recent years to account for the unstructured
and dynamically interpreted nature of learning in disrupted, highly political or socially complex
environments. It is relevant to the SKM model presented in this thesis insofar as it
acknowledges the importance of global disrupters impacting the resources industry and case
company. It also supports the research recommendations for the case organisation to adopt
advanced ICT and Data Mining technologies and identify patterns in unstructured data that
may support Competitive Advantage (Reynolds, 2014; Gupta J. , 2016).
The interview and survey findings revealed a CI culture which supported transfer of knowledge
and Best Practices through various communication channels connecting individuals and
groups. Given the importance of this issue, the company encourages the employees to share
their ideas and Best Practises by presenting awards every year. However, the successful
combination of CI and KM within the company is still dependent on the motivational
disposition and the absorptive capacity of a wide range of individuals and target groups
involved in the knowledge transfer process. Gupta and Govindarajan (2000) ongoing training
of staff, with particular emphasis on motivation to learn and absorb new ideas and principles,
is recommended. The company can promote awards to motivate employees and encourage
them to absorb transferred Best Practices and valuable sources of commercial intelligence and
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useful knowledge from across the organisation’s worldwide constellation of suppliers,
customers and stakeholders (Gupta & Govindarajan, 2000). This follows the example of most
innovative successful companies that form knowledge–sharing networks by bringing together
employees for events such as “Show-and-Tell” sessions, informal brainstorming, and “After-
action Reviews” of projects (Clampitt, 2010). According to Clampitt (2010) these communities
often yield benefits such as higher quality knowledge creation and enhanced employee
motivation (Clampitt, 2010, pp. 144-5).
The interview data highlighted the possibility of engaging a third party provider for advanced
analytics. According to a practical industry derived overview of current Data Mining
applications, techniques, and potential contribution to Competitive Advantage, large firms such
as the case company should concentrate their data analytics within a state of the art internal
Data Mining system (Baicoianu & Dumitrescu, 2010). Under the present arrangement the
company uses its Technical Centre (ATC), incorporating statistical experts to undertake data
analytics. According to the senior manager in charge of this area globally, the Centre
undertakes limited multi variant regression analysis, and does not have a well-developed
capacity for integrated Big Data analytics. In this regard, Data Mining technologies would help
to build this capacity as advanced Data Mining tools allow data analysis and knowledge
extraction, by staff who are not necessarily professionals in statistics (Baicoianu & Dumitrescu,
2010).
Moving from Data Mining issues and opportunities, to broader knowledge creation and
collaboration considerations, several interview respondents indicated the current SharePoint
system presents useability issues - or as one respondent put it ‘is not slick’ (Interviewee 8).
This type of issue, along with broader concerns about the information and knowledge sharing
global platform, may be addressed through adoption of the latest generation of collaboration
software such as Atlassians’ Australian developed “Confluence” software. For benefits and
limitations see (Kohler, 2013, p. 152) and (McIntosh, Zabarovskaya, & Uhlmansiek, 2015)
(Appendix E). As noted by Crosby (2014, P.116) citing Manchester (2013) “…social
networking can become the glue not just across the intranet, but for transactional business
systems too. Project management, document management and customer relationship
management systems, together with business intelligence capabilities and more, can be
intertwined with the ‘social layer’.” The author points to the potential of providing better cross-
functional and cross-system visibility and insights (Crosby, 2014).
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Finally, the case company should promote the role of hard systems supporting activities such
as data management and Data Mining as a mediator for leveraging the value of Knowledge
Management (soft systems) to directly impact Competitive Advantage for the firm. (See
hypothesis testing and statistical evidence presented in section 5.6 and 5.7)
6.5. Limitations of Research and Recommendations for Future Research
Whilst this study has identified significant statistical relationships and qualitative evidence to
advance the thinking on strategic applications for Knowledge Management and Data Mining
within a global corporation and broader industry sector, it is important to qualify this with a
brief discussion of study limitations.
Firstly, each of the constructs examined when testing the hypothesis relating to the SKM model
used survey questions as indicators. Some of these first-order constructs such as ‘Knowledge
Creation’ and ‘Valuable Resource’ were measured by 4 separate indicators. ‘Knowledge
Storage’, “Knowledge Transfer”, “Knowledge Application”, “Analyse Data”, and “Rare
Resource” were measured by two separate indicators, whilst others such as “ETL Data”, “Store
and Manage Data and Provide Data Access”, “Present Data”, and “Inimitable and Non-
substitutable” were measured with only one indicator. (This was partly due to limitations on
the number of questions that could be reasonably incorporated into a survey to be carefully
considered, and fully completed, by busy engineers, technical specialists, line managers, and
supervisors at their work place. The final design and effective communication of this survey to
all 115 globally distributed respondents was the result of extensive discussions with the
research facilitator employed as Global Knowledge Manager for the company). Following the
analysis of the survey data to assess the reliability and validity of the first-order measurement
model, two indicators for ‘Knowledge Creation’ were removed from the quantitative data
analysis. This action was undertaken to reduce the risk to the content validity of the construct
and to maintain parsimony. Most of the first-order constructs were measured with two or more
items and four first-order constructs were measured with one indicator when testing the SKM
model.
Secondly, both qualitative and quantitative data were collected in this mixed method study. In
the qualitative data gathering phase one, a semi-structured interview method was employed.
Due to the nature of semi-structured interviews, not all topics were discussed with all
interviewees in the same level of detail. This related to time constraints and the focus, role, and
priorities of each respondent. For the quantitative data collection, a web-based questionnaire
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was distributed via a link to the survey embedded in an email. Whilst the message of this email
was designed in consultation with the research facilitator, it was not possible for the researcher
to be physically present to directly address respondents’ questions about the survey. While
significant attention was paid to the design and communication of the survey, to make the
questions relevant and understandable to the respondents, there is always the possibility for
misunderstanding some of the questions, underlying assumptions and the intent of the survey.
It should also be noted that the email contact addresses of the researchers were put in the
preface of the questionnaire, and respondents could email any concerns or questions prior to
completing the survey. The questionnaire was also pre-tested by the research facilitator (Global
Knowledge Manager) of the case company to identify any preliminary gaps and concerns. The
survey was distributed to the respondents at the same point in time, with response times ranging
from one day to just over one month. The intention was to gather both the qualitative and
quantitative data as close to one point in time as possible, rather than longitudinally.
By way of a third limitation, three of the senior global managers interviewed for stage 1
(equivalent to 2.6 percent of the survey respondents), answered the survey questionnaire
designed for their reports. This was unintended and due to a minor misunderstanding in the
communication of the survey objectives and distribution protocol.
Finally, the findings and conclusions of this deep case study of the operations of one company
across nine international locations may not be directly generalised in purely quantitative terms
to the other companies in mining or other industry sectors. However, the common
infrastructure, technologies, processes, and contractual arrangements used globally in the
minerals and metals mining industry suggest that valuable lessons may be derived to improve
the design of management and ICT, systems, processes, and practices in this sector.
The conceptual and empirical research undertaken for this study has highlighted the value of
tacit knowledge in the case organisation as a valuable internal resource which directly
contributes to efficiency, effectiveness, and future Competitive Advantage. With this in view,
it is recommended that future research into Strategic Knowledge Management (SKM) adopts
a more dynamic internal and external orientation combining RBV, KBV, SBV, and MBV (see
section 2.2.2). This synthesised approach will allow future researchers to explore the potential
benefits of ICT mediated KM as both an internal capability and valuable asset embedded in
supply chains, customer relationships, and broader stakeholder networks. When combined into
dynamic portfolios of Intellectual Capital, (through the agency of a Global Virtual Team or
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similar integrating mechanism), tacit and explicit knowledge can be aggregated and mobilised
to underwrite the long term survival of large scale global firms.
Some broad observations from findings of the study that would be applicable to the design
operation of a KM system in any large scale multi location business are as follows- Employing
full-time KM manager to build a collaborative environment across multiple sites and networks;
Use of collaborative software platforms for regular meetings and discussions between groups
of staff typically including engineers, designers, technical staff, marketing, HR specialist,
training and development specialists seconded onto global virtual teams.
These teams provide a problem solving, innovation, and systems design, resource for each local
operation and the company as a whole. This local impact and global scalability is more
achievable in large organisation where learning can be derived from lived experience in local
sites and aggregated to a global level.
6.6. Chapter Conclusion
In the increasingly disrupted global market environment of 2016 and beyond, global companies
must move away from traditional notions of organisational capital and economic value
creation. Long-term survival and future Competitive Advantage will depend on the adoption
of new worldviews, business models, organising principles, ICT, and management systems
which support collaborative activity and convert intangible human knowledge into valuable
products, services, and brand assets.
Capital intensive mining companies, and a broad spectrum of other firms operating within
Australia, must find new ways to put intangible assets to work for the purposes of innovation,
differentiation, and creation of value outside their traditional markets and realms of economic
activity. This paradigm shift can be supported through the adoption of an iterative corporate
knowledge and learning strategy situated in a context of market disruption, peripheral
innovation, and high social complexity. This dynamic process can be enabled within the
Strategic Knowledge Management (SKM) framework presented in this study.
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Key Findings Practical Implications
The case company has a strong tradition and culture
of process improvement. The case Company’s
processes over the past 20 years have reflected
advanced Continuous Improvement (CI) thinking,
Activity-Based Costing, and more traditional KM and
Organisational Learning (OL) systems and practices.
This places it in the ‘Midstream’ of Scharmer’s (2009)
management functions, knowledge and social
complexity organisational maturity model
To survive in an increasingly challenging competitive
environment senior decision makers may consider a
radical shift from ‘Midstream’ to ‘Upstream’ thinking
and modes of operating as a response to emerging
complexity in the environment
The senior management thinking in the organisation
corresponds more closely with K1 and K2.
The executive teams and senior managers of the case
company may now need to think about what a means
to move to K3. K3 thinking is not mutually exclusive
with K2 thinking. The company can implement
elements of K3 without abandoning the more
successful systems and practices within the
organisation.
In the case company R&D output as an important
source of innovation.
Knowledge Management processes and Data Mining
technologies can make a positive influence on the
extraction, creation, accumulation, and dissemination
of knowledge for supporting R&D processes.
Yammer is currently been used as a social intranet
system and collaboration software in the case
company. The reported usage is limited in terms of its
contribution to broader Strategic Knowledge
Management processes within the organisation.
Yammer and latest generation collaborative software
provide a platform for Strategic Knowledge
Management when combined with unstructured data
analysis technology linked to advanced Data Mining
systems. Staff relationships and interactions can be
analysed to identify who is having the greatest impact
and engaging collaboratively with other staff
members. In this way, leaders, group champions, idea
generators, and problem solvers can be surfaced.
In the case company the limited “bandwidth” of
existing training systems was viewed by some
respondents as a limitation on the user’s ability to get
The company needs to invest in online technologies to
support increased collaboration and interactive
learning.
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information easily from the SharePoint sites used to
support to the KM activities of Global Virtual Team
members. A SharePoint site provides a collaboration
and communication tool for employees and a channel
for sharing Best Practices.
The Global Virtual Team (GVT) represents a more
developed, overarching version of the Communities
of Best Practice (CoBP) operating across the global
network.
The GVT’s should continue to be supported as self-
contained task teams to identify and share specific
knowledge for customising solutions for non-routine
problems. This approach has consistently added value
for the case company in the post global financial crisis
(2009) period when limited funds have been available
to spend on upgrading the companies ICT
infrastructure.
Table 6.1: Summary of Key Findings from the Study and Practical Implications for the Case
Company
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Key Findings Recommendations
Standard procedures for Knowledge Management
currently used in the case company can “limit autonomy
and creativity” (Interview 7).
The adoption of more integrated and systematic
approaches to Knowledge Management, as
illustrated in the SKM model, is proposed to increase
firm wide creativity and to leverage supporting ICT
and Data Mining capability.
Researched company values and regularly engages
organisational memory systematically mapped from
aggregated individual memories. This approach is
consistent with single-loop learning by detecting and
correcting errors in the same traditional ways and patterns
without changing the governing values of the master
program. The use of double-loop learning to support
Knowledge Management activities in the company is
already evidenced through the GVT and Community of
Practice collaborative network.
Organisational memory may default to outdated
assumptions and reinforce legacy management
practices without adapting to the emerging
environment. Double-loop or explorative learning
can become an important internal resource, as new
knowledge arises from learning processes inside the
firm. There may also be a case for Triple Loop
Learning, consistent with K3 thinking to stimulate
innovation and drive fundamental rethinking of the
firm’s current strategic imperatives, management
practices and systems within the organisation
CI culture is further developed through systems which
support transfer of knowledge and Best Practices through
various communication channels connecting individuals
and groups across the company network. Given the
importance of this knowledge creation, sharing, and
application process, the company encourages the
employees to share their ideas and Best Practises and
supports employee awards every year.
Ongoing training of staff, with particular emphasis
on motivation to learn and absorb new ideas and
principles, is recommended. The company can
promote awards to motivate employees and
encourage them to absorb transferred Best Practices
and valuable sources of commercial intelligence.
The culture of the organisation and Global Virtual
Team arrangements should continue to support the
surfacing and application of useful knowledge from
across the organisation’s worldwide constellation of
suppliers, customers and stakeholders. For example
some events such as “Show-and-Tell” sessions,
informal brainstorming, and “After-action Reviews”
of projects should be formally embedded the
company’s operating procedures.
200
In the view of Global Knowledge Manager and
Interviewee 3, the company needs to engage a third party
provider for advanced analytics to further advance the
Knowledge Management system.
Installation of advanced Data Mining technologies
and an in-house expert would help to build this
capacity as these tools support analysis of structured
and unstructured data and knowledge extraction, by
staff who are not necessarily professionals in
statistics.
Current SharePoint system presents useability issues and it
‘is not slick’ (Interviewee 3).
Adoption of the latest generation of collaboration
software such as Atlassians’ Australian developed
“Confluence” is suggested.
Currently there is no mediation effect of DM (DM is not a
mediator) on the relationships between Knowledge
Management processes and Resource based Competitive
Advantage.
The case company should review legacy systems and
management practices at the interface between
people and technology to optimise the application
and impact of latest generation Data Mining, big data
analysis and collaborative information and
knowledge sharing systems. This would integrate
Data Mining capability into a comprehensive ICT
platform which would support improved knowledge
creation and application through better management
of the interface between people and technology.
This would we re-engage Data Mining activity as a
possible mediator for leveraging the value of
Knowledge Management (soft systems), and
Intellectual Capital embedded in the firms social
networks.
Table 6.2: Summary of Key Findings from the Study and Implementation Recommendations
for the Case Company
201
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216
APPENDICES
APPENDIX A: INTERVIEW SCHEDULES
1- Please briefly describe your role and key responsibilities within the
company and/or o business unit.
2- What kind of data is needed to support decision making in relation to your
role and responsibilities. And why is this required?
3- How would you define knowledge and/or information management? Are
these definitions the same as those commonly used by your company? If
not please explain.
4- What type of knowledge is regarded most valuable within your unit (and
within the whole company internationally). Why?
Do you consider this knowledge as a source of competitive advantage for
your company? Why?
5- What kind of knowledge management practices are employed across the
organisation and within your unit? Is the KM system largely IT or Human
Capital (people) focused? Please explain your answer.
6- Do your managers use these practices as part of their daily work routine?
7- What does your company currently do to support effective knowledge
management in your working area? What would you like it to do in future?
8- Does your company have a Knowledge Management System? (KMS) If
so - please describe its main characteristics as follows-
a. Is the main ICT infrastructure and system used for storage,
transferring or sharing knowledge in your working area? i.e.
intranets, social media, integrated databases (e.g. CRM,
ERP). Please describe how this works.
b. Does your company have a strategy for disseminating and/or
creating valuable knowledge (knowledge strategy)? Please
give supporting examples.
217
c. Does your company encourage organisation- wide learning
and collaboration between teams, managers and leaders from
different business unit or departments? Please explain your
answer with examples.
d. Are your customers, suppliers, and other stakeholders
encouraged to share their experiences with your managers and
staff? Are their experiences tracked and stored in databases?
9- Does your company have a Data mining system? If so - please describe its
main characteristics as follows-
a. Please outline the characteristics and applications of data
mining systems in your company. Is it easy for your
competitors to imitate your data mining practices?
b. Do you have access to valuable and integrated data from
various data sources (such as MS office documents, legacy
systems, files, and archive) for decision making? Would you
please describe how they can are used to support your
decision making?
c. Do you have access to summarised data which is helpful for
decision making? Would you please describe how they can
be useful for decision making?
d. Does your company focuses on analysing both structured and
unstructured data for overcoming complex problems or
creating competitive advantage?
e. What are the main outputs (such as specific reports) provided
by company Data mining systems? Are they valuable and
unique? Or you can obtain similar information from other
systems in your organisation?
f. Is it easy for your competitors to produce the similar outputs
from their systems?
218
10- To what extent do you think current knowledge management practices and
data mining systems within the Alcoa international operations network
support global competitive advantage for the firm? Please support your
answer with examples. In what ways are these systems and practices-
unique, costly to imitate, or value adding for your organisation?
219
APPENDIX B: CONSENT FORM INTERVIEW
Consent Form
Interview
Developing a Model for Competitive Advantage through
Integration of Data Mining within a Strategic Knowledge
Management Framework: A Deep Case Study of a Global Mining
and Manufacturing Company
I have read the participant information sheet, which explains the nature of the research and the
possible risks. The information has been explained to me and all my questions have been
satisfactorily answered. I have been given a copy of the information sheet to keep.
I am happy to be interviewed and for the interview to be sound recorded as part of this research.
I understand that I do not have to answer particular questions if I do not want to and that I can
withdraw at any time without needing to give a reason and without consequences to myself.
I agree that research data from the results of the study may be published provided my name or
any identifying data is not used. I have also been informed that I may not receive any direct
benefits from participating in this study.
I understand that all information provided by me is treated as confidential and will not be
released by the researcher to a third party unless required to do so by law.
Participant’s name: ________________________
Signature of Participant: ________________________ Date: …..../..…../…….
I confirm that I have provided the Information Letter concerning this study to the above
participant; I have explained the study and have answered all questions asked of me.
Signature of researcher: ________________________ Date: …..../..…../…….
220
APPENDIX C: QUESTIONNAIRE SURVEY
Dear Respondent,
I invite you to participate in a research project which aims to develop a practical model for data
mining integration within a broader Strategic Knowledge Management framework. This study
is part of my PhD Degree in Management, supervised by Dr Scott Gardner & Dr Amy Huang
of Murdoch University.
Research Information and Purpose of the Study The research explores the proposition that
Knowledge Management Systems (KMS) aligned to strategy and day to day management
practices represent a significant opportunity for mining and resources firms to add value to
processes, products and services.
With this in mind you are invited to respond to the questions in a web based survey which
explores knowledge management and data mining systems and practises in your business unit
and organisation. Individual responses will be treated in confidence and you will be given the
opportunity to comment on the aggregated survey findings when all the data has been collected.
We hope to identify how KM systems and practices are supporting the strategic goals of your
organisation and the extent of alignment or divergence with a KM best practice framework. Dr
Scott Gardner & Dr Amy Huang will oversee the research and I will collect and analyse the
data and write the final research report. The final thesis results will be shared with your
organisation after data analysis is completed and you may access this information on request.
The aim of this research is to explore how to achieve competitive advantage through integrating
data mining practices into a Strategic Knowledge Management (SKM) framework in mining
and resource organisations.
There are no specific risks anticipated with participation in this study. However, if you do have
any concerns or questions about this study please feel free to email me at
sanaz.moayer@murdoch.edu.au or my supervisors: Dr Scott Gardner at
s.gardner@murdoch.edu.au and Dr Amy Huang at a.huang@murdoch.edu.au. My supervisors
and I are happy to discuss with you any concerns you may have about this study.
Sanaz Moayer
Participant consent
221
You can decide at any time to withdraw your consent to participate in this research and in this
event, any material you have provided will be destroyed. My supervisor and I are happy to
discuss with you any concerns you may have about this study.
Please check the box below if you agree to participate in this research:
I agree to answer this questionnaire.
This study has been approved by the Murdoch University Human Research Ethics Committee
(Approval
2012/227). If you have any reservation or complaint about the ethical conduct of this research,
and wish to talk with an independent person, you may contact Murdoch University’s Research
Ethics Office (Tel. 08 9360 6677 (for overseas studies, +61 8 9360 6677) or e-mail
ethics@murdoch.edu.au). Any issues you raise will be treated in confidence and investigated
fully, and you will be informed of the outcome.
Part A
Please provide your details as below:
Gender
o Male
o Female
o Other
Department
o Accounting and Finance, Legal and Corporate Governance
o Marketing and Sales
o Customer Relationship and Stakeholder Management
o Operational planning department- procedures systems and processes
o Technical Support
o Business unit operations
o Business systems
o IT department- systems and processes
o Human Resources Development and Organisation Development
o Research and Development (R&D)
o Other:
Working years in the company
o Less than 1 year
o 1-4 years
o 5-9 years
o 10-14 years
o 15 years above
222
Position
o Director
o Global Manager
o Technical Manager
o Operational Manager
o Team Leader/ Supervisor
o Research Scientist
o Engineer
o Staff
o Other (Please specify)
Educational Qualification
o High school
o College Diploma
o Bachelor Degree
o Master Degree
o Doctoral Degree
Part B:
Please rate each statement with using the following scale of 1-7.
1=Strongly
disagree 2=Disagree 3=Somewhat
Disagree
4=Neutral
5=Somewhat
Agree
6=Agree
7=Strongly
Agree
1) You are encouraged to seek out and apply new ideas at work. For example attending
different communities of practice or forums you get exposed to new ideas from different parts of
business.
1 (Strongly
Disagree) 2 3 4 5 6
7 (Strongly
Agree)
2) In your workplace there are formal processes for conducting experiments and developing
new ideas. For example challenging assumptions behind standard procedures, innovative use
of company knowledge and resources for problem solving, and piloting new techniques and
processes at specific site
1 (Strongly
Disagree) 2 3 4 5 6
7 (Strongly
Agree)
223
3) These experiments result in improved processes, products or services for the company.
(Please provide brief examples).
1 (Strongly
Disagree) 2 3 4 5 6
7 (Strongly
Agree)
4) In your role you learn from expert networks from across the company. (Please provide brief
examples)
1 (Strongly
Disagree) 2 3 4 5 6
7 (Strongly
Agree)
5) In your role you learn from customers and clients. (Please provide brief examples)
1 (Strongly
Disagree) 2 3 4 5 6
7 (Strongly
Agree)
6) In your role you learn from suppliers. (Please provide brief examples).
1 (Strongly
Disagree) 2 3 4 5 6
7 (Strongly
Agree)
7) When your team completes a key project, task or activity, the details (such as plant operational
information and historical data, relevant experience and knowledge, or best practices) are documented
for ongoing learning and communication purposes. (this may include video or audio files).
1 (Strongly
Disagree) 2 3 4 5 6
7 (Strongly
Agree)
8) Building or maintaining corporate memory or operational history is routine in your working area.
(Corporate memory is a human or IT knowledge base that other employees can learn from. Aspects of
this can be lost when people leave.) (Please provide brief examples).
1 (Strongly
Disagree) 2 3 4 5 6
7 (Strongly
Agree)
224
9) You are encouraged to share your ideas, beliefs and insights with other colleagues. (For example
talking to other employees about your ideas, gathering corporate knowledge via coffee machine
conversations, annual and monthly meetings or operational reviews, sharing via Yammer, Online
training, SharePoint sites, and other virtual spaces)
1 (Strongly
Disagree) 2 3 4 5 6
7 (Strongly
Agree)
10) You are recognised and rewarded formally for idea sharing and reuse. (For example this is linked
to pay, promotion, individual or team bonuses and incentives).
1 (Strongly
Disagree) 2 3 4 5 6
7 (Strongly
Agree)
11) Staff in your work area are open to alternative ways of solving problems (for example working with
Global Virtual Teams (GVTs) or other communities to define problems, share, alternative solutions,
and apply best practices).
1 (Strongly
Disagree) 2 3 4 5 6
7 (Strongly
Agree)
12) Staff in your work area are free to invest time in improvement or innovative use of the intellectual
capability of the company.
1 (Strongly
Disagree) 2 3 4 5 6
7 (Strongly
Agree)
13) Data from various sources (Ie: MS office documents, file sharing platforms, process control
systems, legacy systems and archives) is integrated and used to support decision making in your work
area.
1 (Strongly
Disagree) 2 3 4 5 6
7 (Strongly
Agree)
225
14) Staff in your work area can easily obtain data from relevant databases, data marts, or data
warehouses to support operational decision making.
1 (Strongly
Disagree) 2 3 4 5 6
7 (Strongly
Agree)
15) Specialist staff (engineering, technical or IT) in your work area are able to access and analyse real
time, unstructured data. (Unstructured data is obtained outside routine searches and reporting
requirements. It may be generated by staff conversations and observations from emails, meetings,
conferences blogs and presentations).
1 (Strongly
Disagree) 2 3 4 5 6
7 (Strongly
Agree)
16) Discovery and analysis of unstructured data leads to valuable outcomes for the company. (for
example helpful for solving non-routine problems)
1 (Strongly
Disagree) 2 3 4 5 6
7 (Strongly
Agree)
17) Managers and specialist staff in your area are able to present the results of data analysis in easily
understood formats including graphs, charts, figures and tables to support effective financial, tactical,
and continuous improvement reporting.
1 (Strongly
Disagree) 2 3 4 5 6
7 (Strongly
Agree)
18) Unique technical knowledge provides competitive advantage for the company. (For example unique
expertise in refining and processing)
1 (Strongly
Disagree) 2 3 4 5 6
7 (Strongly
Agree)
226
19) The reports generated in your working area add value for the company.(For example reports
provided by the Manufacturing Execution System (MES)).
1 (Strongly
Disagree) 2 3 4 5 6
7 (Strongly
Agree)
20) You are supported and encouraged to undertake problem solving at work. (To use the valuable
knowledge embodied in employees through their work experiences)
1 (Strongly
Disagree) 2 3 4 5 6
7 (Strongly
Agree)
21) Your business unit or work place provides a supportive learning environment. (A supportive
learning environment enables staff and managers to openly discuss, explore and share new ideas and
different perspectives, and also learn from mistakes).
1 (Strongly
Disagree) 2 3 4 5 6
7 (Strongly
Agree)
22) Your working area often provides unique information for the company.
1 (Strongly
Disagree) 2 3 4 5 6
7 (Strongly
Agree)
23) The company invests in leading edge training, research and development (R&D) to build
technological and human capability at your workplace.
1 (Strongly
Disagree) 2 3 4 5 6
7 (Strongly
Agree)
24) The outputs of electronic reporting, research and development and benchmarking activities
provide a unique point of competitive advantage for the company. (A unique point of competitive
advantage means the output of these activities cannot be easily imitated or substituted)
1 (Strongly
Disagree) 2 3 4 5 6
7 (Strongly
Agree)
227
PENDIX D: STATISTIC RESULTS
Appendix D-1: Profile of Respondents
Statistics
Gender
N Valid 115
Missing 0
Mean 1.2000
Median 1.0000
Skewness 1.520
Std. Error of Skewness .226
Kurtosis .315
Std. Error of Kurtosis .447
Minimum 1.00
Maximum 2.00
Percentiles 25 1.0000
50 1.0000
75 1.0000
Gender
Frequency Percent Valid Percent
Cumulative
Percent
Valid Male 92 80.0 80.0 80.0
female 23 20.0 20.0 100.0
Total 115 100.0 100.0
228
Statistics
Educational
N Valid 115
Missing 0
Mean 3.3130
Median 3.0000
Skewness -.312
Std. Error of Skewness .226
Kurtosis .754
Std. Error of Kurtosis .447
Minimum 1.00
Maximum 5.00
Percentiles 25 3.0000
50 3.0000
75 4.0000
Educational
Frequency Percent Valid Percent
Cumulative
Percent
Valid High school 6 5.2 5.2 5.2
College Diploma 6 5.2 5.2 10.4
Bachelor Degree 60 52.2 52.2 62.6
Master Degree 32 27.8 27.8 90.4
Doctoral Degree 11 9.6 9.6 100.0
Total 115 100.0 100.0
229
Statistics
Department
N Valid 115
Missing 0
Mean 6.9043
Median 6.0000
Skewness .567
Std. Error of Skewness .226
Kurtosis -1.143
Std. Error of Kurtosis .447
Minimum 1.00
Maximum 11.00
Percentiles 25 5.0000
50 6.0000
75 10.0000
Department
Frequency Percent Valid Percent
Cumulative
Percent
Valid Accounting and Finance,
Legal and Corporate
Governance
1 .9 .9 .9
Operational planning
department- procedures
systems and processes
7 6.1 6.1 7.0
Technical Support 48 41.7 41.7 48.7
Business unit operations 17 14.8 14.8 63.5
Business systems 2 1.7 1.7 65.2
IT department- systems and
processes
5 4.3 4.3 69.6
Human Resources
Development and
Organisation Development
2 1.7 1.7 71.3
Research and Development
(R&D)
12 10.4 10.4 81.7
Other 21 18.3 18.3 100.0
Total 115 100.0 100.0
230
Statistics
Position
N Valid 115
Missing 0
Mean 5.8261
Median 6.0000
Skewness -.288
Std. Error of Skewness .226
Kurtosis -.803
Std. Error of Kurtosis .447
Minimum 1.00
Maximum 9.00
Percentiles 25 4.0000
50 6.0000
75 7.0000
Position
Frequency Percent Valid Percent
Cumulative
Percent
Valid Director 1 .9 .9 .9
Global Manager 3 2.6 2.6 3.5
Technical Manager 13 11.3 11.3 14.8
Operational Manager 13 11.3 11.3 26.1
Team Leader/ Supervisor 23 20.0 20.0 46.1
Research scientist 5 4.3 4.3 50.4
Engineer 37 32.2 32.2 82.6
Staff 12 10.4 10.4 93.0
Other 8 7.0 7.0 100.0
Total 115 100.0 100.0
231
Statistics
Workingyears
N Valid 115
Missing 0
Mean 4.0783
Median 5.0000
Std. Deviation 1.13283
Skewness -.819
Std. Error of Skewness .226
Kurtosis -.704
Std. Error of Kurtosis .447
Minimum 1.00
Maximum 5.00
Percentiles 25 3.0000
50 5.0000
75 5.0000
workingyears
Frequency Percent Valid Percent
Cumulative
Percent
Valid Less than 1 year 1 .9 .9 .9
1-4 years 14 12.2 12.2 13.0
5-9 years 21 18.3 18.3 31.3
10-14 years 18 15.7 15.7 47.0
15 years above 61 53.0 53.0 100.0
Total 115 100.0 100.0
232
Appendix D-2: One-Sample Kolmogorov-Smirnov Test
One-Sample Kolmogorov-Smirnov Test
Q1 Q2 Q3 Q4
N 115 115 115 115
Normal Parametersa,b Mean 5.4174 4.9913 5.4000 5.2348
Std. Deviation 1.32442 1.37965 1.02427 1.33988
Most Extreme Differences Absolute .209 .216 .243 .204
Positive .116 .119 .166 .119
Negative -.209 -.216 -.243 -.204
Kolmogorov-Smirnov Z 2.243 2.312 2.603 2.192
Asymp. Sig. (2-tailed) .000 .000 .000 .000
One-Sample Kolmogorov-Smirnov Test
Q5 Q6 Q7 Q8
N 115 115 115 115
Normal Parametersa,b Mean 4.9652 4.8783 4.6609 4.5217
Std. Deviation 1.35031 1.42747 1.34352 1.53525
Most Extreme Differences Absolute .180 .238 .243 .196
Positive .109 .138 .131 .135
Negative -.180 -.238 -.243 -.196
Kolmogorov-Smirnov Z 1.929 2.556 2.607 2.104
Asymp. Sig. (2-tailed) .001 .000 .000 .000
233
One-Sample Kolmogorov-Smirnov Test
Q9 Q10 Q11 Q12
N 115 115 115 115
Normal Parametersa,b Mean 5.5043 4.3304 5.1217 4.6174
Std. Deviation 1.15754 1.66871 1.36464 1.44236
Most Extreme Differences Absolute .266 .143 .204 .239
Positive .160 .092 .121 .117
Negative -.266 -.143 -.204 -.239
Kolmogorov-Smirnov Z 2.850 1.536 2.183 2.567
Asymp. Sig. (2-tailed) .000 .018 .000 .000
One-Sample Kolmogorov-Smirnov Test
Q13 Q14 Q15 Q16
N 115 115 115 115
Normal Parametersa,b Mean 5.0348 4.6957 5.0174 5.0783
Std. Deviation 1.29730 1.57376 1.37000 1.20778
Most Extreme Differences Absolute .289 .246 .189 .169
Positive .168 .143 .124 .136
Negative -.289 -.246 -.189 -.169
Kolmogorov-Smirnov Z 3.102 2.641 2.032 1.808
Asymp. Sig. (2-tailed) .000 .000 .001 .003
234
One-Sample Kolmogorov-Smirnov Test
Q17 Q18 Q19 Q20
N 115 115 115 115
Normal Parametersa,b Mean 5.3217 5.9391 5.1478 6.0435
Std. Deviation 1.18875 1.15688 1.25826 .98579
Most Extreme Differences Absolute .246 .251 .203 .230
Positive .188 .180 .136 .166
Negative -.246 -.251 -.203 -.230
Kolmogorov-Smirnov Z 2.641 2.696 2.178 2.469
Asymp. Sig. (2-tailed) .000 .000 .000 .000
One-Sample Kolmogorov-Smirnov Test
Q21 Q22 Q23 Q24
N 115 115 115 115
Normal Parametersa,b Mean 5.4261 5.6087 4.4174 5.1130
Std. Deviation 1.21439 1.05710 1.70645 1.24791
Most Extreme Differences Absolute .212 .187 .199 .179
Positive .144 .187 .114 .119
Negative -.212 -.175 -.199 -.179
Kolmogorov-Smirnov Z 2.275 2.007 2.132 1.917
Asymp. Sig. (2-tailed) .000 .001 .000 .001
a. Test distribution is Normal.
b. Calculated from data.
235
Appendix D-3: Descriptive Statistics of Variables
Statistics
Q1 Q2 Q3 Q4 Q5 Q6 Q7
N Valid 115 115 115 115 115 115 115
Missing 0 0 0 0 0 0 0
Mean 5.4174 4.9913 5.4000 5.2348 4.9652 4.8783 4.6609
Std. Deviation 1.32442 1.37965 1.02427 1.33988 1.35031 1.42747 1.34352
Minimum 2.00 1.00 2.00 2.00 1.00 1.00 1.00
Maximum 7.00 7.00 7.00 7.00 7.00 7.00 7.00
Statistics
Q8 Q9 Q10 Q11 Q12 Q13 Q14
N Valid 115 115 115 115 115 115 115
Missing 0 0 0 0 0 0 0
Mean 4.5217 5.5043 4.3304 5.1217 4.6174 5.0348 4.6957
Std. Deviation 1.53525 1.15754 1.66871 1.36464 1.44236 1.29730 1.57376
Minimum 1.00 2.00 1.00 1.00 1.00 1.00 1.00
Maximum 7.00 7.00 7.00 7.00 7.00 7.00 7.00
236
Statistics
Q15 Q16 Q17 Q18 Q19 Q20 Q21
N Valid 115 115 115 115 115 115 115
Missing 0 0 0 0 0 0 0
Mean 5.0174 5.0783 5.3217 5.9391 5.1478 6.0435 5.4261
Std. Deviation 1.37000 1.20778 1.18875 1.15688 1.25826 .98579 1.21439
Minimum 1.00 1.00 1.00 2.00 1.00 2.00 1.00
Maximum 7.00 7.00 7.00 7.00 7.00 7.00 7.00
Statistics
Q22 Q23 Q24
N Valid 115 115 115
Missing 0 0 0
Mean 5.6087 4.4174 5.1130
Std. Deviation 1.05710 1.70645 1.24791
Minimum 2.00 1.00 2.00
Maximum 7.00 7.00 7.00
237
Appendix D-4: Correlation Tests between Indicators in the First-order Measurement Model
Correlations
Q1 Q2 Q3 Q4
Spearman's rho Q1 Correlation Coefficient 1.000 .553** .479** .548**
Sig. (2-tailed) . .000 .000 .000
N 115 115 115 115
Q2 Correlation Coefficient .553** 1.000 .689** .429**
Sig. (2-tailed) .000 . .000 .000
N 115 115 115 115
Q3 Correlation Coefficient .479** .689** 1.000 .461**
Sig. (2-tailed) .000 .000 . .000
N 115 115 115 115
Q4 Correlation Coefficient .548** .429** .461** 1.000
Sig. (2-tailed) .000 .000 .000 .
N 115 115 115 115
Q5 Correlation Coefficient .338** .320** .370** .273**
Sig. (2-tailed) .000 .000 .000 .003
N 115 115 115 115
Q6 Correlation Coefficient .292** .221* .219* .279**
Sig. (2-tailed) .002 .018 .019 .003
N 115 115 115 115
238
Correlations
Q5 Q6
Spearman's rho Q1 Correlation Coefficient .338** .292**
Sig. (2-tailed) .000 .002
N 115 115
Q2 Correlation Coefficient .320** .221*
Sig. (2-tailed) .000 .018
N 115 115
Q3 Correlation Coefficient .370** .219*
Sig. (2-tailed) .000 .019
N 115 115
Q4 Correlation Coefficient .273** .279**
Sig. (2-tailed) .003 .003
N 115 115
Q5 Correlation Coefficient 1.000 .318**
Sig. (2-tailed) . .001
N 115 115
Q6 Correlation Coefficient .318** 1.000
Sig. (2-tailed) .001 .
N 115 115
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
239
Correlations
Q11 Q12
Spearman's rho Q11 Correlation Coefficient 1.000 .554**
Sig. (2-tailed) . .000
N 115 115
Q12 Correlation Coefficient .554** 1.000
Sig. (2-tailed) .000 .
N 115 115
**. Correlation is significant at the 0.01 level (2-tailed).
Correlations
Q7 Q8
Spearman's rho Q7 Correlation Coefficient 1.000 .589**
Sig. (2-tailed) . .000
N 115 115
Q8 Correlation Coefficient .589** 1.000
Sig. (2-tailed) .000 .
N 115 115
**. Correlation is significant at the 0.01 level (2-tailed).
240
Correlations
Q9 Q10
Spearman's rho Q9 Correlation Coefficient 1.000 .342**
Sig. (2-tailed) . .000
N 115 115
Q10 Correlation Coefficient .342** 1.000
Sig. (2-tailed) .000 .
N 115 115
**. Correlation is significant at the 0.01 level (2-tailed).
Correlations
Q22 Q23
Spearman's rho Q22 Correlation Coefficient 1.000 .414**
Sig. (2-tailed) . .000
N 115 115
Q23 Correlation Coefficient .414** 1.000
Sig. (2-tailed) .000 .
N 115 115
**. Correlation is significant at the 0.01 level (2-tailed).
241
Correlations
Q18 Q19 Q20 Q21
Spearman's rho Q18 Correlation Coefficient 1.000 .334** .407** .323**
Sig. (2-tailed) . .000 .000 .000
N 115 115 115 115
Q19 Correlation Coefficient .334** 1.000 .413** .390**
Sig. (2-tailed) .000 . .000 .000
N 115 115 115 115
Q20 Correlation Coefficient .407** .413** 1.000 .593**
Sig. (2-tailed) .000 .000 . .000
N 115 115 115 115
Q21 Correlation Coefficient .323** .390** .593** 1.000
Sig. (2-tailed) .000 .000 .000 .
N 115 115 115 115
**. Correlation is significant at the 0.01 level (2-tailed).
242
Appendix D-5: First-order and Second-order Loadings (include all indicators/questions)
243
Appendix D-6: Quality Criteria Overview
244
Appendix D-7: Latent Variable correlations
245
246
247
Appendix D-8: First-order and Second-order Loadings (without questions 5 and 6)
Appendix D-9: collinearity assessment between the constructs
Variables Entered/Removedb
Model
Variables
Entered
Variables
Removed Method
1 KMa . Enter
a. All requested variables entered.
b. Dependent Variable: RCA
248
Coefficientsa
Model
Collinearity Statistics
Tolerance VIF
1 KM 1.000 1.000
a. Dependent Variable: RCA
Variables Entered/Removedb
Model
Variables
Entered
Variables
Removed Method
1 DM, KMa . Enter
a. All requested variables entered.
b. Dependent Variable: RCA
Coefficientsa
Model
Collinearity Statistics
Tolerance VIF
1 KM .675 1.480
DM .675 1.480
a. Dependent Variable: RCA
249
Appendix D-10: Significance (t-values) of the Structural Model Path Coefficients
250
Appendix D-11: Effect sizes ƒ²
251
252
Appendix D-12: Construct Crossvalidated Redundancy
Appendix D-13: Communality
253
APPENDIX E: ATLASSIAN SOFTWARE COLLABORATION
Atlassian software started in 2002 that would be cheap and easy to use; would take little effort
to install and maintain (Kohler, 2013) “Confluence” is developed and marketed by Atlassian
which could be used in the corporate environment. Confluence is straightforward with one-
click installation wizard available. Confluence is a web application and accessible with
compatible web browser. It can be on a desktop system, laptop, and mobile device such as
smartphone or tablet (Kohler, 2013).
Regarding to Kohler (2013, p137-152) Confluence is a great tool for collaboration and can be
used in day-to-day job. In Confluence all content is available for all users with several methods
such as: Mentions, Share content, Like, Status updates (managing and displaying), Working
with notification (managing), Configuring and Enabling workbox notifications, Working with
tasks, Working with tasklists, and Managing tasks on a page.
Also in each collaboration tool, it is important that users can access to their information and
keep up with discussion even when they are on the road. In this regard confluence comes with
a built-in mobile interface. It would be easy to use on mobile devices with a web browser.
Therefore on the phone or other supported mobile devices, users are able to view the confluence
dashboard, pages, blog posts, and user profiles; add comments to a page; like content such as
pages or comments; and manage their personal tasks and notifications. (Kohler, 2013, p. 152).
Confluence is useful to share content or get people involved in their workflow, action, and
content. With mobile interface users enable to search information and manage notifications and
tasks. However some researchers like McIntosh, Zabarovskaya, and Uhlmansiek (2015, p120)
mentioned Confluence relies upon keywords entered, so documentation may be difficult to find
if the person use different terms for the same concepts (McIntosh, Zabarovskaya, &
Uhlmansiek, 2015). For solving this issue, have a strict structure of documentation in
Confluence is suggested. For example using Confluence label as a keyword or tag, which can
be added to pages or attachments, would be useful for categorising, identifying content. Any
users with the permission to edit the page or posts can manage the labels. Also attachments can
have labels, so make it easier to find and filter them. If users click on a label on a page or
attachment, they will be forwarded to the labels view (Kohler, 2013, pp. 116-119).
254
APPENDIX F: KNOWLEDGE MANAGEMENT MODELS &
STRATEGIES
Knowledge Management models are presented with different perspectives on the key
conceptual elements of Knowledge Management. In this part according to Haslinda & Sarinah
(2009, p. 189-198), Dalkir (2005, p. 47-75) and other researchers some Knowledge
Management models are examined as bellow:
F-1: Boisot’s Knowledge Category Models (1987)
Boisot model focuses on knowledge as either codified or uncodified and as diffused or
undiffused in organisation (Haslinda & Sarinah, 2009). Codified knowledge can be easily
prepared for transmission purposes such as financial data (McAdam & McCreedy, 1999). On
the other hand uncodified knowledge is transmitted hard such as experience (Haslinda &
Sarinah, 2009). The diffused knowledge can be easily shared; against undiffused knowledge is
not easily transferred (McAdam & McCreedy, 1999). More explanation is shown below:
Propriety Knowledge Public Knowledge
Personal Knowledge Common Sense
Figure F-1: Boisot’s Knowledge Category Model
Reprinted from (Haslinda & Sarinah, 2009, p. 189)
In this model codified undiffused knowledge is referred to as propriety knowledge which is
prepared to transmit to a small group (McAdam & McCreedy, 1999). The uncodified
undiffused knowledge is referred to as personal knowledge such as experiences, ideas, views,
and perceptions (Haslinda & Sarinah, 2009). The public knowledge is codified diffused
knowledge like books, magazines, newspapers, and libraries (McAdam & McCreedy, 1999).
Finally uncodified diffused knowledge is referred to common sense knowledge (Haslinda &
Sarinah, 2009).
There are a number parallels between Nonaka’s model and Boisot’s model. Nonaka categorised
knowledge to tacit and explicit knowledge, but Boisot referred to codified and uncodified
knowledge. Also in both models the horizontal dimension relates to diffusion knowledge
Codified
Uncodified
Undiffused Diffused
255
through the organisation. In comparison with Boisot model and Wiig model, all features of
Wiig model overlap with the features of Boisot’s model
F-2: Kogut and Zander’s Knowledge Management Model (Kogut & Zander, 1992)
Kogut and Zander established the basis for the knowledge-based theory of the organisations
which are emphasizing the strategic importance of knowledge as a source of Competitive
Advantage (Haslinda & Sarinah, 2009)
Figure F-2: Kogut and Zander’s Knowledge Management Model
Reprinted from (Haslinda & Sarinah, 2009, p. 196)
This view emphasises the firms as a repository of capabilities. Kogut and Zander state that
firms become more efficient by knowledge creation and transformation. Through interaction
to transform knowledge a common understanding is developed by individuals and groups in
the organisations. The difference in knowledge between creators and users determines the
firm’s boundaries (Haslinda & Sarinah, 2009).
The Kogut and Zander’s Knowledge Management model discusses “unsocial sociality” where
people want to become a member of community and at the same time also have a desire to keep
hold of their own individuality (Haslinda & Sarinah, 2009). Firms provide conditions to allow
more knowledge to be created and shared within firms.
Knowledge
Creation
Knowledge
Transfer
Process &
Transformation
Of Knowledge
Knowledge
Capabilities
Individual
“Unsocial
Sociality”
Efficient
Firms/
Competitive
256
F-3: Hedlund and Nonaka’s Knowledge Management Model (Hedlund & Nonaka, 1993)
Hedlund and Nonaka’s model was developed for describing the four levels of carriers of
knowledge in organisations. The four levels of carriers are categorised into the individual, the
group, the organisation, and the interorganisational domains that include important customers,
suppliers, competitors and others (Haslinda & Sarinah, 2009). Hedlund and Nonaka (1993)
argue that Knowledge Management characteristics can have implications for the various types
of activities like innovation and strategies (Haslinda & Sarinah, 2009). It can affect
organisations’ success or failures which can depend on how they create, transfer and exploit
their knowledge resources.
Figure F-3: Hedlund and Nonaka’s Knowledge Management Model
Reprinted from (Haslinda & Sarinah, 2009, p. 190)
F-4: Wiig Model for Building and Using Knowledge (1993)
With the purpose of knowledge to become valuable and useful, it must be organised. The
organised knowledge can be accessed and retrieved simply (Dalkir, 2005).
Wiig defines three forms of knowledge: public knowledge, shared expertise, and personal
knowledge (Dalkir, 2005). Public knowledge is explicit and it is available in the public area.
Public books or information on a public website are examples of public knowledge. Shared
expertise is valuable assets that are held by knowledge workers. This kind of knowledge is
usually shared in communication in works. Personal knowledge is most complete form of
knowledge, but not easy to access (Dalkir, 2005).
In addition more Wiig defines four types of knowledge: Factual, conceptual, expectation, and
methodological. Factual knowledge deals with data and directly observable, verifiable content,
and measurement and so on. Conceptual knowledge involves systems, concepts and
perspectives. Expectational knowledge concerns hypotheses, and expectation. Finally,
methodological knowledge deals with decision making methods, strategies, and other
techniques (Dalkir, 2005).
Knowledge calculus Quality Circle’s documented
analysis of its performance
Organisation chart Supplier’s patents and
documented practices
Cross-cultural
Negotiation Skills Team coordination in
complex work Corporate Culture Customer’s attitudes to
products and expectations
Individual Group Organisation Inter-organisational
Domain
Articulated
Knowledge
Tacit
Knowledge
257
These four types of knowledge and there forms of knowledge which was cited at top, make a
KM matrix that is the basis of the Wiig KM model.
Form of
Knowledge
Type of
Knowledge
Factual Conceptual Expectational Methodological
Public Measurement,
reading
Stability,
balance
When supply
exceeds
demand, price
drops
Look for
temperatures
outside the
norm
Shared Forecast
analysis “Market is hot”
A little water in
the mix is okay
Check for past
failure
Personal The “right”
colour, texture
Company has a
good track
record
Hunch that the
analyst has it
wrong
What is the
recent trend?
Table F-4: The Wiig KM Matrix. Reprinted from (Dalkir, 2005, p. 65)
Wiig KM model is the most practical of the models in existence today and can easily be
integrated into any of the other approaches.
F-5: The Von Krogh and Roos Model of Organisational Epistemology (1995)
The von Krogh and Roos KM model distinguishes individual knowledge and social knowledge.
They manage organisational knowledge with epistemological approach. In this KM model,
knowledge resides in the individuals and social level of organisation. Also it can be viewed in
the relations between individuals. Their approach is connectionist, which provides a solid
theoretical cornerstone for a model of KM (Dalkir, 2005).
Von Krogh, Roos, and Kleine proposed five factors for supporting the successful management
of organisational knowledge for Competitive Advantage and organisational goals. These
factors as follow (Dalkir, 2005):
- The mind-set of the individuals
258
- Communication in the organisation
- The organisational structure
- Relationship between the members
- Management of human resources
F-6: The Nonaka and Takeuchi Knowledge Spiral Model (1995)
The Nonaka and Takeuchi model focuses on tacit and explicit spectrum of knowledge forms.
They argue the successful Japanese enterprises use tacit-driven approach to Knowledge
Management. In some a cultural and educational environment, tacit knowledge can be
converted easily to explicit knowledge along the epistemological dimension. Also it can be
easily transferred and shared from the individual to the groups of organisation along the
ontological dimension (Dalkir, 2005).
The Nonaka and Takeuchi model emphasise to importance of knowledge creation which begins
with the individual. The individual’s private knowledge should be translated into public
organisational knowledge. It means private knowledge should be available for transferring to
others in the company (Dalkir, 2005).
According to Nonaka and Takeuchi, there are four modes of knowledge conversion as bellow
(Dalkir, 2005):
- Socialisation (from tacit knowledge to tacit knowledge)
- Externalisation (from tacit knowledge to explicit knowledge)
- Combination (from explicit knowledge to explicit knowledge)
- Internalisation (from explicit knowledge to tacit knowledge)
259
Figure F-6: The Nonaka and Takeuchi Model of Knowledge Conversion
Reprinted from (Nonaka & Takeuchi, 1995, p. 62)
F-7: Skandia Intellectual Capital Model of Knowledge Management (Chase, 1997);
(Roos & Roos, 1997)
Knowledge Management has been not only seen as a transfer of tacit to explicit knowledge
(Haslinda & Sarinah, 2009) but also it has been as an essentially Intellectual Capital(IC)
(McAdam & McCreedy, 1999). The Skandia Intellectual Capital focused on the importance of
equity, human, customer and innovation in managing the flow of knowledge across the
networks (Haslinda & Sarinah, 2009). This intellectual view of Knowledge Management
ignores the political and social aspects of Knowledge Management (McAdam & McCreedy,
1999).
Skandia Intellectual Capital model emphasis to measurement associated with the decomposed
elements (human, customer and structure) (Haslinda & Sarinah, 2009). This approach attempts
to fit objective measures to subjective elements, so this mechanistic approach to measurement
is more consistent with Nonaka’s process of externalisation and combination (Haslinda &
Sarinah, 2009).
Socialisation
Externalisation
Internalisation Combination
Tacit Knowledge
Explicit
Knowledge
G
en
From
Tacit Knowledge Explicit Knowledge
260
Figure F-7: Skandia Intellectual Capital Model of Knowledge Management
Reprinted from (Roos & Roos, 1997)
F-8: Demerest’s Knowledge Management Model (Demerest, 1997)
Demerest’s Knowledge Management model with wide definition of knowledge emphasise on
the construction of knowledge within organisation (Haslinda & Sarinah, 2009). This model
assumes views knowledge as being linked within social and learning process in organisation
(McAdam & McCreedy, 1999). With the view of this model constructed knowledge is
embodied in organisation can transfer through a process of social interchange, not just through
explicit programs (Haslinda & Sarinah, 2009).
The Demerest’s model is shown below:
Market Value
EquityIntellectual
Capital
Human CapitalStructural
Capital
Customer Capital
Customer BaseCustomer
RelationshipCustomer Potential
Organisational Capital
Innovation Capital
Process Capital
261
Figure F-8-1: Demerest’s Knowledge Management Model
Reprinted from (McAdam & McCreedy, 1999, p. 98)
The model focuses on construction of knowledge which is embedded within the organisation
through a process of social interchange (McAdam & McCreedy, 1999). Following embodiment
there is a process of dissemination of espoused knowledge through the organisation. This
model also includes the ‘use’ element for covering business and employee benefits. (Haslinda
& Sarinah, 2009)
In Figure F-8-1 the solid arrows show the primary flow direction whereas plain arrows show
the more recursive flows (McAdam & McCreedy, 1999). Also more recursive arrows show
that Knowledge Management is not as simple sequential process (Haslinda & Sarinah, 2009).
This model is useful for representing balance view, so it allows Knowledge Management to be
associated with the emerging social paradigm while the contributing to the current paradigm
(Haslinda & Sarinah, 2009).
Figure F-8-2 is a modified version of Demerest’s model which allows Knowledge Management
to be associated with the emerging social paradigm while at the same time contributing to the
current paradigm (Haslinda & Sarinah, 2009).
Knowledge
Construction
Knowledge
Embodiment
Knowledge
Dissemination
Use
262
Figure F-8-2: Demerest’s Knowledge Management Model (Modified)
Reprinted from (McAdam & McCreedy, 1999, p. 98)
F-9: The Choo Sense-Making KM Model (1998)
The Choo sense-making model focuses on sense making, knowledge creation, and decision
making. This model emphasises to how information elements are selected and then fed into
organisational actions. In the sense making stage, individuals make sense of the information
from the external environment and construct common interpretations from the exchange and
discuss information with their experiences. With transformation of personal knowledge via
dialogue, discourse, sharing and so on, knowledge creation is constructed. It expanded to
decision making by providing new knowledge. (Dalkir, 2005).
Decision making is a process of evaluating choices by using exist information and knowledge
and taking action. Dalkir (2005, p.60) identified the basic constraint for organisational decision
making is bounded rationality. The capacity of the human mind for solving complex problem
is very small. These types of problem should be solved by rational behavior in the real world.
Individuals when confronted with a complex world, the mind creates a simple mental model
and attempt to work with that model and solve the problem (Dalkir, 2005).
Important strength of the Choo KM model is the comprehensive treatment to organisational
decision making, which is asking in other KM approaches.
Knowledge
Construction
Knowledge
Embodiment
Knowledge
Dissemination
Use
Scientific Paradigm Social Paradigm
Business Benefits Employee
263
F-10: Boisot I-Space KM Model (1998) (Boisot M. H., 1998)
Boisot developed another model which is called Boisot I-Space KM Model (1998) (Dalkir,
2005). The Boisot KM model is based on the concept of “information good”. Boisot
distinguishes information from data. He cites the information is data which is extracted by
observer with prior knowledge or experience (Dalkir, 2005, p. 66).
The I_Space model has three dimensions: (1) codified-uncodified, (2) abstract-concrete, (3)
diffused-undiffused. This model serves to link together content, information, and Knowledge
Management in an effective way. The codification dimension is related to classification and
categorisation; the abstraction dimension is connected to knowledge creation; and the third
diffusion dimension is related to information access and transfer. Therefore managers with
using this model can manage an organisation’s assets (Dalkir, 2005; Boisot M. H., 1998).
Figure F-10: The Boisot I-Space KM model
Reprinted from (Dalkir, 2005, p. 67)
F-11: Stankosky and Baldanza’s Knowledge Management Framework (Stankosky &
Baldanza, 2001)
Stankosky and Baldanza developed a Knowledge Management framework that represents
enabling factors like learning, leadership, organisation, culture, and technology. This
framework includes wide range of disciplines of Knowledge Management such as cognitive
science, communication, individual and organisational behaviour, psychology, finance,
economics, human resource, management, strategic planning, system thinking, process
reengineering, system engineering, computer technologies and software and library science.
(Haslinda & Sarinah, 2009). The detail is shown below:
Uncodified
Codified
Abstract Concrete
Undiffused
Diffused
264
Figure F-11: Basic Disciplines Underlying Knowledge Management and its Enabling Factors.
Reprinted from (Haslinda & Sarinah, 2009, p. 194)
This model emphasises four major foundations that is critical for Knowledge Management such
as leadership, organisation structure, technology infrastructure and learning. First, learning is
leveraging knowledge. Learning can manage information to build enterprise knowledge and
use to Organisational Learning (OL). The key elements of learning are learning communities,
virtual teams, communication and a culture of trust can be identified as some of the key
elements (Haslinda & Sarinah, 2009). Second, leadership is responsible for making best use of
resources, increasing culture of learning and knowledge sharing, encouraging open dialogue,
and team learning. Key element for leadership is strategic planning, communication, system
view, and business culture (Haslinda & Sarinah, 2009). Third, organisation structure is able to
support communications for capturing tacit and explicit knowledge within the organisation. It
should encourage people for exchanging knowledge. Processes, procedures, performance
management system and communication are the key elements (Haslinda & Sarinah, 2009).
Fourth, technology infrastructure supports to exchange information without formal structures.
It can promote the capture of tacit and explicit knowledge. It also supports knowledge sharing
in organisation. Communication, electronic mail, intranet, internet, data warehousing and
decision support systems are identified as the important elements of technology infrastructure
(Haslinda & Sarinah, 2009).
Enabling Factors
learning,
leadership,
organisation,
structure & culture
technology
Disciplines
Cognitive science
Communication,
Individual & organizational
behaviour,
Psychology,
Finance,
Economics,
Human resource,
Management,
Strategic planning,
System thinking,
Process reengineering,
System engineering,
Computer technologies
Software and library science.
Knowledg
e
Manageme
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F-12: Frid’s Knowledge Management Model (Frid, 2003)
Frid’s Knowledge Management framework includes five level is shown below:
Figure F-12: Frid’s Knowledge Management Model
Reprinted from (McAdam & McCreedy, 1999, p. 98)
The first level knowledge ‘knowledge chaotic’ focuses on understanding and implementation
of Frid framework for Knowledge Management. It includes Knowledge Management vision,
Knowledge Management objectives and Knowledge Management indices (Haslinda & Sarinah,
2009). Second level ‘knowledge aware’ is a step higher than knowledge chaotic. It focuses on
developing a Knowledge Management road map and working in partnership with Knowledge
Management office (Haslinda & Sarinah, 2009). Third level is ‘knowledge focused’ indicates
that organisation should cover the implementation aspects which are identified in the lower
levels. In this level organisations start to focus on five new activities. Organisations should
embed Knowledge Management into process engineering, provide primary Knowledge
Management infrastructure, services, and training, support knowledge community, monitor and
report on management indicates, and finally includes Knowledge Management in budgets
(Haslinda & Sarinah, 2009). The fourth level called ‘knowledge managed’ adopt the suggested
activities in level one, two and three should attempt to embed Knowledge Management in
business plans (Haslinda & Sarinah, 2009). The last level termed ‘knowledge centric’ is the
highest level of all Knowledge Management implementation on Frid’s model. The
differentiating activities in organisation should focus on institutionalising initiatives and
evaluating intellectual assets. All Knowledge Management activities should be given equal
emphasis at this level (Haslinda & Sarinah, 2009), so the Frid’s Knowledge Management with
include five levels can be helpful for implementation Knowledge Management in organisation.
Knowledge Chaotic
Knowledge Aware
Knowledge Focused
Knowledge Managed
Knowledge Centric
Level 1
Level 2
Level 3
Level 4
Level 5
Understand and implement objectives,
vision and other KM Indices
Advocating and adopting departmental
KM vision
Start focusing on new activities
Embed KM in performance
reviews and in business plans
Institutionalize initiatives and
evaluate intellectual assets
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F-13: Complex Adaptive System Model of KM (2004)
The Intelligent Complex Adaptive Systems (ICAS) KM model views the organisation as an
intelligent complex adaptive system-the ICAS model of KM. This model has been applied to
an extensive range of complex situations. Managers enable to elaborate policies and develop
organisational structures with using this model. It is also used to create a sense-making model
which utilised for self-organisation capabilities in the informal communities (Dalkir, 2005).
The key processes in the ICAS KM model can be cited as:
Understanding
Creating new idea
Solving problems
Making decisions
Taking actions to achieve desired results
At last there are four major ways in which the ICAS model for describing organisational
Knowledge Management: (1) creatively, (2) problem solving, (3) decision making, and (4)
implementation (Dalkir, 2005).
F-14: The Inukshuk: A Canadian Knowledge Management Model (Girard, 2005)
This model was designed to help Canadian Government leaders conquer the knowledge
challenges. It includes the enablers of Technology, Leadership, Culture, Process, and
Measurement. The process component is based on the SECI model of Nonaka and Takeuchi
(1995) through socialisation, externalisation, combination, and internalisation.
267
Figure F-14: The Inukshuk: A Canadian Knowledge Management Model
Reprinted from (Girard, 2005)
According to Girard (2005, p15) “The Inukshuk is an excellent model of Canadian knowledge”,
because it is well-known in Canada and play important role in their history and tradition; this
model reminds us people play important role in Knowledge Management and it is impossible
without people; and finally each Knowledge Management implementation will be unique.
F-15: Orzano’s Knowledge Management Model (Orzano, 2008)
This conceptualisation of Knowledge Management framework focuses on effective knowledge
process management to influence on performance and work relationship in ways that enhance
learning and decision making. Orzano (2008, p491) established “by the model suggests KM as
the process by which people in organisations find, share, and develop knowledge for action.”
268
FigureF-15: Knowledge Management Model
Reprinted from (Orzano, 2008, p. 492)
Finding knowledge includes processes which allow organisations to make sense of use data,
information, and knowledge which may be existent but are not codified, analysed, nor
accessible to members. Sharing knowledge entails processes to improve the ability of
knowledgeable organisational members, so in this way members are able to expand their own
learning and knowing. Developing knowledge includes processes which allow members create
new understandings and innovations. Developing new knowledge involves the conversion of
tacit to explicit knowledge. Decision making is equivalent to the problem solving process. This
entails some concepts such as exploration and definition of problems and selection of solutions.
Organisational Learning (OL) is a method of decision making with learning processes. The
KM model promotes education and innovation and provides an implementation strategy for
knowledge creation and learning.
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F-16: Integrated Socio-Technical Knowledge Management Model (Handzic, 2011)
The model proposes three main components as knowledge stocks, knowledge processes and
socio-technical knowledge enablers. Knowledge stocks represent important existing
knowledge (tacit and explicit) in various forms within organisation. Knowledge processes
include various activities for moving knowledge stocks by generating new or transferring
existing knowledge. Through socio-technical initiatives knowledge enablers facilitate
knowledge processes and develop organisational knowledge.
Figure F-16: Integrated Socio-technical Knowledge Management Model
Reprinted from (Handzic, 2011, p. 200)
The model presents the knowledge stocks are the major output of knowledge processes. Also
it suggests that socia-technical factors facilitate knowledge processes and contribute to
knowledge output.
F-17: Knowledge Management Model of Community Business: Thai OTOP (‘‘One
Tambon One Product’’) Champion (Tuamsuk, Phabu, & Vongprasert, 2013)
The conceptual research framework includes three parts such as the process of successful
OTOP business management, the Knowledge Management process for successful OTOP
business, and factors in Knowledge Management that drive OTOP business toward successful
270
(see the Figure F-17). These three parts of model need to support by external organisations for
more knowledge sources (Tuamsuk, Phabu, & Vongprasert, 2013).
Figure F-17: Proposed Knowledge Management Model of Thai OTOP Champions
Reprinted from (Tuamsuk, Phabu, & Vongprasert, 2013, p. 373)
The researchers applied the five steps of the successful OTOP framework in the process of
business management such as setting business strategies, selecting products, product quality
development, marketing and distribution of product, and follow-up and evaluation. Also there
are some steps like knowledge identification, knowledge creation, knowledge storage,
knowledge distribution, knowledge application, and knowledge monitoring/validation in the
Knowledge Management process. In addition more leadership, people, organisational culture,
271
and knowledge and intellectual are the high impact factors which affect on the success of top
OTOP Knowledge Management (Tuamsuk, Phabu, & Vongprasert, 2013).
The KM process is performed by social process within families through story telling, training,
and practices. People obtain knowledge from training, seminars, exhibitions, and visit other
business in their network (Tuamsuk, Phabu, & Vongprasert, 2013).
F-18: Summarised the KM Models
Considering the four basic Knowledge Management process (creation, storage, transfer,
application), the KM models, which are described before, have been summarised in below:
Model Year Knowledge
Creation
Knowledge
Storage/
Retrieval
Knowledge
Transfer
Knowledge
Application
The Boisot
knowledge
category Model
1987 - Propriety
knowledge
- Personal
knowledge
- Public knowledge
Common
sense
Kogut and
Zander’s
Knowledge
Management
Model
1992 Knowledge
Creation
- Knowledge
Transfer
- Process &
Transformatio
n Of
Knowledge
- Knowledge
capabilities
- Individual
“Unsocial
sociality”
Hedlund and
Nonaka’s
Knowledge
Management
Model
1993 - Tacit knowledge-
Organisation
(Corporate
Culture)
- Tacit knowledge-
Inter-
Organisational
Domain
(Customer’s
attitudes to
- Articulated
knowledge-
Inter-
Organisation
al Domain
(Supplier’s
patents and
documented
practices)
- Articulated
knowledge-
Individual
(Knowing
calculus)
- Tacit
knowledge-
Individual
(Cross-
cultural
272
products and
expectations)
Negotiation
Skills)
- Tacit
knowledge-
Group (Team
coordination
in complex
work)
- Articulated
knowledge-
Organisation
(Organisation
chart)
The Wiig
Model for
Building and
Using
Knowledge
1993 - Public
Knowledge
- Personal
knowledge
Shared
experience
The von Krogh
and Roos
Model of
organisational
Epistemology
1995 - Individual
knowledge
- Social knowledge
The Nonaka
and Takeuchi
Knowledge
Spiral Model
1995 Knowledge
creation
Knowledge
conversion
(Socialisation,
Externalisation,
Combination,
Internalisation)
Skandia
Intellectual
Capital Model
of Knowledge
Management
1997 - Equity
- Human
Capital
- Customer
Capital
273
- Innovation
Capital
- Process
Capital
Demerest’s
Knowledge
Management
Model
1997 Knowledge
construction
Knowledge
embodiment
Knowledge
dissemination
Use
The Choo
Sense-making
KM Model
1998 Knowledge
creation
- Sense making
- Decision
making
Boisot I-space
KM model
1998 Codified-Uncodified Abstract-Concrete
- Diffused-
Undiffused
Stankosky and
Baldanza’s
Knowledge
Management
Framework
2001
- Learning
- Leadership
- Organisation,
structure &
culture
- Technology
Frid’s
Knowledge
Management
Model
2003 - Knowledge
Chaotic
- Knowledge
Aware
- Knowledge
Focused
- Knowledge
Managed
- Knowledge
Centric
Complex
Adaptive
System Model
of KM
2004 Creating new
ideas
- Solving
problems
- Making
decisions
- Taking
actions to
achieve
desired
results
The Inukshuk:
A Canadian
2005 Process - Measurement
- Leadership
274
Knowledge
Management
Model (Girard,
2005)
- Technology
- Culture
Orzano’s
Knowledge
Management
Model:
Implications for
Enhancing
Quality in
Health Care
(Orzano, 2008)
2008 - Finding Knowledge
- Developing
Knowledge
Sharing Knowledge
- Decision-
Making
- Organisational
Learning
- Organisational
Performance
Integrated
socio-technical
Knowledge
Management
model
(Handzic, 2011)
2011 Knowledge processes Knowledge stocks Knowledge
processes
Socio-technical
knowledge
enablers
Knowledge
Management
model of
community
business: Thai
OTOP (‘‘One
Tambon One
Product’’)
Champion
(Tuamsuk,
Phabu, &
Vongprasert,
2013)
2013 - Knowledge
identification
- Knowledge creation
Knowledge
storage
Knowledge
distribution
- Knowledge
application
- Knowledge
validation
Table F-18: Summarised Knowledge Management Models with Four Basic Knowledge
Management Processes
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