Filling the Gap: Developing Knowledge Management (KM...

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Filling the Gap: Developing Knowledge Management (KM) Maturity

Assessment Capability in OPM3 for IT Organizations in Pakistan

Author

Farrokh Jaleel

(09-UET/PhD-CASE-EM-40)

Supervisor

Dr. Azhar Mansur Khan

Summer 2014

DEPARTMENT OF ENGINEERING MANAGEMENT

CENTER FOR ADVANCED STUDIES IN ENGINEERING

UNIVERSITY OF ENGINEERING & TECHNOLOGY TAXILA

PAKISTAN

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Filling the Gap: Developing Knowledge Management (KM) Maturity

Assessment Capability in OPM3 for IT Organizations in Pakistan

A dissertation submitted in partial fulfillment of the requirements for the degree of

Doctor of Philosophy (PhD) in Engineering Management

Author

Farrokh Jaleel

(09-UET/PhD-CASE-EM-40)

Approved by:

--------------------------------- Dr. Azhar Mansur Khan

Thesis Supervisor

--------------------- Dr. Memoona Rauf Khan

Member Research Committee EM Department, CASE Islamabad

--------------------- Dr. Nadeem Ehsan

Member Research Committee EM Department, CASE Islamabad

--------------------- Dr. Akhtar Nawaz Malik

Member Research Committee Wah Engineering College, Wah, Pakistan

Summer 2014

DEPARTMENT OF ENGINEERING MANAGEMENT

CENTER FOR ADVANCED STUDIES IN ENGINEERING

UNIVERSITY OF ENGINEERING & TECHNOLOGY TAXILA

PAKISTAN

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Declaration

The substance of this thesis is original work of the author and due references and acknowledgements

have been made, where necessary, to the work of others. No part of the thesis has already been accepted

for any degree, and it is not being currently submitted in candidature of any degree.

Farrokh Jaleel

09-UET/PhD-CASE-EM-40

Thesis Scholar

Countersigned:

--------------------------------------


Thesis Supervisor

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Dedication

This work is dedicated to my parents, who were gracious enough to put up with

my taking so long to find my way in life.

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Acknowledgments

During the course of completion of this dissertation, I have collaborated with many people including

professors, consultants, professionals from various disciplines, project managers and a few colleagues

of mine. Yet, first of all, I express my gratitude and special thanks to my supervisor Dr. Azhar Mansur

Khan, whose advices have been pivotal during the identification and refinement of this dissertation. I

remember the day when I first met him and wished to work with him, he simply expressed his

willingness without even asking a single question about my academic career. As a result, he had to work

as hard as I did but, at the end, we wrapped the work appropriately. I wish and pray a healthier and

brighter future for all of his endeavors.

Then I would like to pay my heartiest thanks to Dr. Memoona Rauf khan, Dr. Nadeem Ehsan and Dr.

Akhtar Nawaz who served as my PhD research committee members, though, Dr. Nadeem was quite

suspicious regarding my capabilities during my PhD proposal defense. I would like to pay a special

thanks to Dr. Ginger Levin whose generous support and guidance have been with me since the start of

my PhD proposal defense and throughout this dissertation. I am especially thankful to her for providing

me access to several contemporary maturity models, reviewing my dissertation and providing other

useful documents and scholarly material.

When I started my Ph. D. program, I was working in Pakistan Telecommunication Mobile Ltd. (PTML)

as a Senior SAP Executive at Islamabad, Pakistan. I would like to thank to my boss Mr. Arshid

Muhammad Khan whose continuous and humble support encouraged me to concentrate on my

dissertation while providing freedom from professional obligations at the workplace. I am also thankful

to my parents who always encouraged me during turbulence and at times when I used to be nervous and

wretched by this cumbersome and tedious work. Finally, I am thankful to everyone who provided

suggestions that helped to improve the final outcome of this dissertation.

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Acronyms (s)

CEO Chief Executive Officer

CMM Capability Maturity Model

CMMI® Capability Maturity Model Integration

ICT Information and Communication Technologies

IEEE Institute for Electrical and Electronics Engineers

IT Information Technology

KBV Knowledge-based view

KM Knowledge Management

KMM Knowledge Management Maturity

KMMM Knowledge Management Maturity Model

KPA Key Process Areas

MM Maturity Model

OGC Office of Government Commerce

OPM3® Organizational Project Management Maturity Model

P3M3 Project, Program, Portfolio Management Maturity Model

PM Project Management

PMI Project Management Institute

PMM Project Management Maturity

PMMM Project Management Maturity Model

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PMP Project Management Professional

POO Project Oriented Organization

PRINCE2® Projects in Controlled Environments

RBV Resource Based View

SCA Sustainable Competitive Advantage

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Abstract

Applications of knowledge management in project management is an active area of research. There are

at least three important reasons for this: re-use of knowledge can substantiate success rates of the

projects significantly, projects can provide a sustainable competitive advantage to the organizations,

employee turnover rates are climbing (Statistics, 2013) to the new heights due to globalizations and

advancements in information and communication technologies.

This research was initiated in the belief that successful completion of projects plays a vital role in

maintaining sustainable competitive advantage for the organizations; which in turn relies on the

efficient exploitation of ‘intangible’ assets of the organizations (Grant, 1991; Jugdev, Mathur, & Fung,

2007b; Jugdev & Thomas, 2002). Successful completion of projects is of more importance when we

talk about Information Technology (IT) organizations because IT organizations are unique in a way that

these are totally dependent on projects. Projects, whether in IT organizations or in any other

organization, are accomplished by implementing practices and processes of project management and

combining various organizational assets and resources in some unique way. That is why assessment of

the extent to which organizations are practicing such project management capabilities is considered

important. To fulfill this need, researchers and management consultancy organizations around the world

developed various project management maturity assessment models over the past three decades. These

models assess various aspects of the organizations but lack in the assessment of the extent to which

organizations are exploiting successfully their ‘intangible’ assets. The Organizational Project

Management Maturity Model (OPM3®) is one of the leading models (PMI, 2011) developed by Project

Management Institute (PMI®) to assess organizational project management maturity. This model,

although the most comprehensive models of its kind, still lacks the capability to assess ‘intangible’

assets of the organizations. Therefore, the objective of my research is to bridge this deficiency and

enhance the capability of OPM3® by making it capable of assessing the extent to which organizations

are managing their ‘intangible’ assets. Organizations possess a breadth of ‘intangible’ assets and some

of these assets are not directly measurable while others are difficult to measure. One of such ‘intangible’

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assets is ‘knowledge’ which is possessed and created by the organizations of all types. Careful

assessment and management of that knowledge is of critical importance for the organizations. This

knowledge lies in organizations at different places and in various forms such as in their processes,

practices, documents, culture, human capital, etc. This study will not only help the IT organizations in

Pakistan but also to the organizations worldwide by creating awareness of the best practices to follow

for managing their knowledge efficiently.

The researcher divided this study in two major phases for data collection and its analysis. In the first

phase, open-ended qualitative interviews were conducted with senior project managers of IT

organizations in two major cities of Pakistan in medium to large organizations to solicit and gather their

opinions about best practices for knowledge management (KM). After performing qualitative data

analysis on this data, we identified major themes and their respective best practices for KM. Based on

these best practices, we developed hypotheses and collected data again from various organizations from

IT sector, both in-country and out-of-country, to validate the results and verify the applicability of best

practices in different industrial sectors and in four countries: Pakistan, UAE, Canada and USA. Various

statistical tests were conducted on these data to look for correlations and variances among groups of

respondents to finally suggest the best practices which are of real worth.

The output of the study is a collection of globally and cross-industries validated knowledge management

best practices capable of guiding organizations ‘what to do' if they want to harness one of their

intangible assets i.e. knowledge. We recommend that these best practices should be incorporated in

OPM3® as they have been statistically tested to have applicability in the organizations worldwide.

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TABLE OF CONTENTS

CHAPTER 1: INTRODUCTION ........................................................................................... 1

1.1 Why IT Projects Fail? .................................................................................................... 2

1.2 Projects, Assets and Sustainable Competitive Advantage ............................................ 7

1.3 Concept of Maturity ....................................................................................................... 8

1.4 IT Industry of Pakistan – Overview and Challenges ..................................................... 9

1.5 Role of Knowledge Management in Project Management ......................................... 11

1.6 Statement of the Problem ............................................................................................. 14

1.7 Research Objectives ..................................................................................................... 15

1.8 Scope of the Study ....................................................................................................... 17

1.9 Research Questions ...................................................................................................... 18

1.10 Significance of the Research ........................................................................................ 19

1.11 Theoretical and Practical Implications ......................................................................... 19

1.12 Hypotheses Traceability ............................................................................................... 20

1.13 Limitations of the Study ............................................................................................... 23

1.14 Definitions of Terms .................................................................................................... 26

CHAPTER 2: LITERATURE REVIEW ................................................................. 28

2.1 Sustainable Competitive Advantage and Projects ........................................................ 28

2.1.1 Projects and Assets of the Organization ................................................................ 31

2.2 Knowledge and Organizational Learning .................................................................... 33

2.2.1 History of Knowledge ............................................................................................ 33

2.2.2 Importance of Knowledge ..................................................................................... 35

2.2.3 Organizational Learning and Organizational Knowledge – Conceptions and

Misconceptions ................................................................................................... 37

2.2.4 Organizational Learning and Knowledge Management ....................................... 39

2.2.5 The Data, Information and Knowledge Paradox ................................................... 40

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2.2.6 Explicit, Implicit and Tacit Knowledge ................................................................. 43

2.2.7 Knowledge in the Organizational Context – The Pragmatic Taxonomy ............... 46

2.3 Knowledge Management and its Elements .................................................................. 47

2.3.1 History of Knowledge Management ...................................................................... 48

2.3.2 Importance of Knowledge Management ................................................................ 49

2.3.3 Benefits of Knowledge Management..................................................................... 51

2.3.4 Elements of Knowledge Management - People, Processes and Technology ........ 52

2.3.5 KM and Sustainable Competitive Advantage ....................................................... 53

2.3.6 Knowledge-Based View (KBV) of Organizations ................................................ 55

2.3.6.1 Organizational Learning as Foundation for KBV ....................................... 56

2.3.7 Knowledge Management in Organizational Settings ............................................ 58

2.4 Capability Maturity Models (CMMs) .......................................................................... 62

2.4.1 History of Maturity Models .................................................................................. 63

2.4.2 An Investigation into Maturity Models ................................................................. 65

2.4.3 Structure of CMMI-based Maturity Models .......................................................... 66

2.5 Capability Maturity Model Integration (CMMI) ......................................................... 68

2.5.1 History and Development of CMMI® ..................................................................... 69

2.5.2 Constellations of CMMI ........................................................................................ 71

2.6 Organizational Project Management Maturity Model (OPM3®) ................................. 71

2.6.1 Development of OPM3® ........................................................................................ 71

2.6.2 Structure of OPM3® ............................................................................................... 73

2.6.3 Advantages of OPM3® ........................................................................................... 76

2.6. Why Improve OPM3® and not any other Project Management Maturity Model? .. 78

2.7 Summary ...................................................................................................................... 79

CHAPTER 3: PHASE ONE - RESEARCH DESIGN, RESULTS &DISCUSSIONS ..... 81

3.1 Research Stance ........................................................................................................... 81

3.2 Research Approach and Method .................................................................................. 82

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3.3 Phase One..................................................................................................................... 84

3.3.1 Development of Interview Protocol ....................................................................... 84

3.3.2 Selection of Samples Organizations and Participants ............................................ 86

3.3.2.1 Selection of Sample Organizations ............................................................. 86

3.3.2.2 Selection of Sample Participants (Interviewee’s) ....................................... 89

3.3.3 Pre-test of Interview Protocol ................................................................................ 91

3.3.4 Conducting the Interviews ..................................................................................... 92

3.3.5 Sorting and Organizing the Data Using QDA Miner Tool .................................... 93

3.3.5.1 Codes and Coding ....................................................................................... 96

3.3.5.2 Coding as Process ....................................................................................... 96

3.4 Qualitative Data Analysis (QDA) ................................................................................ 99

3.4.1 QDA Using QDA Miner Tool ............................................................................. 100

3.4.2 Results .................................................................................................................. 101

3.4.2.1 Demographic Information of Samples (Interviewee's) ............................. 102

3.5 Discussion of the Results ........................................................................................... 108

3.5.1 Availability of Business Analyst ......................................................................... 109

3.5.2 MIS Web Portal ................................................................................................... 110

3.5.3 Standardization of Documents ............................................................................. 111

3.5.4 Documentation ..................................................................................................... 112

3.5.5 Meetings and Discussions .................................................................................... 113

3.5.6 Industry Knowledge + PMBOK .......................................................................... 114

3.5.7 Peer Communication............................................................................................ 115

3.5.8 Templates ............................................................................................................. 115

3.6 Objective(s) of Phase One.......................................................................................... 116

3.7 Theoretical and Practical Outcomes of the First Phase .............................................. 117

3.8 Answers to the Research Question ............................................................................. 117

3.9 Limitations for the Investigation of First Phase ......................................................... 118

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CHAPTER 4: PHASE TWO - RESEARCH DESIGN, RESULTS & DISCUSSIONS 120

4.1 Research Questions .................................................................................................... 121

4.2 Hypotheses ................................................................................................................. 123

4.3 Development of Questionnaire .................................................................................. 126

4.4 Selection of Samples .................................................................................................. 128

4.5 Sorting, Organizing and Coding the Data for SPSS .................................................. 131

4.6 Quantitative Data Analysis ........................................................................................ 132

4.6.1 Demographics Data.............................................................................................. 133

4.6.2 Reliability and Validity ........................................................................................ 138

4.6.2.1 Reliability .................................................................................................. 138

4.6.2.2 Validity ..................................................................................................... 139

4.6.3 Correlation ........................................................................................................... 139

4.6.3.1 Multiple Regression .................................................................................. 140

4.6.4 Results of the Quantitative Analysis .................................................................... 142

4.6.5 Hypotheses Testing for Pakistan .......................................................................... 146

4.6.6 Hypotheses Testing for Other Countries .............................................................. 154

4.6.7 Cumulative Hypotheses Testing .......................................................................... 163

4.7 Discussion .................................................................................................................. 175

4.8 Summary .................................................................................................................... 179

CHAPTER 5: CONCLUSION ........................................................................................... 181

5.1 Answers to Research Questions ................................................................................. 182

5.2 Implications for Policy ............................................................................................... 184

5.3 Limitations of the Study ............................................................................................. 186

5.4 Future Research ......................................................................................................... 187

Appendix A - Interview Protocol .................................................................................. 215

Appendix B - Questionnaire .......................................................................................... 222

Appendix C - Results of Data Analysis ......................................................................... 232

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References ........................................................................................................................ 239

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List of Figure(s)

FIGURE 1-1: THREE CONSTRAINTS OF PROJECT MANAGEMENT .......................................................20

FIGURE 1-2: PROJECT SUCCESS RATES ARE RISING ..........................................................................21

FIGURE 2-1: THEORETICAL FRAMEWORK ........................................................................................49

FIGURE 2-2: DATA, INFORMATION. KNOWLEDGE, WISDOM CONTINUUM .......................................60

FIGURE 2-3: DATA, INFORMATION AND KNOWLEDGE AS HIERARCHY .......................................61

FIGURE 2-4: EXPLICIT, IMPLICIT AND TACIT KNOWLEDGE...............................................................64

FIGURE 2-5: LINES OF DEVELOPMENT OF KM ..................................................................................67

FIGURE 2-6: FIVE LEVELS OF SOFTWARE PROCESS MATURITY ........................................................91

FIGURE 2-7: RELATIONSHIP OF BEST PRACTICES, CAPABILITIES, OUTCOMES AND KPIS ..................95

Figure 3-1: Population Organizations' Size (no. Of employees) ..........................................109

FIGURE 3-2: GEOGRAPHIC DISTRIBUTION OF POULATION’S ORGANIZATIONS ................................110

FIGURE 3-3: BUSINESS OF POPULATION ORGANIZATIONS ..............................................................110

FIGURE 3-4: SNAPSHOT OF ARRANGEMENT OF THE DATA IN QDA MINER .....................................117

FIGURE 3-5: GEOGRAPHIC LOCATION OF INTERVIEWEE'S ................................................................125

FIGURE 3-6: TITLES/DESIGNATIONS OF INTERVIEWEE'S ..................................................................126

FIGURE 3-7: ACADEMIC QUALIFICATION OF INTERVIEW PARTICIPANTS..........................................127

FIGURE 3-8: PARTICIPANTS’ EXPERIENCE AS PMS (IN YEARS) ........................................................128

FIGURE 3-10: THEMES OF THE BEST PRACTICES FOR MANAGING KNOWLEDGE-OF- PROJECTS .......130

FIGURE 4-1: CONCEPTUAL FRAMEWORK.........................................................................................144

FIGURE 4-2: GRAPHICAL REPRESENTATION OF HYPOTHESES ..........................................................146

FIGURE 4-3: DATA ANALYSIS PROCESS ............................................................................................154

FIGURE 4-4: GEOGRAPHIC DISTRIBUTION OF RESPONDENTS ............................................................155

FIGURE 4-5: PROFESSIONAL EXPERIENCE OF RESPONDENTS ............................................................156

FIGURE 4-6: MEAN EXPERIENCE OF RESPONDENTS (IN YEARS) .......................................................157

FIGURE 4-7: DISTRIBUTION OF RESPONDENTS (BY DESIGNATIONS) .................................................158

FIGURE 4-8: ORGANIZATION SIZE (NO. OF EMPLOYEES)...................................................................159

FIGURE 4-9: MEAN SCORES OF OUTCOME VARIABLES FOR EACH OF PREDICTORS ............................160

FIGURE 4-10: REGRESSION STANDARDIZED RESIDUAL - PMC (PAKISTAN) .....................................168

FIGURE 4-11: REGRESSION STANDARDIZED RESIDUAL - SCHEDULE(PAKISTAN)..............................174

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FIGURE 4-12: REGRESSION STANDARDIZED RESIDUAL - SCOPE(PAKISTAN) ...................................176

FIGURE 4-13: REGRESSION STANDARDIZED RESIDUAL - BUDGET (FOR PAKISTAN).........................178

FIGURE 4-14: REGRESSION STANDARDIZED RESIDUAL - PMC (OTHERS COUNTRIES)......................181

FIGURE 4-15: REGRESSION STANDARDIZED RESIDUAL - SCHEDULE (OTHERS COUNTRIES) ..............183

FIGURE 4-16: REGRESSION STANDARDIZED RESIDUAL - SCOPE (OTHERS COUNTRIES)......................185

FIGURE 4-17: REGRESSION STANDARDIZED RESIDUAL - BUDGET (OTHERS COUNTRIES)...................187

FIGURE 4-18: REGRESSION STANDARDIZED RESIDUAL - PMC (CUMULATIVE)..................................190

FIGURE 4-19: REGRESSION STANDARDIZED RESIDUAL - SCHEDULE (CUMULATIVE).........................192

FIGURE 4-20: REGRESSION STANDARDIZED RESIDUAL - SCOPE (CUMULATIVE)................................194

FIGURE 4-21: REGRESSION STANDARDIZED RESIDUAL - BUDGET (CUMULATIVE).............................196

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List of Table(s)

TABLE 1-1: PROJECT SUCCESS RATES VS. COSTS ....................................................................................... 5

TABLE 1-2: UNDERLYING REASONS FOR SUCCESS OF PROJECTS .............................................................6

TABLE 1-3: CLIENTS OF PAKISTANI SOFTWARE Industry..............................................................................11

TABLE 1-4: TRACEABILITY AMONG RESEARCH OBJECTIVES, RESEARCH QUESTIONS AND

HYPOTHESES................................................................................................................... ..................20

TABLE 1-5: DEFINITION OF TERMS ............................................................................................................... 266

TABLE 2-1: TRADITIONAL VS. KNOWLEDGE WORK ................................................................................... 37

TABLE 2-2: COMPARISON OF MATURITY MODELS (MMS) – BY STRUCTURE ....................................... 69

TABLE 2-3: CMMI CONSTELLATIONS ...................................................................................................... ....... 73

TABLE 3-1: RESEARCH OBJECTIVE FOR PHASE ONE .................................................................................. 88

TABLE 3-2: RESEARCH QUESTION FOR PHASE ONE ................................................................................... 88

TABLE 3-3: POPULATION OF ORGANIZATIONS (BY SIZE) ......................................................................... 92

TABLE 3-4: GEOGRAPHIC LOCATION OF INTERVIEWEE'S ....................................................................... 108

TABLE 3-5: TITLES/DESIGNATIONS OF INTERVIEWEE'S .......................................................................... 108

TABLE 3-6: ACADEMIC LEVEL OF INTERVIEWEE'S ................................................................................... 109

TABLE 3-7: INTERVIEWEE'S EXPERIENCE AS PMS (IN YEARS) ................................................................ 110

TABLE 3-8: ORGANIZATION SIZE .......................................................................................................... 113

TABLE 3-9: BEST PRACTICES FOR MANAGING KNOWLEDGE-OF-PROJECTS ............................................ 116

TABLE 3-10: BEST PRACTICE(S) FOR 'AVAILABILITY OF BUSINESS ANALYST' THEME ............................. 116

TABLE 3-11: BEST PRACTICE(S) FOR 'MIS WEB PORTAL' THEME ........................................................... 117

TABLE 3-12: BEST PRACTICE(S) FOR 'STANDARDIZATION OF DOCUMENTS' THEME ................................ 118

TABLE 3-13: BEST PRACTICE(S) FOR 'DOCUMENTATION' THEME ............................................................ 119

TABLE 3-14: BEST PRACTICE(S) FOR 'MEETINGS & DISCUSSIONS' THEME .............................................. 119

TABLE 3-15: BEST PRACTICE(S) FOR 'INDUSTRY KNOWLEDGE + PMBOK ' THEME ................................ 120

TABLE 3-16: BEST PRACTICE(S) FOR 'PEER COMMUNICATION' THEME ................................................... 120

TABLE 3-17: BEST PRACTICE(S) FOR 'TEMPLATES' THEME ..................................................................... 120

TABLE 4-1: RESEARCH OBJECTIVE(S) FOR PHASE TWO ......................................................................... 125

TABLE 4-2: RESEARCH QUESTION(S) FOR PHASE TWO .......................................................................... 126

TABLE 4-3: PREDICTOR AND OUTCOME VARIABLES ............................................................................. 130

TABLE 4-4: QUESTIONNAIRE RESPONSE FACTS ...................................................................................... 135

TABLE 4-5: GEOGRAPHIC DISTRIBUTION OF RESPONDENTS ................................................................... 138

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TABLE 4-6: PROFESSIONAL EXPERIENCE OF RESPONDENTS (IN YEARS) ................................................. 139

TABLE 4-7: PARTICIPANTS’ DESIGNATIONS (BY PERCENTAGE) .............................................................. 140

TABLE 4-8: ORGANIZATION SIZE (NO. OF EMPLOYEES) .......................................................................... 141

TABLE 4-9: INTERNAL CONSISTENCY RESULTS ...................................................................................... 144

TABLE 4-10: MEAN AND STD. DEVIATION FOR SCOPE, SCHEDULE AND COST ESTIMATION .................... 150

TABLE 4-11: CORRELATION AND ANOVA STATISTICS (PAKISTAN) ..................................................... 153

TABLE 4-12: REGRESSION COEFFICIENTS - PMC (PAKISTAN) ............................................................... 153

TABLE 4-13: REGRESSION COEFFICIENTS - SCHEDULE (PAKISTAN) ....................................................... 155

TABLE 4-14: REGRESSION COEFFICIENTS - SCOPE(PAKISTAN) ............................................................... 157

TABLE 4-15: REGRESSION COEFFICIENTS - BUDGET (FOR PAKISTAN) .................................................... 159

TABLE 4-16: CORRELATION STATISTICS (OTHER COUNTRIES) ............................................................... 162

TABLE 4-17: REGRESSION COEFFICIENTS - PMC (OTHERS COUNTRIES) ................................................. 163

TABLE 4-18: REGRESSION COEFFICIENTS - SCHEDULE (OTHERS COUNTRIES) ........................................ 165

TABLE 4-19: REGRESSION COEFFICIENTS - SCOPE (FOR OTHERS) .......................................................... 166

TABLE 4-20: REGRESSION COEFFICIENTS - BUDGET (OTHERS COUNTRIES) ............................................ 168

TABLE 4-21: CORRELATION STATISTICS FOR CUMULATIVE RESPONSES ................................................. 171

TABLE 4-22: REGRESSION COEFFICIENTS - PMC (CUMULATIVE) ........................................................... 172

TABLE 4-23: REGRESSION COEFFICIENTS - SCHEDULE (CUMULATIVE) .................................................. 173

TABLE 4-24: REGRESSION COEFFICIENTS - SCOPE (CUMULATIVE) ......................................................... 175

TABLE 4-25: REGRESSION COEFFICIENTS - BUDGET (CUMULATIVE) ...................................................... 177

TABLE 4-26: SUMMARIZED RESULTS FOR PMC ..................................................................................... 180

TABLE 4-27: SUMMARIZED RESULTS FOR SCHEDULE ESTIMATION CAPABILITY ..................................... 180

TABLE 4-28: SUMMARIZED RESULTS FOR SCOPE DETERMINATION CAPABILITY..................................... 181

TABLE 4-29: SUMMARIZED RESULTS FOR BUDGET DETERMINATION CAPABILITY .................................. 182

TABLE 4-30: SUMMARY OF HYPOTHESIS TESTING ................................................................................ 186

TABLE 5-1: SUMMARIZED PRESENTATION OF CORRELATION BETWEEN KM THEMES AND TRIPLE

CONSTRAINTS ………………………………………………………………………………………….194

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Chapter 1 Introduction

The day businesses have been established, they are striving for competitive

advantage - so that they can ensure enough inflow of enough money to sustain their

existence. This quest for maintaining a sustainable competitive advantage (SCA)

continues to date. The advent and progress of information technologies (IT) has turned

the world into a ‘global village’ where geographical boundaries are virtually

diminished while, easy movement of capital and human resources and time to response

has dropped significantly. Organizations have not only to face rivals in their own

regions but across the world. Organizations’ difficulties continue by facing competition

by the organizations not existing physically at all (i.e. virtual organizations). So, the

challenges organizations are facing in order to sustain and maintain a SCA are

multifaceted. To cope with these pressures organizations have started following a

different paradigm of operating – a project paradigm. In this paradigm organizations

operate by considering even their daily operations in terms of projects. The

organizations which follow this paradigm of managing their daily operations through

projects are termed as “project oriented organizations”. This concept of project-

oriented organizations was first coined by (Garies, 1991). These organizations differ

from traditional organizations in the way they treat their projects and their management.

Project oriented organizations consider the routine work tasks as projects. It helps them

to direct their efforts directly onto planned activities (Barber, 2004; PSEB, 2009). Such

organizations manage projects by managing a network of internal and external projects

and the relationship between the organization and its individual projects (Garies, 1991).

Whether the organization is a project-oriented or a traditional functional organization,

successful completion of projects is considered a source of competitive advantage for

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the it1. Due to the strategic importance of projects, organizations around the world are

investing heavily in assessing their capability to manage projects and the extent to

which they can handle similar or different projects successfully. The ability of an

organization to handle its projects successfully is termed as, “project management

maturity2”.

1.1 Why IT Projects Fail?

A lot of research is being carried out to understand, explain and find ‘why

projects fail’ both in academics and in the IT organizations, but no single answer has

been found due to the diverse, complex and integrated nature of the projects scope and

processes. The most admirable work found to assess the reasons for failure of projects

has been conducted by the Standish Group (Group, 1999). Their main findings suggest

that the basic reasons of failure for projects include: underestimation of project

complexity and ignorance of changes in requirements. Before discussing further the

reasons for the failure of projects and the factors for their success, it is necessary to

describe the meaning of project success or failure and the related literature.

Traditionally, any project is called ‘successful’ if it meets the standard criteria

of scope, time and cost (Meredith & Mantel, 2011). This standard notion is depicted by

the famous triangle of three-constraint (Figure 1-1).

1 See section 2.1 2 See section 1.3 for the definition and discussion of maturity

3

Figure 1-1: Three constraints of project management, Source: (PMI, 2008)

Most of the literature describes project success as we described above. However

the Standish Group sub-divides project success into three types consisting of

completely successful projects, partially completed and completely failed ones. The

projects can be categorized into three categories by resolution types (Group, 1999):

• Successful: The project is completed within time, cost and, scope as originally

specified

• Challenged: The project is completed and operational, but over-budget, over the

time estimate, and with fewer features and functions than initially specified

• Failed: The project is aborted before completion

Improving, understanding and accepting project management as a discipline

and management technique has been considered as a major reason for the improvement

in success rate of IT projects. An excerpt from the report (Group, 1999) mentions this

as follows:

“…five years of the Standish Group’s CHAOS research (Group, 1999, 2001)

shows decided improvement in IT project management. Project success rates are up

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across the board, while cost and time overruns are uniformly down. The best news is

that project management is succeeding more often. In 1994, only 16% of application

development projects met the criteria for success — completed on time, on budget and

with all the features/functions originally specified. By 1998, 26% of projects were

successful (Figure 1-2).”

Figure 1-2: Project success rates are rising3, Source: Group (2001)

This increase in success rates of projects can be attributed, to a large extent, to

the development, improvement and application of improved project management

practices (Group, 1999, 2001). A comparison of the success rates is shown in Table (1-

1). The report categorized projects as being small, medium and large with respect to

their revenues.

3 The data were collected for 23,000 projects in the US for large, medium, and small industries by Standish Group

since 1994.

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Company Size (Revenue in Million $)

Success Rate '94

Success Rate '98

Project cost '94

Project cost '98

Large (>= 500)

9 % 24 % $ 2.3 M $ 1.2 M

Medium (300 - 499)

16 % 28 % $ 1.3 M $ 1.1 M

Small (< 300)

28 % 32 % $ 0.4 M $ 0.6 M

Table 1-1: Project success rates vs. costs4 (1994 vs. 1998), Source: Group (1999)

IT projects are unique from other types of projects in one aspect, namely: both

the product and the tools to create it are intangible and the input (raw material) consists

of human knowledge only. The unique characteristic of IT projects makes them even

more complex to manage. Moreover, globalization and advancements in

communication technologies facilitated the migration of human capital across

countries. The average turnover rates of IT professionals in Pakistan is just two years

(PSEB, 2009). In other words, there is a high human turnover rate in the IT industry,

and IT organizations need to constantly hire new employees.

The global IT market is worth more than USD 275 billion per year and

approximately 200,000 software development projects are executed each year. Most of

these projects fail due to lack of skilled project management professionals and not due

to lack of money (Group, 1999). There is a shortage of skilled project managers having

skills for management and planning of enterprise wide portfolios of projects and

understanding the systems of projects. Purpose of IT software is not just to automate

the business processes - they must create business value by improving customer service

or delivering competitive advantage (Group, 1999, 2001). Due to these reasons

application of project management principles, tools, techniques, and methods to IT

4 Average project costs fell in large and medium companies, while rose in small companies by 50%.

6

software development is empirical.

In a long study, the Standish group (Group, 1999) has identified ten factors

which affect the success of an IT project (Table 1-2). The table shows that the identified

reasons are not related to lack of infrastructure, financial resources, and equipment. In

fact, the reasons depict the factors concerned with people such as user involvement,

executive support, experienced project manager, competent staff, ownership; and

processes such as clear business objectives, small milestones, proper planning. Thus,

both the problems and the solutions lie in people and processes.

What makes a

project successful?

The original CHAOS

study identified 10

success factors. No

project requires all

10 factors to be

successful, but the

more factors, the

higher the confidence

level.

CHAOS Ten

User involvement 20 points

Executive support 15 points

Clear business objectives 15 points

Experienced project manager 15 points

Small milestones 10 points

Firm basic requirements 5 points

Competent staff 5 points

Proper planning 5 points

Ownership 5 points

Other 5 points

Table 1-2: Underlying reasons for success of projects, Source: Group (1999)

Improved project management practices have been fruitful for the success of

projects. Due to these reasons there has been a significant increase in the professional

memberships of project management standardization organizations such as Project

Management Institute (PMI), Association of Project Management (APM) and Office

of Government Commerce (OGC). Due to the proven success of these models and

methodologies organizations around the world are upgrading their PM practices in-line

with any of the known PM standards developed by such organizations. These PM

standardization organizations have developed many PM certifications, models,

methodologies, tools and techniques for the discipline, individual project managers and

for the organizations. Some of the known PM certifications for individual project

7

managers/professionals are: Project Management Professional (PMP®) and Projects in

Controlled Environments (PRINCE2®). Other than these certifications for individuals,

there are some methodologies and models available for the organizations as well. These

are called Project Management Maturity Models (PMMMs). A large number of such

models are developed by academicians, consultancy organizations and PM

standardizations organizations. Some of the known PMMMs are: Project Management

Maturity Model (PMMM), Program Management Maturity Model, PM2 Maturity

Model and Organizational Project Management Maturity Model (OPM3®) etc. All of

these models suggest certain best practices for efficient project management. They also

benchmark organizational project management capability against the best practices.

These models assess existence of project management processes and practices in the

organization across various aspects such as human resources, infrastructure,

governance processes and financial resources etc. All of these are considered to be the

tangible assets of the organizations. Organizations use a mix of their assets to achieve

success in projects. But not all of the assets which organization possess are tangible.

There are intangible assets as well; which organizations possess but sometimes they

are unaware of their existence. Many times even if the organizations are aware of their

intangible assets, they are unable to harness the power of their intangible assets. This

can be a decisive factor in maintaining sustainable competitive advantage for an

organization.

1.2 Projects, Assets and Sustainable Competitive Advantage (SCA)

Intangible assets, rather than the tangible assets, are considered crucial for an

organization to maintain a SCA over the competitors. An optimal mix of both of

tangible and intangible assets are considered to be a major factor for an organization to

8

achieve not only success in its projects but also for it to maintain a sustainable

competitive advantage over its rivals (R. Grant, 1991; Jugdev, et al., 2007b; Jugdev &

Thomas, 2002). Unlike tangible assets, intangible assets encompass a breadth of

resources such as innovation capability, value generation capability, entrepreneurship

capability, intellectual capital and the knowledge. These resources keep on evolving all

the time during the organization’s daily operations and during execution of its projects.

Knowledge is of real worth when it comes to claim a SCA over the competitors. A

deliberate and rigorous effort by the organization to create, organize and share the

knowledge with other employees can prove to be the real decisive factor amongst

competitors. A well-directed effort by the organization can benefit it in many ways

such as: it can reduce the response time of the organization to respond emerging market

demands, prevent reinventing-the-wheel, increase its innovation capability, promote

peer communication among employees, create a collaborative working environment

and provide quick solutions for the problems. Although, the maintenance of

organizational knowledge is of strategic importance but no PMMM has the capability

to assess the existence the best practices adopted by organizations to maintain their

knowledge.

1.3 Concept of Maturity

Maturity in general means fully developed or perfect. Though, there is not a

single agreed upon definition of “maturity” but for the purpose of this study we looked

into a number of dictionaries which provided the following definitions: (1) fully

developed or grown up, (2) of theory, it denotes that they are fully developed or

perfected (Oxford, 2011; Webster, 1988). So, “maturity” is something fully grown up.

If we apply the term of maturity to an organization, it refers to a state where the

9

organization is fully grown and capable of meeting its objectives (Andersen & Jessen,

2003). In this study we are not focused on organizational maturity in general. Instead,

we are concerned with organizational maturity to manage knowledge of its projects5. It

is the ability of organizations to assess and harness their knowledge resources while

executing projects. Let us now discuss in detail the reasons for our interest in assessing

knowledge-of-project management maturity of the organizations.

Although, organizational theorists have conducted studies of organizational

effectiveness and organizational success for many years, yet there is no single approach

or standard for project success. The definition given above is considered as the most

widely accepted definition of ‘project success’. It uses a simple formula that is explicit

and easy to understand. Such measures are typically equated with project success when

they meet the constraints of budget, time and an acceptable level of performance (Pinto

& Slevin, 1988). However, these measures are incomplete, even when taken together.

According to this definition, the projects that met the objectives of schedule, cost and

scope objectives can be counted as successful - but may not have met client's needs and

requirements (Maddison et al., 2008).

1.4 IT Industry of Pakistan – Overview and Challenges

The IT industry of Pakistan industry established by opening up its first company

in 1976 (PSEB, 2009). However, it boomed and attracted local and foreign investors

since the early 1990s. It took only 10 years to develop and attract the attention of policy

makers and the Government. During the IT bubble burst it slowed down, however the

future of Pakistan’s IT industry is promising and it has the potential to become one of

the most profitable industry of the country. In 2008, the industry grew at growth rate

5 See chapter 2 for details of what do we mean by knowledge-of-project management maturity

10

of 37% in revenues, and 27% in terms of technical and professional employment

(PSEB, 2009).

After assessing the huge potential of the industry, the government of Pakistan

(GoP) has undertaken several policy, infrastructure development and up-gradation

projects to promote a domestic software/IT industry and exports of IT services and

products. All of the such policies and actions are documented in the National IT Policy

and its accompanying action plan (MOST, 2000). However, no noticeable actions are

taken to monitor the progress on these actions and initiatives (UNCTAD, 2004). As a

result, the local software development industry lacks the vitality and growth in

comparison of major tier‐1 or tier‐2 software exporting countries (Carmel, 2003). The

only way to establish the industry on solid grounds is to perform a firm‐level analysis

that could unleash the factors behind intra‐industry performance differentials, and

identify the best practices that can be adopted across-the-industry (PSEB, 2009).

One major step taken by the GoP was to formally and explicitly establish an IT

regulation and promotion body in the country namely, Pakistan Software Export Board

(PSEB). The sole objective of PSEB is to strengthen the IT sector in the country,

conduct benchmarking studies, conduct various researches regarding growth of IT

industry and develop strategies to attract as much as possible outsourced/offshore

projects and IT investment in the country. PSEB provides key facts and findings about

the IT industry in the country regularly.

PSEB reported that there are almost 1200 IT organizations in the country

including IT software, hardware, telecommunication, Internet service providers (ISPs)

and call centers. The organizations include small, medium and large organizations - out

of which only three are capability maturity model integration (CMMI) level five

11

certified, a few are level three and level one certified. All the others are following no

standard or methodology for the standardization of their processes. Although the

Pakistani IT industry is not so big but still there is a huge potential in the local IT market

to provide services to foreign organizations by getting outsourced projects due to the

inexpensive labor in the country. The most recent report published by PSEB was in

2009 describing the statistics regarding the clients (foreign and domestic) of IT

organizations in Pakistan (Table 1-3).

Exports vs. Domestic & Products vs. Services % of Total Revenues

Exports-Products

Exports-Services

Domestic-Products

Domestic-Services

N = 54

22.56%

38.52%

23.37%

16.53%

Exports vs. Domestic & Public vs. Private Sectors

Public Sector - Domestic

Public Sector - Foreign

Private Sector - Domestic

Private Sector - Foreign

N = 54

8.51%

5.90%

30.79%

54.77%

Table 1-3: Clients of Pakistani software companies. Source: PSEB (2005)

It can be seen from the table that the major clientele of IT sector of Pakistan consist of

foreign-private sector (54.77%) and export of services (38.52%). It means that the local

IT market has the potential to attract foreign clients provided that the industry follows

and adopt standardized processes to gain trust of foreign investors and clients.

1.5 Role of Knowledge Management (KM) in Project Management (PM)

The steady growth of software industry necessitates search of innovative and

novel ideas. The key focus of industrial and academic research has been to improve the

software development process and system quality - neglecting its management process.

The software industry is unique in its nature and products because both the products

12

and the raw material (intellectual capital) needed to develop the products are intangible.

Due to this unique nature, processes, tools, techniques, methodologies and management

of this industry are also different from the rest of the industries. The software industry

employs a wide variety of tools, methodologies and models to gain an insight and

control of its product life cycles. The software industry is also unique the way products

(software) are developed. For example, developers can work from any part of the world

using advanced communication technologies, products can be developed through

virtual teams and geographically dispersed teams, employees turnover rates are quite

high e.g. two years in Pakistan (PSEB, 2005), people can easily migrate around the

world to other organizations etc. Due to these reasons retention of human capital is a

major challenge for any IT organization; key personnel can leave any time taking

valuable knowledge with them. In this scenario, organizations are facing two major

challenges: (1) retention of key personnel and, (2) if the person leaves the organization

should have access to the valuable knowledge that the person had. Only well-

established knowledge management processes can play a decisive role in this scenario.

KM can play the role to extract the leaving person’s knowledge, making it shareable

with other employees, increase creativity and innovation in the organization (Coakes,

Bradburn, & Blake, 2005; Li, Yezhuang, & Ping, 2005; Owen & Burstein, 2005).

Organizations should formally establish KM systems, tools and methodologies to

assess the extent to which they have established KM processes in their daily operations

and business processes. The application of KM have benefited organization by

providing a net increase in profits, reduction in efforts for product development,

reduction in defects, reduction in administrative costs, demonstrated increase in value

generated, maintaining client relationships, productivity improvement, increase

efficiency, retaining customers and gain a sustainable competitive advantage over the

13

competitors (Anand, Pauleen, & Dexter, 2005; Coakes, et al., 2005; Hahn,

Schmiedinger, & Stephan, 2005; Li, et al., 2005; Owen & Burstein, 2005).

Regardless of the size of the IT industry in Pakistan, it is certain that the industry

is not well understood. Among other important yet unanswerable question, one major

question is: no formal efforts have been made to identify a generalized set of best

practices for the local IT industry. A set of best practices can distinguish better

performers from those that do not perform that well? A lot of studies have been

conducted in the other Asian and European countries on the dynamics of IT industry

such as India (Heeks, 1998, 1999; Heeks, Lai, & Nicholson, 2003; NASSCOM, 2001,

2002, 2003, 2004), China, Japan (Rapp, 1996), Iran (Nicholson & Sahay, 2003) and

Korea (Avron, Tessler, & Miller, 2002).

Several researchers have attempted to reapply the results of these studies in the

context of other countries (UNCTAD, 2002). Some others have developed policy

frameworks and drew policy conclusions (Carmel, 2003b; Heeks & Nicholson, 2002)

or developed generic frameworks for analyzing the competitiveness of IT industries

(Heeks, 1999). A number of studies have been conducted in many countries to identify

and solicit how their IT industries can achieve competitiveness or benefit from the

lessons of others but no such study has been conducted for the IT industry in Pakistan

(PSEB, 2005). To fill this gap we considered it of utmost importance to conduct this

study. So, what is supposed to be explored and tested in this study is:

14

1. What are the best practices for managing knowledge that IT organizations in

Pakistan should follow

2. To what extent adoption of these KM best practices can affect project

management capability of IT organizations in Pakistan in particular and in

USA, Canada and UAE

3. Recommend the globally validated best practices to be included in OPM3®.

1.6 Statement of the Problem

In the recent years, developing countries are getting increasingly interested in

the development of IT industries to gain economic gains. The interest in IT industry is

because of a school of thought that sees IT and software industry as a “great economic

enabler”. This school of thought also argues that, by promoting software industry,

developing countries can compete with the developed nations fast and easily. The

“globalization of work” can reduce the disparities across the nations and provide an

equal opportunity for everybody to participate in the global production and creative

processes. There are many cases in the developing countries where these predictions

have been validated by pilot projects and initial studies (e.g. India, Ireland and Israel,

also known as the three "I's" of the global IT revolution). These countries are now

counted as the new entrants in tier‐1 of software exporting nations. Other countries

such as: Brazil, Mexico, Malaysia, Sri Lanka, Pakistan, Ukraine, Bulgaria, Hungary,

Poland, and Philippines are adopting the examples of these tier‐1 nations (Carmel,

2003).

Given such conditions, this research attempts to show how developing countries

can also follow the experiences of tier-1 countries by developing knowledge-based

15

solutions for their own needs and scenarios. However, due to time and resources

constraints, this study is limited to the identification and analysis of knowledge-based

practices related to project management only.

1.7 Research Objectives

An investigation in the literature by the researcher revealed that many studies

conducted in various countries for the identification and development of a generic

model to guide their IT industries and, how their industries can gain competitive

advantage over their global rivals (Carmel, 2003). However, there is no such study

available for Pakistan – as mentioned in PSEB report as well (PSEB, 2005). Also, we

found many PMMM’s developed by academicians, organizations and consultants

around the world but we did not find any PMM model which could assess KM maturity

of the organization. Nor did we found any KMM model which could assess PM

maturity of the organization. So, there is a clear and obvious need of a model which

could assess both aspects of the organizations. In comparison to KM, where there is no

single renowned organizational capability assessment model available, discipline of

PM is mature enough where there exist many known maturity models developed by

PM standardization organizations. One of the such models is, Organizational Project

Management Maturity Model (OPM3®) developed by Project Management Institute

(PMI). This model has gained acceptability and established rapport around the world

in the organizations of many industries in a short period of time. This model can assess

project management capability of any organization but cannot assess KM capability of

the organization. Organizations adopting this model have reported gains in their

capability to manage projects more efficiently, improvement in response time to market

demands and completion of projects in close approximation of scope, time and cost.

16

Although this model is providing organizations with such advantages but due to its lack

of ability to assess KM capability of the organizations, organizations cannot fully

exploit their capabilities to compete with their competitors.

This research will identify the best practices for Pakistani IT organizations so

that they could gain competitiveness through managing their knowledge-of-project

more effectively to successfully complete their projects. In addition, it will also inspect

to what extent these best practices can affect organizational PM capability both within

Pakistani IT organizations and in the IT organizations of other countries. Moreover, a

comparison will be made to identify any differences in the applicability of best

practices locally and globally. This comparison will enable us to suggest what best

practices can, potentially, be included in OPM3®. Hence, enable OPM3® to assess KM

capability of the organization.

The objectives of the study are:

1. To identify the best practices for managing knowledge-of-project in IT

organizations of Pakistan

2. To test the extent to which adoption of the identified best practices can affect

project management capability of the IT organizations in Pakistan

3. To test the extent to which adoption of the identified best practices can affect

project management capability of the IT organizations in USA, Canada and

UAE

4. To suggest which KM best practices can be considered for incorporation in

OPM3® to make it capable of assessing knowledge-of-project management

capability of the organizations

17

1.8 Scope of the Study

We are interested in enhancing the capability of OPM3®. This model

has been selected because of several reasons. OPM3® is a project management maturity

assessment model, introduced in 2003, and has gained acceptability in the organizations

of many industries around the world. This model is being followed by more than 3,000

organizations, ranging from small to large sized, for assessing and standardizing their

project management processes. PMI has reported numerous successful case studies of

OPM3® implementations and benefits reported by the organizations (PMI, 2011).

Secondly, OPM3® not only assesses organizational project management capability but

also identifies weaknesses and the path for improvement (PMI, 2003). In this regards,

OPM3® is the only PMMM that guides organizations along its complete journey - from

assessment to improvement of its processes.

Basically this research will identify and verify the KM best practices that can

enhance organizational project management competitiveness. Proponents of OPM3®

explicitly state that the well-defined and measureable processes eventually lead to

faster gains, efficient processes, and in-time completion of projects - resulting in saving

of capital, improved product quality, and smooth processes. There are several

evidences depicting adoption of OPM3® in a wide variety of organizations around the

world; which proves its diverse applicability and validity.

OPM3® mentions a characteristic description of effective processes used for

PM process improvement. OPM3® is used as a process improvement model to define

process improvement objectives, establish priorities and lay the foundation for stable,

capable and mature processes to improve project management. It also proposes an

appraisal method to improve the current practices of an organization. The overall goal

18

of this research is to improve the project management capability of organizations by

applying KM practices - this will increase the likelihood of success in projects. Finally,

suggest a set of best practices for organizations of other countries and, suggest the same

to be incorporated in OPM3®.

1.9 Research Questions

As mentioned in previous sections, there is a strong correlation between

successful completion of projects and maintaining sustainable competitive advantage

for the organizations. Sustainable competitive advantage particularly depends upon

some strategic assets; which are believed to be ‘intangible’ in nature. Existing

PMMMs, including OPM3®, do not assess the extent to which any organization is

harnessing its ‘intangible’ assets. Therefore, capabilities of OPM3® are needed to be

enhanced because it is the most widely followed PMM model for assessing the PM

capability of the organizations. However, organizational capability to do successful

projects does not depend only improving the PM processes, rather it depends on a range

of other factors as well. The factors include: ability to benefit from previous

experiences, ability to innovate new and novel ways and ability to create maximum

value from the projects through fulfilling customer requirements and developing the

products etc. OPM3® cannot assess organizational capabilities in these aspects so there

is a clear need to eliminate this deficiency and make this model more helpful for the

organizations. Pakistani IT organizations also need to identify, first of all, the best

practices that they need to follow. This effort can help them gaining competitive

advantage by harnessing the power of their ‘intangible’ assets, which in our study is

‘knowledge’. These deficiencies and lack of existing research in Pakistan gives rise to

our research questions as follows:

19

Q.1. what are the best practices for managing knowledge of IT project management in

the Pakistani IT organizations?

Q.2. How the identified best practices for managing knowledge of projects will affect

project management capability of IT organizations in Pakistan

Q.3. Are the identified best practices for managing knowledge of projects applicable

to the IT organizations in other countries such as USA, Canada and UAE as well?

Q.4. Are the existing best practices in OPM3® pertaining to knowledge management

sufficient, if not, what other practices can be added to make OPM3® more usable?

1.10 Significance of the Research

Some organizations apply project management maturity models within their

departments or organization-wide just to fulfill the certification requirements and

satisfy their customers. However, the real purpose of maturity models is to improve the

current level/state of organizational processes, see any inefficiencies/ deficiencies in

them, and devise ways to improve them. Such efforts can increase robustness and

efficiency in the processes and avoid losses in terms of client turnover rates or financial

losses. The results of this research encourage the organizations, which currently do not

follow any project management maturity model, to use such models to improve their

rates of successful projects.

1.11 Theoretical and Practical Implications

The theoretical and practice implications of this research are as follows. The

theoretical implications are:

Supporting increased attention for the standardized organizational project

20

management processes

Providing more evidence on the use of KM best practices and improvement

in project management capability of the organizations

Giving confidence on the capability of OPM3® in improving project

management processes

Providing evidence that OPM3® can improve project management

processes

The practical implications are:

Exceeding client expectations

Increasing client satisfaction

Reducing project completion times

Avoiding reinvention-of-wheels syndrome

Resulting improvement to the business of IT industry

Proving that adoption of OPM3® best practices will help the organizations

to deliver higher quality and successful projects

Proving that adoption of OPM3® best practices can reduce chances of

project failure

1.12 Hypotheses Traceability

The research objectives, research questions and hypotheses are reported in

the table (Table 1-4) to establish traceability.

Table 1-4: Traceability among research objectives, research questions and Hypotheses

21

Objectives Research Questions Hypotheses

1. To identify the best

practices for managing

knowledge-of-project in

the IT organizations of

Pakistan

Q.1. What are the best practices

for managing knowledge-of-

project in the context of IT

project management in Pakistani

IT organizations?

NA

2. To test the extent to

which adoption of the

identified best practices

for managing knowledge-

of-project can affect

project management

capability of IT

organizations in Pakistan

Q.2. How the identified best


knowledge-of-project will

affect project management

capability of IT organizations in

Pakistan

H2: Adoption of the best

practices for knowledge-

of-project management

will improve project

management capability of

IT organizations in

Pakistan

Q.2.1. How the identified best



affect project 'schedule

estimation’ capability of IT

organizations in Pakistan?

H2a: Adoption of the best




‘schedule estimation’

capability of IT


Q.2.2 How the identified best


knowledge-of-project will affect

‘scope determination capability’

of IT organizations in Pakistan?

H2b: Adoption of the best



will improve ‘scope

determination’ capability

of IT organizations in

Pakistan




project 'budget determination’

capability of IT organizations in

Pakistan?

H2c: Adoption of the best




'budget determination’

22


capability of IT


3. To test the extent to

which adoption of the

identified best practices

can affect project


IT organizations in other

countries

Q.3. Are the identified best


knowledge-of-project applicable

to IT organizations in other

countries?

H3: The identified best


knowledge-of-project



IT organizations in other

countries




affect project 'schedule

estimation’ capability of IT

organizations in other countries?

H3a: Adoption of the best




‘schedule estimation’

capability of the IT

organizations in other

countries

Q.3.2 How the identified best



‘scope determination capability’

of IT organizations other

countries?

H3b: Adoption of the best



will improve ‘scope

determination’ capability

of IT organizations in

other countries




‘project budget determination’

capability of IT and

organizations in other countries?

H3c: Adoption of the best



will improve ‘project

budget determination’

capability of IT

organizations in other

23


countries

1.13 Limitations of the Study

This research is conducted in two phases; first being the qualitative and second

being quantitative. The first phase is qualitative in nature to gather as much as possible

opinions of the interviewees through open-ended questions. Due to the use of open-

ended questions it was not possible to distribute the interview protocol, therefore, the

researcher personally visited the interviewees in two major cities of the country and

conducted face-to-face interviewees with the IT project managers. This provided

richness of data but limited the access to a larger sample of the population due to the

limited resources and access to target sample.

The population of the research is limited due to small IT industry in the country

at one hand and limitation of the researcher to get access to the all organization, at other

hand. Moreover, target sample of the study, both in first and second phase, is highly

experienced, and senior people, i.e. project managers, which were difficult to identify,

access and take appointment to conduct detailed interviews and get the questionnaires

filled. Due to lengthy interviews and senior target sample, it was not possible to collect

data from a larger sample of the population.

Due to the complex and abstract nature of concepts the interviewees' found

difficult to comprehend and express their opinions. The terms and the concepts such as

‘knowledge’ in the context of IT project management are abstract enough and difficult

to envision. Therefore, the respondents were provided with clear descriptions of terms

24

and concepts.

Due to the complex nature of data, short data collection period, limited time,

resources and other such constraints, the researcher has not attempted to focus on more

objectives raised during the study. The researcher is also limited by the access to the

key informants in each organization who were project managers, senior project

managers, consultants and others of similar designations.

The structure of OPM3® comprises of: best practices, capabilities, outcomes,

and key performance indicators (KPIs). All of these cannot be identified in a single

study of interim nature. Therefore, only the best practices are identified and analyzed

in this study as a first step toward improvement of the model - identification of

capabilities, outcomes and KPIs is left for the future research.

The IT industry of Pakistan cannot be considered ‘mature’ enough as there are

only a few large and CMMI® certified organizations - established in three major cities

of the country (i.e. Islamabad, Lahore and Karachi). However, the researcher had

access to only two cities, Islamabad and Lahore. Therefore, interviews in the first phase

were conducted only in these two cities, while the questionnaires were distributed

through a web-based survey to the IT organizations in all three cities.

To validate the results quantitatively and globally, the researcher distributed the

questionnaire through a web-based survey to the IT organizations in various countries

such as USA, Canada, UAE and Pakistan. Although due to the time and access

constraints it was not possible to gather large data and achieve a better response rate

from other countries but still a reasonable response rate was achieved; which provided

confidence about the results and applicability of the results to other organizations

around the world.

25

The proposed approach follows a mixed methods methodology; in the

qualitative phase there is a possibility that not all the constructs or concepts are captured

due to any reason beyond the control of the researcher.

Lastly, but not the least, this kind of research requires an extensive and large

scale study with enough time and resources. Still, with limited resources the researcher

has conducted this study laying the foundation for any such initiative at the government

level.

26

1.14 Definitions of Terms

Following terms (Table 1-5) are used frequently in this study.

Table 1-5: Definition of terms

Term(s) Definition

Organizational project

management

knowledge (Reich

2007)

Process Knowledge – "knowledge about the project structure,

methodology, tasks and time frames. Knowledge allows a team

member to understand his or her part in the overall project, is

expected and when it is to be delivered. It also allows a team or

sub-team to self-organize."

Domain Knowledge – "knowledge of the industry, firm, current

situation, problem/opportunity technical solutions. This

knowledge is spread widely within and outside the project team."

Institutional Knowledge – "knowledge of the history, power

structure and values of the organization really going on” —

which is transferred by means of stories or anecdotes by

organization observers."

Cultural Knowledge – "knowledge of how to manage team

members of different cultures or from groups such as web

designers, IT architects or organizational development experts."

Knowledge

Management (Reich

2007)

"Knowledge management, in the context of a project, is the

application of principles and processes designed to make relevant

knowledge available to the project team."

Methodology

(Maddison, Baker et

al. 1984)

"A recommended collection of philosophies, phases, procedures,

rules, techniques, tools, documentation, management and

training for developers and information systems."

27

A Process Model (SEI

2006)

"A structured collection of practices that describes the

characteristics of effective processes.”

Predictor/Independent

Variable A variable that does not depend on any other variables (Baron and

Kenny 1986).

Outcome/Dependent

Variable A variable that depends on at least one independent or dependent

variable (Baron and Kenny 1986).

Framework Same as a process model

28

Chapter 2 Literature Review

In this chapter, key themes are provided in a logical sequence which lay

foundation for the research hypotheses. First of all, role of projects is discussed in

gaining sustainable competitive advantage (SCA). This logic strengthens the rationale

for conducting this research and show the major reasons that lead to failure of IT

projects. Then, a comprehensive background of various knowledge management

concepts informs and brings the audience to a common level of understanding. After

that, evolution and history of maturity models is described with a basic overview and

description of various renowned maturity models. The next section provides detailed

information about CMMI® and OPM3®, OPM3® is also the main focus of this research.

There are two sections dealing with OPM3®. One describes the evolution and history

of OPM3® and the other is about the OPM3® itself. Also there are some more sections

which discuss improvement of project management processes, role of KM processes in

project management, key factors which influence project management and the factors

which contribute towards failure of projects.

2.1 Sustainable Competitive Advantage (SCA) and Projects

Worldwide, creation of value and sustaining survival in an increasingly

competitive marketplace has become challenging for the organizations. Traditionally,

organizations have always been in competition with their rivals to capture as many

markets of customers as possible. This competition used to limited to rivals in their

specific geographical regions, but this scenario no longer holds true. Due to the

advancement and rapid development of information and telecommunication

technologies (ICT), organizations in any part of the world are now facing cut-throat

29

competition with their rivals globally. Everyday novel types of previously unheard of

business structures are evolving, such as home e-businesses in which individuals can

work directly from their home for any organization in any part of the world. There exist

virtual organizations which do not even exist physically but are evolving rapidly such

as Deloitte. E-businesses such as Amazon and social networking websites such as

Facebook, Twitter and LinkedIn portray another different type of marketing mediums.

Many e-businesses do not require large capital investments hence, any literate person

can start a business very easily. Venture capitalists also exist who are ready to invest

in businesses based on innovative ideas. Thus, ‘ideas’ matter more than anything else.

Many examples of such ideas exist such as Facebook, Google, Microsoft, Apple Inc.,

IBM etc. However, with the ease of starting up, lifespan and maturity of the products

and organizations are shrinking dramatically. Everyday hundreds and thousands of new

products are launched but most of them vanish even without the notice/knowledge of

the large part of the population. This situation is even worse in the Information

Technology (IT) industry where the extent of such forces and factors is faster.

The IT industry was at its peak of challenges and pace of change during last

two decades. In 2008 alone IT products and services crossed USD 1.6 trillion which

depicts a growth of 5.6 percent over the year 2007 (NASSCOM, 2011). This industry

and its products are unique in nature in that except hardware and machines, all the

services and products are intangible in nature. No factory is needed to produce the

software products. Once such products are developed, no variable cost is involved to

produce extra units of the product. The major input component of production of the

products is human capital rather than machines and other tangible assets. People can

work for any organization from any part of the world through internet and online

collaboration tools. Due to these reasons human capital turnover rates are quite high in

30

the IT industry. In Pakistan the average length of tenure of an IT professional at any

organization is just two years (PSEB, 2009). Relocation and career switching has

become very easy due to many reasons such as, transformation of the world into a

“Global Village”, countries are joining trade agreements such as World Trade

Organization (WTO) and European Union etc. Due to this very different nature and

dynamics of both of the industry and its products, the challenges of the industry and

products are also very different.

High human capital turnover rates have posed many risks to the IT

organizations. The risks include: cost overruns, delays in project deadlines which, in

turn, causes dissatisfaction in their clients and the loss of clientele etc. IT organizations

are unique from other organizations in one more aspect, that is, they are project-based

organizations i.e. their existence depends upon successful completion of projects and

achieving client’s satisfaction. In other words, IT organizations can sustain

competitiveness only if they complete their projects within time, cost, resources and

pre-determined quality criteria. Figure 2-1 summarizes the concepts discussed in this

chapter.

31

Figure 2-1: Theoretical framework

The role of projects for maintaining competitiveness is not a fad or a recent

approach. Organizations around the world are exploiting the power of projects for

achieving competitiveness since a long time (Jugdev & Thomas, 2002). However, in

the recent times, the role of projects has been further highlighted by the establishment

of “project management” as a discipline. Many renowned universities are offering

graduate level degree programs and diplomas in project management. Many

standardization organizations are evolving such as, Project Management Institute

(PMI), Association for Project Management (APM), International Project Management

Association (IPMA) etc. These organizations have developed many frameworks and

models to assure success of the projects. These organizations have also introduced

many certifications for individuals and organizations. One of the such certifications for

organizations is organizational project management maturity (OPM3®). These recent

advancements, i.e. introduction of graduate degree programs and certifications, have

surged the awareness and importance of managing projects.

2.1.1 Projects and Assets of the Organization

Organizations execute projects through the efficient exploitation of an efficient

combination of their various assets. The assets include tangible assets (e.g. financial,

equipments, technological infrastructures) and intangible assets (e.g. human capital

skills, knowledge-based, organizational and social assets) (Brush, Greene, Hart, &

Haller, 2001; Jugdev, et al., 2007b). These assets can be classified as strategic and non-

strategic assets. However, only a subset of these assets can be classified as strategic

assets. Strategic assets are the assets contributing to competitive advantage and involve

explicit and tacit knowledge (Eisenhardt & Santos, 2000; Kaplan, Schenkel, Krogh,

32

Weber, & AL., 2001; Kogut, 2000; Nonaka, 1994). Tacit knowledge is classified as,

"the knowledge embedded in a company’s unique internal skills, knowledge, resources,

and practices" (Foss, 1997; Rumelt, Schendel, & Teece, 1994).

There is a clear distinction between strategic assets and basic or generic

competencies or assets. Strategic assets are often intangible in nature. Their

characteristics are: they are valuable, rare (unique), inimitable (difficult to copy),

immobile (organization specific), non-substitutable, durable (long lasting) and have

low tradability. (Amit & Schoemaker, 2006; Barney, 2002; Brush, et al., 2001; Collis

& Montgomery, 1995; R. Grant, 1991; Jugdev & Thomas, 2002, 2002a; Peteraf, 1993;

Priem & Butler, 2001a). Many researchers have examined the relationship between

gaining SCA and the strategic assets of any organization. They have found that there

exists a strong relationship between SCA by and strategic assets (Amit & Schoemaker,

2006; Eisenhardt & Santos, 2000; Jugdev, Mathur, & Fung, 2007a; Jugdev & Thomas,

2002; Kaplan, et al., 2001; Kogut, 2000; Nonaka, 1994; Peteraf, 1993). Strategic assets

are pivotal for any organization but often organizations do not realize their importance

and hence, cannot harness the power of their strategic assets to gain competitive

advantage.

Organizations use a blend of strategic and non-strategic assets combined with

project management processes in their projects; therefore there must be some way to

assess the extent to which organizations are aware of their strategic assets so that

enough effort could be directed to improve and retain them. Successful completion of

projects depends upon a number of factors. The factors include, but not limited to, an

efficient utilization of assets, adoption of standardized project management processes,

practices, tools, techniques and knowledge-based processes. In other words, projects

are accomplished by implementing practices and processes of project management and

33

combining various organizational assets and resources in some unique way (Jugdev, et

al., 2007b; Jugdev & Thomas, 2002).

If usage of strategic assets is of so much importance then, there should be some

method to assess their existence and usage in the organizations. Therefore, this study

attempts to assess the extent to which organizations are able to harness the power of

their strategic assets; which include knowledge-based processes and practices. Projects

are always knowledge intensive endeavors - both in terms of knowledge they require

to be accomplished successfully, and as producer of further knowledge. This

knowledge may lie in daily operations of organizations, in knowledge assets of

employees, produced during the execution of various activities of projects etc.

However, organization may not even know that what ‘knowledge’ they possess and

how that knowledge can provide them competitive advantage, unless a formal attempt

is made to extract, organize and share that. In previous chapter, we have provided many

examples of the benefits organizations can avail just by knowing ‘what they know’ and

making that ‘know-how’ available to other employees. Before describing the ways an

efficient management of ‘knowledge’ can benefit and provide a competitive advantage

to the organizations, let us first discuss what KM is, what are its elements, what it

encompasses, KM maturity models, and what organizations can do to assess and

harness the power of their ‘know-how’, i.e. their knowledge.

2.2 Knowledge and Organizational Learning

2.2.1 History of Knowledge

Around thirty-five thousand years ago, at the base of a cliff in what is now

southeastern France, members of a nomadic hunting tribe crawled through a dark and

narrow passage into a cavern. Holding crude torches before them, they groped deeper

34

into the damp gloom, past the evidence of bears that had made the cave their home.

They lit the fire to light their caves, mixed clays, arranged water sources for them, and

painted pictures of the creatures they often encountered. Leopards, lions, Bison,

rhinoceroses and bears were the animals that threatened them. In their unique ways,

these artists were recorded what they observed and knew, but what were their

intentions, we 21st-century humans cannot be sure. We can just attribute some purpose

to their deeds: to appease their gods, to appeal to the spirits of their predators, or maybe

to train their young men as hunters. Those artists, intentionally, passed their

experiential knowledge to their tribes and apprentices. In this process of creating their

depictions, they unwittingly left the evidence for us (Maier, Hädrich, & Peinl, 2005).

Transfer of knowledge through pictures was their way of communicating and

expressing, we in 21st century have now thousands of languages and hundreds of ways

to do the same.

The implications of knowledge and knowledge management for the

organizations has been rarely researched since a long time. Philosophers and

organizational researchers have been debating to define ‘knowledge’ since the time of

Socrates but there exists no agreement on it (Maier, et al., 2005). The foundations for

the Western thinking about knowledge can be traced back to the times of Socrates.

However, in this study it is neither intended to provide a comprehensive overview of

knowledge definitions, because even a limited review of the work done in philosophy

would fill the bookshelves, nor is it intended to give an all-encompassing definition of

knowledge. Instead, the most important conceptualizations of knowledge which have

made their way into the various classes of KM approaches will be reviewed from the

organizational perspectives (section 2.2.3). There are a number of related terms that

have to be clarified due to the major role that organizational knowledge plays such as

35

capability, competence, expertise or intellectual capital.

A dictionary definition of knowledge is, “the facts, feelings or experiences

known by a person or group of people” (UK, 2010). In other words, knowledge seems

to come from inside the individuals or group of individuals. Knowledge is derived from

information, but it is richer and more meaningful than information. It includes

familiarity, awareness and understanding gained through experience or study, and

results from making comparisons, identifying consequences, and making connections

(Maier, et al., 2005).

The term ‘knowledge’ is used widely –in our daily life, offices, organizations

and businesses, but often quite vaguely within business administration and knowledge

management literature. There exist a large number of KM definitions, which differ not

only between scientific disciplines contributing to KM but also within the KM field.

Moreover, the different definitions of the term 'knowledge' lead to different

perspectives on organizational knowledge6 and thus, to different concepts of an

organization’s way of handling knowledge.

2.2.2 Importance of Knowledge

With the emergence of “knowledge economies", knowledge has been proved to

be the most important factor contributing for the maintenance of sustainable

competitive advantage (Bristow, 2000; Civi, 2000; B. Gupta, Iyer, & Aronson, 2000;

Pan & Scarbrough, 1999; Stonehouse & Pemberton, 1999). Knowledge is becoming

the primary asset and the distinguishing factor that secure the value proposition of

nations in their struggle to win the combinatorial realm of economical and socially

sustainable development. In fact, knowledge can be considered as the critical

6 See section 2.2.3 for detailed discussion on ‘organizational knowledge’

36

foundation for sustainable development and innovation (Laszlo & Laszlo, 2002; Sheng

& Sun, 2007; Sousa, 2006).

With the advent and spread of internet and telecommunication technologies

countries and organizations are now even more concerned about managing their

knowledge. In the 21st century organizations are now transforming into ‘knowledge

organizations', and their workers are considered as ‘knowledge workers’. Knowledge

workers differ from traditional workforce in many diverse ways (Table 2-1) This

transformation of organizations into knowledge-intensive and knowledge-aware

organizations is taking place at an ever-increasing pace. Knowledge has become the

key resource - not labor, raw material or capital. Knowledge represents the key concept

to explain the increasing velocity of the transformation of the way businesses and social

institutions work (Drucker, 1994). According to an estimate, up to 60% of the gross

national product of the United States is supposedly based on information as opposed to

physical goods and services (Delphi 1997). This is not surprising as it is estimated that

the knowledge-intensive development processes of new products and services

comprise eighty to ninety percent of the production costs (Scherrer 1999).

Criterion Traditional office work Knowledge work

Organizational design

Orientation data-oriented communication-oriented

Boundaries organization-internal focus focus across organizational

boundaries, alliances,

coopetition,

(virtual)

networks

Centralization central organizational

design

decentralized organizational

design

Structure Hierarchy network, hypertext

Process highly structured,

deterministic processes

(pre-structured

workflows)

ill-structured, less

foreseeable

processes (ad-hoc

workflows)

37

Group work group, department project team, network,

community

ICT support

Type of contents structured data (e.g., tables,

quantitative data)

semi-structured data, e.g.,

links,

hypertext documents,

container,

messaging/learning objects,

workflows, skill directories

Storage (relational) data base

management

system, data warehouse

document/content

management

systems, experience data

bases,

newsgroups, mail folders etc.

data handling coordination of accesses,

integrity, control of

redundancy

synchronization, information

sharing, distribution of

messages,

search and

retrieval

Coordination workflow management

system

messaging system,

Groupware

Modeling data, business process,

workflow

ontology, user profile,

communication,

activity/work

practice

Table 2-1: Traditional vs. knowledge work, Source: Maier, et al. (2005)

There is also a trend towards more complex problem-solving services where the

majority of employees are well-educated, creative and self-motivated people.

Employees’ roles and their relationships to organizations have been changed

dramatically as knowledge workers are replacing industrial workers. Almost 60% of

the US organizations think that between 60% to 100% of their employees are

knowledge workers (Delphi, 1997). This scenario has coined the term 'knowledge

economy' and has dramatically changed valuation of knowledge work. The concept of

knowledge work was coined in order to stress the corresponding changes in work

processes, practices and places of employees.

2.2.3 Organizational Learning and Organizational Knowledge – Conceptions and

Misconceptions

In this study we are concerned with ‘organizational knowledge’ only, therefore,

38

we will not be looking into the epistemological discussion of knowledge. We will only

discuss different perspectives of ‘organizational knowledge’ existing in literature and

how different organizational theorists have described it – concluding with the most

appropriate definition appropriate for this study.

The question readily arises, “if organizations are just a community of people

which possess knowledge then do the organizations really have their own knowledge?”

Despite the long, intrigue and epistemologically complex discussions, organizational

knowledge still remains an elusive topic that is evolving from several different

literatures. Many authors have argued on this aspect concluding that organizations

literally have knowledge which exists in them (Ashkanasy, Wilderom, & Peterson,

2000; Schneider, 2009). Ashkanasy, Wilderom et al. (2000) and Schneider (2009) talk

about the past times when organizational climate and culture were the ways to talk

about the unique knowledge which characterized or was embedded in an organization.

These earlier analyses provided only some indicators of knowledge - existing but not

being managed. Decades later, we see a significant shift in the debate. Now the

organization’s knowledge is to be managed, as something distinct from the

organization itself (Dierkes, Antal, Child, & Nonaka, 2005; Easterby-Smith & Lyles,

2003; Spender, 1992, 1994; Spender & Marr, 2005). This requires to consider several

assumptions. First, it should be presumed that organizational knowledge is not an easily

identifiable asset that organizations seem to possess but it cannot be managed, stored,

traded and applied like its more tangible financial and physical assets. However, it is

unlike those assets that are intangible and embedded in an organization’s intellectual

capital, intimately tied up with its human constituents and practices/processes

(Spender, 2008). Second, this knowledge is generated by the manageable processes of

organizational learning - with the outcome being managed by the processes of

39

knowledge management. Hence, organizational learning and knowledge management

may seem complementary (Antal, Dierkes, Child, & Nonaka, 2001). Easterby-Smith &

Lyles (2003) helpfully map the two literatures by arguing that ‘organizational learning’,

the ‘learning organization’, ‘organizational knowledge’ and ‘knowledge management’

are quite different terms.

Organizational learning refers to ‘the study of learning processes of and within

organizations’. This definition implies organizations as discrete socio-economic

entities that can learn in ways independent of the individuals within. This attribution

allows the idea of a ‘learning organization’ to emerge (Senge, 1990) where

organizations are conceptualized as coherent entities having the ability to learn like a

biological organism, can adapt purposively and survive in a changing environment.

Organizational knowledge applies to what these learning processes have

generated. This part of the literature typically deals with the nature and location of the

organization’s knowledge (Spender, 1993; Tsoukas & Mylonopoulos, 2004).

2.2.4 Organizational Learning and KM

The organizational learning literature has generally adopted the notion of

learning as behavior change by contrasting behaviors at different points in time.

Learning is framed as more effective behavior at time t2. The knowledge management

literature is more concerned with the identification, collection and diffusion of the

organization’s knowledge. It is less concerned with the change over time. Therefore, it

is turned to other typologies for presenting knowledge (Spender, 2008). This is why so

much of knowledge management’s literature has relied on Polanyi’s explicit/tacit

distinction (Polanyi, 1962) There has also been considerable attention paid to the

distinction between the knowledge held subjectively by individuals and that held by

40

groups, teams and organizations (Spender, 1993, 1996b). Some other researchers

(Blackler, 1995; Yrjo Engeström, 1991; Yrjö Engeström, 2000) have also devised

similar typologies. With such typologies in hand, KM researches can think over

different challenges of, for example, collection and distribution of tacit knowledge

versus collection and distribution of explicit knowledge.

Most of the work in KM presumes the presence of some knowledge and focuses

on realizing its economic potential even if it is not at the right location and easy to find

(Spender, 2008). Generally speaking, the knowledge management agenda deals with

the practicalities of three issues. First, identification of organization’s knowledge

assets. Second, collection and storage of knowledge assets. And finally, delivering the

results to the people who can turn it into value (Spender, 2008; Teece, 2003).

The bulk of KM literature discusses the IT (information technology) systems

design (Alavi & Tiwana, 2003). This diminishes the distinction between IT and

management information systems (MIS), i.e. the difference between engineering an

efficient IT system and maximizing the economic value it delivers. A different part of

this debate deals with ownership and property rights. For example, how the

organization can retain their knowledge when those who carry it leave the organization.

To summarize, organizational learning seems to be about managing the creation of the

organization’s knowledge, while KM is about optimizing the economic value

delivered. Before we delve into further discussion of knowledge management, its

elements and other topics, let us first discuss the data, information and knowledge

paradox.

2.2.5 The Data, Information and Knowledge Paradox

Ackoff was the first person to purport the first working typology for knowledge.

41

He is credited with developing the data, information, knowledge and wisdom (DIKW)

typology (Ackoff, 1989). Ackoff's typologies fit fine into most of the ordinary scenarios

but the technical problem with his categories is that these are nested rather than

mutually exclusive. Thus we progress from data, which he argued is ‘raw fact’, to

‘information’, which is data with meaning, to ‘knowledge’, which is information

contextualized and ‘wisdom’, which is knowledge harnessed to the improvement of the

human condition. Ackoff’s typology fails to provide a system of categories for

theorizing knowledge management’s problematic (Spender, 2008). Also, Ackoff's

typology is not useful for organizational learning as a measure of learning through time,

even when we need to be concerned with notions such as maturity.

Figure 2-2: Data, Information. Knowledge, Wisdom Continuum,

Source: Ackoff (1989)

Similar other typologies can be found in the literature which either extend

Ackoff’s work or suggest the same typology of knowledge with some modifications.

A notable extension of those is that of described by Maier and Hadrich et al (2005).

Maier and Hadrich et al. describe that knowledge is related to many other concepts.

The most often cited relationships are those to data and information (Figure 2-3).

Data refers to the symbols that are ordered to a description of any person, thing,

42

event or activity in the perceived reality or imagination of persons. Data can be

recorded, classified, and stored, but cannot be organized to convey any specific

meaning. Data items can be numeric, alphanumeric, figures, sounds, or images.

Information is data that have been organized so that they have meaning and

value to the recipient. The recipient interprets the meaning and draws conclusions and

implications. It is the result of a person’s interpretation of signals from his or her

environment.

Figure 2-3: Data, information and knowledge as hierarchy, Source: (Maier, et al., 2005)

The description of knowledge provided by Maier and Hadrich et al., seems

more appropriate to knowledge held by any person but is not appropriate in the context

of organizational knowledge because organizations do not have just data and

information but also possess and practice a lot of practices and processes. Hence, this

definition fails to describe that very important aspect of the organizations.

We might spend a lot of time arguing, certainly fruitlessly, about better

definitions of organizational knowledge. If we are able to find organizational

43

knowledge in one place and transfer/share it with others, we can solve the knowledge

management's problematics. This problem can be easier if we could gain some insight

into the problems that organizations confront. For example, if we are concerned with

the retention of expert people’s knowledge i.e. tacit knowledge, as they leave the

organization, it may be helpful to realize that we cannot meet the challenge by simply

asking them to write down everything they know. The implication is that the typology

we need should be based on the action opportunities open to us as we confront

knowledge management’s problematics. This implication leads to the concept of tacit

and explicit knowledge which is a very important aspect and a different

conceptualization of knowledge.

2.2.6 Explicit, Implicit and Tacit Knowledge

Knowledge in the organizations, as well as in the individuals, is often classified

into two types: explicit and tacit. However, a few researchers and theorists suggest that

a third type of knowledge also exists, namely implicit knowledge. All the three types

of knowledge are often depicted and exemplified through the example of an iceberg

which has a tangible portion outside the water level (explicit knowledge - which is

codified and can be shared easily (Brún, 2005; Frappaolo, 2008; Maier, et al., 2005), a

tangible part below the surface of the water (implicit knowledge - which has the

potential to be codified (Frappaolo, 2008; Maier, et al., 2005) and a part deep inside the

darkness of the sea part which is hidden and cannot be discovered (tacit knowledge –

which exists but cannot be codified and is not explicable (Frappaolo, 2008; Maier, et

al., 2005). In this section, we will be discussing these types of knowledge briefly to

provide the readers a basic understanding of them.

Early literature, even today's most of the literature, on knowledge management

44

proposed only two taxonomies of knowledge – explicit and tacit (Brún, 2005; Nonaka,

1994; Polanyi, 1962). There exists a definite agreement among researchers on ‘what

explicit knowledge is’, but there is a lack of consensus on the distinction between

‘implicit’ and ‘tacit’ knowledge. Researchers and theorists have debated a lot on the

nature of tacit and implicit knowledge and their distinguishing traits. A wide array of

literature exists trying to draw a hard line among the two but there exists no consensus

on any discrete distinction between implicit and tacit knowledge. Consequently, the

term ‘tacit knowledge’ is often overused (Brún, 2005) to mention ‘implicit knowledge’

as well. This notion neglects the existence of a type of knowledge which actually exists.

Only recently some researchers have pointed out this lack of epistemological

understanding about types of knowledge. Now we can find various studies

distinguishing between these three types of knowledge. Therefore, in this study will be

distinguishing between the three types of knowledge as follows:

Explicit Knowledge – knowledge which exists in codified form (Brún, 2005;

Frappaolo, 2008; Maier, et al., 2005; Nickols, 2000; Nonaka, 1994; Polanyi, 1962)

Implicit knowledge – knowledge which has not been discovered yet but has the

potential to be discovered and codified (Frappaolo, 2008; Maier, et al., 2005; Nickols,

2000)

Tacit Knowledge – Knowledge which has not been explored yet and, cannot be

explored and codified as well (Frappaolo, 2008; Maier, et al., 2005; Nickols, 2000;

Nonaka, 1994).

45

Figure 2-4: Explicit, implicit and tacit knowledge, Source: (Nickols, 2000)

Explicit knowledge: is the knowledge that can be captured and written down

in documents or databases. Examples of explicit knowledge include documents,

databases, spreadsheets, instruction manuals, written procedures, best practices,

lessons learned and research findings etc (Brún, 2005). Explicit knowledge can be

categorized as either structured or unstructured. In contrast, e-mails, images, training

courses, and audio and video selections are examples of unstructured knowledge

because the information they contain is not referenced for retrieval ,though, the modern

knowledge management systems (KMS), such as the KMS developed by SAP, are able

to retrieve these sources as well for referencing.

Implicit knowledge: is the knowledge which has not been discovered yet but

has the potential to be discovered and codified, The process of turning implicit

knowledge into explicit knowledge is called externalization, the reverse process of

turning explicit into implicit knowledge is called internalization. The distinction

between types of knowledge helps to postulate different KM activities and different

systems to support these activities.

46

Tacit knowledge: is the knowledge that people carry in their heads. It is much

less concrete than explicit knowledge. It is more of an “unspoken understanding” about

something i.e. knowledge that is more difficult to write down in a document or a

database (Brún, 2005). An example might be, knowing how to ride a bicycle – you

know how to do it, you can do it again and again, but could you write down instructions

for someone to learn to ride a bicycle? Tacit knowledge can be difficult to access, as it

is often not known to others. In fact, most people are not aware of the knowledge they

possess themselves or of its value to others. Tacit knowledge is considered more

valuable because it provides context for people, places, ideas and experiences. It

generally requires extensive personal contact and trust to share effectively.

2.2.7 Knowledge in the Organizational Context – The Pragmatic Taxonomy

Due to the problematics of KM7, we posit that KM should not be seen as the

way of mere identification, organization and sharing of data and information as

purported by DIKW framework. In fact, any taxonomy that is based on the way people

act in the organizations would be more appropriate (Spender, 2008). Any such

taxonomy can resolve the problematic of KM. We can, for example, note our ability to

use IT systems to move data around. But a quite different challenge is to reshape other

people’s interpretations or the meanings they might attach to the data being moved.

Meanings are ‘lenses’ people put over the data to bring that data into the world of their

actions as ‘information’. Useful information, therefore, is that which is relevant to that

world and comprises both data and meaning. Such action is in-the-world and thus

conceptually distant from cognition which is in-the-mind. Spender (2007a) suggested

a new typology of knowledge in the context of organizations as: knowledge-as-data,

7 Refer to section 2.2.5 for details

47

knowledge-as-meaning, or knowledge-as-practice. This typology stands specifically

against Ackoff’s DIKW model yet seems most appropriate in the context of

organizational knowledge and organizational knowledge management.

Spenders' typology encapsulates what organizations are doing and practicing

because practice, of course, is always located within a specific context which

determines the data and meaning to be combined. Practice is richer and more complex

than the mere execution of cognition, and cannot be theorized within a framework of

rationality and goal-seeking. Moreover, all of the existing organizational knowledge

management capability assessment models also assess the extent to which any

particular organization is practicing practices to manage its knowledge. Hence, if

organizations follow this typology, they can also assess their KM maturity.

2.3 Knowledge Management and its Elements

In this section we will focus on Knowledge Management (KM), its definition,

various concepts, elements and models. KM is the emerging discipline especially when

one considers managing and capitalizing an organization’s internal intellectual capital

(Davenport & Prusak, 2000; O'Leary, 1998). It is a cross-disciplinary field with its roots

in many disciplines (Figure 2-5). This discipline has drawn insights, ideas, theories,

metaphors and approaches from diverse disciplines such as strategic management,

information systems, psychology, cognitive sciences etc.

48

Figure 2-5: Lines of development of KM, Source: (Maier, et al., 2005)

The tracing of the roots helps to understand the perspective which knowledge

management has or can have on organizations.

2.3.1 History of Knowledge Management

The roots of the term knowledge management (KM) can be traced back to the

late 1960s and early 1970s in the Anglo-American literature. However, it almost took

another 20 years until the term appeared again in the mid 80s in the context as it is still

used today. This time it got a tremendous amount of attention. Concepts of knowledge

management were actually suggested to meet the challenges posed by the globalization

and free trade agreements such as world trade organization (WTO). Emergence of

globalization brought new opportunities and increased competition. Companies

responded by downsizing, merging, acquiring, reengineering and outsourcing. Many

streamlined their workforce and boosted their productivity and their profits by using

49

advances in computer and network technology. However, during these transformations

many organizations lost company knowledge – they no longer “knew what they knew”

(Brún, 2005).

In this regard, some highly notable and innovative work was done by the

authors such as Sveiby and Lloyd (1987), Wiig (1988), just to name a few. The

underlying concepts used and applied in KM, though, have been around for quite some

time. Many authors from a variety of disciplines created, applied and reflected a

number of approaches, concepts, methods, tools and strategies for knowledge

management. In its short history, this field has absorbed a wide array of research

questions which made it interesting and attractive for a large community as diverse as

its authors with backgrounds in psychology, organization science, management

science, computer science etc. At the same time, however, the discipline struggles with

the large number of terms that are used differently and the approaches that are

incommensurable. Organizations and institutions have developed some state-of-the-art

techniques and tools such as, competence management, KM maturity assessment

models, community management, knowledge maps, semantic content management etc.

2.3.2 Importance of KM

In the 1990s, transformation of societies into knowledge societies, and

economies into knowledge economies were the major challenges for organizations.

These transformations significantly increased the pace of innovation and improved

organizational capabilities to handle diverse and distributed knowledge. In knowledge

economy, organizations task execution differs significantly from what people do in

traditional organizations or societies. Also knowledge organizations differ in many

more ways from traditional organizations. The power of knowledge cannot only be

harnessed by the organizations but also by the whole societies and countries; that is

50

why researchers are now focusing on dynamics of knowledge economies.

Knowledge, can create wealth for the countries which do not have opportunities

to exploit natural resources (Brún, 2005). Such countries are focusing more on

transforming their economies into knowledge economies, e.g. Finland and Japan. There

exist many organizations which are based on knowledge work alone e.g. Microsoft,

Google etc. IT organizations are better able to rely on knowledge work because of

nature of their work. As discussed earlier, work and products of IT organizations are

intangible i.e. knowledge-based. Knowledge-based work relies heavily on two factors.

First, it requires highly skilled employees having diverse expertise. Second, it requires

an organizational culture and design conducive for knowledge creation and sharing.

The basic premise of knowledge management is that, knowledge embedded in

people of an organization is its most valuable resource. Apparently, it resembles human

resource management. However, these are totally different perspectives. Human

resource management focuses on managing people whereas, knowledge management

focuses on knowledge that those people carry. The major reason for this focus shift is

the accelerated rate of change caused by information and communication technologies

(ICT). ICT technologies have altered the way organizations and societies used to think.

Today, every task in organizations is comprised of knowledge work and augmented by

ICT technologies. Hence, every worker is a "knowledge worker" and their job is

dependent more on their knowledge than their manual skills. This has made creation,

organization, and sharing of knowledge the most important activities of nearly every

employee in the organizations.

Treating knowledge as an asset, managing human capital and the knowledge

people possess has become of much value for the organizations. Therefore, researchers

51

and organizations have devised many ways to measure value of an organization’s

knowledge assets while measuring the progress and value of knowledge management

initiatives, Skandia pioneered this concept by introducing the value of its knowledge

assets in its balance sheet (Skandia, 1995). The traditional balance sheet is increasingly

being regarded as an incomplete measure of an organization’s worth, as it does not

place a value on intangible assets such as knowledge or intellectual capital. Intellectual

capital is commonly regarded as having three components: human capital (the

knowledge and skills of people), structural capital (the knowledge inherent in an

organization’s processes and systems), and customer capital (customer relationships).

2.3.3 Benefits of KM

Benefits of KM are numerous, diverse, long lasting and impact the

organizations in a multitude of ways. There are many organizations around the world

(Anand, et al., 2005; Coakes, et al., 2005; Hahn, et al., 2005; Li, et al., 2005; Owen &

Burstein, 2005) which successfully followed, implemented and obtained the benefits

of KM. The benefits organization reaped include: improvement in response time to

market, better understanding customer requirements, reduction in errors and repairs

required, reduction in time required to develop new products, more learned workforce,

better organizational cultures and most importantly, increase in the organization’s

financial worth when knowledge of the organization valued.

The notion of knowledge is abstract enough to visualize. This makes it even

harder to identify and calculate its value. Researchers have developed many

methodologies for intellectual capital valuation. One of the such methodologies is

Intellectual capital (IC) methodology. It is the very first methodology developed and

used by Skandia for valuation of its intellectual capital. Using this methodology,

Skandia included non-financial indicators (i.e. intellectual capital) in its organizational

52

performance reports. Though, IC valuation methodology is based on a sound theoretical

basis while most of the other valuation measurement methods are pragmatic ones.

2.3.4 Elements of KM - People, Processes and Technology

One popular and widely-used approach is to think of knowledge management

in terms of three elements or components - people, processes and technology. All of

the KM theoretical and practical concepts, tools, techniques and models address issues

concerning any one or more of these three elements. Therefore, we consider it

important to discuss these elements in detail here.

People: The people element of KM addresses questions such as; does the

culture of any organization support ongoing learning and knowledge sharing? Are

people motivated and rewarded for creating, sharing and using knowledge? Is there a

culture of openness and mutual respect and support? Are people under constant

pressure to act, with no time for knowledge-seeking or reflection? Do they feel inspired

to innovate and learn from mistakes? Such questions are considered utmost important

whenever any organization urges to initiate KM initiatives because getting an

organization’s culture (including values and behaviors) “right” for knowledge

management is typically the most important and yet often the most difficult challenge.

Knowledge management is first and foremost a people issue – although it is

misunderstood mostly as a technological issue (Brún, 2005; Maier, et al., 2005) .

Processes: In order to improve knowledge sharing, organizations often need to

make changes to the way their internal processes are structured, and sometimes even

the organizational structure itself. For example, if an organization is structured in such

a way that different parts of it are competing for resources, then this will most likely be

53

a barrier to knowledge sharing. Looking at the many aspects of “how things are done

around here” in any organization, which processes constitute either barriers to, or

enablers of, knowledge management? How can these processes be adapted, or what

new processes can be introduced, to support people in creating, sharing and using

knowledge?

Technology: A common misconception is that knowledge management is

mainly about technology – getting an intranet, linking people by e-mail, compiling

information databases etc. Technology is often an important enabler of knowledge

management – it can help connect people with information, and people with each other,

but it is not the solution. It is vital that any technology used should “fit” the

organization’s people and processes – otherwise it should simply not be used.

These three components are often compared to the legs of a three-legged stool

– if one is missing, the stool will collapse. However, one leg is viewed as being more

important than the others – people. An organization’s primary focus should be on

developing a knowledge-friendly culture and knowledge-friendly behaviors among its

people, which should be supported by the appropriate processes and may be enabled

through technology.

2.3.5 KM and Sustainable Competitive Advantage (SCA)

KM can help the organizations in maintaining SCA over their competitors that

is why more and more organizations are trying to follow and implement KM systems,

practices and models (Barton, 1992). There has also been a surge shown in the research

and publications in KM depicting its spread, awareness and importance for the

organizations. Since 1991 - when Nonaka coined the term 'learning organization' - to

year 2000 over 8,000 articles and 900 books had been published in just 20 years

54

(Schwartz, 2006). This overwhelming amount of research and interest in the field can

be thought of an indicator of its importance and application to a wide array of industries

and disciplines. Traditionally, organizations have been utilizing various resources to

gain SCA over competitors. These resources include tangible resources such as

financial, human, infrastructure and technological ones. However, tangible resources

cannot be provide SCA to the organizations as they do not fulfill the criteria of being

strategic assets. Strategic assets are the resources providing an SCA to the organizations

(Eisenhardt & Santos, 2000; Jugdev & Thomas, 2002; Scheraga, 1998). There are some

characteristics which make the resources strategically relevant and imperative for

generating SCA. The characteristics are:

1. Resources must be rare. The notion of rare denotes that the resources

must not be easily accessible by competitors. The characteristic of being

rare will hinder any competitive advantage by the competitors

2. Resources must be value creating for the organizations and their

customers. They must significantly contribute for the perceived

customer benefits and improve performance (i.e. effectiveness and

efficiency) of organizational processes. In this perspective, value of a

resource is determined by the relative advantage it can provide when

used in a competitive environment

3. Resources must have diverse applicability's. They should be applicable

in a variety of tasks and markets. Alternatively, resources must be usable

in diverse products, services, and markets

4. Resources must be too difficult to replicable by the rival organization.

It will make them difficult to use

55

5. Resources must be too difficult to substitute.

Organizations can more sustain a competitive advantage the more difficult it is

to acquire the resource from the market or get it through partnerships or other means.

Organizations can maintain their competitive advantage in a number of ways. For

example, the more depreciable the resources are, the lesser sustainable the competitive

advantage will be. In other words, endurance of competitive advantages is dependent

upon the rate of depreciation/obsolescence of the underlying resources. There are some

resources which depreciate quickly, e.g., technological resources and equipment. Rate

of depreciation of such resources is higher due to the increasing pace of technological

advances. On the other hand, reputation and brands are less prone to changes and are a

lot more durable. This conception of the strategic resources and their characteristics is

called Resource-Based View (RBV) of the resources. A number of studies (R. Grant,

1991; Jugdev & Thomas, 2002; Peteraf, 1993; Priem & Butler, 2001a; Spender, 2008)

have been conducted examining ability of RBV to classify organizational resources for

gaining sustainable competitive advantage and its deficiencies to classify

organizational intangible assets such as organizational culture, practices and

knowledge. That is why this view is not appropriate to assess and classify

organizational assets in all the respects. As in this study we are interested in managing

knowledge of the organizations present in the form of practices specifically. Therefore,

researchers have proposed another view to classify knowledge-based practices of the

organizations, called Knowledge-Based View (KBV) (Kogut & Zander, 1992a; Krogh,

Roos, & Slocum, 1994; Nonaka, 1994; Spender, 1996a).

2.3.6 Knowledge-Based View (KBV) of Organizations

In contrast to traditional view of the resources (i.e. RBV), KBV is based on the

distinction between explicit and tacit knowledge (Polanyi, 1962). Tacit knowledge is

56

embedded in the people and is very difficult to articulate, in some instances almost

impossible. This type of knowledge can be made explicit only through the observation

or doing. As knowledge is explored and put into action, some part of it may be made

explicit by converting it into messages or practices which can then be shared and

communicated to other people in the organizations. This distinction between tacit and

explicit knowledge has proven to be particularly important in the dominant knowledge-

based approach to strategy (R. M. Grant, 1996; Kogut & Zander, 1992b). This approach

identifies tacit knowledge as the most strategic resource of firms. The argument is that,

since tacit knowledge is difficult to imitate and relatively immobile, it can lay the

foundation of sustained competitive advantage (Deeds & Decarolis, 1999; R. M. Grant,

1996; A. K. Gupta & Govindarajan, 2000; Spender, 2008).

Knowledge is considered socially constructed and the creation of meaning

occurs in ongoing social interactions present in working practices (S. D. N. Cook &

Brown, 1999; Weick & Roberts, 1993). Instead of a cognitive representation of reality,

knowledge is a creative activity of constructing reality (Krogh, Roos, & Kleine, 1998).

Overall, this approach goes beyond the dominant conception of knowledge as a

resource that can assume tacit or explicit forms. In this newer epistemology, knowledge

is associated with a process phenomenon (Spender, 2008)of knowing that is clearly

influenced by the social and cultural settings in which it occurs. In short, organizational

knowledge can be classified and understood as a series of practices – which are when

adopted and practiced over a period of time are called ‘best practices’.

2.3.6.1 Organizational Learning as Foundation for KBV

Organizational learning is part of the foundation that underlies knowledge-

based thinking. Learning can be defined as the process by which new information is

57

incorporated into the behavior of people, changing their patterns of behavior and

possibly and leading to better outcomes. The initial focus of learning theory was on

individuals (Weick, 1991). More recently, it is conceptualized at the organizational

level and is being viewed as a key process in the adaptation of organizations to the

environment (Argote, 1999).

Penrose’s seminal work on the growth of the firm is an important starting point

for understanding organizational learning (Penrose, 1959). Penrose describes how

learning processes create new knowledge and form the basis of growth of the

organizations through the recombination of existing resources. Shortly thereafter,

(Cyert & March, 1963) developed significant thinking around the concept of

organizational practices. Organizational practices form the basis of collective learning

in organizations. They are seen as executable capabilities for repeated performance that

have been learned by an organization (Cohen et al., 1995). These practices represent a

manifestation of organizational memory in that they encode inferences from history

and guide individual and group behaviors in organizations. Organizational learning is

thus perceived as an adaptive change process that is influenced by past experience,

focused on developing and modifying practices (Nonaka & Takeuchi, 1995).

Capabilities and competencies are often used synonymously. However,

competencies are often focused on knowledge as the underlying resource and are

directly related to an organization’s strategic choices. Organizational competencies are

based on a combination or integration of the individual and organizational knowledge

in an organization. Hence, according to the knowledge-based view, competitive

advantage of an organization depends on how successful it is in exploiting, applying

and integrating its existing capabilities and in exploring and building new capabilities

that can be applied to market. Gaining sustainable competitive advantage through KBV

58

of the organizations emphasizes the need to develop approaches to manage knowledge.

2.3.7 KM in Organizational Settings

Knowledge management emerged as a new business practice and discipline by

the early 1990s. It attracted businesses, academicians, and business consultants

because of its wide applicability and flexibility in terms of its application. Business

journals and conferences started including KM in their agendas. By the mid-1990s, it

became widely acknowledged that the competitive advantage of some of the world’s

leading companies was being carved out from those companies’ knowledge assets such

as competencies, customer relationships and innovation (Anand, et al., 2005; Brún,

2005; Coakes, et al., 2005; Hahn, et al., 2005; Li, et al., 2005; Owen & Burstein, 2005).

By the end of the year 2000, knowledge management had evolved into a quest for more

effective access to tacit8 knowledge — the experiential human understanding that

cannot lend itself to quantification or to management. Organizations first practiced the

concept of KM by keeping better records of their transactions and quantifiable

operations so that less “knowledge” was lost to the organization. But as we looked into

the practice, we learned that what was originally called knowledge was more accurately

redefined as information because it had lost its association with any human experience.

We also found that many had begun to question anyone’s ability to manage knowledge,

being the experiential content of the human mind which is basically the practices people

follow to do their work in the organizations. All of us do manage knowledge - but

unintentionally. Even people in the organizations do it all the time but they do not

know. Each of them possesses knowledge - knowledge gained from experiences,

trainings, informal networks of friends and colleagues etc. Even network of friends and

8 See section 2.2.6 for discussion of explicit, implicit and tacit knowledge

59

colleagues from whom they seek out solutions of problems, is included in their

knowledge managing capabilities. Essentially, they get things done and succeed by

knowing an answer or knowing someone who does. Therefore, whenever any employee

of the organizations leaves the organization, organization loose the knowledge he/she

possess i.e. tacit knowledge.

Recognizing the loss of tacit knowledge, market leaders in almost every

industry started focusing on management of their intellectual capital. Other companies,

who sought to follow market leaders, also started pondering about it. This made KM a

mainstream business objective. Initially, they thought that KM is a mere

implementation of IT technologies and took the approach of implementing KM

solutions. Thinking KM as just the implementation of technological solutions proved a

big misconception. As a results they reaped little benefits and success and it seemed

like KM was just another management fad - destined to be confined to the

"management fad graveyard". However, after closer inspection, it turned out that the

problem was the approach taken to understand KM. Reasons for the limited success

included: Reasons for their limited success included (Brún, 2005):

A technological centered approach - organizations focused on

technologies rather than the business and its people

Too much hype created by the consultants and technology vendor

Organizations overspent - usually on fascinating technologies - with

little or no return on their investments

Most of the KM literature was in its infancy phase, very abstract,

conceptual, and lacking in practical advice. It led to frustration and the

60

inability to translate the theory into practice

Knowledge management was not tied into business processes and ways

of working

A lack of incentives – management did not convey its objectives,

benefits, and concepts correctly to the employees. Hence, they

misunderstood it and took it as extra laborious activity

Insufficient senior executive level interest - just like any other change

management activity, KM requires executive level support. In most of

the cases, executives did not support its implementation activities

Fortunately, organizations recognized these misconceptions and mistakes ssoon

and are beginning to take a more holistic approach to KM. An approach in which the

emphasis is more on people, behavior and ways of working than on technology. This

refinement in the conception of KM in the organizational context can be defined as:

“Knowledge Management is the process of capturing a Company’s collective

expertise, wherever, it resides, in databases, in paper or in people’s heads and

distributing it wherever it can produce maximum pay off” (Hibbard, 1997; Skyrme,

1998).

It is the explicit and systematic management of vital knowledge and its

associated processes of creating, gathering, organizing, diffusing and using. It requires

turning personal knowledge into corporate knowledge that can be widely shared

throughout an organization and appropriately applied".

In this definition, expertise is used as a synonym for knowledge. Expertise is

61

always carried by people and the underlying concept is to make available the collective

or individual expert’s knowledge wherever it improves the organizational performance.

Collective knowledge - in the context of organization - is known as "organizational

knowledge". Organizational knowledge is created by promoting knowledge sharing

activities among the organizational units and individuals. Consequently, the main

objective of KM is to improve the organizational performance by leveraging on the

collective/organizational knowledge. Knowledge of people is the most valuable

resource of any organization. In the 21st century, performance of any organization will

depend more, among many other factors, upon how well the organization is promoting

knowledge creation activities, how well that knowledge is being shared and used to

create value and the best effect.

As KM deals specifically about facilitating and promoting the processes by

which knowledge is created, shared and used in organizations in terms of ‘how that is

done’, therefore, it is complementary to assess to what extent any organization is

following the processes of KM. – the objective of KM maturity models (KMMMs).

There are numerous processes of knowledge management that vary across industries.

The discipline of KM is still in its infancy stage due to many reasons - the most obvious

one being its abstract concept. Therefore, there is no agreed upon standard, a set of best

practices, or framework. However, after much trial and error, academicians and

researchers have developed many frameworks and standards that are now converging

to reach on conclusions. Simply copying the practices or "know-how" may not work

because challenges, problems, people, and working environment of each organization

are different.

Many organizations around the world, especially in the developed countries,

have developed best practices for KM. Some notable work has been done by OGC of

62

Australia (OGC, 2006), yet very limited such work has been done in the developing

countries. No KM tool will work properly if it is not applied the way people think, act,

and behave in organizations because KM is essentially about the people and how they

create, share and use knowledge. But, it does not mean that organizations need to

capture, organize and share all the knowledge of all the employees in the them. There

exists a certain criterion for this. Organizations need to manage only the knowledge

that is most important to them. That might be the knowledge of its most important

people, experts, processes, know-how or anything.

KMMMs are the tools to assess the level to which any organizations is able to

manage its practices. KM is also about ensuring that people have the right knowledge,

at the right time, and at the right place. It can only be done efficiently if the organization

is pursuing explicitly and systematically practices of KM; which in turn is assured and

assessed by KMMMs. Below we will be presenting a detailed critical review of some

of the renowned capability maturity models (CMMs) including project management

maturity models (PMMM’s), knowledge management maturity models (KMMM’s).

Also their history, applicability, pros and cons and various other characteristics will

also be discussed.

2.4 Capability Maturity Models (CMMs)

The term "capability maturity model (CMM)" is used synonymously for a

process improvement approach, and also for the very first process maturity model

(developed by the Software Engineering Institute (SEI) ). This model was based on a

process improvement approach based on a process model. Nearly all of the

contemporary process models are based on this model, except a few. A process model

is defined as a, "structured collection of practices describing characteristics of effective

63

processes". The model includes the practices proven effective by experience and

collected, analyzed and arranged by the experienced industry professionals. A maturity

model is defined as, “a conceptual framework, with constituent parts, that defines

maturity9 in the area of interest". CMMs are considered an effective and efficient

means for analyzing and improving organizational processes. In the sections below,

various maturity models developed for various disciplines are discussed.

2.4.1 History of Maturity Models (MMs)

History of maturity models - and use of computers - dates back to 1960s when

organizations started using IT systems for their operations. With the increase in the

adoption of IT systems, the use of computers also became more flexible and cost

efficient. It significantly increased the demand for software development. At that time,

very few - almost none - standard or "best practices" for software development existed.

Consequently, the growth of IT systems accompanied by growing problems such as,

failure of projects in terms of schedule, cost, scope and incapability to deal with the

complexity. This phenomenon attracted the attention of renowned researchers such as,

Edward Yourdon, Larry Constantine, Gerald Weinberg, Tom DeMarco, and David

Parnas. They observed, analyzed, and studied the software development processes and

published articles and books to professionalize the software development processes.

At that time, in 1980’s, the US military was one of the biggest stakeholders

carrying out various complex software projects. However, scope creep, cost over-runs

and schedule slippage were the most commonly reported complaints during those

projects. Being anxious by this situation, the US Air Force funded a study at Software

Engineering Institute (SEI) to determine the underlying reasons of such failures (Obi,

9 See section 1.3 for detailed discussion of maturity and organizational project management maturity

64

2007; Paulk, 2009; Wikipedia, 2011). At that time, the department of defense appointed

Watts Humphrey (from software engineering institute) to develop some approach to

mitigate the situation. In 1986, Humphrey started his work to develop a process

maturity model. In 1988, he succeeded in developing such a model - he named,

"Capability Maturity Model (CMM). Later on, this model published as a book in 1989

(Humphrey, 1989) as well.

The model proposed by Humphrey was not an entirely new idea. Humphrey

actually based his framework on an earlier developed maturity model, known as

"Quality Management Maturity Grid (QMMG)". QMMG was developed by Philip B.

Crosby in his book "Quality is Free" (Crosby, 1979). However, Humphrey purported

a different and unique perspective of organizational maturity. He proposed that

organizations get maturity in their processes in stages or ladder fashion. Organizations

get more mature as they solve process problems in a specific order. Therefore,

Humphrey's model presents a staged representation (5 stages) of organizational

maturity. His approach differs from Crosby's approach by measuring maturity of the

whole system rather than measuring maturity of each individual process independently.

The full model - including defined process areas and best practices - for each of the

five maturity levels was initiated in 1991 and completed in 1993. The CMM proved

quite a useful and powerful tool in a variety of industries, especially software

development organizations. It enabled the organizations to understand and improve

general business processes performance.

At the same time, internet and IT industry started booming globally and large

IT organizations were facing challenges of meeting projects deadlines and budgets. At

that time they started visualizing the importance of assessing their software

development processes and practices. This need triggered them to adopt CMM® – the

65

only available maturity model at that time. CMM® was later improved and evolved as

Capability Maturity Model Integration (CMMI®). Later on, some variants of CMMI®

were also developed such as P-CMMI®, Dev-CMMI® etc. The CMMI® is considered as

the most renowned CMM, which is the base of many other CMMs developed in various

disciplines. Below we will be presenting some renowned CMMs from a variety of

disciplines, all of which are based on CMMI® and exhibit staged representation, except

OPM3®.

2.4.2 An Investigation into Maturity Models (MMs)

The concepts of process or capability maturity are increasingly being applied in

many disciplines for assessing various aspects of the product or service being

developed or provided. We can call these models capability maturity models (CMMs)

or just maturity models (MMs) in general – for whatever discipline these are developed

for. MMs are widely being used as a means of assessing and improving (DTI, 1994)

the product or service development process. MMs have been developed for a range of

activities (Fraser, Moultrie, & Gregory, 2002) such as quality management (Crosby,

1979, 1996), software development (Niazi, Wilson, & Zowghi, 2005; Paulk, Curtis,

Chrissis, & Weber, 1993), supplier relationships (Macbeth & Ferguson, 1994), R&D

effectiveness (Szakonyi, 1994a, 1994b), product development (McGrath & Romeri,

1994), collaboration (Fraser & Gregory, 2002), product reliability (Sander &

Brombacher, 2000; Tiku, Azarian, & Pecht, 2007), project management (AIPM, 2004;

BMC, 2003; Garies, 2001; Hillson, 2001; Kwak & IBBS, 2000, 2002; Martinelli &

Waddell, March 2007; OGC, 2004, 2006; PMI, 2003; A. Prado; D. Prado, 2006;

Voivedich & Jones, 2001), knowledge management (Boyles et al., 2009; Ehms &

Langen, 2002; Gallagher & Hazlett, 2000; Gottschalk, 2002; Hoss & Schlussel, 2009;

Hubert & Lemons, 2009; Hung & Chou, 2005; Hung, Chou, & Chen, 2005; Klimko,

66

2001; Kochikar, 2000; KPMG, 2000; Kruger & Snyman, 2005; Kulkarni & Freeze,

2004; Kulkarni & Louis, 2003; Langen; Liebowitz & Beckman, 2008; Mohanty &

Chand, 2005; Natarajan, 2005; Pee, Teah, & Kankanhalli, 2009; SAP, 2006; Teah, Pee,

& Kankanhalli, 2006; WisdomSource, 2004), people capability maturity model (SEI,

2009) and business development maturity model (BDII, 2003). These are either the

most commonly known models developed by any organization, institute or

standardization organization or the models developed by various researchers. There are

many other models not available publically but the models mentioned above provide

us an appropriate level of details about their structures and other information, hence,

we can be confident that a critical review of these models would be enough to infer

what characteristics other maturity models based on CMMI possess.

2.4.3 Structure of CMMI-based Maturity Models

One of the lasting contributions of the business reengineering movement is the

view that an enterprise is to be regarded as a set of well-defined processes (Berztiss,

1996; Davenport, 1993). This view of the organizational processes seems quite rational

and has realistic grounds. Therefore, all of the maturity models follow a process

representation of organizations and assess maturity of processes in a staged fashion.

The staged representation of organizational process maturity was purported by Watts

Humphrey (Humphrey, 1988). He had a unique insight of organizational maturity. He

purported that organizations get process maturity in a staged fashion and in a specific

order. Since in this study we are interested in improvement of organizational project

management maturity model (OPM3®), therefore we will be using organizational

maturity as a synonym for organizational project management maturity just to make it

simple and save time of the reader.

Staged-representation of organizational processes improvement has been

67

criticized by many authors (Kerzner, 2001; PMI, 2008b) due to the fact that sometimes

organizations already have enough mature processes which may be declared on later

stages of maturity in the model, while at the same time organizations may not have

mature processes eligible for the first stage.

Some maturity models such as, (BMC, 2003; Fraser & Gregory, 2002; Fraser,

et al., 2002; Hillson, 2001; Hung & Chou, 2005; Macbeth & Ferguson, 1994; Martinelli

& Waddell, March 2007; McGrath & Romeri, 1994; D. Prado, 2006; Sander &

Brombacher, 2000; Szakonyi, 1994a, 1994b; Tiku, et al., 2007) can only assess the

organizational processes while others such as (OGC, 2004; Paulk, et al., 1993; PMI,

2003; SAP, 2006; SEI, 2006a, 2006b, 2009) can assess and suggest the ways of

improvement as well. Maturity models which can assess and suggest ways of

improvement are certainly better than those which only assess the organizational

processes. All the MMs which can assess and suggest improvement portray staged-

representation of maturity (Table 2-2), except OPM3®.

Maturity

Model(s)

Acronym

Structure No. of

Stages

KPA/KPI

Staged Continuous Multi-

Dimensional

Organizational

Project

Management

Maturity Model

OPM3 Yes 4 Not

Definite

Maturity by Project

Category Model MPCM Yes 5 5

Portfolio, Program

& Project

Management

Maturity Model

P3M3 Yes 5 42

Projects in

Controlled

Environments

PRINCE2 Yes 3 32

Project

Management

Maturity Model for

Business

Management

Consultants

PMMM

(BMC)

Yes 5 10

Capability Maturity

Model® for BD-

CMM

Yes 5 22

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Business

Development

Capability Maturity

Model Integration CMMI Yes 5

People Capability

Maturity Model P-CMM Yes 5 21

CMMI® for

Development CMMI-

DEV

Yes Yes 5 4

CMMI® for

Services CMMI-

SVC

Yes 5

Knowledge

Management

Maturity Model for

SAP

KMMM

(SAP)

Yes 5 24

PM Solutions

Project

Management

Maturity Model

PMMM Yes 5 9

Project

Management

Maturity Model

ProMMM Yes 4 4

Table 2-2: Comparison of maturity models (MMs) – by structure

In the following sections, we will be discussing CMMI and OPM3® because

CMMI is the base of most of the MMs existing today. Therefore, it is complementary

to see what advantages it provides to the organizations. Also, a detailed discussion of

this model will be equivalent to discussing all the models which are based on it. On the

other hand, we are interested in suggesting what KM processes can be incorporated in

OPM3®. OPM3® has many advantages over other models. Therefore, fundamental

differences between CMMI® and OPM3® will also be discussed.

2.5 Capability Maturity Model Integration (CMMI)

All the CMMs exhibit a common characteristic - they all are based on CMMI®

(except OPM3®). Hence, they either inherit or extend many or all of the characteristics

of CMMI®. Therefore, instead of providing an overview and discussion of all the

models we think it a better and well-directed approach to provide discussion of CMMI®

only. But before describing CMMI® and its various variants, let us first shed some light

69

of the history and philosophy of development of CMM®.

2.5.1 History and Development of CMMI®

It took the organizations almost two decades to realize that that their

fundamental problem was the inability to manage the organizational processes. And

that, the novel methodologies and tools cannot provide productivity and quality gains

(DoD, 1987). Therefore, organizations needed some way not only to better manage

their projects but also improve their processes each time they executed some project.

The concept and development of CMM, and its descendent CMMI, has its roots in the

discipline of product quality improvement. Principles of product quality existed during

most of the 20th century. In the 1930s, principles of statistical quality control were

introduced by Walter Shewart. His principles were furthered successfully by W.

Edwards Deming and Joseph Juran (Deming 1986; Juran 1988, 1989). Then in 1979,

Philip Crosby presented a product quality framework in his book "Quality is Free"

(Crosby, 1979). Crosby's quality management grid presented a five evolutionary stages

(Figure 2-) of quality improvement. Then in 1985, Watts Humphrey and his colleagues

adopted and applied Crosby's framework to software processes at IBM (Radice,

Harding, Munnis, & Phillips, 1985). Humphrey, in 1986, brought his framework to

Software Engineering Institute (SEI) and modified it by adding the concept of maturity

levels. In this way he laid the foundation of his maturity model i.e. Capability Maturity

Model (CMM), later improved and named Capability Maturity Model Integration

(CMMI). Principles of Humphrey's framework laid the foundation of a maturity

framework that established an engineering foundation for quantitative control of

software processes - which was the basis for continuous process improvement as well.

Due to its base on Crosby's work, CMMI also presented a staged representation of

organizational maturity.

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Both CMM and CMMI models gained a wide acceptance throughout the

software industry. Early versions of Humphrey's maturity framework are described in

SEI technical reports (Humphrey, 1987a, 1987b), papers (Humphrey, 1988), and in his

book, "Managing the Software Process" (Humphrey, 1989). Since 1990, the SEI has

further expanded and refined the model for many other industries e.g. CMMI-DEV,

CMMI-SVC. Expansion of CMMI was funded and supported by many government and

experienced industry professionals.

CMMI, the descendent of CMM, is developed to guide software development

organizations in selecting process improvement strategies by determining current

process maturity and identifying the few most critical issues for software quality and

process improvement. Stages of process improvements are depicted in Figure 2-6

below.

Figure 2-6: Five Levels of Software Process Maturity, Source: (Paulk, et al., 1993)

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CMMI® had five ordinal maturity levels, although contemporary maturity

models do not follow the convention of same number of maturity levels. Maturity level

in CMMI® was described as, “a well-defined evolutionary plateau toward achieving a

mature software process.”

2.5.2 Constellations of CMMI

Initially, CMMI® was a standalone model i.e. it had no constellations, but with

the increasing widespread acceptance of the model, SEI developed variants of the

model to meet diverse needs of the organizations who wished to adopt CMMI® for

processes improvement and for the other aspects as well. The constellations of CMMI

are:

Table 2-3: CMMI Constellations

No. Acronym Maturity Model

1 CMMI-ACQ CMMI® for Acquisition

2 P-CMM People Capability Maturity Model

3 CMMI-DEV CMMI® for Development

4 CMMI-SVC CMMI® for Services

We are only interested in these variants to the extent that these represent

CMMI® and follow the same structure and staged-representation of maturity in any one

specific dimension the model is intended for.

2.6 Organizational Project Management Maturity Model (OPM3®)

2.6.1 Development of OPM3®

In May 1998, PMI chartered a project to create a standard that would describe

how organizations might enhance their capability to manage projects. The PMI

purported two basic reasons for the creation of such a standard. First, promotion of

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projects management as a strategic tool for organizations; second, success of projects

is related to organizational success. This project was the first of its kind. It involved six

integrated projects and 800 volunteers from 35 countries having diverse skills,

experiences, and industrial background. John Schlichter, the organizational project

management maturity model program director, visualized the program strategy. The

team spent almost five years on research and development for creating this standard.

The standard identifies the best practices for project, program and portfolio

management. The OPM3® standard is based on another standard by PMI, the PMBOK

guide. The customers of this project included senior and executive level management

and project management professionals while project management profession was

identified as the audience. The goal of this program was to develop a universal standard

that will benefit each of these customer groups. OPM3® targets organizations, not the

individuals (Schlichter, Friedrich, & Haech, 2003). After the team started development

of OPM3®, it was realized that in addition to PMBOK, some other resources (i.e.

standards for program management and portfolio management) were needed.

Consequently, PMI first developed standard for program management and portfolio

management. Standard for program management addressed management of related

projects as groups and in a coordinated manner to achieve synergistic benefits.

Similarly, standard for portfolio management addressed management and prioritization

of groups of programs which could help the organizations achieve strategic objectives.

Hence, OPM3® incorporates three standards: the PMBOK guide, the standard for

program management and the standard for portfolio management. Hence, it can assess

organizational project, program and portfolio management capabilities and produce

specific outcomes. In addition to these management processes, best practices -

associated with the environment or culture in which these processes are performed -

73

were also identified.

OPM3® is to make organizations capable of achieving consistent and

predictable delivery of projects and enhance their project management capabilities.

Here, the term “organization” is not specifically used for an entire company, agency,

association, or society. It can refer to business units, functional groups, departments, or

sub-agencies within the whole. In the context of OPM3®, the term applies to any groups

intending to make use of the material in this standard.

The need for understanding project management as a holistic system - spanning

the organizational boundaries - further increases when an organization's work is viewed

and performed as multiple projects. Therefore in OPM3®, organizations can address all

three domains of projects: the project management domain, the program management

domain, the portfolio management domain, either one or many domains or any

combination of these - whatever is suitable for organization. Such an approach has

never been taken before in a maturity model. Such unique structure makes OPM3®

scalable, flexible and applicable to most organizations most of the time. In its essence,

OPM3® is also a capability maturity model because it describes development of

capabilities over time, leading to more advanced capabilities. However, it does not

follow the staged-representation perspective of maturity. It explains how organizations

adopting OPM3® get better as they mature.

2.6.2 Structure of OPM3®

We consider it complementary to discuss the structure of OPM3® to inform the

readers what it constitutes. The overview of the structure will help us to describe the

way we aligned our study and its outcomes to the structure of the existing model. As in

this study we are only interested in suggesting the best practices that can potentially be

74

included in OPM3® to assess organizational KM maturity, therefore we will only be

discussing what categories of best practices OPM3® currently has and how these are

organized in OPM3®?

This Standard provides three basic benefits to the organizations: (1) help them

to understand organizational project management, (2) enable them to measure their

project management maturity against a comprehensive and broad set of organizational

project management best practices and, (3) help them for developing an improvement

plan. best practices are the basic building block of OPM3®. Consequently,

organizational project management maturity is assessed and described through the

existence of best practices in OPM3®.

OPM3® describes best practices as, “an optimal way currently recognized by

the industry to achieve a stated goal or objective (PMI, 2003)”. For organizational

project management, this includes the ability to deliver projects predictably,

consistently and successfully to implement organizational strategies. Furthermore, best

practices are dynamic because they evolve over time as new and better approaches are

developed to achieve their stated goal. Using best practices increases the probability

that the stated goal or objective will be achieved.

OPM3® structures best practices such that these are best achieved by

developing and consistently demonstrating their supporting Capabilities10– which are

in turn observed through measurable Outcomes. Capabilities are visualized as

incremental steps, leading up to one or more best practices (Figure 2-7).

10 OPM3 defines a Capability as, “A Capability is a specific competency that must exist in an organization in

order for it to execute project management processes and deliver project.” To date, OPM3® has 488 best practices

that organize 1,773 Capability-Outcome (CO) Statements.

75

Figure 2-7: Relationship of Best Practices, Capabilities, Outcomes and KPIs,

Source: (PMI, 2003)

The existence of a Capability is demonstrated by the existence of one or more

corresponding outcomes. Outcomes are the tangible or intangible result of applying a

capability where a capability may have multiple outcomes. A key performance

indicator (KPI) is a criterion by which an organization can determine, quantitatively or

qualitatively, whether the outcome associated with a capability exists or the degree to

which it exists. A KPI can be a direct measurement or an expert assessment.

Best practices in OPM3® span a wide spectrum of categories, the most

important being the following:

Develop appropriate governance structures

Standardize and integrate processes

Utilize performance metrics

Control and continuously improve processes

Develop commitment to project management

Prioritize projects and align them with organizational strategy

Utilize success criteria to continue or terminate projects

Develop the project management competencies of personnel

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Allocate resources to projects

Improve teamwork

Above categorization of best practices in OPM3® states clearly that currently

there exists no explicit category for managing knowledge-of-project – due to which it

cannot assess a critical aspect of the organizational project management.

In OPM3®, the progression of increasing maturity consists of several

dimensions, or different ways of looking at an organization’s maturity. One dimension

involves viewing best practices in terms of their association with the progressive stages

of process improvement—from Standardize to Measure to Control and to Continuously

Improve. While, another dimension involves the progression of best practices

associated with each of the domains: Project, Program and Portfolio Management. Each

of these progressions is a continuum along which most organizations aspire to advance.

Along with this unique structure, OPM3® has some other very advantageous

characteristics which differentiate it from other MMs.

2.6.3 Advantages of OPM3®

OPM3® is quite different and advantageous from all the other existing

comparable maturity models in many aspects. Let us discuss different aspects of

OPM3®.

First of all, unlike all other maturity models OPM3® does not have a system of

levels of maturity. The basic reason behind this unique structure is that establishing

specific maturity levels can be relatively straightforward if the progression of maturity

is one-dimensional, while OPM3® is a multidimensional (project, program, portfolio)

MM. These multiple perspectives for assessing maturity allow flexibility in applying

OPM3® to the unique needs of the organization. This approach also produces a more

77

robust body of information than is possible with a simpler, linear system of levels

giving the organization greater detail in support of decisions and plans for

improvement.

The scope of OPM3® is global. It has been developed through the participation

and consensus of a diverse group of individuals in the project management profession

representing a cross-section of organizations from 35 countries. It cuts across

boundaries of organizational size and type, is applicable in cultures throughout the

world, virtually any industry, from engineering and construction to information

technology, financial services, government agencies, and manufacturing, to name a

few. This trait of being global makes OPM3® comparable to other renowned MMs such

as CMMI® and PRINCE2® – which are globally 11 applicable to a variety of industries.

The multidimensional approach of assessment of progression of

maturity is one of the other unique characteristic of OPM3®, which has never been

taken in any other MM. This makes OPM3® a distinguished MM amongst all the other

existing and comparable MMs.

Advantages of OPM3® on other MMs are summarized briefly:

OPM3® follows a multi-dimensional structure rather than a straight staged

representation of the processes because of the inherent requirement to assess

project, program and portfolio management maturity assessment of the

organization.

It can assess organizational process maturity not only for projects but also for

programs and portfolio of projects – which is not possible with any other MM.

11 As we collected and analyzed the data from a variety of industries and from many countries.

78

It is based on Project Management Body of Knowledge (PMBOK®), which has

become a de facto standard for project management (PMI, 2003, 2008a) hence,

it has a solid and recognized theoretical base.

As we discussed earlier that both CMM® and CMMI® represent a staged view

of the organizational maturity because of the thinking purported by Humphrey

(Humphrey, 1989) that organizational processes are developed and get mature in stages

based on solving process problems in a specific order. This representation of thinking

organizational process maturity looks quite rational, at first look at least, therefore

many authors have developed MMs based on this view.

2.6.4 Why Improve OPM3® and not any other Project Management Maturity

Model?

Having provided a brief overview of different renowned PM and KM

maturity models, now we consider it appropriate to discuss why OPM3® should be

focused on for improvement than any else model?

1. All other models (whether PM or KM models) are based on CMMI®, which was

developed keeping in mind software development processes, practices, tools,

methodologies and problems, therefore, CMMI® is more appropriate for organizations

which intend to assess and improve their software development PM processes and

practices – on the other hand, OPM3® is a generic PM maturity assessment model

which can be used by organizations from virtually all the industries (as it is developed

by feedback from many industries), which makes OPM3® is more adaptive to the needs

of the organizations.

79

2. OPM3® does not have an overall system of levels of maturity; this unique

structure provides organizations more flexibility if they only wanted to assess and

improve any subpart of the organizations. Moreover, establishing specific maturity

levels can be relatively straightforward if the progression of maturity is one-

dimensional, while OPM3® is a multidimensional MM.

3. OPM3® is based on the PMBOK® which has become a de facto standard for

project, program and portfolio management, while no other model has such a strong

base – even CMMI® is also based on only practices which were found useful in

IBM12(Paulk, 2009) and not on any solid body of knowledge.

4. OPM3® can assess organizational project management maturity in project

management, program management, portfolio management, either one or many

domains or any combination of these - whatever suits the needs and capacity of the

organization. This approach had never been taken before in a maturity model. This

characteristic makes OPM3® scalable and flexible, and therefore applicable to most

organizations most of the time - the hallmark of PMI standards – while no other PMMM

or KMMM has this property.

Based on these objective reasons we have decided that OPM3® is the most

appropriate model to concentrate on and should be improved to make it capable of

assessing the very critical dimension of any organization – managing knowledge-of-

projects.

2.7 Summary

In this chapter we have provided a critical review of our theoretical framework,

12 See section 2.5.1 for details

80

many of its concepts, relationships between concepts, existing capability maturity

models including PM and KM capability maturity models. At the end, the reasons why

we chose OPM3® for this study are provided. There is no debate on that efficient

utilization of intangible assets of the organizations while managing its projects can

provide a sustainable competitive advantage to the organizations, therefore it is

imperative for the organizations to harness the power of their intangible assets. An

exploitation of intangible assets requires an identification, organizations and sharing.

Knowledge-of-projects is amongst such an intangible asset, which should be managed

and shared among other stakeholders. Therefore, it is important for the organizations

to assess the extent to which they are utilizing their intangible assets. However,

unfortunately currently there does not exist any MM through which organizations could

identify, organize, share and assess the their intangible asset, i.e. knowledge-of-project.

Although, various project management and knowledge management maturity models

exist to assess the organizational project management and knowledge management

processes, but all such models exist in isolation to each other (i.e. project management

models) do not have knowledge management assessment capabilities and knowledge

management models do not have project management assessment capabilities.

Therefore, based on various objective criteria we selected OPM3® with the intention of

making it capable of assessing knowledge-of-project management practices of

organizations. In this chapter, we have provided a detailed critical review of relevant

literature. In the following chapter (Chapter 3) research design for first phase of the

study will be discussed.

81

Chapter 3 Phase One - Research Design, Results &

Discussion

The purpose of this chapter is to provide clear description of the objectives,

research paradigm and methodology of phase one of this research. The chapter also

specifies the various issues and characteristics of the sampling process such as expected

samples (participants) for this study, the characteristics of samples and sampling rates

needed (p.88). The methods used for data collection, data organization and data

analysis are also discussed (p.96). Finally, the instruments used both for data collection

and data analysis, are described. At the end of this phase, ethical considerations are

discussed which were taken into account while performing data collection. The detailed

results of data analysis, in the form of tables and figures, have been added to the

appendices.

3.1 Research Stance

A mixed methods approach comprising qualitative and quantitative methods is

followed in this study. A mixed methods approach was followed to fulfill the needs and

objectives of the study. The first objective (p.86) is exploratory in nature while other

objectives are aimed at verifying the impact of predictors on the outcome variable.

Therefore, the study was also conducted in two phases. In the first phase we followed

an interpretivist paradigm because the purpose was not to test the hypothesis. It was to

explore the ideas and opinions of people about the subject which is normally not done

in the context of developing countries (Babbie, 2003; Neuman, 2003). Due to the

different requirements and constraints of each of the paradigms, we believe that

interpretivist paradigm is the most suitable paradigm for addressing the first research

question while the positivistic paradigm is a better approach for addressing second and

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third research questions.

The objectives of the first phase are fulfilled by conducting open-ended

interviews and their results are analyzed using qualitative methods. Results of the first

phase are used as an input for the second phase. In the second phase, an empirical

positivistic paradigm is followed by developing hypotheses, identifying variables,

conducting surveys and analyzing data by using quantitative methods.

Due to the different requirements and constraints of each of the paradigms, we

believe that interpretivist paradigm is the most suitable paradigm for addressing the

first research question while the positivistic paradigm is a better approach for

addressing second and third research questions. In the second phase, the researcher

preferred to use quantitative approaches, such as survey of individuals’ responses

which are time and cost-effective as the variables could be matched to the different

dimensions of the concepts (Wreathall, 1995).

3.2 Research Approach and Method

There are two major schools of thoughts in research: Positivist and interpretive.

The positivist perspective is more applicable in basic sciences or when definite closed-

ended phenomenon is studied (Babbie, 2003). Survey research is the most widely used

approach while employing positivistic perspective in social sciences because it

generates a ‘‘detailed and quantifiable description and a precise map of a

phenomenon”. This perspective was refined during the twentieth century. The

interpretivistic perspective, on the other hand, is more applicable in the field of business

management and when open-ended data and descriptions are needed. The positivistic

paradigm is particularly important in the second phase of our study where we used

questionnaire to collect and analyze the data.

83

In contrast to the positivistic paradigm, the interpretive paradigm is particularly

important in the context of the first phase of this study because of the subjective and

open-ended nature of data we gathered through open-ended interviews. When

subjective data is gathered, a number of people say the same things using different

words. The interpretive perspective caters for such inherently subjective issues. The

basic premise of interpretivism holds that, "realities are multiple rather than singular,

objectivity is a myth and that the meanings ascribed to the words we use are imperfectly

shared" (Chauvel & Despres, 2002).

To summarize, the positivistic perspective defines, measures, codifies and

controls a phenomenon while the interpretivistic perspective focuses on the way people

conceptualize their world and make sense of it. This is generally accomplished through

methods which permit researchers to generate a ‘‘thick description’’ of how individuals

or small groups construe a given reality. Certain types of surveys may be used by the

interpretivists. They are more likely to employ other methods, such as interviews, case

histories, focus groups and delphi techniques.

As discussed earlier, the study has been conducted in two phases due to the

differential nature of the objectives. Thus, objective one requires employing qualitative

methods such as interviews with the target samples. Therefore, the researcher

developed and administered open-ended interviews with experienced IT project

managers to gather their opinions about the best practices that could be adopted by the

organizations to manage their knowledge-of-projects. On the other hand, second, third

and fourth research objectives require quantitative methods to test the relationship

between predictor and outcome variables.

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3.3 Phase One

Only objectives guide the design of Phase one. This strategy is quite in-line

with the interpretive paradigm school of thought which necessitates that whenever the

objective is to explore the patterns or ideas the researcher should not rely on the existing

theories (Babbie, 2003; Neuman, 2003). The objectives(s) and research question(s) for

this phase are narrated in the tables below (Table 3-1, 3-2).

Table 3-1: Research Objective for Phase One

No. Objective (s)

1 To identify the best practices for managing knowledge-of-projects in

the IT organizations of Pakistan

Table 3-2: Research Question for Phase One

No Research Question

Q.1 What are the best practices for managing knowledge-of-projects in the

context of IT project management in Pakistani IT organizations?

3.3.1 Development of Interview Protocol

The major challenge while developing the interview protocol was to describe

knowledge in term of projects. The researcher termed this concept as “Knowledge-of-

projects”. This was a very crucial step of our study because it had to lay the foundation

of our work at the later stages. For this purpose, the researcher relied on the seminal

work of Reich (2007). Reich categorized knowledge-of-project in four categories13,

13 Refer to table1-2 in chapter1

85

namely: process, domain, institutional and cultural. The researcher found this

categorization extremely useful, yet terse and comprehensive as compared to other

descriptions because knowledge is a very elusive concept. It was expected that if,

interviewee are asked questions such as, “In your opinion, what best practices does

your organization need to adopt to manage knowledge of the project”, they would not

even be able to comprehend the question. Reich’s description of knowledge-of-project

resolves this problem. She categorizes the description of knowledge into four

categories. Then, she further describes what sort of knowledge belongs to in each

category. Due to the conciseness and clarity of these conceptual terms, we chose

Reich’s description of knowledge-of-project to develop the interview protocol used in

the first phase of this study.

The protocol comprised of four pages and three sections14. Each section

contained questions corresponding to each step of KM process, namely, capture,

organize and share. Moreover, each section was further subdivided into four areas

according to the categories of knowledge i.e. process, domain, institutional and cultural

knowledge.

This interview protocol was developed as prescribed by Salant & Dillman

(1994). All the questions in this interview protocol were open-ended. The first page of

the protocol contains the title of the protocol, asks a few introductory questions to the

interviewee about their name, professional experience, designations, highest level of

education, city, contact information (including email and telephone number) and name

of employer. The protocol introduces the respondents to the technical terminologies

used. Contact information was also sought to elucidate any confusion in understanding

14 Refer to Appendix A to look at the interview protocol

86

the transcripts while emails were asked for sending the results of the research study to

the interviewee after analysis.

The second, third and fourth pages of the interview protocol contain open-ended

questions asking the interviewees for their opinions about the best practices which the

organizations should adopt to manage, capture, organize and share their knowledge for

process, domain, institutional and cultural knowledge. Finally, the researcher thanked

the respondents for their valuable input.

3.3.2 Selection of Samples Organizations and Participants

The researcher selected the research participants from the two major cities of

Pakistan, Lahore and Islamabad, to conduct the interviews. These participants were

employed by either IT software development organizations or IT departments in

government agencies in either city. It was decided that each interviewee needed to have

a certain amount of work experience due to the complexity, nature and

comprehensiveness of the questions. The sample selection process also comprised two

major steps: (1) selection of the organizations, (2) selection of the participants. Below

we will be discussing the details of each of next step.

3.3.2.1 Selection of Sample Organizations

We utilized a multi-stage sampling technique to select the sample organizations

based on factors such as: organizational size (the number of employees), the type of

business and the geographical location.

To obtain the sample of the organizations in a rational and objective manner, a

listing of all the information technology (IT) organizations based in Pakistan was

obtained from Pakistan Software Export Board (PSEB). As this listing was published

in 2009, it can be considered to be an approximately up-to-date record of the population

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of IT organizations. The list contained a total of 1100 IT organizations including

Software, Hardware, Telecommunication, Internet Service Providers, offshore services

providers etc. This listing helped me to select the sample of organizations so that a

reasonable number of small, medium and large organizations are selected. The

multiple-stage sampling technique was used to classify the organizations based on

different types of businesses. At the first stage of sampling, we limited the sample to

the organizations based in Lahore and Islamabad and did not consider organizations

based in Karachi. At the second stage of sampling we purposively focused on only the

IT software development organizations, so that the remaining organizations were not

considered. This criterion of elimination left us with approximately 500 organizations

(42% of the original 1100 larger population) which could be categorized as software

development organizations. The third stage of sampling consisted of finding those

organizations having an employee base ranging from 70 to more than 500, in order to

obtain a sample of only those organizations large enough to have different functional

departments. The application of these multi-stage sampling criteria yielded a total of

60 IT software development organizations. These 60 organizations comprised small,

medium and large organizations. The conceptualization of small, medium and large

was as follows: small (up to 100 employees), medium (101 to 300) and large (greater

than 300).

Table (3-3) and Figures (3-2,3-3) present describe demographic information of

the organizations which made up the population. The organizations are classified

according to: size, geographic location and type of business.

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Table 3-3: Population of organizations (by size)

Size Frequency Percent

Cumulative

Percent

Small (Up to 100) 16 26.7 26.7

Medium (101 to 300) 27 45.0 71.7

Large (300+) 17 28.3 100.0

Total 60 100.0

It can be seen that 71% of the organizations (71%) are medium in size as they

employ fewer than 300 workers (Table 3-3). Categorization of the organizations by size

(number of employees) was considered as the most appropriate method because almost

all of the IT organizations are private limited companies. Hence, they do not publish

annual reports to show their financial position. Moreover, the organizational

categorization appeared appropriate and consistent with the fact that the IT industry of

Pakistan is not large and mature enough as it has an average organizational size of less

than 100 employees (PSEB, 2009).

Figure 3-1: Population organizations' size (no. of employees)

Figure (3-2) indicates the geographical location of the organizations of the

population. It is noteworthy that 66% of the organizations were based in Lahore (41).

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Figure 3-2: Geographic Distribution of Poulation’s Organizations

Figure (3-3) indicates that most of the organizations are software development

organizations (46), only a few have multiple business such as IT consulting services

and software development (8) while others (6) provide IT services. We selected only

software development organizations to maintain consistency in terminologies and

scope.

Figure 3-3: Business of population organizations

3.3.2.2 Selection of Sample Participants

After finalizing the target organizations, the next logical step was to select a

sample of target participants from the sample of organizations. For this purpose, we

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used a purposive sampling technique which was based on three criteria namely,

professional experience (in years), designation, and the willingness to participate.

These criteria helped us to filter the sample participants from the population. To find

our sample participants, the 60 organizations selected in the third step were contacted

through email, telephones and personal contacts. They were contacted in order to gather

information about number of PMs and their professional experience. It was found that

most of the organizations had one or two project managers, while in certain

organizations, especially the small ones, the owners were acting as the project

managers. These organizations had a total of 80 PMs who had professional experience

ranging from 8 to more than 25 years15. Then we selected respondents based on two

main factors: PMs who were interested in knowledge management and had at least ten

years of project management experience.

After applying these criteria we obtained a sample of 55 PMs. Then an email

containing the objectives of the interview was sent to all the 55 PMs to ask for their

willingness to participate in interview. Only 23 PMs responded and showed their

willingness to participate in the interview. Out of these 23 PMs, 18 PMs were

interviewed due to the unavailability of other 5 PMs at the selected date and time. These

18 PMs were employed by either the IT software development organizations or IT

departments in the government agencies in Lahore and Islamabad. A sample of 18

respondents is considered a reasonable sample size as similar studies by King &

Zeithaml (2003) and Reich (2007) have shown that even less than 18 respondents are

an appropriate number depending upon the complexity of content, depth, seniority of

respondents and the time required to complete the interview.

15 See section 3.4.3 for demographic information of interviewee's

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3.3.3 Pre-test of interview protocol

The main purpose of the pre-test is to study prospective problems before they

become too costly or too late to be corrected. The pre-test provides initial indication

and information on how long data collection can take. It also simulates what will occur

during the data collection scenario. It is an important activity which can provide an

accurate assessment of what can go wrong. If this step is skipped or ignored, the risk

of collecting useless data increases. The risk and probability of collecting useless data

increase when the instruments and data to be collected are qualitative. The pre-test of

interview protocol was conducted before starting a full scale interview process so that

the applicability, usability, reliability and capability of the interview protocol could be

tested.

An open-ended interview protocol is used to collect data. The protocol is

prepared to support the first research question and its objectives. Also, the content

validity of the questions in the protocol was assessed by:

Reviewing the protocol with a senior IT project manager and an expert

in the discipline of maturity models development

A pilot test of the protocol involving a senior project manager

The review of the interview protocol resulted in a few modifications to the

wording of certain questions to enhance their clarity. It was assumed that the interview

protocol is brief and comprehensive enough to elicit the opinions of participants about

the concepts and questions asked. At the implementation stage of the pilot test we

randomly selected two participants from our sample in Islamabad and simulated the

interview process with them. As the participants were selected from the sample

therefore they were considered to be approximately similar to the participants who were

going to be in the main sample. The interviews lasted for at least 45 minutes to 2 hours.

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This process of conducting the pre-test provided us with an indication of the length of

time it would take to conduct the interviews with eighteen PMs. It was deduced that it

would not be possible to conduct more than two interviews per day. The pre-test helped

to clarify a number of questions in the interview protocol such as:

Questions that respondents do not understand

Questions that respondents may misunderstand.

Questions that make respondents uncomfortable.

Ambiguous questions.

Questions that combine two or more issues in a single question.

3.3.4 Conducting the Interviews

Before starting the interview, each participant was introduced to the topic,

scope, objective and utilization of data that had to be collected. The researcher briefed

the participants about the various technical terminologies and terms being used

including a working definition of knowledge in terms of IT projects. The first page of

the protocol also mentioned all the definitions and terms being used for ready reference

during the interviews to facilitate the participants. The interview protocol contained

open-ended questions asking the respondents about their opinions on the best practices

needed to capture, organize and share knowledge-of-project. Enough space was

provided on the protocol so that respondents could write the responses directly on it.

The interview focused on three areas:

1. Best practices needed to capture knowledge of IT projects i.e. process,

domain, Institutional and cultural knowledge.

2. Best practices needed to organize knowledge of IT projects i.e. process,


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3. Best practices needed to share knowledge of IT projects i.e. process,


The researcher ensured that he did not intervene or guide the participants when

they expressed their opinions. This cautious act made it possible to gather as much as

possible information from them. During the interviews the participants shared stories

about their systems, processes, policies, the cumbersome documentation processes they

have to go through to find relevant information, the absence of appropriate knowledge-

based systems and the need to facilitate knowledge sharing through meetings among

the employees.

3.3.5 Sorting and Organizing the Data in QDA Miner

The interviewees recorded their responses on a paper-based interview protocol.

These transcripts needed to be input in a format suitable for transfer to qualitative data

analysis (QDA) software; QDA Miner v 3.2. First of all, the data was carefully

organized in MS Excel before transferring it in QDAMiner. After inputting all the

results to MS Excel, the data were checked for any spelling mistakes. This assured: the

removal of any typing and spelling errors and development of a general

conceptualization of the data. Then all of the data were transferred into QDA Miner.

At this stage, a separate record was created for each participant to make the results and

analysis transparent and distinguishable. For each participant two types of data were

entered in QDA Miner: the basic background information of the PMs and their

responses to the questions. The background information included: each PMs

designation, education level, experience (in years) and city of work. There were four

documents (called document variables in QDA Miner) attached to each type of

knowledge. They are given below:

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- Document variable PR_K represents process knowledge and holds data

for best practices needed to capture, organize and share process

knowledge

- Document variable DOM_K represents domain knowledge and holds

data for best practices needed to capture, organize and share domain

knowledge

- Document variable INST_K represents institutional knowledge and

holds data for best practices needed to capture, organize and share

institutional knowledge

- Document variable CULT_K represents cultural knowledge and holds

data for best practices needed to capture, organize and share cultural

knowledge

A visual snapshot of data organization in QDAMiner is shown in Figure 3-1. It

can be observed that data for each respondent are organized in a multitude of ways. For

example, box ‘a’ displays the case/respondent selected and box ‘b’ displays the

variables for each respondent. These variables may be nominal, ordinal, string or

document variables. Document variables are displayed as tabs (as pointed by box ‘c’)

and can hold as much text data as required in them, this data can be qualitatively

analyzed in various ways. Box ‘d’ points to the area which displays the hierarchy of

codes and major themes of codes. Box ‘e’ points to the area which displays the actual

text as mentioned by the respondents in order to capture, organize and share the

knowledge. Finally, box ‘f’ points to the area where the assigned codes are displayed.

Multiple codes can be assigned to the same text. This functionality of QDA Miner

makes possible to run many analyses, which could otherwise require re-coding of the

data for different analyses. While running any analysis, QDA Miner provides options

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to specify the criteria through which it selects the codes and text. Hence, assignment of

multiple codes to the same text does not violate or override the results of any analysis.

Figure 3-4: Snapshot of arrangement of the data in QDA Miner

All of the basic and response data for each of the eighteen respondents was

organized in the above mentioned format. After entering all the necessary information

in QDA Miner, the next step was to code the data. Coding of data is a common

technique done in order to find patterns in the data. The coding was performed utilizing

the prescribed approach by Strauss & Corbin (1998). Strass and Corbin recommend

three types of coding for QDA: open coding, axial coding, and selective coding. From

these, it was possible to assign codes to the common concepts and the best practices

that the interviewee’s thought could be useful for managing knowledge of the projects.

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Such a coding approach is appropriate and found quite commonly in studies (Sage,

2008; Carter, 2003) addressing qualitative data collection and its analysis. In the

following sections, coding strategy in general, some coding theories, coding processes,

and how we coded our data are discussed.

3.3.5.1 Codes and Coding

This section discusses the codes, coding, text/transcripts coding types, their

purpose and the strategy we adopted to code the interview transcripts. In qualitative

research, coding is the process of searching for concepts, ideas, themes, and categories

that help the researcher to organize and interpret the data. Therefore, qualitative data

analysis is mostly carried out through codes and coding. The researchers assign codes

to concepts based on an explicit criterion. The researcher can develop these codes either

prior to data collection or, they may emerge inductively throughout the coding process.

In the following section, we provide an overview of coding process, describe strategies

for deriving codes and review open, axial and selective coding processes.

3.3.5.2 Coding Process

The assignment and derivation of codes and coding process differ in

quantitative and qualitative research. In qualitative research, coding is the process of

generating ideas and concepts from raw data such as interview transcripts, field notes,

archival materials, reports, newspaper articles, and art. The coding process refers to the

steps a researcher takes to identify, arrange, and systematize the ideas, concepts, and

categories uncovered in the data. During the coding process, the researcher identifies

potentially interesting events, features, behaviors etc and distinguishes them with

labels. At this stage, broader categories are identified. As the same process is repeated,

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the categories are further differentiated or integrated into a smaller number of

categories, relationships and patterns etc (Given, 2008).

In this study, the coding process is broken down into two stages: open and

selective coding. Assignment of open and selective codes is a spiral process, though,

there exist no sharp boundaries in actual practice. In open coding, the data are coded

with attention to smaller details while in selective coding, codes are assigned to

evolving categories at much higher degrees of abstraction. Such an approach is

appropriate whether we want to find patterns, identify categories/themes or develop

theory (Given, 2008; Strauss & Corbin, 1998).

3.3.5.2.1 Open Coding

Open coding is a procedure advocated by Strauss & Corbin (1998). It is

appropriate in situations when the raw data (e.g., interviews) needs to be broken down

so that as many ideas and concepts as possible are identified and labeled. Open coding

is the first step of coding. During this initial stage, the researchers try to bring order

and make sense of the data. It is accomplished through a line-by-line reading of the

data to search and identify as many ideas and concepts as possible without concern for

how they relate to one another (Given, 2008; Strauss & Corbin, 1998)

Researchers may start coding the data by looking for information that concerns

the original goals and interests of the study. This process is done by assigning code

labels to identify occurrences, meanings, activities or phenomena. The researcher

begins to group instances or events that are similar and to distinguish those that differ.

During this process, the same event, incident or activity in the data may be coded in

multiple ways. As we continue examining the data, many new concepts and ideas may

be identified along with those already identified ones. Thus, refinements occur

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throughout the process of open coding. As we proceed with the process, certain

concepts show up repeatedly, whereas others may be less commonly observed or

perhaps, as variations of a concept or theme which has been already recognized. Many

researchers suggest that open coding should continue until nothing new and interesting

emerges. While going through this dynamic exercise, broader categories and their

properties or dimensions are discovered.

We coded the interview transcripts data by following the above mentioned

constraints, rules and philosophies. At the start of open coding, concepts related to the

research questions were identified and labeled with codes. As the researcher became

more familiar with the data, concepts emerged from the data and were labeled and

coded into more abstract categories. This process continued until there was nothing

new to label.

3.3.5.2.2 Selective Coding

During the second step of coding, the data was coded using selective coding.

The analysis of categories was then performed to recognize central themes and their

respective best practices. Such a coding approach is considered appropriate (Given,

2008). It is quite commonly found in studies (Carter, 2003) addressing qualitative data

collection and its analysis. However, the move from open coding to a more focused

systematic coding is not a clearly defined step as this process of moving is not linear.

For instance, if a new idea is discovered later in the process, or as more data are added,

original concepts can arise, and the need to broaden one’s mind to new possibilities

may occur. Nevertheless, as coding progresses, particular categories and themes

emerge as being more salient and central to the key concepts. The data are then more

thoroughly and systematically reviewed with fewer specific concepts or categories to

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determine where and how these are illustrated in the data. The coding process has both

inductive and deductive elements. For example, when confronted with further data,

more codes emerge that are often revised to accommodate the evidence. This pursuit

of a more refined and focused analysis requires that many concepts are re-

conceptualized and incorporated into broader, more abstract categories.

According to Sage (2008) selective coding is a focused and intensive coding

process, addressing questions such as what forms of tools or processes organizations

utilize to execute projects? Suppose the respondents are asked about the tools and

techniques organizations should use to promote collaboration among employees. Here

are a few examples that respondents provided together with the way they were coded:

they should use wiki (coded as online collaboration tool), they should use virtual

meetings (coded as virtual meetings), they should use email lists (coded as email lists).

Eventually, these various forms of knowledge sharing may be combined and

incorporated into a broader category of “ICT tools for knowledge sharing” that includes

collaboration, email lists and virtual meetings. This higher level category may, in turn,

be theoretically reworked and incorporated into an even broader conceptual category

such as “tools for knowledge sharing”. Such successive stages of coding in the

qualitative data enabled analytic discoveries to be made.

3.4 Qualitative Data Analysis (QDA)

Analyses of qualitative research are very different from those of quantitative

research due to the varying objectives of the underlying paradigms i.e. positivistic and

phenomenologist. In positivistic/quantitative research, the objectives are to find

relationships/correlation between variables, assess the strength of correlation, describe

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phenomena existing or measure them. It involves statistical analyses because it deals

with numbers. On the contrary, the objectives of qualitative research are to look for

patterns and opinions by conducting field studies, interviews and case studies etc. It

does not involve statistical analysis because the data are non-numerical.

In the next section we will be discussing the analyses we performed on our data

and its results.

3.4.1 QDA Using QDA Miner Tool

After coding all of the data, we performed analysis of the interview transcripts.

There are numerous software tools available to perform qualitative data analysis. Some

renowned tools are: MaxQDA, WordStat and QDA Miner. These tools differ widely in

their features, as well as in their capabilities to perform QDA. For example, MaxQDA

is suitable in those scenarios where the data needs to be coded without any hierarchies.

If the codes are needed to be assigned in a tree or hierarchical form, then MaxQDA is

not suitable. Similarly, WordStat has certain unique limitations. Thus, both of these

tools could not be used for our study. For this study, we needed a tool which could help

us to: (1) code the data in hierarchical form, (2) assign multiple codes to the same data,

(3) read the data available in multiple text files, (4) perform qualitative data analysis

based on bivariate comparison between groups and, (5) display the results in various

forms such as bar charts and pie charts etc. We found all of these functions in QDA

Miner v3.2. This software is available either as a standalone application or with an

integration of WordStat. WordStat further enhances the capabilities of QDAMiner as

an integrated module. However, we relied on the QDAMiner v3.2 standalone

application as it was found to be appropriate for all of our above mentioned

requirements.

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Qualitative data analysis tools do not perform analyses directly on the data, but

they require that first of all codes must be assigned to the text. After this mandatory

step, the tools perform analyses using those codes. This process is similar to other

statistical analyses tools, such as SPSS, which requires that numeric codes should first

be assigned to the items so that the analyses can be performed using those numeric

codes. Consequently, after assigning the codes to the text/data, a variety of qualitative

analyses can be performed using QDA Miner. Nevertheless, the choice of analyses to

be performed depends upon the objectives and research questions of the study. The first

objective and its respective research question seek to identify the best practices and

their major themes/categories which organizations can adopt to manage their

knowledge-of-project. To fulfill these requirements of the study, QDA Miner provides

two analyses called ‘Coding by Variables’ and ‘Coding Retrieval’. We carried out both

of these analyses. The first analysis, ‘Coding by Variables’ provided categories/themes

of best practices in which all of the best practices can be placed while the second

analysis, ‘Coding Retrieval‘ provided distinct individual best practices. We included

all four types of knowledge in the analysis and then tabulated them with the

‘Title/Designation’ of the respondents. The challenge in such qualitative analyses lies

in the fact that the researcher should follow a rigorous and unbiased coding process

while assigning codes to the data. Then, choosing and running the analysis becomes

very easy and straightforward. To summarize, the qualitative analyses provided us two

things: (1) individual best practices and, (2) themes or major categories of best

practices.

3.4.2.1 Results

This section presents results of the qualitative analysis carried out to find the

major categories/themes and distinct best practices for managing knowledge-of-

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projects from the data collected through interviews from the IT project managers. All

the analyses are performed using QDA Miner v. 3.2 tool. This section presents

comprehensively results of this phase of the study to understand and analyze the

following research question:

What are the best practices for managing knowledge-of-projects in the

context of IT project management in Pakistani IT organizations?

3.4.2.1.1 Demographic Information of Samples (Interviewee's)

All the sixty organization of the population were contacted through various

means to identify the PMs who were willing to participate in the interviews and who

also fulfilled the eligibility criteria. These sixty organizations had almost 75 IT PMs

but only 23 PMs responded and indicated their willingness to participate in the

interviews. However, 18 PMs were actually interviewed because of the unavailability

of other five PMs at the designated date and time. A sample of eighteen people is

considered to be suitable for such studies as mentioned by King & Zeithaml (2003) and

Reich (2007). The breakup of these 18 PMs were such that, 11 PMs were working in

the software houses and government agencies in Lahore while 7 were in Islamabad.

Some of the demographic characteristics of these 18 participants is shown in the figures

and tables (Figures 3-5 to 3-8, Tables 3-4 to 3-7).

Table 3-4: Geographic location of interviewees

City Frequency Percent Islamabad 7 38.9

Lahore 11 61.1

Total 18 100.0

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Figure 3-5: Geographic location of interviewee's

These 18 participants were working in the role of IT PM; although they had

different designations (Table 3-5, Figure 3-6).

Table 3-5: Titles/designations of interviewees

Designation Frequency Percent Project manager 5 27.8

Engineering project manager 1 5.6

System architect 1 5.6

Senior project manager 6 33.3

Software development manager 5 27.8

Total 18 100.0

Figure 3-6: Titles/designations of interviewee's

All the participants had a reasonable level of academic qualifications. It was

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noticed that most of the interview participants held a Masters degree in the related

discipline (72%) while a few held a MPhil degree (11%) and the remaining had only a

Bachelors degree in the related discipline (Table 3-6, Figure 3-7)

Table 3-6: Academic level of interviewees

Educational Level Frequency Percent BSc computer sciences 3 16.7

MSc computer sciences 13 72.2

MPhil 2 11.1

Total 18 100.0

Figure 3-7: Academic qualification of interviewee's

We did not select all the PMs who were eligible for the interviews because of

the very complex and specialized nature of the content that we were inquiring about.

Therefore, only those PMs having minimum of 5 years of project management

experience were selected. The professional experience of the participants (in years) is

shown below (Table 3-7, Figure 3-8).

Table 3-7: Interviewees experience as PMs (in years)

Experience Frequency Percent 5-10 2 11.1

10-15 9 50.0

15-20 4 22.2

20+ 3 16.7

Total 18 100.0

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Figure 3-8: Participants’ experience as PMs (in years)

It can be noticed that the participants had a fairly high level of experience: as

88% had at least 10 years of project management experience.

Table 3-8: Organization size (no. of employees)

Organization size Frequency Percent Cumulative

percent Small (up to 100) 16 26.7 26.7

Medium (101 to 300) 27 45.0 71.7

Large (300+) 17 28.3 100.0

Total 60 100.0

Figure 3-9: Organization size

Almost 73 percent respondents were working in medium to large size

organizations (Table 3-8, Figure 3-9).

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After analyzing demographic information of the research participants in the

sample, the next step was to analyze the qualitative data. At this step, the ‘coding by

variables’ and ‘Coding Retrieval‘ analyses provided us two major results: (1) Themes

of the best practices (Figure 3-10) and, (2) distinct individual best practices (Table 3-9).

The results of ‘coding by variables’ analysis provided the major themes/constellations

of the best practices.

Figure 3-10: Themes of the best practices for managing knowledge-of-project

Each of these themes represents the category of best practices. There

are various best practices in each of these categories. For example, the MIS web

portal theme is a thematic name assigned to refer to the various best practices which

refer to the adoption of the MIS web portal for managing knowledge-of-project.

Thus, the MIS web portal is referred to, 88 times by the participants in different

ways. Under MIS web portal there are many best practices e.g. Email lists, discussion

forums, discussion groups, using wiki, e-diaries, maintenance of central repository

etc. Similarly, other themes contain various best practices in them. ‘Coding

Retrieval‘ analysis provided individual distinct best practices (Table 3-9). Table 3-9

also illustrates the number of times each theme and its respective best practices have

been referred to, and the knowledge process category of each best practice.

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Table 3-9: Best practices for managing knowledge-of-project

KC = Knowledge capture, KO = Knowledge Organization, KS = Knowledge share

No. Description of the Best Practice(s) Freq.

KM Process

Area

KC KO KS

Business Analyst Availability 11

1 BP organizations should hire and retain business

analysts

11

Documentation 32

2 BP develop documentation for minutes of

meetings, templates, project plan etc

10

3 BP take notes during meetings about decisions

made and how they were made i.e. figure out

mind maps of decision makers

9

4 BP organize documented policies and value books

by HR department

2

5 BP maintain policy books and lists of high

achievers with code of conduct

1

6 BP maintain code of conduct and service rule book 1

7 BP document horizontal and vertical

communication channels

1

8 BP develop documents both in electronic and hard

form

6

Industry Knowledge + PMBOK 6

9 BP use project management industry knowledge in

conjunction with PMBOK guidelines

6

MIS Web Portal 88

10 BP Establish/maintain central repository and

intranet portal storing documents with restricted

access functionalities

16

11 BP use common repository of milestones 6

12 BP establishment of e-diaries on department level 8

13 BP develop e-groups according to type of project 12

14 BP use web portal having facilities such as forums,

articles, documents, email lists and wiki

11

15 BP keep documents e.g. project plans, RS, FS in

relevant standard templates on web portal

17

16 BP establishment of restricted access peer behavior

ratings database

3

17 BP organize documents through MIS web portal

using groups & forums

12

18 BP place the code of conduct in central repository 3

Meetings and Discussions 21

19 BP facilitate regular informal meetings to share and

present design & solutions

6

20 BP facilitate formal group discussions on structure,

design and requirements gathering processes

9

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KM Process

Area

KC KO KS

21 BP arrangement of orientation meetings to update

all human resources on domain/process

knowledge

2

22 BP use multimedia technologies such as video

recordings for all trainings

4

Peer Communication 7

23 BP facilitate coordination among different teams 3

24 BP organize orientation sessions for new

employees to introduce them with

organizational culture

2

25 BP promote peer communication through formal

and informal meetings

2

Standardization of Documents 35

26 BP develop standardized employee handbooks for

networking with other employees

3

27 BP develop standardized employee communication

document

4

28 BP maintenance of standardized documents to

develop lists of team structures, schedule of

tasks, roles and responsibilities

28

Templates 7

29 BP develop, use and share best practices

documentation templates

5

30 BP availability of standardized HR documentation

templates

1

31 BP maintain and use standardized templates for

documentation

1

These themes are referred to, most frequently by the interviewees. Hence, the

participants considered the best practices arranged in these themes of significant

importance in managing knowledge-of-projects. At the later stage of quantitative data

analysis, these themes are operationalized and used as variables using the best practices

in them. Development and testing of the hypotheses is discussed in the second phase

of the study (chapter 4).

3.5 Discussion of the Results

In this section, we will discuss results of findings of the first phase of this study

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against the current literature that has been described in chapter two. This phase mainly

focused on identifying and exploring the best practices for managing knowledge-of-

projects. The best practices are collected through open-ended interviews with PMs of

IT organizations in Pakistan. The qualitative approach for gathering different

perspectives about the ways organizations can manage their knowledge-of-projects

enabled us to find patterns in the data and individual factors (i.e. best practices). Also,

collecting the responses from a variety of private and public sector IT organizations,

provides diversity in results which leads to stronger research validity. This study also

contributes to new evidence about practices needed for managing knowledge-of-

projects of IT organizations in the context of Pakistan. Moreover, the results of this

study can be a motivation for many organizations which plan to adopt KM practices

for gaining competitive advantage in the near future.

The response rate (18 participants) for this phase of study is considered high

enough as mentioned by similar studies (King & Zeithaml, 2003; Reich, 2007). The

majority of the participants were highly educated, had more than ten years of project

management experience and were working in medium and large IT organizations.

Although the participants were working under different designations, overall, they were

acting as project managers.

The results of the qualitative analyses are provided as patterns, tables,

frequencies or percentages because there are no hypotheses and hence, no correlational

statistical tests are conducted. As mentioned earlier, we found eight themes and several

best practices. The following sections discuss the results of the study.

3.5.1 Availability of Business Analyst

The 'Availability of business' analyst theme was referred to 11 times by

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participants of the interviews. Participants explicitly mentioned that organizations

should hire and retain specialized business analysts because only business analysts can

solicit a clear scope for projects. The business analyst theme contained only one BP

(Table 3-10).

Table 3-10: Best practice(s) for 'availability of business analyst' theme


11

1 BP Organizations should hire and retain business

analysts 11

Business analysts are getting more and more attention among the organizations,

especially IT organizations. Business analysts are considered particularly important at

the early stages of projects when requirements gathering activities are in progress. Lack

of ability to collect the right requirements is one of the biggest reasons behind failed

projects (see chapter 2, Figure 1-4). Recognizing the importance of business analysts

for projects, the International Institute of Business Analysts (IIBA) has recently been

established. IIBA offers certifications for the individuals pursuing, or who wish to

purue, the careers as business analysts. Business analysts are as important as project

managers. Without them, the chances of project failure are expected to be quite higher.

3.5.2 MIS Web Portal

The 'MIS web portal' theme was referred to 88 times by the participants of

interviews. An eighty eight time referral does not mean that participants explicitly

mentioned that organizations should use the MIS web portal to manage knowledge-of-

project. Instead, they narrated and told a variety of ways that MIS web portal can be

used to capture, organize and share organizational knowledge. All of those ways were

assigned the code ‘MIS web portal’ during the coding process. In actuality, the MIS

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web portal theme contained the following 9 best practices (Table 3-11).

Table 3-11: Best practice(s) for 'MIS web portal' theme


88

1 BP Establish/maintain central repository and

intranet portal storing documents with restricted

access functionalities

16

2 BP use common repository of milestones 6

3 BP establishment of e-diaries on department level 8

4 BP develop e-groups according to type of project 12

5 BP use web portal having facilities such as forums,

articles, documents, email lists and wiki

11

6 BP keep documents e.g. project plans, RS, FS in

relevant standard templates on web portal

17

7 BP establishment of restricted access peer behavior

ratings database

3

8 BP organize documents through MIS web portal

using groups & forums

12

9 BP place the code of conduct in central repository 3

MIS web portals, or project management information systems (PMIS), are

getting more and more attention by organizations for managing their projects. The

reasons behind this are the capabilities of such systems to present up-to-date

information to the project staff and senior management. However, such systems should

be used only to supplement the efforts and be not considered as the sole reason behind

the success or failure of projects. The Project CHAOS report does not even mention

such systems in its top ten reasons. A number of researchers also mention that such

systems are supplementary (Alavi & Tiwana, 2003; Brún, 2005; Maier, et al., 2005).

3.5.3 Standardization of Documents

The ‘standardization of documents’ theme was referred to 35 times by the

participants of interviews. A 35 time referral does not indicate that participants

explicitly mentioned that organizations should use ‘standardization of documents’ to

manage knowledge-of-project, rather they narrated a variety of ways that standardizing

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the documents can be used to capture, organize and share organizational knowledge.

All of those ways were assigned the code ‘standardization of documents’ during the

coding process. In actuality, the ‘standardization of documents’ theme contained the

following best practices (Table 3-12).

Table 3-12: Best practice(s) for 'standardization of documents' theme


35

1 BP develop standardized employee handbooks for


3

2 BP develop standardized employee

communication document

4

3 BP maintenance of standardized documents to

develop lists of team structures, schedule of

tasks, roles & responsibilities

28

Standardization of documents to manage the processes is an old concept. It was

firstly implemented for quality management standards and then, moved on to other

disciplines. The ISO and CMMI standards are all about developing and managing

documentation of the processes. The same concept is found equally applicable for

managing knowledge of projects. However, the interviewees mentioned that such

docuements should be developed and then, their format should also be standardized.

Such standardization can help them in future projects.

3.5.4 Documentation

The ‘documentation’ theme was referred to 32 times by the participants of

interviews. The thirty two times referral does not mean that participants explicitly

mention that organizations should use ‘documentation’ practices to manage

knowledge-of-project, rather, they narrated a variety of ways that documentation can

be used to capture, organize and share organizational knowledge. All of those ways

were assigned the code ‘documentation’ during coding process. In actuality, the

‘documentation’ theme contained 8 best practices (Table 3-83).

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Table 3-83: Best practice(s) for 'documentation' theme

No

. Description of the Best Practice(s)

Freq.

32

1 BP develop documentation for minutes of

meetings, templates, project plan etc

10

2 BP take notes during meetings about decisions

made and how they were made i.e. figure out


9

3 BP organize documented policies and value

books by HR department

2

4 BP organizational values books developed by HR 2

5 BP maintain policy books and lists of high


1

6 BP maintain code of conduct & service rule book 1

7 BP document horizontal & vertical


1

8 BP develop documents both in electronic & hard

form

6

The documentation here differs slightly from the meaning of documentation in

ISO or CMMI standards. It means that organizations should develop documents for

various processes. However, the interviewees mentioned that such docuements should

be developed and then, their format should also be standardized. Such standardization

can help them in future projects.

3.5.5 Meetings and Discussions

The ‘meetings and discussions’ theme was referred to 21 times by the

participants of interviews. A twenty one times referral does not mean that participants

explicitly mentioned that organizations should use ‘meetings and discussions’ to

manage knowledge-of-project. Instead, they narrated and told a variety of ways that

meetings and discussions can help to capture, organize and share organizational

knowledge. All of those ways were assigned the code ‘meetings and discussions’

during coding process. In actuality, the ‘meetings and discussions’ theme contained

four best practices (Table 3-94).

Table 3-94: Best practice(s) for 'meetings & discussions' theme

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21

1 BP facilitate regular informal meetings to share &

present design & solutions

6

2 BP facilitate formal group discussions on structure,

design & requirements gathering processes

9

3 BP arrangement of orientation meetings to update all

human resources on domain/process knowledge

2

4 BP use multimedia technologies such as video


4

Meetings and discussions are always considered important for sharing and

discussing project matters. However, these are found particularly important for eliciting

and sharing knowledge of projects. Organizations can arrange meetings specifically to

elicit the project's knowledge from the staff to make it available for future use and other

employees. In the context of KM, benefits of meetings and discussions can be further

enhanced by turning these in to communities of practice (CoPs). CoPs are a special way

of sharing project knowledge and have been proved extremely beneficial for sharing

project knowledge.

3.5.6 Industry Knowledge and PMBOK

The ‘industry knowledge + PMBOK’ theme was referred to 6 times by

participants of the interviews. Participants explicitly mentioned that the use of PMBOK

practices in conjunction with industrial knowledge can be a source of knowledge which

can, in turn, be organized and shared. This theme contained only one BP (Table 3-105).

Table 3-105: Best practice(s) for 'industry knowledge + PMBOK ' theme


6

1 BP use project management industry knowledge in

conjunction with PMBOK guidelines

6

Project management institute's PMBOK has become a manifesto for managing

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project around the world. However, its contents are quite generic. Therefore, experts

need to use this standard in conjunction with their own industry specific knowledge.

3.5.7 Peer Communication

The ‘peer communication’ theme was referred to 7 times by the participants of

interviews. Seven times referral does not mean that participants explicitly mentioned

that organizations should use ‘peer communication’ to manage knowledge-of-project.

Instead, they narrated and stated a variety of ways in which peer communication can

help to capture, organize and share organizational knowledge. All of those ways were

assigned the code ‘peer communication’ during coding process. In actuality, the ‘peer

communication’ theme contained the following best practices (Table 3-116).

Table 3-116: Best practice(s) for 'peer communication' theme


7

1 BP facilitate coordination among different teams 3

2 BP organize orientation sessions for new employees to

introduce them with organizational culture

2

3 BP promote peer communication through formal &

informal meetings

2

Peer communication is an essential part of almost all of the organizational

activities. Employees need to communicate with each other many times a day. Such

communication can be used as a source of knowledge. The existence of efficient

organizational policies and knowledge management systems can be used to capture

knowledge from such activities. Stenmark (1999) has presented a splendid example of

extracting knowledge from peer communication and has shown the ways KM systems

can be used to extract knowledge from such communication.

3.5.8 Templates

The ‘templates’ theme was referred to 7 times by the participants of interviews.

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A seven times referral does not mean that participants explicitly mentioned that

organizations should use ‘templates’ to manage knowledge-of-project. Instead, they

narrated and told a variety of ways in which templates can help to capture, organize

and share organizational knowledge. All of those ways were assigned the code

‘templates’ during coding process. In actuality, the ‘templates’ theme contained the

following best practices (Table 3-127).

Table 3-127: Best practice(s) for 'templates' theme


7

1 BP develop, use and share best practices documentation

templates

5

2 BP availability of standardized HR documentation

templates

1

3 BP maintain & use standardized templates for

documentation

1

Developing and sharing best practices templates is an established technique of

standardizing processes in the organizations. This technique is also found useful for

managing and sharing knowledge. Organizations can share such templates using their

information systems to motivate the employees to suggest changes in them based on

their experiences gathered during projects. In this way, knowledge of employees can

be observed and shared with other employees.

3.6 Objective(s) of Phase One

Section 3.6.1 to 3.6.5 described all the best practices found for capturing,

organizing and sharing the organizational project management knowledge.

Additionally, the tables also illustrated the total number of referrals to the themes and

the number of times each individual BP was referred to. The results are described using

only tables due to the exploratory nature of work in the first phase. The objective for

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the first phase was as follows:

To identify the best practices for managing knowledge-of-project in the IT

organizations of Pakistan

We managed to fulfill this objective by conducting a rigorous qualitative research and

found various best practices which organizations can adopt to manage their knowledge-

of-project.

3.7 Theoretical and Practical Outcomes of First Phase

The theoretical and practical outcomes of the study are multifaceted. First of

all, it presents a number of best practices suitable for managing organizational project

management knowledge in Pakistani IT organizations supported by the rigorous design

implemented by designing open-ended interview protocol, finding sample

organizations and participants and qualitative analyses. The objective of conducting

qualitative open-ended interviews was to find as many as diverse responses possible

from experts of the discipline. The outcome is the development of a conceptual

framework. This conceptual framework is validated further in the second phase of the

study (chapter 4). This phase of the study seeks to contribute to the understanding and

development of conceptualizations of KM best practices for Pakistani IT organizations.

It will help the organizations increase their probability of successful projects.

Furthermore, managing the organizational knowledge will provide the organizations a

sustainable competitive advantage.

3.8 Answers to the Research Question

Q.1. What are the best practices for managing knowledge-of-project in the context of

IT project management in Pakistani IT organizations?

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The challenge of this phase was to present, for the first time in the history of

Pakistan, a set of best practices for managing organizational project management

knowledge that IT organizations could adopt and hence, leverage the power of their

‘hidden’ knowledge. This intriguing question is answered through a qualitative

assessment of the opinions of experts of the discipline. As a result, several best

practices are discovered for capturing, organizing and sharing the organizational

project management knowledge. The experts regarded some of the best practices to be

more important than others. Hence, they referred to these practices more frequently.

3.9 Limitations of First Phase

This phase of the study follows a qualitative research paradigm and design to

explore the opinions of the experts. The qualitative nature of the phase poses some

limitations to this research such as:

1. The open-ended questions were asked to the participants of the interviews for

the data collection. Open-ended questions have alternating outcomes. On the

one hand they can provide a breadth of open and diverse responses. Yet on the

other hand, the responses can be highly biased and dependent upon the

respondents’ background and personal experiences.

2. The researcher was limited by issues of lack of resources and accessibility to

conduct interviews with the PMs of each IT organization in Pakistan. The

literature review and previous studies indicated there should be a reasonable

sample size to fulfill the objectives. Therefore, the two largest cities of the

country were selected to short list the IT organizations based there.

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3. Due to the busy schedules of interviewees', it was not possible to conduct longer

interviews. Still, the interviews lasted from 45 to 120 minutes which is a

reasonable time for such investigations as illustrated by several studies.

4. The investigation was carried out to find the KM best practices for the IT

organizations of Pakistan only. It can be replicated for any other industry

utilizing the same guidelines as mentioned by the study. This will provide

further depth and refinement to the results.

5. There could be a different coding scheme or wording while assigning codes to

the data before performing qualitative analysis in QDA Miner. Such problems

are inherent in such studies. It does not matter even if there is a little difference

in wording of coding because the wording of the responses is still very similar.

Also the codes are used only to analyze the data and they do not affect the

responses by any means.

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Chapter 4 Phase Two - Research Design, Results &

Discussion

Phase two of the study follows an empirical positivistic paradigm – a paradigm

in which hypotheses are developed and tested by employing statistical methods. The

positivistic paradigm is also known as quantitative research design. It focuses on

exacting measurements for understanding attitudes, and opinions while drawing

correlations and conclusions about how many, who, and when (Cooper & Schindler,

2006). Schindler suggest questionnaire is the most common instrument employed in

quantitative studies and for conducting surveys. Quantitative research is distinguished

by the way the researcher selects the phenomena to study, presentation of questions

designed to provide results and, that could be analyzed statistically as well to offer

precise and objective numerical explanations (Creswell, 2005a).

In the second phase, we developed hypotheses based on results of first phase of

the study. Phase two of the study addresses objective two, three, and four and the

corresponding research questions are two, three and four. This chapter will also provide

clear description of methodology, quantitative methods used and research design. It

will also provide discussion of the expected sample size, characteristics of the samples,

describe the method used for data collection and how that data is analyzed to answer

the research questions. It also discusses detailed account of the way pilot test was

conducted and how the problems faced during it were eliminated before final data

collection.

The chapter comprises three major parts. The first part addresses the process of

primary data collection by means of questionnaires administered to IT PMs and PM

consultants working in Pakistan, UAE, Canada and USA. The rationale behind

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selecting these countries was that that we wanted to cross-validate the results obtained

in Pakistan with results obtained from selected Asian, Middle East and North American

countries. Since this study is conducted in Pakistan, the validation of the results in

Pakistan was required. Countries in the UAE region are technically more advanced than

other Arab countries. Thus, the UAE region was also selected. Then, USA and Canada

are the most advanced countries in North America region. Therefore, these two

countries were selected as being representative of that region.

Section 4.6 describes the results of statistical analyses performed on the data.

Finally, the section 4.7 contains discussion of the results.

4.1 Research Questions

Chapter 2 discussed that contemporary project management maturity models

do not have the capability to assess the extent to which any organization is following

practices to manage its knowledge-of-projects - while several research studies have

linked managing organizational knowledge to competitiveness16. Therefore, this study

seeks to: (1) bridge this gap by suggesting KM best practices which can be incorporated

in one of the contemporary project management maturity models, OPM3®, (2)

contribute to the understanding of which best practices organizations should follow to

manage their knowledge-of-projects and, (3) how adoption of those best practices

would affect project management capability of the organizations in Pakistan and in

other countries. Specifically, this research deals with suggesting KM best practices for

incorporation in OPM3®.

Phase two of the study addresses three objectives and three research questions

16 Refer to chapter 2 for references.

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(Tables 4-1,4-2).

Table 4-1: Research objective(s) for phase two

No. Objective (s)

1 To test the extent to which adoption of the identified best practices can

affect project management capability of IT organizations in Pakistan

2 To test the extent to which adoption of the identified best practices can

affect project management capability of IT organizations in other

countries

3 To suggest which KM best practices can be considered for incorporation

in OPM3® making it capable of assessing the knowledge-of-project

management capability of the organizations

Table 4-2: Research question(s) for phase two

No. Research Question

Q.1. How the identified best practices for managing knowledge-of-project will

affect project management capability of the IT organizations in Pakistan

Q.2. Are the identified best practices for managing knowledge-of-project

applicable to the IT organizations in other countries as well?

Q.3. Are existing best practices in OPM3® pertaining to knowledge

management sufficient, if not, what best practices can be added to make

OPM3® more usable?

These questions are addressed by conducting a quantitative survey among IT

organizations in Pakistan, UAE, USA, and Canada. We collected and analyzed the data

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from these four countries to assure that the identified best practices in Pakistan are

globally valid and applicable. The following section provides details of the specific

hypotheses that flow from the assessment of results of the phase one.

4.2 Hypotheses

This phase of the research is based on two major hypotheses that examine the

relationships between adoption of knowledge-of-project management best practices

and improvement of project management capability of organizations in Pakistan and in

other countries. Each of the two major hypotheses is sub-divided into three sub-

hypotheses The hypotheses are derived from the conceptual framework (Figure4-1).

Figure 4-1: Conceptual framework

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The hypotheses are as follows:

H1: Adoption of the best practices for managing knowledge-of-project will improve

‘project management’ capability of IT organizations in Pakistan

H1a: Adoption of the best practices for managing knowledge-of-project will

improve ‘schedule estimation’ capability of IT organizations in Pakistan

H1b: Adoption of the best practices for managing knowledge-of-project will

improve ‘clear scope determination’ capability of IT organizations in Pakistan

H1c: Adoption of the best practices for managing knowledge-of-project will

improve ‘budget determination’ capability of IT organizations in Pakistan


‘project management’ capability of IT organizations in other countries as well


improve ‘schedule estimation’ capability of IT organizations in other countries

as well


improve ‘clear scope determination’ capability of IT organizations in other

countries as well


improve ‘budget determination’ capability of IT organizations in other countries

as well


‘project management’ capability of IT cumulative organizations of this study

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improve ‘schedule estimation’ capability of IT cumulative organizations of this

study


improve ‘clear scope determination’ capability of cumulative IT organizations

of this study


improve ‘budget determination’ capability of IT cumulative organizations of

this study

Hypotheses are represented graphically below (Figure 4-2).

Figure 4-2: Graphical representation of hypotheses

We have identified several themes and best practices for managing knowledge-

of-projects in the first phase of this study. These themes are used as predictors

(independent variables) while organizational capabilities to determine scope, schedule

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and budget are treated as outcome variables or dependent variables. We have used a

general term, ‘best practices for managing knowledge-of-projects’ in the hypotheses.

Predictor and outcome variables are narrated below (Table 4-3).

Table 4-3: Predictor and outcome variables

No. Predictor (s)

1 Availability of business analyst

2 Documentation

3 Industry knowledge + PMBOK

4 MIS web portal

5 Meetings and discussions

6 Peer communication

7 Standardization of documents

8 Templates

Outcome Variables

1 Scope determination capability Project

management

capability

2 Schedule estimation capability

3 Budget determination capability

4.3 Development of Questionnaire

The survey and questionnaire methods are used to collect the data in this phase

in order to measure the variables and test the hypotheses. Questionnaires technique is

useful whenever the researchers want to gather quantitative data dealing with

measurable numbers that support the defined variables and hypotheses. Also,

questionnaire instrument assures a reasonable reliability and convergent validity in the

content gathered. We wanted to collect the data from a range of samples dispersed

across the countries. In the first phase, we collected, analyzed and discovered best

practices from the data collected from IT project managers only but at the time of

coding special attention was given to the fact that codes should be generic enough so

that best practices could be applicable to the other industries as well. For that reason, a

web-based questionnaire form was developed. There are many web-based

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questionnaire hosting websites available on the internet. We chose

www.Surveymethods.com website to develop and host our survey due to its affordable

cost and a variety of options available to develop the questionnaires. Web-based

questionnaire provided many benefits over paper-based questionnaires. The benefits

included:

- All the questions were mandatory to respond through a special option

provided by the questionnaire hosting website so there was no missing

data

- The website generated a distinct unified resource locator (URL) for each

respondent which could be sent to the participant through email

- The participants can complete the questionnaire at a time and place

convenient to them

- Downloading of questionnaire responses for use in MS Excel or SPSS

was very straight forward as the website provided the options to

download the response data in SPSS compatible format. So, there was

no need to manually code the data in SPSS. This helped to avoid any

erroneous data in SPSS

- The website kept track of the number of completed and partially

completed questionnaires. It made easy to distinguish and separate

completed and partially completed questionnaires

All the questions in the questionnaire were closed-ended and mandatory so

there was no missing data. There were no special circumstances to fill this

questionnaire. Questionnaire was sent to the target participants through their emails so

that they could fill it in their comfortable time. Each questionnaire was accompanied

by a covering letter which asked for some basic background information of the

http://www.surveymethods.com/

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participant, his/her industry and provided a brief description of purpose of the study.

The survey asked less on demographic data of the participants and more for opinions

on the application of KM best practices on the scope, cost and budget estimation

capability of the organizations. It used a 5-point likert scale to indicate their agreement

or disagreement with each item. A rating of 1 indicated that the respondent “strongly

disagree” with the item and a rating of 5 indicated that he or she “strongly agree”. The

center point of the rating scale was labeled “neutral”. There were 130 items in the

questionnaire out of which 6 were for demographic and background information of the

respondents, 31 items were to measure predictor variables and 31 items for each of the

three outcome variables (scope, schedule, budget), totaling to 93 items. The predictor

and outcome variables are measured on continuous scale.

4.4 Selection of Samples

The data collection process is an integral part of this phase of the study; to test

the correlation between adoption of KM best practices and project management

capability of the organizations. After development of the questionnaire, the next step

was to identify the target population, sample size and samples. The questionnaire could

not be distributed randomly among the participants. We needed to make sure that

responses to the questionnaire conform to the research model, hypotheses and

variables. We used multi-stage sampling techniques to select this sample. This

technique is useful and suggests that when the data to be collected is derived from

respondents who are dispersed geographically, it is difficult to obtain access to them.

The nature of the content is difficult to assimilate or is very specialized (Graeff, 1980;

King & Zeithaml, 2003; Reich, 2007). We obtained a listing of IT organizations

working in Pakistan from the PSEB. After applying organizations size and area of

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business criteria, we were left with 150 suitable organizations. The selected

respondents were working in established IT organizations in Pakistan. These

organizations had similarities regarding their competitive environment, value chains,

and terminology. These criteria suggest greater consistency in the industrial context

across competing organizations. This approach increased the likelihood of identifying

a set of respondents (project managers) in IT organizations who could evaluate their

perceptions of importance of KM for organizational project management capability.

Participants from other countries were selected utilizing a similar approach except that

we relied more on snow ball sampling for that due to the very limited access and time

problems. Such approach has been reported in many similar studies (Mehra, 1996;

Porac, Thomas, & Baden-Fuller, 1989; Porac, Thomas, Wilson, Paton, & Kanfer, 1995;

Reich, 2007). Each selected participant needed to meet the following requirements. The

participants needed to be working as a project manager or, in a similar role in:

Either Pakistan, USA, Canada or UAE, in,

An IT software development organization or,

These requirements have been extracted from the research objectives, research

experience and previous studies. Such an approach has been adopted and reported in

other relevant studies too (King & Zeithaml, 2003; Reich, 2007). Approximately 500

PMs fulfilling these requirements were selected from various sources such as: project

managers interviewed in the first phase of the study, PSEB listings, online project

management communities and PMI Islamabad chapter members. After determining the

required sample rates and samples, we sent the questionnaires to the respondents

through email. Sending the questionnaire through email assured that respondents would

have received it and there were no chances that respondents did not receive it. Some

respondents responded immediately while others took some time to respond, those who

130

did not respond were sent the questionnaire again through email.

A total of 132 responses were received (26.4% response rate), out of which 23

incomplete responses (having 50% or more missing data) were ignored, gaining a

response rate of 20% (109), which meets the required sample size of absolute minimum

of five times the number of predictors (Andersen & Jessen, 2003; Brace, Kemp, &

Snelgar, 2003; Miles & Shevlin, 2001). Therefore, a sample size of 109 is considered

enough to predict the model. The twenty three incomplete responses (having more than

50% unanswered questions) were discarded, Table (Table 4-4) illustrates these facts.

Table 4-4: Questionnaire response facts

Received Incomplete Valid

Responses 132 23 109

Percentage 26.4% 17.4% 82.6 %

The 82.6% usable response rate is considered as a positive high response rate

therefore, collected data was deemed to be sufficient to start the analysis. All the

following statistics and analysis are based on the number of the valid responses, i.e.

109 responses.

The following assumptions were made regarding the participants' responses.

Assumptions:

Participants paid enough attention to the survey and gave good efforts

to do it

Participants well understood all the questions and there was no

misunderstanding

Participants answered all the questions in the questionnaire honestly

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The sample size (109 samples) is sufficient to start the statistical

analysis

The following conditions were applied for eliminations of selected questionnaires:

If answered questions are less than 50% of the total questions of the

questionnaire

If two or more questionnaires have 100% identical answers, only one

questionnaire will be taken in account

The high response rate helps to support the analysis results, but it is not

necessary that the high response is indicative of better data and results rather, it just

legitimizes the results of the research. When a research is based on the responses from

a higher percentage of its target population, the findings can be treated as more

accurate.

4.5 Sorting, Organizing and Coding the Data for SPSS

Most of the statistical analyses in Statistical Package for the Social Sciences

(SPSS) are computed using numerical data. This necessitates that each item of each

response must be assigned unique codes. This step was facilitated by questionnaire

hosting website itself. The website provided an option which enabled us to download

the responses data directly in the SPSS compatible format i.e. the website assigned

unique codes to each item of each response automatically. Hence, sorting, organizing

and coding of the data did not require any special efforts. All the questions in the

questionnaire were also mandatory to answer so there was no missing data as well. This

feature helped us to move directly to the statistical analyzes and calculation of other

descriptive statistics.

http://knowledge-base.supersurvey.com/glossary.htm#target_population

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4.6 Quantitative Data Analysis

The SPSS version 17 software tool is used to process and analyze the data in

this phase. SPSS is one of the most widely used, powerful and reliable tools available

with graphical interface for performing statistical analyses. One feature of this tool is

that it caters for missing values automatically by excluding the value from the analysis;

although there were no missing values in our data. Moreover, it provides a lot of

functions for managing, analyzing and presenting the data through a lot of statistical

analyses, descriptive statistics and graphical presentation of the results (Field, 2009).

First of all, reliability of scales is calculated by calculating cronbach alpha to

assure the construct validity. In order to analyze the perceived value of each variable,

the occurrence of a perceived value in each questionnaire was counted. Frequency

analyses and other descriptive statistics are calculated for all the variables for analyzing

meaningful insights in the data. Frequency analysis is an effective mechanism for

comparing and contrasting within, or across the variables. Finally, multiple regression

is conducted on the data to look for any correlations among predictors and outcome

variables. The simplified process of quantitative data analysis followed in this study is

shown below (Figure 4-3).

Figure 4-3: Data analysis process

It states that: (1) raw data is collected, (2) filtered to obtain valid data for

analysis, (3) valid data is put into a database in SPSS for analysis, (4) required analyses

are performed on this data and finally, (5) output and results are analyzed providing

Raw Quantitative Data

Identifying Valid

Responses

Valid Respons

es Database in SPSS

Computing

Analyses in

SPSS

Analysing

Results

Presenting

Results

133

detail explanations and meanings given to values of the results.

4.6.1 Demographics Data

The demographic information for the participants of this phase is described in

the following tables and paragraphs. The information is provided both in the form of

tables (Tables 4-5 to 4-9) and figures (Figures 4-4 to 4-9) narrating important aspects

of the collected data.

We received a total of 109 completed responses. The breakup of the 109

responses was such that 66% (72) responses were from Pakistan and 34% (37) were

from the other countries. The respondents represent small, medium and large

organizations based in different countries from the IT industries. The geographic

distribution of the respondents was as follows (Table 4-5, Figure 4-4).

Table 4-5: Geographic distribution of respondents

Location Freq. Percent Cum.

Percent

Pakistan 72 66 66

UAE 13 12 78

USA 13 12 90

Canada 11 10 100

Total 109 100

Figure 4-4: Geographic distribution of respondents

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We collected responses from different countries to validate if the best practices

are applicable in other environments and countries as well. This topic will be discussed

in detail in the sections to follow.

Table 4-6: Professional experience of respondents (in years)

Experience Freq. Percent Cum.

Percent

0-5 21 19 19

6-10 39 36 55

11-15 26 24 79

16-20 17 16 94

21-25 3 3 97

25+ 3 3 100

Total 109 100

Most of the respondents (81%) had at least 6 to 10 years of professional

experience (Table 4-6, Figure 4-5). Only 19% of the respondents had less than 6 years

of professional experience. It is assumed that more the respondents are experienced,

the better they would be able to answer the questionnaires. A sample of more

experienced respondents strengthens the quality of the responses.

Figure 4-5: Professional experience of respondents

135

Figure 4-6: Mean experience of respondents (in years)

The average professional experience of the project managers is also illustrated

in figure (Figure 4-6). It can be noticed that PMs in almost all the categories had at least

10 years of experience, while senior project managers had approximately 15 years of

experience on average.

Table 4-7: Participants’ designations (by percentage)

Designation Freq. Percent Cum.

Percent

Team Lead 12 11 11

Project Manager 32 29 40

Senior Project Manager 27 25 65

Engineering Manager 9 8 73

Engineering Project Manager 5 5 78

Other 24 22 100

Total 109 100

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Figure 4-7: Distribution of respondents (by designations)

Almost 11% respondents were working as a team lead, 67% in a project

management role, while majority of the participants (78%) were working in an IT

management role (Table 4-7, Figure 4-7). The designation distribution of the

respondents shows that almost all of the respondents were working in any of the senior

management positions.

Table 4-8: Organization size (no. of employees)

No. of

Employees Freq. Percent

Cum.

Percent

Up to 100 21 19 19

101-200 17 16 35

201-300 9 8 43

301-400 4 4 47

401-500 8 7 54

500+ 50 46 100

Total 109 100

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Figure 4-8: Organization size (no. of employees)

Most of the respondents (46%) were working in the large organizations, 35%

were working in medium sized organizations and only 19% were working in the small

(less than 100 employees) organizations (Table 4-8, Figure 4-8). In Pakistan, IT

organizations having more than 500 employees are categorized as large organizations

because almost 80% of IT organizations have less than 100 employees (PSEB, 2009).

Hence, most of the PMs were working in the large organizations.

Each of the above tables and figures provide good indications about the

participants’ total years of professional experience as project manager, organization

size, designations and country of residence. It can be noticed that most of the

participants had at least 6 to 10 years of experience and were working in a senior level

management position at large organizations. These characteristics make the samples

more appropriate for the study.

138

4.6.2 Reliability and Validity

4.6.2.1 Reliability

Reliability is the capability of the research findings to be reproduced in a

different environment or situation. A very common definition of reliability is, “it is the

consistency of measurement” (Babbie, 2003; McMillan & Schumacher, 2001). In other

words, it is the repeatability of the measurement. A measure is assumed reliable if it

produced the same score when a study is repeated. The reliability is usually measured

with Cronbach's alpha (α). Its value lies between zero and one. Cronbach's alpha (a.k.a.,

"the reliability coefficient"), is the most common estimate to determine the consistency

of a survey.

The aim of the reliability tests is to provide indications to the researcher whether

items of the instrument are relevant for desired measurements (Neuman, 2003). In this

study, reliability is estimated using internal consistency technique. It is calculated using

the SPSS v.17 tool. The recommended value for acceptable reliability is 0.70 or higher.

But the range of 0.6 to 0.8 indicates acceptable reliability and 0.95 or higher indicates

very high reliabilities (Field, 2009). Whenever all the items in a questionnaire are aimed

to measure a single construct, that questionnaire is called a scale. However, when the

items of a questionnaire are measuring different constructs, subscales exist, and

Cronbach alpha value for each of the subscales should be calculated separately. Since

this study involves eight predictor and three outcome variables so there were eleven

subscales in the questionnaire to measure each of the eleven constructs. The Cronbach

alpha values for each of the eleven constructs are shown in the table (Table 4-9).

Table 4-9: Internal consistency results

Construct Cronbach’s Alpha

(α)

Availability of business analyst 0.727

http://en.wikipedia.org/wiki/Cronbach%27s_alpha

139

Documentation 0.746

Industry knowledge + PMBOK 0.739

MIS web portal 0.761

Meetings and discussions 0.745

Peer communication 0.754

Standardization of documents 0.749

Templates 0.715

Scope determination capability 0.763

Schedule estimation capability 0.731

Budget determination capability 0.758

4.6.2.2 Validity

Validity is usually assessed along reliability. Validity of an instrument refers to

the degree to which an instrument actually measures what it sets out to measure. In

other words, it is the point to which a measurement gives consistent results. Some

researchers (T. D. Cook & Campbell, 1979; Creswell, 2005b) define it as the "best

available approximation to the truth or falsity of a given inference, proposition or

conclusion". Also, validity is considered the strength of the research conclusions and

somehow ensures that there are no alternative explanations or errors within the

research. Hence, validity can be considered an evidence of the correctness of the study

and that there exists a causal relationship between the predictors and outcomes. Issue

of maintaining validity is a very important consideration in quantitative studies

(Richards, 1999).

In this study, validity is estimated by calculating correlations. We have

employed multiple regression technique to test if there exist any correlations among

the predictors and outcome variables.

4.6.3 Correlation Test

Correlation is one of the most useful and common statistics. Its purpose is to

measure how associated or related two variables are and in which way those variables

140

are related. Correlations tests tell that two variables are related but they do not provide

information about the degree to which the variables are related. When we are interested

in finding the degree of relationship between the variables, we need to perform

regression tests. Here we would like to clarify some difference between the two

terminologies often used in the correlational research. The terms are: “Independent

variables” and “dependent variables”. In correlational research, researchers have very

less control on the independent variables; they just observe the variables or take opinion

about them. Therefore, these terms are actually more appropriate in the experimental

research where researchers have much more control on the variables. Thus, we would

be referring ‘independent variables’ as predictors and the ‘dependent variables’ as

outcomes (APA, 2010; Field, 2009; Leech & Morgan, 2005).

One important assumption to be fulfilled before performing statistical tests is

that the outcome variables should be normally distributed. This assumption does not

need to be fulfilled for the multiple regression (Field, 2009; Leech & Morgan, 2005).

Furthermore, central limit theorem also states that when the sample size increases than

30, sampling distribution tends to be normal so, there is no need to assess the normality

of the distribution separately (Field, 2009; Leech & Morgan, 2005).

Previous sections describe that we are interested in finding out both the

relationship and strength of the relationship between adoption of KM best practices and

project management capability of the organizations. Therefore, simple correlational

tests are not applicable for this purpose. Rather, we needed to employ multiple

regression technique because we had several predictor and outcome variables.

4.6.3.1 Multiple Regression

Multiple regression is used in scenarios when the researcher is interested in

141

finding out the relationship and impact of more than one predictor variable on one

outcome variable. Given that there are several predictors (X1, X2, .... Xn), the unknown

parameters (Y) can be calculated by fitting a model to the data. Multiple regression

can be expressed mathematically as:

Yi = (b0 + b1X1i + b2X2i + ……. bnXni) + Ɛi …………………(1)

Here,

Y is the outcome variable

b1 is the coefficient of first predictor (X1)

b2 is the coefficient of second predictor (X2)

bn is the coefficient of nth predictor (Xn) and,

Ɛi is the difference between the predicted and observed values of Y for the

ith participant

To summarize, in multiple regression, we are seeking for a linear combination

of predictors that correlate maximally with the outcome variable. Another important

difference between simple regression and multiple regression is that, in the former we

can plot a scatter diagram of the two variables because there are only two variables

whereas in multiple regression we cannot do so because there are several variables,

instead we get an equation similar to the above. Due to this reason, results of multiple

regression are reported in the form of a table (APA, 2010; Field, 2009; Leech &

Morgan, 2005).

142

4.6.3.1.1 Assumptions of Multiple Regression

Unlike any other statistical test, multiple regression has its own set of

assumptions that must be taken care of to draw accurate conclusions from it (Field,

2009). The assumptions are:

Variable types: predictor variables can be continuous or categorical

and the outcome variable should be quantitative and continuous.

Non-zero variance: the predictors should not have variances of 0.

No perfect multicollinearity: there should be no perfect linear

relationship between two or more predictors. If two predictors are

perfectly correlated, then the values of b for each variable are

interchangeable. However, perfect collinearity is very rare in real-world

data but, existence of multicollinearity is unavoidable as well. Low

levels of collinearity pose little threat to the models.

Homoscedasticity: it states that variance of the residuals should be

constant at each level of the predictor variables.

Normally distributed errors: it means that residuals of the model are

randomly distributed. In simple words, differences between the

predicted model and the observed data are most frequently zero or close

to zero. It does not mean that the predictors should be normally

distributed (Field 2009).

4.6.4 Results of the Quantitative Analysis

Project management capability of any organization can be decomposed into

three sub-capabilities: schedule estimation capability, scope determination capability

143

and, budget determination capability. Therefore, we can write:

γ = η1+ η2 + η3 ............................................. (2)

where,

γ = Project management capability

η1 = Schedule estimation capability

η2 = Scope determination capability

η3 = Budget determination capability

Hence, multiple regression equation for project management capability can be

rewritten as:

γ = bo + b1 λ1 + b2 λ2+ b3 λ3+ b4 λ4 + b5 λ5 + b6 λ6 + b7 λ7+ b8 λ8 ........ (3)

Where,

λ1 = Business Analyst Availability

λ2 = Meetings and Discussions

λ3 = Industry Knowledge + PMBOK

λ4 = Peer Communication

λ5 = Templates

λ6 = Standardization of Documents

λ7 = Documentation

λ8 = MIS Web Portal

Sub-equations for PMC are:

144

η1 = bo + b1 λ1 + b2 λ2+ b3 λ3+ b4 λ4 + b5 λ5 + b6 λ6 + b7 λ7+ b8 λ8



Now we discuss the details of multiple regression analysis performed on the

collected data. We had a number of predictor and outcome variables. There were eight

predictor and three outcome variables. We analyzed the collective impact of predictors

on each of the schedule, scope and budget determination capabilities of the

organizations. Before performing multiple regression, we calculated mean (µ) and

standard deviations (σ) of the responses for outcome variables against each of the

predictors (Table 4-10).

Table 4-10: Mean and std. deviation for scope, schedule and cost estimation of projects

Predictor (s) Scope Schedule Budget

µ σ µ Σ µ Σ

Business Analyst

Availability

4.6811 .9326 4.2519 .8542 2.7113 .4770

Documentation 3.2659 0.5965 3.3050 0.4590 2.8962 0.4226

Industry Knowledge +

PMBOK

4.7123 .1187 4.5312 .2565 4.8812 .5371

MIS Web Portal 3.7537 0.4451 3.7232 0.6050 3.6795 0.6896

Meetings and Discussions 4.1139 0.3503 4.2822 0.3979 4.2492 0.4786

Peer Communication 4.2405 0.4490 4.4272 0.5954 4.0196 0.6614

Standardization of

Documents

4.0757 0.3724 4.0027 0.6587 3.5436 0.5158

Templates 4.1447 0.3633 4.6458 0.5637 4.4095 0.6546

The mean scores for the three outcome variables are generally above the neutral

point (M= 3) for each of the predictors. This implies that the participants believed that

adoption of KM best practices will significantly improve scope, cost and time

145

estimation capabilities of the organizations. Participants regarded:

‘Business analyst availability’ the most important (M= 4.7123) and

‘Documentation’ as least important (M= 3.3022) to adopt for ‘scope

determination’ of the projects

‘Templates’ the most important (M = 4.6458) and ‘Documentation’ as

least important (M= 3.3050) to adopt for ‘schedule estimation’ of the

projects

‘Industry knowledge + PMBOK‘ the most important (M = 4.8812) and

‘business analyst availability’ as least important (M= 2.7113) to adopt

for ‘budget determination’ of the projects

The mean scores (µ) for the outcome variables against each of the predictor

variables are depicted graphically (Figure 4-9).

Figure 4-9: Mean scores of outcome variables for each of predictors

It can be seen that mean scores of all the variables are above the neutral point

(M=3); except the two items whose mean scores are below the neutral point but still

146

are very close to it.

We have performed three types of multiple regression analyses for this study:

1. First regression analysis analyzes impact of KM best practices on

project management capability for responses collected from Pakistan

2. Second regression analysis analyzes impact of KM best practices on

project management capability for responses collected from other

countries

3. Third regression analysis analyzes impact of KM best practices on

project management capability for the total responses collected from

cumulative organizations of this study

Moreover, each of these analysis is sub-divided into three analyses; one for each

of the scope, schedule and budget determination capabilities of the organizations. Such

hierarchical approach has been adopted to verify each of the hypotheses.

4.6.5 Hypotheses Testing for Pakistan

Following hypotheses were developed for the organizations of Pakistan.


‘project management’ capability of the IT organizations in Pakistan

H1a: Adoption of the best practices for managing knowledge-of-project will improve

‘schedule estimation’ capability of the IT organizations in Pakistan

H1b: Adoption of the best practices for managing knowledge-of-project will improve

‘scope determination’ capability of the IT organizations in Pakistan

147

H1c: Adoption of the best practices for managing knowledge-of-project will improve

‘budget determination’ capability of the IT organizations in Pakistan

Important correlation and ANOVA statistics are summarized and described

(Table 4-11) to test the hypotheses (H1, H1a, H1b, H1c) for the data collected from

Pakistan17. It can be seen that we can explain 69.1% of the variance (R2) in overall

PMC of the organizations in Pakistan if knowledge-of-project management best

practices are adopted. The table also depicts the overall fit for schedule, scope and

budget determination capabilities. The ANOVA (F-ratio) shows that the model is

significantly better at predicting the change in PMC at p < .05, though, the p-value for

the budget estimation capability is a bit high, but still in the allowable range. The F-

ratio also depicts that the regression model fits well to the data.

Table 4-11: Correlation and ANOVA Statistics (Pakistan)

Model PMC(H1) Schedule(H1a) Scope(H1b) Budget(H1c)

R .797 .802 .785 .768 R2 .691 .722 .769 .757 ANOVA

(F-ratio) 24.339 24.343 24.822 27.741

Sig.(p) .000 .002 .000 .047 a. Predictors: (constant), knowledge-of-project management best practices

b. Outcome variable(s): schedule, scope, budget and project management

capability

The regression coefficients (b-values) and VIF (variance inflation factor)

statistics for PMC for the responses collected from Pakistan are also reported in the

table (Table 4-82). First of all, VIF statistics show that, though, there exists some

multicollinearity among predictors but that is within acceptable range i.e. close to one.

So, multicollinearity is not the problem to worry about. The table also depicts

regression coefficients for all the variables. All the t-test are also positive and show the

17 For complete statistics refer to Appendix C

148

significance of relationship (p < 0.05).

Table 4-82: Regression coefficients - PMC (Pakistan)

Model

1

Coefficients

t

Collinearity

Statistics

b

Std.

Error VIF

(Constant) 8.831 .770 11.469

Business Analyst 4.007 .551 7.272 1.132

Meetings and

Discussions

4.122 .638 6.461 1.109

PMBOK & Experience 5.681 .751 7.565 1.102

Peer Communication 3.165 .766 4.132 1.111

Templates 4.398 .687 6.402 .659

Standardization of

Documents

3.032 .574 5.282 .193

Documentation 4.014 .695 5.776 1.239

MIS Webportal 5.010 .754 6.645 1.020

Hence, the regression equation for PMC (γ) can be rewritten as:

γ = 8.831+4.007 λ1 +4.122 λ2+5.681 λ3+ 3.165 λ4 + 4.398 λ5 + 3.032 λ6 + 4.014 λ7+

5.010 λ8

We have also drawn scatter plot of residuals for PMC (Figure 4-10). This plot is

drawn to test the normality of residuals which is an important assumption of multiple

regression. The straight line in this plot represents a normal distribution and the points

represent the observed residuals. In a perfectly normally distributed data, all the points

lie on the line. For our data, it is quite clear that the distribution for residuals is

approximately normal. Hence, the assumption is met.

149

Figure 4-10: Regression standardized residual - PMC (Pakistan)

The regression coefficients (b-values) and VIF statistics for schedule estimation

capability for the responses collected from Pakistan are also reported in the table (Table

4-93). First of all, VIF statistics show that, though, there exists some multicollinearity

among predictors but that is within acceptable range i.e. close to one. So, we did not

have to worry about multicollinearity. The table also depicts regression coefficients for

all the variables. All the t-test are also positive and show the significance of relationship

(p < 0.05).

Table 4-93: Regression coefficients - schedule (Pakistan)

Model

1

Coefficients

t

Collinearity

Statistics

b

Std.

Error VIF

(Constant) 11.850 .798 14.850


Meetings and

Discussions 5.041 .674 7.479 1.109

PMBOK &

Experience 6.023 .624 9.652 1.138


150

Templates 3.063 .594 5.157 .659

Standardization of

Documents 2.021 .791 2.555 .193

Documentation 4.042 .686 5.892 .386

MIS Webportal 5.106 .841 6.071 1.241

Hence, regression equation for schedule estimation capability (η1) can be rewritten as:

η1pak = 11.850 + 4.009 λ1 + 5.041 λ2+ 6.023 λ3+3.110 λ4+3.063 λ5+ 2.021 λ6+ 4.042

λ7+ 5.106 λ8

We have also drawn scatter plot of residuals for schedule estimation capability

(Figure 4-11). This plot is drawn to test the normality of residuals which is an important

assumption of multiple regression. The straight line in this plot represents a normal

distribution and the points represent the observed residuals. In a perfectly normally

distributed data all the points lie on the line. It is clear from the figure that the

distribution for the residuals is approximately normal and hence, the assumption is met.

Figure 4-11: Regression standardized residual - Schedule(Pakistan)

151

The regression coefficients (b-values) and VIF statistics for scope

determination capability for the responses collected from Pakistan are also reported in

the table (Table 4-104) for the responses collected from Pakistan. First of all, VIF

statistics show that, though, there exists some multicollinearity among predictors but

that is within acceptable range i.e. close to one. So, we did not have to worry about

multicollinearity The table also depicts regression coefficients for all the variables. All

the t-test are also positive and show the significance of relationship (p < 0.05).

Table 4-104: Regression coefficients - scope (Pakistan)

Model

1

Coefficients

t

Collinearity

Statistics

b

Std.

Error VIF

(Constant) 10.837 .863 12.557


Meetings and

Discussions 5.850 .632 9.256 1.109

PMBOK &

Experience 6.113 .636 9.612 1.102

Peer

Communication 4.257 .552 7.712 1.166

Templates 4.582 .866 5.290 1.131

Standardization of

Documents 3.374 .691 4.883 1.193

Documentation 3.899 .643 6.064 1.239

MIS Webportal 4.635 .748 6.196 1.310

Hence, regression equation for scope determination capability (η2) can be rewritten

as:

η2pak = 10.837+5.004 λ1+5.850 λ2+ 6.113 λ3+4.257 λ4+4.582 λ5+ 3.374 λ6+ 3.899 λ7

+ 4.635 λ8

We have also drawn scatter plot of residuals for scope determination capability


152



distributed data all the points lie on the line. It is quite clear from the figure that the

distribution for the residuals is approximately normal and hence, the assumption is met.

Figure 4-12: Regression standardized residual - Scope(Pakistan)

The regression coefficients (b-values) and VIF statistics for budget

determination capability for the responses collected from Pakistan are also reported in

the table (Table 4-115). First of all, VIF statistics show that, though, there exists some

multicollinearity among predictors but that is within acceptable range i.e. close to one.

So, we did not have to worry about multicollinearity. The table also depicts regression

coefficients for all the variables. All the t-test are also positive and show the


153

Table 4-115: Regression coefficients - budget (for Pakistan)

Model

1

Coefficients

t

Collinearity

Statistics

b

Std.

Error VIF

(Constant) 7.788 .729 10.683


Meetings and Discussions 4.686 .620 7.558 1.109



Templates 4.138 .654 6.327 1.223

Standardization of

Documents 4.263 .796 5.356 1.193

Documentation 3.229 .532 6.070 1.214

MIS Webportal 5.293 .624 8.482 1.102

Hence, regression equation for budget estimation capability (η3) can be rewritten as:

η3pak = 7.788 + 4.160 λ1+4.486 λ2+5.889 λ3+4.705 λ4+4.138 λ5+ 4.263 λ6+

3.229λ7+5.293λ8

We have also drawn scatter plot of residuals for PMC (Figure 4-13). This plot

is drawn to test the normality of residuals which is an important assumption of multiple


represent the observed residuals. In a perfectly normally distributed data all the points

lie on the line. For our data, it is quite clear that the distribution for residuals is

approximately normal and hence, the assumption is met.

154

Figure 4-13: Regression standardized residual - budget (for Pakistan)

4.6.6 Hypotheses Testing for Other Countries

Now consider the hypotheses for the countries other than Pakistan. The

countries include: UAE, USA and Canada. The hypotheses are:


‘project management’ capability of the IT organizations in other countries


‘schedule estimation’ capability of the IT organizations in other countries


‘clear scope determination’ capability of the IT organizations in other countries


‘budget determination’ capability of the IT organizations in other countries

155



other countries (USA, Canada, UAE)18. It can be seen that we can explain 72.8% of the

variance (R2) in overall PMC of organizations of these countries if, knowledge-of-

project management best practices are adopted. The table also depicts the overall fit for

schedule, scope and budget determination capabilities. The ANOVA (F-ratio) shows

that the model is significantly better at predicting the change in PMC at p < .05. The

F-ratio also depicts that the regression model fits well to the data. The p-value for the

budget estimation capability is a bit high (p ≈ .05), but is in the acceptable range (p <

.05).

Table 4-126: Correlation statistics (other countries)


R .803 .788 .808 .762

R2 .728 .764 .760 .735

ANOVA

(F-ratio) 29.224 25.251 26.016 27.852


b. Outcome variable(s): schedule, scope, budget and project management capability

The regression coefficients (b-values) and VIF statistics for project

management capability for the responses collected from other countries (USA, Canada,

UAE) are also reported in the table (Table 4-137). First of all, VIF statistics show that,

though, there exists some multicollinearity among predictors but that is within

acceptable range i.e. close to one. So, we did not have to worry about multicollinearity.

The table also depicts regression coefficients for all the variables. All the t-test are also

positive and show the significance of relationship (p < 0.05).


156

Table 4-137: Regression coefficients - PMC (others countries)

Model

1

Coefficients

t

Collinearity

Statistics

b

Std.

Error VIF

(Constant) 10.793 .481 21.814





Templates 3.108 .477 6.516 1.132

Standardization of

Documents 4.132 .550 7.513 1.224

Documentation 4.102 .474 8.654 .171

MIS Webportal 6.006 .639 9.399 .828

Hence, the regression equation for PMC (γ )can be rewritten as:

γother = 10.793 + 4.116λ1 + 3.151λ2 + 5.089λ3 + 5.924λ4 + 3.108λ5 + 4.132λ6 +

4.102λ6 + 6.006λ8





lie on the line. It is quite clear from the figure that the distribution for the residuals is


157

Figure 4-14: Regression standardized residual - PMC (others countries)


capability for the responses collected from other countries (USA, Canada and UAE)

are reported in the table (Table 4-148). First of all, VIF statistics show that, though,

there exists some multicollinearity among predictors but that is within acceptable range

i.e. close to one. The table also depicts regression coefficients for all the variables. All


Table 4-148: Regression coefficients - schedule (others countries)

Model

1

Coefficients

t

Collinearity

Statistics

b

Std.

Error VIF

(Constant) 9.853 .641 15.371


Meetings and

Discussions 4.169 .434 9.606 1.104


158


Templates 2.113 .408 5.179 1.366

Standardization of

Documents 4.173 .471 8.860 .552

Documentation 3.076 .451 6.820 .171

MIS Webportal 5.016 .534 9.393 .828

Hence, regression equation for schedule estimation capability (η1) can be rewritten

as:

η1other = 9.853 + 3.098λ1 + 4.169λ2+ 5.754λ3 + 4.060λ4 + 2.113λ5 + 4.173λ6 +

3.076λ7 + 5.016λ8






distribution for the residuals is approximately normal. Hence, the assumption is met.

159

Figure 4-15: Regression Standardized Residual - Schedule (others countries)


determination capability for the responses collected from other countries (USA,

Canada and UAE) are also reported in the table (Table 4-19). First of all, VIF statistics

show that, though, there exists some multicollinearity among predictors but that is

within acceptable range i.e. close to one. So, we did not have to worry about

multicollinearity. The table also depicts regression coefficients for all the variables. All


Table 4-19: Regression coefficients - scope (for Others)

Model

1

Coefficients

t

Collinearity

Statistics

b

Std.

Error VIF

(Constant) 8.849 .742 11.926


Meetings and

Discussions 6.164 .544 11.331 1.117

160



Templates 3.125 .373 8.378 1.316

Standardization of

Documents 6.141 .755 8.134 .596

Documentation 2.102 .429 4.900 .171

MIS Webportal 4.044 .617 6.554 .828

Hence, regression equation for scope determination capability (η2) can be

rewritten as:

η2other = 8.849 + 3.111λ1 + 6.164λ2 + 4.062λ3 + 5.097λ4 + 3.125λ5 + 6.141λ6 +

2.102λ7+ 4.044λ8

We have also drawn scatter plot of residuals for scope determination capability






161

Figure 4-16: Regression standardized residual - scope (others countries)


determination capability for the responses collected from other countries (USA,

Canada and UAE) are reported in the table (Table 4-20). First of all, VIF statistics show

that, though, there exists some multicollinearity among predictors but that is within

acceptable range i.e. close to one. So, we did not have to worry about multicollinearity.

The table also depicts regression coefficients for all the variables. All the t-test are also

positive and show the significance of relationship (p < .05).

Table 4-20: Regression coefficients - budget (others countries)

Model

1

Coefficients

t

Collinearity

Statistics

b

Std.

Error VIF

(Constant) 8.883 .617 28.984


162

Meetings and

Discussions 3.184 .650 4.898 1.146

PMBOK and Experience 3.771 .443 8.512 1.141


Templates 2.065 .611 3.380 .737

Standardization of

Documents 2.146 .705 3.044 .596

Documentation 2.060 .676 3.047 .171

MIS Webportal 3.026 .502 6.028 1.207

Hence, regression equation for budget determination capability (η3) can be

rewritten as:

η3other = 8.883 + 2.060λ1 + 3.184λ2 + 3.771λ3 + 4.166λ4 + 2.065λ5 + 2.146λ6 + .060λ7

+ 3.026λ8

We have also drawn scatter plot of residuals for budget determination capability






163

Figure 4-17: Regression standardized residual - budget (others countries)

4.6.7 Cumulative Hypotheses Testing

This section presents the hypotheses and the results of statistical tests to verify

the impact of knowledge-of-project management best practices on project management

capability (PMC) for the cumulative responses i.e. both from Pakistan and other

countries. The hypotheses are:


‘project management’ capability for the cumulative IT organizations of this study


‘schedule estimation’ capability for the cumulative IT organizations of this study


164

‘scope determination’ capability for the cumulative IT organizations of this study


‘budget determination’ capability for the cumulative IT organizations of this study



the countries Pakistan, USA, Canada and UAE 19. It can be seen that we can explain

76.4% of the variance (R2) in overall project management capability (PMC) of the

organizations in these countries if, knowledge-of-project management best practices

are adopted. The table also depicts the overall fit for schedule, scope and budget

determination capabilities. The ANOVA (F-ratio) shows that the model is significantly

better at predicting the change in PMC at p < .05. Though, the p-value for the budget

estimation capability are a bit high, but are in the acceptable range. The F-ratio also

depicts that the regression model fits well to the data.

Table 4-21: Correlation statistics for cumulative responses


R .801 .796 .781 .717

R2 .764 .774 .774 .763

ANOVA

(F-ratio) 26.504 21.286 24.944 26.918


b. Outcome variable(s): schedule, scope, budget and project management capability

The regression coefficients (b-values) and VIF statistics for project

management capability for the cumulative responses are reported in the table (Table


among predictors but that is within acceptable range i.e. close to one. So,


165

multicollinearity is not the problem to worry about. The table also depicts regression

coefficients for all the variables. All the t-test are also positive and show the


Table 4-22: Regression coefficients - PMC (cumulative)

Model

1

Coefficients

t

Collinearity

Statistics

b

Std.

Error VIF

(Constant) 9.822 .764 12.856


Meetings and

Discussions 3.028 .398 7.608 1.180

PMBOK &

Experience 4.423 .385 11.488 1.123


Templates 4.019 .561 7.164 .815

Standardization of

Documents 2.295 .522 4.397 .298

Documentation 3.103 .425 7.301 .566

MIS Webportal 6.653 .782 8.508 1.127

Hence, regression equation for PMC (γ ) can be rewritten as:

γcum = 9.822 + 4.012 λ1 + 3.028 λ2 + 4.423 λ3 + 3.125 λ4 + 4.019 λ5 +2.295 λ6 + 3.103

λ7 + 6.653 λ8







166

Figure 4-18: Regression standardized residual - PMC (cumulative)


capability for the cumulative responses are reported in the table (Table 4-23). First of

all, VIF statistics show that, though, there exists some multicollinearity among

predictors but that is within acceptable range i.e. close to one. So, we did not have to

worry about multicollinearity. The table also depicts regression coefficients for all the

variables. All the t-test are also positive and show the significance of relationship (p <

0.05).

Table 4-23: Regression coefficients - schedule (cumulative)

Model

1

Coefficients

t

Collinearity

Statistics

b

Std.

Error VIF (Constant)

8.852 .830 10.665


167




Templates 3.203 .440 7.280 .118

Standardization of

Documents 3.252 .400 8.132 .298

Documentation 2.020 .359 5.627 .566

MIS Webportal 5.763 .521 11.061 1.139

Hence, regression equation for schedule estimation (η1) can be rewritten as:

η1cum = 8.852 + 4.020 λ1 + 3.877 λ2 + 5.043 λ3 + 3.238 λ4 + 3.203 λ5 + 3.252 λ6 +

2.020 λ7 + 5.763 λ8







168

Figure 4-19: Regression standardized residual - schedule (cumulative)


determination capability for the cumulative responses are reported in the table (Table





(p < 0.05).

Table 4-154: Regression coefficients - scope (cumulative)

Model

1

Coefficients

t

Collinearity

Statistics

b Std. Error VIF

(Constant) 7.839 .783 10.011


169

Meetings & Discussions 4.273 .565 7.563 1.180



Templates 3.034 .467 6.497 .815

Standardization of

Documents 4.364 .523 8.344 .298

Documentation 2.635 .326 8.083 .566

MIS Webportal 4.515 .526 8.584 1.132

Hence, regression equation for scope determination capability (η2) can be

rewritten as:

η2cum = 7.839 + 4.014 λ1 + 4.273 λ2 + 5.331 λ3 + 3.245 λ4 + 3.034 λ5+4.364 λ6 +

.635 λ7 + 4.515 λ8







170

Figure 4-20: Regression standardized residual - scope (cumulative)


determination capability for the cumulative responses are reported in the table (Table





(p < 0.05).

Table 4-165: Regression coefficients - budget (cumulative)

Model

1

Coefficients

t

Collinearity

Statistics

b

Std.

Error VIF

(Constant) 10.817 .832 13.001


Meetings and

Discussions 3.325 .677 4.911 1.180

171



Templates 3.011 .569 5.292 1.815

Standardization of

Documents 3.294 .592 5.564 1.298

Documentation 2.146 .537 3.996 1.566

MIS Webportal 4.344 .540 8.044 1.392

Hence, regression equation for budget estimation capability (η3) can be

rewritten as:

η3cum = 10.817 + 5.001 λ1 + 3.325 λ2 + 4.048 λ3 + 4.464 λ4 + 3.011 λ5 + 3.294 λ6

+2.146 λ7 + 4.344 λ8







172

Figure 4-21: Regression standardized residual - budget (cumulative)

We think that the readers would be better able to understand results of the study

if these are summarized and presented in tabular format. This will also help the readers

in conceptualizing the differences and reach on a conclusion easily. First of all, look at

the summarized results (Table 4-176) for PMC for hypotheses (H1, H2, H3). It can be

observed that knowledge-of-project management best practices have a significant

impact (p < .05) on project management capability of the organizations in Pakistan,

other countries and for the cumulative organizations. The F-ratios also show that the

regression model fits well to the data and, the model can be used to predict performance

of the organizations for managing their projects.

173

Table 4-176: Summarized results for project management capability (PMC)

Model

PMC(H1)

(Pak)

PMC(H2)

(Other)

PMC(H3)

(Cumulative)

R .797 .803 .801

R2 .691 .728 .764

F 24.339 29.224 26.504

Sig.(p) .000 .000 .000 a. Predictors: (constant), knowledge-of-project management best practices

b. Outcome variable: project management capability (PMC)

The summarized results (Table 4-187) for schedule estimation capability for

hypotheses (H1a, H2a, H3a) are as follows. It can be observed that knowledge-of-

project management best practices have a significant impact (p < .05) on schedule

estimation capability of the organizations in Pakistan, other countries and for

cumulative organizations. The F-ratio also show that the regression model fits well to

the data and, the model can be used to predict performance of the organizations for

estimating schedule for their projects.

Table 4-187: Summarized results for schedule estimation capability

Model Sched(H1a)

(Pak)

Sched(H2a)

(Other)

Sched(H3a)

(Cumulative)

R .802 .788 .796

R2 .722 .764 .774

F 24.343 25.251 21.286


b. Outcome variable: schedule estimation capability

The summarized results (Table 4-198) for scope determination capability for

hypotheses (H1b, H2b, H3b) are as follows. It can be seen that knowledge-of-project

management best practices have a significant impact (p < .05) on scope determination

capability of the organizations in Pakistan, other countries and for cumulative

organizations. The F-ratio also show that the regression model fits well to the data and,

the model can be used to predict performance of the organizations determining scope

174

of their projects.

Table 4-198: Summarized results for scope determination capability

Model Scope(H1b)

(Pak)

Scope (H2b)

(Other)

Scope (H3b)

(Cumulative)

R .785 .808 .781

R2 .769 .760 .774

F 24.822 26.016 24.944


b. Outcome variable: scope determination capability

The summarized results (Table 4-29) for budget determination capability for

hypotheses (H1c, H2c, H3c) are as follows. It can be seen that knowledge-of-project

management best practices have a significant impact (p < .05) on budget determination

capability of the organizations in Pakistan and in the other countries for cumulative IT

organizations (Table 4-199). The F-ratioo also show that the regression model fits well

to the data and, the model can be used to predict performance of the organizations while

they are in the process of determining budget of their projects.

Table 4-29: Summarized results for budget determination capability

Model Budget(H1c)

(Pak)

Budget(H2c)

(Other)

Budget(H3c)

(Cumulative)

R .768 .762 .717

R2 .757 .735 .763

F 27.741 27.852 26.918


b. Outcome variable: budget determination capability

The high p-values (p ≈ .05) for budget determination capability depict the lower

probability of impact of adoption of knowledge-of-project management best practices

on budget determination capability, every time. This may be due to the fact that budget

cannot be determined directly rather, it is dependent upon accurately estimating

schedule and scope of the projects. Hence, if the organizations could be made capable

175

of estimating accurately schedule and scope of the projects through adoption of

knowledge-of-projects management best practices, then budget can be calculated

indirectly from these estimates.

4.7 Discussion

Management of knowledge-of-projects enables the organizations to exploit

their intangible assets which can, in turn, provide sustainable competitive advantage to

them. At the core of this concept is the ability of the organization to successfully

identify its knowledge resources, capture critical knowledge from them, organize that

knowledge and finally, share the knowledge across the organization. We have offered

a novel perspective on managing knowledge-of-projects and empirical validation of the

same by decomposing knowledge into four constituents (process, domain, institutional,

cultural). Knowledge is a vague term. It can include almost hundreds of different facets

of information for any organization. Decomposition of knowledge into constituents

provides a better and clear description of the term 'knowledge'.

From a practical perspective, our results are equally important for researchers

and organizational managers to understand the heterogeneity of projects and managing

the knowledge produced during execution of projects. An identification of the best

practices followed by an empirical analysis shows that managing knowledge-of-

projects can improve organizational project management capability significantly, as

hypothesized too. Our results are interesting because of two major reasons: (1) the

evidences are weak for such work done in the context of developing countries, (2) prior

research shows that only medium to large sized organizations could apply KM

principles and practices as the case studies found in the literature were conducted in

medium/large organizations. However, the results of this study show that small

176

organizations can also adopt the practices of KM, (3) the identified best practices can

potentially be incorporated in OPM3® as these are empirically validated as well.

OPM3® already contains some KM best practices such as, IDs: 3030, 5240,

5250, 5660, 7365, 7375. These best practices emphasize the use/reuse of intellectual

capital, capturing lessons learned, establishing communities of practices and,

establishing project management information systems (PMI, 2008b). However,

OPM3® provides very vague information about these best practices best practices and

does not include detailed information. Our work can be utilized to advance the OPM3®

knowledge foundation if our newly identified best practices are also incorporated in it.

Additionally, any organization cannot assess its KM maturity by just six best practices

(as mentioned by OPM3®). We have identified and tested a significant number of best

practices (31) grouped under eight categories (Table 3-9). We posit that it would be a

notable contribution towards improvement of the model (i.e. OPM3®) if our best

practices are also included in OPM3®. Some researchers (Bhirud, Rodrigues, & Desai,

2005) have also found similar best practices significant in their study. However, their

study was limited to their own organization only and reported the best practices being

practiced there. They suggest that maintenance of a central repository, informal

meetings, documentation of activities, observing key individuals while they work and

during meetings are the best practices for knowledge sharing. Also, Bhirud et al. did

not test the applicability of best practices in other environments/contexts and their best

practices were being used for knowledge sharing only. The role of the identified best

practices in knowledge capture and organization process is not mentioned either. This

study strengthens their findings at one hand, while on the other hand it furthers their

results by finding more best practices and validating those across different countries.

To further our understanding of the impact of adoption of KM best practices on

177

project management capability (PMC) of the, we sub-divided PMC into three

constituents: scope, schedule and budget determination capabilities (also known as

triple constraints). This division enabled to look at the impact of cumulative KM best

practices on each of the triple constraints. It was found that some best practices showed

more significant effect on one or two of triple constraints, while others showed a

significant impact on all the three. Such breakdown approach has been found extremely

useful to look at the individual and cumulative differences in the statistical models

computed for the outcome variables.

Hypothesis H1 stated that the identified best practices will improve the PMC of

IT organizations in Pakistan. Hypothesis H1 is partially supported because the

identified best practices showed a statistically significant impact on schedule and scope

determination capability (p < .05), though, the statistical significance for budget

determination capability is a bit susceptible (p ≈ .05).

Hypothesis H1a, H1b and H1c - the sub-hypotheses of H1 - state that adoption of

the identified best practices will improve the schedule (H1a), scope (H1b) and budget

(H1c) determination capability of the IT organizations in Pakistan. The hypotheses H1a

and H1b were fully supported because the identified best practices showed a statistical

significance (p < .05) for both of these for the organizations in Pakistan but statistical

significance for H1c is a bit high. (p ≈ .05).

Hypothesis H2 states that the identified best practices will improve the PMC of

IT organizations in USA, Canada and UAE. Hypothesis H2 is partially supported

because the identified best practices showed a statistically significant impact on

schedule and scope determination capability (p < .05), though, the statistical

significance for budget determination capability is a bit susceptible (p ≈ .05).

178

Hypothesis H2a, H2b and H2c - the sub-hypotheses of H2 - stated that the

identified best practices will improve the schedule (H2a), scope (H2b) and budget

(H2c) determination capability of IT organizations in USA, Canada and UAE. The

hypotheses H2a and H2b were fully supported because the identified best practices

showed a statistical significance (p < .05) for both of these hypotheses for the

organizations in these countries but statistical significance for H2c is a bit high. (p ≈

.05).

Hypothesis H3 states that the identified best practices will improve the PMC of

IT organization in the cumulative countries of this study. Hypothesis H3 is partially

supported because the identified best practices showed a statistical significance (p <

.05) for PMC.

Hypothesis H3a, H3b and H3c - the sub-hypotheses of H3 - stated that the

identified best practices will improve the schedule (H3a), scope (H3b) and budget

(H3c) determination capability of the IT organization in cumulative organizations of

these countries. The hypotheses H3a and H3b were fully supported because the

identified best practices showed a statistical significance (p < .05) for both of these

hypotheses for the organizations in these countries but statistical significance for H3c

is a bit high. (p ≈ .05)

Following table (Table 4-30) presents the summarized results of hypotheses

testing.

Table 4-30: Summary of Hypothesis Testing

Hypothesis Test Type Hypothesis Testing

H1 Regression Analysis/ANOVA Partially supported

H1a Regression Analysis/ANOVA Supported

179

H1b Regression Analysis/ANOVA Supported

H1c Regression Analysis/ANOVA Partially supported









Hence, we found general support for hypotheses H1, H2 and H3 i.e. the

relationship between predictors and outcome variables are positive and statistically

significant (p < .05). Though, the detailed analysis (correlation between knowledge-of-

project management best practices and each of the triple constraints) revealed that some

best practices are more important for any one of the triple constraints while some were

important for all three i.e. schedule, scope, budget. Overall, we found little support for

hypotheses H1c, H2c and H3c (p ≈ .05). Thus we accept some of the hypotheses while

suggest more investigations for others.

Some of the themes (i.e. peer communication, meeting and discussions) which

are found to have a significant positive impact on project management capability

emphasize on soft factors (i.e. intellectual capital) augmented by hard factors

(technologies) - which strengthens the theoretical foundations of this study too. The

discipline of KM is dominated by ICT tools and techniques; however, more and more

researchers (Ciabuschi, 2005) are recognizing the importance of intellectual capital.

That is why most of the best practices mentioned by the respondents of this study refer

to intellectual capital.

4.8 Summary

In this chapter we discussed in detail some of the basic issues of phase two of

180

our study such as the research design, paradigms it follows and descriptive and

statistical analyses performed. This phase followed a positivistic (quantitative)

paradigm because second and third objectives required causal explanations of the

phenomena by developing and testing hypotheses. Several hypotheses were developed

and tested in this phase. The hypotheses were focused on analyzing if there exist any

relationships between adoption of KM best practices and improvement in project

management capability of organization in Pakistan and in the other countries. The

statistical tests showed a significant impact of adoption of KM best practices on project

management capability as a whole and, on its individual constituents i.e. scope,

schedule and budget. Finally, conclusion of this phase of study is provided at the end.

181

Chapter 5 Conclusion

This research was conducted to investigate the best practices that organizations

can adopt to improve their project management capability in the three dimensions:

schedule estimation capability, scope determination capability and budget

determination capability. To fulfill these objectives the research is conducted in two

phases: qualitative and quantitative. The rationale for conducting a qualitative analysis

was to find out something really applicable in the context of developing countries

because almost all such previous studies had been conducted in the developed

countries. We developed and tested three main and nine sub-hypotheses in order to test

the three dimensions of project management. We examined the organizational

knowledge management based on the process oriented view of any organization by

adopting a mixed-method approach.

This chapter concludes the main findings of this research, provides answers to

research questions, discusses implications for policy, addresses the limitations and

possible areas for future research.

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5.1 Answers to Research Questions

Q.1. What are the best practices for managing knowledge of IT project management

in the Pakistani IT organizations?

This study is a first step towards understanding the nature of knowledge and

knowledge management practices in the Pakistani IT organizations. At the start, there

were no hypotheses rather, there was only one objective; to find the KM best practices

for IT organizations. The Open-ended qualitative interviews were conducted, data were

collected and analyzed. The analysis resulted in a number of major categories of the

best practices and individual best practices. The outcome of this process is a conceptual

framework mentioning the interactions between knowledge-of-project management

best practices and project management capability of the organizations. Hence, an

identification of the KM best practices fulfilled the objective of this phase.

Q.2. How the identified best practices for managing knowledge-of-project will affect

project management capability of the IT organizations in Pakistan?

We developed and tested several hypotheses based on conceptual framework

developed in the first phase to answer this research question. We collected responses

from IT organizations of Pakistan. The purpose of collecting responses from two

industries was to validate the applicability of results across-industries. The results of

statistical tests showed statistical significance (p ≤ .05) for project management

capability of the organizations as a whole. Also, project management capability was

sub-divided into three constituents: schedule, scope and budget to analyze the results

on each of the individual constituents. The statistical tests showed statistical

significance (p ≤ .05) for schedule and scope determination, though, evidence for

budget determination capability is a bit weak (p ≈ .05). To summarize, adoption of best

183

practices for managing knowledge-of-project will improve project management

capability of the IT organizations in Pakistan.

Q.3. Are the identified best practices for managing knowledge-of-project applicable

to the IT organizations in other countries as well?

To answer this research question, we collected data from the IT organizations

of other countries (USA, Canada and UAE). The results of statistical tests showed

statistical significance (p ≤ .05) for project management capability of these

organizations. Also, project management capability was sub-divided into three

constituents: schedule, scope and budget to analyze the results on each of the individual

constituents. The statistical tests showed statistical significance (p ≤ .05) for schedule

and scope determination, though, evidence for budget determination capability is a bit

weak (p ≈ .05). To summarize, adoption of best practices for managing knowledge-of-

project will improve project management capability of the IT organizations of these

countries.

Q.4. Are the existing best practices in OPM3® pertaining to knowledge management

sufficient, if not, what other practices can be added to make OPM3® more usable?

OPM3® contains some KM best practices such as, IDs: 3030, 5240, 5250, 5660,

7365, 7375. These best practices are explicitly mentioned for KM. OPM3® provides

very vague information about these best practices and does not include detailed

information. Additionally, any organization cannot assess its KM maturity by just six

best practices, as mentioned by OPM3®. Hence, six best practices cannot be considered

sufficient for assessing KM maturity of any organization. We have identified and tested

a significant number of best practices (31) grouped under eight categories. We posit

that it would be a notable contribution towards improvement of the model, if our best

184

practices are also included in the OPM3®.

Table 5-1 concludes and presents the results of multiple regression by

categorizing them in conjunction with triple constraints i.e. schedule, scope and budget.

It is acknowledged that the content presents the correlation, not the causality.

Themes of KM best practices

Strong correlation with

Schedule Scope Budget Project mgmt.

capability

Business Analyst Availability

Documentation

Industry Knowledge + PMBOK

Meetings and Discussions

Peer Communication

Standardization of Documents

Templates

MIS Web Portal

Table 5-1: Summarized presentation of correlation between KM themes and triple constraints

5.2 Implications for Policy

This research suggests several policy implications for the regional IT industries,

especially in Pakistan. Reports (PSEB, 2009) demonstrate that no such work has been

done for the IT industry of Pakistan hence, there was a real need of such work. Pakistan

is a developing country and its IT industry is still in its infancy stage and is not big

enough in comparison with the other regional IT industries hence, the industry should

be mentored at government level.

First of all, this research has implications for the importance of hiring some

specialized experts such as business analysts and notes taking personnel in the

185

organizations. An encouragement of this practice by the regulatory authorities, such as

PSEB in Pakistan, can improve success rate of the projects. As mentioned in the

previous chapter, hiring of a business analyst can be of much assistance when

determining the scope of the projects. Failure to determine complete and clear scope of

the projects is one of the biggest reasons of failure of projects around the world (Group,

2001). Hence, this finding suggests that nurturing specialized personnel can be set to

be a first priority for the regulatory authorities.

Second, implication of the study is that the IT regulatory authorities should

establish KM assessment benchmarking systems for the IT organizations. Several case

studies (Anand, et al., 2005; Coakes, et al., 2005; Li, et al., 2005; Owen & Burstein,

2005) indicate that the organizations which adopted KM practices observed at least

twenty percent increase in performance and revenues. Organizations can adopt KM

practices with minimal efforts because, very often, they do not need to spend a lot on

infrastructure - they just need to identify their knowledge assets, capture knowledge

from them and share it throughout the organization.

Finally, with the transformation of the world into a global village, many

countries (Bank, 2007; Government, 2007) are rapidly turning themselves into

knowledge economies. Developed countries have already developed long-term policies

and adopted necessary measures to do so. India, Qatar, UAE, Kuwait and Brazil are the

developing economies transforming themselves swiftly into knowledge economies. In

the midst of this scenario, the government and regulatory authorities of Pakistan also

need to assess the strengths and weaknesses of the country, assess current position on

knowledge-continuum and develop long-term policies.

In conclusion, it is important to note that any organization cannot gain

186

sustainable competitive advantage from its tangibles assets, rather, SCA can be

achieved by harnessing its intangible assets (Amit & Schoemaker, 2006; Eisenhardt &

Santos, 2000; Jugdev, et al., 2007b; Jugdev & Thomas, 2002; Kaplan, et al., 2001). By

considering the relationship between these concepts, we have attempted to extend our

theoretical and empirical understanding of the ways knowledge assets can impact

organizational project management capability.

5.3 Limitations of the Study

It was found that most of the newly identified KM best practices significantly

impacted the organizational project management capability. Nevertheless, there were

some best practices which need to be further investigated to ascertain their real impact.

There are several limitations of this study. The foremost limitation relates to

setting of the study. IT industry represents an interesting and appropriate setting for

investigating impact of knowledge management on organizational project management

capability. However, given the needs of each industry in terms of the importance of

project management knowledge, future studies are needed to augment the external

validity of our findings. Future analyses may also expand on our findings by developing

and testing new and better measurement scales.

There might be some limits in generalizing the findings of this research for all

the IT organizations of Pakistan and other countries; from where samples are taken.

The reason may be that the samples from Pakistan represent most of the major,

renowned (renowned organizations may by small) and established IT organizations.

The sample does not include startup organizations. Another similar limitation is that

the samples collected from other countries are limited by the problem of accessibility

and represent quite a small number of the total organizations of these countries.

187

When the models for project management capability were constructed, three

outcome variables were considered: schedule, scope and budget. There may be many

others such as quality management, value management etc. Similarly, we identified

thirty one best practices but many more could be identified provided that there were a

larger sample size and diverse industries.

Measuring the knowledge base and knowledge assets of a company is not an

easy task. Knowledge is a very abstract and difficult to conceptualize construct. It is

stored either in the mind of an employee in tacit form or in the organizational manuals

in codified form. More best practices are needed to be identified to capture, organize

and share the tacit knowledge of the employees.

5.4 Future Research

The current study extends our understanding of an imperative asset of any

organization i.e. knowledge, by understanding what it means in the context of

organizations and then finding and analyzing the best practices to manage it. There may

be numerous ways to extend the findings of this study. For example, future studies may

expand on our methodological contributions, specifically, researchers may look for

alternative measurements and identify more KM best practices to benchmark

organizational project management performance against these. As mentioned above,

we believe that the measurements and instruments used in this study are appropriate

for our setting, yet other researchers may extend on our findings by applying the same

methodologies to the other settings and industries.

Application of system's thinking and modeling techniques may provide a

unique avenue for such a study in which the relationship between activities is analyzed

by conceptualizing all the constructs as sub-systems of a larger system (where larger

188

system can be any organization/department of organization).

We have identified best practices for project management only, future studies

may adopt the same methodology and extend the work to find the best practices for

program and portfolio management as well. Finding best practices for program and

portfolio management may really be worthy of effort because renowned maturity

models, including OPM3®, do have assessment capabilities for program and portfolio

management.

We have tested the applicability of these best practices in the IT industry only.

Other researchers may validate our findings in different settings, industries and

countries.

Due to time, resources and accessibility problems we had a sample size of 109

responses, though, this sample size was enough to predict the models but we suggest

that future studies may test the results on a larger sample.

In future, a factor analysis can also be run which should include all the best

practices to see if any groups of practices lead to key results.

189

Appendix A – Interview Protocol

Interview Protocol – Best Practices for IT Project-Knowledge Management

Name: ……………………………….. Designation:

………..………………………

Education: ………………………....... Experience (in years):……………...………

Contact info: ………………………… Company: …………....………….…………

City: …………………………………. Date: ……………………....………………

Note: If you want to receive results of this interview phase, please provide your email address in contact info

above

Dear Participants,

It is found & believed that managing the human capital knowledge and following respective best

practices can increase the success rate of projects in any industry. Therefore, we are conducting this

survey to identify the IT project-knowledge management best practices that our IT organizations could

follow & that can help to substantiate their project success rate, in turn.

Your cooperation to take part in this survey is highly appreciated. Your personal information will be kept

confidential and use of information obtained through this survey is purely for academic research

purposes.

Thanks & Regards,

Farrokh Jaleel,

PhD Candidate,

CASE, Islamabad.

…………………………………………………………………………………………………

Terms Used:

Project process knowledge: knowledge about the project structure, methodology, tasks and time

frames.

Project domain knowledge: knowledge of the industry, firm, current situation,

problem/opportunity and potential technical solutions.

Project institutional knowledge: knowledge of the history, power structure and values of the

organization.

Project cultural knowledge: knowledge of how to manage team members from many disciplinary

groups such as web designers, IT architects or organizational development experts.

190

Part 1 - Knowledge Capture

Q.1. In your opinion, what are the best practices that should be followed to capture project

process knowledge?

Best Practices:

1. ..................................................................

2. ..................................................................

3. ..................................................................

4. ..................................................................

5. ..................................................................


domain knowledge?

Best Practices:

1. ..................................................................

2. ..................................................................

3. ..................................................................

4. ..................................................................

5. ..................................................................

191


institutional knowledge?

Best Practices:

1. ..................................................................

2. ..................................................................

3. ..................................................................

4. ..................................................................

5. ..................................................................


cultural knowledge?

Best Practices:

1. ..................................................................

2. ..................................................................

3. ..................................................................

4. ..................................................................

5. ..................................................................

192

Part 2 - Knowledge Organization

Q.5. In your opinion, what are the best practices that should be followed to organize project

process knowledge?

Best Practices:

1. ..................................................................

2. ..................................................................

3. ..................................................................

4. ..................................................................

5. ..................................................................


domain knowledge?

Best Practices:

1. ..................................................................

2. ..................................................................

3. ..................................................................

4. ..................................................................

5. ..................................................................

193


institutional knowledge?

Best Practices:

1. ..................................................................

2. ..................................................................

3. ..................................................................

4. ..................................................................

5. ..................................................................


cultural knowledge?

Best Practices:

1. ..................................................................

2. ..................................................................

3. ..................................................................

4. ..................................................................

5. ..................................................................

194

Part 3 - Knowledge Sharing

Q.9. In your opinion, what are the best practices that should be followed to share/disseminate

project process knowledge?

Best Practices:

1. ..................................................................

2. ..................................................................

3. ..................................................................

4. ..................................................................

5. ..................................................................

Q.10. In your opinion, what are the best practices that should be followed to

share/disseminate project domain knowledge?

Best Practices:

1. ..................................................................

2. ..................................................................

3. ..................................................................

4. ..................................................................

5. ..................................................................

195

Q.11. In your opinion, what are the best practices that should be followed to

share/disseminate project institutional knowledge?

Best Practices:

1. ..................................................................

2. ..................................................................

3. ..................................................................

4. ..................................................................

5. ..................................................................

Q.12. In your opinion, what are the best practices, capabilities, outcomes & KPIs that should

be followed to share/disseminate project cultural knowledge? (Please indicate S/M/C/I with

each)

Best Practices:

1. ..................................................................

2. ..................................................................

3. ..................................................................

4. ..................................................................

5. ..................................................................

Q.13. Is there anything we have not talked about and that you would like to add in the context

of IT project-knowledge management.

1. ..................................................................

2. ..................................................................

3. ..................................................................

---------THANK YOU----------

196

Appendix B - Questionnaire

Knowledge Management Research on Correlation Between Adoption of KM Practices

and Project Management Capability of the Organizations

Questionnaire to measure impact of adoption of KM best practices on project management

capability of the organizations

Dear Participants,

The objective of this survey is to examine the extent to which adoption of KM best practices by an organization

can improve the project management capability of it. The results of this survey will be used for a doctoral study

only and will be kept confidential. It takes 10-15 minutes to complete. Your cooperation to take part in this survey

is highly appreciated. We will not be seeking any personal information.

Best Regards,

Farrokh Jaleel

Email: [email protected]

Under the supervision of


Email:[email protected]

If you want to receive the results of this survey, please provide your email address.

Email:

197

DEMOGRAPHICAL INFORMATION

Please complete the following Information about you. It will help me to analyze the data in a more

meaningful manner. This information is private and confidential and will not be shared with anyone.

1. City: Click here to enter text. 2. Country: Click here to enter text.

3. What best describes the industry you work in:

IT software development Telecom IT consultancy

4. What best describes your Job Title:

Team lead Project manager Senior project manager

Engineering manager Others (please specify): .................................

5. Professional Experience (in Years):

1- 5 6-10 11-15 16-20 21-25 26-30 >30

6. What best describes your organization size (no. of employees):

Small (Upto 100) Medium (101 - 299) Large (300 - 499)

Very large ( 500+)

198

7. How important are/were the following factors on a scale of 1 to 5 in your current/ past projects.

Please mark a tick () in the appropriate box in front of each row.

199

No. Factors Not very

important

Somehow

important

Very

important

1 2 3 4 5

1 Availability of a business

analyst

2 Development of documentation

for minutes of meetings,

templates, project plan etc

3 Notes taken during meetings

about decisions made and how

they were made i.e. figure out


4 Organized documented policies

and value books by HR

department

5 Maintained policy books and

lists of high achievers with

code of conduct

6 Maintained code of conduct &

service rule book

7 Documentation of horizontal &

vertical communication

channels

8 Development of documents

both in electronic & hard form

9 Using project management

industry knowledge in

conjunction with PMBOK

guidelines

10 Establishment of central

repository and intranet portal

storing documents with

restricted access functionalities

11 Using common repository of

milestones

12 Establishment of e-diaries on

department level

13 Development of e-groups

according to type of project

14 Using web portal having

facilities such as forums,

articles, documents, email lists

and wiki

15 Keeping documents e.g. project

plans, RS, FS in relevant

standard templates on web

portal

16 Establishment of restricted

access peer behavior ratings

database

17 Organization of documents

through MIS web portal using

groups & forums

200

18 Placing code of conduct in

central repository

19 Facilitating regular informal

meetings to share & present

design & solutions

20 Facilitation of formal group

discussions on structure, design

& requirements gathering

processes

21 Arrangement of orientation

meetings to update all human

resources on domain/process

knowledge

22 Usage of multimedia

technologies such as video


23 Facilitating coordination

among different teams

24 Organization of orientation

sessions for new employees to

introduce them with


25 Promotion of peer

communication through formal

& informal meetings

26 Development of standardized

employee handbooks for

networking with other

employees


employee communication

document

28 Maintenance of standardized

documents to develop lists of

team structures, schedule of

tasks, roles & responsibilities

29 Development and sharing of

best practices documentation

templates

30 Availability of standardized

HR documentation templates

31 Maintenance & usage of

standardized templates for

documentation

8. How important were the following factors on a scale of 1 to 5 for "project scope

development" activities.


201

No. Factors (s) Not very

important

Somehow

important

Very

important

1 2 3 4 5

1 Availability of a business analyst

2 Development of documentation for

minutes of meetings, templates, project

plan etc

3 Notes taken during meetings about

decisions made and how they were made

i.e. figure out mind maps of decision

makers

4 Organized documented policies and value

books by HR department

5 Maintained policy books and lists of high


6 Maintained code of conduct & service

rule book

7 Documentation of horizontal & vertical


8 Development of documents both in

electronic & hard form

9 Using project management industry

knowledge in conjunction with PMBOK

guidelines

10 Establishment of central repository and

intranet portal storing documents with

restricted access functionalities

11 Using common repository of milestones

12 Establishment of e-diaries on department

level

13 Development of e-groups according to

type of project

14 Using web portal having facilities such as

forums, articles, documents, email lists

and wiki

15 Keeping documents e.g. project plans, RS,

FS in relevant standard templates on web

portal

16 Establishment of restricted access peer

behavior ratings database

17 Organization of documents through MIS

web portal using groups & forums

18 Placing code of conduct in central

repository

19 Facilitating regular informal meetings to

share & present design & solutions

20 Facilitation of formal group discussions

on structure, design & requirements

gathering processes

21 Arrangement of orientation meetings to

update all human resources on

domain/process knowledge

22 Usage of multimedia technologies such as

video recordings for all trainings

202

23 Facilitating coordination among different

teams

24 Organization of orientation sessions for

new employees to introduce them with


25 Promotion of peer communication

through formal & informal meetings

26 Development of standardized employee

handbooks for networking with other

employees

27 Development of standardized employee

communication document

28 Maintenance of standardized documents

to develop lists of team structures,

schedule of tasks, roles & responsibilities

29 Development and sharing of best practices


30 Availability of standardized HR


31 Maintenance & usage of standardized

templates for documentation

203

9. How important were the following factors for "project schedule estimation" activities.



important

Somehow

important

Very

important

1 2 3 4 5



minutes of meetings, templates,

project plan etc


decisions made and how they were

made i.e. figure out mind maps of

decision makers

4 Organized documented policies and

value books by HR department

5 Maintained policy books and lists of

high achievers with code of conduct


service rule book


vertical communication channels

8 Development of documents both in

electronic & hard form

9 Using project management industry

knowledge in conjunction with

PMBOK guidelines

10 Establishment of central repository

and intranet portal storing documents

with restricted access functionalities


milestones


department level

13 Development of e-groups according

to type of project

14 Using web portal having facilities

such as forums, articles, documents,

email lists and wiki


plans, RS, FS in relevant standard

templates on web portal

16 Establishment of restricted access

peer behavior ratings database

17 Organization of documents through

MIS web portal using groups &

forums


repository


meetings to share & present design &

solutions

204


discussions on structure, design &

requirements gathering processes

21 Arrangement of orientation meetings

to update all human resources on

domain/process knowledge

22 Usage of multimedia technologies

such as video recordings for all

trainings

23 Facilitating coordination among

different teams

24 Organization of orientation sessions

for new employees to introduce them

with organizational culture


through formal & informal meetings


employee handbooks for networking

with other employees


employee communication document


documents to develop lists of team

structures, schedule of tasks, roles &

responsibilities

29 Development and sharing of best

practices documentation templates



31 Maintenance & usage of standardized

templates for documentation

205

10. How important were the following factors for "project budget determination"

activities.


206


important

Somehow

important

Very

important

1 2 3 4 5



minutes of meetings, templates,

project plan etc


decisions made and how they were

made i.e. figure out mind maps of

decision makers

4 Organized documented policies

and value books by HR

department

5 Maintained policy books and lists

of high achievers with code of

conduct


service rule book


vertical communication channels

8 Development of documents both

in electronic & hard form

9 Using project management

industry knowledge in conjunction

with PMBOK guidelines

10 Establishment of central repository

and intranet portal storing

documents with restricted access

functionalities


milestones


department level

13 Development of e-groups

according to type of project

14 Using web portal having facilities

such as forums, articles,

documents, email lists and wiki


plans, RS, FS in relevant standard

templates on web portal

16 Establishment of restricted access

peer behavior ratings database

17 Organization of documents

through MIS web portal using

groups & forums


repository


meetings to share & present design

& solutions

207


discussions on structure, design &

requirements gathering processes

21 Arrangement of orientation

meetings to update all human

resources on domain/process

knowledge

22 Usage of multimedia technologies

such as video recordings for all

trainings

23 Facilitating coordination among

different teams

24 Organization of orientation

sessions for new employees to

introduce them with organizational

culture


through formal & informal

meetings


employee handbooks for



employee communication

document


documents to develop lists of team

structures, schedule of tasks, roles

& responsibilities

29 Development and sharing of best

practices documentation templates



31 Maintenance & usage of

standardized templates for

documentation

Thank you for your valuable contribution to this research!

208

Appendix C - Results of Data Analysis (for Pakistan)

Model Summary

Model

R R2 Adjusted R2 Std. Error of

Estimate

1 .797 .691 .654 .093693

a. Dependent Variable: Project management capability

ANOVA

Model Sum of Squares

(SS)

Df Mean Squares

(MS)

F Sig.(p)

1 Regression 2385.030 7 298.129 24.339 .000

Residual 1671.553 100 16.716

Total 4056.583 108


Model Summary

Model R R2 Adjusted R2 Std. Error of

Estimate

1 .802 .722 .619 .079449

a. Dependent Variable: Schedule estimation capability

ANOVA


(SS)

df Mean Squares

(MS)

F Sig.(p)

1 Regression 5732.059 7 716.507 24.343 .002

Residual 2943.398 100 29.433

Total 8675.457 108


Model Summary


Estimate

1 .785 .769 .723 .093495

a. Dependent Variable: Scope determination capability

ANOVA


(SS)

df Mean Squares

(MS)

F Sig.(p)

1 Regression 6371.043 7 796.380 24.822 .000

Residual 3176.551 91 32.086

Total 9547.594 99


Model Summary

209


Estimate

1 .768 .757 .734 .105864

a. Dependent Variable: Budget determination capability

ANOVA


(SS) df

Mean Squares

(MS) F Sig.(p)

1 Regression 4621.058 7 577.632 27.741 .062

Residual 2019.706 97 20.822

Total 6640.764 105


210

Appendix D - Results of Data Analysis (for Other Countries)

Model Summary

Model R R2 Adjusted

R2

Std. Error of

Estimate

1 .803 .728 .708 .087374


ANOVA


(SS) df

Mean Squares

(MS) F Sig.(p)

1 Regression 5732.230 7 716.528 29.224 .001

Residual 2378.214 97 24.518

Total 8110.444 105


Model Summary

Model R R2 Adjusted

R2

Std. Error of

Estimate

1 .788 .764 .714 .074785


ANOVA


(SS) df

Mean Squares

(MS) F Sig.(p)

1 Regression 6125.202 7 765.650 25.251 .000

Residual 3001.157 99 30.321

Total 9126.359 107


Model Summary

Model R R2 Adjusted

R2

Std. Error of

Estimate

1 .808 .760 .700 .098448


ANOVA


(SS) df

Mean Squares

(MS) F Sig.(p)

1 Regression 7031.231 7 878.903 26.016 .000

Residual 3378.271 100 33.783

Total 10409.502 108


Model Summary

211

Model R R2 Adjusted

R2

Std. Error of

Estimate

1 .762 .735 .716 .111987


ANOVA


(SS) df

Mean Squares

(MS) F Sig.(p)

1 Regression 5721.290 7 817.327 27.852 .210

Residual 2871.351 98 29.300

Total 8592.641 105


212

Appendix D - Results of Data Analysis (for Cumulative Countries)

Model Summary

Model R R2 Adjusted

R2

Std. Error of

Estimate

1 .801 .764 .751 .096902


ANOVA


(SS) df

Mean Squares

(MS) F Sig.(p)

1 Regression 6821.089 7 852.636 26.504 .000

Residual 3712.939 98 37.887 .000

Total 10534.028 106


Model Summary


Estimate

1 .796 .774 .731 .082738


ANOVA


(SS) df

Mean Squares

(MS) F Sig.(p)

1 Regression 7173.135 7 896.641 21.286 .000

Residual 4001.685 95 42.123

Total 11174.820 103


Model Summary


Estimate

1 .781 .768 .761 .099276


ANOVA


(SS) df Mean Squares (MS) F Sig.(p)

1 Regression 7487.114 7 935.889 24.944 .002

Residual 3751.986 100 37.520 .000

Total 11239.913 108


213

Model Summary

Model R R2 Adjusted

R2

Std. Error of

Estimate

1 .763 .717 .701 .112058


ANOVA


(SS) df

Mean Squares

(MS) F Sig.(p)

1 Regression 5761.149 7 823.021 26.918 .811

Residual 2935.256 96 30.575

Total 8696.405 103


214

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