Developing a Model for Competitive Advantage through ... · Developing a Model for Competitive...

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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


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


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!

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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

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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).

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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


Qualitative Data Analysis and

Findings

• Introduction

• Interviewee Demographics Background and Roles (Interviewees 1-10)

•Key Findings


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


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


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.

http://searchsqlserver.techtarget.com/definition/data-warehouse

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 (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.

71

- 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)

72

- 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.

74

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


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.

76

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,

77

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.

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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

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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

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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:

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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

http://en.wikipedia.org/wiki/Aluminum

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).

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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.

90

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


Data MiningResource based

Competitive Advantages

92

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


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


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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



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.


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


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)


5.7. 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.

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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)

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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,



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.

138

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

152

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


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


156

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.


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.

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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

[email protected] or my supervisors: Dr Scott Gardner at

[email protected] and Dr Amy Huang at [email protected]. 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

[email protected]). 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


Kurtosis .754


Minimum 1.00

Maximum 5.00


50 3.0000

75 4.0000

Educational


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


Kurtosis -1.143


Minimum 1.00

Maximum 11.00


50 6.0000

75 10.0000

Department


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


Kurtosis -.803


Minimum 1.00

Maximum 9.00


50 6.0000

75 7.0000

Position


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


Kurtosis -.704


Minimum 1.00

Maximum 5.00


50 5.0000

75 5.0000

workingyears


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


Q5 Q6 Q7 Q8

N 115 115 115 115


Std. Deviation 1.35031 1.42747 1.34352 1.53525


Positive .109 .138 .131 .135

Negative -.180 -.238 -.243 -.196


Asymp. Sig. (2-tailed) .001 .000 .000 .000

233


Q9 Q10 Q11 Q12

N 115 115 115 115


Std. Deviation 1.15754 1.66871 1.36464 1.44236


Positive .160 .092 .121 .117

Negative -.266 -.143 -.204 -.239


Asymp. Sig. (2-tailed) .000 .018 .000 .000


Q13 Q14 Q15 Q16

N 115 115 115 115


Std. Deviation 1.29730 1.57376 1.37000 1.20778


Positive .168 .143 .124 .136

Negative -.289 -.246 -.189 -.169


Asymp. Sig. (2-tailed) .000 .000 .001 .003

234


Q17 Q18 Q19 Q20

N 115 115 115 115


Std. Deviation 1.18875 1.15688 1.25826 .98579


Positive .188 .180 .136 .166

Negative -.246 -.251 -.203 -.230


Asymp. Sig. (2-tailed) .000 .000 .000 .000


Q21 Q22 Q23 Q24

N 115 115 115 115


Std. Deviation 1.21439 1.05710 1.70645 1.24791


Positive .144 .187 .114 .119

Negative -.212 -.175 -.199 -.179


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**


N 115 115



N 115 115


Correlations

Q7 Q8



N 115 115



N 115 115


240

Correlations

Q9 Q10



N 115 115



N 115 115


Correlations

Q22 Q23



N 115 115



N 115 115


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


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

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

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


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


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)


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.


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

265

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


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

266

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.

269

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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