The roles and values of personal knowledge management

Southern Cross UniversityePublications@SCU

Theses

2011

The roles and values of personal knowledgemanagementKam Fai CheongSouthern Cross University

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Publication detailsCheong, KF 2011, 'The roles and values of personal knowledge management', DBA thesis, Southern Cross University, Lismore, NSW.Copyright KF Cheong 2011

http://epubs.scu.edu.au

http://epubs.scu.edu.au/theses

mailto:[email protected]

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PPeerrssoonnaall KKnnoowwlleeddggee MMaannaaggeemmeenntt

Cheong, Kam Fai B.Sc. (HK), MBA (UK)

A research thesis submitted to the Graduate College of Management,

Southern Cross University, Australia, in partial fulfillment

of the requirements for the degree of

Doctor of Business Administration

May 2011

i

Statement of Original Authorship

I certify that the substance of this thesis has not currently been submitted for any degree

and has not previously being submitted for any other degree. I also certify that to the best

of my knowledge any help received in preparing this thesis and all sources used have

been acknowledged in this thesis.

……………………………………………….

Cheong, Kam Fai

ii

Acknowledgements

The completion of this dissertation would not have been possible without the support and

inspiration of many individuals and organisations. First and foremost, I wish to express

my heartfelt thanks to my supervisor Professor Eric Tsui who provided me with excellent

guidance, encouragement, assistance and support crucial to the successful completion of

my thesis. His profound knowledge in the field of knowledge management and in

particular to personal knowledge management enabled me to broaden my knowledge and

to complete this dissertation.

Furthermore, I would like to acknowledge the other outstanding academic and

administrative staff members of Southern Cross University and the Hong Kong Institute

of Technology for providing a splendid service throughout the course of my study. I am

especially grateful to: Associate Professor Peter Miller, Dr. Jun Xu, Dr. Simon Pervan

and Dr. Raymond Cheng for giving a lot of valuable advice and support on my research,

Miss Sue White and Miss Betty Yuen for their excellent administrative support. I would

like to also thanks Dr. Eric Cheng of Hong Kong Institution of Education, Miss Teresa

Liew of The Hong Kong Polytechnic University and Mr. Ricky Lee; they have also

provided me a lot of valuable advice, encouragement and support during my research. I

wish to thank Mr. Michael Pomfret for proofreading of this report.

Special thanks to all respondents who have completed my survey and I would also like to

thank those knowledge management organisations for their help in allowing me to

distribute the questionnaires to their members.

Last but not least my wife, Ruth Ma, for staying by me during some tough moments, for

encouraging and supporting me in terms of words and actions. Without her, I would not

have been able to complete this thesis.

iii

Abstract

The topic of Personal Knowledge Management (PKM) has seen accelerated growth

recently although PKM is not new, as our ancestors sought ways to learn better and to

improve their knowledge. From the literature, it is clear that an individual plays an

important role in organisational learning and knowledge management. However, there

has been very little empirical research or significant conceptual development carried out

with PKM (Pauleen 2009a), resulting in very few research publications (a search on Sept

13 2009 revealed that Google Scholar had only 1010 counts, ProQuest 28, EBSCO 22

and Emerald only 6) in this particular field of study, demonstrating that PKM is still an

under-explored or under-researched area (Pauleen 2009a; Tsui 2002b; Zhang 2009).

In the past decade, several scholars (e.g. Frand and Hixon (1999), Avery et al. (2001)

Berman and Annexstein (2003) , Efimova (2005), Wright (2005), Zuber-Skerritt (2005),

Agnihotri and Troutt (2009), and Jarche (2010a) ) have developed models to describe

PKM. Their models shared the same assumption that PKM is playing important roles in

knowledge management and has benefits to both individuals and organisations. However,

there is inadequate research investigating what are the roles and values of PKM. This

research represents the first global survey to investigate this under-explored area and to

unlock our understanding about the roles and values of PKM. There are four research

questions answered in this thesis. The first research question is “What are the roles of

PKM in the KM Process?”, the second is “What are the values of PKM for individuals

and organisations?”, the third is “Is there any correlation between the roles of PKM in

KM Processes and the values of PKM for individuals and organisations?” and the last

one is “ Is there any correlation between the values of PKM for individuals and the

values of PKM for organisations?”

A theoretical model was developed and an online survey was conducted by sending

invitations to the members of KM organisations. Altogether 206 KM participants in 44

different countries/locations completed the survey. The collected data was analysed by

both exploratory data analysis and confirmatory data analysis. Validity and reliability

tests were performed prior to the hypotheses tests that were done by standard regression

and structural equation modelling methods.

iv

The research determined that PKM is playing important roles in KM processes and has

significant values in both individual competences and organisation competences. The

results also showed that there are positive correlations between the roles of PKM in KM

processes and the values of PKM for both individuals and organisations. Moreover,

positive correlations were also found between the values of PKM for individuals and the

values of PKM for organisations.

Towards the end of this study, a PKM 2.0 conceptual model was developed which

consists of four key elements, namely personal information management (PIM), personal

knowledge internalisation (PKI), personal knowledge creation (PKC) and inter-personal

knowledge transferring (IKT). This model sets the foundation for future research and also

for applying PKM in the business environment e.g. business process management.

This research has made significant contributions with implications to both theory and

practice, in four key areas. Firstly, it provided empirical evidence to support Avery et al

(2001)’s PKM Skills Framework. Secondly, it filled the gap in the theory about the roles

and values of PKM and provided empirical evidence to support the assumption used by

many scholars that PKM is playing important roles in KM and has benefits to both

individuals and organisations. Thirdly, an empirical model was developed to describe the

Roles and Values of PKM which can be used for future research and the application of

PKM in organisations. Finally, it provided further support to the published literature

about the importance of individual learning in organisational learning and also supported

the concept that PKM is bridging the gap between individual learning and organisational

learning.

v

Publications resulting from this research

The following papers were originated from this research.

Refereed Journal Articles

Cheong, KFR & Tsui, E 2010, 'The Roles and Values of Personal Knowledge

Management: An exploratory study', VINE: The journal of information and knowledge

management systems, vol. 40, no. 2, pp. 204-227

Cheong, KFR & Tsui, E 2010, 'Exploring the Synergy Between Business Process

Management and Personal Knowledge Management', Cutter IT Journal, vol. 23, no. 5, pp.

28-33.

Cheong, KFR & Tsui, E 2011 Forthcoming, ‘From Skills and Competences to outcome-

based Collaborative Work: Tracking a decade’s development of Personal Knowledge

Management (PKM) Models (Accepted by Knowledge and Process Management Journal)

Book Chapters

Cheong, KFR & Tsui, E 2011, 'Exploring linkage between Personal Knowledge

Management and Organisational Learning', in D Pauleen & G Gorman (eds), Personal

Knowledge Management: Individual, Organisation and Social Perspectives Gower.


Management', in P Miller & R Cheng (eds), Doctoral research in management and

business in Hong Kong, Hong Kong Institute of Technology, Hong Kong

vi

Table of Contents

STATEMENT OF ORIGINAL AUTHORSHIP ........................................................................................ I

ACKNOWLEDGEMENTS .........................................................................................................................II

ABSTRACT ................................................................................................................................................ III

PUBLICATIONS RESULTING FROM THIS RESEARCH...................................................................V

TABLE OF CONTENTS ........................................................................................................................... VI

LIST OF FIGURES................................................................................................................................. VIII

LIST OF TABLES...................................................................................................................................... XI

ABBREVIATIONS.................................................................................................................................. XIII

CCHHAAPPTTEERR 11 -- IINNTTRROODDUUCCTTIIOONN ...............................................................................................................1

1.1 INTRODUCTION...........................................................................................................................2

1.2 BACKGROUND OF THE RESEARCH.......................................................................................2

1.3 JUSTIFICATION OF THE RESEARCH.....................................................................................3

1.4 RESEARCH PROBLEM AND RESEARCH QUESTIONS.......................................................5

1.5 RESEARCH METHODOLOGY AND FINDINGS.....................................................................7

1.6 OUTLINE OF THE REPORT.......................................................................................................9

1.7 DELIMITATION OF SCOPE AND KEY ASSUMPTIONS ....................................................10

1.8 CHAPTER CONCLUSION .........................................................................................................10

CCHHAAPPTTEERR 22 -- LLIITTEERRAATTUURREE RREEVVIIEEWW .................................................................................................12

2.1 INTRODUCTION.........................................................................................................................13

2.2 KNOWLEDGE MANAGEMENT...............................................................................................13

2.3 PERSONAL KNOWLEDGE MANAGEMENT (PKM) ...........................................................34

2.4 THE ROLES AND VALUES OF PERSONAL KNOWLEDGE MANAGEMENT ...............76

2.5 CHAPTER CONCLUSION .........................................................................................................87

CCHHAAPPTTEERR 33 -- RREESSEEAARRCCHH DDEESSIIGGNN AANNDD MMEETTHHOODDOOLLOOGGYY ...........................................................89

3.1 INTRODUCTION.........................................................................................................................90

3.2 RESEARCH PARADIGMS .........................................................................................................91

3.3 RESEARCH METHODOLOGY AND JUSTIFICATION.......................................................93

3.4 THE THEORETICAL MODEL..................................................................................................97

3.5 RESEARCH DESIGN ................................................................................................................101

3.6 DATA ANALYSIS APPROACH...............................................................................................116

3.7 ETHICAL CONSIDERATIONS ...............................................................................................120

3.8 CHAPTER CONCLUSION .......................................................................................................121

CCHHAAPPTTEERR 44 –– FFIINNDDIINNGGSS AANNDD DDAATTAA AANNAALLYYSSIISS ............................................................................122

4.1 INTRODUCTION.......................................................................................................................123

4.2 PILOT STUDY............................................................................................................................123

4.3 DATA PREPARATION .............................................................................................................124

4.4 RESPONDENTS’ PROFILE .....................................................................................................128

4.5 DATA SCREENING...................................................................................................................135

vii

4.6 DESCRIPTIVE STATISTICS ...................................................................................................136

4.7 CONSTRUCTS ASSESSMENT ................................................................................................147

4.8 EXPLORATORY DATA ANALYSIS ......................................................................................192

4.9 CONFIRMATORY DATA ANALYSIS BY STRUCTURED EQUATION MODELLING

(SEM) 218


CCHHAAPPTTEERR 55 -- CCOONNCCLLUUSSIIOONNSS AANNDD IIMMPPLLIICCAATTIIOONNSS .....................................................................230

5.1 INTRODUCTION.......................................................................................................................231

5.2 ANSWERS TO RESEARCH QUESTIONS.............................................................................233

5.3 RESEARCH IMPLICATIONS .................................................................................................259

5.4 RESEARCH CONTRIBUTION................................................................................................266

5.5 RESEARCH LIMITATION ......................................................................................................269


CCHHAAPPTTEERR 66 –– FFUUTTUURREE WWOORRKK ............................................................................................................271

6.1 INTRODUCTION.......................................................................................................................272

6.2 FUTURE RESEARCH ...............................................................................................................272


REFERENCES ..........................................................................................................................................277

APPENDIX 1 – QUESTIONNAIRE........................................................................................................299

APPENDIX 2 – LIST OF KM ORGANISATIONS INVITED TO DISTRIBUTE THE SURVEY ..312

APPENDIX 3 – DISTRIBUTION OF THE RESPONDENTS BY COUNTRY ..................................313

APPENDIX 4 - SEM RESULTS OUTPUT.............................................................................................314

viii

List of Figures

Figure 1. 1: Structure of Chapter 1 ......................................................................................2

Figure 1. 2: Theoretical Model ............................................................................................6

Figure 1. 3: Chapters of this Thesis .....................................................................................9

Figure 2. 1: Structure of Literature Review.......................................................................13

Figure 2. 2: DIKW Hierarchy ............................................................................................16

Figure 2. 3: SEIC Model....................................................................................................19

Figure 2. 4: School of Knowledge Management ...............................................................24

Figure 2. 5: Various Knowledge Management Lifecycle..................................................26

Figure 2. 6: Knowledge Process Categories ......................................................................27

Figure 2. 7: Approaches of studying organisational learning and learning organisations.31

Figure 2. 8: Review of OL Literature ................................................................................33

Figure 2. 9: Individual Learning ........................................................................................34

Figure 2. 10: OADI-Shared Mental Models (SMM) Cycle...............................................37

Figure 2. 11: Experiential Learning Cycle.........................................................................38

Figure 2. 12: Similarities among conceptions of basic adaptive processes: inquiry /

research, creativity, decision-making, problem solving, learning .....................................39

Figure 2. 13 : PKM Development in Past Decade .............................................................47

Figure 2. 14: Berman and Annexstien (2003)’s PKM Model (PK-Book Model) .............58

Figure 2. 15: Efimova (2005)’s PKM Model.....................................................................61

Figure 2. 16: Wright (2005)’s PKM Framework ...............................................................64

Figure 2. 17: Zuber-Skerritt (2005)’s PKM Model (A values and actions model)...........69

Figure 2. 18: Agnihotri and Troutt (2009)’s PKM Model (PKM Skills Tools Fit Model)70

Figure 2. 19: Jarche (2010)’s PKM Model ........................................................................74

Figure 2. 20: Learning Process ..........................................................................................79

Figure 2. 21: Professional Competences............................................................................82

Figure 2. 22: The Effective Knowledge Organisation architectural framework ...............84

Figure 2. 23: Comparison of Knowledge-related element in Effective Knowledge

Organisation.......................................................................................................................85

Figure 3. 1: Structure of Research Methodology...............................................................90

Figure 3. 2: Theoretical Model ..........................................................................................98

Figure 3. 3: The Research Design....................................................................................102

Figure 3. 4: Question 3.1 for Roles of PKM in KM Process ...........................................112

Figure 3. 5: Questions 4.1 for Values of PKM for Individuals........................................112

Figure 3. 6: Questions 5.1 for Values of PKM for Individuals........................................113

Figure 3. 7: Summary of Research Design .....................................................................116

Figure 3. 8: Data Analysis Procedure ..............................................................................117

Figure 4. 1: Structure of Chapter 4 ..................................................................................123

Figure 4. 2 : PKM Adoption ............................................................................................129

Figure 4. 3 : PKM Training..............................................................................................130

Figure 4. 4 : Geographic Distribution of Respondents ....................................................131

Figure 4. 5 : Age Group ...................................................................................................131

Figure 4. 6 : Gender .........................................................................................................132

Figure 4. 7 : Work Position..............................................................................................132

Figure 4. 8 : Organisation Type .......................................................................................133

Figure 4. 9 : Industry........................................................................................................133

Figure 4. 10 : Work Experience.......................................................................................134

Figure 4. 11 : Education...................................................................................................134

Figure 4. 12 : Role of PKM in Locating / Capturing Knowledge....................................137

ix

Figure 4. 13 : The Role of PKM in Creating Knowledge................................................137

Figure 4. 14 : The Role of PKM in Transferring / Sharing Knowledge ..........................138

Figure 4. 15 : The Role of PKM in Applying Knowledge...............................................139

Figure 4. 16 : The Value of PKM in Communication Competence ................................140

Figure 4. 17 : The Value of PKM in Creativity Competence ..........................................140

Figure 4. 18 : The Value of PKM in Problem Solving Competence ...............................141

Figure 4. 19 : The Value of PKM in Learning / Self Development.................................141

Figure 4. 20 : The Value of PKM in Mental Agility Competence ..................................142

Figure 4. 21 : The Value of PKM in Analysis Competence ............................................143

Figure 4. 22 : The Value of PKM in Reflecting Competence .........................................143

Figure 4. 23 : The Value of PKM in External Information Awareness Competence......144

Figure 4. 24 : The Value of PKM in Internal Knowledge Dissemination Competence ..145

Figure 4. 25 : The Value of PKM in Effective Decision Making Competence...............145

Figure 4. 26 : The Value of PKM in Organisation Focus Competence...........................146

Figure 4. 27 : The Value of PKM in Continuous Innovation Competence .....................146

Figure 4. 28 : The Construct Assessment of PKM1 ........................................................149

Figure 4. 29 : Histogram of PKM1 ..................................................................................150

Figure 4. 30 : The construct Assessment of PKM 2 ........................................................151


Figure 4. 32 : Construct Assessment of PKM3................................................................153


Figure 4. 34 : Construct Assessment of PKM4................................................................155








Figure 4. 42 : The Construct Assessment of IV.PKM1 ...................................................163

Figure 4. 43 : Histogram of IV.PKM1.............................................................................164













Figure 4. 56 : The Construct Assessment of OV.PKM1..................................................178

Figure 4. 57 : Histogram of OV.PKM1 ...........................................................................179






x








Figure 4. 70 : Mean Score of the Roles of PKM in KM Cycle........................................205

Figure 4. 71 : Mean Score of PKM Values for Individuals’ Competences .....................206

Figure 4. 72 : Mean Score of PKM Values for Organisations’ Competences.................208

Figure 4. 73 : Model for SEM Analysis...........................................................................219

Figure 4. 74 : Measurement Model..................................................................................223

Figure 4. 75 : Structural Model 1.....................................................................................225

Figure 4. 76 : Structural Model 2.....................................................................................228

Figure 5. 1 : Structure of Chapter 5 .................................................................................232

Figure 5. 2 : The Roles of Retrieving Skill in KM Processes..........................................236

Figure 5. 3 : The Roles of Evaluating Skill in KM Processes .........................................236

Figure 5. 4 : The Roles of Organising Skill in KM Processes.........................................237

Figure 5. 5 : The Roles of Analysing Skill in KM Processes ..........................................238

Figure 5. 6 : The Roles of Collaborating Skill in KM Processes.....................................239

Figure 5. 7 : The Roles of Presenting Skill in KM Processes..........................................239

Figure 5. 8 : The Roles of Securing Skill in KM Processes ............................................240

Figure 5. 9 : PKM Values for Communication Competence...........................................243

Figure 5. 10 : PKM Values for Creativity Competence...................................................243

Figure 5. 11 : PKM Values for Problem Solving Competence........................................244

Figure 5. 12 : PKM Values for Learning / Self Development Competence ....................245

Figure 5. 13 : PKM Values for Mental Agility Competence...........................................246

Figure 5. 14 : PKM Values for Analysis Competence.....................................................247

Figure 5. 15 : PKM Values for Reflection Competence..................................................247

Figure 5. 16 : PKM Values for External Information Awareness Competence ..............249

Figure 5. 17 : PKM Values for Internal Knowledge Dissemination Competence...........250

Figure 5. 18 : PKM Values for Effective Decision Making Competence .......................250

Figure 5. 19 : PKM Values for Organisation Focus Competence ...................................251

Figure 5. 20 : PKM Values for Continuous Innovation Competence..............................252

Figure 5. 21 : Model 1 and Model 2 of SEM Analysis....................................................258

Figure 5. 22 : Roles and Values PKM Model..................................................................268

Figure 6. 1 : Structure of Chapter 6 .................................................................................272

Figure 6. 2: PKM 2.0 Conceptual Model.........................................................................273

xi

List of Tables

Table 1. 1: KM and PKM search counts reported at Sept 13 2009 .....................................3

Table 1. 2: Research Question, Hypotheses and Sub-Hypotheses.......................................8

Table 2. 1: Benefits of Knowledge Management ..............................................................22

Table 2. 2: The Role of Frand and Hixon’s (1999) PKM Model .....................................50

Table 2. 3: The role of Avery et al. (2001)’s PKM Model ................................................57

Table 2. 4: The roles of Berman and Annexstein (2003)’s PKM Model...........................60

Table 2. 5: The Role of Efimova (2005)’s PKM Model....................................................61

Table 2. 6: The Role of Wright (2005)‘s PKM model.......................................................65

Table 2. 7: The Role of Zuber-Skerritt (2005)’s PKM model ...........................................68

Table 2. 8: The roles of Agnihotri and Troutt (2009)’s PKM model ...............................73

Table 2. 9: The roles of Jarche (2010)’s PKM model........................................................75

Table 2. 10: Analysis of PKM Models against KM Processes ..........................................78

Table 2. 11: Mapping of benefit and values to seven individuals competences................83

Table 3. 1: Basic Belief Systems of Alternative Enquiry Paradigms ................................93

Table 3. 2: Quantitative Research versus Qualitative Research ........................................93

Table 3. 3: Research Questions and Hypotheses ...............................................................99

Table 3. 4: Constructs and Variables for Measurement...................................................101

Table 3. 5: Probability and Non-probability Samplings Designs ....................................106

Table 3. 6: Advantages of On-line Survey.......................................................................110

Table 3. 7: Mapping of questions to hypotheses and research questions ........................115

Table 4. 1: Coding Scheme for this Research..................................................................128

Table 4. 2 : Renaming Variables Name...........................................................................192

Table 4. 3 : Assessment of the difference between groups for RPKM by ANOVA Test193

Table 4. 4 : Pro Hoc Comparison of RPKM1 and PKM_Adoption ................................194

Table 4. 5 : Pro Hoc Comparison of RPKM6 and PKM_Adoption ................................194

Table 4. 6 : Assessment of the difference between groups for IVPKM by ANOVA Test

..........................................................................................................................................195

Table 4. 7 : Pro Hoc Test of IVPKM1 and PKM_Adoption ...........................................195

Table 4. 8 : Pro Hoc Test of IVPKM5 and PKM_Adoption ...........................................196

Table 4. 9 : Assessment of the difference between groups for OVPKM by ANOVA Test

..........................................................................................................................................196

Table 4. 10 : Pro Hoc Test of OVPKM1 and PKM_Adoption........................................197

Table 4. 11 : Pro Hoc Test of OVPKM5 and PKM_Adoption........................................197

Table 4. 12 : Assessment of the difference between groups for RPKM and

Respondent_Industry by ANOVA Test ...........................................................................198

Table 4. 13 : Pro Hoc Comparison of RPKM6 and Respondent_Industry......................199

Table 4. 14 : Assessment of between groups for IVPKM and Respondent_Industry by

ANOVA Test ...................................................................................................................200

Table 4. 15 : Pro Hoc Test of IVPKM6 and Respondent_Industry .................................201

Table 4. 16 : Assessment of between groups for OVPKM and Respondent_Industry by

ANOVA Test ...................................................................................................................202

Table 4. 17 : Pro Hoc Test of OVPKM1 and Respondent_Industry ...............................203

Table 4. 18 : Mean Score of PKM Skills in KM Cycle ...................................................204

Table 4. 19 : Mean Score of PKM Values for Individuals Competences........................206

Table 4. 20 : Mean Score of PKM Values for Individuals’ Competences ......................206

Table 4. 21 : Mean Score of PKM Values for Organisations’ Competences ..................207

Table 4. 22 : Linear Regression of PKM Skills and PKM Values for Individuals..........209

Table 4. 23 : Linear Regression of PKM Skills and PKM Values for Organisations......211

xii

Table 4. 24 : Linear Regression of PKM Skills and PKM Values for Organisations......214

Table 4. 25 : Hypotheses Tests Results ...........................................................................217

Table 4. 26 : Reliability Test of Measurement Model.....................................................221

Table 4. 27 : Goodness-of-Fit of Measurement Model....................................................221

Table 4. 28 : Standard Regression of Measurement Model.............................................222

Table 4. 29 : Goodness-of-Fit of Structural Model 1.......................................................224

Table 4. 30 : Standard Regression of Structural Model 1................................................225

Table 4. 31 : Goodness-of-Fit of Structural Model 2.......................................................226

Table 4. 32 : Standard Regression of Structural Model 2................................................227

Table 5. 1 : Research Questions and Hypotheses ............................................................234

Table 5. 2 : The Means Score PKM skills in KM Processes ...........................................235

Table 5. 3 : Mean and Ranking of PKM Skills in Individuals Competences ..................241

Table 5. 4 : Mean and Ranking of PKM Skills in Individuals Competences ..................242

Table 5. 5 : Mean and Ranking of PKM Skills in Organisations Competences..............248

Table 5. 6 : The strength of relationship between the roles of PKM skills in KM Process

and the values of PKM skills in individuals competences...............................................254

Table 5. 7 : The strength of relationship between the roles of PKM skills in KM Process

and the values of PKM skills in organisation competences.............................................255

Table 5. 8 : The strength of relationship between the PKM skills’ values for individual

competences and organisation competences....................................................................257

Table 6. 1: PKM 2.0 Conceptual Model ..........................................................................273

xiii

Abbreviations

AMOS – A statistical software package for SEM, produced by SPSS

d.f. – Degree of Freedom

DV – Dependent Variable

EFA - Exploratory Factor Analysis

H – Hypothesis

HREC - Human Research Ethics Committee

IL – Individual Learning

IV – Independent Variable

KM – Knowledge Management

PCA – Principal Components Analysis

PKM – Personal Knowledge Management

PKMIV – Value of PKM for Individuals

PKMOV – Value of PKM for Organisations

OL – Organisation Learning

RQ – Research Question

SD – Standard Deviation

SEM – Structural Equation Modelling

PASW (SPSS) – Predictive Analytics Software (formerly, Statistical Package for the

Social Science

Chapter 1: Introduction

Copyright@ Ricky K.F. Cheong 2011 Page 1

CChhaapptteerr 11 -- IInnttrroodduuccttiioonn



1.1 Introduction

The research undertaken studies the roles and values of the Personal Knowledge

Management, and this chapter outlines the thesis structure and provides the background

of the research. As shown in figure 1.1, this chapter starts with the introduction (section

1.1) and is followed by a description of the research background (section 1.2), the

justification of the research (section 1.3) and the research problem and research questions

(section 1.4). The research methodology and findings are highlighted (section 1.5) and

followed by the outline of this thesis report (section 1.6). At the end of this chapter, the

limitations of the research scope and key assumptions (section 1.7) are presented before

the chapter conclusion (section 1.8).

Figure 1. 1: Structure of Chapter 1

Source: Developed for this research

1.2 Background of the research

Research in Knowledge Management have been growing rapidly in the past two decades

but Personal Knowledge Management (PKM) is an under-explored / under-researched

area (Pauleen 2009a; Tsui 2002b; Zhang 2009). There has been very little empirical

research or significant conceptual development done with PKM (Pauleen 2009a) with

very few publications in this particular field of study. It can be reflected by the recent

research at Google Scholar and the journal database e.g. ProQuest, EBSCO & Emerald.

The search counts reported at Sept 13 2009, as shown in table 1.1, indicated that the

search counts for “Personal Knowledge Management” had less than 0.25% of

“Knowledge Management”.



Google Scholar ProQuest EBSCO Emerald

PKM KM PKM KM PKM KM PKM KM

1,010 477,000 28 23,112 22 45,480 6 6064 Table 1. 1: KM and PKM search counts reported at Sept 13 2009


In addition, Heisig (2009) analysed 160 KM frameworks and concluded that the

underlying consensuses are detected regarding the basic categories in describing the KM

activities and the critical success factors. The KM framework proposed by Heisig (2009)

from his research has three layers, which are business focus layer, knowledge focus layer

and enabler focus layer. Heisig (2009)’s research reflected most of the previous KM

research that focused on the business, process and enabler, however it lacks focus on the

fundamental element of KM, which is the people focus layer.

This big gap in the research requires more research efforts and this thesis aims to

investigate the roles and values of PKM at both the individual and organisation levels. In

this research, the term “roles” is defined as the position and function of PKM in the KM

processes and the term “values” is defined as the improvement in competencies of

individuals and organisations by practicing the PKM.

1.3 Justification of the research

This section discusses the importance of focusing on this area and justifying the selection

to investigate the roles and values of PKM.

The concept of knowledge work was first introduced by Peter Drucker (1959) and he

emphasised that knowledge worker productivity is the driver for the next level of

economic growth (1999). Knowledge work is often non-sequential, requires individual

decision-making and is self-paced (Ramirez & Nemghard 2004). The capability of

individual workers is valued as the human capital of an organisation. Employee

competence is the muscle behind the people and computers and their knowledge brought

into their ability and competence are the intangible assets for the organisation (Thomas,

B. G. 2000). The competency and proficiency of knowledge workers, among other

factors, underpins the success of an organisation’s KM journey. On the contrary, the

organisations would suffer if incapable staff performed their own tasks poorly, which

leads to poor product and service quality, low productivity, poor customer satisfaction



and ends up reducing the organisation’s competitive advantages in the market.

Obviously, individual knowledge and competence are important factors for organisations’

performance.

David Pauleen (2009a) mentioned that Personal Knowledge Management is helping

individuals to be more effective in personal, organisational and social environments.

From the previous literature, it was clear that the individual is playing an important role

in organisational learning and knowledge management. The main stream of

organisational learning considers individuals as “agents” for organisations to learn

(Argyris, C. & Schon 1978). New knowledge always begins with the individual making

personal knowledge available to others and is the central activity of the knowledge

creation company (Nonaka 1991). A learning organisation should primarily focus on

valuing, managing and enhancing the individual development of its employees

(Scarbrough, Swan & Preston 1998). The relationship between individual and

organisation learning is an important aspect (Kim, D. H. 1993; Matlay 2000). Ahmed et

al. (2002) mentioned that knowledge management involves individuals combining and

sharing their experience, skills, intuition, ideas, judgments, context, motivations and

interpretations. One of the knowledge management strategies proposed by Wiig (1997) is

personal knowledge responsibility.

Pauleen (2009a) argued that the history of PKM begins with the idea of the knowledge

worker by Drucker (1968), but Volkel and Abecker (2008) mentioned that the term PKM

had already been used since Polanyi (1958). Numbers of scholars have tried to define

their own PKM model e.g. Frand and Hixon (1999)’s PKM Model (PIM Model), Avery

et al (2001)’s PKM Model (PKM Skills Model), Berman and Annexstein (2003)’s PKM

Model (PK-Book Model) , Efimova (2005)’s PKM Model (Individuals, Ideas and

Communities Model), Wright (2005)’s PKM Model (Competences Model), Zuber-

Skerritt (2005)’s PKM Model (Values and Actions Model), Agnihotri and Troutt

(2009)’s PKM Model (PKM Skill-Tools Fit model), and Jarche (2010)’s PKM Model

(Aggregate, Understand and Connect Model). All these PKM models shared the same

assumption that PKM is important and could benefit individuals and organisations.

However, there is a shortfall of empirical research to investigate the roles and values of

PKM.



This research is important as it can fill this gap in knowledge and can provide empirical

evidence to support the assumption in previous scholars’ work on PKM. It is also

important to have a roles and values PKM model which can be used for future research as

well as a reference for executives in organisations. It sets the foundation to further

develop the theory, policy and practice of PKM, and benefits both academia and business.

In addition, the author has keen interests in investigating the roles and values of PKM.

The author also believes that this research would lead to an improvement of his own

competences and PKM skills, and as a result can achieve a better performance through a

PKM strategy to manage his work and social life.

1.4 Research problem and research questions

Given the identified knowledge gap, this research addresses the research problem in

relation to the fundamental question “What are the roles and values of personal

knowledge management?” and the following are the proposed research questions.

RQ1: What are the roles of PKM in the Knowledge Management Process?

RQ2: What are the values of PKM for individuals and organisations?

RQ3: Is there any correlation between the roles of PKM in Knowledge

Management Processes and the values of PKM for individuals and organisations?

RQ4: Is there any correlation between the values of PKM for individuals and the

values of PKM for organisations?

Based on the literature review in chapter 2, a theoretical framework on the roles and

values of PKM is shown in figure 1.2. This model illustrates the relationship between the

roles of PKM in the KM process and the values of the PKM for individuals and for

organisations. There are four concepts under this model, namely PKM Skills, KM

Process, PKM Values for Individuals and PKM Values for Organisations.

The first concept is based on the PKM Skill model proposed by Avery et al. (2001) which

consists of seven information skills: (1) retrieving, (2) evaluating, (3) organising, (4)

analysing, (5) Collaborating, (6) Presenting and (7) Securing. This model has been



adopted by various scholars in their PKM researches e.g. Berman and Annexstien (2003),

Wu (2007), Agnihotri and Troutt (2009) and Cheng (2009).

H4

(H4a – H

4g)

Figure 1. 2: Theoretical Model Source: Developed for this research

An analysis of the previous literature was done to investigate the second concept on the

KM processes. There are four generic KM processes identified, as proposed by Seufert,

Back and Krogh (2003). The four generic KM processes are (1) Capture / Locate

knowledge, (2) Create knowledge , (3) Transfer / Share knowledge and (4) Apply

knowledge.

The third concept is related to the values of PKM for individuals which consists of the

meta-competences as proposed by Cheetham and Chivers (1996, 1998), namely (1)

communication, (2) creativity, (3) problem solving, (4) learning / self development, (5)

mental agility, (6) analysis and (7) reflecting. These seven meta-competences have been

tested by 20 different professionals (Cheetham & Chivers 1998) and has influenced many

scholars in their research e.g. Jackson (1998), Boak and Coolican (2001), Foley et al

(2004), Watson et al (2004), Heilmann (2007) and Hashim (2008).

The fourth concept is concerned with the values of PKM for organisations which consists

of the organisation’s competences proposed by Mendelson and Ziegler (1999) and

Ziegler (2008). These are (1) external information awareness, (2) internal knowledge



dissemination, (3) effective decision making, (4) organisation focus and (5) continuous

innovation. Previous research performed by Mendelson and Ziegler confirmed that these

five organisation’s competences were positively correlated to the firm performance.

To answer the research questions, there are five main hypotheses and 23 sub-hypotheses

proposed and these are summarised in table 1.2. These hypotheses were tested by the

collected data and the results are presented in chapter 4.

1.5 Research Methodology and Findings

The quantitative research approach was selected after evaluation of various research

paradigms. The research was designed based on the framework proposed by Sekaran

(2003). A questionnaire using the 5 point-Likert scale with forced closed questions was

constructed from the literature by establishing the questions items to answer the research

questions and to fit the objectives of this research. A pilot test was conducted prior the

main survey to pre-test the questionnaire. The targeted respondents were the knowledge

management participants, and the questionnaire was posted to the online survey platform

(www.surveymonkey.com) and invitations were sent globally to the members /

participants of the knowledge management societies, associations and interest groups.

There were a total of 467 respondents recorded in the main survey. 5 respondents

declined to proceed with the survey in the informed consent stage. 462 respondents

accepted and a total of 213 valid samples were collected. The collected data were

examined, screened and analysed with PASW (SPSS) and AMOS version 18. The data

were analysed by both exploratory data analysis (validity test, reliability test and standard

regression) and confirmatory data analysis (structural equation modelling).

The results concluded that PKM is playing important roles in KM processes. The values

of PKM were found to have significant contributions in both individual competences and

organisational competences. Positive correlations were found between the roles of PKM

and their values in contributing to individual competences and organisation competences,

and were also found between the values of PKM for individual competences and the

values of PKM for organisation competences.



Research Questions Main Hypotheses Sub-Hypotheses

RQ1: What are the

roles of PKM in the

Knowledge

Management Process?

H1. PKM skills are

playing important

roles in the KM

Cycle

N.A.

H2a: PKM can benefit individuals RQ2: What are the

values of PKM for

individuals and

organisations?

H2. PKM can

benefit both

individuals and

organisations H2b: PKM can benefit organisations

H3a : The value of the Retrieving skill for individuals is

positively correlated to its role in PKM Cycle

H3b : The value of the Evaluating skill for individuals is


H3c : The value of the Organising skill for individuals is


H3d : The value of the Analysing skill for individuals is


H3e : The value of the Collaborating skill for individuals

is positively correlated to its role in PKM Cycle

H3f : The value of the Presenting skill for individuals is


H3. The values of

PKM for

individuals are

positively

correlated to the

roles of PKM skills

in the KM process.

H3e : The value of the Securing skill for individuals is


H4a : The value of the Retrieving skill for organisations


H4b : The value of the Evaluating skill for organisations


H4c : The value of the Organising skill for organisations


H4d : The value of the Analysing skill for organisations


H4e : The value of the Collaborating skill for

organisations is positively correlated to its role in PKM

Cycle

H4f : The value of the Presenting skill for organisations is


RQ3: Is there any

correlation between the

roles of PKM in KM

Process and the values

of PKM for individuals

and organisations?

H4. The values of

PKM for

organisations are

positively

correlated to the

roles of PKM skills

in the KM process.

H4e : The value of the Securing skill for organisations is



is positively correlated to its value for individuals








organisations is positively correlated to its value for

individuals

H5f : The value of the Presenting skill for organisations


RQ4: Is there any


values of PKM for

individuals and the

values of PKM for

organisations

H5. The values of

PKM for

individuals are

positively

correlated to the

values of PKM for

the organisation.

H5g : The value of the Securing skill for organisations is

positively correlated to its value for individuals

Table 1. 2: Research Question, Hypotheses and Sub-Hypotheses Source: Developed for this research



1.6 Outline of the Report

This thesis contains six chapters that are outlined in figure 1.3.

Figure 1. 3: Chapters of this Thesis Source: Developed for this research

(1) Chapter One: Introduction

This chapter provides a brief overview on the background to this research, the

research objective, research questions, research gap, research model, and the

research hypotheses. It also presents a brief overview of the methodology and

limitations of this research.

(2) Chapter Two: Literature Review

This chapter overviews the literature in the parent disciplines of knowledge

management and PKM, and it reviews the literature regarding the roles and values

of PKM. This sets the foundation for the development of the theoretical

framework and the research hypotheses discussed in chapter 3.

(3) Chapter Three: Research Design and Methodology

This chapter addresses the choice of research paradigm, development of the

theoretical framework and hypotheses, research design, the population and

sample, the questionnaire design, the administration of the survey, and the data

analysis approach and tools.

(4) Chapter Four: Findings and Data analysis

This chapter presents the results of the data analysis in this research, including the

analysis of respondents’ profiles in term of the PKM profile and demographic

profile. The results of the exploratory data analysis and confirmatory data analysis

are presented. The exploratory data analysis includes validity test, reliability test,

correlations test and simple regressions. The confirmatory data analysis by



structural equation modeling was conducted to test both the measurement model

and hypotheses model.

(5) Chapter Five: Conclusion and Implications

This chapter provides the conclusions and implications for the whole thesis, and

discusses the research conclusion, the research implications for the theory, policy

and practice, research contributions and research limitations.

(6) Chapter Six: Future Work

This chapter presents the direction of future research. A PKM 2.0 model is

developed based on the research results and this model can set the foundation for

future research in the area of Personal Knowledge Management.

1.7 Delimitation of scope and key assumptions

The research was performed by online survey and limited only to those respondents who

could be reached on the Internet. The targeted respondents were affiliated to knowledge

management societies, associations or interest groups, which also is a delimitation of

scope. Although there is no geographic limitation set in this research, the footprint of the

targeted knowledge management organisations has indeed placed a limitation on the

locations of the respondents.

In addition, the delimitation set by the Southern Cross University’s Doctor of Business

Administration (DBA) program is that the limitation of the length of the thesis of

approximately 50,000 words is acknowledged.

A key assumption in this research is that the respondents were able to reflect their own

experience in determine the roles and values of the PKM, at both the individual and

organisation levels.

1.8 Chapter conclusion

This chapter lays the foundation for the thesis and provides an overview of the research.

The background of the research is explained, and the research problems and research

questions are introduced. The research is justified and followed by a brief discussion

about the research methodology. The outline of the thesis is presented and definitions of



the key terms used in this research are highlighted. The delimitation of the scope and key

assumption are discussed. On these foundations, the rest of this thesis presents a detailed

description of the research.

Chapter 2: Literature Review


CChhaapptteerr 22 -- LLiitteerraattuurree RReevviieeww



2.1 Introduction

The aim of the previous chapter is to provide background information about this research

which sets the scene for the rest of the thesis. The purpose of this chapter is to review the

existing literature which sets the foundation of the theoretical framework for this

research. The chapter is divided into 5 sections as shown in Figure 2.1. There are two

parent disciplines of this research, namely Knowledge Management (section 2.2) and

Personal Knowledge Management (section 2.3.). The parent disciplines are the

background for the immediate discipline related to the Roles and Values of Personal

Knowledge Management (section 2.4), and before the end of this chapter a conclusion is

presented (section 2.5).

Figure 2. 1: Structure of Literature Review Source: Developed for this research

2.2 Knowledge Management

The study of Knowledge Management (KM) is not new. The first institution, a library,

dedicated to Knowledge Management was started about 5,000 years ago in Mesopotamia,

when people began to lose track of the thousands of baked-clay tablets used to record

legal contracts, tax assessments, sales and law (Bergeron 2003). The study of knowledge

management has created tremendous interest for participants and researchers. It covers a



broad range of fields, including but not limited to economics, information systems,

organisational behaviour, psychology, strategic management, linguistics, cognitive

science, philosophy, anthropology and sociology (Argote, McEvily & Reagans 2003;

Nonaka & Teece 2001).

This section provides the critical literature review of knowledge management, which

covers discussion on the definition of knowledge, types of knowledge, knowledge

conversion, definition of knowledge management, school of thoughts of knowledge

management, knowledge management process and organisational learning. These are the

foundation of our discussion in the next section related to the PKM.

2.2.1 What is Knowledge?

There is no single agreed definition of knowledge. Grant (2000) mentioned that there is a

philosophical debate as to what knowledge is and valuing what we do not even

understand is unlikely. The philosopher, Plato, defined knowledge as perception and true

judgement. The definitions of Knowledge in the Oxford English Dictionary are:

(1) Information and skills acquired through experience or education

(2) The sum of what is known

(3) Awareness or familiarity gained by experience of a fact or situation.

Van and Spijkervet (1997, p. 36) defined knowledge as the ‘whole set of insights,

experiences and procedures which are considered correct and true and which therefore

guide the thoughts, behaviour and communication of people’. Davenport and Prusak

(2000, p. 5) stated that knowledge is

“a fluid mix of framed experience, values, contextual information,

and expert insight that provides a framework for evaluating and

incorporating new experiences and information. It originates in and

is applied in the minds of knowers. In organisations, it often

becomes embedded not only in documents or repositories but also

in organisational routines, processes, practices, and norms...”



Stewart (2000) mentioned that knowledge is a conclusion drawn from data and

information. This knowledge hierarchy can be traced back in the poetry “The Rock” by

Eliot in 1934 (Sharma 2008). It is a Data, Information, Knowledge & Wisdom (DIKW)

hierarchy.

….

The endless cycle of idea and action,

Endless invention, endless experiment,

Brings knowledge of motion, but not of stillness;

Knowledge of speech, but not of silence;

Knowledge of words, and ignorance of the Word.

….

Where is the Life we have lost in living?

Where is the wisdom we have lost in knowledge?

Where is the knowledge we have lost in information?

Where is the information we have lost in data?

Data is a set of discrete, objective facts about events. Davenport and Prusak (1998)

mentioned that data is most usefully described as structured records of transaction in an

organisation. However, Zack (1999) argued that data is not meaningful as it represents

observations or facts out of the context.

Information is the result of placing data within some meaningful context and often it is in

the form of a message (Zack 1999). Davenport and Prusak (1998) mentioned that

information is usually in the form of a document or an audible or visible communication.

It has a sender and a receiver and the information is aimed at changing the perceptions of

the receiver e.g. judgement or behaviour.

Russell Ackoff (1989) added another layer of “understanding” between knowledge and

wisdom in the knowledge hierarchy, as shown in figure 2.2. Understanding requires

diagnosis and prescription. In Russell Ackoff’s view, the first four layers are related to

the past, i.e. to deal with what has been known, and only wisdom deals with the future

because it is incorporated with the vision and design (Ahsan & Shan 2006).



Figure 2. 2: DIKW Hierarchy

Source: Omegapowers (2008)

(1) Data

Data are the products of observation and are the symbols that represent properties

of objects, events and their environment (Ackoff 1989). Data provides the raw

materials as a set of discrete, objective facts about events (Davenport, H. &

Prusak 1998). Ahsan and Shah (2006) argued that data is the factual information

(as measurements or statistics) used as a basis for reasoning discussion and it can

be structured to become information. However, Ackoff (1989) mentioned that

data are of no value until they are processed into a useable form; therefore, the

difference between data and information is functional, not structural, but data are

usually reduced when they are transformed into information.

(2) Information

Information answers questions that begin with particular words, such as who,

what, where, what, where, when and how many (Ackoff 1989). Ashan and Shah

(2006) mentioned that information is data that has been given meaning by way of

relational connection, and this meaning can be useful but does not have to be.

Information is the analysed data for decision making and in a context to define the

relationship between two or more pieces of data and other information (Loshin

2001; Zikmund 2000). Unlike data, information informs receivers and impacts on

their judgment and behaviour (Davenport, H. & Prusak 1998).



(3) Knowledge

Davenport and Prusak (1998) mentioned that knowledge is information combined

with experience, context, interpretation, reflection and perspective. Ashan and

Shah (2006) described knowledge as an appropriate collection of information

intended to be used. Ackoff (1989) said that knowledge is know-how and it is

what makes possible the transformation of information into instructions. James

(2005) argued that knowledge is blended by many things, and it is usually

subjective, and summarises the meaning of knowledge as an awareness,

understanding or familiarity gained from a blending of information, experience,

skills, principles, rules, values, insight, study, investigation and observation.

(4) Understanding

Ashan and Shah (2006) mentioned that understanding is an interpolative and

probabilistic process, is cognitive and analytical, and it is the process by which

individuals can take knowledge and synthesise new knowledge from the

previously held knowledge. Ackoff (1989) argued that understanding is generally

a man-machine system to facilitate and accelerate learning and adaptation, and

requires diagnosis and prescription, and focuses on efficiency.

(5) Wisdom

Ackoff (1989) argued that wisdom adds value which requires the mental function

of judgment. Wisdom is the judicious application of accumulated knowledge and

experience (James 2005), and is the ability to see through complexity and

discover the fundamental nature of issues or problems. Wisdom is an

extrapolative and non-deterministic, non-probabilistic process, and calls upon all

previous levels of consciousness, and specifically upon special types of human

programming e.g. moral, ethical, codes …etc.

Regardless of how knowledge is defined, many researchers (for example Drucker 1993;

1995; Hamel 2002; Leonard-Barton 1998; Michalisin, Smith and Kline 1997; Nonaka

1991; Pemberton and Stonehouse 2000) viewed that knowledge is the cornerstone for

competitive advantages (James 2005). Drucker (1993) argued that knowledge is not just



another resource alongside the traditional factors of production – labour, capital, and land

– but the only meaningful resource in the new economy.

New knowledge always begins with the individual making personal knowledge available

to others and is the central activity of the knowledge creation company (Nonaka 1991).

The learning that take place from others and the skills shared with others need to be

internalised – that is, reformed, enriched, and translated to fit the company’s self image

and identity (Nonaka & Takeuchi 1995). In the view of Nonaka and Takeuchi (1995),

knowledge not only can be acquired, taught, and trained through manuals, books, or

lectures. Instead, knowledge can be gained in less formal and systematic ways by using

metaphors, pictures or experiences which are highly subjective insights, intuitions, and

hunches.

2.2.2 Types of Knowledge

Knowledge is commonly viewed as two dimensions of “Explicit” and “Tacit”. Explicit

knowledge is deeply ingrained in the traditions of Western management, from Frederick

Taylor to Herbert Simon (Nonaka & Takeuchi 1995); it is in the form of words, numbers

and can be easily communicated and shared in the form of hard data, scientific formulae,

codified procedures, or universal principles (Nonaka & Takeuchi 1995). Best (1989)

mentioned that explicit knowledge is flexible and can often be reorganised to suite our

purpose. It is more precisely and formally expressed than tacit knowledge (Zack 1999).

Polanyi (1996) termed Tacit knowledge, based on the logic that “we know more than we

can tell”. It is something not easily visible and expressible; it is highly personal and hard

to formalise, making it difficult to communicate or to share with others; subjective

insights, intuitions and hunches are classified as tacit knowledge (Nonaka & Takeuchi

1995). It is ‘subconsciously understood and applied, difficult to articulate, developed

from direct experience and custom, and usually shared through highly interactive

conversation, storytelling, and shared experience’ (Zack 1999, p. 46). It is divided into a

technical dimension and a cognitive dimension (Takeuchi 1999). The technical dimension

refers to those skills or crafts said to be ‘know-how’; the cognitive dimension refers to

those ‘taken-for-granted’ beliefs, values and meta-models that shape the way a person

sees the world.



2.2.3 Knowledge Conversion

Nonaka and Takeuchi (1995) proposed the SECI knowledge conversion spiral model as

illustrated in figure 2.3. In the SECI model, there are four modes of knowledge

conversion namely Socialisation, Externalisation, Internalisation and Combination. The

Socialisation is exchanging tacit knowledge by face-to-face communication or shared

experience, Externalisation is converting tacit knowledge to explicit knowledge by

developing concepts to embed the combined tacit knowledge, Internalisation is

converting explicit knowledge to tacit knowledge and Combination is converting explicit

knowledge to explicit knowledge. These conversion processes are interacting in the spiral

of knowledge creation.

Figure 2. 3: SEIC Model Source: Nonaka and Takeuchi (1995)

2.2.4 What is Knowledge Management?

Same as the definition of Knowledge, many researchers have tried to define Knowledge

Management but there is no single definition that is commonly agreed. The following is

the selected KM definitions which provide insight of what KM is really all about. It

includes process oriented; resource based; behavioural and technology based…etc.

aspects.



(1) Knowledge management can be defined as the process of offering the right

knowledge to the right users at the right time and helping people share and put

information into action in ways that strive to improve organisational performance

(Schotte 2003, p. 18).

(2) Knowledge Management includes all methods, instruments and tools that

contribute to the promotion of an integrated core knowledge process – with the

following four core activities as a minimum, to generate knowledge, to store

knowledge, to distribute knowledge and to apply knowledge – in all areas and

levels of the organisation, in order to enhance organisational performance by

focusing on the value creating business process (Mertins, Heisig & Vorbeck 2003,

p. 11).

(3) Knowledge management caters for the critical issues of organisational adaption,

survival and competence in the face of increasingly discontinuous environment

change. Essentially, it embodies organisational processes that seek synergistic

combinations of data and information-processing capacity of information

technologies, and the creative and innovative capacity of human beings (Malhotra,

Y. 2001, p. 9).

(4) Knowledge management is defined as a method designed to simplify and improve

the process of creating, sharing, and using knowledge in an organisation

(Gottschalk 2005).

(5) Knowledge Management is the systematic and deliberate creation, renewal,

application, and leveraging of knowledge and other intellectual capital (IC) assets

to maximise the individual’s and the enterprise’s knowledge-related effectiveness

and returns (Wiig 2004, p. 217).

Apart from the definition of knowledge management, scholars and researchers have

articulated many different knowledge management objectives and benefits. The following

is selected literature which provides insight about what knowledge management can

provide.



(1) Knowledge management provides a competitive advantage for organisations as it

allows the organisation to solve problems and seize opportunities (Earl, M. J. &

Scott 1999; Parlby & Taylor 1999; Zack 1999).

(2) Parlby and Taylor (1999) argued that knowledge management can evaluate core

processes, capture insight about the findings, combine the skills and experience,

innovation and apply new ideas quickly.

(3) Knowledge management contributes to more effective decision making (Ernst &

Young 1999). Parlby and Taylor (1999) argued that decision making performance

may be impacted when the best know-how is not available to the decision makers

when they need it.

(4) Knowledge management improves productivity which includes reduced time to

market, improved innovation and improved personal productivity (Miller 1996).

(5) Knowledge management can improve the performance of organisational processes

(Van & Spijkervet 1997).

(6) Knowledge management can persuading people to share (Havens & Hass 2000)

(7) Knowledge management can build and exploit the organisation’s intellectual

capital affectivity (Wiig 1997).

(8) Knowledge management can make knowledge more visible throughout the

organisation (Allee 1997)

In additions, Cretau and Dfouni (2008, p. 66) summarised the values of knowledge

management in table 2.1



Values of Knowledge Management Scholars

Increase the effective utilisation of

knowledge resource

Breu et al, 2000

Avoid re-inventing the wheel Waruszynski, 2000

Improve the quality of decision-making Chase 1997, Charney and Jordan 2000

Deliver higher quality products and

services

Waruszynski, 2000;

Decrease learning training time Breu et al, 2000; Waruszynski, 2000

Increase internal knowledge sharing Breu et al, 2000; Waruszynski, 2000

Increase external knowledge sharing Breu et al, 2000; Waruszynski, 2000

Help identifying new business

opportunities

Chase 1997, Charney and Jordan 2000,

KPMG 2000

Increase employee satisfaction Breu et al, 2000; Waruszynski, 2000

Increase innovation Waruszynski, 2000

Table 2. 1: Benefits of Knowledge Management Source: Cretau and Dfouni (2008, p. 66)

Patrizi and Levin (2007) also summarised the business value of knowledge management

processes as below:

(1) Increasing revenue by providing re-usable assets

(2) Improving quality as a result of real-time access to the appropriate

resources

(3) Enhancing ability to share best practices through global and local

communities

(4) Improving customer and employee satisfaction as a result of fast and easy

access to accurate and relevant information

(5) Decreasing delivery costs as a result of improved processes for storing and

retrieving knowledge

(6) Decreasing time to “ramp up” new employees with easy availability of

people and other assets to learn

(7) Supporting of employee skill building and improving ability to transfer

knowledge



The above listed benefits and values stated by various scholars highlight the key drivers

for an organisation to practise knowledge management.

2.2.5 Major School of Thoughts of Knowledge Management

To best understand what knowledge management is, it can also be explored by studying

the different school of thoughts of knowledge management. Knowledge management has

been defined by scholars from different disciplines and Earl (2001) classified the school

of knowledge management into three catalogues, namely Technocratic, Economic and

Behavioural. Technocratic includes the systems school, cartographic school and

engineering school which are based on information or management technologies to

govern knowledge work in everyday tasks. Economic is mainly the commercial school of

knowledge management which explicitly creates revenue streams from the exploitation of

knowledge management and intellectual capital. Behavioural includes the organisational

school, spatial school and strategic school which stimulate and orchestrate managers to be

proactive in the creation, sharing, and use of knowledge management as a resource. The

attributes of each school is summarised in figure 2.4 below. Earl (2001) mentioned that

the primary purpose of the seven schools taxonomy is to help executives know what to do

and how to undertake knowledge management initiatives or solutions and improve the

effectiveness of any existing knowledge management program.

2.2.6 Knowledge Management Process

The Knowledge Management Process has been articulated in terms of the knowledge

management cycle by many researchers, e.g. Lethbridge (1994), Wiig (1997) and

Davenport and Prusak (1998), Dilnutt (2000), Schotte (2003), Bergeron (2003), Lytras

and Rouloudi (2003), Alfs (2003), Mertins et al. (2003), and Seufert, Back and Krogh

(2003)…etc.



Fig

ure

2.

4:

Sch

ool

of

Kn

ow

led

ge

Ma

nagem

en

t

Sourc

e: E

arl

(20

01,

p. 2

17)



Dilnutt (2000) described the knowledge management process as Generate, Represent,

Access and Transfer. Schotte (2003) viewed that knowledge management is the cycling

process of Use, Provide, Find, Select, Organise, Distill, Share and Adapt. Bergeron

(2003) argued that knowledge management is the cycle of Create/Acquisition,

Modification, Use, Archiving, Transfer, Translation/Repurposing, Access and Disposal.

Lytras and Rouloudi (2003), based on the research done by Rubenstein-Montano et al.

(2001), summarised knowledge management activities by various researchers in 6 phases,

as in figure 2.5. Lytras and Rouloudi (2003) proposed an integrated model of KM which

consists of Relate/Value, Acquire, Organise, Enable / Reuse, Transfer and Use.

From the previous literature, apparently those different scholars have different

classifications of the process in their KM models. However, Alfs (2003) and Mertins et

al. (2003) stated that it should be a cyclic process which comprises of Generating,

Storing, Distributing and Applying knowledge, similar to the knowledge management

lifecycle proposed by Seufert, Back and Kroch (2003) where there are four generic

knowledge processes that can be distinguished: (1) Locating / Capturing, (2) Sharing /

Transferring, (3) Creating and (4) Applying.

Heisig (2009) analysed 160 knowledge management frameworks and concluded that an

underlying consensus is detected regarding the basic categories describing the knowledge

management activities and the critical success factors. The knowledge management

frameworks proposed by Heisig (2009) from his research has three layers, which are

business focus layer, knowledge focus layer and enabler focus layer. In the knowledge

focus layer, there are four core processes which are Create, Store, Share and Apply. This

classification is also similar to the knowledge process proposed by Alavi and Leidner

(2001) which consists of four the core processes namely: Creation/Construction, Storage/

Retrieval, Transfer and Applicati



Figure 2. 5: Various Knowledge Management Lifecycle Source: Lytras and Rouloudi (2003, p. 244)



The frameworks proposed by both Heisig (2009) and Alavi and Leidner (2001) were

actually similar to the generic processes proposed by Seufert, Back and Krogh (2003), all

consisting of four core processes. Although, the terms used to describe the processes are

different e.g Hisig (2009) termed one of the core process as “Store”, Alavi and Leidner

(2001) termed as “Storage/ Retrieval” and Seufert, Back and Kroch (2003) termed as

“Locate / Capture”, they shared the same functions and purposes that was to acquire and

organise knowledge.

In this research, the Seufert, Back and Krogh (2003)’s knowledge management processes

framework as shown in figure 2.6 was adapted. Instead of a sequential process, the KM

process proposed by Seufert, Back and Krogh (2003) is an interactive process where the

application of knowledge takes the central role.

Figure 2. 6: Knowledge Process Categories Source: Seufert, Back and Krogh (2003, p. 112)

Creating

Applying

Transferring

/ Sharing

Locating /

Capturing



The following describes each of the KM processes applied in this research.

(1) Locating / Capturing Knowledge

Seufert, Back and Krogh (2003) mentioned that locating and capturing

knowledge depends on finding and charting already existing knowledge. It is a big

challenge when there is a widely dispersed knowledge base, and it is required to

have a system to reduce the search cost. This process also includes retrieval,

organising and storage of knowledge which includes written documents,

structured information stored in electronic databases, codified human knowledge

stored in expert systems, documented procedures and processes and the tacit

knowledge acquired by individuals (Tan et al. 1998). The knowledge retrieval

should be from both internal and external sources. Lim (2007) argued that staff

learn from experience. Continuous process improvement could be the internal

sources and external sources should include benchmarking, best practices from

other organisation, attending conferences, hiring consultants, monitoring social,

economic, and technological trends, collaborating with others, building alliances,

forming joint ventures and establishing links with partners. For organising and

storage, Kim, Suh and Kwang (2003) argued that the knowledge should be

structured and stored so that it can be found and delivered quickly. The ease and

ability to access the knowledge repository are critical (Davies et al. 2005).

(2) Sharing / Transferring Knowledge

Sharing / Transferring Knowledge refers to the leveraging of existing knowledge

and generating new values, and explicit knowledge is more easy to transfer

through electronic media or other forms of documents but tacit knowledge is more

difficult since it requires direct interaction with people (Seufert, Back & Krogh

2003). This process is happening at various levels which include the transfer of

knowledge from individuals to a group, between groups, across groups and from

groups to the organisation (Alavi & Leidner 2001). Albino, Garavelli and

Gorgoglione (2004) mentioned that knowledge transfer involves the mechanical,

electronic and interpersonal movement of information and knowledge, both

intentionally and unintentionally. The form of knowledge transfer between tacit



knowledge and explicit knowledge is best described by the SECI model proposed

by Nonaka and Takeuchi (1995), as discussed section 2.2.3.

(3) Creating Knowledge

Seufert, Back and Krogh (2003) argued that creating knowledge is concerned with

the development of new explicit or tacit knowledge by groups or individuals. New

knowledge can be created either through the expansion of the already existing

tacit or explicit knowledge, or through a new method of combining these forms of

knowledge. This process includes development of new content or replacing

existing content for both tacit and explicit knowledge (Pentland 1995). It is

through the processes of socialisation and collaboration as well as individual’s

cognitive processes, that the created knowledge can be shared, amplified, enlarged

and justified (Nonaka 1994). Unlike knowledge capturing that is an adaptive

process, knowledge creation is a generative process (Firestone & McElroy 2004).

Knowledge is created through actions and behaviour such as problem solving and

integration with the organisation existing knowledge stock (Devinney, Midgley &

Soo 2004). Knowledge creation occurs through the application and exploitation of

the acquired information and knowledge, and the outcome of the knowledge

creation includes the production of documents in paper or electronic form,

generation of skills, beliefs, norms, images, intuition and mental models...etc

(Fung 2008).

(4) Applying Knowledge

Applying knowledge comprises the application and usage of knowledge, i.e.

reflection, in actual situations such as in decision making or problem solving

(Seufert, Back & Krogh 2003). It is to create capability by integrating knowledge

(Grant 1996); it is to realize the value of knowledge (Lytras & Rouloudi 2003),

and for an organisation it is to create commercial values for customers (Demarest

1997). It involves the integration of knowledge into an organisation’s business

processes and key applications (Laudon & Laudon 2005). Lytras and Rouloudi

(2003) mentioned that it is a goal-oriented process, and knowledge must be

applied in the context of specific purposes and to construct meanings of higher

values and to support the learning processes.



2.2.7 Organisational Learning

Organisational Learning is an important branch in knowledge management, and actually

the concept of organisational learning can be traced back to the late 1950’s and early

1960’s to scholars like March and Simon (1958) and Cyert and March (1963).

Buchel and Probst (2002) argued that the organisational learning was primarily focusing

on identifying the learning theories and the processes of changing the organisational

knowledge, while knowledge management was taking a proactive role of providing

frameworks / guidelines for active intervention into an organisation’s knowledge base.

This section reviews the literature on organisational learning which provides better

understanding of the knowledge management at the organisational level.

2.2.7.1 Development of Organisational Learning

The development of organisational learning began to attract attention in the 1960’s and

many authors, e.g. Argyris and Schon (1978), Brown and Duguid (1991), Cangelosi and

Dill (1965), Cohen and Sproull (1996), Dodgson (1993), Duncan and Weiss (1979),

Easterby-Smith, Snell and Gherardi (1998), Fiol (1994), Hedberg (1981), Kim (1993),

Lahteenmaki, Toivonen and Mattila (2001), Levitt and March (1988), Matlay (2000),

Rashman, Withers and Jean (2008), Senge (1990), Wang and Ahmed(2003), tried to

illuminate the concept of organisational learning. There were over fifty academic articles

on organisational learning published in the 1980’s (Rashman, Withers & Jean 2008) and

the growth of the organisational learning study was mainly due to (1) the speed of

technological change, (2) the advance of globalisation, and (3) the growing corporate

competition (Easterby-Smith, Snell & Gherardi 1998).

Easterby-Smith, Snell and Gherardi (1998) discussed organisational learning

development by the four sets of issues or assumptions, which are (1) Teleology: the

purposes of organisational learning, (2) Ontology: the essence or fundamental stuff of

organisational learning, (3) Epistemology: the preferred research methodologies for

investigating organisational learning, and (4) Technology: the effectiveness of and

developmental direction for organisational learning interventions. The study of the

organisational learning has been focused on different approaches and Lahteenmaki,



Toivonen and Mattila (2001) summarised various approaches by the learning subject,

learning process and learning precondition, as in the figure 2.2.7.1. The development of

organisational learning has been drawn from a variety of disciplines including

psychology and organisational development, management science, organisation theory,

strategy theory, production management and cultural anthropology (Easterby-Smith,

Snell & Gherardi 1998).

Figure 2. 7: Approaches of studying organisational learning and learning organisations Source: Lahteenmake, Toivonen and Mattila (2001, p. 115)

2.2.7.2 Definition of Organisational Learning

Duncan and Weiss (1979) argued that organisational learning is loosely defined as the

process by which organisations come to have knowledge on action-outcome

relationships. Fiol and Lyles (1985) defined organisation learning as the improving

actions through better knowledge and understanding. Levitt and March (1988) stated that

organisational learning is an instrument of intelligence rather than a transfer or

experience. Senge (1990) defined organisational learning as the process through which

managers seek to improve organisational members’ desire and ability to understand and

manage the organisation and its environment, so that they can make decisions that

continuously raise organisational effectives. Miller (1996) defined organisational learning

as the acquisition of new knowledge by actors who are able and willing to apply



knowledge in making decisions or influencing others in the organisation. Hodgkinson

(2000) defined organisational learning as, “... the coming together of individuals to enable

them to support and encourage one another’s learning which will in the longer term be of

benefit to the organisation”.

The concept of organisational learning has flourished and has been defined in a wide

range of literature (Argyris, C. & Schon 1996; Cohen, M.D. & Sproul 1991; Levitt &

March 1988; Senge 1990). Most of the definitions of organisational learning appear to be

complementary rather than fundamentally original or conceptually different (Matlay

2000). However, Wang and Ahmed (2001) argued that the prevailing concept of

organisational learning bears a strong bias towards the traditional scientific approach to

management, and stresses the importance of system thinking and continuous

improvements (Wang, Catherine L. & Ahmed 2003) .

Fiol and Lyles (1985) summarised the organisational learning literature from 1978 to

1982, as shown in figure 2.8, where the theories are labelled as “learning” or

“adaptation”, and classified the concern of the theories into behavioural development and

cognitive development. This summary provides an insight of organisational learning

theories underlying in modern knowledge management theories.



Figure 2. 8: Review of OL Literature

Source: Fiol and Lyles (1985)

2.2.8 Section Summary of Knowledge Management

The literature of knowledge management concluded that no single definition can explain

knowledge (section 2.2.1) and knowledge management (section 2.2.4). However, it is

clear that knowledge management is a multi-discipline aspect which covers different

schools of thoughts (section 2.2.5) and mainly focuses at the organisation level. The

DIKW knowledge Hierarchy (section 2.2.1) and the types of knowledge, i.e. tacit

knowledge and explicit knowledge (section 2.2.2), and the knowledge conversation

model (section 2.2.3) provide the basic understanding of knowledge transformation from

one form to another. The knowledge management process outlined (section 2.2.6) the

generic model to describe the interaction between capture / locate, share / transfer, create

and apply knowledge. This generic model provided a visual presentation of different



stage of knowledge processes and the definition and development of organisational

learning (section 2.7) provided a better understanding of the underlying theories of

knowledge management at the organisation level.

2.3 Personal Knowledge Management (PKM)

The literature related to knowledge management as discussed in the previous section, is

mainly at the organisational level. It is the traditional view that knowledge management is

primarily focused on enterprise knowledge management (Pauleen 2009a) and most of the

previous research in knowledge management was performed at the enterprise level

(Pauleen 2009a; Tsui 2002b; Zhang 2009). This section discusses another parent

discipline of this research on knowledge management at the individual level, and first

reviews the individual learning, the underlying theory of PKM, followed by the literature

on PKM.

2.3.1 Individual Learning

Forcheri, Molfino and Quarati (2000) defined Individual Learning as the capacity to

build knowledge through individual reflection about external stimuli and sources, and

through the personal re-elaboration of individual knowledge and experience in the light of

interaction with others and with the environment. As in figure 2.9, the requisites for

individual learning are perception of a need, identification of an object (an objective) that

may satisfy that need, and identification of a strategy for reaching that objective.

Figure 2. 9: Individual Learning Source: Forcheri et al (2000, p. 3)



Individual learning is closely linked with organisation learning in knowledge

management. Ahmed et.al (2002) mentioned that knowledge management involves the

individual combining his or her experience, skills, intuition, ideas, judgments, context,

motivations and interpretation. One of the knowledge management strategies proposed

by Wiig (1997) is personal knowledge responsibility. This focuses on individual

responsibility for knowledge-related investments, innovations and the competitive state,

renewal, effective use, and availability to others of the knowledge assets within each

employee’s area of accountability thus being able to apply the most competitive

knowledge to the enterprise’s work. Wiig (2004) defined Knowledge Management as

the systematic and deliberate creation, renewal, application, and leveraging of knowledge

and other intellectual capital (IC) assets to maximise the individual’s and the enterprise’s

knowledge-related effectiveness and returns. It is the combination of people-focused and

enterprise-focused knowledge management.

2.3.2 Linkage between individual learning and organisational learning

An organisation needs to align individual learning with corporate objectives to avoid the

creation of paradoxes. New knowledge always begins with the individual, and making

personal knowledge available to others is the central activity of a knowledge creation

company (Nonaka 1991). Learning starts from individuals and a learning organisation is

founded on the learning process of individuals in the organisation (Wang & Ahmed

2003). The mainstream of organisational learning considers individuals as “agents” for

the organisation to learn (Argyris, C. & Schon 1978). However, Ikehara (1999) argued

that individual learning does not necessarily lead to organisational learning. It is the task

of the learning organisation to integrate individual learning into the organisation’s

learning processes. Hyland and Matley (1997) claimed that a learning organisation can

be defined or measured in terms of the sum total of accumulated individual and collective

learning. However, Field (1997) also argued that it is worth noting that individual

learning does not always yield benefits or contribute to the organisation, because

employees may learn to improve themselves rather than benefit the organisation.

Houle (1961) stated that job-related reasons provide the key to the motivation of

individual learning. These include acquiring skills to solve problems at work; meet

employment expectations or job requirements (Cheng, C. W. 2007; Wynne 2008); stay



abreast of competitors (Lieb 1999); bring additional skills to the workplace (Wynne

2008); advance in one’s job (Lieb 1999; Wynne 2008); enjoy job security (Wynne 2008)

and so on. Self-improvement is reflected in improvements in the working environment.

James (2005) mentioned that organisation effectiveness can be improved through better

access to expertise and past learning experience. A learning organisation should primarily

focus on valuing, managing and enhancing the individual development of its employees

(Scarbrough, Swan & Preston 1998). The relationship between individual and

organisation learning is an important aspect (Kim, D. H. 1993; Matlay 2000). It is

important as all organisations are composed of individuals, and organisations can learn

independently of any specific individual but not independently of all individuals (Kim, D.

H. 1993). Individual learning activities are facilitated or inhibited by the organisation

learning system (Argyris, C. & Schon 1978). The model of organisation learning will

either obscure the actual learning process by ignoring the role of the individual or become

a simplistic extension of individual learning by glossing over organisational complexities

(Kim, D. H. 1993).

Kim (1993) used March and Olsen (1975)’s model and Argyris and Schon (1978)’s single

loop and double loop learning process to propose an integrated model of organisational

learning, named as OADI-Shared Mental Models (SMM) Cycle, as shown in the figure

below. Kim (1993) argued that the cycles of individual learning affect learning at the

organisational level through their influence on the organisation’s shared mental models;

an organisation can learn only through its members, but it is not dependent on any

specific member; the members themselves are influenced by organisational structure and

type of management style and, therefore, can be treated as if they were “extended

individuals”. Kim (1993) further argued that organisational learning is dependent on

individuals improving their mental models and making those mental models explicit is

crucial to the development of new shared mental models. This process allows

organisation learning to be independent of any specific individual.



Figure 2. 10: OADI-Shared Mental Models (SMM) Cycle Source: Kim (1993, p44)

Kim (1993)’s model clearly indicated that linkages exist between individual learning and

organisational learning. However, individual learning needs to lead to behavioural

changes that clearly improve organisational performance and the results of learning must

become a part of the organisational culture and processes (Su 2006).

2.3.3 Individual learning process

The Human Learning process is complicated and many scholars have tried to explain

their understanding of learning processes in different ways e.g. Griffin (1987), Jarvis

(1987) and Kolb (1993). Griffin (1987) attempted to define the basic learning processes

as ‘the inner happenings or experiences that the learner has when engaged in learning….

whether the learner is in a group, with a friend, or alone; when reading a book, listening

to a lecture or reflecting on his or her own experience’. Jarvis (1987) defined learning as



a process of transforming experience into knowledge, skill and attitudes. Kolb (1993)

followed the work of Dewey (1938), Lewin (1942) and Piaget (1970) and modelled the

learning process as experiential learning, which emphasis (1) on the process of adaptation

and learning as opposed to content or outcomes; (2) knowledge is a transformation

process, being continuously created and recreated, not an independent entity to be

acquired or transmitted; (3) learning transforms experience in both its objective and

subjective forms and (4) to understand learning, we must understand the nature of

knowledge, and vice versa.

Figure 2. 11: Experiential Learning Cycle

Source: Kolb (1993)

As shown in figure 2.11, there are four stages in the experiential learning cycle, namely

Concrete Experience, Observation and Reflection, Forming Abstract Concepts and

Testing in New Situations. Kolb (1993) provided an integrated view of the experiential

learning cycle with the problem solving process (Pounds 1965) , the decision making

process (Simon 1947) and the creative process (Wallas 1926) as in figure 2.12.



Figure 2. 12: Similarities among conceptions of basic adaptive processes: inquiry / research,

creativity, decision-making, problem solving, learning

Source: Kolb (1993, p. 151)

The individual learning theory provided underlying principles for PKM and the next

section discusses the definition of PKM, the development of PKM and also the evaluation

of PKM models.

2.3.4 What is Personal Knowledge Management (PKM)?

In the area of knowledge management, existing and past research has tended to focus on

the enterprise level. By comparison, Personal Knowledge Management (PKM) has very

much been under-explored (Tsui 2002b). The competency and proficiency of Individual

Knowledge Workers, among other factors, underpin the success of an organisation’s

knowledge management journey. Individual learning is closely linked with organisational



learning in knowledge management. Ahmed et al. (2002) mentioned that knowledge

management involves individuals combining and sharing their experience, skills,

intuition, ideas, judgments, context, motivations and interpretations. One of the

knowledge management strategies proposed by Wiig (1997) is personal knowledge

responsibility. It means focusing on individual responsibility for knowledge-related

investments, innovations and also on the competitive side, renewal, effective use and

availability to others of the knowledge assets within each employee’s area of

accountability. It also entails being able to apply the most competitive knowledge to the

work of the work.

2.3.4.1 Definitions of Personal Knowledge Management

In the past decades, although there was not much research in this area, several scholars

articulated what is PKM e.g. Frand and Hixon (1999), Avery et al. (2001), Higgison

(2004), Jefferson (2006), Volkel and Abecker (2008), Martin (2008) and Harold Jarche

(2010a). The following shows extracts from the related literature by different scholars,

which provides insights into the definition and nature of PKM.

(1) Frand and Hixon (1999)

PKM is a system designed by individuals for their own personal use (Frand &

Hixon 1999) and “it is a conceptual framework to organise and integrate

information that we, as individuals, feel is important so that it becomes part of our

personal knowledge base. It provides a strategy for transforming what might be

random pieces of information into something that can be systematically applied

and that expands our personal knowledge.”

(2) Avery et al. (2001)

Avery et al. (2001) argued that “PKM assumes that individuals have developed a

self-awareness of their limits and abilities, i.e. what they know and what they can

do. This personal self-awareness is an understanding of how much they know,

how to access the things they know, strategies for acquiring new knowledge and

strategies for accessing new information as needed. In the vast amount of

information available and many means for acquiring new information, individuals



have each mapped out their own areas of expertise and their own methods for

additional learning.”

(3) Higgsion (2004)

Higgison (2004) defined PKM as “managing and supporting personal knowledge

and information so that it is accessible, meaningful and valuable to the

individual; maintaining networks, contacts and communities; making life easier

and more enjoyable, and exploiting personal capital”

(4) Jefferson (2006)

Jefferson (2006) argued that “PKM is focused on bottom up approach, with an

individual perspective to KM. The goal is to allow individuals to choose what

information to collect, how to structure it, and who to share it with. Individuals

need to be able to manage their own information so that is meaningful, accessible

when it needed, can be easily exploited. PKM allows workers to organise both

digital and paper content in such a way to allow them to make sense of the deluge

they are continually exposed to.”

(5) Volkel and Abecker (2008)

Volkel and Abecker (2008) termed “Personal Knowledge Management to denote

the process of the individuals to manage knowledge” and “PKM deals with

embrained, embodied and encoded knowledge i.e. mostly with personal, self-

authored artefacts.”

(6) Jerome Martin (2008)

Martin (2008) argued that “PKM is knowing what knowledge we have and how

we can organise it, mobilise it and use it to accomplish our goal, and how we can

continue to create knowledge.”

(7) Harold Jarche (2010a)

Jarche (2010a) mentioned that “PKM is an individual, disciplined process by

which we make sense of information, observations and ideas. In the past it may



have been keeping a journal, writing letters or having conversations. These are

still valid, but with digital media we can add context by categorising, commenting

or even remixing it. We can also store digital media for easy retrieval”

Irrespective of how PKM is defined by different scholars, the key purpose of PKM is to

provide a framework for individuals to manage new information, integrate it and enrich

each individual knowledge database in an effective manner. Doing this successfully will

empower each individual to easily apply their own personal knowledge to deal with new

and old problems, to learn from new experience and to create new knowledge. It is a

continuous and interactive process which is not independent of other knowledge

management processes.

2.3.4.2 Development of Personal Knowledge Management

Volkel and Abecker (2008) mentioned that the term PKM had already been used since

Polanyi (1958), but Pauleen (2009a) argued that the history of PKM begins with the idea

of the knowledge worker by Drucker (1968). In this section, the development of PKM is

discussed since Frand and Hixon (1999), their work has impacted and drawn the focus of

many scholars in this areas in the past decade.

Frand and Hixon (1999) mentioned that we are living in a sea of data, our challenge is

knowledge and its management so that everyone must listen to a great deal of noise in

order to retrieve the few bits of information that are of value to them. Some problems

appear to be intrinsic knowledge management, whether it is being performed using a

word processor, a formal language-based tool or pencil and paper. These problems

include (1) Categorising or classifying; (2) Naming things and making distinctions

between them; and (3) Evaluating and assessing The PKM framework proposed by Frand

and Hixon (1995) focused on personal information management and failed to address the

importance of inter-personal knowledge activities.

The work of Avery et al (2001) was based on the idea created by Paul Dorsey and

developed another PKM framework which addressed this gap to include collaborating

around, securing and presenting information. Avery et al (2001) believed that PKM

requires to clarify the distinction between data, information and knowledge and believed



that information could become knowledge which entails activities such as comparing,

exploring consequences, making connection to other information and knowledge and

conversing with others. Their proposed PKM framework focused on the information

skills, consisting of seven PKM skills namely (1) Retrieving information; (2) Evaluating

information; (3) Organising information; (4) Collaborating around information; (5)

Analysing information; (6) Presenting information and (7) Securing Information.

Tsui (2002b) provided a technology-centric view on PKM and also explored the various

issues when using the PKM tools available at that time. In the view of Tsui (2002b),

PKM is a collection of processes that individuals need to carry out in their daily activities

in order to manage their own knowledge management work, including gather, classify,

store, search and retrieve knowledge, it is not limited to work-related activities but also to

social activities. Tsui (2002b) suggested that knowledge workers need constantly to (1)

locate the right information quickly, (2) stay abreast with business and technology trend,

(3) switching between learning and practising, (4) create new knowledge and be

innovative, and (5) maintain communications and build trust among peers. These five

suggestions are actually in line with the Avery et al (2001) PKM framework and

communications and working with peers are key elements in PKM. In addition, PKM

should enable innovation and put PKM into practise.

Berman and Annexstein (2003) based on Avery et al. (2001), proposed PKM Skills and

developed a personal knowledge book, “PK-Book”, model to actualise the PKM. Berman

and Annexstein (2003) argued that the ability to actualise the context for PKM is

facilitated by a design combination consisting of (a) a structured and secured container

for the organisation of information, (b) algorithms for the generation of associated

contextual metadata, and (c) utilisation of a contextual engine driven by applications. It is

the natural processes associated with the organisation of focused information which leads

to an ability to actualise the context in information applications, and conversely, through

the usage of context in applications, the focused information unit can be augmented and

improved over time. The PK-Book model seeks to provide users with a natural

organisational structure and methodology, along with a set of associated tools and

applications that together capture and reflect the structure of information as understood

by the individual (Berman & Annexstein 2003).



Pollard (2004) worked out his model of PKM which focused on information acquisition,

information processing and social activities. The information acquisition includes looking

up data, finding / retrieving information & answers, compiling/ researching / reading/

studying / learning, and subscribing to information source. The information processing

activities includes writing / analysing / narrating / interpreting, editing /reviewing /

annotating, and sharing and publishing knowledge work. The social activities include

finding people / experts, connecting to people, collaborating, and interacting. Pollard

(2004) argued that this system has no sequence, no flow and it is just an undifferentiated

set of knowledge activities to describe human intellectual activity.

Efimova (2005) argued that PKM is an interactive process between individuals, other

people and ideas. This is an approach which focuses on supporting knowledge worker

productivity by taking an active perspective in studying their work. Efimova (2005)

defined PKM as managing a one-person enterprise, the knowledge product, e.g. the

processes, tools, relations with partners, customers and suppliers, are connected with

literature on personal effectiveness and time management or personal branding and

networking. Efimova (2005) used Weblog as an example to illustrate PKM work.

Wright (2005) defined PKM as the capacity to access and apply information and

knowledge resources and processes to enhance the effectiveness, productivity and

innovation of individual workers. Wright (2005) mentioned that while PKM was

primarily an unconscious process and occurred naturally, it was more than personal.

Wright (2005) based on an exploratory study of the work and learning processes of highly

skilled experienced knowledge workers and proposed an alternative PKM framework

which linked the problem solving activities with specific cognitive, information, social

and learning competencies.

Zuber-Skerritt (2005) developed a “soft methodology” model of PKM based on the seven

commonly shared values and principles of the action learning and action research

(ALAR) culture in which the generated seven kinds of personal knowledge can be used

for knowledge management in management education and the workplace. Zuber-Skerritt

(2005) described this model as the values and actions for PKM and argued that which can

serve as a practical guide for application in situations where personal knowledge can



contribute to problem solving and effective organisational management for organisation

and all individuals.

Agnihotri and Troutt (2009) argued that technology is a critical part of PKM that

enhances individual efficiency and effectiveness such that it will help users to classify

ideas and information, or to archive interactive emails and other items of they are easy to

locate. Agnihotri and Troutt (2009) referenced to the PKM tools classification by Tsui

(2002b) and addressed the importance of the fit between PKM skills and the tools.

In recent years, the development of PKM started to focus on technologies, e.g. online

tools, Web 2.0 technology and semantic web, which enabled the development of PKM

tools to support the workers to practise PKM in an online and virtual environment.

Pettenati et al. (2007) studied the relationship between social networking software and

PKM skills; Diao, Zuo & Liu (2009) investigated the artificial intelligence in PKM; and

Kim, Breslin and Decker (2009) proposed a wiki-based semantic tagging system for

PKM and Volkel and Haller (2009) proposed a conceptual data structure for PKM.

Pettenati et al. (2007) concluded that social networking tools and methods provide a

tremendous opportunity and context to lead individuals into the learning and knowledge

landscape and PKM skills are the enabling condition and final outcome of social

network-based learning experience.

Diao, Zuo and Liu (2009) argued that although the application of artificial intelligence in

personal knowledge was still at the initial stage, the requirements of artificial intelligence

were increasing. Artificial intelligence can be applied to PKM for (1) intelligent search of

knowledge, (2) automatic classification of knowledge, and (3) conversion of tacit

knowledge. The use of artificial intelligence technology to assist in PKM illustrates the

usefulness of artificial intelligence.

Kim, Breslin and Decker (2009) argued that typical personal management systems do not

provide effective ways for representing knowledge worker’s unstructured knowledge or

ideas. Based on this, a Wiki-based semantic tagging system (Wiki-based social Network

Thin Client – WANT) was proposed to facilitate the collaboration and communication of



the knowledge creation and maintenance. The social semantic cloud of tags (SCOT) was

also suggested to represent tag data at a semantic level, combined with the ontology in

WANT. Kim, Breslin and Decker (2009) mentioned that the PKM system is not only

focused on managing data, but also on connecting people and enabling them to share data

between them.

Volkel and Haller (2009) developed a unified data model called Conceptual Data

Structure (CDS) to bridge the gap between unstructured content (e.g. informal notes) and

formal semantics (e.g. ontologies) by allowing the use of vague semantics and by

subsuming arbitrary relation types under more general ones. The purpose of the CDS

serves as a guideline for future PKM tools, providing a set of crucial structural primitives

as well as providing a knowledge exchange format.

Up to now, the development of PKM is divided into two clusters: skills/activities-centric

and technology-centric. The skills/activities centric mainly focused on the skills for an

individual to manage their knowledge activities e.g. retrieving, analysing and

collaborating information …etc. The technology centric mainly focused on classification

/ selection and development of tools, e.g. data structure and framework for tools

development. The scope of the PKM also expanded from an individual to a more

collaborative focus. Individual focus is mainly concerned with the self

development/reflection and collaborative focus concerned knowledge sharing and

interactive with people, community and society. The timeline of the PKM development

and their focuses are illustrated in figure 2.13.



Frand and Hixon

(1999)

Personal

Information

Management

Avery et al. (2001)

PKM Skills

Eric Tsui (2002)

PKM Tools

(Technology and

Classification) Berman &

Annexstein (2003)

PK-BOOK Model

(for tools

development)Pollard (2004)

Inform

ation

Acquisition &

Process and Social

Activities

Wright (2005)

Cognitive,

inform

ation, social

and learning

competencies

Efimova (2005)

Individual, People

& Ideas

Zuber-Skerritt (2005)

PKM values and

actions

Volkel and Haller

(2009)

Conceptual Data

Structure

Agnihotri and Troutt

(2009)

PKM

Skills-Tools Fit

Pettenati et al.

(2007)

Social networking

software and PKM

skills

Decker (2009)

Wiki-based sem

antic

for PKM

Diao, Zuo &

Liu

(2009)

Artificial intelligence

in PKM

2000 2005 2010

Year

Individuals Focused Collaborative Focused

Legend

Skills /

Activities

Centric

Technologies

Centric

Fig

ure

2.

13 :

PK

M D

evel

op

men

t in

Past

Dec

ad

e

Sourc

e: D

evel

oped

for

this

Res

earc

h



The information technologies tools for PKM have been rapidly developed in the past

decade and the author believes that web 2.0 based tools are playing important roles to

facilitate the practising of the PKM. Tsui (2002b) argued that the IT based PKM tools are

mainly divided into group-based and personal based KM tools. The group-based KM

systems are generally for both intra (within group) and inter (between groups)

collaboration, while personal KM systems are adopted by individual knowledge workers

and operate within the permissible bounds of the enterprise IT framework and security

network. The recently development of Web 2.0 enables a new model of PKM that

involves formal and informal communication, collaboration and social networking tools

(Razmerita, Kirchner & Sudzina 2009).

Setrag (2010) argued that Web 2.0 is about connecting people, inventing communities,

and encouraging collaborative development on the Web; the greatest benefit of Web 2.0

will be realised through the context of collaboration within the enterprise, between

trading partners, and across the Internet. Setrag (2010, p. 6) also mentioned that

“Business processes provide the context of collaboration, and social networking supports

and augments the various phases of the BPM continuous improvement lifecycle.”

In additional to the collaboration, Web 2.0 based PKM tools also plays a multifaceted

role for communicating, sharing and managing knowledge; it enables a new model of

PKM to facilitate interaction, collaboration and knowledge exchanges on the web and in

organisations (Razmerita, Kirchner & Sudzina 2009).

2.3.4.3 Evaluation of Personal Knowledge Management Models

Many scholars have tried to put different aspects, e.g. skills, tools, connection,

communities …etc, of PKM together to form the PKM model and explain the

interactions. This section evaluates the PKM models proposed by different scholars,

including Frand and Hixon (1999)’s PKM Model (PIM Model), Avery et al (2001)’s

PKM Model (PKM Skills Model), Berman and Annexstein (2003)’s PKM Model (PK-

Book Model) , Efimova (2005)’s PKM Model (Individuals, Ideas and Communities

Model), Wright (2005)’s PKM Model (Competences Model), Zuber-Skerritt (2005)’s

PKM Model (Values and Actions Model), Agnihotri and Troutt (2009)’s PKM Model

(PKM Skill-Tools Fit model), and Jarche (2010)’s PKM Model (Aggregate, Understand



and Connect Model) . The models will also be assessed for their roles in the four generic

knowledge management process, as proposed by Seufert, Back & Krogh (2003).

2.3.4.3.1 Frand and Hixon (1999)’s PKM Model (PIM Model)

Frand and Hixon (1999) outlined five PKM techniques as (1) Searching / Finding; (2)

Categorising / Classifying; (3) Naming Things / Making Distinctions; (4) Evaluating /

Assessing; and (5) Integrating / Relating. Individuals would attempt to utilise the

computer to help manage the information explosion in an effective way.

(1) Searching / Finding

Searching / Finding focuses on using tools e.g. database selection tools and search

engines. Individuals need to select appropriate starting points based on the

characteristics of the data and understanding the different value and attributes of

different search engines.

(2) Categorising / Classifying

Categorising / classifying is based on the principles used by library scientists e.g.

Ranganthan, Deway, Cutter and others. It includes the classification schemes,

organising information from general to become more specific, putting items into

the most specific category, and subdividing when new category is required.

(3) Naming Things / Making Distinctions

Naming things / making distinctions focuses how to select or use names that are

meaningful to people. It also requires using unique terms in a consistent manner

for distinct concepts e.g. names, abbreviation, file extensions ...etc. The challenge

is how to select a name which is as complete as necessary and as short as possible

that is able to identify the content and minimise confusion.

(4) Evaluating / Assessing

Evaluating / assessing focuses on the tasks related to determine if the information

is complete and accurate, if any evidence of bias by evaluating the purpose of the

information is provided, if there are any sources that confirm or validate the



information provided, if the information is up to date, if the information is

provided by the authority or expertise of the related topic.

(5) Integrating / Relating

Integrating / relating represents the work by individuals to apply the obtained

information in problem solving and reflection.

The roles of Frand and Hixon’s (1999) PKM model in the four generic knowledge

management processes are summarised in table 2.2. Except for the fifth component,

integrating / relating, which is related to applying knowledge, the other components in the

model were in locating/capturing knowledge. As such, the model lacks the roles of

sharing/ transferring and creating knowledge.

KM Processes (Seufert, Back & Krogh 2003)

Model’s Components Locate /

Capture

Share /

Transfer

Create Apply

(1) Searching / Finding X

(2) Categorising / Classifying; X

(3) Naming Things / Making Distinctions X

(4) Evaluating / Assessing X

(5) Integrating / Relating X

Table 2. 2: The Role of Frand and Hixon’s (1999) PKM Model Source: Developed for this research

The framework proposed by Frand and Hixon (1999) focused on personal information

management by individuals and missed the element of inter-personal knowledge work i.e.

information and knowledge collaborating. However, this model has provided the ground

work for scholars to build other PKM frameworks. The approaches proposed by Frand

and Lippincott (2002) addressed an important area in putting PKM in practise, especially

in handling the information overload.

Frand and Lippincott (2002) followed the idea of “knowledge spiral” by Nonaka and

Takeuchi (1995) to articulate PKM as a strategy to deal with information (and

information overload) and at the same time enables us to build upon or learn from the

information we use, resulting in the growth of our personal knowledge. It is related to the

ideas of transforming information into knowledge which requires us to take a brief look at



some concepts from learning theory, and in building our personal knowledge entails

learning.

The following are PKM tactics suggested by Frand and Lippincott (2002).

(1) Clarify your information needs for each situation.

(2) Plan your information acquisition strategy.

(3) Develop a sourcing strategy for your ongoing information needs.

(4) Identify “push” vs “pull” information.

(5) Adopt naming conventions and stick to them.

(6) File single copies of information

(7) Set criteria for what you want to save or delete.

(8) Work out how and when to process information.

Frand and Lippincott (2002) mentioned that PKM comes into play wherever and

whenever working with information and knowledge, whether it is with paper or electronic

documents, no matter it is email or snail mail, whether it is in our office, home or on the

road. PKM should not focus on the tools used for personal task management e.g. “to do”

lists, calendars, address books, appointment books, and some of the very primitive

personal digital assistants, instead PKM should focus on the content of the tasks

specifically with the information and knowledge management associated with that

content (Frand & Lippincott 2002).

2.3.4.3.2 Avery et al. (2001)’s PKM Model (PKM Skills Model)

Avery et al.(2001) proposed a PKM framework with seven skills. The skills are, in one

sense, problem solving, rather than problem definition, skills. The skills focus on (1)

Retrieving information; (2) Evaluating information; (3) Organising information; (4)

Collaborating around information; (5) Analysing information; (6) Presenting information

and (7) Securing Information. The details of the proposed skills by Avery et al (2001) are

as follows:



(1) Retrieving information.

Avery et al (2001) mentioned that retrieving information “ involves gathering

information not just from print and electronic sources, but through

experimentation and oral inquiry, as well as a broad range of more discipline-

specific techniques. Capabilities required range from the low-tech skills of asking

questions, listening, and following up to skills in using search tools, reading and

note-taking. Concepts of widening and narrowing one’s search, Boolean logic,

and iterative search practices are an important part of the effective exercise of

this PKM skill as are social skills required for more effective oral inquiry. Also,

as the literature on information literacy emphasises, considerable effort should be

placed on framing inquiry even before information retrieval commences. The

effective use of Internet search engines and electronic databases in the inquiry

process requires technology skills as part of the repertoire of PKM skills.”

The challenge here is to identify those nuggets of information, from the large

information environment, which can help to create new knowledge (Avery et al.

2001). It is necessary to be familiar with the search subject and keywords, to

understand the usefulness of different information sources, and to know how to

use the search tools effectively and to be familiar with the concept of widening

and narrowing the scope of the search.

(2) Evaluating information.

Evaluating information skill is “closely related to the skill of retrieving

information. Strategies of information retrieval should be based on practices that

select data and information that pass some evaluative tests. However, evaluation

also takes place after retrieval as the quality and relevance of various pieces of

information are judged as they relate to the problem at hand. We recognise that

difference disciplines tend to emphasise disparate evaluative criteria as they

determine quality and relevance. The greater availability of information in the

current information-rich environments makes this skill of far greater importance

in the electronic age. The intelligent use of some crude electronic tools, such as

“relevance raters,” can be relevant to the effective evaluation of information.”

(Avery et al. 2001).



The challenge here is to be effective in evaluating the quality and relevance of

information from a large amount of information (Dorsey 2001). This skill requires

full understanding of the subject matter and sensitivity to the value of the

available information.

(3) Organising information.

Organising information skill is the core PKM skill identified by Frand and Hixon

(1999). It is “ a central part of the inquiry process focused on making the

connections necessary to link pieces of information. Techniques for organising

information help the inquirer to overcome some of the limitations of the human

information processing system. In some ways the key challenge in organising

information is for the inquirer to make the information his or her own through the

use of ordering and connecting principles that relate new information to old

information. Elementary skills of synthesis and analysis are central to this

process. Technological skills in organising information have become ever more

important as electronic tools such as directories and folders, databases, web

pages, and web portals provide the inquirer with ever more powerful tools to

make connections.” (Avery et al. 2001).

The skill of being able to organize information is the core personal knowledge

management skill identified by Frand and Hixon (1999). The challenge here is to

develop approaches that enable individual knowledge workers to develop

strategies that are consistent with the nature of their work, with their learning

styles, and with the nature of the collaborative relationships they may have

(Dorsey 2001). It requires considerable skill to connect new information to old

information using the mental process of pattern matching or recognition. Skills are

also required to use technologies, for example relational databases, web sites,

personal information software and so on, to store the information in a structural

way (for example, chronological, functional and role-based approaches) and so

on.



(4) Collaborating around information.

Avery et al. (2001) argued that “the interdisciplinary literature on effective teams

and groups is replete with principles for effective collaborative work. Listening,

showing respect for the understanding of others’ ideas, developing and following

through on shared practices, building win/win relationships, and resolving

conflicts are among those underlying principles. Within collaborative inquiry,

partners in inquiry need to learn to have their voice heard and to hear other

voices. Both cultural and more nuts-and-bolts practical issues need to be

attended to. The availability of new electronic tools for collaboration to support

both synchronous and asynchronous communication requires a whole new set of

procedures for efficient information exchange.”.

The challenge here, as it relates to technology, is to identify how information

technology can support the process of working smarter, rather than merely harder,

and to overcome obstacles in the absence of social cues for appropriate behaviour

(Dorsey 2001). It requires the use of different technologies, for example email,

instant messaging, conferencing systems, groupware and so on. These enable

individual knowledge workers to acquire new knowledge and thus help the

organization to achieve its business goals.

(5) Analysing information.

The analysis of information is “fundamental to the process of converting

information into knowledge. At the same time, this is the most discipline-specific

information skill since the models, theories and frameworks that are central to

analysis are frequently tied to the academic disciplines. Analysis builds on the

organisation of information, but goes beyond it in its emphasis on the importance

of respect for standards in public communities. This skill addresses the challenge

of extracting meaning out of data. In some disciplines, electronic tools such as

electronic spreadsheets and statistical software provide the means to analyse

information, but the human element is central in framing the models that are

embodied in that software.” (Avery et al. 2001).



The challenge is to extract meaningful information from data. This is the

fundamental process of converting information into knowledge (Avery et al.

2001). Information technology provides tools, for example spreadsheets and

statistical software, to perform data analysis but the human element is the most

important factor in the analysing process. It is the ability to process the

information and make sense using human experience and knowledge. This ability

is related to the intelligence of individuals, and sometimes intuition and specific

tools, for example data mining tools, also play a key role in performing an

analysis.

(6) Presenting information

Avery et al. (2001) argued that “key to the presentation of information is

audience; this means, as in the case of analysing information, that understanding

disciplinary communities—often an important audience--and their norms and

standards are of central importance. An effective presentation assumes not only

an understanding of audience, but a clear understanding of the purpose of the

presentation as it relates to audience. The history and theory of rhetoric provides

an abundant literature for guidance in the exercise of this skill. The emergence

of new electronic tools and venues for presentations, through computer-based

presentation tools and web sites, makes attention to this information skill even

more important.” (Avery et al. 2001).

It is important to have a clear understanding of the purpose of the presentation as

it relates to the audience (Dorsey 2001). This is the art of composition and

speaking. It is not enough just to prepare a professional looking PowerPoint slide

or a colourful chart. The presenter has to understand the characteristics of the

audience: who they are, what information they require, from what perspective

they will interpret the information and how they will make use of the information

presented. The challenge is to ensure the audience can pick up the information or

knowledge in the context that the presenter has selected.



(7) Securing information

Securing information is frequently being neglected as an information skill (Dorsey

2001). In the view of Avery et al (2001), securing information is “the centrality of

intellectual property issues and the multiplicity of security issues arising from the

explosion of electronically networked environments make security issues more

and more salient. Securing information entails developing and implementing

practices that help to assure the confidentiality, integrity and actual existence of

information. An appreciation of intellectual property issues of copyrights and

patents is very important. Such practices as password management, backup,

archiving and use of encryption are other important elements for the effective

practice of this skill in electronic environments.”

This is frequently neglected as an information skill (Dorsey 2001). It is however

becoming more and more important especially with the rapid development of the

Internet. The importance of keeping information secure is built on the concept of

intellectual property. A business will suffer if its trade secrets are stolen by

competitors. The care of intellectual property, copyright and patents is important

and it should be the concern of everyone, starting from the individual to improve

the state of information security awareness, for example password management,

safeguarding of information sources and so on.

The roles of Avery et al (2001)’s PKM model in the four generic knowledge

management processes are summarised in table 2.3. Retrieving information, evaluating

information and organising information were taking the role in locate/capture knowledge,

the collaborating around information was taking the role in share/transfer knowledge, the

analysing information was involving create knowledge; and the presenting information

and securing information were taking the role of share/transfer and apply knowledge.





Capture

Share /

Transfer

Create Apply

(1) Retrieving information X

(2) Evaluating information X

(3) Organising information X

(4) Collaborating around information X

(5) Analysing information X X

(6) Presenting information X X

(7) Securing Information X X

Table 2. 3: The role of Avery et al. (2001)’s PKM Model Source: Developed for this research

Avery et al (2001)’s PKM model is a more comprehensive model which not only

includes the information management skills but also the skills required for knowledge

sharing .e.g collaborating, presenting and securing. This model has influenced a lot of

PKM scholars in their work e.g. Berman and Annexstien (2003) in the PK-Book Model,

Agnihotri and Troutt (2009)’s PKM Skills-Tools Fit Model, and the recent PKM research

done by Wu (2007) for Teachers in Taiwan and Cheng (2009) for the pre-service teachers

in Hong Kong.

2.3.4.3.3 Berman and Annexstein (2003)’s PKM Model (PK-Book Model)

Berman and Annexstien (2003) developed a Personal Knowledge Book Model “PK-Book

Model”, as shown in figure 2.14, based on the PKM Skills Model proposed by Avery et

al (2001). The PK-Book model adapts the features of the traditional book which (1)

provided to the user an understanding of the structure and topic set in the form of table of

contents (TOC), (2) the pages of the book contain the raw information content in the

forms of textual data, figures, and references; and (3) the index of the book provides a

means to quickly and easily search and locate information contained in the pages of the

book. In addition, Berman and Annexstein (2003) applied new computing technologies to

develop this PK-Book model and to make this PK-Book become a multi-dimensional

object. The formal definition of a PK-Book object (Berman & Annexstein 2003) is as a

tuple B = (T,P,I,E), where T is the table of contents, P is the set of pages, I is the index,

and E is the context engine which applies the contextual metadata in applications.



Figure 2. 14: Berman and Annexstien (2003)’s PKM Model (PK-Book Model)

Source: Berman and Annexstien (2003, p. 5)

(1) PK-Book Table of Content (TOC)

The PK-Book TOC is to facilitate the structural definition of the personal

knowledge which is accomplished by facilitating the processes of outlining,

cataloguing, and categorisation. The formal definition of PK-Book TOC is the

hierarchy of topics and allows cross reference edges that respect the refinement or

subset relations on topics. Hence, PK-Book TOC = (D,T) where D is the directed

acyclic graph (dag) and T is the named topics identified with the nodes.

(2) PK-Book Pages

The PK-Book Pages are designed as an interface to the raw information data,

accessible database, local data, and its visualisation (layout and style

configurations). The Pages are blocked into a vector of frames f1, f2,….,fk…etc.,

where each frame represents a collection of semantically related elements.



(3) PK-Book Index

The PK-Book index is designed to facilitate information search and retrieval

methods. These methods are applied to the book and its contents as well as to the

related resources available through the network. The index allows for the

disambiguating and relevancy determination of terms, and allows integrating with

peer-to-peer sharing.

(4) PK-Book Context Engine

The PK-Book context engine is designed to utilise the contextual metadata (e.g.

time stamping, history, and usage pattern and user profile) stored in the PK-Book

pages. The goal of the context engine is to help augment and improve the

functionality and information content of the PK-Book object over time. The

contextual metadata can also include the external resources e.g. ranking topic

specific information hubs, to determine the authority ranking of content and to

determine the relevancy of information elements with respect to the context of the

PK-Book.

Berman and Annexstein (2003) argued that the PK-Book Model draws inspiration from

the potential synergy between the processes of information organisation and information

contextualisation, and calls these two processes as “manus manum lavat” i.e. one hand

washing the other. The process associated with information organisation include

capturing, converting, cataloguing, categorising, outlining, manual filtering, and

indexing, and the processes associated with information contextualisation include

searching, crawling, browsing, focusing, semantic, evaluation, automatic filtering,

analysing and confirming.

The roles of Berman and Annexstein (2003)’s PKM model in the four generic knowledge

management processes are summarised in table 2.4. All the model’s components were

actually involved in dealing with locate/capture knowledge only and it was lacking a role

in share/transfer, create and apply knowledge.





Capture

Share /

Transfer

Create Apply

(1)PK-Book Table of Content (TOC) X

(2) PK-Book Pages X

(3) PK-Book Index X

(4) PK-Book Context Engine X

Table 2. 4: The roles of Berman and Annexstein (2003)’s PKM Model


The Berman and Annexstein (2003) mentioned that their PK-Book Model was only a

conceptual model for computing design. It was too abstract so that individuals could

hardly follow in building their own PK-Book. Besides, it just focused on the individual

information management and was lacking in the elements for knowledge sharing.

However, this model provided the insight of using computing technology to facilitate the

PKM work.

2.3.4.3.4 Efimova (2005)’s PKM Model (Individuals, Ideas and Communities Model)

Efimova (2005) suggested that a knowledge worker's activities could be mapped as

interactions of an individual, other people and with ideas. The proposed PKM framework

is illustrated in figure 2.15, consisting of (1) Individuals, (2) Ideas and (3) Communities

and Networks.

Efimova (2005) based on the views of Lave and Wenger (1991) and Brown and Duguid

(1991) proposed that new ideas and insights are often developed in the social context,

and defined that conversations and collaboration should be in the middle of the PKM

framework. Making sense of information, organising ideas and creativity are the key

elements for individuals to deal with ideas. Individuals should establish and maintain

relationships with the participants, and awareness, exposure and lurking are the key

processes to interact with ideas within the communities.

Efimova (2005) also argued that conversations require unique personal contributions,

enabling relations between participants, and awareness of a specific domain, its players

and social norms. The participation in conversation requires learning to move from being

an outsider to take a more active position through the participation at the periphery. Trust

and shared understanding between people would enable effective knowledge



development and it is required to establish and maintain a personal network to keep track

of contacts and conversation and to make choices about which communities to join and

which can be ignored. In addition, knowledge workers are faced with a need for personal

information management in order to organise their information, which may be in the form

of paper, digital archives, emails and bookmark collections.

Figure 2. 15: Efimova (2005)’s PKM Model Source (Efimova 2005, p. 8)

The roles of Efimova (2005)’s PKM model in the four generic knowledge management

processes are summarised in table 2.5. The first component “ideas” was taking a role in

both creating and applying knowledge, the “individual” was mainly focusing on

locate/capture knowledge and “communities & networks’ was taking the role in

share/transfer knowledge.



Capture

Share /

Transfer

Create Apply

(1) ideas X

(2) individuals X

(3) communities & networks X

Table 2. 5: The Role of Efimova (2005)’s PKM Model Source: Developed for this research



Efimova (2005)’s PKM model provided the linkage between ideas, individuals and

communities. It provided some important key concept of practising PKM e.g. the

relationship between individual and communities need not just be established but also

need to be maintained; sense making of information and creativity are important for an

individual to generate ideas; and awareness, exposure and lurking are important for ideas

sharing within communities. The element of “Ideas” was introduced to the PKM model

which was not found in previous PKM models. However, this model is less focused on

the applying knowledge and how to better organise the information for future use.

2.3.4.3.5 Wright (2005)’s Personal Knowledge Management Framework (Competences

Model)

Wright (2005) proposed a PKM framework, as shown in figure 2.16, to link the problem

solving activities with specific cognitive, information, social and learning and

development competencies. It is based on the Tissen et al., (1998) proposed model of

knowledge worker competencies and added a fourth competency namely learning and

development. The capacity to apply the competencies requires the support by individuals,

and social and organisational enablers.

(1) Cognitive Competences

Knowledge workers develop and refine their problem solving capabilities through

ongoing learning, including formal training and informal learning, observations

and discussions, as well as work experience. Knowledge workers apply complex

thinking skills e.g. problem identification and definition, pattern recognition,

sense-making, analysis, implementation and monitoring. A variety of heuristic

processes and analytical models are used to solve problems. The workers are not

limited to a single approach to problem solving and will continuously adapt,

modify and refine their problem-solving techniques. As a result, the workers will

acquire advanced cognitive skills which include experimentation, prototyping and

modelling. Communication, negotiation and conflict resolution skills are

developed for application in developing new knowledge, and finally the process

of reflection, including individual reflection and double loop learning and

collaborative reflection within communities of practise, will occur around

particular problem-solving practices.



(2) Information Competences

The core information competences involve sensing and sourcing information

skills i.e. the ability to locate and capture the information in a short time, and

questioning skills which enable knowing what information resources to seek. It is

the ability to assess, organise, structure, present and discard information resources

which are vital elements for efficiently accessing and applying information. It

involves having strong search skills to enable quick assess to the quality and value

of information resources.

(3) Social Competences

Knowledge work is a social interaction. It is a challenge to the knowledge workers

that problem solving in today’s complex environment involves teams, projects,

collaboration and interaction. The problem solving involves working in a

collaborative environment and effective problem solving requires team building

and maintenance activities e.g. communication and conflict resolution skills. How

individual workers utilise their social competencies can be understood by the

concept of social capital (Adler & Kwon 2000; Nahapiet & Ghoshal 1998). The

social capital consists of three inter-related dimensions which are structural,

cognitive and common understanding and relational. Structural defines the ties

and configurations of the social networks which identify the patterns of

connections among workers. Cognitive include the elements of shared language.

Common understandings focus on the essential role of trust, shared norms and

common identification.

(4) Learning and Development Competences

Learning is a continuous process and it is indistinguishable from ongoing work

practices. Learning is contingent and contextual. For routine problems, workers

refine their ability to quickly recognise problem types and act. The novel

problems allow workers to develop stronger pattern recognition and sense-making

capacities and more robust analytical and problem-solving techniques. The new

knowledge is created through experimentation, innovation and prototyping.

Problems outside the expertise of workers can allow the application of reflection



skills. The cognitive, information, social and learning competencies are

continuously improved across the spectrum of problem types workers.

Figure 2.15 illustrates the PKM framework proposed by Wright (2005). This model also

in line with previous PKM models where information and social aspects are the key

elements in PKM. In addition, this model provides a new angle of PKM in terms of

competencies for problem solving. However, as mentioned by Wright (2005), this model

needs further research to understand how knowledge workers work with complex

problems and also to examine the types of problems faced by different workers. Besides,

it also requires study on how experts can be developed and nurtured.

.

Figure 2. 16: Wright (2005)’s PKM Framework

Source: Wright (2005, p. 163)

The roles of Wright (2005)’s PKM model in the four generic knowledge management

processes are summarised in table 2.6. The cognitive competencies and information

competencies have the role in locate/capture knowledge, social competencies are mainly



involving in share/transfer knowledge, and learning and development competencies have

the role to deal with create and apply knowledge.



Capture

Share /

Transfer

Create Apply

(1) cognitive competencies X

(2) information competencies X

(3) social competencies X

(4) learning and development competencies X X

Table 2. 6: The Role of Wright (2005)‘s PKM model


This model introduced an important concept in PKM which is the competences.

Knowledge workers need to develop their competences in order to benefit both

individuals and organisations. The important argument/assumption here was that with

good competences, it will increase the ability to create knowledge and as a result to

improve the capability to deal with problems. This hypothesis requires further research to

support its argument.

2.3.4.3.6 Zuber-Skerritt (2005)’s PKM Model (Values and Actions Model)

Zuber-Skerritt (2005)’s PKM model offered a system of seven values and principles and

seven matching actions. The seven values and principles are, in the view of Zuber-

Skerritt, the most important values and principles in action learning and action research

(ALAR), namely (1) Advancement of knowledge and learning, (2) Collaboration, (3)

Trust, respect and honesty, (4) Imagination and a vision of excellence, (5) Openness, (6)

Non-positivist beliefs, and (7) Success.

(1) Advancement of learning and knowledge

Advancement of learning and knowledge is achieved by experience and reflection

in iterative cycles of reflection and action. It is the essence of action learning.

(2) Collaboration

Collaboration leads to systemic development and synergy of results.

Collaboration, team spirit and “symmetrical communication” accept that everyone



is equal and unique and accept differences positively, and has the capacity to

contribute as best they can to solve a problem.

(3) Trust, respect and honesty

Trust, respect and honesty are the preconditions for the search for truth(s). It is the

heart of action learning and action research.

(4) Imagination and a vision of excellence

Imagination, intuition, and vision of excellence can enrich the pursuit of ideas,

possibilities and ultimately knowledge and appreciation. It can lead to high levels

of performance.

(5) Openness

Openness is to criticism and self-criticism fosters the exploration of multiple

possibilities, rather than single-minded or black and white solutions.

(6) Non-positivist beliefs

Non-positivist beliefs allow the development of grounded theory and reject the

positivist belief that only valid and legitimate knowledge is scientific in nature. It

recognises that knowledge is produced from various sources, including people’s

subjective insight, intuitions and hunches and, as mentioned by Nonaka and

Takeuchi (1995), it must be practical and integrate both explicit and tactic

knowledge.

(7) Success

Success means shared success, accountability, recognition and reward, manifest in

learning and productivity outcomes.

The seven matching actions are:

(1) Reflection on and in action,

(2) Effective use of processes and methods,

(3) Feedback from “Critical friends”,



(4) Leadership development,

(5) Exploration of new opportunities,

(6) Coaching,

(7) Team results.

The model integrated with these seven actions and seven values is illustrated in figure

2.17. Zuber-Skerritt (2005) argued that this model is for developing, accessing and

making explicit one type of knowledge in KM: the experiential, tacit and implicit

knowledge called personal knowledge. This model can help to identity personal

knowledge as:

(1) Knowledge through reflection on action / experience and through developing

concepts and personal theories,

(2) Knowledge through collaboration and effective use of group processes.

(3) Knowledge of oneself (strengths and weakness) and of significant others

through feedback, team building, respecting personal differences, and

understanding what constitutes a winning team.

(4) Knowledge of future goals and envisaged high achievement through vision

building, creative thinking, right-brain activities and developing energy and

motivation for success.

(5) Knowledge of how to explore new opportunities through self-assessment, self-

criticism, and through openness to criticism from others,

(6) Knowledge of our basic beliefs and of the assumptions underpinning our

research and development activities through learning from mentors, coaches

and from the literature, regarding paradigms and epistemology.

(7) Knowledge of team achievement and success through recognition, reward and

celebration.



The roles of Zuber-Skerritt (2005)’s PKM model in the four generic knowledge

management processes are summarised in table 2.7. The “advancement and reflection”

and “success and team result” take the role in apply knowledge; the “collaboration and

effective use of process and methods” and “trust and feedback”; “openness and

exploration of new opportunities”, “non-positivist beliefs and coaching” take the roles in

share/transfer knowledge; and “Imagination and leadership” involve dealing with create

knowledge. This model lacks a role in locate/capture knowledge.



Capture

Share /

Transfer

Create Apply

(1) Advancement and Reflection X

(2) Collaboration and Effective use of

process and methods

X

(3) Trust and Feedback X

(4) Imagination and Leadership

development

X

(5) Openness and Exploration of new

opportunities

X

(6) Non-positivist beliefs and Coaching X

(7) Success and Team results X

Table 2. 7: The Role of Zuber-Skerritt (2005)’s PKM model Source: Developed for this research

Zuber-Skerritt (2005)’s PKM model is a conceptual model to put the PKM in action and

suggested seven actions matching to the seven values / principles of PKM. This model

covered both the individual and collaboration with other people. The underlying principle

is action learning which addressed another important view on PKM that outcome of

learning should be through Reflection. Another contribution of this model is the

identified seven types of personal knowledge which could be used for knowledge

management in the workplace.



Figure 2. 17: Zuber-Skerritt (2005)’s PKM Model (A values and actions model) Source : Zuber-Skerritt (2005, p. 61)

2.3.4.3.7 Agnihotri and Troutt (2009)’s PKM Model (PKM Skill-Tools Fit model)

Agnihotri and Troutt (2009) proposed a PKM Skill-tools fit model based on the previous

work done by Avery etal (2001), Frand and Hixon (1999), Jefferson (2006), Davenport

(1997) and Barth (2004). The proposed model is shown in figure 2.18. There are six

compontents in the model namely (1) PKM Skills, (2) Technology Tools, (3) PKM

Skills-Tools Fit, (4) Utilisation, (5) User’s Context, and (6) Knowledge impact.

The “PKM Skills-tools fit” and “Utilisation” are the mediators, and the “User’s context”

is a moderator.



Figure 2. 18: Agnihotri and Troutt (2009)’s PKM Model (PKM Skills Tools Fit Model)

Source: (Agnihotri & Troutt 2009, p. 333)

(1) PKM Skills

The PKM Skills set for information management, includes the seven PKM skills

proposed by Avery et al (2001): retrieving, evaluating, organising, analysing,

collaborating around, presenting and securing; and the PKM skills proposed by

Hyams (2000): time control, workplace wellness, speedy reading, notation and

research, document structuring, information design, target writing, processing

infrastructure, and filtering techniques. In addition, Agnihotri and Troutt (2009)

argued that workers also need to perform labelling, tagging and indexing in order

for the secured knowledge to be found and reused in the future.

(2) Technology Tools

Agnihotri and Troutt (2009) mentioned that several technology tools are available

for PKM. Based on the categories classified by Barth (2004) and Tsui (2002b),

the PKM tools can be classified as:



(1) Index / Search Tools

(2) Meta-Search Tools

(3) Associative Links

(4) Concept / Mind Mapping

(5) Email management, analysis and unified messaging

(6) Voice recognition tools

(7) Collaboration and synchronisation tools

(8) Learning Tools

This classification is actually obsolete due to the technical advancement in recent

years. Updating of this model is the subject of further research by the author,

where collaborative and social software definitely play a key role in the revision

of this outdated model. Nevertheless, Agnihotri and Troutt (2009) argued that

these tools should not be the focal point, instead it is important to understand how

these tools and techniques can facilitate the process of finding the solutions for

knowledge worker’s needs. It should involve aligning and studying the PKM

skills and tools simultaneously.

(3) PKM Skills-Tools Fit

It is the core of Agnihotri and Troutt (2009)’s PKM model and is based on the

Task-Technology Fit (TTF) theory of Goodhue and Thompson (1995). The TTF

theory stated that there is a positive relationship between the available technology

tools and an individual’s performance; the technology must be utilised and have a

good fit with the tasks. In the context of PKM, there are three dimensions of the

PKM skills-tools fit; it is believed that an individual who tries to find meaning in

retrieved information and to integrate this information into their decision-making

process. As such, the following three dimensions were proposed to measure the

fit.

(1) Quality of information – to assess the proper interpretation of

information and transformation of this into knowledge.

(2) Accessibility of information – to assess if it is easily accessible to get

new information as well as the saved knowledge.



(3) Ease of use of tools – to assess the simplicity or complexity of the

technology used.

(4) User’s Context

The user’s context is referred to the “collection of relevant conditions and

surrounding influences that make a situation unique and comprehensible”

(Degler & Battle 2000). In PKM context, Schwarz (2006) outlined 8 user contexts

as below.

(1) Operational – active applications and services

(2) Organisational – current role of users, projects, department…etc.

(3) Environmental – location, present persons and used hardware,

(4) Historical – previous tasks

(5) Attentional – text scope.

(6) Behavioural – native operations, user actions

(7) Causal – task concepts(goals), tasks/workflow

(8) Informational – touched documents, relevant documents

Agnihotri and Troutt (2009) argued that the PKM skills-tools fit is moderated by

the user’s context.

(5) Utilisation and Knowledge Impact

Agnihotri and Troutt (2009) argued that if the tools can address the concerns of

the user in exercising the PKM skills, it is highly probable that the perceived

utility will be positively affected and there will be great improvement in terms of

utilisation of these PKM skills and tools. Therefore, this utilisation of PKM skills

and tools will lead to positive knowledge impact.

The roles of Agnihotri and Troutt (2009)’s PKM model in the four generic knowledge

management processes are summarised in table 2.8. The “PKM skills” were referenced to

the Avery et al. (2001)’s PKM model and therefore it was dealing with all four

knowledge management processes. The “technology tools” were mainly to deal with

locate/capture and share/transfer knowledge; the remaining model components were

focusing on the apply knowledge.





Capture

Share /

Transfer

Create Apply

(1) PKM Skills X X X X

(2) Technology Tools X X

(3) PKM Skills-Tools Fit X

(4) Utilisation X

(5) User’s Context X

(6) Knowledge impact X

Table 2. 8: The roles of Agnihotri and Troutt (2009)’s PKM model Source: Developed for this research

Agnihotri and Troutt (2009) mentioned that it was a conceptual model only, and future

research was required to empirical test the model. Besides, there were only three

dimensions of fit but some additional dimensions could be worth investigating. However,

this model provided an important concept in PKM research in that the PKM tools should

be a good fit to its purposes and able to facilitate individuals to practise the PKM skills

effectively.

2.3.4.3.8 Jarche (2010)’s PKM Model (Aggregate, Understand and Connect Model)

Jarche (2010a) argued that PKM is of little value unless the results of the knowledge

work are shared by connecting to others and contributing to meaningful conversations. In

this view, Jarche (2010a) proposed a three activities PKM model, as shown in figure 2.19,

namely Aggregate, Understand and Connect. It is enhanced by Jarche’s previous seven

activities model and focuses on streamlining knowledge and sharing with others.



Figure 2. 19: Jarche (2010)’s PKM Model Source: Jarche (2010a)

(1) Aggregate

It includes the observations and notes of information and knowledge and also

looking for good sources of information (people), tagging and noting information

from collaboration.

(2) Understand

It is to evaluate how the information may be useful in various contexts, finding

the right information at the right time and in the right format, and making sense of

it.

(3) Connect

It is the on going conversations while learning and working, including connecting

people to people, ideas to ideas and people to ideas.

The roles of Jarche (2010)’s PKM model in the four generic knowledge management

processes are summarised in table 2.9. The “aggregate” was dealing with locate/capture



knowledge, “understand” was dealing with create knowledge, and “connect” was dealing

with share/transfer and apply knowledge.



Capture

Share /

Transfer

Create Apply

(1) Aggregate X

(2) Understand X

(3) Connect X X

Table 2. 9: The roles of Jarche (2010)’s PKM model


Jarche (2010a) argued that PKM increases the chances of serendipitous learning, and it

increases the likelihood of unexpected discoveries and connections when you

contributing and sharing with others. One of the difficult aspects of PKM is triage, it is

the ability to separate the important from the useless, and it is a time consuming process

to develop good triage techniques. Jarche (2010a) highlighted that the most important

aspect of PKM is making our knowledge not only explicit but public; it is part of the

connecting.

2.3.4.3.9 Summary of Evaluation of PKM Models

Based on the evaluation of the PKM models, it is clear that PKM has evolved from

merely individual activities to something that are more outcome/impact oriented; from

information handling skills to personal competencies, sensemaking and self-reflection;

from individually focused to community and social collaborative focused. Increasingly

the model also provides an alignment of the appropriate technologies. This evolution

necessitates the definition of requirements for a comprehensive PKM model that fully

encapsulates the need for personal information management, knowledge internalisation,

transferring of knowledge and knowledge creation, and learning.

2.3.5 Section summary of Personal Knowledge Management

In this section, the literature of PKM was reviewed to provide a better understanding of

previous scholars’ work in this area. The definitions of individual learning (section 2.3.1),

the linkage between individual learning and organisational learning (section 2.3.2) and

the individual learning process (section 2.3.3) were discussed, and are the underlying

theories of PKM discussed in section 2.3.4. The definitions of PKM (section 2.3.4.1)



were discussed and provided different scholars’ views on the meaning of PKM. The

development of PKM was discussed (section 2.3.4.2) since Frand and Hixon (1999) first

proposed a PKM model focused on personal information management to the recent

development focused on technology for PKM. Eight different PKM models were

evaluated (section 2.3.4.3) which provided in-depth understanding of the different

concepts, processes and the principles proposed by different PKM’s scholars in the past

few decades.

2.4 The Roles and Values of Personal Knowledge Management

The roles and values of PKM emerge as an immediate discipline in this research. This

section reviews and evaluates the related literature in order to develop a research model to

answer the research questions defined in chapter 1. The roles of PKM are discussed in

section 2.4.1, followed by the values of PKM in section 2.4.2.

As discussed in section 2.2.6, the Seufert, Back and Krogh (2003)’s knowledge

management processes (locate/capture, share/transfer, create and apply knowledge) were

adapted to develop the model in this research.

2.4.1 Roles of Personal Knowledge Management

In a previous section (section 2.3.4), eight different PKM models were evaluated. The

models and their corresponding roles in Seufert, Back and Krogh (2003)’s knowledge

management processes were analysed and summarised as shown in table 2.10.

• Frand and Hixon (1999)’s PKM Model (PIM Model) is mainly focused on

locating / capturing knowledge and applying knowledge.

• Avery et al. (2001)‘s PKM Model (PKM Skills Model) is a comprehensive model

which covers all four KM processes. It is also a generic PKM model and

influenced many scholars in their PKM research.

• Berman and Annexstein (2003)’s PKM Model (PK-Book Model) mainly focused

on locating / capturing knowledge.

• Efimova (2005)’s PKM Model (Individuals, Ideas and Communities Model)

mainly dealt with ideas generation and sharing. It lacks a role in applying

knowledge.



• Wright (2005)’s PKM Model (Competencies Model) is also a comprehensive

model which covers all four KM processes but it is mainly focused on problem

solving.

• Zuber-Skerritt (2005)’s PKM Model (Values and Actions Model) lacks a role to

capture/locate knowledge.

• Agnihotri and Troutt (2009)’s PKM Model (PKM Skill-Tools Fit model) covered

all four KM processes but the focus was in matching the skills and tools for PKM.

• Jarche (2010)’s PKM Model (Aggregate, Understand and Connect Model) lacks

a role to apply knowledge.

Based on the evaluation of the different PKM Models, the Avery et al. (2001)’s PKM

Model, Wright (2005)’s PKM Model and Agnihotri and Troutt (2009)’s PKM Model

(PKM Skills Tools Fit Model) are comprehensive models which cover all four generic

knowledge management processes as proposed by Seufert, Back and Krogh (2003).

Agnihotri and Troutt (2009)’s PKM Model (PKM Skills Tools Fit Model) mainly focused

on matching the tools to PKM skills and Wright (2005)’s PKM Model mainly focused on

problem solving.

In reviewing the purpose of this research to explore the roles and values of PKM, a

comprehensive and generic PKM model is required; therefore, the Avery et al. (2001)’s

PKM Model was adapted to develop the new research model.




Model Model’s Components Locate /

Capture

Share /

Transfer

Create Apply

(1) Searching / Finding X

(2) Categorising / Classifying X

(3) Naming Things / Making Distinctions X

(4) Evaluating / Assessing X

Frand and Hixon (1999)’s

PKM Model (PIM Model)

(5) Integrating / Relating X

(1) Retrieving information X

(2) Evaluating information X

(3) Organising information X

(4) Collaborating around information X

(5) Analysing information X X

(6) Presenting information X X

Avery et al (2001)’s PKM

Model (PKM Skills Model)

(7) Securing Information X X

(1)PK-Book Table of Content (TOC) X

(2) PK-Book Pages X

(3) PK-Book Index X

Berman and Annexstein

(2003)’s PKM Model (PK-

Book Model)

(4) PK-Book Context Engine X

(1) ideas X

(2) individuals X

Efimova (2005)’s PKM

Model (Individuals, Ideas

and Communities Model) (3) communities and networks X

(1) cognitive competencies X

(2) information competencies X

(3) social competencies X

Wright (2005)’s PKM

Model (Competences

Model)

(4) learning and development competencies X X

(1) Advancement and Reflection X

(2) Collaboration and Effective use of

process and methods

X

(3) Trust and Feedback X

(4) Imagination and Leadership

development

X

(5) Openness and Exploration of new

opportunities

X

(6) Non-positivist beliefs and Coaching X

Zuber-Skerritt (2005)’s

PKM Model (Values and

Actions Model)

(7) Success and Team results X

(1) PKM Skills X X X X

(2) Technology Tools X X

(3) PKM Skills-Tools Fit X

(4) Utilisation X

(5) User’s Context X

Agnihotri and Troutt

(2009)’s PKM Model (PKM

Skill-Tools Fit model)

(6) Knowledge impact X

(1) Aggregate X

(2) Understand X

Jarche (2010)’s PKM

Model (Aggregate,

Understand and Connect

Model)

(3) Connect X

Table 2. 10: Analysis of PKM Models against KM Processes Source: Developed for this research



2.4.2 The Values of Personal Knowledge Management

Learning is commonly viewed as a process to acquire skills and knowledge e.g. Jarvis

(1987) defined learning as a process of transforming experience into knowledge, skills

and attitudes.

Figure 2. 20: Learning Process

Source: Smith (2009, p. 12)

Based on this model, the values of PKM could be explained by the outcomes of

individual learning, i.e. individual competences, and the outcomes of organisational

learning i.e. the organisation competences.

The following sections provide the relevant literature view about individual competences

and organisation competences.

2.4.2.1 Individuals Competences

The concept of individual competence is widely used in human resource management

(Boyatzis 1982; Burgoyne 1993; Schroder 1989). This refers to a set of skills that an

individual must possess in order to be capable of satisfactorily performing a specified job.

Although the concept is well developed, there is a continuing debate about its precise

meaning, and it continues to remain one of the most diffuse terms in the management

development sector, and the organisational and occupational literature (Collin, 1989).



Competence definition:

(1) Competence refers to a potential ability and / or a capability to function in a given

situation. Competency focuses on one’s actual performance in a situation. This

means that competence is required before one can expect to achieve competency

(Schroeter 2008).

(2) The Concise Oxford English Dictionary defines competence as “ability to do, for

a task” and (interestingly) as “sufficiency of means for living”.

(3) Streumer and Bjorkquist (1998) conclude that in the British literature competence

most often refers to an individual’s capability to perform tasks that have been

assigned to him.

(4) Cheetham and Chivers (2005) provide the general definition of competence:

competence is an effective overall performance within an occupation, which may

range from the basic level of proficiency through to the highest levels of

excellence (Cheetham & Chivers 2005, p. 54).

Many scholars have tried to define what competences are required for different types of

workers. Cheetham and Chivers (1996) proposed a holistic model of professional

competence which consists of four key components: (1) functional competence, (2)

personal or behaviour competence, (3) knowledge/cognitive competence and (4)

values/ethical competence.

(1) Functional competence

It is the ability to perform a range of work-based tasks effectively to produce

specific outcomes. This includes, and indeed requires, the passion of discrete

skills but the emphasis is on putting these to use to achieve specific outcomes.

(2) Personal or Behaviour competence

It is the ability to adopt appropriate, observable behaviours in work-related

situations.



(3) Knowledge / Cognitive competence

It is the possession of appropriate work-based knowledge and ability to put this to

effective use.

(4) Values / Ethical Competence

It is the possession of appropriate personal and professional values and the ability

to make sound judgments based upon these in work-related situations. The

linkage of ethical competence with values emphasises the point that values, like

knowledge, are of little use unless they are effectively applied. This ethical

competence refers to the effective and appropriate application of values in

professional settings.

Fleming (1991) argued that there are competences which work on other competences and

he defined these as Meta-Competence. It is the versatility to deal with a variety of

different problems by being able to draw on appropriate skills and knowledge in the

circumstances. Developing meta-competence is about lining subject-specific knowledge

with the particular competences that should be practised by the learner.

Fleming (1991) did not provide details about which competences are meta-competences

but Cheetham and Chivers (1996, 1998) argued that it should includes (1)

communication, (2) learning and self development, (3) creativity, (4) analysis, (5)

problem solving, (6) mental agility and (7) reflection. His revised professional

competence model is as shown in figure 2.21.



Fig

ure

2.

21:

Pro

fess

ion

al

Com

pet

en

ces

Sourc

e: C

hee

tham

and C

hiv

ers

(199

8,

p. 2

59)



Cheetham and Chivers (1998) claimed that this model has been tested with 20 different

professionals. Their work has influenced a lot of scholars/researchers’ work in

competence studies e.g. Jackson (1998)’s research in teachers-scholars in UK higher

education; Boak and Coolican (2001)’s research in area managers in a large UK fashion

retail company; Foley et al (2004)’s research in the Scottish workforce in the sport and

fitness, play and outdoor sectors; Watson et al (2004)’s research in managerial

competence in the Scottish visitor attraction sector; Heilmann (2007)’s research in

middle management in Finnish information and communication technology sector and

paper business sector and Hashim (2008)’s research on Malaysian managers.

In reviewing the various PKM Models developed by different scholars in section 2.3.4.3,

there were two models (Wright (2005)’s PKM Model (Competencies Model) and Zuber-

Skerritt (2005)’s PKM Model (Values and Actions Model)) that discussed the PKM’s

values or competences which can be mapped to the seven individual competences as

proposed by Cheetham and Chivers (1998), these are summarised in table 2.11.

Cheetham and Chivers (1996,

1998)’s Individuals Competences Wright (2005)’s PKM Model

Zuber-Skerritt (2005)‘s

PKM Model

Communication Cognitive Competences,

Social Competences

Collaboration and Effective

use of process and methods

Learning and Self Development

Cognitive Competences,

Learning and Development

Competences

Advancement and

Reflection

Creativity Learning and Development

Competences

Imagination and Leadership

development

Analysis

Cognitive Competences,

Learning and Development

Competences

Non-positivist beliefs

Problem Solving Cognitive Competences Collaboration and Effective

use of process and methods

Mental Agility Cognitive Competences Openness and Exploration

of new opportunities

Reflection Cognitive Competences Advancement and

Reflection

Table 2. 11: Mapping of benefit and values to seven individuals competences




2.4.2.2 Organisation Competences

An effective knowledge organisation should creates a broad, complex and internally-

consistent dynamic knowledge capability and integrate it with other strategic business

capabilities and with its environment in overall organisational strategies capabilities

architecture (King 2008).

King (2008) argued that an effective knowledge organisation should pursue a hierarchy of

objectives, including (1) improve the quality and range of applications of knowledge; (2)

improve organisational processes for innovation, individual learning, collective learning,

collaborative problem-solving, and knowledge-sharing; (3) improve the quality and the

impacts of the decision and behaviours that are taken by the organisation; and (4)

improve organisational performance.

The effective knowledge organisation architectural framework by King (2008) is shown

in figure 2.22.

Figure 2. 22: The Effective Knowledge Organisation architectural framework

Source: King (2008, p. 30)



Fig

ure

2.

23:

Com

paris

on

of

Kn

ow

led

ge-

rela

ted

ele

men

t in

Eff

ecti

ve

Kn

ow

led

ge

Org

an

isati

on

So

urc

e: K

ing (

2008,

p. 3

0)



Nonaka and Takeuchi (1995) argued that in an effective knowledge organisation, the

knowledge acquired from external sources should be able to be shared widely within the

organisation, stored in the company’s knowledge database, and utilised by the staff to

develop new technologies, services and products. It requires a conversion process to

transform knowledge from outside to inside and back outside again in the form of new

products, services or systems. It requires continuous innovation which can lead to a

competitive advantage.

It is a challenge to measure the effectiveness of an effective knowledge organisation for

both the researcher and the manager. However, frameworks for measuring the

effectiveness of organisation are very mature and one of the models highly relevant to

knowledge organisation is the Organisational IQ (OIQ) framework proposed by

Mendelson and Ziegler (1999). Previous research by Mendelson and Ziegler have shown

that the OIQ is positively correlated to the firm performance, and the OIQ framework

consists of five keys indicator, namely (1) External Information Awareness, (2) Internal

Knowledge Dissemination, (3) Effective Decision Architecture, (4) Organisational Focus

and (5) Information-Age Business Network. Ziegler (2008) enhanced the framework to

replace the Information-Age Business Network by Continuous Innovation.

(1) External Information Awareness

The external information awareness measures the customer dynamics, technology

opportunities and competitive actions. Mendelson and Ziegler (1999) argued that

sharing knowledge within the organisation is important but it is not enough, and

the organisation must acquire knowledge from the outside world, i.e. external

learning at all levels.

(2) Internal Knowledge Dissemination

The internal knowledge dissemination measures the effective flow of information

horizontally, vertically (top down and bottom up) and reviews processes. To

encourage knowledge sharing, an organisation has to develop an effective

structure to allow the free flow of information (Mendelson & Ziegler 1999).



(3) Effective Decision Architecture

The effective decision architecture is to measure the decisions quality, decision

time and sense of ownership and accountability for decisions. The information or

knowledge required to make an effective decision may be located anywhere

within the organisation. The concern is the power diffusion throughout the

organisation so that if the decision maker has the knowledge required to make the

decision (Mendelson & Ziegler 1999).

(4) Organisational Focus

The organisational focus is to measure the scope of the business focus, core

competencies focus and simplification of processes. It means that organisations

can focus on their core competencies and let their business partners who have

complementary capabilities take care of the rest (Mendelson & Ziegler 1999).

(5) Continuous Innovation

Continuous innovation measures the creativity, product development and quality

improvement (Ziegler 2008). Mendelson and Ziegler (1999) argued that an

organisation should introduce new products and to develop new business on an

ongoing basis in order for the organisation to keep growing.

2.5 Chapter Conclusion

This chapter provides a critical literature review for this research, which includes the

literature on Knowledge Management, Personal Knowledge Management and the Roles

and Values of Personal Knowledge Management. In section 2.2, the definition of

knowledge and knowledge management, the DIKW knowledge hierarchy, the knowledge

conversion model and the knowledge management process were discussed. In section 2.3,

the definition of individual learning, the linkage between individual learning and

organisational learning, individual learning process, the definition of personal knowledge

management, the development of personal knowledge management and the evaluation of

different personal knowledge management models were discussed. Section 2.4, showed

the immediate discipline of this research which evaluated the roles of the previous PKM

models in the KM processes and also discussed the relevant literature concerning the

values of PKM in terms of individual competences and organisational competences.



These are the foundations for developing the theoretical model for this research,

discussed in next chapter.

Chapter 3: Research Design & Methodology


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3.1 Introduction

In the previous chapter, the literature review provided the foundation to develop the

conceptual model for this research. The purpose of this chapter is to present the

theoretical framework of this research, and the research design and methodology that was

adopted to answer the research questions in order to achieve the research objective

“Investigate the roles and values of Personal Knowledge Management”.



RQ3: Is there any correlation between the roles of PKM in KM Processes and the

values of PKM for individuals and organisations?



The chapter is divided into 8 sections as shown in Figure 3.1. It starts with a discussion of

the various research paradigms (section 3.2) and provides justification for the chosen

research methodology (section 3.3). Section 3.4 provides details on the theoretical

framework, section 3.5 outlines the research design and data collection method and

section 3.6 describes the approach for data analysis. The ethical considerations are

outlined in section 3.7 and followed by the chapter conclusion in section 3.8.

Figure 3. 1: Structure of Research Methodology




3.2 Research Paradigms

Research paradigms provide a general organising framework for theory and research that

includes the basic assumptions, key issues, models of quality research, and methods for

seeking answers (Neuman 2006). The research paradigms can be identified by three

different elements which are ontology, epistemology and methodology (Guba & Lincoln

1991; Healy & Perry 2000). Ontology is the branch of philosophy (of metaphysics) that is

concerned with issue of existence or being as such, i.e. the nature of the reality (Guba &

Lincoln 1991); epistemology is another branch of philosophy that deal with the origin,

nature and limits of human beings, and questions the relationship between the reality and

the researcher (Guba & Lincoln 1991); and the methodology is a more practical branch of

philosophy (of science) that deals with methods, systems, and rules for the conduct of an

inquiry (Guba & Lincoln 1991), and is the umbrella or collective concepts that together

combine to form a rigour research that is capable of being followed by another person not

involved in the research who can determine the value of the research (Burrell & Morgan

1979).

There are four main research paradigms namely Positivism, Realism, Critical Theory and

Constructivism (Guba & Lincoln 1994; Healy & Perry 2000; Perry, Riege & Brown

1999). Perry, Riege and Brown (1999) based their work on Guba and Lincoln (1994)’s

paper, and is summarised, in table 3.1, given the basic belief systems of the different

paradigms in regard to their position in ontology, epistemology and methodology.

3.2.1 Positivism

Positivism is the approach of natural sciences that it is a rigorous, exact measure and

objective research method (Neuman 2006). Ragin (1994) argued that the positivism is

viewed as condensing data to see the big picture. This paradigm is based on theory

testing, requiring precise quantitative data analysis to test hypotheses (Neuman 2006) and

to analyse the relationships between variables that are consistent across context, place and

time in order to explore the truth (Perry, Riege & Brown 1999). The researcher is

separate from the research process (Guba & Lincoln 1994; Healy & Perry 2000) and

maintains a value-free and theory-free position (Guba & Lincoln 1994; Neuman 2006).



3.2.2 Realism

Realism has elements of both positivism and constructivism and it concerns multiple

perceptions about a single, mind-independent reality (Healy & Perry 2000). Realism

believes that there is a real world to discover even it is only imperfectly apprehensible

(Guba & Lincoln 1994; Healy & Perry 2000). Healy and Perry (2000) mentioned that

Popper has summarised realism as a ‘real’ world which consists of abstract things that are

born of people’s minds but exist independently of any one person. It is largely

autonomous, though created by people. Krauss (2005) argued that realism recognises that

perceptions have a certain plasticity and that there are differences between reality and

people’s perceptions of reality

3.2.3 Critical Theory

Critical theory assumes apprehensive social realities, incorporating historically situated

structures and it aims to critique and transform social, political, cultural, economic, ethnic

and gender values (Perry, Riege & Brown 1999). Neuman (2006) mentioned that critical

theory research is to smash myths and empower people to change society. The researcher

acquires these realities based on the perceptions held by a group of individuals and the

research process is based on the researcher’s experience, expertise and organisational

abilities (Guba & Lincoln 1994).

3.2.4 Constructivism

Constructivism adopts relativism ontology and believes that truth is a construction which

refers to a particular belief system held in a particular context; it has multiple realities

which are socially and experientially based, intangible mental constructions of individual

persons (Perry, Riege & Brown 1999). Guba and Lincoln (1991) argued that the

constructivism believes that realities are ungoverned by any natural laws and truth is

defined as the best informed (amount and quality of information) and most sophisticated

(power with which the information is understood and used) construction on which there is

consensus (although there may be several constructions extant that simultaneously meet

that criterion). The researcher will interactively make a link between the research and the

perceptions of participants in order to create the findings (Guba & Lincoln 1994)



Item Positivism Realism Critical Theory Constructivism

Ontology Naïve realism:

Reality is real and

apprehensible

Critical realism: reality

is ‘real’ but only

imperfectly and

probabilistically apprehensible and so

triangulation from

many sources is

required to try to know

it

Historical realism:

‘virtual’ reality shaped

by social, economic,

ethnic, political, cultural, and gender

values, crystallised

over time.

Critical relativism:

multiple local and

specific ‘constructed’

realities

Epistemology Objectivist: findings true Modified objectivist:

findings probably true

Subjectivist: value

mediated findings

Subjectivist: created

findings

Methodology Experiments/surveys:

verification of hypotheses:

chiefly quantitative

methods

Case studies/

convergent

interviewing:

triangulation,

interpretation of

research issues by qualitative and

quantitative methods

such as structural

equation modelling

Dialogic / dialectical:

researcher is a

‘transformative

intellectual’ who

changes the social

world within which participants live

Hermeneutical /

dialectical: researcher

is a ‘passionate

participant’ within

the world being

investigated

Table 3. 1: Basic Belief Systems of Alternative Enquiry Paradigms Source : Perry, Riege and Brown (1999, p. 17)

3.3 Research Methodology and Justification

There are two common research approaches namely quantitative methodology and

qualitative methodology. Neuman (2006) summarised the differences between these two

methodologies as in table 3.2.

Quantitative Research Qualitative Research

Test Hypothesis that the researcher begins with. Capture and discover meaning once the research becomes

immersed in the data.

Concepts are in the form of distinct variables Concepts are in the form of themes, motifs, generalisations, and

taxonomies.

Measures are systematically created before data collection and

are standardised.

Measures are created in an ad hoc manner and are often

specific to the individual setting or researcher.

Data are in the form of numbers from precise measurement. Data are in the form of words and images from documents,

observations, and transcripts.

Theory is largely causal and is deductive. Theory can be causal or noncausal and is often inductive.

Procedures are standards, and replication is frequent. Research procedures are particular, and replication is very rare.

Analysis proceeds by using statistics, tables, or charts and

discussing how what they show relates to hypotheses.

Analysis proceeds by extracting themes or generalisations from

evidence and organising data to present a coherent, consistent

picture.

Table 3. 2: Quantitative Research versus Qualitative Research

Source: (Neuman 2006, p. 157)



3.3.1 Quantitative Methodology

Quantitative research is more concerned with design, measurement, and sampling

because the deductive approach emphasises on detailed planning prior the data collection

and analysis (Neuman 2006). It is a research approach following the positivism paradigm

and believes that science discovers a single apprehensible reality concerning the research

problem, and if the research is conducted correctly and is based on observation, the

resulting knowledge will be trustworthy (Lincoln & Guba 1985). It is opposite to the

qualitative research approach which seeks to establish reality through an inductive

approach in which phenomena are observed and conclusions drawn on the basis of the

deductive approach. Hence a conclusion is drawn on the basis of logical generalisation

about a known piece of information (Neuman 2006).

The deductive approach is underlying in quantitative research where a hypothesis is

deduced from theory, data is collected and analysed to confirm or discount the theory.

The deductive approach begin with abstract concepts or a theoretical proposition that

outlines the logical connection among concepts and then moves toward concrete,

empirical evidence (Neuman 2006). It is informally called the “top-down” approach and

the ultimately result of deductive approach can lead us to test the hypotheses with specific

data - a confirmation (or not) of our original theories (Trochim 2006).

A quantitative approach is often used for explanatory or causal research, having a focused

or specific research question, comparative large sample size and testing the theory

(Neuman 2006; Perry 1995; Zikmund 2000). Neuman (2006) also argued that quantitative

research is to measure objective facts, and the theory and data are separated. It is

independent of context and the quantitative researcher is detached from the research

context. The methods commonly employed by quantitative researchers include

interviews, surveys, observations, experiments, and the trustworthiness is achieved by

scientific testing of the data reliability and validity (Neuman 2006).

3.3.2 Qualitative Research Methodology

The qualitative research focuses on the issues of richness, texture, and feeling of raw data

because of the inductive approach emphasis in developing insights and generalisation out

of the data collected. It is a research methodology that often relies on interpretive or



critical social science, and it is an non-linear research approach and it applies logic in

practice (Neuman 2006).

Qualitative research has a number of characteristics which include: a focus on

interpretation rather than quantification; an emphasis on subjectivity rather than

objectivity; flexibility in the process of conducting research; an orientation towards the

process rather than the outcome; a concern with context regarding behaviour and situation

as being inextricably linked in forming experience; and finally, an explicit recognition of

the impacts of the research process on the research situation (Cassell & Symon 1994).

An inductive approach is underlying in qualitative research in which the theorising begins

with observing the empirical world and then reflecting on what is taking place, thinking

in increasingly more abstract ways, moving toward theoretical concepts and propositions

(Neuman 2006). Thomas (2003) mentioned that there are three purposes of the general

inductive approach which are to condense extensive and varied raw text data into a brief,

summary format; to establish clear links between research objectives and the summary

findings derived from the raw data and to ensure these links are both transparent and

defensible; to develop a model or theory about the underlying structure of experience or

processes which are evident in the text .

Qualitative research is often used for exploratory studies, and does not have a narrow

focus on specific questions, smaller sample size, and is useful for theory generation

(Neuman 2006; Perry 1995; Zikmund 2000). Neuman (2006) also argued that qualitative

research is to construct social reality and cultural meaning, focus on interactive processes

and events, fuse the theory and data. It is situational constrained and the research is

involved in the researched context. The method commonly employed by qualitative

researchers includes interviews, observations, focus groups, ethnography, case studies

and action research (Neuman 2006). The trustworthiness is achieved by focusing on

credibility, transferability , dependability and confirmability, as mentioned by Lincoln

and Guba (1985).



3.3.3 Justification of Selecting Quantitative Research Methodology

Based on the evaluation of the different research paradigms described in previous

sections and the basic belief systems of different paradigms summarised in table 3.1,

positivism was selected for this research due to the following reasons.

(1) The aim of this research is to investigate the roles and values of the PKM, and

it is believed that everyone is actually practising PKM even if they may not be

aware of it in a systematic view, the relationship between the roles and values

of PKM are a stable pre-existing pattern, consistent across time and context,

and it is discoverable by research. It is inline with the ontology position of

positivism as discussed in section 3.2.1.

(2) The purpose of this research is to find out a generalised theory to explain the

roles and values of PKM. The researcher has to stay objective and minimise

undue influences during the research process. It is inline with the

epistemology position of positivism as discussed in section 3.2.1.

(3) This research tests the theoretical model developed based on the previous

literature as discussed in the chapter 2. There are five main hypotheses to

explore the roles of PKM in knowledge management processes and their

correlations with their values at both individuals and organisations levels; it

focuses on the measurement and testing of theory. It involves a comparatively

large sample size, precise data analysis and requires quantitative methods. It is

inline with the nature and purpose of the quantitative approach as discussed in

section 3.3.1.

Based on the assessment of the ontology, epistemology and methodology as

recommended by various scholars e.g. Guba and Lincoln (1991) and Healy and Perry

(2000), the positivism research paradigm is justified. The quantitative methodology is

always associated to the positivism research paradigms while qualitative methodology is

usually associated to the interpretive / critical realism research paradigms (Neuman 2006;

Perry 1995). Therefore, quantitative research methodology was selected for this research.



3.4 The Theoretical Model

Based on the literature review in the previous sections, the conceptual model for this

research is developed as shown in figure 3.2. The model consists of four concepts which

are PKM skills, KM Process, PKM Values for the individual and PKM values for the

organisation.

The PKM skills are the underlying measurement of the roles in the KM Process and the

values of PKM for both individuals and organisations. There are seven PKM skills as

proposed by Avery et al.(2001): namely information retrieving skill (PKM1),

information evaluating skill (PKM2), information organising skill (PKM3), information

analysing skill (PKM4), collaborating around information skill (PKM5), information

presenting skill (PKM6) and information securing (PKM7).

The KM processes includes four interactive processes as suggested by Seufert, Back and

Korgh (2003, p. 112), which are locate / capture knowledge (KMC1), create knowledge

(KMC2), share / transfer knowledge (KMC3) and apply knowledge (KMC4).

The PKM values for individuals are measured by the seven individuals’ competences as

proposed Cheetham and Chivers (1996, 1998), namely communication (ICOMC),

creativity (ICREC), problem solving (IPBSC), learning / self development (ILSDC),

mental agility (IMEAC), analysis (IANAC) and reflecting (IREFC).

The PKM values for organisations are measured by the five organisation competences as

suggested by Mendelson and Ziegler (1999) and Ziegler (2008), and are external

information awareness (OEIAC), internal knowledge dissemination (OIKDC), effective

decision making (OEDMC), organisation focus (OORFC) and continuous innovation

(OCOIC).



H5

(H5a – H5g)

H3

(H3a-H3g)

H4

(H4a – H

4g)

Figure 3. 2: Theoretical Model Source: Developed for this research

3.4.1 Proposed Hypotheses

To answer the research questions, the following main hypotheses are proposed to fill the

gap and answer the research questions.

H1. PKM skills are playing important roles in the KM Cycle

H2. PKM can benefit both individuals and organisations

H3. The values of PKM for individuals are positively correlated to the roles of

PKM skills in the KM process.

H4. The values of PKM for organisations are positively correlated to the roles of


H5. The values of PKM for individuals are positively correlated to the values of

PKM for the organisation.

These five main hypotheses were further developed into twenty-three sub hypotheses as

shown in table 3.3. Hypothesis H1 was to answer the research question RQ1; Hypothesis

H2 (Sub-hypotheses H2a and H2b) was to answer the research question RQ2; Hypothesis

H3 (Sub-hypotheses H3a to H3g) and Hypothesis H4 (Sub-Hypotheses H4a to H4g) were

to answer the research question RQ3; and Hypothesis H5 (Sub-Hypotheses H5a to H5g)

was to answer the research RQ5.




RQ1: What are the

roles of PKM in the

Knowledge

Management

Process?

H1. PKM skills

are playing

important roles in

the KM Processes

N.A.

H2a: PKM can benefit individuals RQ2: What are the

values of PKM for

individuals and

organisations?

H2. PKM can

benefit both

individuals and










H3e : The value of the Collaborating skill for individuals is




H3. The values of

PKM for

individuals are

positively

correlated to the

roles of PKM skills

in the KM process.

H3g : The value of the Securing skill for individuals is


H4a : The value of the Retrieving skill for organisations is


H4b : The value of the Evaluating skill for organisations is


H4c : The value of the Organising skill for organisations is


H4d : The value of the Analysing skill for organisations is


H4e : The value of the Collaborating skill for organisations




RQ3: Is there any

correlation between

the roles of PKM in

KM Process and the

values of PKM for

individuals and

organisations?

H4. The values of

PKM for

organisations are

positively

correlated to the

roles of PKM skills

in the KM process.



H5a : The value of the Retrieving skill for organisations is


H5b : The value of the Evaluating skill for organisations is


H5c : The value of the Organising skill for organisations is


H5d : The value of the Analysing skill for organisations is


H5e : The value of the Collaborating skill for organisations




RQ4: Is there any

correlation between

the values of PKM

for individuals and

the values of PKM

for organisations

H5. The values of

PKM for

individuals are

positively

correlated to the

values of PKM for

the organisation.



Table 3. 3: Research Questions and Hypotheses




3.4.2 Definition of Variables

Based on the literature review, the proposed theoretical model and hypotheses, the

variables in each concept are summarised in table 3.4 below. These variables are used to

develop the survey instrument, for data collection.

Concept Variables Operation Definitions References

PKM1: Retrieving Capability to gather information from wide range of

source and make use of tools from low-tech skills of

asking, listening and following up using search

tools, reading and note-taking to effective use of

PKM tools build on technology.

(Agnihotri & Troutt 2009; Avery et al.

2001; Berman & Annexstein 2003;

Frand & Hixon 1999; Wright, K. 2005)

PKM2: Evaluating Capability to select data and information, and to

determine the quality and relevance of the collect

information.




PKM3: Organising Capability to making the connections necessary to

link pieces of information.




PKM4:

Collaborating

Capability to share the information effectively

which involve listening, showing respect for the

understanding of others’ ideas, developing and

following through on shared practices, building

win/win relationships, and resolving conflicts are

among those underlying principles.



Wright, K. 2005)

PKM5: Analysing Capability to convert information into knowledge

which involves skills to extract the meaning

information from data.




PKM6: Presenting Capability to present the information to audience

effectively.



Wright, K. 2005)

PK

M

PKM7: Securing Capability and the sense to protect the information

to assure the confidentiality, integrity, actual

existence of information. Awareness and safeguard

of Intelligent Property and copyright.



Wright, K. 2005)

KMC1: Capture /

Locate Knowledge

Finding and charting existing knowledge. It involves

creating clarity in the existing knowledge.

(Seufert, Back & Krogh 2003)

KMC2: Create

Knowledge

Developing of new explicit or implicit knowledge

either through the expansion of already existing

knowledge or through a new method of combing

implicit and explicit knowledge.


KMC3: Share / Transfer

Knowledge

Leveraging of exiting knowledge in groups,

individuals or in organisations to generate value.

Sharing the implicit knowledge and explicit

knowledge in different forms.


KM

Pro

cess

KMC4: Apply

Knowledge

Application and usage of the knowledge in actual

situation e.g. decision making or problem solving.




ICOMC:

Communication Competence

Ability to persuasion, assertive and negotiation...etc (Cheetham & Chivers 1996, 1998;

Wright, K. 2005; Zuber-Skerritt 2005)

ICREC: Creativity

Competence

Ability to generate ideas, be innovative, share your

thought...etc

(Cheetham & Chivers 1996, 1998;


IPBSC: Problem

Solving

Competence

Ability to to identify the core problem area, select

the appropriate solution

and apply the solution to tackle the problem.



ILSDC: Learning

and Self

Development

Competence

Ability to use appropriate learning approach to

achieve the learning objectives.



IMEAC: Mental

Agility

Competence

Ability to always to keep your mind fresh and ready

to respondent any challenge.



IANAC: Analysis

Competence

Ability to make conclusion from the available

information.



Indiv

idu

al C

om

pet

ences

IREFC: Reflection

Competence

Ability to your knowledge, skills and experience to

perform task / duty.



OEIAC: External

Information

Awareness

Ability to improve the customer dynamics, the

technology opportunities and competitiveness.

(Mendelson & Ziegler 1999; Nonaka &

Takeuchi 1995)

OIKDC: Internal

Knowledge

Dissemination

Ability to improve the effectiveness of information

flow both horizontally, vertically (top down and

bottom up) and the review process.

(King 2008; Mendelson & Ziegler

1999; Nonaka & Takeuchi 1995)

OEDMC: Effective Decision

Making

Ability to improve the decisions quality, decision

time and sense of ownership and accountability for

decisions


1999)

OORFC:

Organisational

Focus

Ability to improve the scope of business focus, core

competencies focus and simplification of processes.


1999)

Org

anis

atio

n C

om

pet

ence

s

OCOIC: Continue

Innovation

Ability to allow creativity, product development and

quality improvement

(King 2008; Nonaka & Takeuchi 1995;

Ziegler 2008)

Table 3. 4: Constructs and Variables for Measurement


3.5 Research Design

Research design is the master plan to specify the methods and procedures to be used in

collecting and analysing the required information and it involves the decisions, as shown

in figure 3.3, regarding the purpose of the study (exploratory, descriptive, hypothesis

testing), its location (i.e. the study setting), the type of investigation, the extent of any

research inference, its temporal aspects (time horizon), the unit of analysis, the sampling

size, the data collection method and the measurement and measures (Sekaran 2003).



Figure 3. 3: The Research Design Source: (Sekaran 2003, p. 118)

3.5.1 Purpose of the Study

Sekaran (2003) referred the purpose of study to different types of research, including

exploratory research, descriptive research, hypotheses (predictive) research and case

study. However, Davis (2005) and Copper and Schindler (2006) argued that the major

types of business research be classified as exploratory, descriptive/predictive, and causal

(explanatory).

(1) Exploratory Research

Exploratory research is used when there is a lack of a clear idea of the problem and

through the extensive preliminary work to explore and develop concepts more clearly,

establish priorities, develop operational definitions, and improve the final and

rigorous research design to perform comprehensive investigation (Copper &

Schindler 2006; Sekaran 2003).

Exploratory research can be done by interviewing individuals and through focus

groups (Sekaran 2003), and also can be done by a combination of literature searches,

experience surveys, single or multiple case studies (Davis, D. 2005) such that the

researcher can identify the significant variables and their relationships in the problem

situation, develop theories and formulate hypotheses and conclude if additional

research is not feasible or could lead to a more rigorous study at a later stage (Copper

& Schindler 2006; Davis, D. 2005; Sekaran 2003).



(2) Descriptive Research

In contrast to exploratory study, descriptive research is typically structured with

clearly stated hypotheses or investigation questions, usually using standardised

questionnaires, to describe the characteristics of the variables of interest in a

particular situation (Copper & Schindler 2006; Davis, D. 2005; Sekaran 2003). The

goal of descriptive research is to offer the researcher a profile or to describe relevant

aspects of the phenomena of interest from an individual, organisation, industry-

oriented, or other perspective (Sekaran 2003).

Descriptive research is usually performed by quantitative analysis in terms of

frequencies, means, standard deviations, correlational studies, cross-tabulations …etc.

(Copper & Schindler 2006; Sekaran 2003).

(3) Causal Research

The causal research is done when it is necessary to establish a definitive cause-and-

effects relationship (Sekaran 2003). The concern of causal analysis is how one

variable affect or is responsible for changes in another variable, and their relationship

can be described as either symmetrical, reciprocal or asymmetrical (Copper &

Schindler 2006).

Davis (2005) argued that causal research is usually done by experimental design

which is either carried out as a field experiment (a realistic setting) or a laboratory

experiment (an artificial setting).

In this research, it is a combination of exploration, descriptive and causal (hypotheses

testing) study. The exploration study was first done by a comprehensive literature review

as discussed in chapter 2, the theoretical model was then developed and hypotheses were

formulated. The descriptive study was performed by questionnaires and rigorous

statistical analysis. The hypotheses testing was first performed by classical statistics

analysis, e.g. factor analysis, reliability test, correlations test and simple regression, and

followed by confirmatory analysis by structured equation modelling.

3.5.2 Type of Investigation



Davis (2005) mentioned that the type of investigation is either a causal or correlational

study. The causal study is done when it is necessary to establish a definitive cause-and-

effect relationship and correlational study is used when it is merely to identify the

important factors associated with the problem. Neuman (2006) mentioned that

correlational study seeks to establish the extent of a possible relationship between two or

more variables. For example, causal study can investigate if smoking causes cancer and

for correlational study can study if smoking and cancer are related (Davis, D. 2005).

In this research, the relationship between the roles and values of PKM was investigated; it

was not to study the absolute cause-and-effect relationship, instead it was to delineate the

importance associated between the roles and values. Therefore, correlation study has been

chosen for this research.

3.5.3 Extent of the researcher’s Interference

A correlation study is conducted in the natural environment with minimum interference

by the researcher on the normal flow of work (Sekaran 2003). The researcher should

collect the data without influencing the respondents and to analysis the data in an

objective manner in order to come up with the findings. In this research, as discussed in

the previous section, it is a correlations study and therefore minimum interference is

required. One of the most common approaches for collecting the data with minimum

interference is by using questionnaires and this is discussed in the data collection section

later.

3.5.4 Study Setting

The study setting is the environment within which the study is running, and it is important

to define how the data is collected and analysed. The study setting is either contrived or

non-contrived, and correlational studies are invariably conducted in non-contrived

settings (Sekaran 2003). The contrived setting is mainly for experiment study which

would be either a field study, e.g. in a factory, or a laboratory experiment study, e.g. in an

artificial laboratory environment.

In this research, the objective is to have generalisability of findings; therefore, the study

setting should be non-contrived and the researcher should be objective in collecting the



data and analysing the data. In addition, the targeted respondents should not be limited to

any particular geographic location, industry, age group, gender, working experience and

education …etc. The only criterion of the targeted respondents is they should have basic

understanding of knowledge management to ensure the respondents can understand and

answer the questions correctly. The growth of knowledge management in the past

decades has attracted a lot of people from different backgrounds to affiliate to various

knowledge management associations or societies and also to interest groups; therefore,

members of these bodies were candidates meeting all our criteria.

3.5.5 Unit of analysis

The unit of analysis refers to the level of aggregation of the data collected during the

subsequent data analysis stage (Davis, D. 2005). In this research, the PKM is the subject

of investigation which is at the individual level rather than a group of people or an

organisation. Although the benefit of PKM can be reflected at the organisation level,

individuals as the agents of organisational learning (Argyris, C. & Schon 1978) should be

able to reflect their experience in practicing PKM at both the individual and organisation

levels; therefore, the unit of analysis for this research was an individual.

3.5.6 Sampling Design

It is practically impossible to collect data from the entire population and a sample survey

is used. The sample survey allows gathering a sizeable amount of information from a

relatively large sample (Kerlinger 1992).

Zikmund (2003) provided several sampling methods that can be used by researchers,

including random sampling, probability sampling, stratified sampling, convenience

sampling, judgment sampling, quota sampling and snowball sampling. The sampling

methods can be divided into two major types: probability and non-probability.

Davis (2000) summarised different sampling methods as in table 3.5 and he also

suggested five considerations of sampling method: cost, accuracy, time, acceptance of

results and generalisability of results. The requirement of a targeted respondent was

discussed in section 3.5.4 so that members of knowledge management associations or

societies and interest groups are appropriate respondents. Simple random sampling within



the targeted respondents group was selected in order to allow for better generalisability of

findings.

Sampling Design Description Advantages Disadvantages

Probability Sampling

1. Sample random

sampling

All elements in the

population are considered

and each element has an

equal chance of being

chosen as the subject.

High generalisability

of findings

Not as efficient as

stratified sampling.

2. Systematic sampling Every nth element in the

population is chosen

starting from a random

point in the population

frame.

Easy to use if

population frame is

available.

Systematic biases are

possible.

3. Stratified random

sampling

Proportionate

Disproportionate

Population is first divided

into meaningful segments;

thereafter subjects are

drawn in proportion to

their original numbers in

the population

Based on criteria other

than their original

population numbers

Most efficient among

all probability designs.

All groups are

adequately sampled

and comparisons

among groups are

possible.

Stratification must be

meaningful. More time-

consuming than simple

random sampling or

systematic sampling.

Population frame for

each stratum is essential.

4. Cluster sampling Groups that have

heterogeneous members

are first identified; then

some are chosen at

random; all the members

in each of the randomly

chosen groups are studied.

In geographic clusters,

costs of data

collection are low.

The least reliable and

efficient among all

probability sampling

designs since subsets of

clusters are more

homogeneous than

heterogeneous.

5. Area sampling Cluster sampling within a

particular area or locality.

Cost-effective. Useful

for decisions relating

to a particular

location.

Takes time to collect

data from an area.

6. Double sampling The same sample or a

subset of the sample is

studied twice.

Offers more detailed

information on the

topic of study.

Original biases, if any,

will be carried over.

Individuals may not be

happy responding a

second time.

Non Probability Sampling

7. Convenience

sampling

The most easily accessible

members are chosen as

subjects

Quick, convenient,

less expensive.

Not generalisable at all.

8. Judgement sampling Subjects selected on the

basis of their expertise in

the subject investigated.

Sometimes, the only

meaningful way to

investigate.

Generalisability is

questionable; not

generalisable to entire

population.

9. Quota sampling Subjects are conveniently

chosen from targeted

groups according to some

predetermined number or

quota.

Very useful where

minority participation

in a study is critical.

Not easily generalisable.

Table 3. 5: Probability and Non-probability Samplings Designs

Source: (Davis, D. 2005, p. 280)



3.4.6.1 Sampling Size

The sampling size affects the precision and confidence level of the research. Roscoe

(1975) proposed four rules of thumb for determining sample size: (1) the size of sample

that is appropriate for most research is between 30 to 500 samples, (2) a minimum

sample size of 30 is necessary for each sub-group when samples are broken into sub-

samples, (3) in multivariate research, the sample size should be several times as large as

the number of variables in the study, (4) sample size as small as 10 to 20 is possible for

the simple experimental research with tight experimental control.

The sampling size number can also be calculated by Cochran’s sample size formula as

below.

N0 = t2 x s

2 / d

2

Where t = 1.96 for the 0.05 alpha level

s = 0.83 for a 5 point scale and 6 standard deviations

d = 0.15 for a 5 point scale and an 0.03 margin of error

Therefore, the sample size required is N0 = (1.96)2 x (0.83)

2 / (0.15)

2 = 118

Based on the Cochran’s formula, the minimum sampling size is 118. However, there are

up to 21 variables in the research model, and the target sample size would be better 10

times (Roscoe, 1975; Barlett et al, 2001) the variables and therefore the target sample size

in this research is 210. This size (>200) is also appropriate for Structural Equation

Modelling (SEM) analysis by maximum likelihood estimation (Byrne 2010; Kline 2005).

3.5.7 One-shot or Longitudinal study (Time Horizon)

The study can be done either just one time or over a period of time, called one-shot or

cross sectional study, and in the case of the longitudinal study, the data is collected more

than one time, to study the effect of change (Sekaran 2003). In this research, one-shot

study was selected; it was to collect the respondents’ experience of their role of PKM

applied in their individual knowledge management processes and the perceived values or



their experience that the benefit of PKM in improving their competences at both the

individual and organisation levels.

3.5.8 Data Collection Method

Sekaran (2003) mentioned that the common data collection methods are interview,

observation and survey by questionnaire. Interview is a conversation with a purpose

(Berg 2004) and data can be captured in the form of informants’ speech, appearance,

attitude and emotion. Open-end questions are always asked during the interview with

follow up questions. It allows in-depth communications to be established (Patton 2002).

Observation involves paying close attention, watching, and listening carefully (Neuman

2006). In additional to looking at the physical surroundings, the researcher will observe

people and their actions, noting each person’s observable characteristics: age, sex, race,

and stature (Neuman 2006). Both interview and observation are used by qualitative

research, and for quantitative research a questionnaire survey is commonly used. A

questionnaire is a pre-formulated written set of questions to which respondents record

their answers, normally using closed ended questions (Sekaran 2003). Survey by

questionnaire has advantages when there are many questions that have to be obtained and

the respondents are geographically dispersed; therefore, a questionnaire survey is selected

in this research.

3.5.8.1 Survey Administration

Traditionally, the method for the administration of a questionnaire consists of face-to-

face, mail and telephone. Recently, the web-based / online survey is very popular. This

section evaluates these four different types of survey methods and selects the appropriate

one for this research.

(1) Fact-to-face Survey

Dillmam (1978) viewed that a face-to-face survey has the advantages of high

response rate as it allows long and complex questionnaires and avoids missing

data. It also allows the interviewer to observe the respondent’s reactions to the

questions (Neuman 2006). However, the disadvantage of a face-to-face survey

that it is very time consuming and the cost is very high. Besides, a face-to-face

survey may introduce bias due to the respondent’s or interviewer’s characteristics,

such as the appearance, tone and voice (Neuman 2006).



(2) Telephone survey

A telephone survey is another popular survey type and may be thought as a

compromise between a mail survey and a face-to-face survey and therefore

combines many advantages of each type (Sekaran 2000). This method has a high

response rate (Dillmam 1978; Neuman 2006) and it almost guarantees an

immediate response(Neuman 2006). However, it may not always be convenient to

the respondent to answer the questions at the time of calling, or not suitable for a

long questionnaire with boring questions; and therefore the control of completing

the questions may have problem (Dillmam 1978) . Same as face-to-face

interviews, it may introduce bias by the respondent’s and interviewer’s

characteristics and the cost to implement the survey is also high. Therefore, it is

also inappropriate to use a telephone survey in this research.

(3) Mail survey

A mail survey sends the questionnaire directly to the respondent and it is by far

the cheapest type of survey (Neuman 2006). As this type of survey has no direct

contact with the respondent, it can eliminate the bias from the interviewer’s and

respondent’s characteristics (Dillmam 1978; Neuman 2006). Besides, it is an

excellent survey method to have accuracy on sensitive data (Davis, D. 2005). It

can allow the respondent to have more time to collect the information in order to

answer the questions. However, the down size of this type of survey is the low

response rate. Although Dillmam (1978) rated that the response rate for this

method is good, previous experience has shown it is not the case. Sekaran (2003)

stated that return rates of mail questionnaires are typically low, and 30% response

rate is considered acceptable. However, the response rate can be improved by

good planning and it is still the most popular method of survey. Therefore, it is

selected for this research.

(4) Online Survey

Recently, a lot of surveys are done by Internet based methods e.g. web

questionnaire or email. Davis (2005) stated that it is excellent in controlling

interview effects, very good in time needed, good in flexibility, anonymity of

respondents, and accuracy on sensitive data. Neuman (2006) stated that the



computer-assisted method is simple and quick, more accurate in information

gathering, plus it is fast and easy in data analysis. Ticehurst and Veal (2000)

argued that the advantages of online surveys include the widespread reach to

potential respondents, low cost, potential for a quick response, computerised data

formats, and the ability to monitor the response process. In addition, Chung

(2007) summarised the advantages of on-line surveys, as shown in table 3.6.

Based on the assessment of different survey methods, online survey was selected for this

research. The self-completed questionnaire with structured questions was sent to the

members associated to the knowledge management bodies as in appendix 2.

Table 3. 6: Advantages of On-line Survey

Source : (Chung 2007, p. 86)



3.5.9 Measurement and Measure Design (Questionnaires Design)

Discussed in section 3.4, there are five main hypotheses proposed, as below, to answer

the research questions.









To test the hypotheses, the questionnaire was developed, as shown in appendix 1. There

were 7 sections in the questionnaire: section 1 was the project information and inform

consent, section 2 was the operational definitions, section 3 was to determine the roles of

PKM, sections 4 and 5 were to determine the values of PKM for individuals and

organisations respectively, section 6 was to determine the demographic factors and

section 7 was to collect respondents comment about the survey and to ask if the

respondents want to receive a copy of the survey report. The following provides more

details about the questionnaire design for section 3 to 6.

(1) Section 3 - The Roles of PKM in KM Processes

As discussed in the chapter 2 that there were four generic KM processes as suggested

by Seufert, Back and Korgh (2003, p. 112). The respondents were asked to rate the

importance of the seven PKM skills in each KM process and a 5 point Likert scale

was used. An example of the questions for Locating / Capturing Knowledge is as

shown in figure 3.4.



Figure 3. 4: Question 3.1 for Roles of PKM in KM Process


(2) Section 4 – The Values of PKM for Individuals

There were seven meta-competences (Cheetham & Chivers 1996, 1998) used to

measure the values of PKM for individuals. A 5 point Likert scale ( 1 lowest to 5

highest) was also used to measure the values and an example of the question is as

shown in figure 3.5.

Figure 3. 5: Questions 4.1 for Values of PKM for Individuals




(3) Section 5 – The Values of PKM for Organisations

There were five organisation competences (Mendelson & Ziegler 1999; Ziegler 2008)

used to measure the values of PKM for organisations. A 5 point Likert scale ( 1

lowest to 5 highest) was also used to measure the values and an example of the

question is as shown in figure 3.6.

Figure 3. 6: Questions 5.1 for Values of PKM for Individuals Source: Developed for this research

(4) Section 6 – Demographic Information

This section was mainly to obtain the demographic information of the respondents,

and the first two questions were related to their background on the adoption of PKM,

and the rest of the questions were related to their country of abode, age, gender, work

position, organisation type, industry, work experience and education. At the end of

this section, the respondent was asked to provide an email address if they want to

receive the brief report of this research, and a space was provided for the respondents

to provide any comments.

The questionnaire was designed based on the suggestions by Frazer and Lawley (2000),

that it is within 12 pages, simple, to the point, and easy to read. The items were also

designed within 24 words as suggested by Horst (1968) and Andrews (1984). The

questions were also designed according to the rules suggested by Ticehurst and Veal



(2000) in that jargon, leading questions and ambiguities were avoided. The sequence of

the questions was based on the order suggested by Ticehurst and Veal (2000) in that easy

and relevant questions were asked first. The questionnaire included an information sheet,

brief literature to describe the definition of the PKM and also a question to confirm the

respondent’s consent. A complete set of the survey pack posted in the online survey tools

appears in appendix 1.

The mapping of the questions to the hypotheses and research questions is illustrated in

table 3.7. The composite variables for data analysis were constructed based on this table.

3.5.10 Pilot Survey

Zikmund (2003) mentioned that a pilot survey is a collective term for a pre-test on a

questionnaire and it uses a small number of samples prior to collecting the data in a larger

survey. Copper and Schindler (2006) pointed out that pre-test is the final step to improve

the survey and the purpose is to (1) discover ways to increase the participant response, (2)

increase the likelihood that participants will remain engaged till the completion of the

survey, (3) discover target question groups where researcher training is needed, and (4)

explore ways to improve the overall quality of the survey data. In addition, Veal (2006)

argued that the purpose of the pilot test is to test the questionnaire wording, sequence,

layout, completion time and also the analysis procedures.

In this research, a pilot survey was performed in order to enhance the questionnaire

design. A total of 50 participants were invited, and 37 completed samples were returned.

The pilot participants were the KM participants known by the researcher in KM

activities/conferences. The respondents’ comments about the survey instrument were

carefully reviewed and some clarifications were done by phone. There were some minor

changes in the wordings used in the questionaries based on the feedback, and the revised

questionnaire is attached in the appendix



Research

Question Hypotheses

Sub

Hypotheses

Independent

Variable

Section –

Question.Sub-

question

Dependent

Variables

Section –

Question.Sub-

question

KMC1 3-1.1 to 3-1.7 - -

KMC2 3-2.1 to 3-2.7 - -

KMC3 3-3.1 to 3-3.7 - -

RQ1 H1 -

KMC4 3-4.1 to 3-4.7 - -

ICOMC 4-1.1 to 4-1.7 - -

ICREC 4-2.1 to 4-2.7 - -

IPBSC 4-3.1 to 4-3.7 - -

ILSDC 4-4.1 to 4-4.7 - -

IMEAC 4-5.1 to 4-5.7 - -

IANAC 4-6.1 to 4-6.7 - -

H2a

IREFC 4-7.1 to 4-7.1 - -

OEAIC 5-1.1 to 5-1.7 - -

OIKDC 5-2.1 to 5-2.7 - -

OEDMC 5-3.1 to 5-3.7 - -

OORFC 5-4.1 to 5-4.7 - -

RQ2 H2

H2b

OCOIC 5-5.1 to 5-5.7 - -

H3a RPKM1 3-X.1 IVPKM1 4-X.1

H3b RPKM2 3-X.2 IVPKM2 4-X.2

H3c RPKM3 3-X.3 IVPKM3 4-X.3

H3d RPKM4 3-X.4 IVPKM4 4-X.4

H3e RPKM5 3-X.5 IVPKM5 4-X.5

H3f RPKM6 3-X.6 IVPKM6 4-X.6

H3

H3g RPKM7 3-X.7 IVPKM7 4-X.7

H4a RPKM1 3-X.1 OVPKM1 5-X.1

H4b RPKM2 3-X.2 OVPKM2 5-X.2

H4c RPKM3 3-X.3 OVPKM3 5-X.3

H4d RPKM4 3-X.4 OVPKM4 5-X.4

H4e RPKM5 3-X.5 OVPKM5 5-X.5

H4f RPKM6 3-X.6 OVPKM6 5-X.6

RQ3

H4

H4g RPKM7 3-X.7 OVPKM7 5-X.7

H5a IVPKM1 4-X.1 OVPKM1 5-X.1

H5b IVPKM2 4-X.2 OVPKM2 5-X.2

H5c IVPKM3 4-X.3 OVPKM3 5-X.3

H5d IVPKM4 4-X.4 OVPKM4 5-X.4

H5e IVPKM5 4-X.5 OVPKM5 5-X.5

H5f IVPKM6 4-X.6 OVPKM6 5-X.6

RQ4 H5

H5g IVPKM7 4-X.7 OVPKM7 5-X.7

Remark: X = 1 to 7

Table 3. 7: Mapping of questions to hypotheses and research questions




3.5.11 Summary of Research Design

The research design is summarised in figure 3.7. It was a combination of exploration,

description and hypotheses testing research, and the type of investigation was

correlations. The researcher stayed objective and there was minimal interference to the

research process, the study setting was noncontrived, the unit of analysis was the

individual, simple random sampling was used, target respondents were the knowledge

management participants affiliated to the knowledge management associations / societies

/ interest groups. It was a one-shot study, and a 5 point Likert scale was used to collect

the respondents’ feedback to the roles and values of PKM. The target sample size was

210, and the survey was done by online questionnaire and a pre-test was performed to

validate the questionnaire design.

Problem Statement

Data Analysis

Figure 3. 7: Summary of Research Design Source: Developed for this research

3.6 Data Analysis Approach

Section 3.5 described the research design and this section discusses the data analysis

procedure and the tools used in the research. The details of the data analysis and the

results are discussed in chapter 4.

3.6.1 Data Analysis Tools

All the data were collected by the online survey tools and a web page was created by the

researcher using the web tools provided by Survey Monkey. An URL link

(http://www.surveymonkey.com/s.aspx?sm=0472MavY0kXG1ogVk37IRg_3d_3d) was



generated to access the questionnaire and this link was sent to the target respondents to

invite them to participate in this research. The URL link was disabled after the data

collection was completed.

The data collected by the online survey was stored in the provided site and the author

could download the results in excel file format. For the data analysis, the PASW (SPSS)

and AMOS (version 18) was used for quantitative analysis. The PASW was used for

exploratory data analysis and AMOS was used for confirmatory data analysis.

3.6.2 Data Analysis Procedure

There were seven steps, as illustrated in figure 3.8, designed to perform the data analysis.

Figure 3. 8: Data Analysis Procedure


(1) Data Preparation

It was the first step in data analysis and this process involved error and omission

detection, clarify the unclear data, checking for completeness, consistency…etc,

in order to achieve maximum data quality standard before processing the data

(Copper & Schindler 2006; Davis, D. 2005). In this research, the 3 steps approach

was used, namely data editing, data coding and data entry.

(2) Respondents Profile

The respondents’ profile was the first analysis of the collected data to understand

the respondents’ characteristics. Here, the respondents’ profile was divided into



two parts; the first part was related to the PKM profile of the respondents and the

second part was the personal profile. The PKM profile concerns the stage of PKM

adoption which was based on the adoption stage classified by Rogers (1962), and

their previous training on PKM. The personal profile was demographic

information as discussed in the questionnaire design in section 3.4.9. The analysis

was done by the statistical values of frequency and percentage…etc.

(3) Data Screening

The data screening was mainly focused on any missing data and outliers for the

collected data in section 1 to section 3 of the questionnaire, on the roles and

values of the PKM. Both univariate and multivariate outliers were checked. For

the univariate outliers, the histograms, box-plots and z-scores were checked. For the

multivariate, the Mahalanobis distance was calculated and checked to see if it

exceeded the chi-square values for the corresponding degree of freedom. The records

with substantial missing data and significant outlier problems were removed from the

dataset before performing the next step in the analysis.

(4) Descriptive Statistics

Descriptive statistics provided clear understanding, with graphical displays, for

the roles and values of PKM. The data concerned were in section 1 to section 3 of

the questionnaire. It tested hypothesis H1 and hypothesis H2. All data were

collected by the 5 point Likert Scale from 1 to 5 and the minimum, maximum,

mean score and standard deviations were reported. A score above 3 was

considered as important or significant.

(5) Constructs Assessment

Constructs assessment were used to test the validity, reliability and normality of

the composite variables created for hypotheses analysis in the next step. The

validity was assessed by Pearson correlations and Principal Components analysis

(PCA). For the Pearson correlations, the item-to-item correlations should exceed

0.3 and item-to total correlations should exceed 0.5 (Hair, J. F. et al. 1998); for

the PCA, there should be only eigenvalues greater than 1 and the loading factors

were all > 0.5. The Cronbach’s Alpha coefficient was used for reliability testing,



the values >0.6 were acceptable for the exploratory study, >0.7 were acceptable,

>0.8 were good and >0.9 were excellent, as suggested by Hair et al (1998) and

Sekaran (2003). The normality was assessed by the Z-score and both skewness and

kurtosis were checked to see if they exceeded the absolute value of 2.58 (samples

<300), as proposed by Tabachnick and Fidell (2001). Transformation was performed

for those construct did not meet this requirement.

(6) Exploratory Data Analysis

The exploratory data analysis was performed in two steps. The first step was to

check if the scoring was associated with the PKM adoption stages of the

respondents. It was to access the generalisability of the data before the second step

of testing the hypotheses by simple regression. The first step was using ANOVA

which was recommended by Manning and Munro (2004, p. 82) ANOVA is

suitable if one variable is related to another variable when the variables are scale

or ratio types. The second step was to test hypotheses H3 to H5 and simple

regression was used. Instead of just checking the correlation between variables,

simple regressions provide additional information about the coefficients of the

regressions equations such that a researcher can have a better understanding about

the relationships between the independent and dependent variables (Manning &

Munro 2004).

(7) Confirmatory Data Analysis

The exploratory data analysis provided evidence of the hypotheses testing and the

confirmatory approach by structural equation modelling, as recommended by

Byrne (2010), that was used on the analysis of a structural theory on the roles and

values of PKM. The analysis was performed in two stages to test the measurement

model and hypotheses model. In the SEM analysis, the Maximum Likelihood

(ML) estimation was used. The goodness of fit was assessed by the normed chi-

square, χ2 /df (<3.0), CFI, IFI and TFI (> 0.9) and RMSEA (< 0.08), as

recommended by various scholars e.g. Kline (2005), Byrne (2010) and Hu and

Bentler (1999).



This section provides a guideline on the data analysis in this research. The results,

conclusion and the implications are presented in chapter 4 and chapter 5.

3.7 Ethical Considerations

All efforts related to this research were conducted in a manner which conforms to

acceptable ethical standards, and abides by the guidelines of the Ethics Committee of the

University. The issues to be addressed in this research are Informed Consent, Privacy,

and Confidentiality. The aim is, of course, not to cause any harm to the research

participants (Rubin & Rubin 1995).

The Informed Consent procedure which ensures that participants understand all the facts

of the research that may be relevant to them in making a decision to participate in the

study, and requests their permission before proceeding with the survey (Emory & Cooper

1991). Before taking the survey, an information sheet was provided about the purpose,

procedure and the respondents’ right to withdraw from the survey at any time. The

respondents were asked for their consent before answering any question.

For the Privacy and Confidentiality issues, the data presented in the research report were

coded and no particular information was disclosed, so that individual respondents would

not be traceable. All collected data have been kept in safe storage by the author.

This research project has abided by all the ethical standards and principles required by

Southern Cross University and its Human Research Ethics Committee (HREC), and

ethics approval has been obtained from HREC with the approval number was ECN-08-

159.

The cover letter of this questionnaire also indicated that if the participants had any

questions, they could directly contact the researcher and supervisor by mail or e-mail. So,

based on all of the above factors, there were no ethical risks taken and no possible harm

to the respondents.




This chapter has discussed research paradigms, research methodology, research design,

data collection methods, data analysis approaches and ethical considerations. Based on

the evaluation, a positivism paradigm and a quantitative research approach were selected.

The research design was based on the framework proposed by Sekaran (2003). A survey

by online questionnaire was used, with a targeted sample size of 210, and the targeted

respondents selected were the knowledge management participants in affiliated

knowledge management associations / societies / interest groups. The data analysis was

performed by PASW (SPSS) and AMOS. A seven steps analysis approach was defined

and both exploratory data analysis and confirmatory data analysis were undertaken.

Finally, the ethical issues were discussed and ethics approval was obtained from HREC

prior to data collection.

Chapter 4: Findings & Data Analysis


CChhaapptteerr 44 –– FFiinnddiinnggss aanndd DDaattaa AAnnaallyyssiiss



4.1 Introduction

The previous chapter provided the background on the research design which included

justification of the research methodology, survey instrument and the data analysis

method. The conceptual model for this research, the constructs and the variables were

also discussed in Chapter 3. This chapter presents the results of this study. The structure

of this chapter is illustrated in Figure 4.1. A pilot study was conducted and the findings

have contributed to fine tuning the survey instrument prior to the main survey. The

collected data were carefully prepared and examined before the data analysis. Data

screening was performed to check for any missing data and outliers. The validity and

reliability of the constructs were tested and normality was tested if transformation was

required. The hypotheses were tested by exploratory data analysis and followed by

confirmatory data analysis. The exploratory data analysis was performed by classical

statistics analysis and confirmatory data analysis was performed by a Structured Equation

Model (SEM). The chapter conclusion is presented at the end of this chapter.

Figure 4. 1: Structure of Chapter 4


4.2 Pilot Study

As discussed in chapter 3, a total of 50 participants were invited, and 37 respondents

completed the survey. The data from these 37 respondents were analysed by exploratory

factor analysis (EFA) and Cronbach’s Alpha coefficient. The results indicated that most

of the constructs meet the criteria that item-to-item correlations > 0.3 and item-to-total

correlations > 0.5; the Cronbach’s Alpha of all constructs were > 0.6 and it was

acceptable for exploratory research (Hair, J. et al. 1998). There was no item removed



from the questionnaire, even though a few constructs marginally failed the validity test. It

was due to the number of samples in the pilot being small; the EFA could only to provide

an initial assessment of the survey instrument. However, the respondents’ comments

about the survey instrument were carefully reviewed and some clarifications were done

by phone. There were some minor changes in the wording used in the questionaries based

on the feedback, and the revised questionnaire is attached in the appendix.

4.3 Data Preparation

Data preparation is an important step prior to any data analysis. It is to purify and refine

the data for subsequent final data analysis, and the carefully orchestrated pre-analytical

process helped to reduce analytical errors and lead to a better understanding of the nature

of the study data (Davis, D. 2005). Cooper and Schindler (2006) mentioned that data

preparation included editing, coding and entry, to ensure the accuracy of the data and

their conversion from a raw form to reduced and classified forms that were more

appropriate for analysis. In this research, a 3 step approach was used.

4.3.1 Data Editing

Data editing is a process to give clarity, readability, consistency, and completeness to the

collected data (Davis, D. 2005). It was the first step in the pre-analytical process and the

purpose was to detect any error and omission. It was to correct any errors where possible

and to certify that maximum data quality standards were achieved; it was also done to

guarantee that the data are accurate, consistent with the question intent and other

information in the survey, uniformly entered, complete and arranged to simply coding

and tabulation (Copper & Schindler 2006).

In this research, the data were collected by an online survey tool called Survey Monkey.

There were altogether 467 respondents recorded, 5 respondents declined to proceed

during the informed consent stage. The data was checked for any missing data and those

samples with a substantial number of unanswered questions were removed. As a result,

213 samples were valid and the valid sample rate was 46%. The data were downloaded

from the online survey and stored into an Excel formatted file for editing.



The data for the closed questions were automatically coded and reported during the

survey. However, the data for open-ended questions needed to be checked and edited.

There were two open-ended questions edited, namely “Country” and “Industry” in the

demographic section. “Country ” was edited to fix any problems e.g., typo mistake, small

/ capital letters and full / short name entered by different respondents for the same

country. “Industry” was edited to re-group the data entered for “other industry” to the

defined or new category after carefully analysing the entered data.

The final dataset was exported in an appropriate format such that it could be imported to

the analysis software, i.e. PASW (SPSS) v.18.

4.3.2 Data Coding

Data coding was to translate the collected data into numerical codes for the purpose of

transferring data to a data storage medium and for subsequent computer analysis (Davis,

D. 2005). Coopper and Schindler (2006) mentioned that categories were the partitions of

a data set of a given variable and it was a coding process applying rules to partition a

body of data, for both closed and open-ended questions.

Most of the collection instruments were planned with the coding of variables in mind and

it was referred to as pre-coded questionnaires (Davis, D. 2005). In this research, the

coding was planned before the start of data collection such that the online survey could be

developed based on the defined coding scheme. Except the questions for demographic

data, the data to measure the roles and values of PKM all used the 5 point Likert Scale.

The coding scheme in this research is as shown in table 4.1. To eliminate human error

and obtain coding accuracy, the coded questionnaires were re-checked for accuracy after

the data collection, as proposed by Sekaran (2003).



Question Variables Name Variables Description Code Description

1 – 4 KMC1.PKMx

KMC2.PKMx

KMC3.PKMx

KMC4.PKMx

(x= 1to 7)

The role of PKM Skills (PKM1 to

PKM7) in 4 different stages of

KM Cycle.

KMC1 = Capture/Locate

KMC2= Create

KMC3= Share / Transfer

KMC4=Apply

1=Less Important

2=Somewhat Important

3=Important

4=Very Important

5=Critical

5 – 11 ICOMC.PKMx

ICREC.PKMx

IPBSC.PKMx

ILSDC.PKMx

IMEAC.PKMx

IANAC.PKMx

IREFC.PKMx

(x= 1to 7)

The values of PKM Skills (PKM1

to PKM7) for individuals.

ICOMC = Communication

Competence

ICREC = Creativity Competence

IPBSC = Problem Solving

Competence

ILSDC = Learning / Self

Development Competence

IMEAC = Mental Agility

Competence

IANAC = Analysis Competence

IREFC = Reflecting Competence

1=Lowest

2=Lowest to Middle

3=Middle

4=Middle to Highest

5=Highest

12 – 16 OEIAC.PKMx

OIKDC.PKMx

OEDMC.PKMx

OORFC.PKMx

OCOIC.PKMx

(x= 1to 7)

The values of PKM Skills (PKM1

to PKM7) for organisations.

OEIAC = External Information

Awareness Competence

OIKDC = Internal Knowledge

Dissemination Competence

OEDMC = Effective Decision

Making Competence

OORFC = Organisation Focus

Competence

OCOIC = Continuous Innovation

Competence

1=Lowest

2=Lowest to Middle

3=Middle

4=Middle to Highest

5=Highest

17 PKMAdoption The stage of PKM adoption

defined by Rogers (1962)

1= Knowledge Stage

2=Persuasion Stage

3=Decision Stage

4=Implementation Stage



5=Confirmation Stage

18 PKMTraining If the respondent has attended any

course / seminar / workshop about

PKM

1= Yes

2= No

19 Country The respondents’ country of living Open-ended (Free Text)

20 Age The age of the respondents 1= < 20

2= 20 – 29

3= 30 – 39

4= 40 – 49

5= 50 – 59

6= 60 – 69

7= >69

21 Gender The gender of the respondent 1=Male

2=Female

22 WorkPosition

The work position of the

respondents

1= Directors and Senior

Management

2= Managers and

Administrators

3= Professionals

4= Associate Professional

5= Clerks and Service Workers

6= Others

23 OrganisationType

The type of organisation the

respondents are working for

1= Private Company

2= Government

3= Non Government

Organisation / Non profit

Organisation (e.g. Charity and

University...etc)

24 Industry

The industry in which the

respondents are working

1= Community, Social and

Personal Services

2= Construction

3= Education

4= Financing, Insurance, Real

Estate and Business Services

5= Manufacturing

6= Professional Services

7= Technology

8= Transport, Storage and

Communication



9= Wholesale, Retail,

Import/Export Trade,

Restaurant and Hotel

10= Other

25 WorkExperience

The number of years of working

experience of the respondents

1= < 1 year

2.= 1 to 5 year

3= 6 to 10 yea

4.= 11 to 15 ye

5= 16 to 20 ye

6= 21 to 25 ye

7= > 25 years

26 Education The education level of the

respondents

1= Doctoral Degree

2= Master Degree

3= Bachelor Degree

4= Associate Degree / Higher

Diploma

Table 4. 1: Coding Scheme for this Research

Source: Developed for this research (Data analysis from PASW (SPSS))

4.3.3 Data Entry

Data entry was to convert the gathered information to a medium for viewing and

manipulation (Copper & Schindler 2006). It was an advantage to use computer based

survey tools sp that the data were already in digital format when the respondents were

submitting the survey. It could save time for data entry and reduce human errors

introduced during data entry. The collected data were downloaded from the online survey

tools to an Excel format file. The file was checked for any blank data and also checked

against the coding scheme. The blank data were all replaced by “No Data” to avoid

confusion in the analysis and the edited Excel file was imported to the PASW (SPSS) for

analysis.

4.4 Respondents’ Profile

Out of 467 respondents, 462 respondents agreed to do the survey and a total of 213 valid

samples were collected. The completion rate (total valid samples / total respondents) was

46%; it is common that the dropout rate for a web survey is quite high (Galesic 2006).The

research on dropout rates for web surveys performed by Galesic (2006), indicated that for

an over 120 items survey, like the case in this research which was over 129 items, the

dropout rate would be greater than 50%.



The demographic information was collected in terms of their PKM profile and personal

profile. The PKM profile included the adoption and training of PKM and the personal

profile included the country of abode, age, gender, work position, organisation type,

industry, work experience and education.

4.4.1 PKM Profile

The adoption of PKM was measured based on the 5 stages of the adoption process as

suggested by Rogers (1962), namely the knowledge stage, persuasion stage, decision

stage, implementation stage and confirmation stage. In the knowledge stage, the

individual is first exposed to PKM but lacks information about PKM and has not been

motivated to find more information about PKM. In the persuasion stage, the individual is

interested in PKM and actively seeks information or details about PKM. In the decision

stage, the individual takes on the concept of the PKM and makes a decision to adopt or

reject PKM. In the implementation stage, the individual uses PKM and may search for

further information about PKM. In the confirmation stage, the individual continues using

PKM and may use PKM to its fullest potential. There were totally 206 respondents who

answered this question and 51 (23.9%) respondents were in the knowledge stage, 32 (15

% ) in the persuasion stage, 17 (8%) in the decision stage, 60 (28.2% ) in the

implementation stage and 46 (21.6%) in the confirmation stage.

Figure 4. 2 : PKM Adoption


The PKM training was to measure if the respondent has attended any course, seminar and

workshop on PKM. There were 205 respondents who answered this question, and 77



(36.2%) have attended some kind of PKM training and 128 (60.1%) have not attended

any PKM training.

Figure 4. 3 : PKM Training


4.4.2 Personal Profile

The targeted respondents were the participants in Knowledge Management bodies and

there were no geographic limitations in this research. There were totally 213 valid

samples but not all answered the demographic information. 197 respondents answered the

country of abode question and 203 respondents answered the remaining questions. The

personal profiles were analysed based on the available data and it should provide good

representation of the respondents’ profile as the answered ratio is higher than 92%.

4.4.2.1 Country

There were a total of 197 respondents who answered this question; the respondents were

distributed in 42 different countries/cities around the world which including countries in

North and South America (36%), Asia (35.5%), Europe (20.3%), Africa (4.1%) and

Oceania (4.1%).



Figure 4. 4 : Geographic Distribution of Respondents


4.4.2.2 Age

A total of 206 respondents answered this question, 1 (0.5%) respondent was below 20, 9

(4.2%) respondents were between 20 to 30, 67 (31.5%) respondents were between 30 to

40, 69 (32.4%) respondents were between 40 to 50, 44 (20.7%) respondents were

between 50 to 60, 13 (6.1%) respondent were between 60 to 70 and 3 (1.4%) respondents

were over 70 years old.

Figure 4. 5 : Age Group


4.4.2.3 Gender

There were altogether 206 respondents who answered this question. 125 (58.7%)

respondents were male and 81 (38%) respondents were female.



Figure 4. 6 : Gender


4.4.2.4 Work Position

Altogether 206 respondents answered this question. There were 34 (16%) respondents

working as directors and senior management, 47 (22.1%) were working in managerial

and administrative position, 94 (44.1%) were professionals, 5 (2.3%) were associate

professionals, 4 were clerks and service workers and 22 (10.3%) were holding other

positions.

Figure 4. 7 : Work Position


4.4.2.5 Organisation Type

Altogether 206 respondents answered this question. There were totally 91 (42.7%)

respondents working in private companies, 44 (20.7%) were working in government and

71 (33.3%) respondents were working in non-government or non-profit organisations.



Figure 4. 8 : Organisation Type


4.4.2.6 Industry

Altogether 206 respondents answered this question. There were a total of 46 (21.6%)

respondents working for community, social and personal services; 7 (3.3%) in

construction; 38 (17.8%) in education, 25 (11.7%) in financing, insurance, real estate and

business services; 9 (4.2%) in manufacturing; 29 (13.6%) in professional services; 16

(7.5%) in technology; 15 (7%) in transport, storage and communication; 6 (2.8%) were in

wholesale, retail, import/export trade, restaurant and hotel; and 15 (7%) were in

miscellaneous industries.

Figure 4. 9 : Industry




4.4.2.7 Work Experience

Altogether 206 respondents answered this question. There were 3 (1.4%) respondents

who had less than 1 year’s working experience; 12 (5.6%) had between 1 to 5 years; 35

(16.4%) had between 6 to 10 years; 41 (19.2%) had between 11 to 15 years; 36 (16.9%)

had between 16 to 20 years; 19 (8.9%) had between 21 to 25 years; and 60 (28.2%)

respondents had over 25 years working experience.

Figure 4. 10 : Work Experience


4.4.2.8 Education

Altogether 206 respondents answered this question. There were 44 (20.7%) respondents

with a doctoral degree; 133 (62.4%) had a master degree; 26 (12.7%) had a bachelor

degree; and 2 (0.9%) respondents had an associate diploma or higher diploma.

Figure 4. 11 : Education




4.5 Data Screening

The data should be screened carefully prior to any analysis to ensure accuracy. The data

should also be screened for any missing data, outliers and distribution normality (Hair, J. F.

et al. 1998; Tabachnick & Fidell 2001). In this section, we focus on the missing data and

outliers. The distribution was checked during the assessment of the constructs in next section

to determine if any transformation was required.

4.5.1 Missing Data

Missing data appears if the respondents could not understood or if there were any ambiguities

in the questions asked, or there was any information the respondents did not want to provide.

In this research, missing data refers to incomplete responses (Malhotra, N. K. 2004; Manning

& Munro 2004).

There were 249 out of 462 samples having substantial missing data in the section measuring

the roles and values of PKM and all were deleted from the analysis, as suggested by

Tabachnick and Fidell (2001). As a result, the remaining valid samples numbered 213. Out of

the 213 valid samples, there were 9 samples having missing data in demographic information;

these 9 samples were kept for subsequent analysis as the demographic information was

merely help understand the profile of the respondents. The missing demographic information

in these 9 samples was labelled with “No Data”, such that it could be identified by PASW

(SPSS) as missing data.

4.5.2 Outliers

Outliers are samples having extreme scores on an individual variable, or presenting an

unusual pattern across a set of variables (Manning & Munro 2004). The extreme score on a

single variable is called an univariate outlier and the extreme score on more than 1 variable is

called a multivariate outlier (Kline 2005). It was suggested to remove the outliers from the

data set as it could distort the statistical analysis (Hair, J. F. et al. 1998; Tabachnick & Fidell

2001). Kline (2005) mentioned that there was no single definition of “extreme” but a

common rule of thumb was that scores more than three standard deviations beyond the mean

might be outliers.

For the univariate outliers, the histograms, box-plots and z-scores were checked and there

was no potential outlier that could be identified. For the multivariates, the Mahalanobis

distances were calculated by PASW (SPSS) and checked against the chi-square (39.252, DF



= 16). There were 7 samples identified as multivariate outliers in cases 79, 102, 120, 136,

144, 203 and 211. These cases were removed from the data sets and the final number of valid

samples for the subsequent analyses was 206.

4.6 Descriptive Statistics

This section provides statistics and graphical displays for the roles and values of PKM.

The PKM was represented by the seven PKM skills, namely information retrieving skill

(PKM1), evaluating skill (PKM2), organising skill (PKM3), analysing skill (PKM4),

collaborating skill (PKM5), presenting skill (PKM6) and securing skill (PKM4). These

seven variables were used to measure the roles in KM cycles, the values for individuals

and values for organisations. The descriptive statistics are reported in the following sub-

sections.

4.6.1 Descriptive Statistics of PKM’s Roles in KM Cycle

The roles of PKM in the KM Cycle were measured by the seven PKM skills in the four

different phases of the KM cycle. The four different phases were 1. Locating / Capturing

Knowledge (KMC1), 2. Creating Knowledge (KMC2), 3. Sharing / Transferring

Knowledge (KMC3) and 4. Applying Knowledge (KMC4). The roles of PKM in the four

KM cycles were measured by the seven PKM skills. The 5 point Likert-scale from 1 to 5

was used; 1 was less important, 2 was somewhat important, 3 was important, 4 was very

important and 5 was critical.

4.6.1.1 The Roles of PKM in Locating/Capturing Knowledge (KMC1)

The results show that the mean for PKM1 was 4.25 (SD = 0.917), PKM2 was 4.18 (SD =

0.922), PKM3 was 3.96 (SD = 0.933), PKM4 was 3.99 (SD = 1.014), PKM5 was 3.59

(SD = 1.031), PKM6 was 3.22 (SD = 1.15) and PKM7 was 3.28 (SD = 1.125). All PKM

skills scored above 3 which indicates they were all playing important roles in locating /

capturing knowledge for which PKM1 and PKM2 were more than very important, and

PKM 3 to PKM7 were between important and very important. The radar chart below

illustrates the role (mean score) of the seven PKM skills in Locating / Capturing

Knowledge.



Roles of PKM in Locating / Capturing Knowledge (KMC1)

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)

Analysing (PKM4)Collaborating (PKM5)

Presenting (PKM6)

Securing (PKM7)

Figure 4. 12 : Role of PKM in Locating / Capturing Knowledge


4.6.1.2 The Roles of PKM in Creating Knowledge (KMC2)




skills scored above 3 which indicates they were all playing important roles in sharing /

transferring knowledge for which PKM2 to PKM5 were more than very important, and

PKM 1, PKM6 and PKM7 were between important and very important. The radar chart

below illustrates the role (mean score) of the seven PKM skills in Creating Knowledge.

Roles of PKM in Creating Knowledge (KMC2)

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 4. 13 : The Role of PKM in Creating Knowledge




4.6.1.3 The Roles of PKM in Sharing / Transferring Knowledge (KMC3)




skills scored above 3 which indicates they were all playing important roles in sharing /

transferring knowledge for which PKM3, PKM5 and PKM6 were more than very

important, and PKM 1, PKM2, PKM4 and PKM7 were between important and very

important. The radar chart below illustrates the role (mean score) of the seven PKM skills

Sharing / Transferring Knowledge.

Roles of PKM in Sharing / Transferring Knowledge (KMC3)

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 4. 14 : The Role of PKM in Transferring / Sharing Knowledge Source: Developed for this research (Data analysis from PASW (SPSS))

4.6.1.4 The Roles of PKM in Applying Knowledge (KMC4)




skills scored above 3 which indicates they were all playing important roles in applying

knowledge for which PKM2, PKM4 and PKM5 were more than very important, and

PKM 1, PKM3, PKM6 and PKM7 were between important and very important. The radar

chart below illustrates the role (mean score) of the seven PKM skills Applying

Knowledge.



Roles of PKM in Applying Knowledge (KMC4)

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 4. 15 : The Role of PKM in Applying Knowledge Source: Developed for this research (Data analysis from PASW (SPSS))

4.6.2 Descriptive Statistics of PKM’s Values for Individuals

The values of PKM for individuals were measured by the seven PKM skills contributed

to individuals’ competences. There are seven individuals’ competences namely 1.

Communication Competence (ICOMC), 2. Creativity Competence (ICREC), 3. Problem

Solving Competence (IPBSC), 4. Learning / Self Development Competence (ILSDC), 5.

Mental Agility Competence (IMENA), 6. Analysis Competence (IANAC) and 7

Reflecting Competence (IREFC). The values were measured by the 5 point Likert-scale

from 1 to 5, 1 was the lowest value when 5 was the highest value.

4.6.2.1 Communication Competence (ICOMC)




skills scored above 3 for which PKM5 and PKM6 were more than 4 , and PKM 1, PKM2,

PKM3, PKM4 and PKM7 were between 3 and 4. The radar chart below illustrates the

value (mean score) of the seven PKM skills in this competence.



Values of PKM in Communication Competence (ICOMC)

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 4. 16 : The Value of PKM in Communication Competence Source: Developed for this research (Data analysis from PASW (SPSS))

4.6.2.2 Creativity Competence (ICREC)




skills scored above 3 for which PKM2, PKM4, PKM5 and PKM6 were more than 4 , and

PKM 1, PKM3 and PKM7 were between 3 and 4. The radar chart below illustrates the


Values of PKM in Creativity Competence (ICREC)

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 4. 17 : The Value of PKM in Creativity Competence Source: Developed for this research (Data analysis from PASW (SPSS))

4.6.2.3 Problem Solving Competence (IPBSC)






skills scored above 3 for which PKM2, PKM3, PKM4 and PKM5 were more than 4, and

PKM 1, PKM6 and PKM7 were between 3 and 4. The radar chart below illustrates the


Values of PKM in Problem Solving Competence (IPBSC)

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 4. 18 : The Value of PKM in Problem Solving Competence Source: Developed for this research (Data analysis from PASW (SPSS))

4.6.2.4 Learning / Self Development Competence (ILSDC)




skills scored above 3 for which PKM1 to PKM4 were more than 4 , and PKM 5 to PKM7

were between 3 and 4. The radar chart below illustrates the value (mean score) of the

seven PKM skills in this competence.

Values of PKM in Learning / Self Development Competence

(ILSDC)

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 4. 19 : The Value of PKM in Learning / Self Development




4.6.2.5 Mental Agility Competence (IMEAC)




skills scored above 3 for which PKM2 to PKM4 were more than 4 , and PKM1, PKM 5

to PKM7 were between 3 and 4. The radar chart below illustrates the value (mean score)

of the seven PKM skills in this competence.

Values of PKM in Mental Agility Competence (IMEAC)

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 4. 20 : The Value of PKM in Mental Agility Competence


4.6.2.6 Analysis Competence (IANAC)




skills scored above 3 for which PKM2 to PKM4 were more than 4 , and PKM1, PKM 5

to PKM7 were between 3 and 4. The radar chart below illustrates the value (mean score)

of the seven PKM skills in this competence.



Values of PKM in Analysis Competence (IANAC)

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 4. 21 : The Value of PKM in Analysis Competence Source: Developed for this research (Data analysis from PASW (SPSS))

4.6.2.7 Reflecting Competence (IREFC)




skills scored above 3 for which PKM2 to PKM4 were more than 4, and PKM1, PKM 5 to

PKM7 were between 3 and 4. The radar chart below illustrates the value (mean score) of

the seven PKM skills in this competence.

Values of PKM in Reflecting Competence (IREFC)

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 4. 22 : The Value of PKM in Reflecting Competence Source: Developed for this research (Data analysis from PASW (SPSS))

4.6.3 Descriptive Statistics of PKM Values for Organisations

The values of PKM for organisations were measured by the seven PKM skills that

contribute to organisations’ competences. There were five organisations’ competences

namely 1. External Information Awareness Competence (OEIAC), 2. Internal Knowledge



Dissemination Competence (OIKDC), 3. Effective Decision Making Competence

(OEDMC), 4. Organisation Focus Competence (OORFC) and 5. Continuous Innovation

Competence (ICOIC). The values were measured by the 5 point Likert-scale from 1 to 5,

1 was the lowest value when 5 was the highest value.

4.6.3.1 External Information Awareness Competence (OEIAC)




skills scored above 3 for which PKM1 to PKM5 were more than 4 , and PKM6 and



Values of PKM in External Information Awareness Competence

(OEIAC)

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 4. 23 : The Value of PKM in External Information Awareness Competence


4.6.3.2 Internal Knowledge Dissemination Competence (OIKDC)




skills scored above 3 for which PKM2 to PKM6 were more than 4 , and PKM1 and





Values of PKM in Internal Knowledge Dissimilation Competence

(OIKDC)

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 4. 24 : The Value of PKM in Internal Knowledge Dissemination Competence Source: Developed for this research (Data analysis from PASW (SPSS))

4.6.3.3 Effective Decision Making Competence (OEDMC)




skills scored above 3 for which PKM2 to PKM6 were more than 4, and PKM1 and PKM7



Values of PKM in Effective Decision Making Competence

(OEDMC)

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 4. 25 : The Value of PKM in Effective Decision Making Competence Source: Developed for this research (Data analysis from PASW (SPSS))

4.6.3.4 Organisation Focus Competence (OORFC)






skills scored above 3 for which PKM2 to PKM6 were more than 4, and PKM1 and PKM7



Table 4.6.3.4

Values of PKM in Organization Focus Competence (OORFC)

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 4.6.3.4

Figure 4. 26 : The Value of PKM in Organisation Focus Competence Source: Developed for this research (Data analysis from PASW (SPSS))

4.6.3.5 Continuous Innovation Competence (ICOIC)




skills scored above 3 for which PKM1 to PKM6 were more than 4, and PKM7 were

between 3 and 4. The radar chart below illustrates the value (mean score) of the seven

PKM skills in this competence.

Values of PKM in Continuous Innovation Competence (OCOIC)

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 4. 27 : The Value of PKM in Continuous Innovation Competence




4.7 Constructs Assessment

In this research, there were three main concepts measured, the roles of PKM in KM

processes (PKM), values of PKM for the individual (IV_PKM) and values of PKM for

the organisation (OV_PKM). There were seven constructs in each of these concepts.

These seven constructs that represent the PKM skills are, Information Retrieving

(PKM1), Information Evaluating (PKM2), Information Organising (PKM3), Information

Analysing (PKM4), Information Collaborating (PKM5), Information Presenting (PKM6),

Information Securing (PKM7).

The validity and reliability of each construct were assessed by Exploratory Factor

Analysis (EFA) and Cronbach’s Alpha Coefficients. The normality was checked to

determine if any transformation was required prior to performing any multivariate

analysis.

The EFA used Pearson correlations and Principal Components Analysis (PCA). For the

Pearson correlations, the item-to-item correlations should exceed 0.3 and item-to total

correlations should exceed 0.5 (Hair, J. F. et al. 1998). For the PCA, there should be only

one eigenvalue greater than 1 and the loading factors are all > 0.5. For the Cronbach’s

Alpha coefficient, the values >0.6 were acceptable for exploratory study, >0.7 were

acceptable, >0.8 were good and >0.9 were excellent as suggested by Hair et al (1998) and

Sekaran (2003).

Data normality is a common prerequisite in most multivariate analyses (Kline 2005). A

skewness and kurtosis test was done by PASW (SPSS) and the Z-score calculated for each of

the four composite variables. This Z-Score was used to check the significance of the

skewness by dividing the statistical value of skewness and the standard error of skewness

(Tabachnick & Fidell 2001). For samples size less than 300, the data will be non-normal if

the skewness is significant, which is indicated when the absolute value of the Z-score exceeds

the Z standard value of 2.58 (Tabachnick & Fidell 2001). The same testing was performed to

determine if there was significant kurtosis.

The following sections describe the assessment of each of the constructs.



4.7.1 Roles of PKM in KM Process Cycle (PKM)

There were seven constructs under this concept which measured the roles of PKM in each

of the KM Processes, namely 1. Capture/Locate (KMC1), 2. Transfer (KMC2), 3. Create

(KMC3) and 4. Apply (KMC4). The following section assesses each constructs in this

concept.

4.7.1.1 The Roles of Information Retrieving (PKM1)

A composite variable PKM1 was calculated by taking the average of the 4 fours

variables, namely KMC1.PKM1, KMC2.PKM1, KMC3.PKM1 and KMC4.PKM1. The

Pearson correlations of these four variables and the composite variable were calculated by

PASW (SPSS). The item-to-item correlations should exceed 0.3 and item-to-total

correlations should exceed 0.5, as suggested by Hair et al (1998). KMC4.PKM1 was

found not to meet this requirement and was removed. A new composite variable PKM1A

was created by the remaining three variables. The Pearson correlations were recalculated

and the results indicated that the item-to-item correlations exceeded 0.3 (from 0.324 to

0.601, p<0.01) and the item-to-total exceeded 0.5 (from 0.717 to 0.864, p<0.01).

The PCA was performed and there was only one eigenvalue greater than 1 and the

loading factors were all > 0.5 (from 0.772 to 0.782), which indicated that these three

variables measured the same construct (Hair, J. F. et al. 1998).

The reliability of this construct was measured by Cronbach’s Alpha and the result was

0.724, which indicated acceptable reliability, as suggested by Hair et al (1998) and

Sekaran (2003).



PKM1A

Pearson Correlations Factor Loading

Principal Component Analysis Cronbach’s Alpha

Figure 4. 28 : The Construct Assessment of PKM1


The normality of distribution of the scores of the composite variable PKM1A was

investigated. Values of skewness and kurtosis were calculated for the distribution of

scores for this variable. The value for skewness -0.26 was divided by the standard error of

skewness 0.169 to yield a z-score of -1.89 which was interpreted to be not significant, as

it did not exceed the absolute value of 3.29 (p<0.001) (Tabachnick & Fidell 2001). The

same procedure was performed for kurtosis which yielded a z-score of -0.769, and did not

exceed the absolute value of 3.29 (p<0.001) so was interpreted to be not significant in

kurtosis. The histogram of PKM1A is shown below and the curve shows a normal

distribution. Therefore, transformation was not required.



Figure 4. 29 : Histogram of PKM1 Source: Developed for this research (Data analysis from PASW (SPSS))

4.7.1.2 The Roles of Information Evaluating (PKM2)




PASW (SPSS) and the results indicated that the item-to-item correlations exceeded 0.3

(from 0.331 to 0.571, p<0.01) and item-to-total exceeded 0.5 (from 0.711 to 0.824,

p<0.01). It met the criteria suggested by Hair et al (1998) that the item-to-item

correlations should exceed 0.3 and item-to-total correlations should exceed 0.5.

A PCA was performed and there was only one eigenvalue greater than 1 and the loading

factors were all > 0.5 (from 0.693 to 0.810), which indicated that these four variables

measured the same construct (Hair, J. F. et al. 1998).


0.771, which indicated acceptable reliability, as suggested by Hair et al (1998) and

Sekaran (2003).



PKM2


Principal Components Analysis Cronbach’s Alpha

Figure 4. 30 : The construct Assessment of PKM 2


The normality of distribution of the scores of the composite variable PKM2 was


scores for this variable. The value for skewness -0.854 was divided by the standard error

of skewness 0.169 to yield a z-score of -5.04 which was interpreted to be negatively

significant as it exceeded the absolute value of 3.29 (p<0.001) (Tabachnick & Fidell

2001). The same procedure was performed for kurtosis which yielded a z-score of 3.4,

which, exceeded the absolute value of 3.29 (p<0.001) and it was interpreted to be

positively significant kurtosis.

The transformation was done by reflection and followed by a square root transformation.

The result was then reflected again in order to maintain the rank order of the original raw

values for easy interpretation, as suggested by Manning and Munro (2004). The new

variable was labelled as TPKM2 after the transformation and the z-score for skewness

was -2.09 and kurtosis is -0.59. Both values were less than the absolute value of 3.29



(P<0.001) and were interpreted as not significant in skewness and not significant in

kurtosis (Tabachnick & Fidell 2001).

The histograms of PKM2 before and after the transformation (TPKM2) are shown below

and the curve shows a normal distribution after the transformation. TPKM2 was used in

subsequent analysis.

Before Transformation After Transformation

Figure 4. 31 : Histogram of PKM2


4.7.1.3 The Roles of Information Organising (PKM3)








A PCA was performed and there was only one eigenvalue greater than 1 and the loading

factors were all > 0.5 (from 0.696 to 0.805), which indicated that these four variables

measured the same construct (Hair, J. F. et al. 1998).




0.749 which indicated acceptable reliability, as suggested by Hair et al (1998) and

Sekaran (2003).

PKM3



Figure 4. 32 : Construct Assessment of PKM3 Source: Developed for this research (Data analysis from PASW (SPSS))




of skewness 0.169 to yield a z-score of -3.43, which was interpreted to be a negatively



and did not exceed the absolute value of 3.29 (p<0.001) and was interpreted to be not

significant in kurtosis.







was -0.76 and kurtosis is -1.03. Both values were less than the absolute value of 3.29



The histograms of PKM3 before and after the transformation (TPKM3) are as shown

below and the curve shows a normal distribution after the transformation.




4.7.1.4 The Roles of Information Analysing (PKM4)









loading factors were all > 0.5 (from 0.759 to 0.820), which indicated that these four





0.783 which indicated acceptable reliability, as suggested by Hair et al (1998) and

Sekaran (2003).

PKM4



Figure 4. 34 : Construct Assessment of PKM4








which exceeded the absolute value of 3.29 (p<0.001) and was interpreted to be positively


The transformation was done by reflection and followed by a log transformation. The

result was then reflected again in order to maintain the rank order of the original raw

values for easy interpretation as suggested by Manning and Munro (2004). The new




was -2.63 and kurtosis was -0.616. Both values were less than the absolute value of 3.29



The histograms of PKM4 before and after the transformation (TPKM4) are shown below

and the curve shows a normal distribution after the transformation.




4.7.1.5 The Roles of Information Collaborating (PKM5)









0.518, p<0.01) and item-to-total correlations exceeded 0.5 (from 0.768 to 0.825, p<0.01).


loading factors were all > 0.5 (from 0.742 to 0.852) which indicated that these three





0.695 which indicated acceptable reliability for exploratory study, as suggested by Hair et

al (1998) and Sekaran (2003).

PKM5A








of skewness 0.169 to yield a z-score of -3.35 which was interpreted to be a negatively



and did not exceed the absolute value of 3.29 (p<0.001) and was interpreted not to be







variable was labelled as TPKM5A after the transformation and the z-score for skewness

was -0.82 and kurtosis was -1.40, both values were less than the absolute value of 3.29



The histograms of PKM5A before and after the transformation (TPKM5A) are shown





4.7.1.6 The Roles of Information Presenting (PKM6)









0.517, p<0.01) and item-to-total correlations exceeded 0.5 (from 0.701 to 0.807, p<0.01).


loading factors were all > 0.5 (from 0.673 to 0.819), which indicated that these three





0.656 which indicated acceptable reliability for exploratory study, as suggested by Hair et

al (1998) and Sekaran (2003).

PKM6A


















variable was labelled as TPKM6A after the transformation and the z-score for skewness

was -1.04 and kurtosis -1.20. Both values were less than the absolute value of 3.29



The histograms of PKM6A before and after the transformation (TPKM6A) are shown





4.7.1.7 The Roles of Information Securing (PKM7)





(from 0.423 to 0.643, p<0.01) and item-to-total correlations exceeded 0.5 (from 0.753 to

0.871, p<0.01). It met the criteria suggested by Hair et al (1998) that the item-to-item








0.835 which indicated good reliability, as suggested by Hair et al (1998) and Sekaran

(2003).

PKM7



Figure 4. 40 : The Construct Assessment of PKM7 Source: Developed for this research (Data analysis from PASW (SPSS))




of skewness (0.169) to yield a z-score of -1.36 which was interpreted to be not

significant, as it did not exceed the absolute value of 3.29 (p<0.001) (Tabachnick &

Fidell 2001). The same procedure was performed for kurtosis which yielded a z-score of -

0.93, and did not exceed the absolute value of 3.29 (p<0.001) and was interpreted to be

not significant in kurtosis. The histogram of PKM7 is shown below and the curve shows a

normal distribution, therefore, no transformation was required.



Figure 4. 41 : Histogram of PKM7 Source: Developed for this research (Data analysis from PASW (SPSS))

4.7.2 Values of PKM for individual (IV_PKM)

There were seven constructs under this concept which measured the PKM values for

individuals in term of communication competence (ICOMC), creativity competence

(ICREC), problem solving competence (IPBSC), learning / self development competence

(ILSDC), mental agility competence (IMEAC), analysis competence (IANAC) and

reflection competence (IREFC). The following section assesses each constructs in this

concept.

4.7.2.1 The Value of Information Retrieving Skill for Individual (IV.PKM1)

A composite variable IV.PKM1 was calculated by taking the average of the seven

variables, namely ICOMC.PKM1, ICREC.PKM1, IPBSC.PKM1, ILSDC.PKM1,

IMEAC.PKM1, IANAC.PKM1 and IREFC.PKM1. The Pearson correlations of these

seven variables and the composite variable were calculated by PASW (SPSS) and the

results indicated that the item-to-item correlations exceeded 0.3 (from 0.384 to 0.698,

p<0.01) and item-to-total exceeded 0.5 (from 0.679 to 0.826, p<0.01). It met the criteria

suggested by Hair et al (1998) that the item-to-item correlations should exceed 0.3 and

item-to-total correlations should exceed 0.5.








(2003).

IV.PKM1



Figure 4. 42 : The Construct Assessment of IV.PKM1 Source: Developed for this research (Data analysis from PASW (SPSS))

The normality of distribution of the scores of the composite variable IV.PKM1 was



of skewness 0.169 to yield a z-score of -2.0 which was interpreted to be not significant as

it did not exceed the absolute value of 3.29 (p<0.001) (Tabachnick & Fidell 2001). The

same procedure was performed for kurtosis which yielded a z-score of -0.91, not

exceeding the absolute value of 3.29 (p<0.001) and was interpreted to be not significant

in kurtosis.



The histogram of IV.PKM1 is shown below and the curve shows a normal distribution;

therefore, no transformation was required.

Figure 4. 43 : Histogram of IV.PKM1


4.7.2.2 The Value of Information Evaluating Skill for Individual (IV.PKM2)














(2003).



IV.PKM2















variable was labelled as TIV.PKM2 after the transformation and the z-score for skewness






The histograms of IV.PKM2 before and after the transformation (TIV.PKM2) are shown





4.7.2.3 The Value of Information Organising Skill for Individual (IV.PKM3)
















(2003).

IV.PKM3


























4.7.2.4 The Value of Information Analysing Skill for Individual (IV.PKM4)












loading factors were all > 0.5 (from 0.696 to 0.860) which indicated that these four



0.904 which indicated excellent reliability, as suggested by Hair et al (1998) and Sekaran

(2003).

IV.PKM4



Figure 4. 48 : The Construct Assessment of IV.PKM4










exceeding the absolute value of 3.29 (p<0.001) and was interpreted to be positive






was -3.42 and kurtosis was -1.57. The z-score for skewness was still higher than the

absolute value of 3.29 (P<0.001) but the z-score for kurtosis was good. Invert

transformation was then applied to check if there was any further improvement and the

result indicated that it has negatively significant kurtosis (z-score = -3.7). As invert

transformation created the negatively significant kurtosis problem, log transformation

was used for TIV.PKM4 for subsequent analysis.






4.7.2.5 The Value of Information Collaborating Skill for Individual (IV.PKM5)
















(2003).

IV.PKM5









of skewness 0.169 to yield a z-score of -3.31, which was interpreted to have slightly

negative skewness as it exceeded the absolute value of 3.29 (p<0.001) (Tabachnick &

Fidell 2001). The same procedure was performed for kurtosis which yielded a z-score of -

0.44, and did not exceed the absolute value of 3.29 (p<0.001) and was interpreted to be

not significant in kurtosis.















4.7.2.6 The Value of Information Presenting Skill for Individual (IV.PKM6)




seven variables and the composite variable were calculated by PASW (SPSS) and


item-to-total should exceed 0.5. The results indicated that IPCSC.PKM6, ILSDC.PKM6,

IMEAC.PKM6 and IANAC.PKM6 did not meet this requirement and were removed. A

new composite variole IV.PKM6A was created by the remaining three variables. The

Pearson correlations were recalculated and the results indicated that the item-to-item

correlations exceeded 0.3 (from 0.417 to 0.624, p<0.01) and item-to-total exceeded 0.5

(from 0.712 to 0.813, p<0.01).





0.631 which indicated acceptable reliability for exploratory research, as suggested by

Hair et al (1998).

The normality of distribution of the scores of the composite variable IV.PKM6A was










IV.PKM6A



Figure 4. 52 : The Construct Assessment of IV.PKM6





variable was labelled as TIV.PKM6A after the transformation and the z-score for

skewness was -2.060 and kurtosis was -1.50. Both values were less than the absolute

value of 3.29 (P<0.001) and were interpreted as not significant in skewness and not

significant in kurtosis (Tabachnick & Fidell 2001).

The histograms of IV.PKM6A before and after the transformation (TIV.PKM6A) are

shown below and the curve shows a normal distribution after the transformation.






4.7.2.7 The Value of Information Securing Skill for Individual (IV.PKM7)













0.959 which indicated excellent reliability, as suggested by Hair et al (1998) and Sekaran

(2003).



IV.PKM7







of skewness 0.169 to yield a z-score of -1.33 which was interpreted to be not significant

as it did not exceed the absolute value of 3.29 (p<0.001) (Tabachnick & Fidell 2001). The

same procedure was performed for kurtosis and yielded a z-score of -2.15, not exceeding

the absolute value of 3.29 (p<0.001) and was interpreted not to be significant in kurtosis.

The histogram of IV.PKM7 is as shown below and the curve shows a normal distribution;




Figure 4. 55 : Histogram of IV.PKM7 Source: Developed for this research (Data analysis from PASW (SPSS))

4.7.3 Values of PKM for organisation (OV_PKM)

There were seven constructs under this concept which measured the PKM values for the

organisation in terms of external information awareness competence (OEIAC), internal

knowledge dissemination competence (OIKDC), effective decision making competence

(OEDMC), organisation focus competence (OORFC) and continuous innovation

competence (OCOIC). The following section assesses each constructs in this concept.

4.7.3.1 The Value of Information Evaluating Skill for Organisation (OV.PKM1)

A composite variable OV.PKM1 was calculated by taking the average of the seven

variables, namely OEIAC.PKM1, OIKDC.PKM1, OEDMC.PKM1, OORFC.PKM1 and

OCOIC.PKM1. The Pearson correlations of these seven variables and the composite

variable were calculated by PASW (SPSS) and the results indicated that the item-to-item


(from 0.712 to 0.850, p<0.01). It met the criteria suggested by Hair et al (1998) that the

item-to-item correlations should exceed 0.3 and item-to-total correlations should exceed

0.5.








(2003).

OV.PKM1



Figure 4. 56 : The Construct Assessment of OV.PKM1 Source: Developed for this research (Data analysis from PASW (SPSS))

The normality of distribution of the scores of the composite variable OV.PKM1 was





same procedure was performed for kurtosis and yielded a z-score of -1.05, not exceeding

the absolute value of 3.29 (p<0.001) and was interpreted not to be significant in kurtosis.

The histogram of OV.PKM1 is shown below and the curve shows a normal distribution;

therefore, no transformation is required.



Figure 4. 57 : Histogram of OV.PKM1 Source: Developed for this research (Data analysis from PASW (SPSS))

4.7.3.2 The Value of Information Evaluating Skill for Organisation (OV.PKM2)








0.5.






(2003).



OV.PKM2















variable was labelled as TOV.PKM2 after the transformation and the z-score for






The histograms of OV.PKM2 before and after the transformation (TOV.PKM2) are



Figure 4. 59 : Histogram of OV.PKM2


4.7.3.3 The Value of Information Organising Skill for Organisation (OV.PKM3)








0.5.








(2003).

OV.PKM3


























4.7.3.4 The Value of Information Analysing Skill for Organisation (OV.PKM4)








0.5.








(2003).

OV.PKM4



Figure 4. 62 : The Construct Assessment of OV.PKM4
























4.7.3.5 The Value of Information Collaborating Skill for Organisation (OV.PKM5)










0.5.





0.842 which represented good reliability, as suggested by Hair et al (1998) and Sekaran

(2003).

OV.PKM5












2001). The same procedure was performed for kurtosis which yield a z-score of 0.223,















4.7.3.6 The Value of Information Presenting Skill for Organisation (OV.PKM6)










0.5.





0.842 which represented good reliability, as suggested by Hair et al (1998) and Sekaran

(2003).

OV.PKM6














significant of kurtosis.













4.7.3.7 The Value of Information Securing Skill for Organisation (OV.PKM7)







correlations exceeded 0.3 (from 0.657 to 0.803, p<0.01) and item-to-total exceed 0.5



0.5.





0.925 which represented excellent reliability, as suggested by Hair et al (1998).

OV.PKM7












same procedure was performed for kurtosis which yielded a z-score of -1.11, and did not

exceed the absolute value of 3.29 (p<0.001) and was interpreted to be not significant in

kurtosis.

The histogram of OV.PKM7 is shown below and the curve shows a normal distribution;


Figure 4. 69 : Histogram of OV.PKM7 Source: Developed for this research (Data analysis from PASW (SPSS))

4.7.4 Construct Renaming Prior Hypotheses Tests

The table 4.2 shows a summary of the variables’ name before and after the transformation

process for rectifying the non-normality distribution, as discussed in the previous section.

For easy reference and interpretation, the variables’ names were renamed in the new

variable name column. The variables for the roles of PKM were renamed to RPKM1 to

RPKM7, the variables for the values of PKM for individuals were renamed to IVPKM1

to IVPKM7, and the variables for the values of PKM for organisations were renamed to

OVPKM1 to OVPKM7.



Old Variable Name Transformation

Transformed Variable Name

New Variable Name

PKM1A No Transformation TPKM1A RPKM1

PKM2 Reflect – SQRT- Reflect TPKM2 RPKM2

PKM3 Reflect – SQRT- Reflect TPKM3 RPKM3

PKM4 Reflect – LN – Reflect TPKM4 RPKM4

PKM5A Reflect – SQRT- Reflect TPKM5A RPKM5

PKM6A Reflect – SQRT- Reflect TPKM6A RPKM6

PKM7 No Transformation TPKM7 RPKM7

IV.PKM1 No Transformation TIV.PKM1 IVPKM1

IV.PKM2 Reflect – SQRT- Reflect TIV.PKM2 IVPKM2


IV.PKM4 Reflect – LN – Reflect TIV.PKM4 IVPKM4


IV.PKM6A Reflect – SQRT- Reflect TIV.PKM6A IVPKM6

IV.PKM7 No Transformation TIV.PKM7 IVPKM7

OV.PKM1 No Transformation TOV.PKM1 OVPKM1

OV.PKM2 Reflect – SQRT- Reflect TOV.PKM2 OVPKM2


OV.PKM4 Reflect – LN – Reflect TOV.PKM4 OVPKM4



OV.PKM7 No Transformation TOV.PKM7 OVPKM7

Table 4. 2 : Renaming Variables Name Source: Developed for this research (Data analysis from PASW (SPSS))

4.8 Exploratory Data Analysis

The process to ensure the validity and reliability of the constructs was explained in

section 4.7. The normality assumption for multivariate analysis was also assessed and the

constructs which did not have normality distribution were transformed. All the measures

were ready for subsequent analyses and this section described the results of the

hypotheses testing.

4.8.1 Assessment of mean score differ among the different stages of PKM Adoption

Prior the hypotheses tests, the ANOVA test was performed to assess if the mean score

results of the roles and values of PKM were significantly different at different stages of

PKM Adoption. The test was performed by selecting PKM_Adoption as the factor and

RPM1 to RPKM 7, IVPKM1 to IVPKM7 and OVPKM to OVPKM7 as the dependents.

There are 5 stages of the adoption process, as suggested by Rogers (1962), namely

knowledge stage, persuasion stage, decision stage, implementation stage and

confirmation stage. In the knowledge stage, the individual is first exposed to PKM but

lacks information about PKM and has not been motivated to find more information about



PKM. In the persuasion stage, the individual is interested in PKM and actively seeks

information or details about PKM. In the decision stage, the individual takes on the

concept of PKM and makes a decision to adopt or reject PKM. In the implementation

stage, the individual uses PKM and may search for further information about PKM. In the

confirmation stage, the individual continues using PKM and may use PKM to its fullest

potential.

The results of the ANOVA tests are illustrated in table 4.3 for RPKM1 to RPKM7, table

4.6 for IVPKM1 to IVPKM7 and table 4.9 for OVPKM1 to OVPKM7.

Table 4. 3 : Assessment of the difference between groups for RPKM by ANOVA Test


As illustrated in table 4.3, there were one or more groups differing in RPKM 1 (F=2.775,

p=0.028) and RPKM6 (F=2.898, p=0.023) as p < 0.05. The Post-hoc (Tukey HSD)

comparison is shown in table 4.4 and table 4.5. The results show that the mean score

differences between the knowledge stage and the persuasion stage for RPKM1 and

RPKM6 were significant, at the 0.05 level.



Lower

Bound

Upper

Bound

Persuasion Stage -0.52031* .18232 .038 -1.0223 -.0183

Decision Stage -.19688 .23097 .914 -.8329 .4391

Implementation Stage -.39579 .15627 .088 -.8261 .0345

Confirmation Stage -.40422 .16562 .109 -.8603 .0518

Knowledge Stage 0.52031* .18232 .038 .0183 1.0223

Decision Stage .32344 .24561 .681 -.3529 .9997

Implementation Stage .12452 .17719 .956 -.3634 .6124

Confirmation Stage .11609 .18549 .971 -.3947 .6269

Knowledge Stage .19688 .23097 .914 -.4391 .8329

Persuasion Stage -.32344 .24561 .681 -.9997 .3529



Knowledge Stage .39579 .15627 .088 -.0345 .8261


Decision Stage .19891 .22695 .905 -.4260 .8238

Confirmation Stage -.00843 .15996 1.000 -.4489 .4320

Knowledge Stage .40422 .16562 .109 -.0518 .8603


Decision Stage .20735 .23348 .901 -.4356 .8503

Implementation Stage .00843 .15996 1.000 -.4320 .4489

RPKM1 Knowledge Stage

Persuasion Stage

Decision Stage

Implementation

Stage

Confirmation Stage

Multiple Comparisons

Tukey HSD

Dependent Variable (I) PKM_Adoption (J) PKM_Adoption Mean

Difference (I-

J) Std. Error Sig.

95% Confidence Interval

* The mean difference is significant at the 0.05 level

Table 4. 4 : Pro Hoc Comparison of RPKM1 and PKM_Adoption


Lower

Bound

Upper

Bound

Persuasion Stage -0.19063* .06036 .016 -.3568 -.0244

Decision Stage -.14306 .07647 .337 -.3536 .0675



Knowledge Stage 0.19063* .06036 .016 .0244 .3568

Decision Stage .04757 .08132 .977 -.1764 .2715



Knowledge Stage .14306 .07647 .337 -.0675 .3536




Knowledge Stage .11334 .05174 .188 -.0291 .2558


Decision Stage -.02973 .07514 .995 -.2366 .1772


Knowledge Stage .06859 .05484 .721 -.0824 .2196


Decision Stage -.07447 .07731 .871 -.2873 .1384


RPKM6 Knowledge Stage

Persuasion Stage

Decision Stage

Implementation

Stage

Confirmation Stage


Tukey HSD


Difference (I-

J) Std. Error Sig.



Table 4. 5 : Pro Hoc Comparison of RPKM6 and PKM_Adoption




Table 4. 6 : Assessment of the difference between groups for IVPKM by ANOVA Test


Lower

Bound

Upper

Bound

Persuasion Stage -0.4732* .17194 .050 -.9466 .0002

Decision Stage -.19195 .21782 .904 -.7917 .4078



Knowledge Stage 0.4732* .17194 .050 -.0002 .9466

Decision Stage .28125 .23162 .743 -.3566 .9191



Knowledge Stage .19195 .21782 .904 -.4078 .7917




Knowledge Stage .38124 .14737 .077 -.0246 .7870


Decision Stage .18929 .21402 .902 -.4001 .7786


Knowledge Stage .31188 .15619 .271 -.1182 .7420


Decision Stage .11993 .22019 .982 -.4864 .7262


IVPKM1 Knowledge Stage

Persuasion Stage

Decision Stage

Implementation

Stage

Confirmation Stage


Tukey HSD


Difference (I-

J) Std. Error Sig.



Table 4. 7 : Pro Hoc Test of IVPKM1 and PKM_Adoption Source: Developed for this research (Data analysis from PASW (SPSS))

As illustrated in table 4.6, there were one or more groups differing in IVPKM 1 (F=2.523,

p=0.042) and IVPKM5 (F=3.935, p=0.004) as p < 0.05. The Post-hoc (Tukey HSD)

comparison is shown in table 4.7 and table 4.8. The results show that the mean score

differences between the knowledge stage and the persuasion stage for IVPKM1 were

significant at the 0.05 level, the mean score differed between the knowledge stage and the



persuasion stage, the implementation stage and the confirmation stage for IVPKM5 were

significant, at the 0.05 level.

Lower

Bound

Upper

Bound

Persuasion Stage -0.1655* .05666 .031 -.3216 -.0096

Decision Stage -.10011 .07178 .632 -.2978 .0975

Implementation Stage -0.1488* .04856 .021 -.2825 -.0151

Confirmation Stage -0.1784* .05147 .006 -.3201 -.0367

Knowledge Stage 0.1655* .05666 .031 .0096 .3216

Decision Stage .06548 .07632 .912 -.1447 .2757



Knowledge Stage .10011 .07178 .632 -.0975 .2978




Knowledge Stage 0.1488* .04856 .021 .0151 .2825


Decision Stage .04870 .07052 .958 -.1455 .2429


Knowledge Stage 0.1784* .05147 .006 .0367 .3201

Persuasion Stage .01281 .05764 .999 -.1459 .1715

Decision Stage .07829 .07256 .817 -.1215 .2781


IVPKM5 Knowledge Stage

Persuasion Stage

Decision Stage

Implementation

Stage

Confirmation Stage


Tukey HSD


Difference (I-

J) Std. Error Sig.



Table 4. 8 : Pro Hoc Test of IVPKM5 and PKM_Adoption Source: Developed for this research (Data analysis from PASW (SPSS))

Table 4. 9 : Assessment of the difference between groups for OVPKM by ANOVA Test


As illustrated in table 4.9, there were one or more groups differing in OVPKM1

(F=3.109, p=0.017) and OVPKM5 (F=3.313, p=0.012) as p < 0.05. The Post-hoc (Tukey

HSD) comparison is shown in table 4.10and table 4.11. The results show that the mean



score differences between the knowledge stage and the persuasion stage for OVPKM1

were significant at the 0.05 level, the mean score differences between the knowledge

stage and the implementation stage, and between the knowledge stage and the

confirmation stage for OVPKM5 were significant at the 0.05 level.

Lower

Bound

Upper

Bound

Persuasion Stage -0.6003* .17219 .005 -1.0745 -.1262

Decision Stage -.19413 .21814 .900 -.7948 .4066



Knowledge Stage 0.6003* .17219 .005 .1262 1.0745

Decision Stage .40625 .23197 .405 -.2325 1.0450



Knowledge Stage .19413 .21814 .900 -.4066 .7948

Persuasion Stage -.40625 .23197 .405 -1.0450 .2325

Implementation Stage -.04539 .21434 1.000 -.6356 .5448


Knowledge Stage .23953 .14759 .485 -.1669 .6459


Decision Stage .04539 .21434 1.000 -.5448 .6356


Knowledge Stage .29497 .15642 .329 -.1358 .7257


Decision Stage .10083 .22052 .991 -.5064 .7081


OVPKM1 Knowledge Stage

Persuasion Stage

Decision Stage

Implementation

Stage

Confirmation Stage


Tukey HSD


Difference (I-

J) Std. Error Sig.



Table 4. 10 : Pro Hoc Test of OVPKM1 and PKM_Adoption Source: Developed for this research (Data analysis from PASW (SPSS))

Lower

Bound

Upper

Bound

Persuasion Stage -0.1035 .05413 .314 -.2526 .0455

Decision Stage -.04402 .06857 .968 -.2328 .1448

Implementation Stage -0.1351* .04640 .032 -.2629 -.0074

Confirmation Stage -0.1570* .04917 .014 -.2925 -.0217

Knowledge Stage .10351 .05413 .314 -.0455 .2526

Decision Stage .05949 .07292 .926 -.1413 .2603



Knowledge Stage .04402 .06857 .968 -.1448 .2328




Knowledge Stage 0.1351* .04640 .032 .0074 .2629


Decision Stage .09116 .06738 .658 -.0944 .2767


Knowledge Stage 0.1570* .04917 .014 .0217 .2925


Decision Stage .11305 .06932 .480 -.0778 .3039


OVPKM5 Knowledge Stage

Persuasion Stage

Decision Stage

Implementation

Stage

Confirmation Stage


Tukey HSD


Difference (I-

J) Std. Error Sig.



Table 4. 11 : Pro Hoc Test of OVPKM5 and PKM_Adoption




Based on the results from the ANOVA and Post-Hoc (Turkey HSD) comparison, among

the 21 constructs, there were only 6 constructs (RPKM1, RPKM6, IVPKM1, IVPKM5,

OVPKM1 and OVPKM5) having one or more group differences were significant at level

0.05. Those other constructs have no significant differences between the mean score at

different stages of PKM adoption.

4.8.2 Assessment of mean scores of respondents in different industries

The ANOVA test was performed to assess if the respondent’s mean score results of the

roles and values of PKM were significantly different in different industries. The test was

performed by selecting Respondent_Industry as the factor and RPM1 to RPKM 7,

IVPKM1 to IVPKM7 and OVPKM to OVPKM7 as the dependents.

The results of the ANOVA tests are illustrated in table 4.12 for RPKM1 to RPKM7, table

4.14 for IVPKM1 to IVPKM7 and table 4.16 for OVPKM1 to OVPKM7.

Table 4. 12 : Assessment of the difference between groups for RPKM and Respondent_Industry by

ANOVA Test Source: Developed for this research (Data analysis from PASW (SPSS))

As illustrated in table 4.12, there were one or more groups differing in RPKM 6

(F=2.516, p=0.010) as p < 0.05. The Post-hoc (Tukey HSD) comparison is shown in table

4.13. The results show that the mean score differences between the education and

manufacturing industry for RPKM6 were significant, at the 0.05 level.



Lower

Bound

Upper

Bound

2. Construction -.14246 .10636 .943 -.4830 .1981

3. Education -.09984 .05853 .791 -.2873 .0876

4. Financing, Insurance, Real Estate & Business Services .02105 .06530 1.000 -.1880 .2301

5. Manufacturing .26207 .10636 .295 -.0785 .6026

6. Professional Services .01193 .06170 1.000 -.1856 .2095

7. Technology .15084 .08011 .680 -.1057 .4073

8. Transport, Storage & Communication -.05208 .07804 1.000 -.3020 .1978

9. Wholesale, Retail, Import/Export Trade, Restaurant and Hotel -.07707 .11377 1.000 -.4413 .2872

10. Other .10714 .08011 .944 -.1494 .3636

1. Community, Social & Personal Services .14246 .10636 .943 -.1981 .4830

3. Education .04262 .10813 1.000 -.3036 .3888

4. Financing, Insurance, Real Estate & Business Services .16351 .11194 .906 -.1949 .5219

5. Manufacturing .40453 .13992 .115 -.0435 .8525

6. Professional Services .15439 .10988 .924 -.1974 .5062

7. Technology .29330 .12117 .320 -.0947 .6813

8. Transport, Storage & Communication .09038 .11982 .999 -.2933 .4740

9. Wholesale, Retail, Import/Export Trade, Restaurant and Hotel .06539 .14563 1.000 -.4009 .5317

10. Other .24960 .12117 .558 -.1384 .6376


2. Construction -.04262 .10813 1.000 -.3888 .3036


5. Manufacturing 0.3619* .10813 .033 .0157 .7081


7. Technology .25068 .08245 .078 -.0133 .5147

8. Transport, Storage & Communication .04776 .08045 1.000 -.2098 .3053


10. Other .20698 .08245 .270 -.0570 .4710

1. Community, Social & Personal Services -.02105 .06530 1.000 -.2301 .1880

2. Construction -.16351 .11194 .906 -.5219 .1949

3. Education -.12089 .06815 .751 -.3391 .0973

5. Manufacturing .24102 .11194 .493 -.1174 .5994

6. Professional Services -.00912 .07089 1.000 -.2361 .2179

7. Technology .12980 .08738 .896 -.1500 .4096

8. Transport, Storage & Communication -.07313 .08549 .998 -.3469 .2006

9. Wholesale, Retail, Import/Export Trade, Restaurant and Hotel -.09811 .11900 .998 -.4791 .2829

10. Other .08609 .08738 .993 -.1937 .3659

1. Community, Social & Personal Services -.26207 .10636 .295 -.6026 .0785

2. Construction -.40453 .13992 .115 -.8525 .0435

3. Education -0.3619* .10813 .033 -.7081 -.0157

4. Financing, Insurance, Real Estate & Business Services -.24102 .11194 .493 -.5994 .1174

6. Professional Services -.25014 .10988 .409 -.6020 .1017

7. Technology -.11122 .12117 .996 -.4992 .2768



10. Other -.15493 .12117 .957 -.5429 .2331


2. Construction -.15439 .10988 .924 -.5062 .1974

3. Education -.11177 .06471 .779 -.3190 .0954


5. Manufacturing .25014 .10988 .409 -.1017 .6020

7. Technology .13891 .08473 .827 -.1324 .4102



10. Other .09521 .08473 .982 -.1761 .3665


2. Construction -.29330 .12117 .320 -.6813 .0947

3. Education -.25068 .08245 .078 -.5147 .0133


5. Manufacturing .11122 .12117 .996 -.2768 .4992




10. Other -.04370 .09894 1.000 -.3605 .2731

1. Community, Social & Personal Services .05208 .07804 1.000 -.1978 .3020

2. Construction -.09038 .11982 .999 -.4740 .2933

3. Education -.04776 .08045 1.000 -.3053 .2098


5. Manufacturing .31415 .11982 .215 -.0695 .6978


7. Technology .20293 .09728 .540 -.1085 .5144


10. Other .15922 .09728 .828 -.1522 .4707


2. Construction -.06539 .14563 1.000 -.5317 .4009

3. Education -.02277 .11543 1.000 -.3924 .3468


5. Manufacturing .33914 .14563 .376 -.1272 .8054


7. Technology .22791 .12773 .744 -.1811 .6369

8. Transport, Storage & Communication .02498 .12645 1.000 -.3799 .4299

10. Other .18421 .12773 .912 -.2248 .5932


2. Construction -.24960 .12117 .558 -.6376 .1384

3. Education -.20698 .08245 .270 -.4710 .0570


5. Manufacturing .15493 .12117 .957 -.2331 .5429


7. Technology .04370 .09894 1.000 -.2731 .3605



8. Transport, Storage &

Communication

9. Wholesale, Retail,



10. Other

RPKM6 1. Community, Social &

Personal Services

2. Construction

3. Education

4. Financing, Insurance,

Real Estate & Business

Services

5. Manufacturing

6. Professional Services

7. Technology


Tukey HSD

Dependent Variable (I) Respondent_Industry (J) Respondent_Industry Mean

Difference

(I-J) Std. Error Sig.

95% Confidence


Table 4. 13 : Pro Hoc Comparison of RPKM6 and Respondent_Industry Source: Developed for this research (Data analysis from PASW (SPSS))



ANOVA

Sum of Squares df Mean Square F Sig.

Between Groups 8.695 9 .966 1.689 .094

Within Groups 108.100 189 .572

IVPKM1

Total 116.795 198

Between Groups .479 9 .053 .909 .518


IVPKM2

Total 11.537 198

Between Groups .531 9 .059 .938 .493


IVPKM3

Total 12.416 198

Between Groups 1.426 9 .158 1.382 .199


IVPKM4

Total 23.093 198

Between Groups 1.012 9 .112 1.768 .077


IVPKM5

Total 13.033 198

Between Groups 1.104 9 .123 1.961 .046


IVPKM6

Total 12.926 198

Between Groups 12.214 9 1.357 1.292 .244

Within Groups 198.545 189 1.051

IVPKM7

Total 210.759 198

Table 4. 14 : Assessment of between groups for IVPKM and Respondent_Industry by ANOVA Test


As illustrated in table 4.14, there were one or more groups differing in IVPKM 6


4.15. The results show that the mean score differences between other industries and the

transport, storage and communication industry for IVPKM6 were significant at the 0.05

level.



Lower

Bound

Upper

Bound

2. Construction .00792 .10162 1.000 -.3174 .3333

3. Education -.04244 .05592 .999 -.2215 .1366


5. Manufacturing .06580 .10162 1.000 -.2596 .3912


7. Technology .07668 .07654 .992 -.1684 .3217



10. Other .20938 .07654 .167 -.0357 .4544


3. Education -.05036 .10331 1.000 -.3812 .2804

4. Financing, Insurance, Real Estate & Business Services -.05604 .10695 1.000 -.3985 .2864

5. Manufacturing .05788 .13368 1.000 -.3702 .4859


7. Technology .06876 .11577 1.000 -.3019 .4395



10. Other .20146 .11577 .771 -.1692 .5722


2. Construction .05036 .10331 1.000 -.2804 .3812


5. Manufacturing .10824 .10331 .989 -.2226 .4390


7. Technology .11912 .07877 .886 -.1331 .3713



10. Other .25182 .07877 .051 -.0004 .5041


2. Construction .05604 .10695 1.000 -.2864 .3985

3. Education .00568 .06511 1.000 -.2028 .2142

5. Manufacturing .11392 .10695 .987 -.2285 .4564


7. Technology .12480 .08349 .893 -.1425 .3921



10. Other .25750 .08349 .070 -.0098 .5248


2. Construction -.05788 .13368 1.000 -.4859 .3702

3. Education -.10824 .10331 .989 -.4390 .2226



7. Technology .01088 .11577 1.000 -.3598 .3816



10. Other .14358 .11577 .965 -.2271 .5143


2. Construction .03430 .10498 1.000 -.3018 .3704

3. Education -.01607 .06183 1.000 -.2140 .1819


5. Manufacturing .09218 .10498 .997 -.2440 .4283

7. Technology .10306 .08095 .958 -.1561 .3623



10. Other .23576 .08095 .109 -.0234 .4950


2. Construction -.06876 .11577 1.000 -.4395 .3019

3. Education -.11912 .07877 .886 -.3713 .1331


5. Manufacturing -.01088 .11577 1.000 -.3816 .3598




10. Other .13270 .09453 .925 -.1700 .4354


2. Construction .11900 .11448 .989 -.2476 .4856

3. Education .06863 .07686 .997 -.1775 .3147


5. Manufacturing .17687 .11448 .872 -.1897 .5434


7. Technology .18775 .09294 .586 -.1098 .4853


10. Other 0.32045* .09294 .024 .0229 .6180


2. Construction .10459 .13914 .999 -.3409 .5501

3. Education .05422 .11028 1.000 -.2989 .4073


5. Manufacturing .16246 .13914 .976 -.2831 .6080


7. Technology .17334 .12204 .919 -.2174 .5641


10. Other .30605 .12204 .271 -.0847 .6968


2. Construction -.20146 .11577 .771 -.5722 .1692

3. Education -.25182 .07877 .051 -.5041 .0004


5. Manufacturing -.14358 .11577 .965 -.5143 .2271


7. Technology -.13270 .09453 .925 -.4354 .1700

8. Transport, Storage & Communication -0.32045* .09294 .024 -.6180 -.0229



Tukey HSD


Difference


95% Confidence


Communication




IVPKM6 1. Community, Social &

Personal Services

2. Construction

3. Education



Services

5. Manufacturing


7. Technology

10. Other


Table 4. 15 : Pro Hoc Test of IVPKM6 and Respondent_Industry




Table 4. 16 : Assessment of between groups for OVPKM and Respondent_Industry by ANOVA Test


As illustrated in table 4.16, there were one or more groups differing in OVPKM1


4.17. The results show that there were no mean score differences between groups

significant at the 0.05 level.

Based on the results from the ANOVA and Post-Hoc (Turkey HSD) comparison, among

the 21 constructs, there were only 3 constructs (RPKM6, IVPKM6 and OVPKM1) having

one or more group differences that were significant at the level 0.05. The other constructs

have no significant differences between the mean scores for different industries.



Lower

Bound

Upper

Bound

2. Construction -.11429 .30568 1.000 -1.0931 .8645

3. Education -.07778 .16823 1.000 -.6164 .4609


5. Manufacturing .80000 .30568 .217 -.1788 1.7788


7. Technology -.08571 .23024 1.000 -.8229 .6515


9. Wholesale, Retail, Import/Export Trade, Restaurant and Hotel .10000 .32698 1.000 -.9470 1.1470

10. Other .21429 .23024 .995 -.5229 .9515

1. Community, Social & Personal Services .11429 .30568 1.000 -.8645 1.0931

3. Education .03651 .31078 1.000 -.9586 1.0316

4. Financing, Insurance, Real Estate & Business Services -.08571 .32172 1.000 -1.1158 .9444

5. Manufacturing .91429 .40215 .411 -.3734 2.2019

6. Professional Services .48762 .31580 .872 -.5236 1.4988

7. Technology .02857 .34827 1.000 -1.0866 1.1437

8. Transport, Storage & Communication -.13905 .34438 1.000 -1.2417 .9636

9. Wholesale, Retail, Import/Export Trade, Restaurant and Hotel .21429 .41857 1.000 -1.1259 1.5545

10. Other .32857 .34827 .995 -.7866 1.4437


2. Construction -.03651 .31078 1.000 -1.0316 .9586


5. Manufacturing .87778 .31078 .136 -.1173 1.8729


7. Technology -.00794 .23697 1.000 -.7667 .7508



10. Other .29206 .23697 .966 -.4667 1.0508


2. Construction .08571 .32172 1.000 -.9444 1.1158

3. Education .12222 .19587 1.000 -.5049 .7494

5. Manufacturing 1.00000 .32172 .065 -.0301 2.0301


7. Technology .11429 .25114 1.000 -.6899 .9184


9. Wholesale, Retail, Import/Export Trade, Restaurant and Hotel .30000 .34202 .997 -.7951 1.3951

10. Other .41429 .25114 .822 -.3899 1.2184

1. Community, Social & Personal Services -.80000 .30568 .217 -1.7788 .1788

2. Construction -.91429 .40215 .411 -2.2019 .3734

3. Education -.87778 .31078 .136 -1.8729 .1173

4. Financing, Insurance, Real Estate & Business Services -1.00000 .32172 .065 -2.0301 .0301

6. Professional Services -.42667 .31580 .940 -1.4378 .5845

7. Technology -.88571 .34827 .253 -2.0009 .2294

8. Transport, Storage & Communication -1.05333 .34438 .075 -2.1560 .0493

9. Wholesale, Retail, Import/Export Trade, Restaurant and Hotel -.70000 .41857 .810 -2.0402 .6402

10. Other -.58571 .34827 .804 -1.7009 .5294


2. Construction -.48762 .31580 .872 -1.4988 .5236

3. Education -.45111 .18599 .317 -1.0466 .1444

4. Financing, Insurance, Real Estate & Business Services -.57333 .20374 .139 -1.2257 .0790

5. Manufacturing .42667 .31580 .940 -.5845 1.4378

7. Technology -.45905 .24351 .679 -1.2388 .3207

8. Transport, Storage & Communication -.62667 .23792 .209 -1.3885 .1351

9. Wholesale, Retail, Import/Export Trade, Restaurant and Hotel -.27333 .33646 .998 -1.3507 .8040

10. Other -.15905 .24351 1.000 -.9388 .6207


2. Construction -.02857 .34827 1.000 -1.1437 1.0866

3. Education .00794 .23697 1.000 -.7508 .7667


5. Manufacturing .88571 .34827 .253 -.2294 2.0009


8. Transport, Storage & Communication -.16762 .27958 1.000 -1.0628 .7276


10. Other .30000 .28436 .988 -.6105 1.2105


2. Construction .13905 .34438 1.000 -.9636 1.2417

3. Education .17556 .23121 .999 -.5648 .9159


5. Manufacturing 1.05333 .34438 .075 -.0493 2.1560


7. Technology .16762 .27958 1.000 -.7276 1.0628

9. Wholesale, Retail, Import/Export Trade, Restaurant and Hotel .35333 .36342 .993 -.8103 1.5170

10. Other .46762 .27958 .809 -.4276 1.3628

1. Community, Social & Personal Services -.10000 .32698 1.000 -1.1470 .9470

2. Construction -.21429 .41857 1.000 -1.5545 1.1259

3. Education -.17778 .33176 1.000 -1.2400 .8845


5. Manufacturing .70000 .41857 .810 -.6402 2.0402


7. Technology -.18571 .36711 1.000 -1.3612 .9897


10. Other .11429 .36711 1.000 -1.0612 1.2897


2. Construction -.32857 .34827 .995 -1.4437 .7866

3. Education -.29206 .23697 .966 -1.0508 .4667


5. Manufacturing .58571 .34827 .804 -.5294 1.7009


7. Technology -.30000 .28436 .988 -1.2105 .6105


9. Wholesale, Retail, Import/Export Trade, Restaurant and Hotel -.11429 .36711 1.000 -1.2897 1.0612


Tukey HSD


Difference


95% Confidence

OVPKM1 1. Community, Social &

Personal Services

2. Construction

3. Education



Services

5. Manufacturing


7. Technology


Communication




10. Other


Table 4. 17 : Pro Hoc Test of OVPKM1 and Respondent_Industry Source: Developed for this research (Data analysis from PASW (SPSS))



After the assessment of the mean score differences among the different stages of PKM

adoption and the respondents’ industries, hypotheses testing was undertaken. There were

five main hypotheses, as mentioned in chapter 2. The first two hypotheses, H1 and H2,

used the mean score rated by the respondents in each variable to measure the roles and

values of the PKM to determine if the hypotheses were substantiated or not. The

hypotheses H3 to H5 used linear regression analysis of the composite variables of the

roles of PKM (RPKM1 to RPKM7), the composite variables of the values of PKM for

individuals (IVPKM1 to IVPKM7) and the values of PKM for organisations (OVPKM1

to OVPKM7).

4.8.3 Hypothesis H1: PKM Skills are playing important roles in KM Cycle

The roles of PKM were measured by the 5 point Likert-scale from1 to 5, 1 was less

important, 2 was somewhat important, 3 was important, 4 was very important and 5 was

critical. The mean score for the seven PKM skills in the four KM processes are illustrated

in table 4.18 and figure 4.70.

KMC1 KMC2 KMC3 KMC4

Mean

Std. Deviation Mean

Std. Deviation Mean

Std. Deviation Mean

Std. Deviation

PKM1 4.25 .917 3.62 1.013 3.45 1.119 3.52 1.090

PKM2 4.18 .922 4.25 .869 3.77 1.075 4.14 .943

PKM3 3.96 .933 4.08 .904 4.10 .827 3.84 1.000

PKM4 3.99 1.014 4.43 .810 3.87 .966 4.39 .774

PKM5 3.59 1.031 4.07 .921 4.44 .799 4.06 .993

PKM6 3.22 1.150 3.83 1.080 4.53 .730 3.94 1.055

PKM7 3.28 1.125 3.27 1.174 3.42 1.135 3.28 1.151

Table 4. 18 : Mean Score of PKM Skills in KM Cycle Source: Developed for this research (Data analysis from PASW (SPSS))

The PKM skills scored 3.22 to 4.25 in locating / capturing knowledge (KMC1), 3.27 to

4.43 in creating knowledge (KMC2), 3.42 to 4.53 in sharing / transferring knowledge

(KMC3) and 3.28 to 4.39 in applying knowledge (KMC4). All seven PKM skills scored

above 3 in all KM processes, which indicated that the PKM skills were playing important

roles in the KM Cycle. Therefore, this hypothesis was substantiated.



Roles of PKM in KM Cycle

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Locate / Capture Create Share / Transfer Apply

Figure 4. 70 : Mean Score of the Roles of PKM in KM Cycle Source: Developed for this research (Data analysis from PASW (SPSS))

4.8.4 Hypothesis H2: PKM can benefit both individuals and organisations

There were two sub-hypotheses, namely H2a and H2b. H2a is to test if PKM could

benefit individuals and H2b was to test if PKM could benefit organisations. The values of

PKM were measured by the 5 point Likert-scale from 1 to 5, 1 was the lowest and 5 was

the highest.

4.8.3.1: H2a: PKM can benefit individuals.

The mean scores for the seven individual competences are illustrated in tables 4.19 and

4.20 and figure 4.71. The PKM skills were scored 3.09 to 4.56 in communication

competence (ICOMC), 3.06 to 4.33 in creativity competence (ICREC), 3.62 to 4.62 in

problem solving competence (IPBSC), 3.54 to 4.44 in learning / self development

competence (ILSDC), 3.06 to 4.35 in mental agility competence (IMEAC), 3.00 to 4.52

in analysis competence (IANAC) and 3.16 to 4.39 in reflecting competence (IREFC).



ICOMC ICREC IPBSC ILSDC

Mean

Std. Deviation Mean

Std. Deviation Mean

Std. Deviation Mean

Std. Deviation

PKM1 3.37 1.100 3.67 1.081 3.96 .904 4.08 .992

PKM2 3.71 1.009 4.09 .917 4.43 .734 4.30 .881

PKM3 3.82 1.037 3.96 .949 4.03 .894 4.24 .914

PKM4 3.95 .909 4.33 .864 4.62 .643 4.44 .786

PKM5 4.15 .922 4.17 .895 4.00 .963 3.85 1.026

PKM6 4.56 .715 4.00 .980 3.62 1.061 3.54 1.167

PKM7 3.09 1.129 3.06 1.138 3.12 1.117 3.18 1.234

Table 4. 19 : Mean Score of PKM Values for Individuals Competences


IMEAC IANAC IREFC

Mean

Std. Deviation Mean

Std. Deviation Mean

Std. Deviation

PKM1 3.91 1.023 3.78 .996 3.67 1.040

PKM2 4.19 .910 4.52 .703 4.15 .941

PKM3 4.07 .968 4.19 .850 4.09 .906

PKM4 4.35 .818 4.70 .596 4.39 .805

PKM5 3.83 .992 3.70 1.053 3.92 1.067

PKM6 3.60 1.090 3.52 1.146 3.72 1.155

PKM7 3.06 1.220 3.00 1.183 3.16 1.209

Table 4. 20 : Mean Score of PKM Values for Individuals’ Competences


Values of PKM for Individuals

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

ICOMC ICREC IPBSC ILSDC IMEAC IANAC IREFC

Figure 4. 71 : Mean Score of PKM Values for Individuals’ Competences Source: Developed for this research (Data analysis from PASW (SPSS))



All seven PKM skills were scored above 3 in all individuals competences, which

indicated that PKM could benefit individuals. Therefore, this hypothesis was

substantiated.

4.8.3.2: H2a: PKM can benefit organisations.

The mean scores for the five organisation competences are illustrated in table 21 and

figure 4.72. The PKM skills were scored 3.29 to 4.34 in external information awareness

competence (OEIAC), 3.42 to 4.38 in internal knowledge dissemination competence

(OIKDC), 3.33 to 4.58 in effective decision making competence (OEDMC), 3.30 to 4.26

in organisation focus competence (OORFC) and 3.43 to 4.38 in continuous innovation

competence (OCOIC).

OEIAC OIKDC OEDMC OORFC OCOIC

Mean

Std. Deviation Mean

Std. Deviation Mean

Std. Deviation Mean

Std. Deviation Mean

Std. Deviation

PKM1 4.21 .963 3.92 1.021 3.88 .986 3.76 1.007 4.01 .965

PKM2 4.26 .842 4.00 .955 4.41 .771 4.03 .934 4.22 .876

PKM3 4.08 .896 4.29 .827 4.20 .793 4.14 .875 4.09 .906

PKM4 4.34 .828 4.07 .897 4.58 .699 4.26 .842 4.37 .778

PKM5 4.12 .978 4.37 .784 4.22 .881 4.22 .859 4.38 .792

PKM6 3.88 1.120 4.38 .810 4.10 .924 3.94 .968 4.07 .973

PKM7 3.29 1.190 3.42 1.185 3.33 1.205 3.30 1.187 3.43 1.194

Table 4. 21 : Mean Score of PKM Values for Organisations’ Competences


All seven PKM skills were scored above 3 in all organisations competences, which

indicated that PKM could benefit organisations. Therefore, this hypothesis was

substantiated.



Values of PKM for Organisations

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

OEIAC OIKDC OEDMC OORFC OCOIC

Figure 4. 72 : Mean Score of PKM Values for Organisations’ Competences


4.8.5 Hypothesis H3: The values of the PKM for individuals are positively correlated to

the roles of the PKM skills in KM Cycle

There were seven sub-hypotheses, namely H3a to H3g, to test if the values of each PKM

skill for individuals were positively correlated to the roles of each PKM skill in the KM

cycle. As discussed in section 4.7, seven composite variables were created to represent

the roles of PKM (RPKM1 to RPKM7) and seven composite variables were created to

represent the values of PKM for individuals (IVPKM1 to IVPKM7). Linear regression

tests were performed and the results are summarised in table 4.22.



ANOVA Coefficients

R

R Square F Sig. B Beta T sig

(Constant) 1.116 6.366 .000

RPKM1 to IVPKM1 .736 .542 241.512 .000 .706 .736 15.541 .000

(Constant) -.491 -6.801 .000

RPKM2 to IVPKM2 .754 .569 269.518 .000 .714 .754 16.417 .000

(Constant) .436 5.105 .000

RPKM3 to IVPKM3 .704 .495 200.187 .000 .744 .704 14.149 .000

(Constant) .607 8.746 .000

RPKM4 to IVPKM4 .716 .513 215.052 .000 .674 .716 14.665 .000

(Constant) .513 6.584 .000

RPKM5 to IVPKM5 .700 .490 195.987 .000 .682 .700 14.000 .000

(Constant) .866 10.242 .000

RPKM6 to IVPKM6 .548 .300 87.429 .000 .520 .548 9.350 .000

(Constant) .268 1.521 .130

RPKM7 to IVPKM7 .760 .577 278.340 .000 .854 .760 16.684 .000

Table 4. 22 : Linear Regression of PKM Skills and PKM Values for Individuals Source: Developed for this research (Data analysis from PASW (SPSS))

4.8.5.1 H3a : The value of the Retrieving skill for individuals is positively correlated to

its role in PKM Cycle The regression result with F=241.512, p<0.0005, indicated that there are significant

correlations between the role of retrieving skill in KM cycle (RPKM1) and its value for

individuals (IVPKM1). The correlations R=0.736 and R2 = 0.542 indicated that 54.2% of

IVPKM1 was predicted by RPKM1 and the relationship could be also described by the

equation IVPKM1 = 0.706 x RPKM1 + 1.116. The results indicated that there was a

positive correlation between RPKM1 and IVPKM1; therefore, this hypothesis was

substantiated.

4.8.5.2 H3b : The value of the Evaluating skill for individuals is positively correlated to


correlations between the role of evaluating skill in KM cycle (RPKM2) and its value for


IVPKM2 is predicted by RPKM2 and the relationship could be described by the equation

IVPKM2 = 0.714 x RPKM2 – 0.491. The results indicated that there was a positive

correlation between RPKM2 and IVPKM2; therefore, this hypothesis was substantiated.



4.8.5.3 H3c: The value of the Organising skill for individuals is positively correlated to

its role in PKM Cycle The regression result with F=200.187, p<0.0005 indicated, that there are significant

correlations between the role of organising skill in KM cycle (RPKM3) and its value for



IVPKM3 = 0.744 x RPKM3 +0.436. The results indicated that there was a positive


4.8.5.4 H3d: The value of the Analysing skill for individuals is positively correlated to


correlations between the role of analysing skill in KM cycle (RPKM4) and its value for





4.8.5.5 H3e : The value of the Collaborating skill for individuals is positively correlated

to its role in PKM Cycle The regression result with F=195.987, p<0.0005, indicated that there are significant

correlations between the role of collaborating skill in KM cycle (RPKM5) and its value

for individuals (IVPKM5). The correlations R=0.700 and R2 = 0.490 indicated that 49%

of IVPKM5 is predicted by RPKM5 and the relationship could be described by the

equation IVPKM5 = 0.682 x RPKM5 +0.513. The results indicated that there was a


substantiated.

4.8.5.6 H3f : The value of the Presenting skill for individuals is positively correlated to


correlations between the role of presenting skill in KM cycle (RPKM6) and its value for

individuals (IVPKM6). The correlations R=0.548 and R2 = 0.300 indicated that 30% of






4.8.5.7 H3g: The value of the Securing skill for individuals is positively correlated to its

role in PKM Cycle The regression result with F=278.340, p<0.0005, indicated that there are significant

correlations between the role of securing skill in KM cycle (RPKM7) and its value for





4.8.6 Hypothesis H4: The values of PKM for organisations are positively correlated to

its roles in KM Cycle

There were seven sub-hypothesis, namely H4a to H4g, to test if the values of each PKM

skill for organisations were positively correlated to the roles of each PKM skill in the KM

cycle. As discussed in section 4.7, seven composite variables were created to represent

the role of PKM (RPKM1 to RPKM7) and seven composite variables were created to

represent the values of PKM for organisations (OVPKM1 to OVPKM7). Linear

regression tests were performed and the results are summarised in table 4.23.

ANOVA Coefficients

R

R Square F Sig. B Beta T sig

(Constant) 1.391 7.477 .000

RPKM1 to OVPKM1 .703 .494 198.909 .000 .680 .703 14.104 .000

(Constant) .476 6.183 .000

RPKM2 to OVPKM2 .742 .550 249.307 .000 .732 .742 15.789 .000

(Constant) -.460 -5.721 .000

RPKM3 to OVPKM3 .704 .495 200.337 .000 .701 .704 14.154 .000

(Constant) .514 7.138 .000

RPKM4 to OVPKM4 .722 .521 222.135 .000 .710 .722 14.904 .000

(Constant) -.125 -1.460 .000

RPKM5 to OVPKM5 .566 .320 96.104 .000 .526 .566 9.803 .000

(Constant) .925 10.285 .000

RPKM6 to OVPKM6 .492 .242 65.035 .000 .477 .492 8.064 .000

(Constant) .878 4.395 .000

RPKM7 to OVPKM7 .670 .448 165.779 .000 .747 .670 12.876 .000

Table 4. 23 : Linear Regression of PKM Skills and PKM Values for Organisations Source: Developed for this research (Data analysis from PASW (SPSS))



4.8.6.1 H4a : The value of the Retrieving skill for organisations is positively correlated


correlations between the role of retrieving skill in KM cycle (RPKM1) and its value for

organisations (OVPKM1). The correlations R=0.703 and R2 = 0.494 indicated that 49.4%

of OVPKM1 is predicted by RPKM1 and the relationship could be also described by the

equation OVPKM1 = 0.680 x RPKM1 + 1.391. The results indicated that there was a

positive correlation between RPKM1 and OVPKM1; therefore, this hypothesis was

substantiated.

4.8.6.2 H4b : The value of the Evaluating skill for organisations is positively correlated


correlations between the role of evaluating skill in KM cycle (RPKM2) and its value for

organisations (OVPKM2). The correlations R=0.742 and R2 = 0.550 indicated that 55%

of OVPKM2 is predicted by RPKM2 and the relationship could be described by the

equation OVPKM2 = 0.732 x RPKM2 +0.476. The results indicated that there was a


substantiated.

4.8.6.3 H4c: The value of the Organising skill for organisations is positively correlated


correlations between the role of organising skill in KM cycle (RPKM3) and its value for



equation OVPKM3 = 0.701 x RPKM3 -0.460. The results indicated that there was a


substantiated.

4.8.6.4 H4d: The value of the Analysing skill for organisations is positively correlated


correlations between the role of analysing skill in KM cycle (RPKM4) and its value for







substantiated.

4.8.6.5 H4e : The value of the Collaborating skill for organisations is positively

correlated to its role in PKM Cycle The regression result with F=96.104, p<0.0005, indicated that there are significant

correlations between the role of collaborating skill in KM cycle (RPKM5) and its value

for organisations (OVPKM5). The correlations R=0.566 and R2 = 0.320 indicated that

32% of OVPKM5 is predicted by RPKM5 and the relationship could be described by the

equation OVPKM5 = 0.526 x RPKM5 -0.125. The results indicated that there was a


substantiated.

4.8.6.6 H4f : The value of the Presenting skill for organisations is positively correlated


correlations between the role of presenting skill in KM cycle (RPKM6) and its value for





substantiated.

4.8.6.7 H4g: The value of the Securing skill for organisations is positively correlated to

its role in PKM Cycle

The regression result with F=165.779, p<0.0005, indicated that there are significant

correlations between the role of securing skill in KM cycle (RPKM7) and its value for





substantiated.



4.8.7 Hypothesis H5 : The values of PKM for organisations are positively correlated to

its values for individuals

There were seven sub-hypotheses, namely H5a to H5g, to test if the values of each PKM

skill for organisations were positively correlated to its value for individuals. As discussed

in section 4.7, seven composite variables were created to represent the values of PKM for

individuals (IVPKM1 to IVPKM7) and seven composite variables were created to

represent the values of PKM for organisations (OVPKM1 to OVPKM7). Linear

regression tests were performed and the results are summarised in table 4.24.

ANOVA Coefficients

R R

Square F Sig. B Beta T sig

(Constant) .987 5.765 .000

IVPKM1 to OVPKM1 .779 .606 313.85

2 .000 .786 .779 17.716 .000

(Constant) 1.113 34.742 .000

IVPKM2 to OVPKM2 .795 .632 349.63

2 .000 .829 .795 18.698 .000

(Constant) -.468 -6.383 .000

IVPKM3 to OVPKM3 .738 .545 244.47

1 .000 .695 .738 15.636 .000

(Constant) .248 3.338 .001

IVPKM4 to OVPKM4 .784 .614 324.39

9 .000 .819 .784 18.011 .000

(Constant) -.367 -4.819 .000

IVPKM5 to OVPKM5 .707 .499 203.41

3 .000 .674 .707 14.262 .000

(Constant) .637 6.667 .000

IVPKM6 to OVPKM6 .596 .355 112.31

7 .000 .609 .596 10.598 .000

(Constant) .895 6.556 .000

IVPKM7 to OVPKM7 .800 .639 361.52

0 .000 .794 .800 19.014 .000

Table 4. 24 : Linear Regression of PKM Skills and PKM Values for Organisations Source: Developed for this research (Data analysis from PASW (SPSS))

4.8.7.1 H5a : The value of the Retrieving skill for organisations is positively correlated

to its value for individuals The regression result with F=313.852, p<0.0005, indicated that there are significant

correlations between the value of retrieving skill for organisations (OVPKM1) and its

value for individuals (IVPKM1). The correlations R=0.779 and R2 = 0.606 indicated that

60.6% of OVPKM1 is predicted by IVPKM1 and the relationship could be also described

by the equation OVPKM1 = 0.786 x IVPKM1 + 0.987. The results indicated that there

was a positive correlation between OVPKM1 and IVPKM1; therefore, this hypothesis

was substantiated.



4.8.7.2 H5b : The value of the Evaluating skill for organisations is positively correlated


correlations between the value of evaluating skill for organisations (OVPKM2) and its



by the equation OVPKM2 = 0.829 x IVPKM2 + 1.113. The results indicated that there


was substantiated.

4.8.7.3 H5c : The value of the Organising skill for organisations is positively correlated


correlations between the value of organising skill for organisations (OVPKM3) and its



by the equation OVPKM3 = 0.695 x IVPKM3 -0.468. The results indicated that there was

a positive correlation between OVPKM3 and IVPKM3; therefore, this hypothesis was

substantiated.

4.8.7.4 H5d : The value of the Analysing skill for organisations is positively correlated


correlations between the value of analysing skill for organisations (OVPKM4) and its





substantiated.

4.8.7.5 H5e : The value of the Collaborating skill for organisations is positively

correlated to its value for individuals The regression result with F=203.413, p<0.0005, indicated that there are significant

correlations between the value of collaborating skill for organisations (OVPKM5) and its







substantiated.

4.8.7.6 H5f : The value of the Presenting skill for organisations is positively correlated


correlations between the value of presenting skill for organisations (OVPKM6) and its



by the equation OVPKM6 = 0.609 x IVPKM6 +0.637. The results indicated that there


was substantiated.

4.8.7.7 H5g: The value of the Securing skill for organisations is positively correlated to

its value for individuals The regression result with F=361.520, p<0.0005, indicated that there are significant

correlations between the value of securing skill for organisations (OVPKM7) and its



by the equation OVPKM7 = 0.794 x IVPKM7 +0.895. The results indicated that there


was substantiated.

4.8.8 Summary of Hypotheses Testings

The results of the hypotheses tests are summarised in table 4.25. In short, all tested

hypotheses were supported by the analyses. All seven PKM skills were found to play

important roles in the KM processes; PKM can provide benefits to improve the individual

and organisation competences; the values of PKM for individuals were positively

correlated to their roles in the KM processes; the values of PKM for organisations were

positively correlated to their roles in the KM processes and the values of PKM for

organisations were positively correlated to the values of PKM for individuals.



Hypotheses Results

H1: PKM skills are playing important roles in KM Cycle Supported

H2: PKM can benefit to both individuals and organisations Supported

H2a: PKM can benefit to individuals Supported

H2b: PKM can benefit to organisations Supported

H3. The values of the PKM for individuals are positively correlated to the roles of the

PKM skills in KM Cycle.

Supported

H3a : The value of the Retrieving skill for individuals is positively correlated to


Supported

H3b : The value of the Evaluating skill for individuals is positively correlated to


Supported

H3c : The value of the Organising skill for individuals is positively correlated to


Supported

H3d : The value of the Analysing skill for individuals is positively correlated to


Supported

H3e : The value of the Collaborating skill for individuals is positively correlated

to its role in PKM Cycle

Supported

H3f : The value of the Presenting skill for individuals is positively correlated to


Supported

H3e : The value of the Securing skill for individuals is positively correlated to its

role in PKM Cycle

Supported

H4. The values of PKM for organisations are positively correlated to its roles in KM

Cycle.

Supported

H4a : The value of the Retrieving skill for organisations is positively correlated


Supported

H4b : The value of the Evaluating skill for organisations is positively correlated


Supported

H4c : The value of the Organising skill for organisations is positively correlated


Supported

H4d : The value of the Analysing skill for organisations is positively correlated to


Supported

H4e : The value of the Collaborating skill for organisations is positively

correlated to its role in PKM Cycle

Supported

H4f : The value of the Presenting skill for organisations is positively correlated


Supported

H4e : The value of the Securing skill for organisations is positively correlated to


Supported

H5. The values of PKM for organisations are positively correlated to its values for

individuals.

Supported

H5a : The value of the Retrieving skill for organisations is positively correlated

to its value for individuals

Supported

H5b : The value of the Evaluating skill for organisations is positively correlated


Supported

H5c : The value of the Organising skill for organisations is positively correlated


Supported

H5d : The value of the Analysing skill for organisations is positively correlated


Supported

H5e : The value of the Collaborating skill for organisations is positively

correlated to its value for individuals

Supported

H5f : The value of the Presenting skill for organisations is positively correlated


Supported

H5g : The value of the Securing skill for organisations is positively correlated to

its value for individuals

Supported

Table 4. 25 : Hypotheses Tests Results Source: Developed for this research (Data analysis from PASW (SPSS))



4.9 Confirmatory Data Analysis by Structured Equation Modelling (SEM)

The hypotheses testing in section 4.8 provides an exploratory data analysis of the roles

and values of PKM. The SEM analysis in this section is intended to provide an

alternative way, the confirmatory approach as recommended by Byrne (2010), for the

analysis of the structural theory about the roles and values of PKM. SEM permits the

measurement of several variables and their interrelationships simultaneously and it is a

powerful statistical technique that combines measurement modelling or confirmatory

factor analysis (CFA) and structural modelling into a simultaneous statistical test (Hoe

2008).

In this section, the model was built based on the literature review in chapter 2 and the

results in section 4.8. The model for SEM testing is shown in figure 4.73. A partial

aggregation approach was used, as recommend by Baggozzi and Heatherton (1994).

Unlike the total aggregation approach, a partial aggregation approach was used to assess

the complex structural models and could retain the idea of a single underlying factor

(Bogozzi & Heatherton 1994; Heidt & Scott 2007; Hoe 2008). Heidt & Scott (2007)

proposed that in partial aggregation approach, a composite variable be created from the

items of each separate dimension of the construct and it becomes the single indicator of a

single factor model. SEM confirmatory factor analysis can then be performed to test an

overall model. This approach can provide greater substantive content for each variable

within a smaller matrix, less distraction from accumulated errors and thereby, greater

reliability (Bentler & Wu 1995; Leohlin 1992).



Figure 4. 73 : Model for SEM Analysis


The roles of PKM in the KM Process was represented by the latent variable R_PKM, the

PKM values for individuals was represented by the latent variable IV_PKM and the PKM

values for organisations was represented by the latent variable OV_PKM. Each latent

variable was constructed by the seven observed variables on the PKM skills towards the

latent variables.

The analysis was performed by AMOS 18, based on the data prepared for the PASW

(SPSS) analysis, as mentioned in section 4.7.

4.9.1 The Process of SEM Analysis in this research

Bryne (2010) mentioned that an SEM model can be decomposed into two sub-models: a

measurement model and a structural model; the measurement model defines the relations

between the observed variable and latent variables and the structural model defines the

relations between the latent variables. Kline (2005) suggested six basic steps to perform

the SEM, namely 1). Specify the model, 2). Determine if the model is identified, 3).

Select Measures, 4). Use a computer program to estimate the model, 5). If necessary,

respecify the model and 6). Describe the final model.



In this research, the processes of SEM analysis were divided into two stages. Stage 1 was

to assess the measurement model and stage 2 was to assess the structural model. The six

basic steps suggested by Kline(2005) were underlying the process in each stage. Besides,

Kline (2005) also suggested that data screening for SEM was crucial, as in all

multivariable analyses, which include the assessment of the normality, reliability and

validity. The reliability and validity of the latent variables were assessed in the

measurement model, as described in next section.

In SEM analysis, the non-normality of the data can be handled in two ways. Kline (2005)

stated that one way to deal with non-normality was transformation, and Bryne (2010)

mentioned that it could be handled by using the estimation approach, which does not have

normality assumed e.g. the Satorra-Bentler scaled statistical approach. In this research,

the transformation approach was selected as the transformation has been done in the

sections 4.8 during the exploratory data analysis, and also the Satorra-Bentler analysis is

not available in AMOS.

In this research, Maximum Likelihood (ML) estimation was selected. This estimation

procedure requires a sample size range from 200 to 500 (Byrne 2010; Hair, J. F. et al.

1998; Kline 2005). Hence, our final valid samples size of 206 was appropriate.

4.9.2 The Measurement Model

The measurement model, as illustrated in figure 4.74, has three latent variables, namely

R_PKM, IV_PKM and OV_PKM. Each latent variable has seven observed variables.

RPKM1 to RPKM7 were the observed variables for R_PKM, IV_PKM1 to IV_PKM7

were the observed variables for IV_PKM1 and OVPKM1 to OVPKM7 were the observed

variables for OV_PKM.

The reliability of the constructs was measured by the Cronbach’s Alpha coefficient and

the result is as shown in table 4.26. The Cronbach’s Alpha coefficient for R_PKM was

0.815, IV_PKM was 0.807 and IV_PKM was 0.829, all of which exceeded 0.8 and

considered to have good reliability, as suggested by Hair et al (1998).



Latent Variables

Cronbach’s

Alpha

Reliability (Hair, J.

F. et al. 1998).

R_PKM(RPKM1 to RPKM7) 0.815 Good

IV_PKM(IVPKM1 to IVPKM7) 0.807 Good

OV_PKM (OVPKM1 to OVPKM7) 0.829 Good

Table 4. 26 : Reliability Test of Measurement Model


The goodness-of-fit statistics for the measurement model are illustrated in table 4.27. The

results supported the model fit with normed chi-square, χ2 /df less than 3.0, CFI, IFI and

TFI greater than 0.9 and RMSEA less than 0.08, which indicated a good fit and provided

additional support for the model. Based on this criterion, the findings suggested that the

model fitted the sample data well and were significant at p<0.01.

Goodness-of-Fit Statistics : Measurement model

Fit Measures Acceptable Level

Model

Fit Result

Acceptable

(Yes/No)

CMIN/DF (χ2/df) 1< χ

2/df < 3.0

(Kline 2005)and (Byrne 2010)

1.774 Yes

CFI >0.9 (Hu & Bentler, 1999) 0.977 Yes

IFI >0.9 (Hu & Bentler, 1999) 0.978 Yes

TFI >0.9 (Hu & Bentler, 1999) 0.965 Yes

RMSEA <0.08 (Byrne 2010) 0.061 Yes

Conclusion: Good Fit

Table 4. 27 : Goodness-of-Fit of Measurement Model

Source: Developed for this research (Data analysis from AMOS)

Table 4.28 illustrates the standardised regression weights, the standard error (S.E.) and

critical ratio (C.R.) values of the indicator variables of the measurement model, as shown

in Figure 4.74. The standardised regression weights were greater than 0.50 and

significant, (p<0.01) and suggested that the indicators were good measures of the latent

constructs and provide evidence of convergent validity.



Standard

Regression

Estimate

S.E. C.R. *2

P

RPKM1 <--- R_PKM .790 Note *1

RPKM2 <--- R_PKM .888 .024 14.619 ***

RPKM3 <--- R_PKM .843 .023 13.821 ***

RPKM4 <--- R_PKM .879 .036 14.193 ***

RPKM5 <--- R_PKM .645 .027 9.905 ***

RPKM6 <--- R_PKM .605 .028 9.181 ***

RPKM7 <--- R_PKM .564 .089 8.898 ***

IVPKM1 <--- IV_PKM .833 Note *1

IVPKM2 <--- IV_PKM .892 .020 16.596 ***

IVPKM3 <--- IV_PKM .840 .022 15.216 ***

IVPKM4 <--- IV_PKM .880 .034 14.075 ***

IVPKM5 <--- IV_PKM .732 .023 12.436 ***

IVPKM6 <--- IV_PKM .677 .025 11.079 ***

IVPKM7 <--- IV_PKM .600 .090 10.490 ***

OVPKM1 <--- OV_PKM .831 Note *1

OVPKM2 <--- OV_PKM .928 .018 20.151 ***

OVPKM3 <--- OV_PKM .914 .019 17.573 ***

OVPKM4 <--- OV_PKM .909 .030 17.158 ***

OVPKM5 <--- OV_PKM .817 .023 13.500 ***

OVPKM6 <--- OV_PKM .742 .024 12.372 ***

OVPKM7 <--- OV_PKM .632 .092 11.046 *** Note 1: Constrained to fixed status, no S.E. and z-value calculated by AMOS.

Note 2: z-scores exceeding ± 1.96 (p<0.01) are considered significant

Table 4. 28 : Standard Regression of Measurement Model Source: Developed for this research (Data analysis from AMOS)



Figure 4. 74 : Measurement Model




4.9.3 The Structural Model

The measurement model was tested and described in previous section. This section

evaluates the structural models and re-visits the hypotheses of H3, H4 and H5 more

comprehensively by SEM. The strength and relationships between the latent variables of

the structural model were examined.

4.9.3.1 The Structural Model 1

The first model, as shown in figure 4.75, was examined. The goodness-of-fit statistics for

the structural model are illustrated in table 4.29. The results supported the model fit with

normed chi-square, χ2 /df less than 3.0, CFI, IFI and TFI greater than 0.9 and RMSEA

less than 0.08, which indicated a good fit and provides additional support to the model.

Based on this criterion, the finding suggested that the model fitted the sample data well

and was significant at p<0.01.

Goodness-of-Fit Statistics : structural model 1


Model

Fit Result

Acceptable

(Yes/No)


2/df < 3.0


2.077 Yes




RMSEA <0.08 (Byrne 2010) 0.072 Yes


Table 4. 29 : Goodness-of-Fit of Structural Model 1


As shown in figure 4.75, the standard regression coefficients of the roles of PKM

(R_PKM) ranged from 0.537 to 0.870, the values of PKM for individuals (IV_PKM)

ranged from 0.592 to 0.884 and the values of PKM for organisations (OV_PKM) ranged

from 0.617 to 0.928. The results indicated that all factors were strongly loaded onto their

own construct which showed a suitable representation of these three latent variables.

Furthermore, the paths in the structural model 1, as shown in table 4.30, were significantly

different from zero, with the standardised regression coefficients between R_PKM and

IV_PKM was 0.916 and between R_PKM and OV_PKM was 0.890.



Standard

Regression

Estimate

S.E. C.R. *1

P

OV_PKM <--- R_PKM .890 .067 13.585 ***

IV_PKM <--- R_PKM .916 .062 15.070 *** Note 1: z-scores exceeding ± 1.96 (p<0.01) are considered significant

Table 4. 30 : Standard Regression of Structural Model 1


Figure 4. 75 : Structural Model 1 Source: Developed for this research (Data analysis from AMOS

4.9.3.2 The Structural Model 2

The structural model 1 tested the relationships between the latent variables R_PKM and

IV_PKM and R_PKM and OV_PKM. A path was added between IV_PKM and

OV_PKM to build the model 2, as shown in figure 4.76. It was to test holistically, the



relationships between the three latent variables and also to compare any changes of the

standard regression coefficients between R_PKM and IV_PKM and RPKM and

OV_PKM.

The goodness-of-fit statistics for the structural model are illustrated in table 4.31. The

results supported the model fit with normed chi-square, χ2 /df less than 3.0, CFI, IFI and

TFI greater than 0.9 and RMSEA less than 0.08, which indicated a good fit and provided

additional support to the model. Based on this criterion, the findings suggested that the

model fitted the sample data well and was significant at p<0.01.

Goodness-of-Fit Statistics : Structural model 2


Model

Fit Result

Acceptable

(Yes/No)


2/df < 3.0


1.774 Yes




RMSEA <0.08 (Byrne 2010) 0.061 Yes


Table 4. 31 : Goodness-of-Fit of Structural Model 2


As shown in figure 4.76, the standard regression coefficients of the roles of PKM

(R_PKM) ranged from 0.564 to 0.888, the values of PKM for individuals (IV_PKM)

ranged from 0.600 to 0.892 and the values of PKM for organisations (OV_PKM) ranged

from 0.632 to 0.928. The results indicated that all factors were strongly loaded onto their

own construct, which showed a suitable representation of these three latent variables.

Furthermore, all paths in the structural model were significantly different from zero, with the

standardised regression coefficients of each construct ranging from 0.280 to 0.871. Therefore,

all three latent variables of R_PKM, IV_PKM and OV_PKM were found to be strongly

related to each other.

This structural model was than used for re-testing the hypotheses H3 to H5.



4.9.3.3 Hypothesis H3: The values of the PKM for individuals are positively correlated

to the roles of the PKM skills in KM Cycle

This hypothesis evaluated the correlation between the roles of PKM (R_PKM) and the

values of PKM for individuals (IV_PKM). The analysis found (see figure 4.76 and table

26) that the standard regression coefficient relating R_PKM and IV_PKM was 0.871

(p<0.001 and CR=14.13). This indicated that IV_PKM was positively correlated to

R_PKM. Therefore, this hypothesis was supported.

4.9.3.4 Hypothesis H4: The values of PKM for organisations are positively correlated to

its roles in KM Cycle

This hypothesis evaluated the correlation between the roles of PKM (R_PKM) and the

values of PKM for organisations (OV_PKM). The analysis found (see figure 4.76 and

table 4.32) that the standard regression coefficient relating R_PKM and OV_PKM was

0.280 (p<0.001 and CR=3.193). This indicated that OV_PKM was positively correlated

to R_PKM. Therefore, this hypothesis was supported

4.9.3.5 Hypothesis H5: The values of PKM for organisations are positively correlated to

its values for individuals

This hypothesis evaluated the correlation between the values of PKM for individuals

(IV_PKM) and the values of PKM for organisations (OV_PKM). The analysis found (see

figure 4.76 and table 4.32) that the standard regression coefficient relating IV_PKM and

OV_PKM was 0.642 (p<0.001 and CR=7.161). This indicated that OV_PKM was

positively correlated to IV_PKM. Therefore, this hypothesis was supported

Standard Regression Estimate S.E. C.R. *1

P

IV_PKM <--- R_PKM .871 .062 14.131 ***

OV_PKM <--- R_PKM .280 .089 3.193 .001

OV_PKM <--- IV_PKM .642 .091 7.161 *** Note 1: z-scores exceeding ± 1.96 (p<0.01) are considered significant

Table 4. 32 : Standard Regression of Structural Model 2




Figure 4. 76 : Structural Model 2 Source: Developed for this research (Data analysis from AMOS)

4.9.4 SEM Conclusion

SEM analysis was performed to test the measurement model and the structural model.

The results indicated that the measurement model, as shown in figure 4.9.2, and the

structural model, as shown in figure 4.75 (model 1) and figure 4.76 (model 2), were well

fitted for the analysis. The hypotheses H3, H4 and H5 were re-tested by the structural

model which provided a confirmatory analysis of the results from the exploratory analysis

described in section 4.8. The results of the SEM analysis indicated that all three

hypotheses were supported. Besides, it was noticed that the standard regression

coefficient between R_PKM and IV_PKM and R_PKM and OV_PKM changed when a

path was added between IV_PKM and OV_PKM. The coefficient between R_PKM and

IV_PKM was changed from 0.92 (model 1) to 0.87 (model 2) and R_PKM and OV_PKM

was changed from 0.89 (model 1) to 0.28 (model 2). The R_PKM to OV_PKM

coefficient was significant reduced for model 2, while the standard regression coefficient

of the new path between IV_PKM and OV_PKM was 0.64, which indicated that the

OV_PKM is significantly described by IV_PKM.




This chapter discussed the results of the data analysis performed by using PASW (SPSS)

18 and AMOS 18. The data preparation and screening were formed and the respondents

profile and the descriptive statistics were presented. The analysis was done by

exploratory data analysis and followed by confirmatory data analysis. The validity and

reliability of the constructs were tested and found to be acceptable. The normality was

also checked and transformation was undertaken prior the hypotheses testing. The

exploratory data analysis found that all hypotheses in this research were supported, and

the hypotheses related to the correlations between the roles of PKM, the values of PKM

for individuals, and the values of PKM for organisations were re-tested by SEM to

provide confirmatory results. The SEM was performed to test both the measurement

model and the structural model. The goodness-of-fit results indicated that both the

measurement model and structural model were suitable for analysis. The results of the

SEM on the structural model also indicated that the tested hypotheses were supported

which provided confirmation to the exploratory analysis results.

In order to draw an appropriate conclusion in answering the research questions, the

research results presented in this chapter have been considered in conjunction with the

literature review as presented in Chapter 2. The conclusions of this research are presented

together with the implications of the findings in next chapter to form the final part of this

thesis.

Chapter 5: Conclusions and Implications


CChhaapptteerr 55 -- CCoonncclluussiioonnss aanndd IImmpplliiccaattiioonnss



5.1 Introduction

Chapter 1 provided the background and objectives of this research and identified the

following research questions.



RQ3: Is there any correlation between the roles of PKM in the KM Process and

the values of PKM for individuals and organisations?



To answer the research questions, chapter 2 reviewed the literature in relation to

knowledge management, personal knowledge management, individual learning,

individual competences, organisational learning, and organisation competences. A gap

existing due to inadequate research that relates to the roles and values of the personal

knowledge management was recognised.

Based on the literature review, a conceptual model was developed, as described in

chapter 3, and the following main hypotheses were proposed to fill the gap in the

literature and answer the research questions.









Chapter 3 also provided the details on the research design and methodology employed in

the research. A quantitative approach was selected as the most appropriate method to test

the conceptual model. To collect the data, an online survey was used and invitations were



sent to knowledge management participants affiliated with the knowledge management

associations, interest groups and societies …etc. The methods used to analyse the data

and consideration of the ethical aspects were also discussed in this chapter.

The previous chapter (chapter 4) provided the data analysis results by both exploratory

data analysis and confirmatory data analysis. The software statistical analysis tools,

PASW (SPSS) 18 and AMOS 18 were used. Prior to carrying out the hypotheses tests,

the validity and reliability were tested for each construct, and the normality was also

checked to determine if any transformation was required. The exploratory data analysis

was performed by classical statistical analysis methods e.g. PCA and linear regression

testing. The confirmatory data analysis was performed using a Structural Equation Model

(SEM). All the proposed hypotheses in this research were supported by the results from

both the exploratory and confirmatory data analysis.

This chapter provides the conclusions and the theoretical and practical implications of

this research, and has seven sections. Section 5.1 is the introduction, section 5.2 provides

the answers to the research questions, section 5.3 describes the implications from the

research findings, section 5.4 describes the research contributions, section 5.5 presents

research limitations and section 5.6 provides the conclusion of this chapter. The structure

is illustrated in figure 5.1 below.

Figure 5. 1 : Structure of Chapter 5




5.2 Answers to Research Questions

In order to answer the research questions, five main hypotheses and twenty-three sub

hypotheses were developed. Table 5.1 summarises the related hypotheses in answering

the research questions. Hypothesis H1 was to answer the research question RQ1;

Hypothesis H2 (Sub-hypotheses H2a and H2b) was to answer the research question RQ2;

Hypothesis H3 (Sub-hypotheses H3a to H3g) and Hypothesis H4 (Sub-Hypotheses H4a

to H4g) were to answer the research question RQ3; and Hypothesis H5 (Sub-Hypotheses

H5a to H5g) was to answer the research RQ5.

This section provides conclusions of the relevant hypotheses in answer to the research

questions.

5.2.1 RQ1: What are the roles of PKM in the Knowledge Management Process?

Hypothesis H1 was proposed to answer the research question RQ1.


Under this hypothesis, the seven PKM skills were measured to examine the roles of PKM

in the KM Processes.

The literature review in chapter 2 stated that Avery et al. (2001) defined PKM as an

overall structured process for intentionally managing information and turning it into

useful knowledge. There were seven PKM skills in the proposed PKM framework,

namely (1) Retrieving information; (2) Evaluating information; (3) Organising

information; (4) Collaborating around information; (5) Analysing information; (6)

Presenting information; and (7) Securing information.

Seufert, Back and Krogh (2003) suggested four generic knowledge processes,

Locating/Capturing (KMC1), Creating (KMC2), Sharing/Transferring (KMC3) and

Applying (KMC4). These four processes were interactive instead of sequential and the

application of knowledge took the central role in the KM cycle.




RQ1: What are the roles

of PKM in Knowledge

Management Process?

H1. PKM skills

are playing

important roles in

the KM Cycle

N.A.

H2a: PKM can benefit individuals RQ2: What are the values

of PKM for individuals

and organisations?

H2. PKM can

benefit both

individuals and










H3e : The value of the Collaborating skill for individuals




H3. The values of

PKM for

individuals are

positively

correlated to the

roles of PKM skills

in the KM process.

H3e : The value of the Securing skill for individuals is











organisations is positively correlated to its role in PKM

Cycle



RQ3: Is there any


roles of PKM in KM

Process and the values of

PKM for individuals and

organisations?

H4. The values of

PKM for

organisations are

positively

correlated to the

roles of PKM skills

in the KM process.

H4e : The value of the Securing skill for organisations is











organisations is positively correlated to its value for

individuals

H5f : The value of the Presenting skill for organisations


RQ4: Is there any


values of PKM for

individuals and the values

of PKM for organisations

H5. The values of

PKM for

individuals are

positively

correlated to the

values of PKM for

the organisation.



Table 5. 1 : Research Questions and Hypotheses




The respondents were asked to rate (1 to 5, 1 is the lowest and 5 is the highest) the

importance of the role that the seven PKM skills were playing in their KM processes. The

results, as discussed in chapter 4, confirmed that the seven PKM skills were playing

important roles in all four KM processes. Table 5.2 summarises the mean scores of the

seven PKM skills in the four KM processes.

KMC1 KMC2 KMC3 KMC4

Mean

Std. Deviation Mean

Std. Deviation Mean

Std. Deviation Mean

Std. Deviation

PKM1 4.25 .917 3.62 1.013 3.45 1.119 3.52 1.090

PKM2 4.18 .922 4.25 .869 3.77 1.075 4.14 .943

PKM3 3.96 .933 4.08 .904 4.10 .827 3.84 1.000

PKM4 3.99 1.014 4.43 .810 3.87 .966 4.39 .774

PKM5 3.59 1.031 4.07 .921 4.44 .799 4.06 .993

PKM6 3.22 1.150 3.83 1.080 4.53 .730 3.94 1.055

PKM7 3.28 1.125 3.27 1.174 3.42 1.135 3.28 1.151

Table 5. 2 : The Means Score PKM skills in KM Processes Source: Developed for this research

(1) Retrieving Skill (PKM1)

Retrieving skill (PKM1), as shown in figure 5.2, was found to play an important

role in creating knowledge (KMC2), sharing / transferring knowledge (KMC3)

and applying knowledge (KMC4), and a very important role in capturing /

locating knowledge. The result was supported by previous scholars’ work in

which retrieving skill is an important skill of PKM (Agnihotri & Troutt 2009;

Avery et al. 2001; Berman & Annexstein 2003; Frand & Hixon 1999; Wright, K.

2005).

(2) Evaluating Skill (PKM2)

Evaluating skill (PKM2), as shown in figure 5.3, was found to play an important

to very important role in sharing / transferring knowledge and a very important to

critical role in capturing / locating knowledge (KMC1), creating knowledge

(KMC2) and applying knowledge (KMC4). This result is in line with various

scholars’ views that evaluating skill is an important skill in PKM (Agnihotri &

Troutt 2009; Avery et al. 2001; Berman & Annexstein 2003; Frand & Hixon

1999; Wright, K. 2005).



The Roles of Retrieving Skill

in KM Processes

0

1

2

3

4

5Locate / Capture (KMC1)

Create (KMC2)

Transfer / Share (KMC3)

Apply (KMC4)

Figure 5. 2 : The Roles of Retrieving Skill in KM Processes

Source: Developed for this Research

The Roles of Evaluating Skill

in KM Processes

0

1

2

3

4


Create (KMC2)


Apply (KMC4)

Figure 5. 3 : The Roles of Evaluating Skill in KM Processes Source: Developed for this Research



(3) Organising Skill (PKM3)

Organising skill (PKM3), as shown in figure 5.4, was found to play an important

to very important role in locating / capturing knowledge (KMC1) and applying

knowledge (KMC4) and very important to critical role in creating knowledge

(KMC2) and sharing / transferring knowledge (KMC4). The result was supported

by the arguments of various scholars that organising skill is indeed an important

skill in PKM (Agnihotri & Troutt 2009; Avery et al. 2001; Berman & Annexstein

2003; Frand & Hixon 1999; Wright, K. 2005).

The Roles of Organising Skill

in KM Processes

0

1

2

3

4


Create (KMC2)


Apply (KMC4)

Figure 5. 4 : The Roles of Organising Skill in KM Processes Source: Developed for this Research

(4) Analysing Skill (PKM4)

Analysing skill (PKM4), as shown in figure 5.5, was found to play a close to very

important role in capturing / locating knowledge (KMC1) and sharing /

transferring knowledge (PKM3) and a more than very important role in creating

knowledge (KMC2) and applying knowledge (PKM4). This result is in line with

different scholars’ views that it is an important skill in PKM (Agnihotri & Troutt

2009; Avery et al. 2001; Berman & Annexstein 2003; Frand & Hixon 1999;

Wright, K. 2005).



The Roles of Analysing Skill

in KM Processes

0

1

2

3

4


Create (KMC2)


Apply (KMC4)

Figure 5. 5 : The Roles of Analysing Skill in KM Processes Source: Developed for this Research

(5) Collaborating Skill

Collaborating skill (PKM5), as shown in figure 5.6, was found to play an

important to very important role in capturing / locating knowledge (KMC1) and a

very important to critical role in creating knowledge (KMC2), sharing /

transferring knowledge (KMC3) and applying knowledge (KMC4). It provided

empirical evidence to support different scholars’ views that collaborating skill is

important for PKM (Agnihotri & Troutt 2009; Avery et al. 2001; Berman &

Annexstein 2003; Wright, K. 2005).

(6) Presenting Skill (PKM6)

Presenting skill (PKM6), as shown in figure 5.7, was found to play an important

role in capturing / locating knowledge (KMC1), a close to very important role in

creating knowledge (KMC2) and applying knowledge (KMC4), and between very

important to critical role in sharing / transferring knowledge (KMC3). It was in

line with many scholars’ views that presenting skill is indeed an important PKM

skill (Agnihotri & Troutt 2009; Avery et al. 2001; Berman & Annexstein 2003;

Wright, K. 2005).



The Roles of Collaborating Skill

in KM Processes

0

1

2

3

4


Create (KMC2)


Apply (KMC4)

Figure 5. 6 : The Roles of Collaborating Skill in KM Processes


The Roles of Presenting Skill

in KM Processes

0

1

2

3

4


Create (KMC2)


Apply (KMC4)

Figure 5. 7 : The Roles of Presenting Skill in KM Processes




(7) Securing Skill (PKM7)

Securing skill (PKM7), as shown in figure 5.8, was found to play an important

role in capturing / locating knowledge (KMC1), creating knowledge (KMC2),

sharing / transferring knowledge (KMC3) and applying knowledge (KMC4). It is

noted that securing information skill is the lowest important skill among the other

PKM skills in all KM processes; however, the roles are still important. This result

is in line with different scholars’ views, such as Agnihotri and Troutt (2009),

Avert et al (2001), Berman and Annexstein (2003) and Wright (2005). Besides, it

also echoed Avery et al (2001)’s view that securing information skill is frequently

neglected by knowledge workers as an important skill.

The Roles of Securing Skill

in KM Processes

0

1

2

3

4


Create (KMC2)


Apply (KMC4)

Figure 5. 8 : The Roles of Securing Skill in KM Processes


5.2.1.1 Conclusion for Research Question 1

The results of this hypothesis confirmed that the seven PKM skills suggested by Avery et

al (2001) were all playing important roles in KM processes. The seven PKM skills were

rated differently in the knowledge management process and table 5.2, figures 5.2 to 5.8

summarises and provides a visual presentation of their roles in KM processes. It provides

empirical evidence to support the PKM framework proposed by Avery et al (2001).



5.2.2 RQ2: What are the values of PKM for individuals and organisations?

A hypothesis H2 was proposed to answer the research question RQ2. This hypothesis was

further divided into two sub-hypotheses to answer the question at the individual and

organisation levels.


H2a: PKM can benefit individuals

H2b: H2a: PKM can benefit organisations

Under these hypotheses, the values were measured in terms of the benefit contributed to

the improvement of competences by the seven PKM skills.

5.2.2.1 H2a: PKM can benefit individuals

The results, in table 5.3 and table 5.4, as discussed in chapter 4, confirmed that the seven

PKM skills have significant values (all above 3 in the 5 point Likert Scale) for the

individual competences. This result supported the hypothesis and concluded that PKM

can provide benefits to an individual’s competence in term of communication, creativity,

problem solving, learning and self development, mental agility, analysis and reflection.

This result supported the arguments of Wright (2005)’s PKM competence model and

Zuber-Skerritt (2005)’s PKM values and actions model.

Competences Communication Creativity Problem Solving

Learning / Self Development

PKM Skills Mean Rank Mean Rank Mean Rank Mean Rank

Retrieving 3.37 6 3.67 6 3.96 5 4.08 4

Evaluating 3.71 5 4.09 3 4.43 2 4.30 2

Organising 3.82 4 3.96 5 4.03 3 4.24 3

Analysing 3.95 3 4.33 1 4.62 1 4.44 1

Collaborating 4.15 2 4.17 2 4.00 4 3.85 5

Presenting 4.56 1 4.00 4 3.62 6 3.54 6

Securing 3.09 7 3.06 7 3.12 7 3.18 7

Table 5. 3 : Mean and Ranking of PKM Skills in Individuals Competences




Competences Mental Agility Analysis Reflection

PKM Skills Mean Rank Mean Rank Mean Rank

Retrieving 3.91 4 3.78 4 3.67 6

Evaluating 4.19 2 4.52 2 4.15 2

Organising 4.07 3 4.19 3 4.09 3

Analysing 4.35 1 4.70 1 4.39 1

Collaborating 3.83 5 3.70 5 3.92 4

Presenting 3.60 6 3.52 6 3.72 5

Securing 3.06 7 3.00 7 3.16 7

Table 5. 4 : Mean and Ranking of PKM Skills in Individuals Competences


(1) Communication Competence

Figure 5.9 illustrates the value of the PKM skills for communication competences.

The results indicated that all seven PKM skills (scored 3.09 to 4.56) have

significant values in communication competence. The presenting skill (4.56),

collaborating skill (4.15) and analysing skill (3.95) were the top three scored PKM

skills, followed by evaluating skill (3.82), organising skill (3.71) and retrieving

skill (3.71). Securing skill (3.09) was the lowest scored PKM skill.

(2) Creativity Competence

Figure 5.10 illustrates the value of the PKM skills for creativity competence. All

seven skills (scored 3.06 to 4.33) were found to have significantly contributed to

this competence. Analysing skill (4.33) scored the highest among all PKM Skills,

the second was collaborating skill (4.17) and the third was evaluating skill (4.09).

Presenting skill (4.00), organising skill (3.96) and retrieving skill (3.67) were

ranked from 4 to 6 and among all seven PKM skills and securing skill (3.06) was

the lowest scored.



PKM Values for

Communication Competence

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 5. 9 : PKM Values for Communication Competence


PKM Values for

Creativity Competence

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 5. 10 : PKM Values for Creativity Competence Source: Developed for this research



(3) Problem Solving Competence

Figure 5.11 illustrates the values of the PKM skills for problem solving

competence. The highest scored skill was analysing skill (4.62), the second was

evaluating skill (4.43) and was followed by organising skill (4.03). Collaborating

skill (4.00), retrieving skill (3.96) and presenting skill (3.62) were ranked from 4

to 6 respectively and the lowest scored skill was again the securing skill (3.12).

All seven PKM skills scored above 3 (3.12 to 4.62), which shows that they all

have significant values for problem solving competence.

PKM Values for

Problem Solving Competence

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 5. 11 : PKM Values for Problem Solving Competence Source: Developed for this research

(4) Learning / Self Development Competence

Figure 5.12 illustrates the value of the PKM skills for learning / self development

competences. The results indicate that all seven PKM skills (scores 3.18 to 4.44)

have significant values in this competence. Analysing skill (4.44), evaluating skill

(4.30) and organising skill (4.24) had the top three scores and were followed by

retrieving skill (4.08), collaborating skill (3.85) and presenting skill (3.54).

Securing skill (3.18) was the lowest scored PKM skill.



PKM Values for

Learning / Self Development Competence

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 5. 12 : PKM Values for Learning / Self Development Competence Source: Developed for this research

(5) Mental Agility Competence

Figure 5.13 illustrates the values of the PKM skills for mental agility competence.

All seven skills (scores 3.06 to 4.35) were found to have significantly contributed

to this competence. Analysing skill (4.35) scored the highest among all PKM

skills, the second was evaluating skill (4.19) and the third was organising skill

(4.07). Retrieving skill (3.91), collaborating skill (3.83) and presenting skill (3.6)

were ranked from 4 to 6 respectively. Among all the seven PKM skills, as for

other competencies, securing skill (3.06) was the lowest.



PKM Values for

Mental Agility Competence

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 5. 13 : PKM Values for Mental Agility Competence


(6) Analysis Competence

Figure 5.14 illustrates the values of the PKM skills for analysis competence.

Analysing skill (4.7), evaluating skill (4.52) and organising skill (4.19) were the

top three scored PKM skills, followed by retrieving skill (3.78), collaborating skill

(3.70) and presenting skill (3.52). The lowest scored skill was again securing skill

(3.0). All seven PKM skills scored above 3 (3.0 to 4.7), which shows that they all

have significant values for analysis competence.

(7) Reflection Competence

Figure 5.15 illustrates the value of the PKM skills for reflection competence. The

results indicated that all seven PKM skills (scores 3.16 to 4.39) have significant

values for reflection competence. Analysing skill (4.39), evaluating skill (4.15)

and organising skill (4.09) were the top three scored PKM skills, followed by

collaborating skill (3.92), presenting skill (3.72) and retrieving skill (3.67).

Securing skill (3.16) was the lowest scored PKM skill.



PKM Values for

Analysis Competence

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 5. 14 : PKM Values for Analysis Competence


PKM Values for

Reflection Competence

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 5. 15 : PKM Values for Reflection Competence




5.2.2.2 H2b: PKM can benefit organisations

The respondents were asked to rate (1 to 5, 1 is the lowest and 5 is the highest) the value

of the seven PKM skills in each of the organisation competences. The results, as

discussed in chapter 4, confirmed that the seven PKM skills all scored values above 3 in

each of the organisation competences. As shown in table 5.5, the mean score and ranking

of the seven PKM skills in the seven individual competences are summarised. This result

supported the hypothesis and concluded that PKM can benefit organisations in terms of

the competences in external information awareness, internal knowledge dissemination

competence, effective decision making, organisation focus and continuous innovation.

.

Organisation

Competences

External Information Awareness

Internal Knowledge

Dissemination

Effective Decision Making

Organisation Focus

Continuous Innovation

PKM Skills Mean Rank Mean Rank Mean Rank Mean Rank Mean Rank

Retrieving 4.21 3 3.92 6 3.88 6 3.76 6 4.01 6

Evaluating 4.26 2 4.00 5 4.41 2 4.03 4 4.22 3

Organising 4.08 5 4.29 3 4.20 4 4.14 3 4.09 4

Analysing 4.34 1 4.07 4 4.58 1 4.26 1 4.37 2

Collaborating 4.12 4 4.37 2 4.22 3 4.22 2 4.38 1

Presenting 3.88 6 4.38 1 4.10 5 3.94 5 4.07 5

Securing 3.29 7 3.42 7 3.33 7 3.30 7 3.43 7

Table 5. 5 : Mean and Ranking of PKM Skills in Organisations Competences


(1) External Information Awareness

Figure 5.16 illustrates the values of the PKM skills for external information

awareness competence. The highest scored skill was analysing skill (4.34), the

second was evaluating skill (4.26) and followed by retrieving skill (4.21).

Collaborating skill (4.12), organising skill (4.08), presenting skill (3.88) were

ranked from 4 to 6 respectively. The lowest scored skill was again securing skill

(3.29). All seven PKM skills were scored above 3 (3.29 to 4.34), which shows

that all these are have significant values for this competence.



PKM Values for

External Information Awareness Competence

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 5. 16 : PKM Values for External Information Awareness Competence


(2) Internal Knowledge Dissemination

Figure 5.17 illustrates the values of the PKM skills for internal knowledge

dissemination competence. All seven skills (scores 3.42 to 4.38) were found to

have significantly contributed to this competence. Presenting skill (4.38) has the

highest scored among all the PKM Skills, the second was collaborating skill

(4.37) and the third was organising skill (4.29). Analysing skill (4.07), evaluating

skill (4.00) and retrieving skill (3.88) ranked from 4 to 6 among all seven PKM

skills; securing skill (3.42) was the lowest.



PKM Values for

Internal Knowledge Dissimilation Competence

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 5. 17 : PKM Values for Internal Knowledge Dissemination Competence


PKM Values for

Effective Decision Making Competence

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 5. 18 : PKM Values for Effective Decision Making Competence




(3) Effective Decision Making

Figure 5.18 illustrates the values of the PKM skills for effective decision making

competences. The results indicated that all seven PKM skills (scores 3.33 to 4.58)

have significant values for this competence. Analysing skill (4.58), evaluating

skill (4.41), collaborating skill (4.22) were the top three scored PKM skills.

Organising skill (4.20), presenting skill (4.1) and retrieving skill (3.88) ranked

from 4 to 6 respectively. Securing skill (3.33) was the PKM skill with the lowest

score.

(4) Organisation Focus

Figure 5.19 illustrates the values of the PKM skills for organisation focus

competence. The highest scored skill was analysing skill (4.26), the second was

collaborating skill (4.22) and followed by organising skill (4.13). Ranked from 4

to 6 were evaluating skill (4.03), presenting skill (3.94) and retrieving skill (3.76)

respectively. The lowest scored skill was again securing skill (3.3).

All seven PKM skills were scored above 3 (3.3 to 4.26) which shows that these

are all have significant values for this competence.

PKM Values for

Organization Focus Competence

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 5. 19 : PKM Values for Organisation Focus Competence Source: Developed for this research



(5) Continuous Innovation

Figure 5.20 illustrates the value of the PKM skills for continuous innovation

competence. The results indicated that all seven PKM skills (scores 3.43 to 4.38)

have significant values for this competence. Collaborating skill (4.38), presenting

skill (4.37) and evaluating skill (4.22) were the top three scored PKM skills.

Organising skill (4.09), presenting skill (4.07) and retrieving skill (4.01) were

ranked from 4 to 6, and securing skill (3.43) was the lowest scored PKM skill.

PKM Values for

Continuous Innovation Competence

0

1

2

3

4

5Retrieving (PKM1)

Evaluating (PKM2)

Organising (PKM3)


Presenting (PKM6)

Securing (PKM7)

Figure 5. 20 : PKM Values for Continuous Innovation Competence



Both hypotheses H2a and H2b were supported by the test results. It indicated that PKM

can benefit both individual competences and organisation competences. In this research,

the individual competences were measured in terms of the meta-competences proposed

by Cheetham and Chivers (1996, 1998), which consist of communication, creativity,

problem solving, learning / self development, mental agility, analysis and reflection. The

organisation competences were measured in terms of the organisation IQ proposed by

Mendelson and Ziegler (1999) and Ziegler (2008), which consists of external information



awareness, internal knowledge dissemination, effective decision making, organisation

focus and continuous innovation.

5.2.3 RQ3: Is there any correlation between the roles of PKM in the KM Process and

the values of PKM for individuals and organisations?

Two hypotheses H3 and H4 were proposed to answer the research question RQ3. These 2

hypotheses were further divided into 14 sub-hypotheses (H3a to H3g and H4a to H4g) to

answer the question at the individual and organisation levels.

H3 - The values of PKM for individuals are positively correlated to the roles of


H4 - The values of PKM for organisations are positively correlated to the roles of


As discussed in chapter 4, these two hypotheses were tested by simple regression analysis

and structural equation modelling analysis. The simple regression was done by ANOVA

test on each of the sub-hypotheses (each PKM skill) and the structural equation modelling

provided a holistic view of the relationship.

5.2.3.1 H3: The values of PKM for individuals are positively correlated to the roles of


There are seven sub-hypotheses for this main hypothesis to test the strength of the

relationships in each PKM skill. The analyses were performed by ANOVA and SEM, as

described in chapter 4. The results supported this hypothesis and indicated that the value

of each PKM skill in individual competences has a significant relationship and was

positively correlated to their roles in the KM processes. This implies that the more

important the role of the PKM skill in a KM process, the higher the values for the

individual competences.

The strength of the correlations tested by ANOVA in terms of R2 are summarised in table

5.6 below and denote the percentage (30% to 57.7%) of the PKM skill’s values in

individuals competence (dependent variables), as explained by the roles played in the KM



processes (independent variables). The SEM results indicated that the standard

regression between the roles of PKM in the KM process (R_PKM) and the values of

PKM for individuals (IV_PKM) was 0.92, which indicated a significant positive

correlation between R_PKM and IV_PKM.

PKM Skills

R2

between Roles and Values

Retrieving .542

Evaluating .569

Organising .495

Analysing .513

Collaborating .490

Presenting .300

Securing .577

Table 5. 6 : The strength of relationship between the roles of PKM skills in KM Process and the

values of PKM skills in individuals competences


The implication of the result is that the PKM skills are influencing factors affecting the

values of the individual competences, i.e. improving the PKM Skills would lead to

improving individual competences.

5.2.3.2 H4: The values of PKM for organisations are positively correlated to the roles of


There are seven sub-hypotheses for this main hypothesis to test the strength of the

relationships in each PKM skill. The analyses were performed by ANOVA and SEM, as

described in chapter 4. The results supported this hypothesis and indicated that the values

of each PKM skill in organisation competences have a significant relationship and are

positively correlated to their roles in KM processes. This implies that the more important

the roles of the PKM skill in a KM process, the higher the values for the organisation

competencies.

The strength of the correlations tested by ANOVA in term of R2 are summarised in table

5.7 below which denotes the percentage (32% to 67%) of the PKM Skill’s values in



organisation competence (dependent variables), explained by the roles played in the KM

processes (independent variables). The SEM results indicated that the standard regression

between the roles of PKM in KM process (R_PKM) and the values of PKM for

organisations (OV_PKM) was 0.89, which indicated a significant positive correlation

between R_PKM and OV_PKM.

PKM Skills

R2

between Roles and Values

Retrieving .494

Evaluating .550

Organising .495

Analysing .521

Collaborating .320

Presenting .492

Securing .670

Table 5. 7 : The strength of relationship between the roles of PKM skills in KM Process and the

values of PKM skills in organisation competences


The implication of the result is that the roles of the PKM skills in KM processes are the

influencing factors to the values of the organisation competences, i.e. improving the PKM

skills for individual employees would lead to improvement in the organisation

competences.


The conclusion of the research question is that the roles of PKM skills in KM processes

were positively correlated to their contributions to the individual competences and

organisation competences. By improving the PKM skills for individuals, it would not

only lead to improvement of individual competences but also lead to improvement in the

organisation competences.

5.2.4 RQ4: Is there any correlation between the values of PKM for individuals and the




Hypothesis H5 was proposed to answer the research question RQ4. This hypothesis was

further divided into 7 sub-hypotheses (H5a to H5g) to answer the question at the

individual and organisation levels.

H5 - The values of PKM for individuals are positively correlated to the values of


As discussed in chapter 4, the hypotheses were tested by simple regression analysis and

structural equation modelling analysis. The simple regression was done by ANOVA

testing on each of the sub-hypotheses (each PKM skill), and the structural equation

modelling provided a holistic view of the relationship.

The results were presented in chapter 4 which supported the proposed hypotheses. It

indicated that the value of each PKM skill for individual competences has a significant

relationship and was positively correlated to their values for organisation competences. It

also implied that the benefits of PKM skills for organisation competences were

contributed by the benefits of PKM skills for individual competences.

The strengths of the correlations in term of R2 are summarised in table 5.8 below which

denotes the percentage (32% 5o 67%) of the PKM skill’s values in organisation

competence (dependent variables) explained by the roles played in the KM processes

(independent variables). The SEM results showed that the standard regression between

the values of PKM for individuals (IV_PKM) and the values of PKM for organisations

(OV_PKM) was 0.64, which indicated a significant positive correlation between

IV_PKM and OV_PKM.

From the SEM analysis, there were two models tested, as shown in figure 5.21. The first

model neglected the relationship between the IV_PKM and OV_PKM and the second

model connected these two latent variables together. By comparing the results between

the first model and second model, it was noticed that the standard regression coefficient

between the R_PKM and OV_PKM changed from 0.89 to 0.28 and to 0.64 between

IV_PKM and OV_PKM. Model 2 also indicated that the standard regression coefficient

of OV_PKM between IV_PKM (0.64) was greater than between R_PKM (0.28). The



results implied that there was an indirect benefit of PKM for the organisation that came

from the values of PKM for individuals (IV_PKM), i.e. a large portion of the OV_PKM

was actually influenced by IV_PKM.

PKM Skills

R2

between values for

individual competences and

organisation competences

Retrieving .606

Evaluating .632

Organising .545

Analysing .614

Collaborating .499

Presenting .355

Securing .639

Table 5. 8 : The strength of relationship between the PKM skills’ values for individual competences

and organisation competences Source: Developed for this research


This conclusion supported the argument by Argyris and Schon (1978) that individuals

are the agents for organisational learning and also supported the argument that PKM

skills benefitting individuals will turn into values for the organisation i.e. the

improvement of the individual competences by PKM skills can contribute to the

organisation competences.



Model 1

Model 2

Figure 5. 21 : Model 1 and Model 2 of SEM Analysis




5.2.5 Summary of the Research Questions Conclusion

There were four research questions answered in this thesis. The research questions were

defined to provide in-depth understanding of the roles of PKM and the values of PKM for

both individuals and organisations. In summary, the research findings concluded that

PKM has important roles in KM processes (section 5.2.1). The values of PKM were

found to have significant contribution (section 5.2.2) in both individual competences and

organisational competences. Positive correlations were found between the roles of PKM

and their values in contributing to individual competences and organisation competences

(section 5.2.3), and also between the values of PKM for individual competences and the

values of PKM for organisation competences (section 5.2.4).

These conclusions have impact on the future of PKM research as well as providing a

significant contribution to both theory and practice in the field of PKM.

5.3 Research Implications

This section outlines the implications of the findings from this research for the

development of theory, policy and practice.

5.3.1 Implications to Theory

The literature review, as discussed in chapter 2, provided an overview of the theories in

various disciplines which includes knowledge management, organisational learning,

personal knowledge management and individual learning. The findings from this research

contribute to the development of PKM theories in various disciplines, as discussed below.

(1) Implication for the Roles of PKM

The significant changes to the world economy, e.g. globalisation, the technology

revolution and the transition from the industrial age to the information and

knowledge age, in the past two decades have made many traditional sources of

competitive advantage obsolete (Durgin 2006), and the increasing competitive

pressures requires both organisations and individuals to have greater flexibility

and skills (Pauleen 2009a). The required skills should allow the tackling of the

major challenges e.g. emergence of knowledge work, onset of knowledge based



environment, increasing of virtual collaboration and the information

overload…etc. The roles of PKM have become more and more important, and

actually all seven PKM skills were found to play important roles in KM processes.

It implies that the KM processes require individuals to make use of their PKM

skills in locating/capturing, creating, transferring/sharing and applying

knowledge. The retrieving and evaluating skills are the paramount important skills

in locating / capturing knowledge. Organising skill, analysing skill are the highest

important skills in creating knowledge. Collaborating, presenting skill and

securing skill represent the three most important skills for transferring / sharing

knowledge. For applying knowledge, the most important PKM skills are

analysing, evaluating and collaborating.

These findings have the implications that PKM is a valuable area of study in KM

theory development, in particular to the theory related to KM process planning

and execution. PKM would be an enabler for KM process execution and by

providing resources and support to practice PKM would help to achieve the

desired outcome of the KM processes.

(2) Implication for the values of PKM for individuals

The findings for RQ1 confirmed that all seven PKM skills have significant value

in individual competences e.g. communication, creativity, problem solving,

learning / self-development, mental agility, analysis and reflection competence.

This finding has implications that PKM should be an area of study in the field of

individual learning theory. The individual competences would be improved by

practising PKM skills.

(3) Implication for the values of PKM for organisations

The findings for RQ2 confirmed that all seven PKM skills have significant values

in organisation competences e.g. external information awareness, internal

knowledge dissemination, effective decision making, organisation focus and

continuous innovation. This finding has implications that PKM should be an area

of study in the field of organisation learning theory. The organisation competence

would be improved by practising PKM at the individual employee level.



(4) Implication for the correlations between the roles of PKM and the values of

PKM

The findings of RQ3 echoed the findings of RQ1 and RQ2 related to the roles and

values of the PKM. It confirmed that the roles are positively correlated to the

values. This finding has implications for our better understanding about the

strength of the relationship between the role of each PKM skill to its value at both

the individual and organisation level. This relationship revealed the degree of

importance of each PKM skill in each KM process, and the benefits to be gained

in each of the competences.

(5) Implication for the correlations between the values of PKM for individuals

and values of PKM for organisations

The findings of RQ4 in this research indicated that there was a significant

relationship between the values of PKM for individual competence and values of

PKM for organisation competence. This finding is in line with the organisational

learning theory, described by Argyris and Schon (1978), that an individual is

acting as an agent in organisational learning. It does imply that PKM provides a

direct linkage between individual learning and organisation learning. By

improving the PKM skills of individual employees, it not only leads to the

improvement of individual competences but also to the improvement of

organisational competences.

The results from this research provide empirical evidence to support the assumption by

previous scholars that PKM is playing an important role in KM and has benefits for both

individuals and organisations. The following three PKM basic theories are summarised

from this research which provides the foundation for PKM development.

(1) The degree of the value contributed from the PKM skills in individual

competences is directly proportional to the degree of the role played in the

KM processes.



(2) The degree of the value contributed from the PKM skills in organisation

competences is directly proportional to the degree of the role played in the

KM processes.

(3) The degree of the value contributed from the PKM skills in organisation

competences is directly proportional to the degree of the value contributed by

the PKM skills to individual competences.

5.3.2 Implications to Policy and Practise

The values of the PKM have been examined in this research and the findings support the

proposed theoretical model, as defined in chapter 2. It has been concluded that PKM can

benefit both individual competences and organisation competences. It does imply that

both the individual and the organisation should have a strategy to put PKM into practice

and the following are the general guidelines contributed by this research for the

formulation of the PKM strategies.

5.3.2.1 PKM as a strategy for individuals

An individual PKM strategy provides a framework for individuals to improve their PKM

skills, to acquire their own knowledge and to transfer the knowledge to others. The

following framework provides general guidelines for defining and implementing a PKM

strategy.

(1) Treat PKM skills as a valuable asset for self development

Individuals should develop, practice and demonstrate their PKM skills whenever

possible in their daily personal situation and working environment. Smart use of

PKM skills is the key to success. Retrieving, evaluating, analysing and organising

skills enable an individual to have better personal information management and to

become an effective knowledge worker under the emerging knowledge based

environment and can tackle information overload. Collaborating, presenting and

securing skills enable an individual to have better inter-personal skills to deal with

the knowledge sharing. The PKM skills also improve problem solving and



reflection competence which directly benefit individuals to perform better, both in

their working and social environment.

(2) Develop the PKM skills which are required to meet individual learning

objectives and are aligned to organisational learning objectives

Individuals should understand what PKM skills are required in order to meet their

individual needs and align them to their organisational needs. Financial analysts

may focus on developing analytical aspects to get job advancement, while sales

managers may focus on developing presentation skills e.g. learning a new

language to develop a new market.

(3) Establish your own trustable information network through evaluating skill.

Individuals should build their own information network and evaluate its

trustworthiness. This can be achieved by simply developing an information

directory manually or by means of the PKM tools. Individuals should realise that

the information source, especially information available on the Internet, needs to

be evaluated very carefully.

(4) Develop a consistent approach to organising information

A consistent approach to managing information is important (Dorsey 2001). It can

help individuals to retrieve information fast, when needed. The consistent

approach can be achieved by systematically classifying the information by the

source of information, type of information or by the chronological order of the

information. The key is consistency in terms of how to classify the information

and the location of the storage.

(5) Build your own collaboration network

PKM is not an individual task and it should include peer-to-peer knowledge

sharing. Collaboration has been viewed as an important element in PKM (Avery

et al. 2001; Efimova 2005; Jarche 2010a; Wright, K. 2005) and therefore

individuals should develop their own collaboration network by identifying what

and how they can learn from their collaboration group members. It is not

necessary to have a formal collaboration structure but the individual should enjoy



and know the benefits of collaboration activities. A PKM 2.0 framework (Cheong

& Tsui 2010) which emphasises inter-personal knowledge transfer is discussed in

section 5.6.

(6) Identify and leverage of PKM Tools

There are many PKM toolkits available e.g. email, instant messaging, web log

(blog), search engine …etc. Agnihotri and Troutt (2009) mention that PKM Skills

and Tools should be matched in order to achieve optimised performance.

Therefore, individuals should be able to choose the appropriate PKM tools to fit

their own learning objectives, whether they are personal-related, social-related or

job-related. Tsui (2002a) classified PKM tools by their function as (1)

Index/Search, (2) Meta-search, (3) Associative links, (4) Information capturing

and Sharing, (5) Email management, analysis and Unified Messaging, (6) Voice

recognition, (7) collaboration and synchronisation and (8) Learning.

5.3.2.2 PKM Strategy for organisation

Organisational learning is not only the effort of individuals, and the organisation should

provide a positive learning environment to yield a positive outcome from individual

learning (Starbuck & Hedberg 2001). Kessels and Poell (2004) stated that knowledge

productivity requires personal involvement and individual learning, in a favourable social

context, and this work environment should be transformed into a conducive learning

environment, which should be able to encourage employees to become self-directed

learners, to pursue their interests, to find personal meaning, and to adapt to and if

necessary, change their life circumstances. Recently, there is increasing focus both in

academia and the business world on the topic of PKM for organisations e.g. a special

issue of a journal on PKM (Pauleen 2009b) published at On-line Information Review in

2009; the second international workshop PKM2010 (2010) was held at Duisburg,

Germany; a PKM seminar (Jarche 2010b) was held at IBM and a one-day PKM course

(2010) was held by The iSchool Institute of the University of Toronto at November 2010.

Therefore, an organisation should have a strategy to facilitate implementation of PKM.

The following is a general framework to guide an organisation in its task of implementing

their organisational PKM strategy.

(1) Treat PKM Skills as an asset for organisation.



Intellectual capital is the economic value of the intangible assets of organisational

and human capital and it is embedded in both people and systems (Stiles &

Kulvisaechana 2003). The stock of human capital consists of human (the

knowledge, skills and abilities of people), social (the valuable relationships among

people) and organisation (the processes and routines within the firm) (Wright, P.

M., Dunford & Snell 2001). Stiles and Kulvisaechana (2003) argued that there is a

large and growing body of evidence that demonstrates a positive linkage between

the development of human capital and organisational performance. Therefore, an

organisation should value individual PKM skills as an asset for business

development. Organisations should help individuals to maintain and improve their

PKM skills to meet organisational learning objectives. To manage an individual’s

PKM skills, it is necessary for the organisation itself to develop a battery of skills

and the means for measuring them.

(2) Develop a PKM Skills inventory as part of Human Capital Management.

Human capital is the productive wealth embodied in labour, skills and knowledge

(OECD 2001). The PKM skills inventory should be an element of human capital

management such that an organisation can easily access the learning capability of

each individual, knowing their strengths and weaknesses and matching the PKM

skills to different job profiles. The PKM skills inventory should cover all seven

skills of (1) Retrieving information; (2) Evaluating information; (3) Organising

information; (4) Collaborating around information; (5) Analysing information; (6)

Presenting information and (7) Securing Information.

(3) PKM Skills are part of the performance measurement and reward system.

The PKM skills should be measured regularly as part of performance evaluation

and as a factor when rewarding individuals. It will ensure that individuals are

aware of the importance of maintaining and improving their PKM skills. It is

important that different job profiles have different requirements regarding PKM

skills. The organisation should evaluate the PKM skills of individuals based on

their own job profiles.



(4) Develop an individual learning plan to acquire and improve PKM skills.

Having the PKM skills inventory and performance evaluation in place, the

organisation should work with individuals to develop a learning plan to improve

their PKM skills. This will help the individual to achieve the required PKM skills

for the next level of work and also will help the organisation to achieve the

organisational learning objectives via the individual learning agents.

(5) Leverage on IT based PKM tools to embed individual learning processes into

the organisational learning process.

The organisation can make use of IT technology to facilitate the implementation

of the PKM strategy. Tsui (2002a) argued that the PKM technologies should be

bottom-up, easy to install and powerful, search, information extraction and

categorisation tools. It is equally important that these tools are aligned to support

the tasks that are commonly performed by individuals. This means that the PKM

tools should enable the encapsulation of an individual’s learning processes into

the organisational learning process. By using the PKM tools, the individual user is

acting as a Learning Agent to provide input and feedback to the organisational

learning processes, while at the same time they can gain knowledge from it and

can contribute to the knowledge creation processes. Some knowledge can also be

captured by PKM tools and be available for future retrieval. Appropriate IT based

PKM toolkits cannot only motivate individuals to practice PKM but also can

improve their work performance.

5.4 Research Contribution

The research reported in this thesis has provided at least four major contributions which are

explained below. On top of that, three journal papers and two book chapters have been

published.

5.4.1 Avery et al (2001)’s PKM Skills Framework

This research provided empirical evidence to validate Avery et al (2001)’s PKM Skills

Framework. Avery et al (2001) based their work on the idea created by Paul Dorsey and

developed a conceptual framework in PKM Skills. Although this framework has

influenced various scholars in their work in PKM e.g. Berman and Annexstien (2003),



Wu (2007), Agnihotri and Troutt (2009) and Cheng (2009), this PKM skills framework

has not been thoroughly tested.

Berman and Annexstien (2003) based their work on Avery et al.’s PKM Skills framework

and developed another conceptual framework named PK-Book Model. It is focused on

personal information management and is mainly for software application design only.

Wu (2007) performed a PKM research based on Avery et al (2001)’s PKM Skills

Framework and the research focused on teachers in Taiwan only. Cheng (2009) also did

PKM research and his research focused on the pre-service teachers in Hong Kong only.

Although the research performed by Wu (2007) and Cheng (2009) have contributed to

the field of PKM, it was limited to teachers and pre-service teachers only and also applied

to a specific geographic location.

This research is the first global survey that focused on the roles and values of PKM, and

has no demographic, e.g. country of abode, industry, work position, education…etc,

limitation. The study included a rigorous testing of Avery et al (2001)’s PKM skills

framework by quantitative analysis. The research findings, as discussed in previous

sections, showed that all seven PKM skills proposed by Avery et al (2001) were playing

important roles in the four generic KM processes (locate / capture, create, transfer / share,

and apply knowledge) proposed by Seufert, Back and Krogh (2003). The values of the

seven PKM skills were examined, and the results indicated that there are significant

contributions to the individual competences in term of the meta-competences

(communication, creativity, problem solving, learning / self development, mental agility,

analysis and reflection) proposed by Cheetham and Chivers (1996, 1998). They also have

significant contributions to organisational competences in term of the organisational IQ

(external information awareness, internal knowledge dissemination, effective decision

making, organisation focus and continuous innovation) proposed by Mendelson and

Ziegler (1999) and Ziegler (2008).

5.4.2 PKM Theory for Roles and Values

This research has shed light onto the theoretical gap between the roles and values of

PKM. The literature indicates a lack of research on the roles and values of PKM and as a



result the importance and values of the PKM are not fully understood. This research

provides empirical evidence to support the assumption by many scholars in their PKM

work that PKM is important and has benefits to both individuals and organisations. This

research also generated 3 basic theories of PKM as discussed in section 5.3.1, and is the

first global survey of its kind, and has provided a foundation for further research in this

field of study.

5.4.3 Roles and Values Model of PKM

A roles and values model of PKM has been developed. The theoretical model tested in

this research describes the roles and values of PKM. The model was redrawn and is

presented as shown in figure 5.22. The model was tested by both exploratory data

analysis and confirmatory data analysis, and can provide a foundation for further

research. It can be applied in various disciplines e.g. knowledge management,

organisational learning, individual learning, business process management, human

resource management…etc.

Figure 5. 22 : Roles and Values PKM Model Source: Developed for this research

Problem Solving

Communication

Creativity

Analysis

Learning

Mental Agility

Reflection

Personal Meta Competences

Problem Solving

Communication

Creativity

Analysis

Learning

Mental Agility

Reflection

Problem Solving

Communication

Creativity

Analysis

Learning

Mental Agility

Reflection

Personal Meta Competences

Retrieving Information

Evaluation Information

Organising Information

Collaborating Information

Analysing Information

Presenting Information

Securing Information

PKM Skills















PKM Skills


Internal Knowledge Dissemination

Effective Decision Architecture

Organisational Focus

Continue Innovation

Organisational Performance


Internal Knowledge Dissemination

Effective Decision Architecture

Organisational Focus

Continue Innovation

Organisational Performance

Locate / capture

Transfer / Share

Create

Apply

KM Process

Locate / capture

Transfer / Share

Create

Apply

KM Process



5.4.4 Individual learning in organisational learning

This research has supplemented previous literature on the importance of individual

learning in organisational learning. As discussed in chapter 2 (section 2.3.1.1), previous

organisational learning theory addressed the importance of individual participation. For

example, Argyris and Schon (1978) argued that the main stream of organisational

learning considers individuals as “agents” for organisations to learn; Nonaka (1991)

argued that new knowledge always begins with the individual, and making personal

knowledge available to others is the central activity of the knowledge creation company.

Scarbrough, Swan and Preston (1998) mentioned that a learning organisation should

primarily focus on valuing, managing and enhancing the individual development of its

employees; Kim (1993) and Matley (2000) argued that the relationship between

individual and organisation learning is an important aspect. This research has confirmed

the previous literature about the argument of the importance of individual learning in

organisation learning and PKM bridging the gap between individual learning and

organisation learning. By practicing PKM, not only the individual competence but also

the organisation competence would be improved.

5.5 Research Limitation

This study has few limitations that need to be acknowledged. Although this research has

provided relevant and interesting insights into the understanding of roles and values of PKM,

it is important to recognise the limitations.

The first limitation is the background of the targeted respondents in that they were all

knowledge management participants, even though the demographic information analysis

showed that the respondents were distributed over wide range of countries, industries, work

positions, age groups, work experience and education. They all had a background in

knowledge management and potentially had bias in valuing the roles and values of the PKM

in this research. However, the author believes that knowledge management participants are

still the preferred targeted respondents as PKM is an under-explored discipline and most

people may not be aware of it, in a systematic manner, even if they are doing the PKM work

in their daily activities. By having a background in knowledge management, they could easily

understand the concepts and answer the survey questions more accurately. Further research is

required to extend the target respondent beyond the knowledge management communities.



The second limitation is that the individual competences and organisation competences in this

research were those generic competences which are suitable in most situations; however, it is

acknowledged that certain individuals or organisations may require some specific

competences which were not covered in the theoretical model. Hence, further research is

required by augmenting the existing set of competences to meet different contextual

requirements.


This research investigated the roles and values of PKM. A theoretical model was

developed, as described in chapter 3, based on the literature review, as discussed in

chapter 2. There was a gap found in the literature where there was a lack of research

investigating the roles and values of PKM. The research questions and hypotheses were

proposed to address this research problem and aimed to provide better understanding in

this area.

Quantitative research was used based on the evaluation of the various research paradigms

and methodologies discussed in chapter 3. An online questionnaire was used to

administrate the survey. The online survey enabled this study to reach a wide range of

respondents in different countries in a timely and cost effective manner.

The data analysis, as discussed in chapter 4, used a two steps approach to perform

exploratory data analysis and confirmatory data analysis. The exploratory data analysis

was performed by factor analysis, correlation analysis and simple regression using

ANOVA…etc. The confirmatory data analysis was carried out by structural equation

modelling, and the data analysis was performed by the software tools PASW (SPSS) 18

and AMOS 18.

Finally, the conclusions and implications are presented in this chapter. The research

found that PKM was playing an important role in KM processes and had significant value

in both individual competences and organisation competences. In addition, the study

showed that there were positive correlations between the roles of PKM in KM processes

and the values of the PKM for both individuals and organisations. Moreover, it was found

that there were positive correlations between the values of PKM for individuals and the

values of PKM for organisations.

Chapter 6: Future Work


CChhaapptteerr 66 –– FFuuttuurree WWoorrkk



6.1 Introduction

The previous chapter provided the conclusions and implications of the research findings.

This final chapter of the thesis proposes further research work to be done in the future. A

conceptual model namely PKM 2.0 is proposed and a research agenda is outlined at the

end of section 6.2. Section 6.3 provides a chapter conclusion to summarise the proposed

future research work.

Figure 6. 1 : Structure of Chapter 6


6.2 Future Research

Following the findings of this research, a conceptual model of PKM 2.0 was developed.

There are four core components in this model, namely Personal Information Management

(PIM), Personal Knowledge Internalisation (PKI), Personal Wisdom Creation (PCW) and

Inter-Personal Knowledge Transferring (IKT). The interaction of the components is

illustrated in figure 6.2 and table 6.1 and provides more a detailed view of the model in

terms of the required skill/competence, the belonging aspect of the DIKW transformation,

the inherent knowledge conversion and the involved KM process.



Figure 6. 2: PKM 2.0 Conceptual Model Source: Developed for this research

PKM 2.0

Components

Personal

Information

Management

(PIM)

Personal

Knowledge

Internalisation

(PKI)

Personal

Wisdom

Creation

(PWC)

Inter-Personal

Knowledge

Transferring

(IKT)

Skill / Competence

Ret

riev

ing

Eval

uat

ing

Org

an

isin

g

An

aly

sis

Lea

rnin

g /

Sel

f

Dev

elo

pm

en

t

Ref

lect

ion

Pro

ble

m S

olv

ing

Cre

ativ

ity

Men

tal

Ag

ilit

y

Secu

rin

g

Pre

sen

tin

g a

nd

Co

mm

un

icat

ion

Co

llab

ora

tin

g

DIKW

Transformation

Layer

Data

� �

Information

Information

� �

Knowledge

Knowledge

� �

Wisdom

Information /

Knowledge

� � Information /

Knowledge

Knowledge

Conversion

Explicit

� �

Explicit

Explicit

�

Tacit

Tacit

� �

Tacit/Explicit

Explicit / Tacit

� �

Explicit / Tacit

KM Process Capture /

Locate

Create Apply Transfer / Share

Table 6. 1: PKM 2.0 Conceptual Model




The PKM 2.0 framework was developed based on the DIKW hierarchy defined by

Russell Ackoff (1989), the knowledge conversion framework suggested by Nonaka and

Takeuchi (1995) and the KM process described by Seufert et al. (2003).

(1) Personal Information Management (PIM)

PIM is the focus of many scholars in the area of PKM. It is the process of

capturing or locating knowledge, as defined by Seufert et al. (2003). It transforms

data to information and vice versa, and it deals with past knowledge, as argued by

Russell Ackoff (1989). Knowledge conversion is in the form of explicit

knowledge (from one media, e.g. hard copy, to another media, e.g. electronic

copy), and is the combination process as suggested by Nonaka and Takeuchi

(1995). The PIM is the foundation of PKM 2.0, where individuals are able to

create their own knowledge database for immediate or future use in this process.

The required skills / competences in PIM are retrieving, evaluating and

organising, which are the skills playing significant roles in capture / locate

knowledge, based on the results of this research.

(2) Personal Knowledge Internalisation (PKI)

PKI is the process of creating knowledge in the KM cycles, suggested by Seufert

et al. (2003). It transforms information to knowledge and vice versa. It requires

understanding of past knowledge and current information / knowledge available to

an individual. It is the understanding aspect as mentioned by Russell Ackoff

(1989), between knowledge and wisdom. Knowledge conversion is mainly in the

form of explicit to tacit knowledge; it is the internalisation process in the SECI

model (Nonaka & Takeuchi 1995). PKI is beyond PIM as it turns past knowledge

into new knowledge.

The required competences in PKI are analysis, learning / self development and

reflection. Based on the results from this research, the highest scored PKM skills

in creating knowledge were evaluating, organising and analysing; these PKM

skills were also the biggest contributor for analysis competence, learning / self

development competence and reflection competence.



(3) Personal Wisdom Creation (PWC)

PWC is the process of applying knowledge in the KM cycles, suggested by

Seufert et al. (2003). It transforms knowledge to wisdom, putting the knowledge

in practise to tackle the current challenges and prepare for the future, as argued by

Ackoff (1989), who stated that wisdom is dealing with the future. The knowledge

conversion in this process is between tacit to tacit/explicit; it involves the

socialisation and externalisation process in the SECI model (Nonaka & Takeuchi

1995). PWC is beyond PKI as it puts knowledge in practice in tackling the daily

challenges from personal life, social life and work.

The required skills / competences in PWC are problem solving, creativity and

mental agility. Based on the research results, it was found that organising,

evaluating, analysing and collaborating were the highest scored PKM skills in

applying knowledge. These PKM skills were also the essential skills for problem

solving competence, creativity competence and mental agility competence.

(4) Interpersonal Knowledge Transferring (IKT)

IKT plays an important role in PKM 2.0 which maximises the knowledge work by

others to form a knowledge collaborating environment for individuals. It is the

process to share / transfer knowledge in the KM cycles suggested by Seufert et al.

(2003). It transforms the information and knowledge in both explicit and tacit

forms. It is a bidirectional transfer through different social activities in both face-

to-face and virtual models. IKT is beyond PIM, PKI and PWC as it positions

PKM 2.0 in an interactive and collaborating mode. It links the networked

individuals together and gears the distributed process of socialisation,

externalisation, combination and internalisation (Nonaka & Takeuchi 1995) in a

meshed knowledge network to increase the knowledge flow and knowledge

quality.

The required skills / competence in IKT are securing, presenting and

communication, and collaborating. Based on the findings from this research, it

was found that collaborating and presenting are the essential PKM skills for



sharing / transferring knowledge. They are also the essential skills for

communication competence. Although securing information skills always scored

the lowest among the seven PKM skills in all KM process, it is believed that it is

an essential PKM skill in sharing / transferring knowledge, and actually securing

information skill obtained the highest score in the sharing / transferring

knowledge process.

The concept of PKM 2.0 is a dynamic framework where PIM, PKI, PCW and IKT are

working in an interactive mode. It is leveraging the knowledge and wisdom of the

networked individuals to produce a meshed knowledge network. This PKM 2.0 model

still needs to be validated. It is recommended to undertake future research based on this

conceptual model to investigate the relationship between these 4 interactive components.

The research agenda should also include the following:

(1) Explore and test the interaction and correlations between PIM, PKI, PCW and

IKT.

(2) Validate the skills and competences associated with each concept of the PKM 2.0

model.

(3) Apply the PKM 2.0 model in different individual and organisation contexts.

(4) Explore and test any influencing factors between the individual and the

organisation.

(5) Explore how the recent development of Web 2.0-based PKM tools can enable

PKM 2.0 model to maximise the contribution to competency improvement.


At the end of this research, a PKM 2.0 conceptual model was developed which consists

of four key elements, namely personal information management (PIM), personal

knowledge internalisation (PKI), personal knowledge creation (PKC) and inter-personal

knowledge transferring (IKT). Future research is proposed, based on this conceptual

model, which can set the foundations for the future research in the area of personal

knowledge management.

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Appendix


Appendix 1 – Questionnaire

Appendix


Appendix


Appendix


Appendix


Appendix


Appendix


Appendix


Appendix


Appendix


Appendix


Appendix


Appendix


Appendix


Appendix 2 – List of KM Organisations invited to distribute the Survey

KM Organisations

actKM Forum

American Society for Information Science and Technology (ASIS&T) Special Interest

Group on Knowledge Management (SIG-KM)

Boston KM Forum

BRINT Institute (Knowledge Management)

ChinaKM.com

Gurteen Knowledge Forum

Hong Kong Knowledge Management Society

International Federation of Library Associations and Institutions (KM Interests Group)

KM Africa

KM Australia

KM for Development (KM4Dev)

KM Talk

Knowledge Board

Knowledge Management Professional Society (KMPro)

Knowledge Management Resource Centre (KMRC) - Hong Kong Polytechnic

University

Knowledge Management Society of Japan

Knowledge Management.uk.net

LawyerKM

Multimedia Knowledge Management (MMKM)

Personal Knowledge Management Community (Max Volkel)

personalKM @ Twitter

Swiss Knowledge Management Forum

Appendix


Appendix 3 – Distribution of the respondents by country

Country of Abode


Appendix


Appendix 4 - SEM Results Output

Appendix 4.1 – Measurement Mode

Model Fit Summary

CMIN

Model NPAR CMIN DF P CMIN/DF

Default model 116 241.253 136 .000 1.774

Saturated model 252 .000 0

Independence model 42 4815.561 210 .000 22.931

Baseline Comparisons

Model NFI

Delta1

RFI

rho1

IFI

Delta2

TLI

rho2 CFI

Default model .950 .923 .978 .965 .977

Saturated model 1.000 1.000 1.000

Independence model .000 .000 .000 .000 .000

Parsimony-Adjusted Measures

Model PRATIO PNFI PCFI

Default model .648 .615 .633

Saturated model .000 .000 .000

Independence model 1.000 .000 .000

NCP

Model NCP LO 90 HI 90

Default model 105.253 65.828 152.528


Independence model 4605.561 4383.307 4835.063

FMIN

Model FMIN F0 LO 90 HI 90

Default model 1.177 .513 .321 .744

Saturated model .000 .000 .000 .000

Independence model 23.491 22.466 21.382 23.586

RMSEA

Model RMSEA LO 90 HI 90 PCLOSE

Default model .061 .049 .074 .070

Independence model .327 .319 .335 .000

Appendix


AIC

Model AIC BCC BIC CAIC

Default model 473.253 501.143

Saturated model 504.000 564.590

Independence model 4899.561 4909.659

ECVI

Model ECVI LO 90 HI 90 MECVI

Default model 2.309 2.116 2.539 2.445

Saturated model 2.459 2.459 2.459 2.754


HOELTER

Model HOELTER

.05

HOELTER

.01

Default model 140 151

Independence model 11 12

Appendix


Appendix 4.2 – Structural Model 1

Model Fit Summary

CMIN


Default model 116 282.521 136 .000 2.077




Model NFI

Delta1

RFI

rho1

IFI

Delta2

TLI

rho2 CFI

Default model .941 .909 .969 .951 .968








NCP


Default model 146.521 102.174 198.633



FMIN


Default model 1.378 .715 .498 .969



RMSEA


Default model .072 .061 .084 .001


Appendix


AIC





ECVI


Default model 2.510 2.294 2.764 2.646

Saturated model 2.459 2.459 2.459 2.754


HOELTER

Model HOELTER

.05

HOELTER

.01



Appendix


Appendix 2.3 – Structural Model 2

Model Fit Summary

CMIN


Default model 116 241.253 136 .000 1.774




Model NFI

Delta1

RFI

rho1

IFI

Delta2

TLI

rho2 CFI

Default model .950 .923 .978 .965 .977








NCP


Default model 105.253 65.828 152.528



FMIN


Default model 1.177 .513 .321 .744



RMSEA


Default model .061 .049 .074 .070


Appendix


AIC





ECVI


Default model 2.309 2.116 2.539 2.445

Saturated model 2.459 2.459 2.459 2.754


HOELTER

Model HOELTER

.05

HOELTER

.01



The roles and values of personal knowledge management

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