© COPYRIG

HT UPM

UNIVERSITI PUTRA MALAYSIA

INTEGRATED TACIT KNOWLEDGE SHARING MODEL AMONG

MEDICAL PRACTITIONERS OVER SOCIAL MEDIA WITH MODERATOR AND MEDIATOR INTERACTION EFFECT

ASRA AMIDI

FSKTM 2018 1

© COPYRIG

HT UPMINTEGRATED TACIT KNOWLEDGE SHARING MODEL AMONG

MEDICAL PRACTITIONERS OVER SOCIAL MEDIA WITH MODERATOR AND MEDIATOR INTERACTION EFFECT

By

ASRA AMIDI

Thesis Submitted to the School of Graduate Studies, Universiti Putra Malaysia, in Fulfillment of the Requirements for the Degree of Doctor of Philosophy

January 2018

© COPYRIG

HT UPM

COPYRIGHT

All material contained within the thesis, including without limitation text, logos, icons,

photographs and all other artwork, is copyright material of Universiti Putra Malaysia

unless otherwise stated. Use may be made of any material contained within the thesis

for non-commercial purposes from the copyright holder. Commercial use of material

may only be made with the express, prior, written permission of Universiti Putra

Malaysia.

Copyright © Universiti Putra Malaysia

© COPYRIG

HT UPM

DEDICATION

Dedicated To My Beloved Husband, Mohammad

© COPYRIG

HT UPM

i

Abstract of thesis presented to the Senate of Universiti Putra Malaysia in fulfillment

of the requirement for the degree of Doctor of Philosophy

INTEGRATED TACIT KNOWLEDGE SHARING MODEL AMONG MEDICAL PRACTITIONERS OVER SOCIAL MEDIA WITH MODERATOR AND MEDIATOR INTERACTION EFFECT

By

ASRA AMIDI

January 2018

Chairman : Associate Professor Yusmadi Yah Binti Jusoh, PhD Faculty : Computer Science and Information Technology

Codification of tacit knowledge is significant challenge of knowledge management.

The importance of tacit knowledge has been pointed out in relation to decision-

making, time-management, quality and competitiveness. Tacit knowledge of medical

practitioners is an important source of experiential know-how. However, due to

various operational and technical reasons, such health-care knowledge is not entirely

harnessed and put into professional practice. There are still issues with the socio-

technical factors that influence the success of knowledge management implementation

in the dissemination of tacit knowledge. Many of the traditional methods of knowledge

diffusion like manuals and lectures are unsuitable for tacit knowledge. Hence, with

the advent of social web tools, several studies argued that these new technologies may

provide new opportunities to facilitate tacit knowledge sharing among medical

practitioners. In spite of these arguments, there is still a lack of understanding and an

empirical research on how social media may facilitate tacit knowledge sharing. The

First objectives of this study are to explore the potential contributions of socio-

personal and technical factors which support tacit knowledge sharing among medical

practitioners in social media. The second objective of the study was to propose an integrated tacit knowledge sharing model among medical practitioners via social media. The third objective of this study is to develop and evaluate the prototype of the tacit knowledge sharing model.

Critical review of existing model and synthesizing the component of existing model

was conducted to identify the factors influencing tacit knowledge sharing over social

media. There are two influential dimensions namely socio personal and technical were

identified. Therefore, conceptual model of this study was developed. Next, experts in

© COPYRIG

HT UPM

ii

knowledge management and social media review and validate the conceptual model. A pilot study was conducted to have the reliability and validity of the instrument.

The empirical study was conducted to validate tacit knowledge sharing model. There

are 366 medical practitioner in the state of Selangor in Malaysia participated in this

study. The collected data was analysed using Structural Equation Modelling (SEM-

PLS).The empirical results showed that the model properly fit the data and the

structural model demonstrated that the technical factors: perceived ease of use,

perceived usefulness and perceived enjoyment, significantly played a positive role in

tacit knowledge sharing. The analytical results also revealed that the socio-personal

factors which comprise of trust, motivation, self-efficacy, performance expectation

and frequency of use of social media, have positive significant effect on tacit

knowledge sharing whereas commitment influence was not significant. Conclusively,

attitude to use social media has a partial mediating effect on the relation between the

technical factors and tacit knowledge sharing. On the other hand however, the results

were supportive of the moderating effect of work experience on attitude towards using

social media and the technical factors (except for perceived ease of use which was not

significant). Based on the literature review and empirical study results a model named

as “Integrated tacit knowledge sharing model among medical practitioners over social

media” was proposed.

A prototype named as Medical Knowledge Sharing (MEDKSH) was developed based

on the proposed model. Then, an expert validation was conducted to verify the model.

The analysis of the four expert reviewers revealed that the prototype has the potential

to support tacit knowledge sharing. As for the evaluation of acceptance among the

users of MEDKSH, a technology acceptance test was conducted, with 51 participants.

The results from the survey indicated that MEDKSH is an effective, easy, enjoyable

and useful tool that can help medical practitioners to share tacit Therefore MEDKSH

can be used by medical practitioner in performing their work, and sharing their work

experiences and ideas.

The proposed model is unique in terms of contributing and updating the existing

literature in the area of ICT support for tacit knowledge sharing, in particular, by

demonstrating that social media is one of the recent enablers of tacit knowledge

sharing. A major contribution of this study is the provision of new theoretical and

practical insights that help explain how socio personal and -technical factors affect

tacit knowledge sharing in social media. It is hoped that these insights will help

builders and managers of knowledge-based virtual communities better promote online

knowledge sharing behaviors and improve the sustainability of such communities in

the future.

© COPYRIG

HT UPM

iii

Abstrak tesis yang dikemukakan kepada Senat Universiti Putra Malaysia sebagai

memenuhi keperluan untuk ijazah Doktor Falsafah

MODEL PERKONGSIAN PENGETAHUAN TACIT BERSEPADU DALAM KALANGAN PENGAMAL PERUBATAN MELALUI MEDIA SOSIAL

DENGAN KESAN INTERAKSI PENYEDERHANA DAN PENGANTARA

Oleh

ASRA AMIDI

Januari 2018

Pengerusi : Profesor Madya Yusmadi Yah Binti Jusoh, PhD Fakulti : Sains Komputer dan Teknologi Maklumat

Kodifikasi pengetahuan tacit merupakan cabaran penting dalam pengurusan

pengetahuan. Kepentingan pengetahuan tacit telah ditunjukkan dalam hubungan

pembuatan keputusan, pengurusan masa, mutu dan keberdayasaingan. Pengetahuan

tacit di kalangan pengamal perubatan merupakan sumber pengetahuan yang penting.

Walau bagaimanapun, disebabkan beberapa isu pengoperasian dan teknikal, maka

pengetahuan mengenai penjagaan kesihatan tidak dapat dimanfaatkan dan dipratikkan

sepenuhnya secara profesional. Terdapat juga isu berhubung dengan faktor sosio-

teknikal yang mempengaruhi kejayaan pelaksanaan pengurusan pengetahuan dalam

menyebarkan pengetahuan tacit. Banyak kaedah-kaedah tradisional penyebaran

pengetahuan seperti manual dan syarahan tidak sesuai untuk pengetahuan tacit. Oleh

itu, dengan adanya alat penjaringan sosial, beberapa kajian mengatakan bahawa

teknologi baru ini mampu mewujudkan peluang baru dalam memudahkan perkongsian

pengetahuan tacit dalam kalangan pengamal perubatan. Meskipun hujah tersebut,

masih terdapat kekurangan dalam memahami konsep dan penyelidikan secara kaji

selidik mengenai bagaimana media sosial boleh membantu memudahkan lagi

perkongsian pengetahuan tacit. Objektif pertama kajian ini adalah untuk menyiasat

potensi faktor sosio-peribadi dan teknikal yang menyokong perkongsian pengetahuan

tacit dalam kalangan pengamal perubatan dalam media sosial. Seterusnya objektif

kedua kajian ini adalah untuk mencadangkan model perkongsian pengetahuan tacit bersepadu dalam kalangan pengamal perubatan melalui media sosial. Objektif ketiga

kajian ini adalah untuk membangunkan dan menilai prototaip bagi model perkongsian

pengetahuan tacit.

© COPYRIG

HT UPM

iv

Kritikan sorotan literatur dan mensintesis komponen terhadap model sedia ada telah

dijalankan bagi mengenalpasti faktor yang mempengaruhi perkongsian pengetahuan

tacit melalui media sosial. Sebanyak dua (2) dimensi faktor yang berpengaruh telah

dikenalpasti iaitu sosio-peribadi dan teknikal. Sehubungan dengan itu, model

konseptual kajian ini telah dibangunkan. Seterusnya, pakar dalam bidang pengurusan

pengetahuan dan juga media sosial telah mengesahkan model konseptual tersebut.

Kajian perintis telah dijalankan untuk memastikan kebolehpercayaan dan kesahan

instrumen.

Kajian selidik telah dilaksanakan untuk menentusahkan model perkongsian

pengetahuan tacit. Seramai 336 pengamal perubatan di negeri Selangor, Malaysia

telah menyertai dalam kajian ini. Data yang dikumpulkan telah dianalisa dengan

menggunakan Model Persamaan Struktural (SEM-PLS). Keputusan kaji selidik

menunjukkan bahawa model tersebut sesuai dengan data yang dikutip, dan model

struktural menunjukkan bahawa factor-faktor teknikal: persepsi kemudahan

penggunaan, persepsi penggunaan dan persepsi keseronokan adalah signifikan positif

dalam perkongsian pengetahuan tacit. Hasil analisa juga mendapati bahawa faktor

sosio-peribadi yang terdiri daripada kepercayaan, motivasi, keberkesanan diri,

jangkaan prestasi dan kekerapan penggunaan media sosial mempunyai kesan positif

yang signifikan terhadap perkongsian pengetahuan tacit, manakala pengaruh

komitmen adalah tidak signifikan. Kesimpulannya, tingkah laku menggunakan media

sosial mempunyai kesan pengantaraan separa terhadap hubungan antara faktor

teknikal dan perkongsian pengetahuan tacit. Namun sebaliknya, hasil analisa juga

menyokong kesan moderating bagi pengalaman kerja terhadap tingkah laku dalam

penggunaan media sosial dan faktor teknikal (kecuali persepsi kemudahan

penggunaan adalah tidak signifikan). Berdasarkan sorotan literatur dan dapatan kajian

selidik, satu model yang dinamakan sebagai “Model Perkongsian Pengetahuan Bersepadu Tacit dalam Kalangan Pengamal Perubatan Melalui Media Sosial” telah dicadangkan.

Prototaip yang dikenali sebagai Medical Knowledge Sharing (MEDKSH) telah

dibangunkan berdasarkan model yang dicadangkan. Seterusnya, pengesahan pakar

telah dijalankan untuk mengesahkan model tersebut. Seramai empat (4) orang pakar

telah mengesahkan bahawa prototaip tersebut berpotensi menyokong perkongsian

pengetahuan tacit. Ujian penerimaan teknologi MEDKSH telah dijalankan dan

melibatkan seramai 51 pengguna sistem. Hasil daripada tinjauan kajian menunjukkan

bahawa MEDKSH merupakan sistem aplikasi yang efektif, mudah digunakan,

menyeronokkan dan berguna dalam membantu pengamal perubatan untuk berkongsi

pengetahuan tacit. Justeru itu, MEDKSH juga boleh digunakan untuk melaksanakan

kerja harian, berkongsi pengalaman kerja dan pandangan dalam kalangan pengamal

perubatan.

Model yang dicadangkan adalah unik dari aspek sumbangan dan nilai tambah kepada

kajian-kajian lepas dalam bidang sokongan ICT khususnya perkongsian pengetahuan

tacit, secara umumnya, media sosial merupakan salah satu faktor pembolehdaya

terkini dalam perkongsian pengetahuan tacit. Sumbangan utama kajian ini adalah

© COPYRIG

HT UPM

v

memperkenalkan teori baharu dan gambaran praktikal yang dapat membantu

menerangkan bagaimana faktor sosio-peribadi dan faktor teknikal mempengaruhi

perkongsian pengetahuan tacit melalui media sosial. Pemahaman daripada kajian ini

diharap dapat membantu pengasas dan pengurus komuniti maya yang berpengetahuan

supaya mengalakkan tingkah laku perkongsian pengetahuan secara dalam talian serta

meningkatkan kemampanan komuniti maya pada masa akan datang.

© COPYRIG

HT UPM

vi

ACKNOWLEDGEMENTS

I would like to thank all these people without whose support this accomplishment

would not have been possible. First and foremost, I would like to thank my supervisor

Assoc. Prof. Dr.Yusmadi Yah Jusoh, a mentor with a big heart who embraces and

supports me in this academic and life journey. Thank you for your encouragement,

guidance, and patience through this wonderful yet challenging journey. Second, I

would like to thank and acknowledge my dissertation committee members, Assoc.

Prof. Dr. Marzana A. Jabar and Prof. Dr. Rusli Haji Abdullah, you both were so

wonderful to work with. I valued your feedback and was truly blessed to have you

serve on my committee.

I am grateful to my family, my parents for their encouragement, support, and prayers,

To my sibling for cheering me on and believing in me and helping whenever I needed

extra hands Last, but not least, I would like to express my special thanks to my

husband, Mohammad, who has despite my many long days and nights spent writing,

and countless mood swings, has supported, pushed and loved me through the very end.

Thank you for listening when I needed. You’re my rock and I could not have gotten through this without your support.

© COPYRIG

HT UPM

© COPYRIG

HT UPM

viii

This thesis was submitted to the Senate of Universiti Putra Malaysia and has been

accepted as fulfillment of the requirement for the degree of Doctor of Philosophy. The

members of the supervisory committee were as follows:

Yusmadi Yah Binti Jusoh, PhD Associate Professor

Faculty of Computer Science and Information Technology

Universiti Putra Malaysia

(Chairman)

Marzanah A. Jabar, PhD Associate Professor

Faculty of Computer Science and Information Technology

Universiti Putra Malaysia

(Member)

Rusli Haji Abdullah, PhD Professor

Faculty of Computer Science and Information Technology

Universiti Putra Malaysia

(Member)

ROBIAH BINTI YUNUS, PhD Professor and Dean

School of Graduate Studies

Universiti Putra Malaysia

Date:

© COPYRIG

HT UPM

ix

Declaration by graduate student

I hereby confirm that:

� this thesis is my original work;

� quotations, illustrations and citations have been duly referenced;

� this thesis has not been submitted previously or concurrently for any other degree

at any other institutions;

� intellectual property from the thesis and copyright of thesis are fully-owned by

Universiti Putra Malaysia, as according to the Universiti Putra Malaysia

(Research) Rules 2012;

� written permission must be obtained from supervisor and the office of Deputy

Vice-Chancellor (Research and Innovation) before thesis is published (in the form

of written, printed or in electronic form) including books, journals, modules,

proceedings, popular writings, seminar papers, manuscripts, posters, reports,

lecture notes, learning modules or any other materials as stated in the Universiti

Putra Malaysia (Research) Rules 2012;

� there is no plagiarism or data fascination/ falsification in the thesis, and scholarly

integrity is upheld as according to the Universiti Putra Malaysia (Graduate

Studies) Rules 2003 (Revision 2012-2013) and the Univeisiti Putra Malaysia

(Research) Rules 2012. The thesis has undergone plagiarism detection software.

Signature: Date:

Name and Matric No.: Asra Amidi, GS35248

© COPYRIG

HT UPM

x

Declaration by Members of Supervisory Committee

This is to confirm that:

� the research conducted and the writing of this thesis was under our supervision;

� supervision responsibilities as stated in the Universiti Putra Malaysia (Graduate

Studies) Rules 2003 (Revision 2012-2013) are adhered to.

Signature :

Name of

Chairman of

Supervisory

Committee : Associate Professor Dr. Yusmadi Yah binti Jusoh

Signature :

Name of

Member of

Supervisory

Committee : Associate Professor Dr. Marzanah A. Jabar

Signature :

Name of

Member of

Supervisory

Committee : Professor Dr. Rusli Haji Abdullah

© COPYRIG

HT UPM

xi

TABLE OF CONTENTS

Page

ABSTRACT i

ABSTRAK iii

ACKNOWLEDGEMENTS vi

APPROVAL vii

DECLARATION ix

LIST OF TABLES xvi

LIST OF FIGURES xix

LIST OF ABBREVIATIONS xxi

CHAPTER

1 INTRODUCTION 1 1.1 Background 1 1.2 Background to the Research Problem 2 1.3 Problem Statement 3 1.4 Research Questions 5 1.5 Research objectives 5 1.6 Delimitation and Research Scope 6 1.7 Thesis Outline 7

2 LITERATURE REVIEW 8 2.1 Introduction 8 2.2 Knowledge 8

2.2.1 A taxonomy of knowledge 9 2.2.2 Tacit knowledge 9

2.3 Knowledge sharing frameworks 18 2.3.1 Hendriks (1999) – classical view of knowledge sharing 18 2.3.2 Nonaka et al. (1995) – dynamic theory of knowledge

creation 19 2.3.3 Szulanski (2000) – framework of knowledge sharing

stickiness 19 2.4 The impact of Web 2.0 on knowledge sharing 21

2.4.1 Sharing tacit knowledge 22 2.4.2 Information technology and tacit knowledge sharing 23

2.5 The role of social media in healthcare context 24 2.6 Difficulties of tacit knowledge sharing using information

technology 26 2.6.1 Sharing mechanisms 27 2.6.2 Media richness 27 2.6.3 Degree of tacitness 28 2.6.4 Lack of trust 29 2.6.5 Privacy 29

© COPYRIG

HT UPM

xii

2.7 Social media 30 2.7.1 Social networking 31 2.7.2 Blog 32 2.7.3 Wikis 32 2.7.4 Content communities 33

2.8 Characteristics of social media 33 2.8.1 User-generated content 34 2.8.2 Peer-to-peer interaction 34 2.8.3 Networking 35 2.8.4 Multimedia oriented 35

2.9 Managing and sharing tacit knowledge through social media 36 2.10 Tacit knowledge capture strategies 38 2.11 Existing knowledge sharing model and components 39 2.12 Gap analysis 47 2.13 Conceptual Model 48 2.14 Supporting theories for conceptual model 50

2.14.1 TAM theory 51 2.14.2 Social capital theory 52 2.14.3 Social cognitive theory 52 2.14.4 UTAUT theory 53 2.14.5 Self-determination theory 53

2.15 Summary 54

3 RESEARCH METHODOLOGY 55 3.1 Introduction 55 3.2 Phase I - Literature review 57

3.2.1 Selection criteria 57 3.3 Phase – Analysis of the existing knowledge sharing model 59

3.3.1 Hypotheses development 60 3.4 Phase - Pilot study 61

3.4.1 Step 1 – Background 62 3.4.2 Step 2 – Questionnaire conceptualization 63 3.4.3 Step 3 – Format 63 3.4.4 Step 4 – Establishing validity 64

3.4.4.1 Kappa analysis 65 3.4.5 Step 5 – Establishing Reliability (Pilot Test) 68

3.5 Phase – Empirical study 74 3.5.1 Sample size 75 3.5.2 Sampling method 76 3.5.3 Data collection 76 3.5.4 Data analysis 77

3.6 Phase - Model implementation 80 3.6.1 Prototype development 80 3.6.2 Model validation 82 3.6.3 Technology acceptance test 82

3.7 Summary 83

© COPYRIG

HT UPM

xiii

4 MODEL DEVELOPMENT 84 4.1 Introduction 84 4.2 Theoretical background 84 4.3 Hypotheses for main effect model 87

4.3.1 Socio personal factor hypotheses 87 4.3.1.1 Trust 87 4.3.1.2 Motivation 88 4.3.1.3 Performance expectation 88 4.3.1.4 Commitment 89 4.3.1.5 Knowledge sharing self-efficacy 90 4.3.1.6 Frequency use of social media 91

4.3.2 Technical factor hypotheses 91 4.4 Hypothesis for interaction effect model 95

4.4.1 Moderator effect of experience 95 4.4.2 Mediation effect of attitude to use social media 96

4.5 Summary 100

5 PROTOTYPE DEVELOPMENT 101 5.1 Introduction 101 5.2 Communities of practice as enablers for tacit knowledge sharing 101 5.3 Medical knowledge sharing in online community 104 5.4 The MEDKSH system 105 5.5 Platform design and functionality 106 5.6 System development 109

5.6.1 Architecture description 109 5.6.2 User interface design 110

5.7 Prototype implementation 111 5.7.1 Prototype Feature 112

5.7.1.1 Trust: 112 5.7.1.2 Performance expectation 114 5.7.1.3 Knowledge sharing self-efficacy 114 5.7.1.4 Frequency of social media use 116 5.7.1.5 Sharing tacit knowledge 116 5.7.1.6 Motivation 117 5.7.1.7 Attitude 118 5.7.1.8 Technology acceptance model in MEDKSH

online community 119 5.8 Model validation 120 5.9 Summary 120

6 RESULTS AND DISCUSSION 121 6.1 Introduction 121 6.2 Demographic characteristics of the respondents 121

6.2.1 Age 122 6.2.2 Gender 123 6.2.3 Discipline 123 6.2.4 Work experience 124

© COPYRIG

HT UPM

xiv

6.3 Descriptive statistic of construct in model evaluation 124 6.3.1 Descriptive result of current use of social media 125 6.3.2 Frequency of using social media 126 6.3.3 Sharing tacit knowledge 127 6.3.4 Commitment 127 6.3.5 Trust 128 6.3.6 Knowledge sharing self-efficacy 129 6.3.7 Motivation 129 6.3.8 Performance expectation 130 6.3.9 Perceived enjoyment 131 6.3.10 Perceived usefulness 131 6.3.11 Perceived ease of use 132 6.3.12 Attitude toward social media usage 133

6.4 Missing data analysis 133 6.4.1 Outlier 134 6.4.2 Normality test 134 6.4.3 Assessment of common method variance 135 6.4.4 Multi Collinearity 136

6.5 PLS based structural equation modelling (SEM) approach 137 6.5.1 Measurement model 138 6.5.2 Convergent validity 138 6.5.3 Discriminant validity 141

6.6 Path analysis 149 6.6.1 Assessing structural model (Inner Model) 153 6.6.2 Predictive relevance Q² 154 6.6.3 Effect size f2 and Q² 155

6.7 Mediator effect of attitude toward social media usage 156 6.7.1 Analysis mediation 156 6.7.2 Test of mediation 158

6.8 Moderating effect of work experience 161 6.9 Importance -Performance matrix analysis 163 6.10 Hypotheses testing 165 6.11 Discussion of hypotheses for main effect model 166 6.12 Discussions of hypothesis for interaction model 169

6.12.1 Discussions of mediating effect of attitude toward social

media usage 169 6.12.2 Discussion of moderation effect of work experience 171

6.13 Prototype validation 174 6.13.1 Expert validation 174 6.13.2 Technology acceptance test 176 6.13.3 Data collection and analysis 176 6.13.4 Demographic information of the participant 176 6.13.5 Descriptive analysis of technology acceptance test 178

6.14 Measurement of scales 183 6.15 Summary 184

© COPYRIG

HT UPM

xv

7 CONCLUSION AND FUTURE WORK 186 7.1 Introduction 186 7.2 Answer to research questions 186 7.3 Theoretical contribution 189 7.4 Practical contribution 191 7.5 Research limitations 191 7.6 Future work 192

REFERENCES 194 APPENDICES 223 BIODATA OF STUDENT 283 LIST OF PUBLICATIONS 284

© COPYRIG

HT UPM

xvi

LIST OF TABLES

Table Page

2.1 Description and characteristic of explicit and tacit knowledge 12

2.2 Overview of social media 31

2.3 Five common knowledge practices mediated by social media 36

2.4 Studies of tacit knowledge capture strategies 39

2.5 Summarized findings of existing knowledge sharing models 45

2.6 Items considered for the research model and their sources 50

3.1 Hypotheses list 60

3.2 List of model constructs and supporting references 63

3.3 Computation of a CVI and Kappa for items scale with experts 66

3.4 Analysis of the reliability for the pilot study 69

3.5 Summary of Cronbach’s Alpha for the measures in the pilot study 74

3.6 Rules of thumb for selecting PLS-SEM or CB-SEM 78

3.7 The Criteria of assessment on measurement model using PLS-SEM 79

3.8 The criteria of assessment on structural model using PLS-SEM 80

5.1 MEDKSH web page topics 106

5.2 Demographic information of prototype 107

6.1 Demographic profiles of respondents 122

6.2 Descriptive statistic related to the respondents' social media usage 125

6.3 Frequency of social media usage 127

6.4 Descriptive statistic related to tacit knowledge sharing 127

6.5 Descriptive statistic related items to commitment 128

6.6 Descriptive statistic for related items to trust 128

© COPYRIG

HT UPM

xvii

6.7 Descriptive statistic related items to knowledge-sharing self-efficacy 129

6.8 Descriptive statistic related items to motivation 130

6.9 Descriptive statistic related to performance expectation 130

6.10 Descriptive statistic related to perceived enjoyment (PE) 131

6.11 Descriptive statistic related to perceived usefulness (PU) 132

6.12 Descriptive statistic related to perceived ease of use 132

6.13 Descriptive statistic related to attitude toward social media usage 133

6.14 Outlier test 134

6.15 Normality test 135

6.16 Common-method variance result (CMV) 136

6.17 Collinearity assessment based on VIF for all three models 137

6.18 PLS-SEM 137

6.19 The result of convergent validity 140

6.20 Loading and cross loading of constructs for discriminant validity

assessment 142

6.21 Correlation of latent variables and discriminant Validity (Fornell-

Larcker) 146

6.22 Correlation of latent constructs and discriminant validity (HTMT

method) 148

6.23 List of hypotheses and relative paths 150

6.24 Test of the total effects of IVs on DV without mediator using

bootstrapping 153

6.25 Results of R² and Q² Values in the model 154

6.26 Results of effect size f2 and Q² for all exogenous constructs for STK 155

6.27 Test of the total effects using bootstrapping 158

6.28 Test of the mediation effects using bootstrapping (indirect effect, ab) 161

© COPYRIG

HT UPM

xviii

6.29 Test of the total effects using bootstrapping 163

6.30 Index values and total effects for the IPMA 164

6.31 List of hypotheses and relative paths 165

6.32 Demographic profiles of respondents for technology acceptance test 177

6.33 Level of acceptance of MEDKSH among users 179

6.34 Result of reliability test for technology acceptance test 183

© COPYRIG

HT UPM

xix

LIST OF FIGURES

Figure Page

2.1 SECI model 14

2.2 SECI model - the concept in practice 15

2.3 Tacit knowledge example in Nonaka’s two dimension 17

2.4 Stickiness and the process of knowledge transfer 20

2.5 Chain of data, information, and knowledge 20

2.6 Degree of tacitness 29

2.7 Conceptual model of tacit knowledge sharing in social media 49

3.1 Research methodology 56

3.2 Details of literature review sources and screening process 59

3.3 Systematic development of questionnaires 62

3.4 Process of Knowledge Sharing in online communities 81

4.1 Theoretical integrated model for tacit knowledge sharing (main

effect model) 94

4.2 Theoretical integrated model for tacit knowledge sharing (interaction

model) 99

5.1 Model implementation phases 101

5.2 Use case diagram for MEDKSH 105

5.3 Community of practice life cycle 109

5.4 Architecture of prototype system 110

5.5 Graphical user interface (GUI) map 111

5.6 Registration page of MEDKSH 113

5.7 Registration requirements of MEDKSH 113

5.8 New updates in MEDKSH 114

© COPYRIG

HT UPM

xx

5.9 An example thread from the discussion forum 115

5.10 An example of total thread and last visit in MEDKSH 116

5.11 Thread, post number and reputation of members in MEDKSH 118

5.12 Number of rating for posts in MEDKSH 119

6.1 The result of frequency distribution for age profile of respondents 122

6.2 The result of frequency distribution for gender of respondents 123

6.3 The result of frequency distribution for discipline of respondents 123

6.4 Years of work experience of the respondents 124

6.5 Mean score for the respondents’ current use of social media 126

6.6 Initial path model without moderators and mediators 152

6.7 Path model including attitude as a mediator 157

6.8 Path diagram for mediation model 159

6.9 Path model including work experience as moderator 162

6.10 IPMA (Priority Map) 164

6.11 Results of SEM analysis model 173

6.12 Mean score of construct in prototype evaluation 175

6.13 Demographic information of the participants 178

6.14 Mean score for perceived usefulness 180

6.15 Mean score of perceived ease of use 181

6.16 Mean scores for perceived enjoyment 181

6.17 Mean score for attitude 182

6.18 Mean score for intention to use 183

© COPYRIG

HT UPM

xxi

LIST OF ABBREVIATIONS

AT Attitude

AVE Average Variance Extracted

COM Commitment

COP Community of Practice

CR Composite Reliability

FAQ Frequency Asked Question

FSMU Frequency Use of Social Media

GUI Graphical User Interface

HTMT Heterotrait Monotrait

ICT Information and Communication Technology

IPMA Importance-Performance Matrix Analysis

IS Information System

IT Information Technology

KSSE Knowledge Sharing Self-Efficacy

KM Knowledge Management

KMS Knowledge Management System

KS Knowledge Sharing

MEDKSH Medical Knowledge Sharing

MOT Motivation

OCOP Online Community of Practice

PE Perceived Enjoyment

PER Performance Exception

PEOU Perceived Ease of Use

PLS Partial Least Square

PLS-SEM Partial Least Square Structural Equation Modeling

PU Perceived Usefulness

SCT Social Cognitive Theory

SD Standard Deviation

SDT Self Determination Theory

SEM Structural Equation Modeling

STK Sharing Tacit Knowledge

TAM Technology Acceptance Technology

UGC User Generated Content

© COPYRIG

HT UPM

xxii

UTAUT Unified Theory of Acceptance And Use Of Technology

VIF Variance Inflator Factor

WE Work Experience

© COPYRIG

HT UPM

1

CHAPTER 1

1 INTRODUCTION

1.1 Background

Knowledge management is centralized on knowledge sharing. An organization thrives

on its knowledge and knowledge sharing activities which serve as its most strategic

asset in ensuring its success and competitive advantage. According to Nonaka (1994),

there are two dimensions of knowledge namely tacit and explicit. Tacit knowledge

constitutes internal individual processes mostly stored exclusively in human beings

including experiences, reflections, internalizations or individual talents which cannot

be achieved and imparted in the same manner as explicit knowledge. In contrast,

explicit knowledge can be achieved, stored and imparted mechanically or

technologically, using mediums such as information systems (IS) or handbooks.

Tacit knowledge, being technical or cognitive, is manifested as mental models, values,

principles, observations, insights and assumptions derived from certain trainings,

habits, and personal experiences (Elizabeth A, 2001). For employees of an

organization, tacit organizational knowledge is manifested as their abilities, talents,

and skills and their most valuable assets. Specific knowledge can only be kept,

recovered and initiated by human intellect, and in cases of retirees, such knowledge is

taken with them when they leave the organization.

In the medical field, knowledge sharing among medical practitioners is pertinent in

the quest to improve patient care. Medical practitioner who share information about

their respective clinical experiences, skills, know-how, or know-who, could bring

about noteworthy impacts on the quality of their medical diagnosis and decision-

making (Abidi et al., 2005; Steininger et al., 2010). Such tacit knowledge sharing in

the healthcare setting, according to the Knowledge Management (KM) perspective,

need to be harnessed and facilitated as the practitioners are usually not co-located but

need to exchange critical information nevertheless (Abidi et al., 2005).

In this vein, Abidi et al. (2009) indicated that tacit knowledge sharing among medical

practitioner is not well facilitated by traditional information technologies (IT) because

instead of assisting in the interaction among the knowledge owners, traditional IT is

more focused on information management. More viable forms of technologies for tacit

knowledge sharing are those that run on free-form, real-time, and interactive

communication and collaboration platforms (Marwick, 2001; Panahi et al., 2013).

Several studies have suggested for experts to take advantage of the advent of social

media tools including blogs, online social networks and wikis to facilitate the sharing

of tacit and experiential knowledge amongst them (Tsai et al., 2010). That being said,

further empirical research is still needed to fully understand the mechanisms of social

media in facilitating tacit knowledge sharing and the ways to maximize its benefits in

© COPYRIG

HT UPM

2

the healthcare context. This study therefore aims to investigate the potentials of social

media in facilitating tacit knowledge sharing among medical practitioners by

presenting findings from a review of relevant literature and a survey on medical

practitioners.

1.2 Background to the Research Problem

Knowledge sharing has been defined from various perspectives and levels in an

attempt to understand it better. According to Lee et al. (2002), knowledge sharing

constitutes the deliberate act whereby knowledge is made reusable via its transmission

from one individual to another. However, Alajmi (2011) cautioned that knowledge

sharing in practice is actually a complex phenomenon to study and understand. This

complexity is attributed to two reasons which are related to the two types of

knowledge: explicit and tacit. Explicit knowledge is easy to capture, codify and written

as technical or academic data which in turn can be presented in the form of journals,

manuals, documents and patents (Nonaka et al., 1995; Nonaka et al., 2009). Tacit

knowledge, on the other hand, is much more personalized i.e. experience-based, job

specific and contextualized which makes it difficult to be articulated completely

despite the fact that it is transferrable via conversations and narratives, and capable of

being shifted into explicit knowledge and vice versa (Gourlay, 2004). Although tacit

knowledge holds a central role in leveraging the general effectiveness of knowledge

in organizations (Wah, 2000), it is often difficult for employees to communicate and

share tacit knowledge such as ideas, skills, values and mental models with others

(Holste et al., 2010; Nonaka et al., 1995) because this type of knowledge is not easily

converted into explicit knowledge which is easier to be stored and transferred (Smoyer

(2009). Due to its impersonal and non-exclusive nature, explicit knowledge is

considered to be relatively cheaper to acquire as compared to tacit knowledge which

is dubbed expensive and highly valuable as it involves complex methods of

transmission and acquisition such as shared activities, observation of behavior, and

direct contact with co-workers. As such, employees may feel disinclined to share this

prized commodity without getting anything in return.

Recent advancements in IT has made it possible for knowledge to be captured, stored,

processed, retrieved, and communicated in a much easier and more affordable manner,

but this only mainly relates to individual explicit knowledge (Reychav et al., 2010)

when in fact the most valuable and important human knowledge exists in tacit forms

(Abidi et al., 2005; Zhong Hao et al., 2016). The sharing and managing of tacit

knowledge is considered to be very demanding (Long, 2005). Whereby storytelling,

conversations, one-on-one discussions and discussion groups, deemed to be the

traditional methods for doing so (Alavi et al., 2001; Panahi et al., 2013; Parker, 2011;

Venkitachalam et al., 2012).

Tacit knowledge particularly is highly regarded as an organization’s most strategically-crucial resource (Grant, 1996), which makes up an estimated 80% of the

overall vital knowledge (Callahan, 2006), and is asserted to be the only renewable and

© COPYRIG

HT UPM

3

sustainable base for ensuring the organization’s viable position and competitiveness

(Nonaka et al. (2009).

McDermott (2000) asserts that the sharing of tacit knowledge is the “real gold” in KM activities. Penciuc et al. (2010) further reiterate that the loss of tacit knowledge is

equivalent to the loss of its main asset for example its “collective memory” to a certain degree. Although the exploitation of tacit knowledge is considered challenging, the

success in doing so could intensify the organizational knowledge capital and make the

decision-making process more effective (Chen Le et al., 2010; Harlow, 2008; Jabar et

al., 2010; Purkis et al., 2006). Apart from organizational performance, tacit knowledge

is also well recognized to contribute to the aspects of competitive advantage, problem

solving, and organizational learning (Abidi et al., 2005; Chen et al., 2010; Pathirage

et al., 2007). Despite being an undercapitalized resource, tacit healthcare knowledge

can be of central significance in improving healthcare quality and delivery (Abidi et

al., 2005; Panahi et al., 2014; Sim et al., 2001; Steininger et al., 2010). From the

perspective of healthcare KM, tacit knowledge is the inherent knowledge of medical

experts (Stewart et al., 2012) which is more noteworthy than explicit knowledge due

to the complexity of finding the right treatment for patients (Steininger et al., 2010).

There are another two further categories of tacit knowledge namely: 1) basic tacit

knowledge or routine experiential knowledge, which consists of theoretical

knowledge, evidence-based interpretations as well as experiences acquired from

routine clinical problems that could be easily transferred during casual peer

conversations or when replying to queries; and 2) complex tacit knowledge or intuitive

experiential knowledge, which consists of progressively accumulating knowledge

acquired by the expert as he responds to uncommon and high acuity clinical problems.

This type of tacit knowledge is deeply entrenched and therefore not easily expressed,

but demonstrates the expert’s natural capabilities in clinical situations that are

challenging. Abidi et al. (2009) suggest that one of the main issues in healthcare

knowledge management today involves the capturing, formalizing, measuring,

addressing, and operationalizing of healthcare tacit knowledge.

1.3 Problem Statement

A review of current published literature shows a gap in research concerning the use of

social media for the effective and efficient sharing of tacit knowledge. The numerous

discussions about the transferability of tacit knowledge and the methods involved have

still failed to lead to the establishment of a valid transfer model. Although

theoretically, the transfer of tacit knowledge has been modeled fairly well, practical

issues still exist due to the fact that tacit knowledge is significantly linked to its owner

and his personality. Thus, the nurturing of tacit knowledge sharing and the codifying

of tacit knowledge is presented as the main challenge in this respect (Kirchner et al.,

2009). Codification, or formalization of tacit knowledge into explicit and available

resources, is the vital factor of successful KM. it was acknowledged that it would be

difficult to codify highly tacit knowledge to explicit knowledge (McIver et al., 2012)

© COPYRIG

HT UPM

4

Although researchers like Yoshida et al., (2011) and Grant (2013) have focused on the

use of technology to enable this conversion. Agbim et al., (2013) asserted that the

exchange of tacit knowledge to new tacit knowledge should happen through

socialization processes. This was supported by Venkitachalam et al., (2012) position

that ICT could support the flow of tacit knowledge between individuals and aid in its

codification.

Although it has been established previously that tacit knowledge is the fundamental

base of the healthcare sector (Panahi et al., 2015), makes up approximately 80% of the

total vital knowledge (Callahan, 2006) and is greatly necessary for higher performance

(Podgórski, 2010), such healthcare knowledge has not been fully harnessed or

practiced professionally due to various operational and technical disadvantages (Abidi

et al., 2005).

Tacit healthcare knowledge in tandem with explicit healthcare knowledge could

significantly improve the quality and delivery of healthcare. Readily available ICT

tools such as the email and databases do not provide the appropriate setting needed for

transferring tacit knowledge; hence, Luoma et al. (2009) suggested the use of more

open and unstructured solutions. The growing popularity of social media has prompted

health educators to discover new ways of communicating information (Antheunis et

al., 2013). However, considering that the use of social media in healthcare is still in

its initial stages, its full capabilities and advantages remain uncovered (Sarringhaus,

2011).

There are six main elements to the use of social media for information sharing as

identified by Panahi et al. (2015), namely: 1) focus on a larger audience, 2) more rapid

dissemination, 3) tailored and filtered information feed, 4) broadcasting of current

information, 5) documentation of knowledge and experiences, and 6) retrievability.

These elements facilitate the accessibility of existing knowledge and information to

the clinical communities, which in turn creates a higher possibility of tacit knowledge

sharing. Despite the suggestion of several authors (Hsia et al., 2006; Panahi et al.,

2016; Steininger et al., 2010; Stewart et al., 2012; Zheng et al., 2010b) that social web

tools enable tacit knowledge sharing, there is yet sufficient empirical evidence to

support this notion. In the healthcare context specifically, very few studies have

explored the potentials of social media for tacit knowledge sharing among medical

practitioners (Panahi et al., 2016).

Several studies such as those by Alajmi (2011); Li (2010); Ma et al. (2011);

Tamjidyamcholo et al. (2014); Zhao (2010), Papadopoulos et al. (2013); Razmerita,

Phillips-Wren, et al. (2016); Tamjidyamcholo et al. (2013) have investigated the

individual and human factors that encourage knowledge sharing via social media, but

have neglected to put specific focus on tacit knowledge. Other researches such as those

by Matschke et al. (2014); Nielsen et al. (2014); Vuori et al. (2012); Yuan et al. (2016)

have discovered several technological factors that influence knowledge sharing via the

social media platform, and while these studies have offered insight into the

© COPYRIG

HT UPM

5

affordances of these factors, none of it have specifically addressed them when it

involves tacit knowledge.

As such, there is currently very little insight on the effectiveness of these human or

technical factors in encouraging tacit knowledge sharing via social media. Luoma et

al. (2009) and Panahi et al. (2015) however argue that successful tacit knowledge

sharing is not dependent on ICT factors alone because of the highly personalized

nature of tacit knowledge. Therefore, this study takes on a more comprehensive

approach by including technical and human related factors into the investigation. This

research also tackles the ongoing debate among researchers concerning the practicality

of ICT for tacit knowledge sharing (Levy, 2013; Panahi et al., 2015; Steininger et al.,

2010) particularly among medical practitioner. There is a need to re-conceptualize

tacit knowledge sharing via social media by investigating the experiences of medical

practitioner in using social media for that purpose; hence, the proposed integrated

model combines both human and technological factors in investigating tacit

knowledge sharing in the healthcare sector.

1.4 Research Questions

Generally, this study aims to investigate and ascertain the prospective contributions of

social media in enabling tacit knowledge sharing among medical practitioner. The

main research questions are framed as below:

1. What are the constructs (technical-socio personal) that influence sharing tacit

knowledge over social media?

2. What is the relationship between technical and socio personal constructs in tacit

knowledge sharing over social media?

3. How to examine the relationship between the Constructs (technical-socio

personal) and sharing tacit knowledge in social media?

1.5 Research objectives

Several specific objectives were considered to achieve the main research goal and to

answer the research questions. Below are the set objectives:

1. To identify the constructs that influence tacit knowledge sharing over social

media (Chapter 2).

2. To propose an integrated tacit knowledge sharing model among medical

practitioners over social media (Chapters 4, 6).

3. To evaluate integrated tacit knowledge sharing model effect for medical

practitioner through prototype development (Chapters 5, 6).

© COPYRIG

HT UPM

6

1.6 Delimitation and Research Scope

The purpose and scope of this research is to recognize the contribution of social media

in tacit knowledge sharing among medical practitioner in healthcare sector. To achieve

this, an organizational rather than a philosophical definition of tacit knowledge is

adopted in this research. The question of whether tacit knowledge can be researched,

observed, and operationalized have long been a subject of debate. Polanyi (1967)

indicated that tacit knowledge is inexpressible and inconvertible into explicit

knowledge.

Face-to-face conversation has been argued to be the best method in transmitting tacit

knowledge because it can include the use of a wider variety of metaphors - an aspect

that IT is incapable of doing (Haldin-Herrgard, 2000; Johannessen et al., 2001;

Tsuchiya, 1993). Nonaka et al. (1995) have, to some extent, changed the philosophical

view of tacit knowledge as previously introduced by Polanyi. In their SECI

(Socialization, Externalization, Combination, and Internalization) model, the

researchers suggested that new knowledge is created through iterative social

interaction, where tacit knowledge is made explicit. Knowledge that can be articulated

and expressed in certain situations is categorized into several types of tacit knowledge

according to the degree of its tastiness and impressibility (Oguz et al., 2011). The

desirability of transferring knowledge is influenced by its tastiness. For the purpose of

this study, the organizational definition of “tacit knowledge” is used in view of its applicability and adoptability (Oguz et al., 2011; Panahi et al., 2014). The

phenomenon of tacit knowledge sharing via IT can be better understood through the

use of this definition rather than Polanyi’s, which is more inclined to exclude IT in the study of the subject.

The target population in this research included medical practitioners in public and

private hospitals and clinics. A medical practitioner is a skilled individual in the

field of science of medicine (Marriam-Webster, 2017). The term ‘medical practitioner’ is used differently worldwide. In the United States, the term is used to

refer to all physicians. Meanwhile, in the United Kingdom, it is used to refer to a

general practitioner or specialist in internal or general medicine to differentiate it from

a surgeon (Martin, 2015). For the purpose of this study, the general definition of

medical practitioner is used to refer to all groups of doctors including general

practitioners, specialists, and surgeons involved in clinical practice.

The participants in this study were limited to the state of Selangor in Malaysia. The

size of the population is sufficient enough to reflect the entire Malaysian medical

practitioner. Considering that the study has no intention of exploring the nature or

application of medical tacit knowledge by any specific groups of medical practitioner,

the participants were therefore not limited to any specific categories of medical

practitioner, at least not in the early stages. The aim was on the mechanisms of tacit

knowledge sharing, and on examining the potential of social media in enabling this

process amongst medical practitioner.

© COPYRIG

HT UPM

7

There are several reasons for choosing the healthcare sector in Malaysia. medical

practitioner from all specialties were originally targeted for data collection because it

was impossible for the researcher to enlist a sufficient number of specific groups of

medical practitioner who used social media regularly due to the inaccessibility of such

groups to the researcher at the time. The study also initially investigated social media

as a whole rather than focusing on one specific group of social web sites such as social

networks, wikis, or blogs.

Since the study aimed to achieve a holistic view of the contributions of social media

to tacit knowledge sharing, examining only specific types of social media tools will

not help in achieving that goal. Moreover, studying a single tool is difficult because

most social media platforms today use a combination of different tools which render

it essential to study their relationship with each other.

1.7 Thesis Outline

This thesis is organized into seven chapters. Chapter 1 introduces the phenomenon

under investigation, discusses the research problems, highlights the research

objectives and research questions, and summarizes the importance of this study to both

research and practice. In order to clarify the relevant concepts and demarcate the topic

and perspective of this study, a literature review is presented in Chapter 2 which

touched on prior studies on knowledge sharing and social media, the challenges of

sharing tacit knowledge through information technology, the factors that could

possibly impact tacit knowledge sharing, as well as existing knowledge sharing

models. Chapter 3 explains the research methodology which consists of six phases

namely: Phase I, literature review; Phase II, synthesize existing knowledge sharing

model, analyze model components, propose model, hypothesis development; Phase

III, expert validation, questionnaire design, and pilot study and; Phase IV, evaluate

research model; and Phase V, prototype development, model implementation and

model validation. Phase VI reporting research result and discussing the finding.

Chapter 4 proposes a research model, which comprises a main effects model and an

interaction model, to frame the research investigation. Chapter 5 explicates the model

implementation covering prototype design and development which involves

requirements gathering and analysis, flowchart, and architecture design. This chapter

also represents prototype implementation and preparation of model validation.

Chapter 6 provides a thorough description of the data analysis and research results.

The chapter begins with a description of the field research setting, the data collection

process and the participants, followed by a preliminary analysis of the data.

Subsequently, both the main effects model and interaction model are estimated with

the data collected from the field setting. Issues such as common method covariance is

also discussed. Chapter 7 summarizes and concludes this dissertation. First, it provides

a discussion on the study’s findings in relation to research questions outlined previously. Next, the theoretical and practical contributions are outlined and the

limitations of study described. Lastly, future research opportunities are suggested,

followed by some concluding remarks.

© COPYRIG

HT UPM

194

REFERENCES

Abidi, S. S. R., Cheah, Y. N., & Curran, J. (2005). A knowledge creation info-structure to

acquire and crystallize the tacit knowledge of health-care experts. Information Technology in Biomedicine, IEEE Transactions on, 9(2), 193-204.

Abidi, S. S. R., Hussini, S., Sriraj, W., Thienthong, S., & Finley, G. A. (2009). Knowledge sharing for pediatric pain management via a Web 2.0 framework. Paper presented at the

MIE.

Adams, J., Khan, H. T., Raeside, R., & White, D. I. (2007). Research methods for graduate business and social science students: SAGE publications India.

Adis, A.-A. A., Ing, G. P., Osman, Z., Razli, I. A., Pang, Y. X., Sondoh Jr, S. L., & Majid, M.

R. A. (2015). Mediation Role of Attitude towards Product Placement in Social Media.

Journal of Sustainable Development, 8(3), 79.

Agbim, K. C., Owutuamor, Z. B., & Oriarewo, G. O. (2013). Entrepreneurship Development

and Tacit Knowledge: Exploring the Link between Entrepreneurial Learning and

Individual Know-How. Journal of Business Studies Quarterly, 5(2), 112.

Agichtein, E., Castillo, C., Donato, D., Gionis, A., & Mishne, G. (2008). Finding high-quality content in social media. Paper presented at the Proceedings of the 2008 International

Conference on Web Search and Data Mining.

Ajzen, I. (2001). Nature and operation of attitudes. Annual review of psychology, 52(1), 27-

58.

Al-Busaidi, K., & Olfman, L. (2014). Knowledge Workers’ Attitude Toward Inter-Organizational Knowledge Sharing System In The Education Sector. Paper presented at

the Proceedings of the 19 th UKAIS Conference on Information Systems, Oxford,

England.

Al-Qdah, M. S., & Salim, J. (2013). A Conceptual Framework for Managing Tacit Knowledge

through ICT Perspective. Procedia Technology, 11, 1188-1194.

Alajmi, B. M. (2011). The intention to share: professionals’ knowledge sharing behaviors in online communities. Rutgers University-Graduate School-New Brunswick.

Alavi, M., & Leidner, D. E. (2001). Review: Knowledge management and knowledge

management systems: Conceptual foundations and research issues. MIS quarterly, 107-

136.

Ali, N. a., Whiddett, D., Tretiakov, A., & Hunter, I. (2012). The use of information

technologies for knowledge sharing by secondary healthcare organisations in New

Zealand. International journal of medical informatics, 81(7), 500-506.

Allen, J. P. (2010). Knowledge-sharing successes in Web 2.0 communities. Technology and Society Magazine, IEEE, 29(1), 58-64.

Allison, P. D. (1999). Multiple regression: A primer: Pine Forge Press.

© COPYRIG

HT UPM

195

Ambrosini, V., & Bowman, C. (2001). Tacit knowledge: Some suggestions for

operationalization. Journal of Management Studies, 38(6), 811-829.

Andersen, P. (2007). What is Web 2.0?: ideas, technologies and implications for education

(Vol. 1): JISC Bristol, UK.

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review

and recommended two-step approach. Psychological bulletin, 103(3), 411.

Anderson, P. (2007). What is Web 2.0? Ideas, technologies and implications for education.

JISC Technology and Standards

Antheunis, M. L., Tates, K., & Nieboer, T. E. (2013). Patients’ and health professionals’ use of social media in health care: motives, barriers and expectations. Patient education and counseling, 92(3), 426-431.

Appelbaum, S. H., Gunkel, H., Benyo, C., Ramadan, S., Sakkal, F., & Wolff, D. (2012).

Transferring corporate knowledge via succession planning: analysis and solutions–Part

1. Industrial and Commercial Training, 44(5), 281-289.

Aral, S., Dellarocas, C., & Godes, D. (2013). Introduction to the special issue—social media

and business transformation: a framework for research. Information systems research, 24(1), 3-13.

Ardichvili, A. (2008). Learning and knowledge sharing in virtual communities of practice:

Motivators, barriers, and enablers. Advances in developing human resources, 10(4), 541-

554.

Ardichvili, A., Page, V., & Wentling, T. (2003). Motivation and barriers to participation in

virtual knowledge-sharing communities of practice. Journal of Knowledge Management, 7(1), 64-77.

Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys.

Journal of marketing research, 396-402.

Askay, D. A., & Spivack, A. J. (2010). The multidimensional role of trust in enabling creativity within virtual communities of practice: A theoretical model integrating swift, knowledge-based, institution-based, and organizational trust. Paper presented at the

System Sciences (HICSS), 2010 43rd Hawaii International Conference on.

Asrar-ul-Haq, M., & Anwar, S. (2016). A systematic review of knowledge management and

knowledge sharing: Trends, issues, and challenges. Cogent Business & Management, 3(1), 1127744.

Bakhuisen, N. (2014). Knowledge Sharing using Social Media in the Workplace: A chance to expand the organizations memory, utilize weak ties, and share tacit information? (master

of science), VU University Amsterdam, VU University Amsterdam. (1954938)

Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change.

Psychological review, 84(2), 191.

© COPYRIG

HT UPM

196

Bandura, A. (1989). Human agency in social cognitive theory. American psychologist, 44(9),

1175.

Bandura, A., & Locke, E. A. (2003). Negative self-efficacy and goal effects revisited. Journal of applied Psychology, 88(1), 87.

Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social

psychological research: Conceptual, strategic, and statistical considerations. Journal of personality and social psychology, 51(6), 1173.

Bautista-Frias, L., Romero-Gonzalez, R. M., & Morgan-Beltran, J. (2012). Knowledge maps

in the conversion of tacit knowledge as a competitive strategy. Journal of Competitiveness Studies, 20(3/4), 47.

Benner, P., & Tanner, C. (1987). How expert nurses use intuition. AJN The American Journal of Nursing, 87(1), 23-34.

Bennett III, R. H. (1998). The importance of tacit knowledge in strategic deliberations and

decisions. Management decision, 36(9), 589-597.

Bhanumathi, P., & Rathb, J. (2014). Knowledge Management Challenges and Opportunities

in Indian Small and Medium Enterprises (SMEs). TSM Business Review, 2(1), 57.

Bilgihan, A., Barreda, A., Okumus, F., & Nusair, K. (2016). Consumer perception of

knowledge-sharing in travel-related Online Social Networks. Tourism management, 52,

287-296.

Biswanath Dutta, D., Devika P. Madalli, D., Chandra, R., Iyer, R. S., & Raman, R. (2015).

Enabling organizations to implement smarter, customized social computing platforms by

leveraging knowledge flow patterns. Journal of Knowledge Management, 19(1), 95-107.

Biswanath Dutta, D., Devika P. Madalli, D., Tangaraja, G., Mohd Rasdi, R., Ismail, M., &

Abu Samah, B. (2015). Fostering knowledge sharing behaviour among public sector

managers: a proposed model for the Malaysian public service. Journal of Knowledge Management, 19(1), 121-140.

Blackler, F. (1995). Knowledge, knowledge work and organizations: an overview and

interpretation. Organization studies, 16(6), 1021-1046.

Blattberg, R. C., & Hoch, S. J. (1990). Database models and managerial intuition: 50%

model+ 50% manager. Management science, 36(8), 887-899.

Blessing, L. T., & Chakrabarti, A. (2009). DRM: A Design Reseach Methodology: Springer.

Blumenberg, S., Wagner, H. T., & Beimborn, D. (2009). Knowledge transfer processes in IT

outsourcing relationships and their impact on shared knowledge and outsourcing

performance. International Journal of Information Management, 29(5), 342-352.

Bock, G.-W., Zmud, R. W., Kim, Y.-G., & Lee, J.-N. (2005). Behavioral intention formation

in knowledge sharing: Examining the roles of extrinsic motivators, social-psychological

forces, and organizational climate. MIS quarterly, 87-111.

© COPYRIG

HT UPM

197

Bogner, A., Littig, B., & Menz, W. (2009). Interviewing experts: Palgrave Macmillan

Basingstoke.

Braun, M. T. (2013). Obstacles to social networking website use among older adults.

Computers in Human Behavior, 29(3), 673-680.

Bughin, J., Byers, A. H., & Chui, M. (2011). How social technologies are extending the

organization. McKinsey Quarterly, 20(11), 1-10.

Cabrera, A., Collins, W. C., & Salgado, J. F. (2006). Determinants of individual engagement

in knowledge sharing. The International Journal of Human Resource Management, 17(2), 245-264.

Cain, J. (2011). Social media in health care: the case for organizational policy and employee

education. American Journal of Health-System Pharmacy, 68(11), 1036.

Callahan, S. (2006). Want to manage tacit knowledge? Communities of practice offer a

versatile solution. White Paper Anecdote.[online] available: http://www. anecdote. com. au/papers/Want_to_manage_tacit_knowledge. pdf.

Camelo-Ordaz, C., García-Cruz, J., Sousa-Ginel, E., & Valle-Cabrera, R. (2011). The

influence of human resource management on knowledge sharing and innovation in Spain:

the mediating role of affective commitment. The International Journal of Human Resource Management, 22(07), 1442-1463.

Canali, C., Garcia, J. D., & Lancellotti, R. (2008). Impact of social networking services on the performance and scalability of web server infrastructures. Paper presented at the

Network Computing and Applications, 2008. NCA'08. Seventh IEEE International

Symposium on.

Caputo, A. (2009). Making the complex simple For better business decisions. Business Information Review, 26(1), 28-34.

Carlsson, S. A. (2003). Knowledge managing and knowledge management systems in inter

organizational networks. Knowledge and Process Management, 10(3), 194-206.

Carrillo, F. J., Brachos, D., Kostopoulos, K., Eric Soderquist, K., & Prastacos, G. (2007).

Knowledge effectiveness, social context and innovation. Journal of Knowledge Management, 11(5), 31-44.

Carroll, E. (1988). The role of tacit knowledge in problem solving in the clinical setting. Nurse Education Today, 8(3), 140-147.

Casimir, G., Ngee Keith Ng, Y., & Liou Paul Cheng, C. (2012). Using IT to share knowledge

and the TRA. Journal of Knowledge Management, 16(3), 461-479.

Casta, J. A., \#241, eda, Mu\, F., \#241, oz-Leiva, & Luque, T. (2007). Web Acceptance Model

(WAM): Moderating effects of user experience. Inf. Manage., 44(4), 384-396.

doi:10.1016/j.im.2007.02.003

Castañeda, J. A., Muñoz-Leiva, F., & Luque, T. (2007). Web Acceptance Model (WAM):

Moderating effects of user experience. Information & Management, 44(4), 384-396.

© COPYRIG

HT UPM

198

Chai, S., Das, S., & Rao, H. R. (2011). Factors affecting bloggers' knowledge sharing: An

investigation across gender. Journal of Management Information Systems, 28(3), 309-

342.

Chai, S., & Kim, M. (2010). What makes bloggers share knowledge? An investigation on the

role of trust. International Journal of Information Management, 30(5), 408-415.

Chan, C. K., & Chan, Y.-Y. (2011). Students' views of collaboration and online participation

in Knowledge Forum. Computers & Education, 57(1), 1445-1457.

Chatterjee, S. (2014). Managing constraints and removing obstacles to knowledge

management. IUP Journal of Knowledge Management, 12(4), 24.

Chatti, M. A., Klamma, R., Jarke, M., & Naeve, A. (2007). The Web 2.0 driven SECI model based learning process. Paper presented at the Advanced Learning Technologies, 2007.

ICALT 2007. Seventh IEEE International Conference on.

Chen, C.-J., & Hung, S.-W. (2010). To give or to receive? Factors influencing members’ knowledge sharing and community promotion in professional virtual communities.

Information & Management, 47(4), 226-236.

Chen, D., Hu, N., & Liu, L. (2007). Corporate blogging and firm performance: An empirical study. Paper presented at the Wireless Communications, Networking and Mobile

Computing, 2007. WiCom 2007. International Conference on.

Chen, L., & Mohamed, S. (2010). The strategic importance of tacit knowledge management

activities in construction. Construction Innovation, 10(2), 138-163.

Chen, R. (2013). Member use of social networking sites—an empirical examination. Decision support systems, 54(3), 1219-1227.

Chen, S. S., Chuang, Y.-W., & Chen, P.-Y. (2012). Behavioral intention formation in

knowledge sharing: Examining the roles of KMS quality, KMS self-efficacy, and

organizational climate. Knowledge-Based Systems, 31, 106-118.

Chen, Y.-J., & Chen, Y.-M. (2012). Knowledge evolution course discovery in a professional

virtual community. Knowledge-Based Systems, 33, 1-28.

Chennamaneni, A., & Teng, J. T. (2011). An integrated framework for effective tacit

knowledge transfer.

Chennamaneni, A., Teng, J. T., & Raja, M. (2012). A unified model of knowledge sharing

behaviours: theoretical development and empirical test. Behaviour & Information Technology, 31(11), 1097-1115.

Chin, W. W. (1998). Commentary: Issues and opinion on structural equation modeling:

JSTOR.

Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable

modeling approach for measuring interaction effects: Results from a Monte Carlo

simulation study and an electronic-mail emotion/adoption study. Information systems research, 14(2), 189-217.

© COPYRIG

HT UPM

199

Chiu, C.-M., Hsu, M.-H., & Wang, E. T. (2006). Understanding knowledge sharing in virtual

communities: An integration of social capital and social cognitive theories. Decision support systems, 42(3), 1872-1888.

Choi, J. H., Lev, B., & Kim, H.-L. (2014). Exploring Determinants of Knowledge Sharing in a Social Network of Practice. Paper presented at the Proceedings of the Seventh

International Conference on Management Science and Engineering Management.

Chumg, H.-F., Cooke, L., Fry, J., & Hung, I.-H. (2015). Factors affecting knowledge sharing

in the virtual organisation: Employees’ sense of well-being as a mediating effect.

Computers in Human Behavior, 44, 70-80.

Chung, L. H. (2001). The role of management in knowledge transfer. Paper presented at the

Third Asian Pacific Interdisciplinary Research in Accounting Conference.

Cochran, W. G. (2007). Sampling techniques: John Wiley & Sons.

Cohen, J. (1988). Statistical power analysis for the behavior science. Lawrance Eribaum Association.

Cohen, L., Manion, L., & Morrison, K. (2013). Research methods in education: Routledge.

Collins, H. M. (2001). Tacit knowledge, trust and the Q of sapphire. Social studies of science, 31(1), 71-85.

Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure

and initial test. MIS quarterly, 189-211.

Connell, J., Schechtman, G. M., & Hasty, B. (2012). KM Issues with Retiring and Transient Explosive Ordnance Disposal (EOD) Airmen and Their Impact on Air Force EOD Response Capabilities. Paper presented at the System Science (HICSS), 2012 45th

Hawaii International Conference on.

Cooper, C. P., Gelb, C. A., Rim, S. H., Hawkins, N. A., Rodriguez, J. L., & Polonec, L. (2012).

Physicians who use social media and other internet-based communication technologies.

Journal of the American Medical Informatics Association, 19(6), 960-964.

Cooper, J., & Schindler, M. (2008). Perfect Sample Size in Research: Macmillan. New Jersey.

Coopmans, C., & Button, G. (2014). Eyeballing expertise. Social studies of science,

0306312714531472.

Cordery, J. L., Cripps, E., Gibson, C. B., Soo, C., Kirkman, B. L., & Mathieu, J. E. (2015).

The operational impact of organizational communities of practice: A Bayesian approach

to analyzing organizational change. Journal of Management, 41(2), 644-664.

Cumberland, D. M., & Githens, R. P. (2010). Tacit Knowledge Barriers within Franchise

Organizations. Online Submission.

Cummings, J. N. (2004). Work groups, structural diversity, and knowledge sharing in a global

organization. Management science, 50(3), 352-364.

© COPYRIG

HT UPM

200

Curran, J. A., Murphy, A. L., Abidi, S. S. R., Sinclair, D., & McGrath, P. J. (2009). Bridging

the gap: knowledge seeking and sharing in a virtual community of emergency practice.

Evaluation & the Health Professions, 32(3), 314-327.

Daft, R. L., & Lengel, R. H. (1986). Organizational information requirements, media richness

and structural design. Management science, 32(5), 554-571.

Dalkir, K. (2013). Knowledge management in theory and practice: Routledge.

Dave, B., & Koskela, L. (2009). Collaborative knowledge management—A construction case

study. Automation in construction, 18(7), 894-902.

Davenport, E., & Hall, H. (2002). Organizational knowledge and communities of practice.

Annual review of information science and technology, 36(1), 171-227.

Davenport, T. H., & Prusak, L. (1998). Working knowledge: How organizations manage what they know: Harvard Business Press.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of

information technology. MIS quarterly, 319-340.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to

use computers in the workplace1. Journal of applied social psychology, 22(14), 1111-

1132.

Davis, L. J. (2010). Social Networking Sites as Virtual Communities of Practice: A Mixed Method Study: ERIC.

De Choudhury, M., Sundaram, H., John, A., & Seligmann, D. D. (2009). What makes conversations interesting?: themes, participants and consequences of conversations in online social media. Paper presented at the Proceedings of the 18th international

conference on World wide web.

Dearstyne, B. W. (2005). Blogs: the new information revolution? Information Management, 39(5), 38.

Deci, E. L., & Ryan, R. M. (1985). The general causality orientations scale: Self-determination

in personality. Journal of research in personality, 19(2), 109-134.

Del Giudice, M., Della Peruta, M. R., Maggioni, V., & Chase, R. (2015). A model for the

diffusion of knowledge sharing technologies inside private transport companies. Journal of Knowledge Management, 19(3).

Delen, D., & Al-Hawamdeh, S. (2009). A holistic framework for knowledge discovery and

management. Communications of the ACM, 52(6), 141-145.

DeLyser, D., & Sui, D. (2013). Crossing the qualitative-quantitative divide II: Inventive

approaches to big data, mobile methods, and rhythmanalysis. Progress in Human Geography, 37(2), 293-305.

Dennis, A. R., & Valacich, J. S. (1999). Rethinking media richness: Towards a theory of media synchronicity. Paper presented at the Systems Sciences, 1999. HICSS-32. Proceedings

of the 32nd Annual Hawaii International Conference on.

© COPYRIG

HT UPM

201

Dorsey, D. W. (2003). Hiring for knowledge-based competition. Managing knowledge for sustained competitive advantage: Designing strategies for effective human resource management, 21, 155.

Droege, S. B., & Hoobler, J. M. (2003). Employee turnover and tacit knowledge diffusion: A

network perspective. Journal of Managerial Issues, 50-64.

Dzekashu, W. G., & McCollum, W. R. (2014). A quality approach to tacit knowledge capture:

Effective practice to achieving operational excellence. International Journal of Applied Management and Technology, 13(1).

Elefant, C., & Black, N. (2010). Social media for lawyers: The next frontier.

Elias, S. M., Smith, W. L., & Barney, C. E. (2012). Age as a moderator of attitude towards

technology in the workplace: Work motivation and overall job satisfaction. Behaviour & Information Technology, 31(5), 453-467.

Elizabeth A, S., Aaron. (2001). The role of tacit and explicit knowledge in the workplace.

Journal of Knowledge Management, 5(4), 311-321.

Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer Mediated Communication, 13(1), 210-230.

Ellison, N. B., & Boyd, D. (2013). Sociality through social network sites. The Oxford handbook of internet studies, 151-172.

Endres, M. L., Endres, S. P., Chowdhury, S. K., & Alam, I. (2007). Tacit knowledge sharing,

self-efficacy theory, and application to the Open Source community. Journal of Knowledge Management, 11(3), 92-103.

Evans, C., Professor Raymond Hackney, D., Rauniar, R., Rawski, G., Yang, J., & Johnson, B.

(2014). Technology acceptance model (TAM) and social media usage: an empirical study

on Facebook. Journal of Enterprise Information Management, 27(1), 6-30.

Eysenbach, G. (2008). Credibility of health information and digital media: New perspectives

and implications for youth. Digital media, youth, and credibility, 123-154.

F. Hair Jr, J., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014). Partial least squares

structural equation modeling (PLS-SEM) An emerging tool in business research.

European Business Review, 26(2), 106-121.

Fahey, L., & Prusak, L. (1998). The eleven deadliest sins of knowledge management.

California management review, 40(3), 265-276.

Falk, R. F., & Miller, N. B. (1992). A primer for soft modeling: University of Akron Press.

Fang, Y.-H., & Chiu, C.-M. (2010). In justice we trust: Exploring knowledge-sharing

continuance intentions in virtual communities of practice. Computers in Human Behavior, 26(2), 235-246.

Farrell, A. M. (2010). Insufficient discriminant validity: A comment on Bove, Pervan, Beatty,

and Shiu (2009). Journal of Business Research, 63(3), 324-327.

© COPYRIG

HT UPM

202

Feng, K., Chen, E. T., & Liou, W. (2005). Implementation of knowledge management systems

and firm performance: an empirical investigation. Journal of Computer Information Systems, 45(2), 92-104.

Fetterhoff, T., Nila, P., & McNamee, R. C. (2011). Accessing internal knowledge:

Organizational practices that facilitate the transfer of tacit knowledge. Research Technology Management, 54(6), 50.

Fichter, D. (2005). The many forms of e-collaboration: Blogs, wikis, portals, groupware,

discussion boards, and instant messaging. Online, 29(4), 48-50.

Fielding, N. G., Lee, R. M., & Blank, G. (2008). The SAGE handbook of online research methods: Sage.

Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to

theory and research.

Flanagin, A. J. (2002). The Elusive Benefits of the Technological Support of Knowledge

Management. Management Communication Quarterly, 16(2), 242-248.

Fleck, J. (1996). Informal information flow and the nature of expertise in financial services.

International Journal of Technology Management, 11(1-2), 104-128.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable

variables and measurement error. Journal of marketing research, 39-50.

Foss, N. J., & Pedersen, T. (2002). Transferring knowledge in MNCs: The role of sources of

subsidiary knowledge and organizational context. Journal of International Management, 8(1), 49-67.

Fowler Jr, F. J. (2013). Survey research methods: Sage publications.

Franco, M., & Mariano, S. (2007). Information technology repositories and knowledge

management processes: A qualitative analysis. Vine, 37(4), 440-451.

Gabbay, J., & le May, A. (2004). Evidence based guidelines or collectively constructed

“mindlines?” Ethnographic study of knowledge management in primary care. Bmj, 329(7473), 1013.

Gefen, D., Karahanna, E., & Straub, D. W. (2003). Inexperience and experience with online

stores: The importance of TAM and trust. IEEE Transactions on engineering management, 50(3), 307-321.

Gefen, D., & Straub, D. W. (2000). The relative importance of perceived ease of use in IS

adoption: A study of e-commerce adoption. Journal of the Association for Information Systems, 1(1), 8.

Gilchrist, A. (2007). Can Web 2.0 be used effectively inside organisations? Bilgi Dünyası, 8(1), 123-139.

Goffin, K., & Koners, U. (2011). Tacit knowledge, lessons learnt, and new product

development. Journal of Product Innovation Management, 28(2), 300-318.

© COPYRIG

HT UPM

203

Gordeyeva, I. (2010). Enterprise 2.0: theoretical foundations of social media tools influence

on knowledge sharing practices in organizations.

Gormley, S. (2012). Understanding participation in knowledge-sharing in virtual communities of practice on the HSELanD elearning portal [thesis]/by Sandra Gormley. University of Dublin, Trinity College.

Götz, O., Liehr-Gobbers, K., & Krafft, M. (2010). Evaluation of structural equation models

using the partial least squares (PLS) approach Handbook of partial least squares (pp.

691-711): Springer.

Gourlay, S. (2004). Knowing as semiosis: steps towards a reconceptualization of'tacit

knowledge'. Haridimos Tsoukas and Nikolaos Mylonopoulos. Organizations as knowledge systems. Knowledge, learning and dynamic capabilities, Basingstoke & New York: Palgrave Macmillan, 86-105.

Grant, R. M. (1996). Toward a knowledge based theory of the firm. Strategic management journal, 17(S2), 109-122.

Grant, R. M. (2013). The development of knowledge management in the oil and gas

Industry/El desarrollo de la dirección del conocimiento en la industria del petroleo y gas.

Universia Business Review(40), 92.

Greenhalgh, J., Flynn, R., Long, A. F., & Tyson, S. (2008). Tacit and encoded knowledge in

the use of standardised outcome measures in multidisciplinary team decision making: A

case study of in-patient neurorehabilitation. Social science & medicine, 67(1), 183-194.

Groves, R. M. (1987). Research on survey data quality. The Public Opinion Quarterly, 51,

S156-S172.

Guang-bin, C., Yi-Jun, L., & Liang, D. (2010). Study on the incentive mechanism model of tacit knowledge sharing. Paper presented at the Information Management and

Engineering (ICIME), 2010 The 2nd IEEE International Conference on.

Hair, J. F. (1998). Multivariate data analysis.

Hair, J. F., Anderson, R. E., Babin, B. J., & Black, W. C. (2010). Multivariate data analysis: A global perspective (Vol. 7): Pearson Upper Saddle River, NJ.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152.

Hair Jr, J. F., & Hult, G. T. M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM): Sage Publications.

Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM): Sage Publications.

Haldin-Herrgard, T. (2000). Difficulties in diffusion of tacit knowledge in organizations.

Journal of Intellectual capital, 1(4), 357-365.

Hall, H. (2001). Input-friendliness: motivating knowledge sharing across intranets. Journal of Information Science, 27(3), 139-146.

© COPYRIG

HT UPM

204

Hamel, C. (2009). Determinants of participation in an Online Community of Practice (OCoP). University of Ottawa (Canada).

Hanson, C., West, J., Neiger, B., Thackeray, R., Barnes, M., & McIntyre, E. (2011). Use and

acceptance of social media among health educators. American Journal of Health Education, 42(4), 197-204.

Hara, N., & Hew, K. F. (2007). Knowledge-sharing in an online community of health-care

professionals. Information Technology & People, 20(3), 235-261.

Hariharan, A. (2015). Knowledge Management Is Fun. The Journal for Quality and Participation, 38(2), 34.

Harlow, H. (2008). The effect of tacit knowledge on firm performance. Journal of Knowledge Management, 12(1), 148-163.

Harman, H. H. (1960). Modern factor analysis.

Hartwick, J., & Barki, H. (1994). Explaining the role of user participation in information

system use. Management science, 40(4), 440-465.

Hashimoto, H., Yoshida, I., Teramoto, Y., Tabata, H., & Han, C. (2011). Extraction of tacit knowledge as expert engineer's skill based on mixed human sensing. Paper presented at

the RO-MAN, 2011 IEEE.

Hau, Y. S., Kim, B., Lee, H., & Kim, Y.-G. (2013). The effects of individual motivations and

social capital on employees’ tacit and explicit knowledge sharing intentions.

International Journal of Information Management, 33(2), 356-366.

Heidemann, J., Klier, M., & Probst, F. (2012). Online social networks: A survey of a global

phenomenon. Computer Networks, 56(18), 3866-3878.

Hendriks, P. (1999). Why share knowledge? The influence of ICT on the motivation for

knowledge sharing. Knowledge and Process Management, 6(2), 91.

Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path

modeling in international marketing New challenges to international marketing (pp. 277-

319): Emerald Group Publishing Limited.

Hildreth, P. M., & Kimble, C. (2002). The duality of knowledge. Information research, 8(1).

Hildrum, J. M. (2009). Sharing Tacit Knowledge Online: A Case Study of e-Learning in

Cisco's Network of System Integrator Partner Firms: Research Paper. Industry and Innovation, 16(2), 197-218.

Hislop, D. (2002). Mission impossible? Communicating and sharing knowledge via

information technology. Journal of Information Technology, 17(3), 165-177.

Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia computer-mediated

environments: Conceptual foundations. The Journal of Marketing, 50-68.

Holden, R. J., & Karsh, B.-T. (2010). The technology acceptance model: its past and its future

in health care. Journal of biomedical informatics, 43(1), 159-172.

© COPYRIG

HT UPM

205

Hollenbeck, J. R., & Klein, H. J. (1987). Goal commitment and the goal-setting process:

Problems, prospects, and proposals for future research. Journal of applied Psychology, 72(2), 212.

Holste, J. S., & Fields, D. (2010). Trust and tacit knowledge sharing and use. Journal of Knowledge Management, 14(1), 128-140.

Holzmann, V., Dubnov, S., Schwartz, M. A., Bohlander, K. M., Rosselet, U., Wentland, M.,

Pratama, A. P. (2011). Understanding the Collaboration Enigma. International Journal of Knowledge, Culture and Change Management, 10(7), 69-81.

Honeycutt, M. A., & Director, W. S. (2008). The brain drain from a private sector perspective:

The case of Duke Energy. Duke Energy presentation, September, 23, 2008.

Hong, S. J., Thong, J. Y., Moon, J.-Y., & Tam, K.-Y. (2008). Understanding the behavior of

mobile data services consumers. Information Systems Frontiers, 10(4), 431-445.

Howells, J. R. (2002). Tacit knowledge, innovation and economic geography. Urban studies, 39(5-6), 871-884.

Hsia, T.-L., Lin, L.-M., Wu, J.-H., & Tsai, H.-T. (2006). A framework for designing nursing

knowledge management systems. Interdisciplinary Journal of Information, Knowledge, and Management, 1(1), 13-23.

Hsu, C.-L., & Lin, J. C.-C. (2008). Acceptance of blog usage: The roles of technology

acceptance, social influence and knowledge sharing motivation. Information & Management, 45(1), 65-74.

Hsu, M.-H., Ju, T. L., Yen, C.-H., & Chang, C.-M. (2007). Knowledge sharing behavior in

virtual communities: The relationship between trust, self-efficacy, and outcome

expectations. International Journal of Human-Computer Studies, 65(2), 153-169.

Hu, P. J.-H., Clark, T. H., & Ma, W. W. (2003). Examining technology acceptance by school

teachers: a longitudinal study. Information & Management, 41(2), 227-241.

Huang, X., Hsieh, J., & He, W. (2014). Expertise dissimilarity and creativity: The contingent

roles of tacit and explicit knowledge sharing. Journal of applied Psychology, 99(5), 816.

Huber, G. P. (1991). Organizational learning: The contributing processes and the literatures.

Organization science, 2(1), 88-115.

Huh, J., Jones, L., Erickson, T., Kellogg, W. A., Bellamy, R. K., & Thomas, J. C. (2007).

BlogCentral: the role of internal blogs at work. Paper presented at the CHI'07 extended

abstracts on Human factors in computing systems.

Hult, G. T. M., Ketchen, D. J., & Nichols, E. L. (2002). An examination of cultural

competitiveness and order fulfillment cycle time within supply chains. Academy of management Journal, 45(3), 577-586.

Hung, H.-T., & Yuen, S. C.-Y. (2010). Educational use of social networking technology in

higher education. Teaching in higher education, 15(6), 703-714.

© COPYRIG

HT UPM

206

Hur, W.-M., Ahn, K.-H., & Kim, M. (2011). Building brand loyalty through managing brand

community commitment. Management decision, 49(7), 1194-1213.

Huysman, M., & Wulf, V. (2006). IT to support knowledge sharing in communities, towards

a social capital analysis. Journal of Information Technology, 21(1), 40-51.

Hvorecký, J., Šimúth, J., & Lipovská, A. (2015). Ways of delivering tacit knowledge in e-learning. Paper presented at the Interactive Collaborative Learning (ICL), 2015

International Conference on.

Ibrahim, F., & Reid, V. (2009). What is the value of knowledge management practices?

Electronic journal of knowledge management, 7(5), 567-574.

Ifinedo, P. (2016). The moderating effects of demographic and individual characteristics on

nurses' acceptance of information systems: A canadian study. International journal of medical informatics, 87, 27-35.

Islam, M. A., Agarwal, N. K., & Ikeda, M. (2014). Library adoption of knowledge

management using Web 2.0 A new paradigm for libraries. IFLA journal, 40(4), 317-330.

Jabar, M. A., Sidi, F., & Selamat, M. H. (2010). Tacit knowledge codification. Journal of Computer Science, 6(10), 1170.

Jadin, T., Gnambs, T., & Batinic, B. (2013). Personality traits and knowledge sharing in online

communities. Computers in Human Behavior, 29(1), 210-216.

Jarrahi, M. H., & Sawyer, S. (2013). Social technologies, informal knowledge practices, and

the enterprise. Journal of Organizational Computing and Electronic Commerce, 23(1-2),

110-137.

Jarvenpaa, S. L., & Staples, D. S. (2000). The use of collaborative electronic media for

information sharing: an exploratory study of determinants. The Journal of Strategic Information Systems, 9(2), 129-154.

Jashapara, A. (2004). Knowledge management: An integrated approach: Pearson Education.

Jasimuddin, S. M., Klein, J. H., & Connell, C. (2005). The paradox of using tacit and explicit

knowledge: strategies to face dilemmas. Management decision, 43(1), 102-112.

Jennex, M. E., & Durcikova, A. (2013). Assessing knowledge loss risk. Paper presented at the

System Sciences (HICSS), 2013 46th Hawaii International Conference on.

Jeon, S., Kim, Y. G., & Koh, J. (2011). An integrative model for knowledge sharing in

communities-of-practice. Journal of Knowledge Management, 15(2), 251-269.

Jin, S. A. A., & Phua, J. (2014). Following celebrities’ tweets about brands: The impact of Twitter-based electronic word-of-mouth on consumers’ source credibility perception, buying intention, and social identification with celebrities. Journal of Advertising, 43(2),

181-195.

© COPYRIG

HT UPM

207

Johannessen, J. A., Olaisen, J., & Olsen, B. (2001). Mismanagement of tacit knowledge: the

importance of tacit knowledge, the danger of information technology, and what to do

about it. International Journal of Information Management, 21(1), 3-20.

John, M. (2010). Physician-Generated Content on Social Media Sites-DocCheck Survey of Its

Members. Pharma Marketing News, 9(9), 1-6.

Joia, L. A., & Lemos, B. (2010). Relevant factors for tacit knowledge transfer within

organisations. Journal of Knowledge Management, 14(3), 410-427.

Jolaee, A., Md Nor, K., Khani, N., & Md Yusoff, R. (2014). Factors affecting knowledge

sharing intention among academic staff. International Journal of Educational Management, 28(4), 413-431.

Jong Won, H., Brush, T., & Bonk, C. (2012). An analysis of teacher knowledge and emotional

sharing in a teacher blog community Virtual professional development and informal learning via social networks (pp. 219-239): Information Science Reference Hershey, PA.

Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: Structural equation modeling with the SIMPLIS command language: Scientific Software International.

Jung Won, H., Brush, T., & Bonk, C. (2012). An analysis of teacher knowledge and emotional

sharing in a teacher blog community Virtual professional development and informal learning via social networks (pp. 219-239): Information Science Reference Hershey, PA.

Kamel Boulos, M. N., & Wheeler, S. (2007). The emerging Web 2.0 social software: an

enabling suite of sociable technologies in health and health care education. Health Information & Libraries Journal, 24(1), 2-23.

Kang, M., & Schuett, M. A. (2013). Determinants of sharing travel experiences in social

media. Journal of Travel & Tourism Marketing, 30(1-2), 93-107.

Kankanhalli, A., Tan, B. C., & Wei, K.-K. (2005). Contributing knowledge to electronic

knowledge repositories: an empirical investigation. MIS quarterly, 113-143.

Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and

opportunities of Social Media. Business horizons, 53(1), 59-68.

Ke, W., Tan, C. H., Sia, C.-L., & Wei, K.-K. (2012). Inducing intrinsic motivation to explore

the enterprise system: The supremacy of organizational levers. Journal of Management Information Systems, 29(3), 257-290.

Ketikidis, P., Dimitrovski, T., Lazuras, L., & Bath, P. A. (2012). Acceptance of health

information technology in health professionals: An application of the revised technology

acceptance model. Health informatics journal, 18(2), 124-134.

Kim, H.-W., Chan, H. C., & Gupta, S. (2007). Value-based adoption of mobile internet: an

empirical investigation. Decision support systems, 43(1), 111-126.

Kim, J. W., Choi, J., Qualls, W., & Han, K. (2008). It takes a marketplace community to raise

brand commitment: the role of online communities. Journal of Marketing Management, 24(3-4), 409-431.

© COPYRIG

HT UPM

208

Kim, S. (2012a). Factors affecting the use of social software: TAM perspectives. Electronic Library, The, 30(5), 690-706.

Kim, S. (2012b). Factors affecting the use of social software: TAM perspectives. The Electronic Library, 30(5), 690-706.

Kirchner, K., Razmerita, L., & Sudzina, F. (2009). New forms of interaction and knowledge

sharing on Web 2.0 Web 2.0 (pp. 1-16): Springer.

Kline, J., & Alex-Brown, K. (2013). The Social Body of Knowledge: Nurturing

Organizational Social Capital via Social Media Based Communities of Practice.

Technical Communication, 60(4), 279-292.

Kline, R. B. (2015). Principles and practice of structural equation modeling: Guilford

publications.

Koh, J., Kim, Y.-G., Butler, B., & Bock, G.-W. (2007). Encouraging participation in virtual

communities. Communications of the ACM, 50(2), 68-73.

Kontos, P. C., & Naglie, G. (2009). Tacit knowledge of caring and embodied selfhood.

Sociology of health & illness, 31(5), 688-704.

Koopman, J., Howe, M., Hollenbeck, J. R., & Sin, H.-P. (2015). Small sample mediation

testing: Misplaced confidence in bootstrapped confidence intervals. Journal of applied Psychology, 100(1), 194.

Ku, E. (2014). Distributed fascinating knowledge over an online travel community.

International Journal of Tourism Research, 16(1), 33-43.

Kummer, T.-F., Schäfer, K., & Todorova, N. (2013). Acceptance of hospital nurses toward

sensor-based medication systems: a questionnaire survey. International journal of nursing studies, 50(4), 508-517.

Kwahk, K.-Y., & Park, D.-H. (2016). The effects of network sharing on knowledge-sharing

activities and job performance in enterprise social media environments. Computers in Human Behavior, 55, 826-839.

Lakhani, K. R., & Von Hippel, E. (2003). How open source software works:“free” user-to-

user assistance. Research policy, 32(6), 923-943.

Lau, A. S. (2011). Hospital-based nurses’ perceptions of the adoption of Web 2.0 tools for knowledge sharing, learning, social interaction and the production of collective

intelligence. Journal of medical Internet research, 13(4), e92.

Lee, C.-K., & Al-Hawamdeh, S. (2002). Factors impacting knowledge sharing. Journal of Information & Knowledge Management, 1(01), 49-56.

Lee, W., & Paris, C. M. (2013). Knowledge sharing and social technology acceptance model:

Promoting local events and festivals through Facebook. Tourism Analysis, 18(4), 457-

469.

© COPYRIG

HT UPM

209

Lee, W., Xiong, L., & Hu, C. (2012). The effect of Facebook users’ arousal and valence on intention to go to the festival: Applying an extension of the technology acceptance model.

International Journal of Hospitality Management, 31(3), 819-827.

Leech, N. L., & Onwuegbuzie, A. J. (2007). An array of qualitative data analysis tools: A call

for data analysis triangulation. School psychology quarterly, 22(4), 557.

Leonard, N., & Insch, G. S. (2005). Tacit knowledge in academia: a proposed model and

measurement scale. The Journal of psychology, 139(6), 495-512.

Lerman, K. (2007). Social information processing in news aggregation. Internet Computing, IEEE, 11(6), 16-28.

Levy, M. (2013). Stairways to heaven: implementing social media in organizations. Journal of Knowledge Management, 17(5), 741-754.

Li, H., & Liu, Y. (2014). Understanding post-adoption behaviors of e-service users in the

context of online travel services. Information & Management, 51(8), 1043-1052.

Li, W. (2010). Virtual knowledge sharing in a cross-cultural context. Journal of Knowledge Management, 14(1), 38-50.

Liao, C., To, P.-L., & Hsu, F.-C. (2013). Exploring knowledge sharing in virtual communities.

Online Information Review, 37(6), 891-909.

Lietsala, K., & Sirkkunen, E. (2008). Social media. Introduction to the tools and processes of participatory economy.

Lin, C. P. (2007). To share or not to share: Modeling tacit knowledge sharing, its mediators

and antecedents. Journal of business ethics, 70(4), 411-428.

Lin, C. P., & Joe, S.-W. (2012). To share or not to share: Assessing knowledge sharing,

interemployee helping, and their antecedents among online knowledge workers. Journal of business ethics, 108(4), 439-449.

Lin, K. Y., & Lu, H.-P. (2011). Why people use social networking sites: An empirical study

integrating network externalities and motivation theory. Computers in Human Behavior, 27(3), 1152-1161.

Lindmark, S. (2009). Web 2.0: where does Europe stand? Institute for Prospective Technological Studies (IPTS), Luxembourg2009.

Ling, K., Beenen, G., Ludford, P., Wang, X., Chang, K., Li, X. Rashid, A. M. (2005). Using

social psychology to motivate contributions to online communities. Journal of Computer Mediated Communication, 10(4), 00-00.

Liu, S. M. (2009). What shapes employees' decisions to share knowledge in real work practices—an exploration of knowledge sharing processes and factors shaping workers' knowledge sharing when performing a task: University of Washington.

Long Alex Lai, I. (2005). Knowledge management for Chinese medicines: a conceptual

model. Information Management & Computer Security, 13(3), 244-255.

© COPYRIG

HT UPM

210

Lopez-Nicolas, C., & Soto-Acosta, P. (2010). Analyzing ICT adoption and use effects on

knowledge creation: An empirical investigation in SMEs. International Journal of Information Management, 30(6), 521-528.

Luarn, P., & Lin, H.-H. (2005). Toward an understanding of the behavioral intention to use

mobile banking. Computers in Human Behavior, 21(6), 873-891.

Ludwick, D. A., & Doucette, J. (2009). Adopting electronic medical records in primary care:

lessons learned from health information systems implementation experience in seven

countries. International journal of medical informatics, 78(1), 22-31.

Lulic, I., & Kovic, I. (2013). Analysis of emergency physicians' Twitter accounts. Emergency Medicine Journal, 30(5), 371-376.

Luoma, S., & Okkonen, J. (2009). Capturing competence–using wiki for transferring tacit knowledge. Paper presented at the Proceedings of the European Conference on

Intellectual Capital in Haarlem, The Netherlands.

Lynn, M. R. (1986). Determination and quantification of content validity. Nursing research, 35(6), 382-386.

Ma, Q., & Liu, L. (2004). The technology acceptance model: A meta-analysis of empirical

findings. Journal of Organizational and End User Computing (JOEUC), 16(1), 59-72.

Ma, W. W. (2012). Online knowledge sharing. Encyclopedia of Cyber Behavior, 394-402.

Ma, W. W., & Chan, A. (2014). Knowledge sharing and social media: Altruism, perceived

online attachment motivation, and perceived online relationship commitment. Computers in Human Behavior, 39, 51-58.

Ma, W. W., & Yuen, A. H. (2011). Understanding online knowledge sharing: An interpersonal

relationship perspective. Computers & Education, 56(1), 210-219.

Malterud, K. (1995). The legitimacy of clinical knowledge: towards a medical epistemology

embracing the art of medicine. Theoretical medicine, 16(2), 183-198.

Marriam-Webster, D. (2017). The Merriam-Webster Dictionary: Merriam-Webster,

Incorporated.

Martin, E. (2015). Concise medical dictionary: Oxford Quick Reference.

Martin-Niemi, F., & Greatbanks, R. (2010). The ba of blogs: Enabling conditions for

knowledge conversion in blog communities. Vine, 40(1), 7-23.

Marwick, A. D. (2001). Knowledge management technology. IBM systems journal, 40(4),

814-830.

Mason, G. (1996). Recent advances in questionnaire design for program evaluation. The Canadian Journal of Program evaluation, 11(1), 73.

© COPYRIG

HT UPM

211

Mateos-Aparicio, G. (2011). Partial least squares (PLS) methods: Origins, evolution, and

application to social sciences. Communications in Statistics-Theory and Methods, 40(13),

2305-2317.

Matschke, C., Moskaliuk, J., Bokhorst, F., Schümmer, T., & Cress, U. (2014). Motivational

factors of information exchange in social information spaces. Computers in Human Behavior, 36, 549-558.

Matthee, C. (2011). Towards the two-way symmetrical communication model: The use of Social media to create dialogue around brands. Nelson Mandela Metropolitan

University.

Matzat, U. (2013). Do blended virtual learning communities enhance teachers' professional

development more than purely virtual ones? A large scale empirical comparison.

Computers & Education, 60(1), 40-51.

Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational

trust. Academy of management review, 20(3), 709-734.

Mayfield, A. (2008a). What is social media: V1.

Mayfield, A. (2008b). What is social media? iCrossing. E-kirja ladattavissa< http://www. icrossing. com.

Mayfield, M. (2010). Tacit knowledge sharing: techniques for putting a powerful tool in

practice. Development and learning in organizations, 24(1), 24-26.

McAdam, R., Mason, B., & McCrory, J. (2007). Exploring the dichotomies within the tacit

knowledge literature: towards a process of tacit knowing in organizations. Journal of Knowledge Management, 11(2), 43-59.

McAfee, A. P. (2006). Enterprise 2.0: The dawn of emergent collaboration. MIT Sloan management review, 47(3), 21-28.

McDermott, R. (2000). Knowing in community. IHRIM journal, 1-12.

McGinn, C. A., Grenier, S., Duplantie, J., Shaw, N., Sicotte, C., Mathieu, L., Gagnon, M.-P.

(2011). Comparison of user groups' perspectives of barriers and facilitators to

implementing electronic health records: a systematic review. BMC medicine, 9(1), 46.

McGowan, B. S., Wasko, M., Vartabedian, B. S., Miller, R. S., Freiherr, D. D., &

Abdolrasulnia, M. (2012). Understanding the factors that influence the adoption and

meaningful use of social media by physicians to share medical information. Journal of medical Internet research, 14(5).

McIver, D., Lengnick-Hall, C. A., Lengnick-Hall, M. L., & Ramachandran, I. (2012).

Integrating knowledge and knowing: A framework for understanding knowledge-in-

practice. Human Resource Management Review, 22(2), 86-99.

Meyers, L. S., Gamst, G., & Guarino, A. J. (2006). Applied multivariate research: Design and interpretation: Sage.

© COPYRIG

HT UPM

212

Meyerson, D., Weick, K. E., & Kramer, R. M. (1996). Swift trust and temporary groups. Trust in organizations: Frontiers of theory and research, 166, 195.

Mirza, R. S. (2009). Knowledge management and clinical framework for cross country

healthcare organizations. Master, Department of Interaction and System Design, Blekinge Institute of Technology, Sweden.

Mládková, L. (2011). Sharing tacit knowledge within organizations: Evidence from the Czech

Republic.

Mohamed, M., Stankosky, M., & Murray, A. (2006). Knowledge management and

information technology: can they work in perfect harmony? Journal of Knowledge Management, 10(3), 103-116.

Mohr, D. C., Schueller, S. M., Montague, E., Burns, M. N., & Rashidi, P. (2014). The

behavioral intervention technology model: an integrated conceptual and technological

framework for eHealth and mHealth interventions. Journal of medical Internet research, 16(6), e146.

Moore, T. D., & Serva, M. A. (2007). Understanding member motivation for contributing to different types of virtual communities: a proposed framework. Paper presented at the

Proceedings of the 2007 ACM SIGMIS CPR conference on Computer personnel

research: The global information technology workforce.

Murray, S. R., & Peyrefitte, J. (2007). Knowledge type and communication media choice in

the knowledge transfer process. Journal of Managerial Issues, 111-133.

Nahapiet, J., & Ghoshal, S. (1998). Social capital, intellectual capital, and the organizational

advantage. Academy of management review, 23(2), 242-266.

Nath, A. K. (2012). Web 2.0 technologies for effective knowledge management in organizations: a qualitative analysis: The University of North Carolina at Greensboro.

Nezakati, H., Amidi, A., Yah Jusoh, Y., Shayesteh, M., Abdul Aziz, Y., &

Sohrabinezhadtalemia, R. (2015). Review of Social Media Potential on Knowledge

Sharing and Collaboration in Tourism Industry. Social and Behavioral Sciences, 172,

120–126.

Nicolopoulou-Stamati, P., Matiatos, I., Kotampasi, C., Stamatis, P., Sasco, A. J., Protopapa,

E., & Hens, L. (2015). Training in environmental health necessitates tacit knowledge.

Environment, Development and Sustainability, 17(2), 299-314.

Nielsen, P., & Razmerita, L. (2014). Motivation and Knowledge Sharing through Social

Media within Danish Organizations Creating Value for All Through IT (pp. 197-213):

Springer.

Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization science, 5(1), 14-37.

Nonaka, I., & Konno, N. (2005). The concept of “5, 4”: building a foundation for knowledge creation. Knowledge management: critical perspectives on business and management, 2(3), 53.

© COPYRIG

HT UPM

213

Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation: Oxford university press.

Nonaka, I., Toyama, R., & Konno, N. (2000). SECI,< i> Ba</i> and Leadership: a Unified

Model of Dynamic Knowledge Creation. Long range planning, 33(1), 5-34.

Nonaka, I., & Von Krogh, G. (2009). Perspective-tacit knowledge and knowledge conversion:

Controversy and advancement in organizational knowledge creation theory.

Organization science, 20(3), 635-652.

Norland-Tilburg, E. (1990). Controlling error in evaluation instruments. Journal of extension, 28(2), 23-41.

Nov, O. (2007). What motivates wikipedians? Communications of the ACM, 50(11), 60-64.

Nov, O., Naaman, M., & Ye, C. (2010). Analysis of participation in an online photo sharing

community: A multidimensional perspective. Journal of the American Society for Information Science and Technology, 61(3), 555-566.

O'Dell, C., & Hubert, C. (2011). Building a knowledge-sharing culture. The Journal for Quality and Participation, 34(2), 22.

Oguz, F., & Sengün, A. E. (2011). Mystery of the unknown: revisiting tacit knowledge in the

organizational literature. Journal of Knowledge Management, 15(3), 445-461.

Oh, S. (2012). The characteristics and motivations of health answerers for sharing

information, knowledge, and experiences in online environments. Journal of the American Society for Information Science and Technology, 63(3), 543-557.

Okoroafor, H. (2014). The barriers to tacit knowledge sharing in franchise organizations.

Knowledge Management Research & Practice, 12(1), 97-102.

O'reilly, T. (2007). What is Web 2.0: Design patterns and business models for the next

generation of software. Communications & strategies(65).

Pai, F.-Y., & Yeh, T.-M. (2014). The effects of information sharing and interactivity on the

intention to use social networking websites. Quality & Quantity, 48(4), 2191-2207.

Panahi, S., Watson, J., & Partridge, H. (2012a). Potentials of social media for tacit knowledge

sharing amongst physicians: preliminary findings.

Panahi, S., Watson, J., & Partridge, H. (2012b). Social media and tacit knowledge sharing:

developing a conceptual model. World academy of science, engineering and technology(64), 1095-1102.

Panahi, S., Watson, J., & Partridge, H. (2013). Towards tacit knowledge sharing over social

web tools. Journal of Knowledge Management, 17(3), 379-397.

Panahi, S., Watson, J., & Partridge, H. (2014). Social media and physicians: Exploring the

benefits and challenges. Health informatics journal, 1460458214540907.

© COPYRIG

HT UPM

214

Panahi, S., Watson, J., & Partridge, H. (2015). Information encountering on social media and

tacit knowledge sharing. Journal of Information Science, 0165551515598883.

Panahi, S., Watson, J., & Partridge, H. (2016). Social media and physicians: exploring the

benefits and challenges. Health informatics journal, 22(2), 99-112.

Papadopoulos, T., Stamati, T., & Nopparuch, P. (2013). Exploring the determinants of

knowledge sharing via employee weblogs. International Journal of Information Management, 33(1), 133-146.

Parker, M. (2011). Exploring tacit knowledge transfer: How community college leaders ensure institutional memory preservation: Northcentral University.

Paroutis, S., & Al Saleh, A. (2009). Determinants of knowledge sharing using Web 2.0

technologies. Journal of Knowledge Management, 13(4), 52-63.

Pathirage, C. P., Amaratunga, D. G., & Haigh, R. P. (2007). Tacit knowledge and

organisational performance: construction industry perspective. Journal of Knowledge Management, 11(1), 115-126.

Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk

with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101-134.

Penciuc, D., Abel, M.-H., & Van Den Abeele, D. (2010). Modelling and architecture of a workspace for tacit knowledge management in railway transport. Paper presented at the

Tools with Artificial Intelligence (ICTAI), 2010 22nd IEEE International Conference on.

Pereira, C. A. B., Ferreira, J. J. M., & Alves, H. M. B. (2012). Tacit knowledge as competitive

advantage in relationship marketing: a literature review and theoretical implications.

Journal of Relationship Marketing, 11(3), 172-197.

Podgórski, D. (2010). The use of tacit knowledge in occupational safety and health

management systems. International Journal of Occupational Safety and Ergonomics, 16(3), 283-310.

Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method

biases in behavioral research: a critical review of the literature and recommended

remedies. Journal of applied Psychology, 88(5), 879.

Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems

and prospects. Journal of Management, 12(4), 531-544.

Polanyi, M. (1967) Sense-giving and sense-reading, Philosophy, 42(162), pp. 301–325.

Polit, D. F., & Beck, C. T. (2006). The content validity index: are you sure you know what's

being reported? Critique and recommendations. Research in nursing & health, 29(5),

489-497.

Popadiuk, S., & Choo, C. W. (2006). Innovation and knowledge creation: How are these

concepts related? International Journal of Information Management, 26(4), 302-312.

© COPYRIG

HT UPM

215

Pope, C., Smith, A., Goodwin, D., & Mort, M. (2003). Passing on tacit knowledge in

anaesthesia: a qualitative study. Medical education, 37(7), 650-655.

Priest, J., Thomas, L., & Bond, S. (1995). Developing and refining a new measurement tool:

If we cannot find a test to suit your circumstances, we may have to develop our own.

Researchers from the University of Newcastle describe how they developed the

Newcastle Satisfaction with Nursing Scales. Nurse Researcher, 2(4), 69-81.

Purkis, M. E., & Bjornsdottir, K. (2006). Intelligent nursing: accounting for knowledge as

action in practice. Nursing Philosophy, 7(4), 247-256.

Putnam, R. D. (1993). The prosperous community. The american prospect, 4(13), 35-42.

Rai, A., Patnayakuni, R., & Seth, N. (2006). Firm performance impacts of digitally enabled

supply chain integration capabilities. MIS quarterly, 225-246.

Räisänen, T., & Oinas-Kukkonen, H. (2008). A system architecture for the 7C knowledge

environment. Information Modelling and Knowledge Bases XIX, 166, 217.

Ramayah, T., Chiun, L. M., Rouibah, K., & May, O. S. (2014). Identifying Priority Using an

Importance-Performance Matrix Analysis (IPMA): The Case of Internet Banking in

Malaysia. International Journal of E-Adoption (IJEA), 6(1), 1-15.

Ramirez, A., Walther, J. B., Burgoon, J. K., & Sunnafrank, M. (2002). Information Seeking

Strategies, Uncertainty, and Computer Mediated Communication. Human Communication Research, 28(2), 213-228.

Razmerita, L., Kirchner, K., & Nabeth, T. (2014). Social Media in Organizations: Leveraging

Personal and Collective Knowledge Processes. Journal of Organizational Computing and Electronic Commerce, 24(1), 74-93.

Razmerita, L., Kirchner, K., & Nielsen, P. (2016). What factors influence knowledge sharing

in organizations? A social dilemma perspective of social media communication. Journal of Knowledge Management, 20(6), 1225-1246.

Razmerita, L., Phillips-Wren, G., & Jain, L. C. (2016). Advances in knowledge management:

an overview Innovations in Knowledge Management (pp. 3-18): Springer.

Redlitz, J. (2013). The Baby Boom Bust: Strategies to Overcome Retirement Brain Drain.

Remedios, L., Clarke, D., & Hawthorne, L. (2008). The silent participant in small group

collaborative learning contexts. Active learning in higher education, 9(3), 201-216.

Restler, S. G., & Woolis, D. D. (2007). Actors and factors: Virtual communities for social

innovation. Electronic journal of knowledge management, 5(1).

Reychav, I., & Weisberg, J. (2010). Bridging intention and behavior of knowledge sharing.

Journal of Knowledge Management, 14(2), 285-300.

Richey, R. C., & Klein, J. D. (2014). Design and development research: Methods, strategies, and issues: Routledge.

© COPYRIG

HT UPM

216

Ridings, C. M., Gefen, D., & Arinze, B. (2002). Some antecedents and effects of trust in virtual

communities. The Journal of Strategic Information Systems, 11(3), 271-295.

Ringle, C. M., Sarstedt, M., & Straub, D. (2012). A critical look at the use of PLS-SEM in

MIS Quarterly. MIS Quarterly (MISQ), 36(1).

Rogers, E. M. (2010). Diffusion of innovations: Simon and Schuster.

Rosen, L. D., Carrier, L. M., & Cheever, N. A. (2013). Facebook and texting made me do it:

Media-induced task-switching while studying. Computers in Human Behavior, 29(3),

948-958.

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic

motivation, social development, and well-being. American psychologist, 55(1), 68.

Safko, L. (2010). The social media bible: tactics, tools, and strategies for business success:

John Wiley & Sons.

Sarkkinen, H. (2009). The role of social media in customer communication in business-to-

business markets. Master, Department of Marketing, Faculty of Economics and Business Administration, University of Oulu.

Sarringhaus, M. M. (2011). The great divide: Social media's role in bridging healthcare's

generational shift. Journal of Healthcare Management, 56(4), 235-244.

Schiuma, G., Vuori, V., & Okkonen, J. (2012). Knowledge sharing motivational factors of

using an intra-organizational social media platform. Journal of Knowledge Management, 16(4), 592-603.

Schutz, D. M. (2013). Seeking and Sharing Knowledge Using Social Media in an Organization: The Impact of Social Influence, Organization Structure and Social Capital: ERIC.

Schutz, D. M., Yoo, Y., Wattal, S., & Fesenmaier, D. R. (2013). Seeking and sharing knowledge using social media in an organization: The impact of social influence, organization structure and social capital: Temple University Libraries.

Segars, A. H. (1997). Assessing the unidimensionality of measurement: A paradigm and

illustration within the context of information systems research. Omega, 25(1), 107-121.

Shao, Z., Feng, Y., & Liu, L. (2012). The mediating effect of organizational culture and

knowledge sharing on transformational leadership and Enterprise Resource Planning

systems success: An empirical study in China. Computers in Human Behavior, 28(6),

2400-2413.

Sharratt, M., & Usoro, A. (2003). Understanding knowledge-sharing in online communities

of practice. Electronic Journal on Knowledge Management, 1(2), 187-196.

Sim, I., Gorman, P., Greenes, R. A., Haynes, R. B., Kaplan, B., Lehmann, H., & Tang, P. C.

(2001). Clinical decision support systems for the practice of evidence-based medicine.

Journal of the American Medical Informatics Association, 8(6), 527-534.

© COPYRIG

HT UPM

217

Sinclair, N., Martin-Niemi, F., & Greatbanks, R. (2010). The ba of blogs: Enabling conditions

for knowledge conversion in blog communities. Vine, 40(1), 7-23.

Skyrme, D. J. (2011). Beneath the fad: the future of Knowledge Management.

Smith, P. A., & Anklam, P. (2009). Ten years of net work. The Learning Organization, 16(6),

415-426.

Smoyer, E. W. (2009). Identifying knowledge sharing relationships in the telecommunication industry: University of Phoenix.

Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation

models. Sociological methodology, 13(1982), 290-312.

Song, D. (2009). The tacit knowledge-sharing strategy analysis in the project work.

International Business Research, 2(1), P83.

Song, J., & Zahedi, F. M. (2005). A theoretical approach to web design in e-commerce: a

belief reinforcement model. Management science, 51(8), 1219-1235.

Soo, K. (2006). Why workers share knowledge: A case study. Unpublished doctoral dissertation. Indiana University.

Stefanone, M. A., & Jang, C. Y. (2007). Writing for friends and family: The interpersonal

nature of blogs. Journal of Computer Mediated Communication, 13(1), 123-140.

Steininger, K., Ruckel, D., Dannerer, E., & Roithmayr, F. (2010). Healthcare knowledge

transfer through a web 2.0 portal: an Austrian approach. International Journal of Healthcare Technology and Management, 11(1), 13-30.

Stenmark, D. (2000). Leveraging tacit organizational knowledge. Journal of Management Information Systems, 17(3), 9-24.

Stewart, S. A., & Abidi, S. S. R. (2012). Applying social network analysis to understand the

knowledge sharing behaviour of practitioners in a clinical online discussion forum.

Journal of medical Internet research, 14(6), e170.

Strahovnik, V., & Mecava, B. (2009). Storytelling and Web 2.0 Services: A synthesis of old

and new ways of learning. eLearning Papers(15), 3.

Sun, H., & Zhang, P. (2006). Causal relationships between perceived enjoyment and perceived

ease of use: An alternative approach. Journal of the Association for Information Systems, 7(9), 24.

Sun, Y., Liu, L., Peng, X., Dong, Y., & Barnes, S. J. (2014). Understanding Chinese users’ continuance intention toward online social networks: an integrative theoretical model.

Electronic Markets, 24(1), 57-66.

Surong, Z., & Wenping, W. (2010). Transfer efficiency of tacit knowledge in knowledge-based enterprises. Paper presented at the Networking and Digital Society (ICNDS), 2010 2nd

International Conference on.

© COPYRIG

HT UPM

218

Sutton, J., Palen, L., & Shklovski, I. (2008). Backchannels on the front lines: Emergent uses of social media in the 2007 southern California wildfires. Paper presented at the

Proceedings of the 5th International ISCRAM Conference.

Swift, L. (2014). Online communities of practice and their role in educational development: a

systematic appraisal. Community Practitioner, 87(4), 28-31.

Swift, M. L., & Virick, M. (2013). Perceived Support, Knowledge Tacitness, and Provider

Knowledge Sharing. Group & Organization Management, 38(6), 717-742.

Szolnoki, G., & Hoffmann, D. (2013). Online, face-to-face and telephone surveys—Comparing different sampling methods in wine consumer research. Wine Economics and Policy, 2(2), 57-66.

Szulanski, G. (2000). The process of knowledge transfer: A diachronic analysis of stickiness.

Organizational behavior and human decision processes, 82(1), 9-27.

Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics, 5th. Needham Height, MA: Allyn & Bacon.

Taiwo, A. A., & DOWNE, A. G. (2013). The theory of user acceptance and use of technology

(UTAUT): A meta-analytic review of empirical findings. Journal of Theoretical & Applied Information Technology, 49(1).

Tamjidyamcholo, A., Baba, M. S. B., Shuib, N. L. M., & Rohani, V. A. (2014). Evaluation

model for knowledge sharing in information security professional virtual community.

Computers & Security, 43, 19-34.

Tamjidyamcholo, A., Baba, M. S. B., Tamjid, H., & Gholipour, R. (2013). Information

security–Professional perceptions of knowledge-sharing intention under self-efficacy,

trust, reciprocity, and shared-language. Computers & Education, 68, 223-232.

Tee, M. Y., & Karney, D. (2010). Sharing and cultivating tacit knowledge in an online learning

environment. International Journal of Computer-Supported Collaborative Learning, 5(4), 385-413.

Thoene, M. A., & Buszko, A. (2014). Quantitative model of tacit knowledge estimation for

pharmaceutical industry. Engineering Economics, 25(1), 40-46.

Tsai, K.-H., & Hsu, T. T. (2014). Cross-Functional collaboration, competitive intensity,

knowledge integration mechanisms, and new product performance: A mediated

moderation model. Industrial Marketing Management, 43(2), 293-303.

Tsai, T., & Cheng, M. (2010). Programmer perceptions of knowledge-sharing behavior under

social cognitive theory. Expert systems with Applications, 37(12), 8479-8485.

Tsai, W., & Ghoshal, S. (1998). Social capital and value creation: The role of intrafirm

networks. Academy of management Journal, 41(4), 464-476.

Tseng, F.-C., & Kuo, F.-Y. (2014). A study of social participation and knowledge sharing in

the teachers' online professional community of practice. Computers & Education, 72, 37-

47.

© COPYRIG

HT UPM

219

Tsuchiya, S. (1993). Improving knowledge creation ability through organizational learning. Paper presented at the ISMICK'93 Proceedings, International Symposium on the

Management of Industrial and Corporate Knowledge.

Turel, O., Serenko, A., & Giles, P. (2011). Integrating technology addiction and use: An

empirical investigation of online auction users. MIS quarterly, 35(4), 1043-1062.

Türel, Y. K., & Johnson, T. E. (2012). Teachers' Belief and Use of Interactive Whiteboards

for Teaching and Learning. Educational Technology & Society, 15(1), 381-394.

Van Acker, F., Vermeulen, M., Kreijns, K., Lutgerink, J., & van Buuren, H. (2014). The role

of knowledge sharing self-efficacy in sharing Open Educational Resources. Computers in Human Behavior, 39, 136-144.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance

model: four longitudinal field studies. Management science, 46(2), 186-204.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of

information technology: Toward a unified view. MIS quarterly, 425-478.

Venkatesh, V., Speier, C., & Morris, M. G. (2002). User acceptance enablers in individual

decision making about technology: Toward an integrated model. Decision sciences, 33(2), 297-316.

Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information

technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 36(1), 157-178.

Venkatraman, N. (1989). Strategic orientation of business enterprises: The construct,

dimensionality, and measurement. Management science, 35(8), 942-962.

Venkitachalam, K., & Busch, P. (2012). Tacit knowledge: review and possible research

directions. Journal of Knowledge Management, 16(2), 357-372.

Vinzi, V. E., Chin, W. W., Henseler, J., & Wang, H. (2010). Handbook of partial least squares: Concepts, methods and applications: Springer Science & Business Media.

Voigt, K., & Ernst, M. (2010). Use of Web 2.0 applications in product development: An

empirical study of the potential for knowledge creation and exchange in research and

development. International Journal of Engineering, Science and Technology, 2(9).

Von Krogh, G. (2012). How does social software change knowledge management? Toward a

strategic research agenda. The Journal of Strategic Information Systems, 21(2), 154-164.

doi:http://dx.doi.org/10.1016/j.jsis.2012.04.003

Von Krogh, G., Ichijo, K., & Nonaka, I. (2000). Enabling knowledge creation: How to unlock the mystery of tacit knowledge and release the power of innovation: Oxford university

press.

Vuori, V., & Okkonen, J. (2012). Knowledge sharing motivational factors of using an intra-

organizational social media platform. Journal of Knowledge Management, 16(4), 592-

603.

© COPYRIG

HT UPM

220

Wagner, C. (2004). Wiki: A technology for conversational knowledge management and group

collaboration. The Communications of the Association for Information Systems, 13(1),

58.

Wagner, C. (2006). Breaking the knowledge acquisition bottleneck through conversational

knowledge management. Information Resources Management Journal (IRMJ), 19(1),

70-83.

Wah, L. (2000). Making knowledge stick. The knowledge management yearbook, 2001, 145-

156.

Wahab, S. A., Rose, R. C., & Osman, S. I. W. (2011). Measuring the effects of relationship

quality and mutual trust on degree of inter-firm technology transfer in international joint

venture. International Business Research, 4(3), p116.

Wahlroos, J. K. (2011). Social media as a form of organizational knowledge sharing. Helsingfors universitet.

Wang, H., Chung, J. E., Park, N., McLaughlin, M. L., & Fulk, J. (2011). Understanding online

community participation: a technology acceptance perspective. Communication Research, 0093650211408593.

Wang, S., & Noe, R. A. (2010). Knowledge sharing: A review and directions for future

research. Human Resource Management Review, 20(2), 115-131.

Wang, W.-T., & Hou, Y.-P. (2015). Motivations of employees’ knowledge sharing behaviors: A self-determination perspective. Information and Organization, 25(1), 1-26.

Wasko, M. M., & Faraj, S. (2000). “It is what one does”: why people participate and help others in electronic communities of practice. The Journal of Strategic Information Systems, 9(2), 155-173.

Wasko, M. M., & Faraj, S. (2005). Why should I share? Examining social capital and

knowledge contribution in electronic networks of practice. MIS quarterly, 35-57.

Watson, S., & Hewett, K. (2006). A multi theoretical model of knowledge transfer in

organizations: determinants of knowledge contribution and knowledge reuse. Journal of Management Studies, 43(2), 141-173.

Wenger, E. (2002). Communities of practice. Encyclopedia of the social sciences, 1, 2339-

2342.

Wenger, E. (2011). Communities of practice: A brief introduction.

White, M. T. (2006). Diagnosing PVS and minimally conscious state: the role of tacit

knowledge and intuition.

Whyte, G., & Classen, S. (2012). Using storytelling to elicit tacit knowledge from SMEs.

Journal of Knowledge Management, 16(6), 950-962.

Widmer, L. (2012). Brain drain: risk management street smarts are on the verge of

disappearing thanks to a mass exodus of veteran talent. Will companies be able to transfer

the knowledge? Risk Management, 59(7), 14-18.

© COPYRIG

HT UPM

221

Wilmott, D., & Knox, I. (2014). In a virtual world, can a community of interest inform

practice? International Employment Relations Review, 20(1), 56.

Witherspoon, C. L., Bergner, J., Cockrell, C., & Stone, D. N. (2013). Antecedents of

organizational knowledge sharing: a meta-analysis and critique. Journal of Knowledge Management, 17(2), 250-277.

Wollan, R., Smith, N., & Zhou, C. (2010). The social media management handbook: Everything you need to know to get social media working in your business: John Wiley

& Sons.

Wynne, C. W. (1998). Issues and Opinion on structural Equation Modelling. Management Information Systems quarterly, 22(1), 1-8.

Xie, K., Durrington, V., & Yen, L. L. (2011). Relationship between Students' Motivation and

their Participation in Asynchronous Online. Journal of Online Learning and Teaching, 7(1), 17.

Yang, H. C., & Wang, Y. (2015). Social sharing of online videos: Examining American

consumers’ video sharing attitudes, intent, and behavior. Psychology & Marketing, 32(9),

907-919.

Yassin, F., Salim, J., & Sahari, N. (2013). The influence of organizational factors on

knowledge sharing using ICT among teachers. Procedia Technology, 11, 272-280.

Ye, S., Chen, H., & Jin, X. (2006). An empirical study of what drives users to share knowledge

in virtual communities Knowledge Science, Engineering and Management (pp. 563-575):

Springer.

Ye, X., & Huirong, Z. (2010). Literature Review on Enterprise Tacit Knowledge Management. Paper presented at the E-Business and E-Government (ICEE), 2010 International

Conference on.

Yi, J. (2006). Externalization of tacit knowledge in online environments. International Journal on E-learning, 5(4), 663-674.

Yin, R. K. (2013). Case study research: Design and methods: Sage publications.

Yoshida, I., Teramoto, Y., Tabata, H., Han, C., & Hashimoto, H. (2011). Tacit knowledge extraction of skillful operation from expert engineers. Paper presented at the Advanced

Intelligent Mechatronics (AIM), 2011 IEEE/ASME International Conference on.

Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G. (2007). Technology acceptance: a meta-

analysis of the TAM: Part 1. Journal of Modelling in Management, 2(3), 251-280.

Yu, J., Jiang, Z., & Chan, H. C. (2011). The influence of sociotechnological mechanisms on

individual motivation toward knowledge contribution in problem-solving virtual

communities. IEEE Transactions on Professional Communication, 54(2), 152-167.

Yu, T.-K., Lu, L.-C., & Liu, T.-F. (2010). Exploring factors that influence knowledge sharing

behavior via weblogs. Computers in Human Behavior, 26(1), 32-41.

© COPYRIG

HT UPM

222

Yuan, D., Lin, Z., & Zhuo, R. (2016). What drives consumer knowledge sharing in online

travel communities?: Personal attributes or e-service factors? Computers in Human Behavior, 63, 68-74.

Zaim, H., Gürcan, Ö. F., Tarım, M., Zaim, S., & Alpkan, L. (2015). Determining the Critical Factors of Tacit Knowledge in Service Industry in Turkey. Procedia-Social and Behavioral Sciences, 207, 759-767.

Zakaria, N., Amelinckx, A., & Wilemon, D. (2004). Working together apart? Building a

knowledge sharing culture for global virtual teams. Creativity and innovation management, 13(1), 15-29.

Zhang, A. M., Zhu, Y., & Hildebrandt, H. (2009). Enterprise Networking Web Sites and

Organizational Communication in Australia. Business Communication Quarterly, 72(1),

114-119.

Zhang, X., & Bartol, K. M. (2010). The influence of creative process engagement on employee

creative performance and overall job performance: a curvilinear assessment. Journal of applied Psychology, 95(5), 862.

Zhang, X., Liu, S., Deng, Z., & Chen, X. (2017). Knowledge sharing motivations in online

health communities: A comparative study of health professionals and normal users.

Computers in Human Behavior, 75, 797-810.

Zhao, L. (2010). Sharing knowledge in virtual communities: Factors affecting a member's

intention to share.

Zhao, L., Detlor, B., & Connelly, C. E. (2016). Sharing Knowledge in Social Q&A Sites: The

Unintended Consequences of Extrinsic Motivation. Journal of Management Information Systems, 33(1), 70-100.

Zheng, Y., Li, L., & Zheng, F. (2010a). Social media support for knowledge management.Paper presented at the Management and Service Science (MASS), 2010 International

Conference on.

Zheng, Y., Li, L., & Zheng, F. (2010b). Social media support for knowledge management.Paper presented at the 2010 International Conference on Management and Service

Science.

Zhong, H., Ozsoy, E., & Nof, S. Y. (2016). Co-Insights framework for collaborative decision

support and tacit knowledge transfer. Expert systems with Applications, 45, 85-96.

Zhong, X., & Qu, K. (2012). Research on the model construction of teachers’ tacit knowledge sharing based on social software. Procedia Engineering, 29, 223-228.

Zhou, T. (2011). Understanding online community user participation: a social influence

perspective. Internet Research, 21(1), 67-81.

Zikmund, W., Babin, B., Carr, J., & Griffin, M. (2012). Business Research Methods: Cengage

Learning. H4 B.