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1
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
2
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
3
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:
--------------------------------------
Dr. Azhar Mansur Khan
Thesis Supervisor
4
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.
5
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.
6
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
7
PMP Project Management Professional
POO Project Oriented Organization
PRINCE2® Projects in Controlled Environments
RBV Resource Based View
SCA Sustainable Competitive Advantage
8
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’
9
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.
10
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
11
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
12
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
13
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
14
References ........................................................................................................................ 239
15
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
16
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
17
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
18
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
1
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
2
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
4
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.
5
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
practices for managing
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
practices for managing
knowledge-of-project will
affect project 'schedule
estimation’ capability of IT
organizations in Pakistan?
H2a: Adoption of the best
practices for knowledge-
of-project management
will improve project
‘schedule estimation’
capability of IT
organizations in Pakistan
Q.2.2 How the identified best
practices for managing
knowledge-of-project will affect
‘scope determination capability’
of IT organizations in Pakistan?
H2b: Adoption of the best
practices for knowledge-
of-project management
will improve ‘scope
determination’ capability
of IT organizations in
Pakistan
Q.2.3. How the identified best
practices for managing
knowledge-of-project will affect
project 'budget determination’
capability of IT organizations in
Pakistan?
H2c: Adoption of the best
practices for knowledge-
of-project management
will improve project
'budget determination’
22
Objectives Research Questions Hypotheses
capability of IT
organizations in Pakistan
3. To test the extent to
which adoption of the
identified best practices
can affect project
management capability of
IT organizations in other
countries
Q.3. Are the identified best
practices for managing
knowledge-of-project applicable
to IT organizations in other
countries?
H3: The identified best
practices for managing
knowledge-of-project
will improve project
management capability of
IT organizations in other
countries
Q.3.1. How the identified best
practices for managing
knowledge-of-project will
affect project 'schedule
estimation’ capability of IT
organizations in other countries?
H3a: Adoption of the best
practices for knowledge-
of-project management
will improve project
‘schedule estimation’
capability of the IT
organizations in other
countries
Q.3.2 How the identified best
practices for managing
knowledge-of-project will affect
‘scope determination capability’
of IT organizations other
countries?
H3b: Adoption of the best
practices for knowledge-
of-project management
will improve ‘scope
determination’ capability
of IT organizations in
other countries
Q.3.3. How the identified best
practices for managing
knowledge-of-project will affect
‘project budget determination’
capability of IT and
organizations in other countries?
H3c: Adoption of the best
practices for knowledge-
of-project management
will improve ‘project
budget determination’
capability of IT
organizations in other
23
Objectives Research Questions Hypotheses
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 -
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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
82
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,
domain, Institutional and cultural knowledge.
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3. Best practices needed to share knowledge of IT projects i.e. process,
domain, Institutional and cultural knowledge.
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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No. Description of the Best Practice(s) Freq.
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
No. Description of the Best Practice(s) Freq.
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
No. Description of the Best Practice(s) Freq.
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
No. Description of the Best Practice(s) Freq.
35
1 BP develop standardized employee handbooks for
networking with other employees
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
mind maps of decision makers
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
achievers with code of conduct
1
6 BP maintain code of conduct & service rule book 1
7 BP document horizontal & vertical
communication channels
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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No. Description of the Best Practice(s) Freq.
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
recordings for all trainings
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
No. Description of the Best Practice(s) Freq.
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
No. Description of the Best Practice(s) Freq.
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
No. Description of the Best Practice(s) Freq.
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
H2: Adoption of the best practices for managing knowledge-of-project will improve
‘project management’ capability of IT organizations in other countries as well
H2a: Adoption of the best practices for managing knowledge-of-project will
improve ‘schedule estimation’ capability of IT organizations in other countries
as well
H2b: Adoption of the best practices for managing knowledge-of-project will
improve ‘clear scope determination’ capability of IT organizations in other
countries as well
H2c: Adoption of the best practices for managing knowledge-of-project will
improve ‘budget determination’ capability of IT organizations in other countries
as well
H3: Adoption of the best practices for managing knowledge-of-project will improve
‘project management’ capability of IT cumulative organizations of this study
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H3a: Adoption of the best practices for managing knowledge-of-project will
improve ‘schedule estimation’ capability of IT cumulative organizations of this
study
H3b: Adoption of the best practices for managing knowledge-of-project will
improve ‘clear scope determination’ capability of cumulative IT organizations
of this study
H3c: Adoption of the best practices for managing knowledge-of-project will
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
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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.
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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
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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.
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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
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
η2 = bo + b1 λ1 + b2 λ2+ b3 λ3+ b4 λ4 + b5 λ5 + b6 λ6 + b7 λ7+ b8 λ8
η3 = 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.
H1: Adoption of the best practices for managing knowledge-of-project will improve
‘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
Business Analyst 4.009 .628 6.384 1.132
Meetings and
Discussions 5.041 .674 7.479 1.109
PMBOK &
Experience 6.023 .624 9.652 1.138
Peer Communication 3.110 .685 4.540 1.332
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
Business Analyst 5.004 .545 9.182 1.132
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
(Figure 4-12). This plot is drawn to test the normality of residuals which is an important
152
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 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
significance of relationship (p < 0.05).
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
Business Analyst 4.160 .629 6.614 1.132
Meetings and Discussions 4.686 .620 7.558 1.109
PMBOK & Experience 5.889 .573 10.277 1.102
Peer Communication 4.705 .589 7.988 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
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 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:
H2: Adoption of the best practices for managing knowledge-of-project will improve
‘project management’ capability of the IT organizations in other countries
H2a: Adoption of the best practices for managing knowledge-of-project will improve
‘schedule estimation’ capability of the IT organizations in other countries
H2b: Adoption of the best practices for managing knowledge-of-project will improve
‘clear scope determination’ capability of the IT organizations in other countries
H2c: Adoption of the best practices for managing knowledge-of-project will improve
‘budget determination’ capability of the IT organizations in other countries
155
Important correlation and ANOVA statistics are summarized and described
(Table 4-126) to test the hypotheses (H2, H2a, H2b, H2c) for the data collected from
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)
Model PMC(H1) Schedule(H1a) Scope(H1b) Budget(H1c)
R .803 .788 .808 .762
R2 .728 .764 .760 .735
ANOVA
(F-ratio) 29.224 25.251 26.016 27.852
Sig.(p) .000 .001 .000 .048 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 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).
18 For complete statistics refer to Appendix C
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
Business Analyst 4.116 .454 9.066 1.011
Meetings and Discussions 3.151 .507 6.215 1.124
PMBOK & Experience 5.089 .546 9.320 1.141
Peer Communication 5.924 .534 11.094 1.255
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
We have also drawn scatter plot of residuals for PMC (Figure 4-14). 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 quite clear from the figure that the distribution for the residuals is
approximately normal. Hence, the assumption is met.
157
Figure 4-14: Regression standardized residual - PMC (others countries)
The regression coefficients (b-values) and VIF statistics for schedule estimation
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
the t-test are also positive and show the significance of relationship (p < 0.05).
Table 4-148: Regression coefficients - schedule (others countries)
Model
1
Coefficients
t
Collinearity
Statistics
b
Std.
Error VIF
(Constant) 9.853 .641 15.371
Business Analyst 3.098 .389 7.964 1.136
Meetings and
Discussions 4.169 .434 9.606 1.104
PMBOK & Experience 5.754 .430 13.381 1.111
158
Peer Communication 4.060 .536 7.575 1.334
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
We have also drawn scatter plot of residuals for schedule estimation capability
(Figure 4-15). 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 quite clear from the figure that the
distribution for the residuals is approximately normal. Hence, the assumption is met.
159
Figure 4-15: Regression Standardized Residual - Schedule (others countries)
The regression coefficients (b-values) and VIF statistics for scope
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
the t-test are also positive and show the significance of relationship (p < 0.05).
Table 4-19: Regression coefficients - scope (for Others)
Model
1
Coefficients
t
Collinearity
Statistics
b
Std.
Error VIF
(Constant) 8.849 .742 11.926
Business Analyst 3.111 .512 6.076 1.312
Meetings and
Discussions 6.164 .544 11.331 1.117
160
PMBOK & Experience 4.062 .489 8.307 1.141
Peer Communication 5.097 .510 9.994 1.255
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
(Figure 4-16). 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 quite clear from the figure that the
distribution for the residuals is approximately normal. Hence, the assumption is met.
161
Figure 4-16: Regression standardized residual - scope (others countries)
The regression coefficients (b-values) and VIF statistics for budget
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
Business Analyst 2.060 .583 6.964 1.232
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Meetings and
Discussions 3.184 .650 4.898 1.146
PMBOK and Experience 3.771 .443 8.512 1.141
Peer Communication 4.166 .545 7.644 1.018
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
(Figure 4-17). 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 quite clear from the figure that the
distribution for the residuals is approximately normal. Hence, the assumption is met.
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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:
H3: Adoption of the best practices for managing knowledge-of-project will improve
‘project management’ capability for the cumulative IT organizations of this study
H3a: Adoption of the best practices for managing knowledge-of-project will improve
‘schedule estimation’ capability for the cumulative IT organizations of this study
H3b: Adoption of the best practices for managing knowledge-of-project will improve
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‘scope determination’ capability for the cumulative IT organizations of this study
H3c: Adoption of the best practices for managing knowledge-of-project will improve
‘budget determination’ capability for the cumulative IT organizations of this study
Important correlation and ANOVA statistics are summarized and described
(Table 4-2121) to test the hypotheses (H3, H3a, H3b, H3c) for the data collected from
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
Model PMC(H3) Schedule(H3a) Scope(H3b) Budget(H3c)
R .801 .796 .781 .717
R2 .764 .774 .774 .763
ANOVA
(F-ratio) 26.504 21.286 24.944 26.918
Sig.(p) .000 .000 .002 .044 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 statistics for project
management capability for the cumulative responses are reported in the table (Table
4-22). 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,
19 For complete statistics refer to Appendix C
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
significance of relationship (p < 0.05).
Table 4-22: Regression coefficients - PMC (cumulative)
Model
1
Coefficients
t
Collinearity
Statistics
b
Std.
Error VIF
(Constant) 9.822 .764 12.856
Business Analyst 4.012 .448 8.955 1.117
Meetings and
Discussions 3.028 .398 7.608 1.180
PMBOK &
Experience 4.423 .385 11.488 1.123
Peer Communication 3.125 .345 9.058 1.225
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
We have also drawn scatter plot of residuals for PMC (Figure 4-18). 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 quite clear from the figure that the distribution for the residuals is
approximately normal. Hence, the assumption is met.
166
Figure 4-18: Regression standardized residual - PMC (cumulative)
The regression coefficients (b-values) and VIF statistics for schedule estimation
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
Business Analyst 4.020 .630 6.381 1.117
167
Meetings and Discussions 3.877 .447 8.673 1.180
PMBOK & Experience 5.043 .735 6.861 1.123
Peer Communication 3.238 .619 5.231 1.132
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
We have also drawn scatter plot of residuals for schedule estimation capability
(Figure 4-19). 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 quite clear from the figure that the
distribution for the residuals is approximately normal. Hence, the assumption is met.
168
Figure 4-19: Regression standardized residual - schedule (cumulative)
The regression coefficients (b-values) and VIF statistics for scope
determination capability for the cumulative responses are reported in the table (Table
4-154). 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 4-154: Regression coefficients - scope (cumulative)
Model
1
Coefficients
t
Collinearity
Statistics
b Std. Error VIF
(Constant) 7.839 .783 10.011
Business Analyst 4.014 .570 7.042 1.117
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Meetings & Discussions 4.273 .565 7.563 1.180
PMBOK & Experience 5.331 .562 9.486 1.123
Peer Communication 3.245 .428 7.582 1.124
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
We have also drawn scatter plot of residuals for PMC (Figure 4-20). 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 quite clear from the figure that the distribution for the residuals is
approximately normal. Hence, the assumption is met.
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Figure 4-20: Regression standardized residual - scope (cumulative)
The regression coefficients (b-values) and VIF statistics for budget
determination capability for the cumulative responses are reported in the table (Table
4-165). 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 4-165: Regression coefficients - budget (cumulative)
Model
1
Coefficients
t
Collinearity
Statistics
b
Std.
Error VIF
(Constant) 10.817 .832 13.001
Business Analyst 5.001 .596 8.391 1.117
Meetings and
Discussions 3.325 .677 4.911 1.180
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PMBOK & Experience 4.048 .758 5.340 1.232
Peer Communication 4.464 .664 6.723 1.268
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
We have also drawn scatter plot of residuals for PMC (Figure 4-21). 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 quite clear from the figure that the distribution for the residuals is
approximately normal. Hence, the assumption is met.
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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.
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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
Sig.(p) .002 .001 .000 a. Predictors: (constant), knowledge-of-project management best practices
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
Sig.(p) .000 .000 .002 a. Predictors: (constant), knowledge-of-project management best practices
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
Sig.(p) .047 .048 .044 a. Predictors: (constant), knowledge-of-project management best practices
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
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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
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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
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H1b Regression Analysis/ANOVA Supported
H1c Regression Analysis/ANOVA Partially supported
H2 Regression Analysis/ANOVA Partially supported
H2a Regression Analysis/ANOVA Supported
H2b Regression Analysis/ANOVA Supported
H2c Regression Analysis/ANOVA Partially supported
H3 Regression Analysis/ANOVA Partially supported
H3a Regression Analysis/ANOVA Supported
H3b Regression Analysis/ANOVA Supported
H3c 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.
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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. ..................................................................
Q.2. In your opinion, what are the best practices that should be followed to capture project
domain knowledge?
Best Practices:
1. ..................................................................
2. ..................................................................
3. ..................................................................
4. ..................................................................
5. ..................................................................
191
Q.3. In your opinion, what are the best practices that should be followed to capture project
institutional knowledge?
Best Practices:
1. ..................................................................
2. ..................................................................
3. ..................................................................
4. ..................................................................
5. ..................................................................
Q.4. In your opinion, what are the best practices that should be followed to capture project
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. ..................................................................
Q.6. In your opinion, what are the best practices that should be followed to organize project
domain knowledge?
Best Practices:
1. ..................................................................
2. ..................................................................
3. ..................................................................
4. ..................................................................
5. ..................................................................
193
Q.7. In your opinion, what are the best practices that should be followed to organize project
institutional knowledge?
Best Practices:
1. ..................................................................
2. ..................................................................
3. ..................................................................
4. ..................................................................
5. ..................................................................
Q.8. In your opinion, what are the best practices that should be followed to organize project
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
Dr. Azhar Mansur Khan
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
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
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
recordings for all trainings
23 Facilitating coordination
among different teams
24 Organization of orientation
sessions for new employees to
introduce them with
organizational culture
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 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.
Please mark a tick () in the appropriate box in front of each row.
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
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
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
organizational culture
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
documentation templates
30 Availability of standardized HR
documentation templates
31 Maintenance & usage of standardized
templates for documentation
203
9. How important were the following factors for "project schedule estimation" activities.
Please mark a tick () in the appropriate box in front of each row.
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 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
18 Placing code of conduct in central
repository
19 Facilitating regular informal
meetings to share & present design &
solutions
204
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
23 Facilitating coordination among
different teams
24 Organization of orientation sessions
for new employees to introduce them
with organizational culture
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 documentation templates
30 Availability of standardized HR
documentation templates
31 Maintenance & usage of standardized
templates for documentation
205
10. How important were the following factors for "project budget determination"
activities.
Please mark a tick () in the appropriate box in front of each row.
206
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 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
18 Placing code of conduct in central
repository
19 Facilitating regular informal
meetings to share & present design
& solutions
207
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
23 Facilitating coordination among
different teams
24 Organization of orientation
sessions for new employees to
introduce them with organizational
culture
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 documentation templates
30 Availability of standardized HR
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
a. Dependent Variable: Project management capability
Model Summary
Model R R2 Adjusted R2 Std. Error of
Estimate
1 .802 .722 .619 .079449
a. Dependent Variable: Schedule estimation capability
ANOVA
Model Sum of Squares
(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
a. Dependent Variable: Schedule estimation capability
Model Summary
Model R R2 Adjusted R2 Std. Error of
Estimate
1 .785 .769 .723 .093495
a. Dependent Variable: Scope determination capability
ANOVA
Model Sum of Squares
(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
a. Dependent Variable: Scope determination capability
Model Summary
209
Model R R2 Adjusted R2 Std. Error of
Estimate
1 .768 .757 .734 .105864
a. Dependent Variable: Budget determination capability
ANOVA
Model Sum of Squares
(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
a. Dependent Variable: Budget determination capability
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
a. Dependent Variable: Project management capability
ANOVA
Model Sum of Squares
(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
a. Dependent Variable: Project management capability
Model Summary
Model R R2 Adjusted
R2
Std. Error of
Estimate
1 .788 .764 .714 .074785
a. Dependent Variable: Schedule estimation capability
ANOVA
Model Sum of Squares
(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
a. Dependent Variable: Schedule estimation capability
Model Summary
Model R R2 Adjusted
R2
Std. Error of
Estimate
1 .808 .760 .700 .098448
a. Dependent Variable: Scope determination capability
ANOVA
Model Sum of Squares
(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
a. Dependent Variable: Scope determination capability
Model Summary
211
Model R R2 Adjusted
R2
Std. Error of
Estimate
1 .762 .735 .716 .111987
a. Dependent Variable: Budget determination capability
ANOVA
Model Sum of Squares
(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
a. Dependent Variable: Budget determination capability
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
a. Dependent Variable: Project management capability
ANOVA
Model Sum of Squares
(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
a. Dependent Variable: Project management capability
Model Summary
Model R R2 Adjusted R2 Std. Error of
Estimate
1 .796 .774 .731 .082738
a. Dependent Variable: Schedule estimation capability
ANOVA
Model Sum of Squares
(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
a. Dependent Variable: Schedule estimation capability
Model Summary
Model R R2 Adjusted R2 Std. Error of
Estimate
1 .781 .768 .761 .099276
a. Dependent Variable: Scope determination capability
ANOVA
Model Sum of Squares
(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
a. Dependent Variable: Scope determination capability
213
Model Summary
Model R R2 Adjusted
R2
Std. Error of
Estimate
1 .763 .717 .701 .112058
a. Dependent Variable: Budget determination capability
ANOVA
Model Sum of Squares
(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
a. Dependent Variable: Budget determination capability
214
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