a contingency view on the effect of project management maturity on perceived performance

A contingency view on the effect of project management maturity on perceived performance

Luciano Cerqueira Torres

A thesis

submitted for the degree of

Doctor of Philosophy in Strategy, Programme and Project Management

April 9, 2014

Luciano Cerqueira Torres, 2014

CERTIFICATE OF AUTHORSHIP/ORIGINALITY

I certify that the work in this thesis has not previously been submitted for a degree nor has

it been submitted as part of requirements for a degree except as fully acknowledged

within the text.

I also certify that the thesis has been written by me. Any help that I have received in my

research work and the preparation of the thesis itself has been acknowledged. In addition,

I certify that all information sources and literature used are indicated in the thesis.

Luciano Cerqueira Torres

Wednesday, 9 April 2014


Acknowledgements

This thesis would not be completed without the help of Dr. Ginger Levin, my advisor,

who always went the extra mile helping me any day of the week, with very detailed and

timely reviews, suggestions, and messages of encouragement and support. She was also

extremely helpful in securing the use of the ProjectFRAMEWORK® maturity model and

helping its customization for the use in this thesis.

I would also like to thank Professor Ralf Müller, for all the support given in the classes of

the program and throughout. Also, the workshops held by Professor Ralf Müller and

Professor Rodney Turner were crucial to the development of this thesis, and their help

during those intense sessions was key for me to be able to define the research model, the

research questions and the methodology – most importantly the one held in Warsaw in

July 2008.

I would like to thank Christophe Bredillet who took me in the program and supported the

start of this undertaking in any way he could, including getting me a very cozy apartment

in Lille.

And finally I would like to thank Vivian, my wife, for enduring this journey with me.


Table of Contents CERTIFICATE OF AUTHORSHIP/ORIGINALITY .................................................... 2

Acknowledgements .............................................................................................................. 3

Table of Contents ................................................................................................................. 4

List of Figures ...................................................................................................................... 8

List of Tables ..................................................................................................................... 10

List of Acronyms and Abbreviations ................................................................................. 13

Abstract .............................................................................................................................. 14

Chapter 1 – Introduction .................................................................................................... 16

1.1. Background .......................................................................................................... 16

1.1.1. Maturity Models ............................................................................................ 16

1.1.2. Contingency .................................................................................................. 19

1.1.3. Performance .................................................................................................. 20

1.2. Research Question ............................................................................................... 21

1.3. Methodology ........................................................................................................ 21

1.4. Summary of the Results ....................................................................................... 22

1.5. Structure of the Thesis ......................................................................................... 25

1.5.1. Chapter 2 – Literature Review ...................................................................... 25

1.5.2. Chapter 3 – Methodology ............................................................................. 25

1.5.3. Chapter 4 – Data Analysis ............................................................................ 25

1.5.4. Chapter 5 – Conclusions ............................................................................... 25

1.6. Summary .............................................................................................................. 26

Chapter 2 – Literature Review ........................................................................................... 27

2.1. Introduction .......................................................................................................... 27

2.2. Project Management Maturity ............................................................................. 27

2.3. Statistical Process Control ................................................................................... 28

2.4. Maturity Models ................................................................................................... 30

2.4.1. Stages of Growth ........................................................................................... 30

2.4.2. The Quality Management Maturity Grid ...................................................... 30

2.4.3. The Capability Maturity Model (CMM) ....................................................... 32

2.4.3.1. Level 1 – The Initial Level ..................................................................... 34

2.4.3.2. Level 2 – The Repeatable Level ............................................................ 34

2.4.3.3. Level 3 – The Defined Level ................................................................. 34


2.4.3.4. Level 4 – The Managed Level ............................................................... 35

2.4.3.5. Level 5 – The Optimizing Level ............................................................ 35

2.4.4. The People CMM .......................................................................................... 35

2.4.5. The CMM Integrated (CMMI) ...................................................................... 35

2.4.6. Maturity Models ............................................................................................ 37

2.4.7. Project Management Maturity Models ......................................................... 39

2.4.7.1. OPM3 – Organizational Project Management Maturity Model (PMI,

2013b) 40

2.4.7.2. PM2 - Project Management Maturity Model (Crawford, 2007) ............ 43

2.4.7.3. ProjectFRAMEWORK® (Levin et al., 2013c) ...................................... 45

2.4.7.4. Project-oriented company Maturity Model (Gareis & Füssinger, 2007) 46

2.4.7.5. Project Management Maturity Model (Kerzner, 2005) ......................... 47

2.4.7.6. PM3M - Portfolio, Programme and Project Management Maturity

Model (OGC, 2010a) ............................................................................................. 48

2.4.7.7. MGP – Project Management Maturity (Prado, 2008) ............................ 49

2.4.7.8. Risk Maturity Model (Hillson, 1997) and ProMMM – Project

Management Maturity Model (Hillson, 2003) ....................................................... 49

2.4.7.9. CPMEM – Cultural Project Management Effectiveness Model (Piney,

2004) 51

2.4.8. The Value of Project Management Maturity Models ................................... 51

2.4.8.1. Strategic Value ....................................................................................... 51

2.4.8.2. Benchmarking ........................................................................................ 52

2.4.8.3. Project Management Performance ......................................................... 53

2.4.9. Criticisms of Maturity Models ...................................................................... 55

2.4.10. Process Capability and Project Management Maturity ............................... 56

2.5. Contingency Theory ............................................................................................. 57

2.5.1. Contingency Theory in Project Management Research ................................ 62

2.5.2. Studies of Project Management Using Contingency .................................... 64

2.6. Performance ......................................................................................................... 69

2.7. Studies of Maturity and Contingency .................................................................. 72

2.8. Summary .............................................................................................................. 74

2.8.1. Summary of concepts .................................................................................... 74

2.8.2. Knowledge gap and justification for the research topic ................................ 75

Chapter 3 – Methodology .................................................................................................. 76


3.1. Research Philosophy ............................................................................................ 76

3.1.1. Ontology and Epistemology ......................................................................... 76

3.1.2. Philosophies and Research Methods ............................................................. 77

3.2. Research Methods ................................................................................................ 80

3.3. Research Model ................................................................................................... 81

3.4. Research Methodology ........................................................................................ 81

3.4.1. Instrument Design ......................................................................................... 81

3.4.1.1. Project Management Maturity ............................................................... 83

3.4.1.2. Contingency ........................................................................................... 85

3.4.1.3. Performance ........................................................................................... 85

3.4.2. Pilot ............................................................................................................... 87

3.4.3. Ethical Considerations .................................................................................. 88

3.5. Sampling .............................................................................................................. 88

3.6. Data Analysis ....................................................................................................... 89

3.7. Data Check ........................................................................................................... 90

3.8. Summary .............................................................................................................. 91

Chapter 4 – Data Analysis ................................................................................................. 93

4.1. The Sample .......................................................................................................... 93

4.2. Project Management Maturity ............................................................................. 96

4.2.1. Reliability of the Scale .................................................................................. 98

4.3. Project Context ..................................................................................................... 99

4.4. Performance ....................................................................................................... 101

4.4.1. Factor Analysis ........................................................................................... 103

4.5. Review of Research Model and Hypotheses Definition .................................... 106

4.6. Regression Analysis ........................................................................................... 107

4.6.1. Impact on Team .......................................................................................... 108

4.6.2. Organizational performance ........................................................................ 111

4.6.3. Impact on customer ..................................................................................... 114

4.6.4. Project Financial Results ............................................................................. 117

4.6.5. Preparing for the future ............................................................................... 121

4.6.6. Project impact on business .......................................................................... 122

4.6.7. Project efficiency ........................................................................................ 122

4.6.8. Internal Efficiency ...................................................................................... 124

4.6.9. Overall Performance ................................................................................... 127


4.6.10. Summary of Results .................................................................................. 129

4.7. Summary ............................................................................................................ 131

Chapter 5 – Discussions and Conclusions ....................................................................... 132

5.1. Project management maturity ............................................................................ 132

5.2. Contingency applied to project management ..................................................... 132

5.3. Impact of maturity on performance ................................................................... 133

5.4. Industry of the project ........................................................................................ 134

5.5. Project management maturity, context and performance .................................. 135

5.5.1. Impact on team ............................................................................................ 135

5.5.2. Organizational Performance ....................................................................... 137

5.5.3. Impact on Customer .................................................................................... 138

5.5.4. Project Financial Results ............................................................................. 141

5.5.5. Internal Efficiency ...................................................................................... 144

5.5.6. Overall Performance ................................................................................... 144

5.5.7. Variance ...................................................................................................... 145

5.5.8. Discussion on Counterbalancing Contingency Factors .............................. 146

5.6. Contributions to Theory ..................................................................................... 146

5.7. Contributions to Practice .................................................................................... 147

5.8. Limitations of the Research ............................................................................... 147

5.9. Opportunities for Future Research ..................................................................... 148

5.10. Summary .......................................................................................................... 149

References ........................................................................................................................ 151

Appendix A – Questionnaire ....................................................................... 168

Appendix B – Additional analysis figures and charts .................................. 183


List of Figures Figure 1 – Control chart of process not under statistical control, adapted from Shewhart

(1939, p. 114) ............................................................................................................. 29

Figure 2 – Control chart of process under statistical control, adapted from Shewhart

(1939, p. 114) ............................................................................................................. 29

Figure 3 – Building blocks of OPM3 (PMI, 2013b) .......................................................... 41

Figure 4 – Sample Results of OPM3 assessment, from PMI (2008, p. 6) ......................... 42

Figure 5 – Project-oriented company Maturity Model, adapted from Füssinger (2006, p. 2)

.................................................................................................................................... 46

Figure 6 – Kerzner's Project Management Maturity Model levels of maturity ................. 48

Figure 7 – Goals and Methods Matrix, adapted from Turner & Cochrane (1993, p. 95) .. 65

Figure 8 – Shenhar & Dvir Diamond Model (Shenhar & Dvir, 2007, p. 14) .................... 68

Figure 9 – Research Model ................................................................................................ 81

Figure 10 – Missing data analysis for maturity ................................................................. 97

Figure 11 – Missing value analysis for performance variables ....................................... 102

Figure 12 – Boxplot for Stakeholder Value ..................................................................... 102

Figure 13 – Scree Plot of Performance Factors ............................................................... 105

Figure 14 – Regression line for impact on team .............................................................. 136

Figure 15 – Regression line for impact on team, project industry ................................... 137

Figure 16 – Regression line for organizational performance and project strategic goals 138

Figure 17 – Regression lines for organizational performance and industry of the project

.................................................................................................................................. 138

Figure 18 – Regression line for impact on customer and goals ....................................... 139

Figure 19 – Regression line for impact on customer and novelty ................................... 140

Figure 20 – Regression line for impact on customer and industry of the project ............ 141

Figure 21 – Regression line for project financial results and complexity ....................... 142

Figure 22 – Regression line for project financial results and technology ........................ 143

Figure 23 – Regression lines for project financial results and industry of the project .... 143

Figure 24 – Internal efficiency and age of organization .................................................. 144

Figure 25 – Regression lines for overall performance and industry of the project .......... 145

Figure 26 – Scatterplot for impact on team regression .................................................... 188

Figure 27 – Scatterplot for impact on team and project industry .................................... 189


Figure 28 – Scatterplot for organizational performance and project industry ................. 189

Figure 29 – Scatterplot for impact on customer regression ............................................. 189

Figure 30 – Scatterplot for impact on customer and project industry regression ............ 190

Figure 31 – Scatterplot for project financial results regression ....................................... 190

Figure 32 – Scatterplot for project financial results and project industry regression ...... 190

Figure 33 – Scatterplot for internal efficiency, using age of organization as moderator . 191

Figure 34 – Scatterplot for internal efficiency and industry of the project as moderator 191


List of Tables Table 1 – Nolan's Stages of Growth (Gibson & Nolan, 1974) .......................................... 30

Table 2 – Adapted from Crosby (1996, p. 32) ................................................................... 31

Table 3 – Performance Improvements from Gibson et al (2006) ...................................... 54

Table 4 – Organizational structures, (Perrow, 1967) ......................................................... 60

Table 5 – Typology for project strategy from Pich & Loch (2002) ................................... 66

Table 6 – Comparison of four research philosophies in management research (M.

Saunders et al., 2009, p. 119) ..................................................................................... 79

Table 7 – Analysis of maturity models as instruments ...................................................... 83

Table 8 – Project Contingency Constructs ......................................................................... 85

Table 9 – Project Performance Questionnaire, adapted from Shenhar & Dvir (2007) ...... 86

Table 10 – Organizational Performance Constructs .......................................................... 87

Table 11 – Sources and the number of responses .............................................................. 93

Table 12 – Country distribution ......................................................................................... 94

Table 13 – Role of respondent distribution ........................................................................ 95

Table 14 – Descriptive Statistics for Project Management Maturity ................................. 96

Table 15 – EM Means for Project Management Maturity ................................................. 97

Table 16 – Descriptive statistics for maturity variables after filling missing data ............ 98

Table 17 – Reliability analysis for maturity variables ....................................................... 98

Table 18 – Pearson correlation indexes for maturity variables .......................................... 99

Table 19 – Descriptive Statistics for Context Variables .................................................... 99

Table 20 – Descriptive Statistics For Ratio Variables After Transformation .................. 100

Table 21 – Frequencies For Project Customer ................................................................. 100

Table 22 – Frequencies for Project Strategic Goal .......................................................... 100

Table 23 – Frequencies for Project Industry .................................................................... 101

Table 24 – Descriptive Statistics to The Project Contributed to Stakeholder Value ....... 103

Table 25 – KMO and Bartlet's test for Performance Variables ....................................... 103

Table 26 – Rotated Component Matrix for Performance (coefficients above 0.5) ......... 104

Table 27 – Reliability Tests for Performance Factors ..................................................... 105

Table 28 – Regression for impact on team ...................................................................... 108

Table 29 – Regression for impact on team with reduced terms ....................................... 109

Table 30 – Regression for impact on team and project strategic goal ............................. 110

Table 31 – Regression for impact on team and project industry ..................................... 110


Table 32 – Regression for impact on team and project industry ..................................... 111

Table 33 – Regression for organizational performance ................................................... 111

Table 34 – Regression for organizational performance, with goals as interaction term .. 112

Table 35 – Regression for organizational performance and project strategic goal .......... 112

Table 36 – Regression for organizational performance and project strategic goal .......... 113

Table 37 – Regression for organizational performance and project industry .................. 113

Table 38 – Regression for organizational performance and government projects .......... 114

Table 39 – Regression for impact on customer ............................................................... 114

Table 40 – Regression for impact on customer with reduced terms ................................ 115

Table 41 – Regression for impact on customer and project strategic goal ...................... 116

Table 42 – Regression for impact on customer and project industry .............................. 116

Table 43 – Regression for impact on customer and project industry .............................. 117

Table 44 – Regression for project financial results ......................................................... 118

Table 45 – Regression for project financial results using reduced interaction terms ...... 118

Table 46 – Regression for project financial results and project strategic goal ................ 119

Table 47 – Regression for project financial results and project strategic goal ................ 119

Table 48 – Regression for project financial results and industry of the project .............. 120

Table 49 – Regression for project financial results and industry of the project .............. 120

Table 50 – Regression for preparing for the future .......................................................... 121

Table 51 – Regression for project impact on business .................................................... 122

Table 52 – Regression for project efficiency ................................................................... 123

Table 53 – Regression for project efficiency and project strategic goal .......................... 123

Table 54 – Regression for project efficiency and industry of the project ........................ 124

Table 55 – Regression for internal efficiency .................................................................. 125

Table 56 – Regression for internal efficiency using company age as interaction term ... 125

Table 57 – Regression for internal efficiency and project strategic goal ........................ 126

Table 58 – Regression for internal efficiency and industry of the project ....................... 126

Table 59 – Regression for internal efficiency and industry of the project with reduced

terms ......................................................................................................................... 127

Table 60 – Regression for overall performance ............................................................... 127

Table 61 – Regression for overall performance and project strategic goal ..................... 128

Table 62 – Regression for overall performance and project strategic goal ..................... 128

Table 63 – Regression for overall performance and industry of the project ................... 129

Table 64 – Summary of results ........................................................................................ 129


Table 65 – Performance factors and link to maturity ...................................................... 133

Table 66 – Significant contingency factors ..................................................................... 135

Table 67 – Factors influencing the impact of project management maturity on

performance ............................................................................................................. 150

Table 68 – Descriptive statistics for performance questions before treatment for missing

values ....................................................................................................................... 183

Table 69 – Descriptive statistics for performance questions after treatment for missing

values ....................................................................................................................... 184

Table 70 – Performance questions anti-image correlation diagonals (Measure of Sample

Adequacy) ................................................................................................................ 186

Table 71 – Reliability test for impact on team ................................................................. 186

Table 72 – Reliability tests for organizational performance ............................................ 186

Table 73 – Reliability tests for impact on customer ........................................................ 187

Table 74 – Reliability tests for project financial results .................................................. 187

Table 75 – Reliability tests for preparing for the future .................................................. 187

Table 76 – Reliability tests for project impact on business ............................................. 187

Table 77 – Reliability tests for project efficiency ............................................................ 188

Table 78 – Reliability tests for internal efficiency ........................................................... 188


List of Acronyms and Abbreviations

CMM Capability Maturity Model

CMMI Capability Maturity Model Integration

CPI Cost Performance Index

CPMEM Cultural Project Management Effectiveness Model

EDP Electronic Data Processing

EM Expectation-maximization

ESI Educational Services Institute

EVM3 Earned Value Management Maturity Model

IMSI Integrated Management Systems Inc.

IPD-CMM Integrated Product Development Capability Maturity Model

IS Information Systems

IT Information Technology

NTCP Novelty, Technology, Complexity and Pace

OGC Office of Government Commerce

OPM3 Organizational Project Management Maturity Model

PM3M Portfolio, Programme and Project Management Maturity Model

PMBOK Project Management Body of Knowledge

PMI Project Management Institute

PMMM Project Management Maturity Model

PRINCE2 Projects in Controlled Environments 2

RMM Risk Maturity Model

SEI Software Engineering Institute

SECM Systems Engineering Capability Model

SMCI Standardize, Measure, Control and Improve

SPI Schedule Performance Index


Abstract

For over two decades now a number of project management maturity models have

appeared with the promise to use them to improve the project delivery capabilities of

organizations. Although many practitioners, consultants and researchers claim success in

implementing those models, the real benefits of those models are yet to be proven. It is

time we look at project management maturity from a contingency perspective and start

from the assumption that not all projects and organizations are the same, seeking to learn

more about the dynamics of maturity, context and performance. This thesis addresses the

topic of the value of maturity models using contingency theory.

The research question formulated is “What are the factors that influence the impact of

project management maturity on performance?” Taking a critical realist view, the thesis

uses quantitative methods to answer the question, using different context factors and

different performance perspectives.

The research model, common to studies using contingency theory, is composed of the

study of the main relationship between maturity and performance and of moderating

factors impacting the relationship. Two general hypotheses were defined, the first being

maturity has a significant positive relationship with performance, and the second that this

relationship is moderated by contingency factors. The second hypothesis was then

detailed according to the contingency factors investigated, being split into three different

hypotheses.

A questionnaire was designed using mostly existing instruments. Maturity was measured

based on the ProjectFRAMEWORKTM model. The moderating factors were taken from

contingency research applied to project management, such as the NTCP (Novelty,

Technology, Complexity and Pace) model from Shenhar & Dvir (2007) and the Goals and

Methods matrix from Turner & Cochrane (1993). For performance, a multi-dimension

measure was used to collect different aspects of project and organizational performance,

both tangible and intangible levels.


During a two-month period, 211 responses from a questionnaire were collected. The data

were analyzed, and the hypotheses were tested using moderated hierarchical regression

analysis.

The results show that different context factors, such as technology, complexity and

novelty of the project, clarity of goals and methods, strategic goal of the project and

industry do play a role in the value obtained by maturity in different performance aspects

of the project. More specifically, the results show that low-technology projects benefit

more from higher maturity than high-technology projects, likening high maturity

organizations to the mechanistic profile from classical contingency theory. On the other

hand, project novelty plays a contrary role, maximizing the value of maturity, perhaps

driven by the “liability of newness” effect. Those results characterize further steps toward

a better theoretical understanding of the value of maturity in project management – and

ultimately could lead to improvements on existing maturity models, or to the creation of

new, more “mature”, project management maturity models.


Chapter 1 – Introduction

1.1. Background

As companies struggle to deliver projects, they recognize that delivering successful

projects cannot depend only on the effort and skills of individuals but also on

organizational capabilities to globally support their project managers. To evaluate their

current capabilities, processes, tools, policies, systems, and analyze the gaps between

them and the best practices of the industry, they turn to project management maturity

models (Cooke-Davies, Schlichter, & Bredillet, 2001).

As the review of the literature will show, even though there are a number of claims that

increasing the project management maturity bring organizational benefits (Ibbs & Kwak,

2000; Pennypacker & Grant, 2003), the context where improving maturity brings value is

not yet fully understood. This thesis intends to look at the claimed benefits of achieving

high levels of project management maturity contextualized into the environment and

nature of the projects of the organizations.

1.1.1. Maturity Models

The maturity models, as they are known today, originated from the combination of

concepts for quality management from Deming and Juran, together with statistical quality

control from Shewhart (Cleland & Ireland, 2006; Humphrey, 1989).

The assumption is that the project processes were similar to production processes to run

an assembly line; therefore the same quality concepts could be applied: if the processes

and their inputs and outputs are not standardized, the results are unpredictable. As

performing the same activities will produce the same results, standardizing the processes

would bring the performance to a controlled and predictable state (Humphrey, 1989). This

would allow improvements in the performance by making changes to the process. If the

process is not standardized, those incremental improvements are not possible (Humphrey,

1989).


These ideas inspired the creation of the successful Capability Maturity Model (CMM)

from the Software Engineering Institute (SEI) at the Carnegie-Mellon University (Paulk,

Curtis, Chrissis, & Weber, 1993), which inspired a number of project management

maturity models starting from the late 1980s (Cooke-Davies, 2007).

Those models inherited from the Capability Maturity Model (CMM) the strong emphasis

of standardization and control of the project management processes (Pasian, Sankaran, &

Boydell, 2012).

When the first maturity models were being developed, the understanding of project

success was a more operational one, measuring project efficiency using the iron triangle

of cost, schedule and adherence to technical specifications (Shenhar & Stefanovic, 2006).

Assessing project performance using those measures is convenient, as the data is readily

available at the end of project, and it’s immediacy allows continuous improvement using

the feedback on incremental improvements to the process and its impact on performance

(Pinto & Slevin, 1988; Shenhar, Levy, & Dvir, 1997). However the view of project

performance has evolved from operational to a more strategic one, which takes into

account other factors such as the long-term effects of the project after the project is

finished, and the actual value it brings to the users (Jugdev & Müller, 2005).

One other operational measurement of performance, the compliance to requirements and

technical specifications, is often taken as a proxy for the customer satisfaction, however it

does not consider that, for some projects, some customers do not have a clear

understanding of the requirements upfront (Shenhar et al., 1997). One often cited example

of such disparity of operational versus strategic measures of project performance is the

Sidney opera house, whose costs were 14 times higher than the budget and it took 15

years to finish, by no means an example of project performance using the iron triangle,

but today it represents an engineering masterpiece (Shenhar & Dvir, 2007). For those

reasons, this thesis will look at many performance indicators and the impact of maturity

on all of them, to attempt an increase in our understanding of the impact of maturity on

more strategic project performance indicators.

Maturity models are used by the organization to assess the situation in which the

organization currently is placed in regards to processes standardization, and assist in the


planning of the implementation of the next processes assuming that an organization must

follow a predictable path to maturity (Cooke-Davies, 2007). For that it assumes there is a

general set of steps that can be followed by any organization to that end – even though

there are too many project environments – different industries, markets, strategies and

types of projects – raising doubts that a single project management maturity model can

exist and offer a global “development path” to a single “perfected end-state” and be

applicable to all organizations (Cooke-Davies, 2007). This thesis will address this

question, by looking not only at the performance obtained by increasing the project

management maturity, but also looking for differences in this relationship in different

project contexts and industries.

There is empirical evidence that achieving higher project management maturity brings

value, even if sometimes not a strong one. Early studies tried to find the relationship with

project efficiency, with mixed results. Flowe & Thordahl (1994) found a link between

maturity and CPI/SPI, but only for certain maturity levels and project sizes. Herblseb et

al. (1997) found, in general, a link between maturity and several dimensions of

performance, but customer satisfaction, in certain cases, actually dropped when maturity

increased – which also was the result in another study done by Gibson et al (2006). Ibbs et

al. (Ibbs & Kwak, 2000; Ibbs, Reginato, & Kwak, 2004) found a link between maturity

and the variation of Cost and Schedule Performance Indicators (CPI/SPI), but a very weak

link between maturity and the actual CPI/SPI. In their extensive research project on the

value of project management, Mullaly and Thomas showed that higher levels of project

management maturity increase the intangible value that is obtained from project

management in the organization, but they could not find evidence of increase in tangible

value (Thomas & Mullaly, 2008). Also, they emphasized in their study the link between

the value of project management and its fit to the organization’s context. One important

result of their research is that the sustainability of the value is correlated to the degree of

this fit (Mullaly & Thomas, 2009). In other words, the concept of value of project

management and fit to the environment is dynamic, and the misfit between the project

management maturity and the organization project context would impact the future value

of project management, even if at the present moment project management is providing

value for the organization (Mullaly & Thomas, 2009).


The study of project context and how it affects the value of project management maturity

is possible through the use of contingency theory, as explained next.

1.1.2. Contingency

In fact, by looking at projects as temporary organizations, we can argue that projects are

subject to the contingency theory, which proposes (in regard to organizational structures):

1. “There is no best way to organize”

2. “Any way of organizing is not equally effective” (Galbraith, 1973, p. 2)

It means that organizations operating in different environments, with higher or lower

uncertainties, instability and complexity, must have different structures in order to cope

with the requirements of the environment (Betts, 2011), which conflicts with the

generalist approach of maturity models.

However, contingency theory may explain why some projects can actually benefit from

higher organizational maturity. Contingency theory proposes a scale of structural

archetypes, having in the extremes the mechanistic and organic structures, with

mechanistic being better suited to cope with stable and simple market and technology

settings, while organic is adequate for complex, unpredictable and ever changing

environments (Mintzberg, 1979). In the mechanistic structure, the activities are

coordinated with clear definition of rules and procedures, while in the organic structure

the coordination shifts to the definition of the end goals and by allocating personnel with

the required skills, without much concern with the methods employed (Galbraith, 1977;

Mintzberg, 1979). Clear definition of rules and procedures is a pre-condition for

achieving higher maturity levels (Pasian et al., 2012), therefore, it is possible that higher

maturity results in higher performance in those environments with less inherent

uncertainty.

As uncertainty of environments can be one strong moderating factor, there are possibly

others that also influence this link. In the literature reviewed, there were studies of

contingency factors impacting performance obtained by maturity using factors such as

knowledge of methods and goals (Pasian, 2011), organizational culture (Yazici, 2009b),

baseline changes (Flowe & Thordahl, 1994) or risk profiles (Bahli, Sidenko, & Borgman,


2011). Some studies were focused on a specific type of project such as product

development (Dooley, Subra, & Anderson, 2001). This thesis intends to look at this

question by investigating the effect of project management maturity on perceived

performance of different types of projects exploring variables that influence this link,

derived from the contingency theory.

1.1.3. Performance

Project management maturity, as an organizational asset, is designed to improve the

project management effectiveness and performance (Kwak & Ibbs, 2002). In order to

understand the claimed increase in performance, it is necessary to look at the concepts of

project performance and organization performance.

Project performance is, according to a number of researchers, a multi-dimensional

concept (Jugdev & Müller, 2005; Shenhar et al., 1997). A model to measure success must

take into account the assessment of a range of stakeholders over different time scales

(Turner, Zolin, & Remington, 2009). Shenhar (2007) proposed a list of measures that

cover a wide spectrum of project situations and time horizons, as well as the point of view

of different stakeholders. The measures were used in the definition of the performance

construct. They are:

• Project efficiency

• Impact on the customer

• Impact on the team

• Business and direct success

• Preparation for the future

Similarly, the construct of organization performance must be, according to the literature

(Chenhall & Langfield-Smith, 2007; Kaplan & Norton, 1996; Venkatraman &

Ramanujam, 1986), multidimensional, taking into account financial and non-financial

measures.

For this thesis, the instrument to measure performance will be created based on

dimensions used in existing literature researching the impact of different strategies on


organizational performance (Bisbe & Otley, 2004; Denison & Mishra, 1995; Gupta &

Govindarajan, 1984; W. R. King & Teo, 2000; Nahm, Vonderembse, & Koufteros, 2003;

Tracey, Vonderembse, & Lim, 1999; Ward & Duray, 2000; Yazici, 2009b). The

dimensions are divided in those related to a financial perspective: sales growth rate,

profitability; and non-financial: customer satisfaction, market share, internal efficiency

and overall business performance.

1.2. Research Question

From the background of the research exposed, the research question can be formulated as

“What are the factors that influence the impact of project management maturity on

performance?”

1.3. Methodology

The research methodology took a critical realist view. The method chosen for the research

is quantitative. The justification for the methods and a discussion on the implications are

presented in Chapter 3.

The research model uses maturity as independent variable, and the dependent variables

are project and organizational performance. The moderating variables are project novelty,

technology, complexity, pace, the knowledge of project goals and methods, strategic goal

and industry of the project.

Four high-level hypotheses were proposed initially, which were subsequently expanded

after factor analysis on the performance variables. They are:

• H1: Organizational project management maturity has a positive relationship on

performance.

• H2: Project context affects how organizational project management maturity is

related to performance.

• H3: Strategic goal of the project affects how organizational project management

maturity is related to performance

• H4: Industry of the project affects how organizational project management



The questionnaire was developed based on existing measurements for project

management maturity, contingency factors, project and organizational performance. The

measurement for project management maturity involved an analysis of a number of

existing models to select one that was adequate for an online survey and, at the same time,

captured the construct as faithfully as possible considering the conceptualization of

maturity done in the literature review, as discussed in Chapter 2.

The data were collected via an online survey with professionals involved on projects.

Since the test of the hypotheses would be made with regressions, the data were pre-tested

for normality and other aspects required by regression analysis.

The performance dimensions analyzed were the results of the factor analysis performed in

the data collected, they are:

• Impact on team

• Organizational performance

• Impact on customer

• Project financial results

• Preparing for the future

• Project impact on business

• Project efficiency

• Internal efficiency

• Overall performance

To search for moderating variables, regression analysis was used with interaction terms as

variables (Saunders, 1956; Sharma, Durand, & Gur-Arie, 1981), using multiple

hierarchical regression analysis to verify the impact of the moderation.

1.4. Summary of the Results

The hypothesis H1 was highly supported, as a significant positive relationship was found

for seven of the nine factors for project performance. The supported hypotheses are below



performance. Performance is measured as

• H1a: Impact on team: supported

• H1b: Organizational performance: supported

• H1c: Impact on customer: supported

• H1d: Project financial results: supported

• H1e: Preparing for the future: not supported

• H1f: Project impact on business: not supported

• H1g: Project efficiency: supported

• H1h: Internal efficiency: supported

• H1i: Overall performance: supported

The hypothesis H2 was also supported for some of the sub-hypotheses. They are listed

below


related to performance. Performance is measured as

• H2a: Impact on team: supported, moderating factor is knowledge of project

goals

• H2b: Organizational performance: not supported

• H2c: Impact on customer: supported, moderating factors are knowledge of

project methods and novelty of the project

• H2d: Project financial results: supported, moderating factors are

complexity and technology of the project



• H2g: Project efficiency: not supported

• H2h: Internal efficiency: supported, moderating factor is age of

organization

• H2i: Overall performance: not supported

The results show that the positive relationship between maturity and performance is

affected by moderating factors.


Using project strategic goal as moderator, the results are listed below

• H3: Project strategic goal affects how organizational project management maturity

is related to performance. Performance is measured as

• H3a: Impact on team: not supported

• H3b: Organizational performance: supported, for infrastructure as project

strategic goal

• H3c: Impact on customer: not supported

• H3d: Project financial results: not supported




• H3h: Internal efficiency: not supported

• H3i: Overall performance: not supported

And finally for industry of the project, the results are


is related to performance. Performance is measured as

• H4a: Impact on team: supported, for consumer electronics as industry

• H4b: Organizational performance: supported, for government as industry

• H4c: Impact on customer: supported, for software as industry

• H4d: Project financial results: supported, for telecommunications as

industry




• H4h: Internal efficiency: supported, for consulting as industry

• H4i: Overall performance: supported, for software as industry

A full discussion of the results is presented in Chapter 5.


1.5. Structure of the Thesis

The thesis follows the structure described below.

1.5.1. Chapter 2 – Literature Review

This chapter presents a review of the existing literature of the concepts under study and

the existing research linking those concepts, namely project management maturity,

contingency theory and project performance. The justification to study the topic, and the

managerial problem being addressed, is gradually built based on the literature.

1.5.2. Chapter 3 – Methodology

Here the methodology of the study is presented and justified. The philosophical

underpinnings and its implications are discussed. The research question is formulated,

based on the topic under study, and the philosophical stance is adopted. The initial

hypotheses are presented, and the methods for data analysis are discussed.

1.5.3. Chapter 4 – Data Analysis

In this chapter the results of the analysis of the data collected are reported. The descriptive

statistics are presented. The moderating factors are uncovered using regression analysis,

which enable us to refine the main hypotheses defined in Chapter 3. The research model

and the refined hypotheses are then tested using moderated hierarchical regression

analysis.

1.5.4. Chapter 5 – Conclusions

To conclude, the statistical results are then analyzed against the original problem and the

theory, and conclusions are drawn based on the study. Suggestions for practitioners are

presented, limitations of the research are discussed and possible future research topics are

proposed.


1.6. Summary

This chapter introduced the thesis, presenting the justification for the research and the

background of the problem addressed. The methodology is briefly presented, with the

research questions and hypotheses. Finally the structure of the thesis is explained.


Chapter 2 – Literature Review

2.1. Introduction

This chapter will review the literature based on four core concepts, which are part of this

thesis. The first concept is project management maturity models, and its underlying

concepts of maturity stages and statistical process control. A review of project

management maturity models will be presented. Then, there is a discussion of the

potential value of project management maturity models and their common criticisms.

The second concept is contingency theory. Its theoretical foundation will be reviewed,

along with its applications in project management theory and its weaknesses. The third

concept is performance. There will be an analysis of the constructs and their application

on project management and organizational theory research.

Before closure, there is a literature review of contingency theory applied to project

management maturity. Finally the chapter will present a summary of the concepts, the

current knowledge gap and justification for the research.

2.2. Project Management Maturity

In order to study the concept of project management maturity it is necessary to start by

defining the terms. Mature, according to the Merriam Webster Dictionary, is “having

completed natural growth and development”, and “having attained a final or desired state”

(“Mature,” 2013), and maturity is the state of being mature.

Projects are, according to the Project Management Institute (PMI) (PMI, 2013a, p. 3), “A

temporary endeavor undertaken to create a unique product, service or result”, and project

management is “The application of knowledge, skills, tools, and techniques to project

activities to meet the project requirements” (PMI, 2013a, p. 3). And according to the

Office of Government Commerce (OGC), projects are “a temporary organization that is

created for the purpose of delivering one or more business products according to an

agreed business case” (OGC, 2009, p. 3), and project management is “the planning,

delegating, monitoring and control of all aspects of the project, and the motivation of


those involved, to achieve the project objectives within the expected performance targets

for time, cost, quality, scope, benefits and risks.” (OGC, 2009, p. 4).

Applying the concept of project management maturity to organizations, it would mean a

state where the organization is in a perfect condition to achieve its objectives and to deal

with its projects (Andersen & Jessen, 2003).

In this context, a maturity model is a model that identifies gaps between the current

organizational situation and the intended one, which can be closed by succeeding

development activities (Mettler & Rohner, 2009). The maturity models describe the

current situation of the organization, either in a sequence of discrete levels of maturity

(Becker, Knackstedt, & Pöppelbuß, 2009), or in a continuum (Cleland & Ireland, 2006;

PMI, 2013b), for a class of entities. It contains a desired or typical evolution path of these

entities. Typically, these entities are organizations or processes (Becker et al., 2009).

The concept of project management maturity has, in the literature and in practice, a strong

connection with the view that project management capability is achieved through

definition of repeatable and predictable project management processes that are under

statistical control (Pasian et al., 2012). For that reason it is necessary to understand the

concept of process control.

2.3. Statistical Process Control

Shewhart (1939), in his study of quality control in manufacturing, proposed that the

variations in the manufacturing processes should be controlled using statistical tools. He

defended that the continuous analysis of the process variation, using control charts, and

the gradual elimination of the assignable causes of variation, would cause the

manufacturing process to reach a state of statistical control (Shewhart, 1939). In this state

all variation would be caused by the normal randomness (or chance) of the system

(Shewhart, 1939). Figure 1 presents an example of a control chart of a process that is not

in statistical control from Shewhart’s studies. In the chart, the dots represent the inspected

quality of resistors coming out of an assembly line. After removing all special causes, or

assignable causes, the process enters statistical control, and the resulting chart is shown in

Figure 2.


Figure 1 – Control chart of process not under statistical control, adapted from Shewhart (1939, p. 114)

Shewhart defines an assignable cause as a “one that can be found by experiment without

costing more than it is worth to find it” (Shewhart, 1939, p. 30). Consequently, the

benefit of removing assignable causes and achieving a state of statistical control is that the

costs of non-conformance are reduced to an economic minimum (Shewhart, 1939).

Figure 2 – Control chart of process under statistical control, adapted from Shewhart (1939, p. 114)

Deming (1982) advocated that using statistical tools to control the process performance,

as Shewhart proposed, would increase efficiency and reduce waste in the form of

inspections and rework – the companies should cease the dependence on inspections to

achieve quality, and instead build quality in the product in the first place (Deming, 1982).

In addition it allowed the organizations to focus on the system, the ultimate responsible


for the variations in the process, instead of blaming the people involved, which had

limited influence in the outcome (Deming, 1982).

The work of Deming and Shewhart was influential in the conception of the majority of the

maturity models, as will be shown later.

2.4. Maturity Models

2.4.1. Stages of Growth

The concept of modeling of organizational evolution in stages, called “stages of growth”,

have been used widely in both organizational research and information systems research

(Greiner, 1972; W. R. King & Teo, 1997). In information systems (IS), the most famous

model of stages of growth is the one describing the assimilation of computing technology

in organizations described by Gibson & Nolan (1974).

In this model, it was described four stages that a department of Electronic Data Processing

(EDP) goes through when faced with growth in IS spending and assimilation in the

organization. The model takes into account three types of growth: growth in computer

applications, growth in the specialization of EDP personnel, and a growth in formal

management techniques and organization (Gibson & Nolan, 1974). The phases are

described in the table below. Table 1 – Nolan's Stages of Growth (Gibson & Nolan, 1974)

Growth of Applications Growth of Personnel Specialization Formal Management Techniques

Stage 1

Cost reduction accounting applications

Specialization for computer efficiency Lax management

Stage 2

Proliferation of applications in all functional areas

Specialization to develop variety of applications

Sales-oriented management

Stage 3

Moratorium on new applications, emphasis on control

Specialization for control and effectiveness assurance

Control-oriented management

Stage 4

Database applications Specialization for database technology and teleprocessing

Resource-oriented planning and control

2.4.2. The Quality Management Maturity Grid

Crosby, from the standpoint of quality management, developed another model, which he

called the quality management maturity grid. Its purpose was to introduce “managers and


executives to the actions that were involved in running a successful quality management

effort.” (Crosby, 1996, p. 31). According to Crosby experience implementing quality

management, companies follow a similar path in their adoption of quality concepts. Along

this path there are patterns of problems, behaviors and challenges that are common across

organizations, as they go through the improvement steps. Crosby modeled these patterns

in what he called the quality management maturity grid.

The grid demonstrated, among other things, that quality management was not confined to

the quality department, being a key factor the attitude of all managers toward quality

management (Crosby, 1979, 1996). The grid is presented in Table 2.

According to Crosby the model accelerated the process of adoption of quality

management, offering managers a roadmap to implement quality management in their

organizations. All they needed to do is to assess their organization in the grid, so the

required missing steps to improve quality would be clearly identified (Crosby, 1979), and

in case of deterioration of the implementation program the grid can also be read in reverse

order in order to identify the steps to put the program back on track (Crosby, 1979).

Table 2 – Adapted from Crosby (1996, p. 32)


2.4.3. The Capability Maturity Model (CMM)

Crosby’s concepts inspired IBM to apply them to assessing software processes (Radice,

Harding, Munnis, & Phillips, 1985). They devised a model, also with five stages (from

lowest to highest): traditional, awareness, knowledge, skills & wisdom, and integrated

management system. The five stages were used to assess eleven attributes that they

deemed important to software development, similar to Crosby measurement categories.

They used the model to assess the capabilities of different IBM development sites, and the

main purpose of the model was to compare the capabilities and share knowledge of

processes and tools between them, but they recognized the potential value of the model in

planning process improvement activities (Radice et al., 1985).

Humphrey, who led the work at IBM, brought the model to the Software Engineering

Institute (SEI) from Carnegie Mellon University (CMU), initially defining for the

Department of Defense of the United States a similar assessment model to evaluate

software contractors (Humphrey et al., 1987). Humphrey combined the software maturity

framework inspired by Crosby with the principles of process statistical control from

Deming, to develop a software process maturity model with maturity levels, similar to

Crosby maturity grids. This model was soon identified as valuable for any organization to

assess their current level, and to plan the necessary steps to implement the changes that

combined would allow more successful software projects (Humphrey, 1988, 1989).

The main assumption was that, since process effectiveness and efficiency has such a high

importance in manufacturing environments, they could be just as applicable to software

development – in his own words: “While there are important differences, these concepts

are just as applicable to software as they are to automobiles, cameras, wristwatches, and

steel. A software-development process that is under statistical control will produce the

desired results within the anticipated limits of cost, schedule, and quality” (Humphrey,

1988, p. 74). Without statistical control, according to Humphrey, continuous progress is

not possible (Humphrey, 1989), as “When a process is under statistical control, repeating

the work in roughly the same way will produce roughly the same result.” (Humphrey,

1988, p. 74), which enables the organization to achieve better results by improving the

process. To reach a state of statistical control, it was necessary to ensure that the processes


were stabilized, in other words, that the results were repeatable (Humphrey, 1988).

According to Humphrey’s model, as suggested by Crosby earlier, in order to reach this

state the organization must follow a series of stages, or maturity levels, each one acting as

a foundation, or pre-requisite, to the next (Humphrey & Curtis, 1991). And by positioning

the organization in the maturity structure, managers and software professionals can better

identify where improvement actions will be most relevant (Humphrey, 1989).

Humphrey’s framework defined the principles from which the SEI created the Capability

Maturity Model (CMM) (Paulk, Curtis, Chrissis, & Weber, 1991). The CMM is based in

the five levels of maturity defined by Humphrey.

The CMM model starts by describing undisciplined organizations – In undisciplined

organizations, the general outcome is projects that run over budget and late (Paulk et al.,

1991). Better tools and methods cannot have their benefit realized in those undisciplined

organizations. If there are successful projects, they relied on heroic efforts of individuals,

and future successes depend on the availability of the same people. That does not provide

a "basis for long term productivity and quality improvement throughout an organization"

(Paulk et al., 1991, p. 1).

According to CMM, the path to discipline involves becoming a mature organization – the

difference between mature and immature software organizations is that in immature

organizations, processes are generally improvised, or if they are documented they are

ignored. The organization reacts to problems, solving immediate crises. In immature

organizations, "there is no objective basis for judging product quality or for solving

product or process problems. Therefore, product quality is difficult to predict." (Paulk et

al., 1991, p. 2). A mature software organization has "an organization-wide ability to for

managing software development and maintenance process" (Paulk et al., 1991, p. 2). This

means the process is correctly communicated to all relevant people, it is consistent to the

actual work methods, and it is maintained and updated.

To achieve maturity, it is needed a framework that assists the gradual improvement of

organizational processes – so the necessary foundation is built to support the increase in

maturity. This framework combines three concepts: process performance, process

capability and process maturity. Process capability is the means to predict the outcomes of


a project. Process performance is the actual result obtained from following a software

process. It’s important to notice that the CMM expects that the project context may keep

the organization from achieving the process performance predicted by the process

capability – for instance, changes in the technology may impose a learning curve to the

project staff which will directly impact the performance (Paulk et al., 1993).

Software process maturity is the combination of the organization's software process and

the consistency with which it is applied in projects throughout the organization. The

CMM assumption is that process maturity indicates process capability, which allows

gradual and consistent improvements on performance (Paulk et al., 1991). The levels

proposed by CMM are explained below.

2.4.3.1. Level 1 – The Initial Level

At this level, the process capability is unpredictable, as the processes are constantly

changing or even being dropped in crisis situations. Performance depends on the

capabilities of the managers and individuals assigned to the project. The products

resulting from the project frequently are functional, even though they are constantly over

budget and schedule. The management has no visibility into the software process.

2.4.3.2. Level 2 – The Repeatable Level

At the repeatable level, procedures for managing projects are established so that planning

and managing new projects is based on experience with similar past projects. Even if the

specific practices for projects may vary, the project management practices are

documented and enforced. The process capability is disciplined, because the project is

tracked and planned, and previous successes can be repeated.

2.4.3.3. Level 3 – The Defined Level

At the defined level, both the management and the engineering processes are documented

and enforced, and the processes are coherent as a whole. The standard processes are

known and supported throughout the organization. The projects tailor the processes to

their needs. Cost, schedule and scope are tracked and under control.


2.4.3.4. Level 4 – The Managed Level

At the managed level, the organization sets quantitative goals for quality of the products

and processes. All processes are instrumented to collect measurements of their

performance, and the data for project measurements are collected in a database across the

organization. The variation in project performance is very narrow, and when meaningful

deviations occur they can be easily distinguished from random noise of the process, and

actions are taken to correct the situation.

2.4.3.5. Level 5 – The Optimizing Level

At the optimizing level, the entire organization commits to continuous process

improvement. Weaknesses in the process are identified and fixed proactively, preventing

defects. When defects occur, their cause is analyzed and the process is evaluated and

changed, to prevent its reoccurrence.

2.4.4. The People CMM

SEI recognized that managing the people is essential to reach process maturity, therefore

they created a companion model to manage the workforce in a disciplined way – the

People CMM (Curtis, Miller, & Hefley, 2001).

Defining five stages of maturity as the CMM, the People CMM intends to gradually

improve the processes to manage the workforce moving from the lower stages, where

there is a low awareness of the value and the importance of managing the people as part

of the business, to the higher stages, where the competencies of the workforce are

managed proactively to ensure high performance. At the highest stages, the process to

manage competencies are measured and continuously improved, similarly to the CMM

higher levels.

2.4.5. The CMM Integrated (CMMI)

The CMM model, created for software projects, triggered the creation of similar maturity

models for other engineering areas, such as the Systems Engineering Capability Model


(SECM) (Electronic Industries Alliance, 2002) and the Integrated Product Development

Capability Maturity Model (IPD-CMM). While all those models were being developed or

in the process of dissemination, including the draft of the second version of CMM, SEI

took the opportunity to approach the industry and seek consensus to release a unified

model, called the CMM Integration (CMMI Product Team, 2010). The CMMI model

expanded the CMM, including the full life cycle of product development and not only

software engineering. It also expanded the coverage of organizational processes, risk

management and measurement (CMMI Product Team, 2000). However it was still based

in statistical control of the development processes, following the fundamental premise is

that “the quality of a system or product is highly influenced by the quality of the process

used to develop and maintain it” (CMMI Product Team, 2010, p. 5).

The CMMI model also incorporated two different representations of maturity levels, the

staged representation from the CMM and the continuous representation from the SECM.

In the staged representation, the maturity level characterize the overall state of

organizational maturity in processes relative to the model (CMMI Product Team, 2010),

whereas the continuous representation uses capabilities levels to characterize the maturity

of the organization processes relative to an individual process area. For each process area

a level is assessed according to the definitions below:

• 0: Incomplete – not performed or partially performed

• 1: Performed – process that accomplishes the needed work

• 2: Managed – process that is planned, executed, monitored, controlled and

reviewed

• 3: Defined – when it’s managed and tailored for each project

One of the effects of the staged representation is that the sequence of improvements is

pre-defined in the model, as fulfilling the requirements of one stage works as a foundation

for the next. In the continuous, the order in which the improvements are made is not

prescribed, so the organization can choose according to the business objectives and

associated risks. Also, the staged representation allows comparisons at the organizational

level of maturity, whereas the continuous the comparisons are made at the individual

process level.


In regards to the impact of project context to performance and process maturity, as stated

before, CMM recognized that new technology may impact project performance despite

process capabilities – "For instance, radical changes in the application or technology

undertaken may place a project' s staff on a learning curve that causes their project's

performance to fall short of the organization's full process capability." (Paulk et al., 1991,

p. 4). When the model evolved to CMMI, though, the view changed and maturity was

seen as an enabler of adoption of new technology – “Effective processes also provide a

vehicle for introducing and using new technology in a way that best meets the business

objectives of the organization.” (CMMI Product Team, 2010, p. 4).

2.4.6. Maturity Models

The CMM and CMMI family of models was very successful and influential in the

software industry (Bollinger & McGowan, 2009). Its success inspired a number of other

maturity models, starting from the late 1990s. Today there are more than 150 such models

(De Bruin, Freeze, Kaulkarni, & Rosemann, 2005) addressing issues from information

technology, business management, innovation, project management, and others.

What is common among all the models, as inherited from the CMM, is the strong

emphasis of standardization and control of processes (Pasian et al., 2012), and the

assumption that an organization must follow a predictable path to a mature end state

(Cooke-Davies, 2007).

The models will typically have a combination of components described as the following

(adapted from Fraser, Moultrie, & Gregory, 2002):

• A number of maturity levels

• A descriptor for the level, such as initial, repeatable, defined, managed and

optimized

• A summarized description for each level, with their characteristics and

peculiarities

• A set of process areas, or dimensions, or both (Gareis, 2004; PMI, 2013b). This

allows the assessment of maturity to be performed in different dimensions or

perspectives, instead of a single value for the whole organization

• A number of elements or activities for each process area and dimension


• A description of each activity, as it might be performed at each maturity level

The purpose of the models is to define the stages of maturation paths, its characteristics

and the logical relationship between them (Röglinger, Pöppelbuß, & Becker, 2012). They

can be used for three different applications (De Bruin et al., 2005; Röglinger et al., 2012):

• Descriptive, which assesses the organization current state of maturity, in a single

point in time, without any provision for improving the maturity (Fraser et al.,

2002; Mettler & Rohner, 2009)

• Prescriptive, which provides guidance in actions to reach higher levels of maturity,

enabling the development of an improvement roadmap (Mettler & Rohner, 2009;

Prananto, McKay, & Marshall, 2003)

• Comparative, allowing benchmarking of the organization processes with other

groups inside the organization, with other organizations within or across industries

and regions (Hillson, 2003; Ibbs et al., 2004; Kwak & Ibbs, 1998; Pennypacker &

Grant, 2003; Pennypacker, 2006)

It is possible to also consider those applications as a life cycle of model development,

which starts as a prescriptive model, to understand the domain situation, and evolving so

it can act as a prescriptive or comparative model (De Bruin et al., 2005).

The models also differ in their approach to determine the current maturity stage. Similar

to CMMI, there are continuous and staged models (Cleland & Ireland, 2006; Cooke-

Davies et al., 2001):

• Continuous: model that defines a baseline from which an organization can be

assessed from different perspectives. The elements to be improved, and the rate of

improvement, can be tailored to the organization’s needs.

• Staged: model that defines a number of steps and criteria for each step. All areas

are considered essential in order to move up the maturity levels.

The continuous model allows a multidimensional view of the organizational maturity, in

which the organization is not in a single stage, but in different maturity stages depending

on the perspective used (CMMI Product Team, 2010; Gareis, 2004; PMI, 2013b). A

continuous model could also provide a faster feedback loop than the staged model, as it


assesses smaller incremental improvements to the maturity – facilitating a faster cycle of

continuous improvement (Lubianiker & Levin, 2001).

2.4.7. Project Management Maturity Models

Since project management is an integral part of the software development process,

naturally the concept of organizational maturity also migrated to project management

(Cooke-Davies et al., 2001). Starting from the 1990s, a number of project management

models appeared in the marketplace.

Below there is a list of project management maturity models compiled by Iqbal (2012),

updated with the Prado model and additional references

• Programme Management Maturity Model by Russ Martinelli and Jim Waddell

• Cultural Project Management Effectiveness Model (CPMEM) by Project

Management Global Solutions (Piney, 2004)

• Integrated Management Systems Inc. (IMSI) Project Management Assessment

Model by Steve J. Holmes and Robert T. Walsh

• Project Risk Maturity Model (RMM) by Martin Hopkinson, QinetiQ, UK

• Risk Maturity Model by David Hillson (Hillson, 1997)

• Project Management Maturity Model (ProMMM) by David Hillson (Hillson,

2003)

• Earned Value Management Maturity Model (EVM3) by Ray W. Stratton,

Management Concepts

• Berkeley Project Management Process Maturity Model (PM)2 Model by Kwak &

Ibbs (2002)

• Project Management Maturity Model (PMMM or KPM3) by Kerzner (2005)

• Portfolio Management Maturity Model by Pennypacker (2005)

• Project Management Maturity Model (PMMM) by Crawford (2006)

• Prado Project Management Maturity Model (Prado-PMMM), by Prado (2008)

• PRINCE2 Maturity Model (P2MM) by OGC (2010b)

• Portfolio, Programme and Project Management Maturity Model (P3M3) by OGC

(2010a)


• ProjectFRAMEWORKTM Project Management Maturity Model by ESI

International (Levin, Artl, & Ward, 2013c; Levin, Hill, Defilippis, Ward, &

Shaltry, 1999)

• ProgramFRAMEWORKTM Program Management Maturity Model by ESI

International (Levin, Artl, & Ward, 2010b, 2013b)

• PortfolioFRAMEWORKTM Portfolio Management Maturity Model by ESI

International (Levin, Artl, & Ward, 2010a, 2013a)

• Organizational Project Management Maturity Model (OPM3) by PMI (2013b)

Most of the models have five levels, similar to the CMM (De Bruin et al., 2005), while

the criteria to assess organizations in each level may differ (Cleland & Ireland, 2006).

Also, most models used functional areas from the standard Project Management Body of

Knowledge (PMBOK®) (PMI, 2013a, 2013b), or the methodology Projects in Controlled

Environments version 2 (PRINCE2®) (OGC, 2010b). Some of the models are described

below.

2.4.7.1. OPM3 – Organizational Project Management Maturity Model (PMI, 2013b)

OPM3 is a maturity model developed under the sponsorship of the PMI by hundreds of

volunteers (PMI, 2013b). The first version was published in 2003, and the latest version is

the third, published in 2013. The OPM3 it’s explicitly related to the PMI standards for

project, program and portfolio management, and its model differs considerably from other

models based on CMM (Cooke-Davies, 2007). The main building blocks of the model are

the following:

• Best practices: The organizational project management maturity of the

organization is assessed by the existence of the best practices. They are the

methods to achieve an objective or goal, and are defined by volunteers of the

industry during the standard development process carried by PMI. Every best

practice contains a list of capabilities and outcomes, and a best practice is

considered achieved when the organization consistently demonstrate the presence

of all capabilities (PMI, 2013b). If all but one capability is demonstrated, the best

practice is not considered achieved.


• The capabilities represent the collection of people, processes and technology that

enables an organization to deliver projects (PMI, 2013b). Incrementally, the

capabilities enable an organization to achieve a best practice, even if the model

does not prescribe an order to which the capabilities must be fulfilled. There are

dependencies between capabilities and best practices, in which capabilities of one

best practice can be required for achieving other best practices, so some best

practices require the attainment of certain capabilities and other best practices. The

analysis of these dependencies will guide the improvement plans of the

organization.

• The outcomes are results, tangible and intangible, that are used to verify the

existence of a capability. A tangible outcome is, for instance, the presence of a

policy or template in the organization. An intangible outcome is a verbal

acknowledgement of the policy. The presence of one outcome is enough to

achieve a capability, even if the capability has a list of possible outcomes.

• KPI, or Key Performance Indicators, are the criteria for which an organization can

determine, quantitatively or qualitatively, if an outcome is present and to what

extent it is present. The indicators can be directly measured or assessed by an

expert.

The relationship between those blocks is expressed in the Figure 3 below.

Figure 3 – Building blocks of OPM3 (PMI, 2013b)


The OPM3 standard contains a list of hundreds of such best practices, divided in two

types:

• “Domain: Portfolio, Program, and Project with Process Improvement Stage:

Standardize, Measure, Control and Improve (SMCI)

• Organizational Enabler: Non-domain-based processes, belonging to environmental

and cultural aspects of the organization” (PMI, 2013b, p. 47).

The organization enablers underpin the implementation of SMCI practices. And the SMCI

practices are classified in the different domains of project, program and portfolio. Based

on the reported presence of the best practices, the maturity of the organization is assessed

in a multidimensional format, containing the dimensions of project, program, and

portfolio management in one focus area, and standardize, measure, control and

continuously improve in the other. The maturity can be assessed based on the dimensions

prioritized by the organization (PMI, 2013b). This representation of the results is the

greatest difference compared to CMMI. Instead of discrete levels of organizational

maturity, the OPM3 model assesses the maturity in a matrix, having different scales, as

shown in Figure 4.

Figure 4 – Sample Results of OPM3 assessment, from PMI (2008, p. 6)


OPM3 also describes a life cycle for process improvement, while using the model to

increase maturity. The life cycle is similar to the Plan-Do-Check-Act (PDCA) cycle

(Deming, 1982), and defines the following steps:

• Acquire knowledge, covering the contents of the standard;

• Perform assessment, providing means to compare organizations to the standard;

• Manage improvements, assisting in organizational changes to increase the project

management maturity (PMI, 2013b).

The steps can be used differently depending on the approach the organization is taking

towards organizational project management. The model proposes three alternatives, which

are similar to the different purposes of a maturity model as described by De Bruin et al

(2005), they are:

• Comparative: for organizations adopting elements of organizational project

management, as a means to compare against the OPM3 model to determine the

extent of their implementation

• Design: organizations who have not an approach to organizational project

management can use the best practices to design their approach to implement

organizational project management

• Improvement: organizations who lack a process improvement and strategy

execution framework in place, could use the model to determine their

improvement plan

2.4.7.2. PM2 - Project Management Maturity Model (Crawford, 2007)

The PM2 is a developed by the consulting company PM Solutions. Based on CMM, it

defines five levels of maturity (Crawford, 2006):

• Initial process

• Structured process and standards

• Organizational standards and institutionalized process

• Managed process

• Optimizing process


The model combines the five levels with knowledge areas from the PMBOK (PMI,

2013a) – integration, scope, time, cost, quality, human resources, communication, risk

and procurement and breaks them down to 42 key components (Grant & Pennypacker,

2006). The assessment of the maturity level is done for each of the components, which are

aggregated by knowledge areas, and a global maturity level can be calculated for the

organization.

The components are a breakdown of the knowledge areas. As an example, the

components for scope management are:

• Scope planning and management, which is the process to define, verify and

control the project scope

• Business requirements definition, processes and standards to collect business-

related requirements for the project

• Technical requirements definition, processes and standards to collect technical

requirements for the project

• Work breakdown structure, looks at how formal is the process to identify the

scope of the project

• Scope change control, processes to incorporate additions and changes to the scope

The model defined three areas as key points to rapidly develop a project management

culture and accelerate the increase in project management maturity. They are components

that are not directly taken from the PMBOK as the others, but according to the author are

very important in the acceleration of the improvement process. They are

• Project Office: the formation of a project office, according to the model, helps the

project teams by providing support in the areas of scheduling, status reporting,

project management tools and training The project office facilitates improvements

in project management maturity by acting as the main focal point for the

consistent application of project management processes and methodologies

• Management oversight: also a main point of the CMM model, the PM2 model

assumes that the institutionalization of project management processes can not

happen if it do not have full support from the leaders of the organization. The

management must empower the project manager and hold him accountable for the


success of the project, to send a message to the organization of the importance of

the role (Crawford, 2006).

• Professional development: here borrowing from the People CMM (Curtis et al.,

2001), the PM2 model recognizes the need to develop the project manager in terms

of technical, management and leadership skills, and to continuously improving the

skills of the people behind the projects.

2.4.7.3. ProjectFRAMEWORK® (Levin et al., 2013c)

ProjectFRAMEWORK® is a maturity model developed for the consulting firm ESI

International. It was first published in 1999, had a second version in 2006 and the latest

version in 2013, according to changes in the PMI Standards.

As other models it contains performance objectives based on the nine knowledge areas of

PMBOK. It describes five levels of maturity, which are: Ad hoc, consistent, integrated,

comprehensive and optimizing, similar to CMMI levels. The model is structured in the

following components for each knowledge area and maturity level:

• Objectives – Objectives for the maturity level

• Commitment to perform – actions that must be taken by the organization to ensure

that the process are established

• Ability to perform – preconditions that must exist in the organization to enable

process implementation

• Activities performed – specific tasks necessary to implement the objectives

• Evaluation – metrics that can be used to determine a given maturity level

• Verification – organization oversight and activities to verify that the process are

being performed properly

Those components are also used in the CMM for Software (Paulk et al., 1993). The

assessment defines a discrete maturity level for the organization using a staged approach,

based on the presence of the components on the knowledge areas.

The ProjectFRAMEWORK® model also contains some concepts from the People CMM

(Curtis et al., 2001). For instance, the model supports the creation of organization-wide

strategic long-term plan to develop the competencies and workforce required for project


management. Also, it supports the formal definition of an integrated compensation system

that rewards individual and team performance.

The model is part of a group of three individual models, assessing maturity of project,

program and portfolio management in the organization (Levin et al., 2013a, 2013b).

2.4.7.4. Project-oriented company Maturity Model (Gareis & Füssinger, 2007)

This is a multidimensional model to assess maturity of organizations. The maturity is

described in a spider-web chart containing eight dimensions, as shown in Figure 5.

Figure 5 – Project-oriented company Maturity Model, adapted from Füssinger (2006, p. 2)

The assessment is made with a questionnaire with 74 questions, assessing items according

to the dimensions of the spider web. It includes in one model the dimensions of project,

program and portfolio management, as does OPM3 (PMI, 2013b). Also, the model

includes other organizational dimensions, they are ((Füssinger, 2006)

• Assurance of management quality of a project or program – proposes audits and

consulting to improve the quality of the management of the project

• Assignment of a project or program – in addition to the portfolio management

processes to start and cancel projects, it proposes that during the assignment of the

project a decision is taken whether to start or not the project

• Personnel management – similar to concepts introduced by the People CMM

(Curtis et al., 2001), this model assesses the processes to recruit, and continuously

develop the competences of the people responsible for projects, including the

project managers and project members


• Organizational design – this dimension includes the creation of a project

management office, a project portfolio group and corporate standards for

managing projects

• Business process management – this dimension assesses the capabilities of the

organization to manage their operational business processes, in a similar fashion to

what has been proposed by different business process maturity models (Jochem,

Geers, & Heinze, 2011; Röglinger et al., 2012; Smith & Fingar, 2004)

The maturity result is given as a percentage of compliance of the organization with the

model. A maturity result for the organization is also calculated, using a weighted average

of the results for the dimensions (Gareis & Füssinger, 2007).

2.4.7.5. Project Management Maturity Model (Kerzner, 2005)

Kerzner’s model is composed of five levels of maturity, similar to the CMM group of

models, although their meanings are somewhat different:

• Level 1: Common Language – basic knowledge on project management and the

terminology

• Level 2: Common Processes – organization recognizes that project management

processes need to be defined, so that success in one project can be repeated in

others

• Level 3: Singular Methodology – organization recognizes the effect of

consolidating all corporate methodologies, having project management in the

center

• Level 4: Benchmarking – organization recognizes that process improvement is

necessary and performs benchmarking continuously

• Level 5: Continuous Improvement – in this level, the organization is capable of

evaluating information from benchmarking and decides if it should be adopted in

the methodology

The assessment is done with questionnaires for each of the levels, ranging from 15 to 30

questions for each level, that gives a score for the level. Each level has defined criteria to


interpret the score, and to assess if the level has been achieved or not. There are no

knowledge areas, only questions related to the concept the level describes.

Figure 6 – Kerzner's Project Management Maturity Model levels of maturity

2.4.7.6. PM3M - Portfolio, Programme and Project Management Maturity Model

(OGC, 2010a)

The United Kingdom Office of Government Commerce (OGC) develops this model. The

first version was published in 2006, and the second in 2010.

The model in fact is a group of three individual models, assessing the maturity of project,

program and portfolio management in the organization. All of the individual models

follow the CMM in defining five levels of maturity: awareness of process, repeatable

process, defined process, managed process and optimized process. Where it differs from

many other maturity models is that, instead of using the key process areas from the

PMBOK, the maturity is described in process perspectives common to project, program

and portfolio management. They are:

• Management control

• Benefits management

• Financial management

• Stakeholder management

• Risk management

• Organizational governance

• Resource management


The three models contain a self-assessment questionnaire for each model, with one

question per process perspective. The questionnaires contain the criteria to assess the level

of each process perspective. OGC offers a separate project management maturity model

based on PRINCE2, using the same basic five levels and process perspectives for

organizations which adopt the PRINCE2 methodology (OGC, 2010b).

2.4.7.7. MGP – Project Management Maturity (Prado, 2008)

The MGP model (Maturidade em Gerenciamento de Projetos in Portuguese) is a model

developed by Prado. Its five levels are named: initial, known, standardized, managed and

optimized, which are equivalent to the CMMI levels. There are five dimensions to the

assessment:

• Technical and contextual competence

• Methodology

• Automation and use of IT systems

• Organizational structure

• Strategic alignment

• Behavioral competence

The assessment questionnaire contains ten questions per level, except level 1 which is not

assessed. The model documents the criteria for the calculation of the organizational level,

and level 1 is assumed if level 2 is not achieved.

2.4.7.8. Risk Maturity Model (Hillson, 1997) and

ProMMM – Project Management Maturity Model (Hillson, 2003)

The risk maturity model was defined to assist organizations implementing risk

management processes. As the other models, it assumes that the implementation of risk

processes has to be treated as a project, and a model to guide the organization through the

steps necessary for the implementation can accelerate the process. It was succeeded by the

ProMMM model, which incorporates all project management processes. What is common

between the risk maturity model and ProMMM is the approach to include more

components than the traditional framework of processes and standards, which according


to the model are important but insufficient to assess the project management maturity of

an organization. As such, the model proposes additional elements in the assessment. They

are:

• Organizational culture: covers the belief structure, and how the members of the

organization think, which guides the decisions and assumptions. In an immature

organization, according to the model, the culture does not recognize the

importance of project management and is resistant to change. As the organization

matures, the culture changes and people start to recognize the value of practices

and process to apply project management, and the value of applying them

proactively

• Experience: analyzes the project management experience of the organization and

of its individuals. The experience is what indicate what is known and what the

people are capable of doing, and how they understand the principles and practices

of project management. The mature organization has individuals with experience

and formal training in project management, whereas the immature has no

experience in using it.

• Application: is the extent to which the organization actually practices in terms of

project management. In immature organizations, the application is patchy and

inconsistent, and as the organization matures, it starts to apply consistently,

routinely across the whole organization.

The model defines four levels of maturity, they are:

• Naïve: in which the organization have no awareness of the value of project

management, and the processes (if they exist) are reactive without any learning

from past experiences

• Novice: One organization that began to experiment with project management but

has no formal structure or process in place.

• Normalized: At this stage, the organization has implemented formal project

management processes and they are consistently applied. Even if the benefits are

not always obtained, the organization understands the value of the processes.

Typically, according to the model, most organizations will aim at this level in their

improvement initiatives.


• Natural: level at which an organization has a fully project based culture. Project

information is used to improve business processes and gain competitive

advantage.

2.4.7.9. CPMEM – Cultural Project Management Effectiveness Model (Piney,

2004)

This model, as many others listed here, uses the PMBOK (PMI, 2013a) knowledge

process areas to assess the maturity of the organization. However, the results are based on

weight scores obtained by consulting senior management about priorities and critical

success factors for projects, using a questionnaire that is part of the model. By having a

weighted score, the model claims that the model is tuned for the needs of the

organization; therefore the buy-in obtained from management for the maturity

improvement initiative is stronger.

2.4.8. The Value of Project Management Maturity Models

Humphrey’s original software maturity framework was developed to improve the

business performance of software projects based on a framework to evaluate software

suppliers (Humphrey, 1989; Humphrey et al., 1987). Since then, many authors have

claimed that increasing the maturity brings a number of benefits for the organization

(Cooke-Davies, 2007).

A number of those benefits will be discussed below.

2.4.8.1. Strategic Value

One view of project management maturity advocates that, since it is through projects that

an organization implements its strategy, the capabilities to consistently deliver projects

obtained by increase the maturity in project management are strategic to the organization

(Schlichter, 2001).

This view guided the development of some models, more importantly the OPM3 model

(Friedrich, Schlichter, & Haechk, 2003). As the model integrated all best practices for

management of project, programs and portfolio, an organization adopting the model


would be able to successfully implement the defined strategies, gaining key competitive

advantage (Schlichter, 2001). Still on strategy, according to some authors the commitment

to improve the project management maturity with a maturity model has to be treated as

strategic, as it is a long-term endeavor, and it impacts how the organization implements its

business strategy (Kerzner, 2005; OGC, 2010b; PMI, 2013b). Kerzner defines in his

model the concept of strategic planning for project management, as the development of a

methodology to increase project performance, using a project management maturity

model (Kerzner, 2005).

In that sense, high levels of project management maturity could be claimed as the

provider of agility to the organization, enabling it to rapidly implement strategy, and

change, via projects – quickly adapting to changes in the environment and opportunities

as they appear (Schlichter, McEver, & Hayes, 2010).

A few of those claims have been contested by some authors, such as the link between

project management maturity and competitive advantage (Jugdev & Thomas, 2002b), for

which a longer discussion is presented later in this chapter.

2.4.8.2. Benchmarking

Benchmarking is the process of researching for new methods, practices and processes

being adopted by other organizations, from the same or different industries (Camp, 1989).

The purpose of the search is to compare the performance and practices of one’s own

organization with the ones from the best performing companies. With the results of the

comparison, an organization can plan the implementation and the adaptation of those

practices (Camp, 1995).

The benchmarking process can be one performed by one organization targeting other

organizations directly, collecting data and performing the gap analysis (Camp, 1989), or

alternatively a maturity model can be used as a benchmarking tool, as it is composed of

practices commonly used by successful organizations (Ibbs et al., 2004). In fact, many

authors use the term benchmarking for the process of assessing an organization maturity

and, based on the results, defining the improvement steps (Hillson, 2003; Ibbs et al., 2004;

Mullaly, 2006; Pennypacker & Grant, 2003).


In the maturity model developed by Kerzner (2005), the activity of benchmarking project

management best practices from other organizations is the requirement for one of the

levels. The model describes a process similar to Camp (1995), in which organizations are

selected to be benchmarked, data is collected and shared between the organizations and

the best practices are implemented after the gap analysis.

2.4.8.3. Project Management Performance

Some studies sought to find empirical evidence of increase in performance linked to an

increase in process maturity. Using CMM and CMMI, a number of studies were published

to find such evidence, with mixed results (Flowe & Thordahl, 1994; Galin & Avrahami,

2006; Gibson et al., 2006; Harter & Krishnan, 2000; Herbsleb et al., 1997; Jiang, Klein,

Hwang, Huang, & Hung, 2004; Jung & Goldenson, 2009; Subramanian, Jiang, & Klein,

2007). In general, all of these studies found some correlation between the maturity and

project performance, measured in project cost performance indicators (CPI) and schedule

performance indicators (SPI), however, with different results depending on the level

achieved and the project context. Flowe & Thordahl (1994) found correlation between

CPI and maturity between CMM levels 1 and 2, but not between levels 2 and 3; also, both

cost and performance were correlated with maturity when the scope baseline suffered less

than 15% of changes during the project, but they were not correlated when changes were

more than 15% of the scope baseline.

Gibson et al (2006) collected data from 35 case studies of companies who invested in

CMMI based processes. Those companies were mainly big enterprises, and the

improvement efforts were performed in small or big business units. CMMI was applied to

engineering disciplines, mostly software and systems engineering. The data was collected

before and after the improvement initiatives started, and the results are shown in increase

of the baseline before the CMMI-based process improvement, or of ratio of return on

investment. The data was collected using a number of different measures, as it was taken

from several case studies, and it was grouped into the following performance categories:

cost, schedule, productivity, quality, customer satisfaction, and return on investment

estimated. The averages are shown below in Table 3.


Table 3 – Performance Improvements from Gibson et al (2006)

Performance Category

Median improvement Number of data points

Lowest improvement Highest improvement

Cost 34% 29 3% 87% Schedule 50% 22 2% 95% Productivity 61% 20 11% 329% Quality 48% 34 2% 132% Customer satisfaction 14% 7 -4% 55% Return on investment 4.0:1 22 1.7:1 27.7:1

This result is important, because it shows the impact of maturity in a number of

performance measures, not only cost and schedule performance – it found a median

improvement of 14% in customer satisfaction measures linked to maturity improvements,

even if one of the cases had a decrease of 4%.

Some empirical studies were conducted using project management maturity models as

well. Ibbs et al. (2004) conducted an analysis of return of investment in project

management and concluded that an increase in project management maturity improved

pure project performance and the consistency of performance, measured in CPI and SPI.

Füssinger (2006) applied a model in Austrian organizations and concluded that high

project management maturity led to more consistent project results. Pennypacker (2006)

found evidence of an increase in project performance when maturity increased, using

several performance measurements – schedule, budget, customer satisfaction, resource

allocation, optimization, strategic alignment, estimating quality, employee satisfaction

and portfolio optimization. Yazici (2009a) measured business performance indicators,

external and internal, and found a positive correlation between business performance and

project management maturity. O’Hara & Levin (2000) studies concluded that project

management maturity correlated with CPI and SPI performance indicators, but most

importantly, maturity had an even higher correlation with CPI and SPI consistency,

meaning that higher maturity may lead to higher predictability but not necessarily to

higher performance.


In their extensive research project in the value of project management, Mullaly & Thomas

(2008) showed that higher levels of project management maturity increase the intangible

value that is obtained from project management in the organization. However, an

important result of their research is that the sustainability of the value is correlated to the

degree of “fit” of the project management implementation to the company’s context

(Mullaly & Thomas, 2009). This concept will be explored in the section of this chapter

dedicated to the contingency theory.

2.4.9. Criticisms of Maturity Models

A number of authors have criticized the concept of maturity models. In regard to the

statement that a maturity model represents a global “development path” to a single

“perfected end-state”, Cooke-Davis (2007) argues that there is neither a universal

description of this perfect condition nor an agreement on the steps to achieve it. Models

contain the steps to reach higher levels but not the factors that actually influence evolution

and change (King & Kraemer, 1984). Additionally, there are too many project

environments – different industries, markets, strategies and types of projects – raising

doubts that such a path or state can exist and be applicable to all organizations (Cooke-

Davies, 2004), instead, there could be multiple maturation paths (King & Teo, 1997). This

was confirmed by study from Mullaly & Thomas (2014) in the value of project

management. In their study, although they found correlation of organizational maturity

and the attainment of intangible value, this was only found in a macro level – there was

no correlation between the different practices associated with higher maturity and the

different types of value obtained.

In regard to the strategic value of project management maturity, Jugdev and Thomas

(2002a, 2002b) explored the models through the resource-based view of the firm. Their

conclusion is that project management maturity models, and its common sets of levels,

practices and processes, are easily available for competing firms, therefore are imitable

and not a source of competitive advantage, even if its value can lead to competitive parity.

Other criticisms are related to the high number of maturity models published for similar

applications, without clear justification or motivation for developing a new model (Becker

et al., 2009), the lack of construct validity and empirical evidence for the models (Cooke-


Davies, 2007; Fraser et al., 2002; Mullaly, 2006), and the inherent complexity of maturity

models, making their application difficult (Jugdev & Mathur, 2012).

2.4.10. Process Capability and Project Management Maturity

When the first maturity models were developed, the understanding of project success was

more operational, and measured project efficiency using the iron triangle of cost, schedule

and adherence to technical specifications (Shenhar & Stefanovic, 2006). Continuous

improvement based on statistical control requires fast feedback on incremental

improvements to the process and its impact on performance, so using cost, schedule and

technical performance measures to assess performance is convenient, as the data is readily

available at the formal end of project (Pinto & Slevin, 1988; Shenhar et al., 1997).

However, to be able to efficiently deliver projects is valuable as an enabler for the

organization (Jugdev & Thomas, 2002b), but alone it is not enough to cause positive

business impact (Shenhar & Stefanovic, 2006). For that reason, the view of project

performance has evolved from operational to a more strategic one, which takes into

account other factors such as the lasting effects of the project outcome long after the

project is finished, and the actual value it brings to the users (Jugdev & Müller, 2005).

When maturity models focus on the process capability, the important strategic aspects are

not covered (Shenhar & Stefanovic, 2006).

Also, maturity models assess the process maturity in terms of institutionalization and

repeatability and not the maturity in managing those processes as part of the business

(Röglinger et al., 2012; Smith & Fingar, 2004). Therefore the processes could be mature

but do not generate value for the organization, in other words, the process are efficient but

not effective (Bollinger & McGowan, 2009; Fraser et al., 2002).

Some authors argue that mature processes and performance are not compatible: process

maturity is based on reproducibility and uniformity, which is the aim of manufacturing

where the concept originated – contrasting with projects, that are unique by definition

(Bollinger & McGowan, 1991; Kujala & Artto, 2000). In addition, the commitment of

organizations to rigid maturity models may push them away from actions that will bring

down the maturity level, such as improvements to the process (Bollinger & McGowan,

1991) or implementing projects with high risk, in which the potentially high payoff is


essential to the survival of the organization (DeMarco & Lister, 1999). Herbsleb et al.

(1997) tried to refute some of those concerns by surveying companies that had been

through CMM-based software process improvement programs – and the majority of

respondents disagreed or strongly disagreed that the organization had become more rigid

and bureaucratic; they also looked at the risk acceptance of lower and higher maturity

organizations, and found out that higher maturity organizations are significantly more

willing to take risks (Herbsleb et al., 1997).

A number of studies of project management maturity models defend that they are

compatible with high performance, as long as process capability is complemented by

other strategic factors, as process capability alone is not sufficient to predict project

management success (Cooke-Davies, 2004, 2007; Teague & Cooke-Davies, 2007). These

studies suggest the addition of other perspectives to measure maturity. They are:

• Organization strategy: considers the attainability of the organization strategies, in

the form of the vision, mission, objectives and goals, and its alignment with the

organization projects and programs (Cleland & Ireland, 2006; Cooke-Davies,

2005; Hartman & Skulmoski, 1998; Kerzner, 2005; Lee & Anderson, 2006;

Shenhar & Stefanovic, 2006).

• Organization attitude and culture: considers the general attitude and culture of the

organization toward acceptance of project management, adaptability and risk

(Andersen & Jessen, 2003; Cooke-Davies & Arzymanow, 2003; Hartman &

Skulmoski, 1998; Hillson, 2003; Pasian et al., 2012; Suares, 1998)

• Competence: considers the availability of competent project and program

managers (Cooke-Davies & Arzymanow, 2003; Curtis et al., 2001; Hillson, 2003;

Levin et al., 2013c; Prado, 2008; Skulmoski, 2001)

2.5. Contingency Theory

This thesis intends to look at project management maturity models from a contingency

theory perspective. In order to understand the concept presented by the contingency

theory, it is important to discuss the distinction between the views of the organization as

open and closed systems.


For many years the study of organizations adopted a closed systems approach (Scott,

2004), which is a rational model that looks at organization as a group of variables and

relationships that can be controlled and manipulated in order to achieve the desired goal

(Thompson, 1967) while the uncertainties can be removed from the system.

The classic schools that follow this approach are, for instance, the scientific management

proposed by Taylor (1911). He looked at the organization from the perspective of the

tasks executed at the shop floor and proposed the standardizing and optimizing work,

sequencing tasks, and organizing the tasks into jobs and departments (Taylor, 1911).

Fayol (1919) studied the role of management, the chain of command and delegation of

authority – how managers can be divided to cope with complex systems. Weber (1968)

proposed the view of the organization as a bureaucracy, using staffing and structure to

handle cases and clients. All of those studies focused on actors (workers, managers) and

processes, with little attention to the environment in which they operated (Scott, 2004).

This view, design-driven, formalized and prescriptive, was challenged by proponents of

open systems, which placed the organization as a responsive system, subject to an

external environment, working in larger and more encompassing systems (Scott, 2004). In

open systems, uncertainty is expected, and it is assumed that the systems contain more

variables that can be comprehended at one time, and some of the variables are subjected

to influences that cannot be predicted or controlled (Thompson, 1967).

In the context of open systems, the contingency theory appeared to propose that the best

structure for an organization depends on the environment to which the organization

relates (Betts, 2011). This view assumes that “1. There is no best way to organize” and “2.

Any way of organizing is not equally effective” (Galbraith, 1973, p. 2). Accordingly,

organizations whose structures are more adequate to its environment (concept of “fit”) are

more prepared for survival and can achieve higher performance (Drazin & Ven, 1985).

Starting from the 1950s, a group of researchers started publishing studies of organizations

under the view of the contingency theory. Woodward (1958) studied different

manufacturing organizations and how their structures differed in terms of number of

levels of hierarchy, span of control of the first line managers and the ratio of managers to

total personnel. Also, she looked at the environment in terms of the technical systems, if

they were made to produce unique individual units, mass-market units, or continuous

processes such as chemical production. The different structures alone were not related to


the organization performance, but the combination of structure and technical systems

were good predictors of performance, confirming the theory of fit.

Burns & Stalker (1961) defined the concepts of mechanistic and organic systems of

management, as systems that can be designed for an organization to cope with stable or

dynamic environments. The mechanistic approach is appropriate for stable conditions,

and is characterized by specialization of functional tasks, by the focus on the activities

performed rather than the outcomes, by the strong hierarchical structure, and by clearly

defined responsibilities and obligations for each member of the organization (Burns &

Stalker, 1961). The organic, appropriate for changing conditions, is characterized by the

fact that the individual tasks and roles are continuously redefined through interaction with

others, in which members contribute their knowledge and experience to the purpose of the

organization. The responsibilities are not limited nor well defined, and problems cannot

be transferred to others in the hierarchy (Burns & Stalker, 1961).

Lawrence & Lorsch (1967) went further and looked at the organization as a larger set of

smaller subsystems, each coping with sub-environments. According to the uncertainty of

the tasks performed by each subsystem, the structure of the subsystem must be adequate

to cope with this uncertainty. As a whole, the organization should promote differentiation

between subsystems dealing with environments with different attributes.

Perrow (1967) proposed an integrated framework, extending the work of Burns & Stalker

(1961), taking into account the complexity of the technology and the stability of the raw

materials, defining four basic types of organization structures to cope with different

environments. The types differed in terms of the independence (discretion) of workers to

perform their technical tasks and of the middle managers (supervisors); how are the

control performed (via planning or constant feedback); and the independence of the

teams. The four types are described as decentralized, formal centralized, flexible

centralized, and flexible decentralized, and the profiles are shown in Table 4.


Table 4 – Organizational structures, (Perrow, 1967)

The organization described in cell 2, the flexible and polycentralized, is the archetype of

the organic organization described by Burns & Stalker (1961), whereas the organization

profile of cell 4 is the mechanistic. The left two cells are organizations well suited for

situations where the problems, or exceptions, are rare and most of the work is routine – so

the technical worker has low discretion to solve those exceptions, and need to escalate to

his supervisor when one is encountered. The technical planning of the activities can be

performed, as the exceptions are few and can be treated as they occur. On the right hand

side of the table, the exceptions are the norm, therefore the technical worker must have

independence to solve the problems as part of the activities. The planning needs to be

constantly updated, so it needs continuous feedback. As for the top half of the matrix, the

organizations are structured to handle problems that are hard to analyze, so the supervisor

needs power and independence to analyze those problems. Whereas in the bottom half,

the problems have solutions that can be found by experts, therefore the supervisory power

is low as they rely on the technical workers to solve the exceptions (Perrow, 1967).

Mintzberg (1979) analyzed a number of studies of organization structures under the

theory of contingency, and proposed a framework in which the performance of the

organization would depend not only on the fit between parameters of the organization

design and contingency factors (external and internal), but also in the internal consistency

of these parameters. The contingency factors used by Mintzberg were the following:


• Age and size of the organization: the age and size of the organization would

predict the formalization of behavior, the job specialization and differentiation

between units, and the complexity of its administrative systems

• Production systems: expanding on the work from Woodward (1958), Mintzberg

suggested that technical systems for mass production, which are regulating and

standardized, indicate a bureaucratic and mechanistic structure for operations, as

opposed to organic structures found in technical systems used for production of

individual units and prototypes.

• Stability of the environment: the more dynamic the environment, the more organic

the structure. Similarly, stable environments would indicate mechanistic

structures.

• Complexity of the environment: the more complex the environment, the more

decentralized the structure.

• Power: organizations that are submitted to external controls have a structure that is

more formal and decentralized. Also, organizations in which its members have

power needs tend to generate structures that are excessively centralized.

Contingency theory, as any theory, has its share of criticism. One that is common is the

difficulty in explaining the causation between the variables, and the assumption in some

studies that if correlation between design characteristics and environment is found in

organizations, it is the best fit, without considering the effectiveness of the design (Drazin

& Ven, 1985). Another criticism is the assumption that the relationships between

variables are symmetrical, where some relationships could be linear, and others could be

curvilinear (Betts, 2011), such as the task orientation of employees in very low or very

high uncertainty environments as studied by Lawrence & Lorsch (1967).

Similarly, the age of the organization and industry can change the dynamics of the

mechanistic and organic structures, as young organizations may require formal structures

to cope with uncertainties caused by the “liability of newness” (Stinchcombe, 1965). This

was confirmed by Sine, Mitsuhashi, & Kirsch (2006) in their study of internet startups: in

their study, they linked the success of new ventures to structures closer to the mechanistic

approach, such as team formalization, functional specialization and administrative

intensity (Sine et al., 2006).


Another criticism to contingency theory is the possible lack of practical value, as they

tend to be overwhelmingly difficult to apply in the industry giving the complexity of

combinations of environment and industry characteristics (Betts, 2011), and because the

environment changes faster than the organizations can adapt their structures (Mintzberg,

1979). Despite the criticism, it is a theory that is powerful because of its simplicity and at

the same time large scope, addressing many factors that other theories do not (Betts,

2011).

The main line of thought that can be extracted from contingency theory research is the

duality of mechanistic and organic structures, the first is better suited to cope with stable

and less complex market and technology environments, while the second is one that is

adequate for complex, unpredictable and ever changing environments (Mintzberg, 1979).

According to Galbraith, the coordination mechanisms change according to the

uncertainties of the tasks – tasks that contain less uncertainty are coordinated with direct

supervision and clear definition of rules and procedures, while more uncertainty is

coordinated with definition of goals for the output and required skills (Galbraith, 1977;

Mintzberg, 1979). Definition of rules and procedures is a pre-condition for achieving

higher maturity levels according to project management maturity models (Pasian et al.,

2012), therefore, according to contingency theory, it is better suited for tasks that have

less inherent uncertainty.

2.5.1. Contingency Theory in Project Management Research

For many years, projects have been studied as entities detached from their environments,

but this view changed when projects were viewed as temporary organizations, which led

to applying contingency theory to projects as well (Hanisch & Wald, 2012). Many studies

on project management theory appeared using the contingency view, and it is recognized

as one of the nine schools of project management research (Bredillet, Anbari, & Turner,

2008; Bredillet, 2008; Turner, Anbari, & Bredillet, 2013), which are

• The optimization school: The project as a machine – in this school of research, the

project is compared to a machine that can be optimized using tools such as gantt

charts and earned value management


• The modeling school: the project as a mirror – an evolution of the optimization

school, the modeling school integrates the individual components of project

management to get a full view of the system. Here the focus is on the integration

of hard and soft systems and modeling a total project management system

• The governance school: the project as a legal entity – views the project as a

temporary organization, as such investigates the mechanisms of governance of the

project and its parent organization

• The behavior school: the project as a social system – the behavior school studies

the social aspects of the project such as organizational behavior, leadership,

communication, team building and human resources management

• The success school: the project as a business objective – focus on project success

factors, which are elements of the project that can be influenced to increase the

likelihood of success, and the success criteria, which are the measures by which

the successful outcome of the project can be evaluated

• The decision school: the project as a computer – investigate aspects of initiation

and approval for funding of projects. Its focus is on the decision making process at

the early stages of the projects.

• The process school: the project as an algorithm – aims to define structured

processes to achieve the project objectives. This school investigates effectiveness

of processes used to manage projects in different environments

• The marketing school: the project as a billboard – focuses in the marketing of the

project to stakeholders, internal and external, starting from the identification of

their needs going to the alignment of project management to company strategies,

selling the viewpoint of project management as a tactical and operational matter.

• The contingency school: the project as a chameleon – this school recognizes the

different contexts between projects and organizations and attempts to adapt the

project management process to the environmental needs of the project. It research

typologies and project categorizations to align project capability with strategy, and

seeks to find methods to adapt the organization approaches to different types of

projects


The use of contingency theory in project management studies increased considerably

since the last ten years and it is an important and ever growing foundation for a number of

studies in project management (Hanisch & Wald, 2012).

2.5.2. Studies of Project Management Using Contingency

Several studies applied contingency theory to investigate project management – and many

of them proposed categorization models to assess the relevant contextual variables of the

project.

Henderson & Clark (1990) created a project categorization model based on the degree of

innovation, introducing the concept of architectural innovation, positioned in the middle

between incremental and radical innovation. In projects described as architectural

innovation, the effort and complexity can be underestimated, stressing the communication

channels, processes and structures of the firm.

Turner & Cochrane (1993) proposed a 2x2 matrix project typology, according to the

uncertainty of goals and methods, resulting in four types of projects:

• Type 1 projects: when both goals and methods are well defined. Typically are

represented by engineering projects. They are well defined and have a solid

foundation.

• Type 2 projects: when the goals of the projects are well defined but not the

methods to achieve them. A good example of this type is product development.

• Type 3 projects: when goals are not well defined, but the methods are. Software

development projects tend to fit this description, as it is difficult to specify the

requirements early in the project, and they typically are discovered as the project

evolves.

• Type 4 projects: when neither the goals nor the methods are well defined. Blue sky

research activities belong to this type, as well as organizational development.

The four types are represented in Figure 7.


Figure 7 – Goals and Methods Matrix, adapted from Turner & Cochrane (1993, p. 95)

For each type, it was proposed to use different techniques to deal with project start and

implementation. The project startup of Type-1 projects, for instance, would focus on

detailing the scope of work, project organization and constraints of quality, cost and time.

The startup of Type-4 projects would, instead, focus on ensuring the project context and

purpose is well defined, before developing the objectives and methods.

Davila (2000) applied a contingency model to measure the impact of management control

systems to project performance. The contingency factors used in his model included

technology and market uncertainty.

Pich & Loch (2002) defined different strategies to cope with uncertain project

environments and adequacy of information, grouped by a 2x2 matrix as well. In this

matrix, one dimension divides projects in the extent of the learning that happens during

the project – some projects start with a clear plan to achieve its objectives from the

beginning to the end, and the plan hardly changes during the project; other projects learn

and adapt itself during the project, only providing detailed plans for the next phases.

The other dimension, inspired by natural sciences and tactics for survival of species, is

split by two different strategies: selectionism, in which a number of different projects with


the same goal but different methods are started to increase the chances of success, even if

some of the projects fail; and optimization, which attempts to evolve and change a single

project according to the problems encountered (see Table 5). Each one of the strategies is

adequate to deal with a type of environment, according to the complexity, ambiguity and

uncertainty of the project environment. In this view, traditional project management,

enforced by maturity models, would be placed in the cell where no learning occurs during

the project, and an optimization approach is taken, called the instructionist strategy –

scope is defined as early as possible in the project, and detailed planning is made before

project execution. It assumes the information is adequate – always available for the

project team and with low ambiguity.

Table 5 – Typology for project strategy from Pich & Loch (2002)

Optimization Selectionism

Lear

ning

Learning Strategy Learning occurs by scanning for unknown-unknowns, and using original problem solving. Detailed plans are only provided for the next phases, based on overall vision. Project provides capacity for replanning.

Learning and Selectionism Multiple projects exchange information to increase learning. Projects can be stopped or merged based on success of one candidate.

No-

Lear

ning

Instructionist Strategy Uses detailed plans with critical paths, adding buffers to cope with unknown-unknowns. Manage risks with risks lists and contingency plans. Tracks progress using percentage completions.

Selectionist Strategy Plan multiple trial projects, hedging against anticipated events. Choice of winner is ex post. Success is shared between “winners” and “losers”, as winners cannot be predicted.

Shenhar & Dvir (Shenhar & Dvir, 2007; Shenhar, 2001), starting from the earlier studies

in contingency theory applied to project management, developed a model containing

different dimensions of uncertainty and complexity to categorize projects, aiming to use it

to define the methodology to be used in management of the project. Based on studies of

contingency theory, they used three dimensions: uncertainty, complexity and pace.

Uncertainty is split between market uncertainty and technology uncertainty. The model is

called Novelty, Technology, Complexity and Pace (NTCP). According to the project type

in the dimensions, a different management style must be adopted.

The degree of novelty of the project is important in planning the definition of project

requirements. A completely new product do not have a market, therefore market research


and customer surveys are of little value, and the definition is based on market trials and

gradual changes to project requirements. The requirements freeze must happen only at

later stages of the project, as the results of the market trials are known. On the other hand,

projects that produce only incremental changes from previous products or new product

generations can rely on existing data from market surveys, and need to ensure a timely

product introduction with a relatively low cost, therefore the requirements must be frozen

as early as possible in the project.

The technology level is independent of the project novelty and affects other

characteristics of the management of the project, although both interact in their effect on

the project. Higher technology levels will require increased design and development time

and effort. It means it will have a later design freeze, as the risks and uncertainties

involved with the adoption of new technology are mitigated. High technology projects

also require better interaction between team members, to collectively solve problems

encountered during the project. In lower technology projects, the priority is on increasing

the project management efficiency, delivering the project with the lower cost and in the

shorter time possible, therefore the design is frozen as early as possible to avoid rework,

and the communication between team members is more formal and simplified.

The complexity is related to the size and number of different integrations involved in

delivering the project. The more complex the project gets, the more difficult it is to the

project manager to manage all the changes and communication required. Low complexity

projects tend to have a more informal communication structure, as the team is small and

working on a single location. High complexity projects require a great deal of formalism

and bureaucracy to manage all the communication of the project, documenting

agreements and contracts between different groups.

The pace of the project is the criticality of the time constraints imposed in the project.

Higher time constraints can be imposed by reasons of market window of opportunity or

by external events, such as an emergency and natural disasters. A very high pace project

may require an organization to change their internal structures, from a matrix organization

to a pure project organization, sometimes co-located in what is called skunk works

structure. These teams have to be very focused, and they need to be given high autonomy

to solve the problems as they appear in the project. Such projects don’t do any formal


documentation or non-essential, bureaucratic activities. The model was validated through

a series of case studies (Shenhar & Dvir, 2007, Chapters 5A, 6A)

Figure 8 – Shenhar & Dvir Diamond Model (Shenhar & Dvir, 2007, p. 14)

Müller & Turner (2007) studied the impact of the leadership style to project success,

moderated by a set of contingency variables related to the project. They discovered that

leadership competency was correlated to project success, and the correlation was

moderated by project complexity, the strategic importance of the project, contract type,

culture and life cycle of the project. The conclusion was that different leadership styles

are appropriate for different types of projects, for instance

• Medium and high complexity projects require transformational leadership

(emotional resilience, communication, sensitivity), more than transactional

leadership.

• In regards to the strategic importance of the project, repositioning projects require

a more transactional leadership (motivation) whereas renewal projects would

require a more transformational style of leadership (self-awareness,

communication)

• For fixed price contracts, sensitivity and communication are important leadership

competences, whereas in remeasurement contracts influence and communication

are important, all related to a transactional leadership style


• In regards to the life cycle of the project, conscientiousness and communication

are important throughout the project. At the design stage, managing resources

competence is important, and motivation and sensitivity are important at the

commissioning stage. Strategic perspective correlates negatively with project

success except during feasibility and closeout phases, indicating that during

project execution the project manager must focus on the tasks at hand, and let the

strategic side be managed by other stakeholders.

• On home based projects (as opposed to expatriate), motivation and managing

resources are important, whereas strategic perspective detrimental to project

success

• As for application areas, on engineering projects, motivation is important and

vision is detrimental to project success, therefore a more transactional style is

needed. On information systems projects, self-awareness and communication are

important (among others with less importance), and vision is detrimental –

therefore, in general a transformation style is important.

As a summary, the general view of contingency theory applied to project management

advocates that projects cannot be studied without considering the context, and the

effectiveness of the project organization is determined by its fit to the environment

(Hanisch & Wald, 2012). It’s important to notice that, even though there is a diversity of

frameworks, variables and constructs for performance to analyze project contingencies

such as the frameworks described above from Turner & Cochrane (1993), Shenhar &

Dvir (2007) and Müller & Turner (2007), and other studies using degree of innovation

(Bisbe & Otley, 2004; Henderson & Clark, 1990), uncertainty (Davila, 2000) and learning

and optimization (Pich & Loch, 2002), there is not yet a consensus of the set of variables

that are relevant to analyze project contingencies and the structural needs to cope with

those contingencies (Sauser, Reilly, & Shenhar, 2009).

2.6. Performance

Project management maturity, as an organizational asset, is designed to improve the

project management effectiveness and performance (Kwak & Ibbs, 2002). Furthermore,

any research on organizational theory is only relevant to practitioners if there is emphasis

on organizational effectiveness (Nahm et al., 2003). Therefore, the impact of project


management maturity, mediated by contingency factors, must be measured in terms of an

increase in project and organizational performance. In order to understand the claimed

increase in performance, it is necessary to look at the concepts of project performance and

organization performance.

Project performance is, according to a number of researchers, a multi-dimensional

concept (Jugdev & Müller, 2005; Shenhar et al., 1997). Shenhar (2007) proposed a list of

measures that cover a wide spectrum of project situations and time horizons, as well as

the points of view of different stakeholders. The measures were divided into the following

• Project efficiency – measures the degree of efficiency of project management, in

terms of meeting the schedule and the budget of the project. Indicates if the project

was well managed. It’s a short-term measure, usually available as soon as the

project ends. However its importance diminishes as the time passes.

• Impact on the customer – represents the perspective of the stakeholder whose

perception is arguably the most important to assess the project success. It indicates

not only if the project met the requirements and specifications, but also if the

results improved the business of the customer, and how his needs were addressed.

It can be measured quantitatively, using metrics such as improvement in process

indicators, or qualitatively, with customer satisfaction interviews. The effect of

this success measure lasts longer then the project efficiency.

• Impact on the team – measures the satisfaction and morale of the team, if they

developed new skills and felt energized by the project. It has a financial impact in

the organization, promoting retention of the team and learning.

• Business and direct success – measures the impact on the organization bottom

line. It assesses sales, income, profits, cost savings, cash flow and other financial

measures. This dimension becomes significant only some time after the project is

finished, and cannot be measured in the short term.

• Preparation for the future – measures the project outcome in creating or exploring

new technologies, new markets, new organizational competencies and building the

future of the organization. It only affects the organization years after the project is

delivered.


A model to measure success must take into account the assessment of a range of

stakeholders over different time scales (Turner et al., 2009), even if they are sometimes

conflicting and the stakeholders do not agree (Cheung, Zolin, Turner, & Remington,

2010).

Turner & Zolin (2012) performed an study of different measures to measure project

success, using the perspective of different stakeholders and timescales, and arrived to nine

scales. They are:

• Stakeholder satisfaction – general satisfaction of all stakeholders, such as

contractors, suppliers, project executive and investor

• Project executive satisfaction – measures the satisfaction from the project

executive perspective

• Product satisfaction – measures if the resulting product or prototype is useful for

the customer and operator

• Product efficiency – measures if the resulting product or prototype achieved the

expected performance and efficiency

• Satisfaction with specifications – takes into account if the specifications are

appropriate, from the point of view of the customer, operator and investor

• Project manager satisfaction – scale that measures if the project manager had high

satisfaction and morale during the project, if there was enough recognition and

opportunities for personal growth

• Contractor satisfaction – if the contractor and supplier are satisfied with

performance and contract compliance

• Supplier profitability – if the supplier was allowed to profit from the project

• Public stakeholder – measures the social costs and environmental effects of the

project

Similarly to project performance, the construct of organization performance must be,

according to the literature (Chenhall & Langfield-Smith, 2007; Kaplan & Norton, 1996;

Venkatraman & Ramanujam, 1986), multidimensional, taking into account financial and

non-financial measures.


Different dimensions for organizational performance have been used in the literature

researching the impact of different strategies on organizational performance, such as

management control systems (Bisbe & Otley, 2004), organizational culture (Denison &

Mishra, 1995), business strategy and managerial characteristics (Gupta & Govindarajan,

1984), information systems planning (W. R. King & Teo, 2000), organizational structure

(Nahm et al., 2003), manufacturing technology (Tracey et al., 1999; Ward & Duray,

2000) and also project management maturity (Yazici, 2009b). This is another place to add

something on each one. The dimensions are divided in those related to a financial

perspective: sales growth rate and profitability; and non-financial: customer satisfaction,

market share, internal efficiency and overall business performance.

2.7. Studies of Maturity and Contingency

As it is an important concept, there are a number of studies on project management

maturity related to environmental factors. In an early study, Flowe & Thordahl (1994)

investigated the relationship between CMM ratings and performance, moderated by a set

of variables. They measured project success using Cost and Schedule Performance

Indexes (CPI and SPI) from earned value analysis,. The sample was composed of

acquired projects for the Department of Defense (DoD) of the United States, excluding

internal projects. In general, their results were that CPI was correlated with the CMM

rating, but only from level 1 to 2, not from 2 to 3. SPI showed significant difference

between levels 1 and 3 and between 2 and 3. Using moderators for baseline volatility,

they discovered that CPI and SPI are impacted by maturity levels in projects with less

than 15% baseline changes, whereas in projects with more than 15% changes there was no

statistically significant impact. They also used project size as moderating factor – as the

projects were delivering software, the measure was in thousands of lines of code, or

KLOC. The results were that projects with less than 100K LOC had maturity impacting

CPI and SPI whereas in projects with more than 100K there was no significant

relationship. The results of this study are important, even if it was restricted only to one

aspect of performance, which is project efficiency. It is also interesting that in projects

that are bigger and more complex, the impact of maturity in efficiency is weaker.


One factor studied is the industry of the organization, and how it influence maturity levels

of different project management practices (Cooke-Davies & Arzymanow, 2003; Grant &

Pennypacker, 2006; Pennypacker & Grant, 2003). Although the results from Grant &

Pennypacker did not find significant deviation of maturity among industries, Cooke-

Davies & Arzymanow found that there is evidence of higher maturity in what they call

“industries of origin”, or industries that adopted project management relatively earlier

than others – this finding is consistent with Mintzberg’s hypothesis that the structure of

the organization reflects the age of the appearance of the industry (Mintzberg, 1979).

Skulmoski (2001) looked at the topic from a competence perspective and suggested that

there must be a fit between the project management maturity of the organization, the

required competencies of the project context and the competencies of the project

members, in order to achieve project performance.

Pasian et al (2012) studied emergent factors as potentially predictors of project

management maturity, effective in different project contexts using Turner & Cochrane’s

goals and methods project typology (Turner & Cochrane, 1993). She did a textual analysis

of a number or maturity models of different disciplines, including but not restricted to

project management, looking for maturity factors beyond process control. From the

factors that were found, a case study was performed in two universities with e-learning

projects of Type-3 (when methods are known, but project goals are not well defined at the

outset) according to Turner & Cochrane (1993) typology. They found that, beside defined

processes, other factors were also important in a project management maturity model.

They were

• Customer involvement – as the projects were of type-3, and the goals were not

very well defined, it was deemed of high importance to have the customer

involvement in the project as a key part of the framework

• Adaptable variants – the maturity framework must be also be adaptable to

different project contexts, as the project organization itself, to be mature, needs to

be adaptable do changing conditions

• Dynamic non-events – giving the uncertainties of type-3 projects, the human

factors must be taken into account, in the projects and in the maturity framework.

Aspects such as motivation, attitude and loyalty of the people involved in the


project must be managed (Pasian, Sankaran, & Boydell, 2011; Pasian et al., 2012;

Pasian, 2011)

Giving the apparent inconsistency of the requirement of fit between different project

structures for different project contexts, and the rigidity of project management maturity

models, other authors also suggested the development of maturity models that are

adaptable to different situations. Mettler & Rohner (2009) and Ofner, Huener & Otto

(2009) proposed models in which the assessment strategy would be customized and

adapted for the organization structure; while the reference model would be the superset of

best practices. The team developing the OPM3 model, recognizing the need to adapt

maturity models to the context of the organization, attempted to adopt a contingency

framework in the initial development of the model, but until it’s third release it was not

realized (Schlichter et al., 2010).

According to Mullaly & Thomas (2014), project management maturity models should, at

the very minimum, take into account the context and contingency variables the

organization face – in order to define how project management is implemented and which

practices should be utilized.

2.8. Summary

2.8.1. Summary of concepts

In this literature review, the concept of maturity, how it was inspired from the total quality

movement and its emphasis on standardization and statistical process control is described.

As such, the Capability Maturity Model appeared in the software industry, which inspired

similar maturity models in many other disciplines, including project management. A

number of project management maturity models were developed from the beginning of

the 1990s, largely inspired by SEI’s CMM. The purpose of a maturity model is to define

levels or stages of maturity, or a value to describe the maturity in a continuum, indicating

a path the organization must follow in order to achieve maximum performance in a given

discipline (Cooke-Davies, 2007).


The basic composition of a maturity model is (adapted from Pöppelbuß & Röglinger,

2011):

• Basic information about the application domain, target group and purpose

• Description of maturity levels, maturation paths and criteria for level assessment

• Improvement procedures for achieving higher levels

Also in this literature review the concept of contingency was discussed, which can be

summarized as

3. “There is no best way to organize”

4. “Any way of organizing is not equally effective” (Galbraith, 1973)

It means that organizations operating in different environments, with higher or lower

uncertainties, instability and complexity must have different structures in order to cope

with the requirements of the environment.

Finally, the literature review looked at the concept of performance, and how can it be used

to measure effectiveness of higher maturity levels in different environments.

2.8.2. Knowledge gap and justification for the research topic

In the literature reviewed, there were some studies of contingency factors impacting

performance obtained by maturity. Some of them were restricted to a few factors such as

knowledge of methods and goals (Pasian, 2011), organizational culture (Yazici, 2009b),

baseline changes (Flowe & Thordahl, 1994) or risk profiles (Bahli et al., 2011). Some

studies were focused on a specific type of project such as product development (Dooley et

al., 2001). But published studies on effectiveness of applying project management

maturity models using the combination of different project contexts are not available. This

thesis intends to look at this question by investigating the effect of project management

maturity on perceived performance, exploring situational variables that influence this link.

In broader terms, there is a need for research in contextual application of project

management and to demonstrate how the overall discipline works coherently to deliver

projects successfully (Cooke-Davies, 2007; Morris, 2000). Specifically, there is not a

consensus on one project management framework to address project contingencies

(Sauser et al., 2009). The results of this research may advance knowledge in this area.


Chapter 3 – Methodology

This chapter presents the methodology for the study. It covers the philosophical

underpinnings, the research model, the instrument design and the procedures for data

collection and analysis.

3.1. Research Philosophy

No discussion about the research methodology is complete without concern for the

research philosophy, which underpins the strategy adopted by the researcher (Saunders,

Lewis, & Thornhill, 2009). Management research deals with the creation and legitimation

of knowledge related to management issues, and the philosophical worldview of the

researcher carries assumptions that are key to the methodology that will be adopted in

generating this knowledge (Remenyi, Williams, Money, & Swartz, 1998). According to

Guba & Lincoln, “Questions of methods are secondary to the questions of paradigm,

which we define as the basic belief system or world view that guides the investigation, not

only in choices of method but in ontologically and epistemologically fundamental ways.”

(Guba & Lincoln, 1994, p. 105).

In the following sections a summary of the existing standpoints will be presented.

3.1.1. Ontology and Epistemology

Ontology refers to the researcher’s view of the nature of reality (M. Saunders et al., 2009).

Two aspects represent competing paradigms on ontology, one being the objectivism, in

which social entities exist and are independent of social actors. The other is subjectivism,

in which social phenomena are created from the perception of the social actors concerned

with its existence (M. Saunders et al., 2009).

Management researchers adopting an objective perspective study management in terms of

particular aspects, such as the formal structure of the organization, the operating

procedures in place and job descriptions. It assumes those aspects will have different

structures, but its essence is the same in organizations (Saunders et al., 2009). A

subjective view is different as it considers those objective aspects to be less important


than the way the managers attach meaning to them. The subjectivist view is often attached

to the term constructionism, which views reality as socially constructed by actors

(Saunders et al., 2009).

Epistemology concerns the nature of knowledge and what do we accept as valid

knowledge (Saunders et al., 2009). There are two opposite views in the epistemological

continuum that are relevant to management and organizational science, which are

positivism and relativism (Rousseau, Manning, & Denyer, 2008). Those views will be

discussed in the next section, along with the philosophies that represents intermediate

views in the continuum.

3.1.2. Philosophies and Research Methods

The positivism philosophy is based in logic, and it assumes reality is observable – and

from those observations the researcher seeks causality connections, to derive laws and

generalizations, similarly to physical and natural scientists (Remenyi et al., 1998).

Positivist researchers typically structure their investigation with hypotheses generated by

existing theories, which are tested and confirmed, or refuted, which leads to further theory

development (Saunders et al., 2009). The research method can be quantitative or

qualitative (Saunders et al., 2009).

Relativism or interpretivism is opposite to the positivist view and advocates that the world

is not composed by a single objective reality but of a series of multiple socially

constructed realities (Remenyi et al., 1998). Interpretivists consider the world to be too

complex to be reduced to simple laws and generalizations of cause and effect, as is the

case for positivists (Saunders et al., 2009). Relativists investigate instead the explanations

and narratives of social actors, with a goal of understanding their perspectives (Rousseau

et al., 2008). In a study using a relativist stance it is not possible, or desirable, to define

the steps and hypothesis – instead, the study unfolds as the research proceeds, and early

collection of evidence suggests how subsequent phases will be (Remenyi et al., 1998), a

process called grounded theory (Edmondson & Mcmanus, 2007). In grounded theory, the

researcher approaches the enquiry with an open mind as to what kind of theory will

emerge from the study – of course, preconceptions coming from previous literature

reviewed and the researcher’s own experience cannot be avoided, but they must be


acknowledged in the study (Remenyi et al., 1998). The research methods are primarily

qualitative (Saunders et al., 2009).

Standing between the two poles is the philosophy of critical realism. In critical realism, it

is accepted the epistemological stance that an objective reality exists and is knowable, at

the same time recognizing that the understanding of reality is mediated by human

perception and cognition (Rousseau et al., 2008). Critical realism does not advocate one

method over the other, recognizing that every method has shortcomings, mixing

qualitative and quantitative methods instead and adopting triangulation across methods

and forms of data (Rousseau et al., 2008). Methodological triangulation is the use of

different methods, such as qualitative and quantitative, to study the same phenomena, in

the same or subsequent studies (Tashakkori & Teddlie, 1998).

A fourth research paradigm is called pragmatism. The pragmatism view does not require a

prior choice of philosophical stance from the researcher and advocates the choice of the

methodology according to the research question under study (Saunders et al., 2009). This

paradigm is often criticized for ignoring the role theory plays and “focus on can

something be made to work not why it works” (Rousseau et al., 2008, p. 18).

A table is presented below, from Saunders et al (2009), which compares the four

philosophies.


Table 6 – Comparison of four research philosophies in management research (M. Saunders et al., 2009, p. 119)

The choice of the research method is, ultimately, driven by the background and

philosophical preferences of the researcher (Remenyi et al., 1998). This thesis will take a

critical realism philosophy. Management and organizational science is a human science

and contains multiple levels of complexity in organizations, teams, markets, social

institutions, and those multiple levels require multiple methods to increase our

understanding and create knowledge (Rousseau et al., 2008). Furthermore, project

management maturity and success contain elements of socially constructed and physical

external realities (Cooke-Davies et al., 2001; Jugdev & Müller, 2005), therefore a critical

realist approach is needed to investigate these concepts.


Finally, while a more interpretivist perspective would mean a gradual development of the

hypothesis, a critical realism approach allow us to investigate and prove, or reject, the pre-

defined hypothesis that project management maturity impacts performance, mediated by

contingency factors.

3.2. Research Methods

According to Edmondson & Mcmanus (2007), depending on the prior work on the theory

under study, a different methodology may be a better fit for the research. What they call

“nascent theory” requires qualitative methods and open-ended inquiries in order to

advance our knowledge and formulate a new theory, whereas a mature theory would call

for quantitative methods to test formal hypothesis, adding new mechanisms and

boundaries for existing theories. In this model, if the methodological fit is low the

researcher may face problems with the theoretical contribution of the research – in case of

using quantitative methods with nascent theory, the constructs are still emerging, and the

significant associations may be found by chance. In the other extreme, using qualitative

methods with mature theory may incur in findings that only “reinvent the wheel” or that

are too obvious to contribute to new knowledge (Edmondson & Mcmanus, 2007).

Contingency theory can be considered mature because of its 50 years of application in

organizational theory (Hanisch & Wald, 2012). Extant research of contingency theory

applied to project management range from theoretical (Artto, Martinsuo, Dietrich, &

Kujala, 2008; Pich & Loch, 2002; Turner & Cochrane, 1993) and purely qualitative

(Pasian et al., 2012; Sauser et al., 2009; Shenhar & Dvir, 2007) to the use of mixed

methods (Müller & Turner, 2007; Shao, Müller, & Turner, 2012). At the same time,

project management maturity is a stable concept with a number of published quantitative

studies (Ibbs et al., 2004; Pennypacker, 2006; Yazici, 2009a). Therefore it is intended

with this research to build upon existing theory and test a contingency model of the effect

of project management maturity on performance, which could be added to existing project

management theory. By adding contingency mechanisms to understand the impact of

maturity in performance, we are adding new boundaries to the existing theory, therefore

according to Edmondson & Mcmanus’ (2007) model of methodology fit the most

appropriate methodology for such investigation is a quantitative method.


3.3. Research Model

The literature review presented the existing research in project management maturity and

contingency theory. The knowledge gap that was uncovered is the understanding of the

impact of contingency factors in the application of project management maturity models.

The following research question can then be formulated: “What are the factors that

influence the impact of project management maturity on performance?”

The unit of analysis is the business unit, which can be assessed in regard to its project

management maturity. The theoretical perspective is the one from the contingency theory.

The research model is shown in Figure 9.

Figure 9 – Research Model

The research model is the classic model for contingency theory studies using the

interaction approach (Drazin & Ven, 1985; Venkatraman, 1989), in which contextual

variables act as moderating factor between organizational structure (in this case, project

management maturity) and performance.

3.4. Research Methodology

3.4.1. Instrument Design

This section describes the instruments used to measure the variables from the hypotheses

and research model of this thesis. In order to define valid instruments, the factors below

must be taken into account (Cooper & Schindler, 2006; Rudestam & Newton, 2007):

• Existing instruments are preferable to the development of new instruments if they

exist – newly developed or modified versions of existing ones lack the pretests and

are questionable in terms of reliability and validity. The development of new


instruments requires its own research steps and can be alone the topic of a

dissertation

• Appropriateness of the instrument – the instrument must be adequate for the target

population of the study, and it must conceive the phenomenon studied in terms

similar to the manner it has been conceptualized in the thesis

• Validity, reliability, and structure of the instrument – reliability means the ability

for the instrument to provide consistent results, validity indicates the instrument

measures what it allegedly measures, and structure refers to the number and

meaning of subscales of an instrument. At the instrument definition stage, the only

way to maximize reliability and validity is through existing literature.

• Procedures to administer the instrument – the procedures to collect the data using

the instrument, for instance via self-reported questionnaire or interviews with

qualified personnel, must be taken into account. In order to test the complexity of

the instrument, a pilot must be performed to gather participant feedback on items,

which they found ambiguous or difficult to understand.

The use of self-reported questionnaires is recognized as indispensable in organizational

research, but the researcher must be aware of the inherent problems of using the tool

(Podsakoff & Organ, 1986). The main problems, according to Podsakoff & Organ (1986)

are:

• Common method variance – when using the same source to report two or more

variables, any defect in the source can contaminate the variables in the same

fashion and direction, producing erroneous correlations

• Consistency motif – people tend to be consistent in their answers, based on what

they believe to be true, producing again illusory correlations, which only support

their beliefs

• Social desirability – respondents may answer questions in a way to present

themselves in a favorable light.

In order to mitigate the problems of self-reporting, the following actions were taken, as

suggested by Conway & Lance (2010) and Podsakoff & Organ (1986):


• Ensure construct validity – construct validity was ensured by utilizing existing

instruments, which were tested in the literature using both quantitative and

qualitative methods

• Lack of overlap of different constructs – the constructs were validated against

overlap by the advisors of the thesis and by the pilot of the questionnaire

• Use of different scales and methods – the scale for maturity, contingency and

success were different, and the instruments came from different sources

• Design of the questionnaire – the questions for different variables were grouped

by their concepts in different pages, in order to provide a separation for the

respondents

• Anonymity – in order to mitigate the social desirability problem, the introduction

of the survey made clear the answers were anonymous

In the following sections, the choice of instruments for the constructs of project

management maturity, project context and performance will be presented.

3.4.1.1. Project Management Maturity

The literature review has shown a number of existing project management maturity

models. In order to operationalize the construct for the research, it was necessary to select

one of existing models according to the criteria described above for appropriateness and

procedures.

Table 7 presents the analyzed maturity models.

Table 7 – Analysis of maturity models as instruments

Model Rating Concepts Applicability

MGP - Darci-Prado 5 levels Project management, strategic alignment, organizational and competence.

Simple, 40 questions

P3M3 – OGC 5 levels Project management, benefits management, organizational and financial.

Simple, nine questions for project, nine for program and


nine for portfolio management

Project oriented Company Maturity Model – Gareis

Continuous Project, programme, quality assurance, assignment of a project or programme, project portfolio coordination and networking between projects, organizational design, personnel, process

Long, 74 questions

OPM3 – PMI Continuous Project, Program and Portfolio. Organizational enablers, standardize, measure, control and continuously improve.

Complex, 488 best practices to be assessed by certified consultant

PMMM - PM Solutions 5 levels Rating as the average maturity

Knowledge areas from PMBOK Simple, survey with 42 questions

ProjectFRAMEWORK – ESI

5 levels Rating as fulfilling pre-requisites

Knowledge areas from PMBOK Simple, survey with 11 questions

The chosen model is the ProjectFRAMEWORK based on the following reasons:

• Appropriateness: the model must represent, as closely as possible, the construct as

defined by the theoretical foundation adopted. As seen in the literature review, the

core concept of organizational maturity is the standardization of processes and

statistical process control as defined by Shewhart (1939), and the progressive five

levels described by Humphrey (Humphrey, 1989), as the ProjectFRAMEWORK

model is structured. In regard to the concept of project management, the model

must adopt processes as recognized by practitioners for being project management

processes. ProjectFRAMEWORK adopts the PMBOK, which is one recognized

standard for project management processes (PMI, 2013a).

• Applicability of procedures: giving the quantitative nature of the study, the

instrument must be applicable to a large quantity of subjects via a questionnaire.

From the options analyzed, ProjectFRAMEWORK was one of the most compact,

being able to be formatted in 11 questions, one per PMBOK knowledge area and

one for overall maturity.


3.4.1.2. Contingency

The situational variables, selected according to the literature and existing research, are

related to the project type, more specifically their novelty, pace, complexity and use of

technology (Shenhar & Dvir, 2007); industry (Cooke-Davies & Arzymanow, 2003;

Shenhar & Dvir, 2007); uncertainty and adequacy of information (Pich & Loch, 2002;

Turner & Cochrane, 1993); the goal of the project in the organization (Shenhar & Dvir,

2007; Thomas & Mullaly, 2008); and the strategic importance and application area

(Müller & Turner, 2007; Shenhar & Dvir, 2007).

The operationalization of the constructs is described in Table 8.

Table 8 – Project Contingency Constructs

Construct Operationalization Source Industry Industry name Shenhar & Dvir (2007)

Novelty Derivative, Platform, New to the Market, New to the World

Technological Uncertainty Low-Tech, Medium-Tech, High-Tech, Super High-Tech

Complexity Component/Material, Assembly, System, Array

Pace Regular, Fast/Competitive, Time-critical, Blitz

Business Goal Operational or Strategic Customer External or Internal Strategic Goal Money-Making Project, Money-

Saving Project, Utility/Infrastructure, Maintenance/Keep the Lights On Project, Building the Future, Exploring new ideas, Problem Solving Project

Uncertainty of Goals Goals were well understood / goals were not well understood

Turner & Cochrane (1993)

Uncertainty of Methods Methods were well understood / Methods were not well understood

3.4.1.3. Performance

For this thesis, performance is being measured in two units of analysis: the performance

of the project and the performance of the organization.

The instrument to measure performance of the organization was created based on

dimensions used in existing literature researching the impact of different strategies on


organizational performance (Bisbe & Otley, 2004; Denison & Mishra, 1995; Gupta &

Govindarajan, 1984; W. R. King & Teo, 2000; Nahm et al., 2003; Tracey et al., 1999;

Ward & Duray, 2000; Yazici, 2009b). The dimensions are divided in those related to a

financial perspective: growth rate of sales, profitability; and non-financial: customer

satisfaction, market share, internal efficiency, and overall business performance.

The instrument selected to measure project performance is the one from Shenhar & Dvir

(2007). There are other models in the literature that measure performance from the point

of view of many stakeholders using multiple timescales, such as the model defined by

Müller & Turner (2007) and Turner & Zolin (2012) – as an opportunity for future

research, the impact of maturity in performance can be investigated using a combination

of those or more models to triangulate the results.

Table 9 – Project Performance Questionnaire, adapted from Shenhar & Dvir (2007)

Construct Question

Project Efficiency The project was completed on time or earlier.

The project was completed within or below budget.

The project had only minor changes.

Impact on Customer The project improved the customer’s performance.

The customer was satisfied.

The project met the customer requirements.

The customer is using the project result.

The customer will come back for future work.

Impact on the Team The project team was highly motivated and satisfied.

The team was highly loyal to the project.

The project team had high morale and energy.

The team felt that working on this project was fun.

Team members experienced personal growth.

Team members wanted to stay in the organization.

Business and Direct Organization Success The project was an economic business success.

The project increased the organization’s profitability.

The project has a positive return on investment.

The project increased the organization's market share.

The project contributed to stakeholder value.

The project contributed to the organization's direct performance.


Preparation for the Future The project outcome will contribute to future projects.

The project will lead to additional new products.

The project will help create new markets.

The project created new technologies for future use.

The project contributed to new business processes.

The project developed better managerial capabilities.

Table 10 – Organizational Performance Constructs

Construct Question Source

Sales growth, market share growth, profitability

The rate of sales growth of my organization improved as a result of its projects

Gupta & Govindarajan, 1984 Tracey, Vonderembse, & Lim, 1999 Denison & Mishra, 1995

The profitability of my organization improved as a result of its projects

The market share of my organization improved as a result of its projects

Overall business performance The overall business performance of my organization improved as a result of its projects

Denison & Mishra, 1995

Internal efficiency, customer satisfaction

The internal efficiency of my organization improved as a result of its projects

King & Teo, 2000

The customer satisfaction with my organization improved as a result of its projects

The scale for the performance questions was a five-point Likert scale: 1- Strongly Agree,

2- Disagree, 3-Agree, 4-Strongly Agree, 5- N/A. The “N/A” option is important to allow

answers when the respondent cannot emit an opinion on the question, either because it

does not apply to that particular case or because the respondent does not know (Shao et

al., 2012). This case is treated as missing data, as explained in Chapter 4.

3.4.2. Pilot

A pilot of the questionnaire was conducted with five project managers during one week.

The feedback collected from the pilot led to changes in the questionnaire, which are

summarized below.

• In the maturity questionnaire, there is one question to measure the global

organizational maturity of the organization, which more than one pilot respondent


said was the most difficult question. For the final version, the question was moved

to the end of the maturity section, as at that stage the respondent is more familiar

with the structure of the maturity questions.

• There are two versions of the ProjectFRAMEWORK maturity questionnaire, one

with 11 questions related to the knowledge areas, and one with 26 questions

related with the best practices. One respondent answered the 26 question version;

the other four responded using the 11 question version. All respondents gave the

feedback that generally the questionnaire was quite long, but the respondent of the

26 questions version claimed it was longer than acceptable for an online survey.

Therefore it confirmed our decision to use the 11-question version, and they were

shortened further with a review by the original author of the model, with care to

keep the original concepts.

• The question about the project duration did not have the time unit specified. The

question was changed to make it clear it should be answered in months.

3.4.3. Ethical Considerations

The researcher must take in consideration ethical aspects for the study, most importantly

issues of informed consent and protection of confidentiality (Czaja & Blair, 2005). Those

were addressed by informing the participants in the survey introduction text that their

participation was voluntary, and all information provided was confidential. The survey is

presented in Appendix A.

3.5. Sampling

There are two main techniques to select the sample of the survey: probability and non-

probability sampling. In the probability sampling, the probability of each case being

selected in the population is known, even if it is not necessarily equal (Remenyi et al.,

1998). However, in business research this is often not possible, which is due to limited

resources or the inability to specify the sampling frame or population – for that case, there

are techniques that are called non-probability sampling (Saunders et al., 2009). This will

be the case for this research.


In non-probability sampling, the technique used for this research was the snowball

sampling, in which some cases in the population are contacted, and they are asked to

identify additional cases and so on.

3.6. Data Analysis

The starting point of the analysis is a study of a regression, which can be stated in the

form of

where x is the independent variable, and predictor of the dependent variable y, according

to the coefficient b1.

The moderator variable influences the regression, and it can be classified into pure

moderator or quasi moderator (Sharma et al., 1981).

Pure moderators influence the regression but are not related to the independent variable,

as expressed by the variable z in the equation below

Whereas quasi-moderators not only influence the regression but are also related to the

independent variable, as shown in variable z in the equation below

To study the moderators of the model, considering all cases above, moderated hierarchical

regression analysis was used. The method has been used extensively in the study of

moderating factors and its impact on performance (Carson, Madhok, Varman, & John,

2003; Tatikonda & Rosenthal, 2000; Zhu & Sarkis, 2004). There is one caveat in this

approach that is the multicollinearity between the terms and their cross products, in this

example, between x, z and xz, which may underestimate the effect of the moderator in the

regression – to tackle this problem, the predictor and independent variables will be

standardized, as suggested by Dunlap & Kemery (1987).


3.7. Data Check

The data collected was screened to check against problems that could affect the analysis.

They could either inflate or deflate correlations, which would incur in type-1 or type-2

errors in the analysis (Tabachnick & Fidell, 2007). A list of those pre-tests is shown

below:

• Missing values: missing data points are a common problem in any research. In the

case of this thesis, respondents skipping questions or answering “N/A” could

cause missing data points. The data were checked for missing data, and depending

on the severity and on the distribution of the missing data, different strategies were

used, including the imputation of those missing values by the means of the

variable, or by estimation of the missing value using existing values (Tabachnick

& Fidell, 2007), or even dropping the variable if no other approach is possible.

• Normality: for regression analysis, it is assumed the variables follow a normal

distribution. There are two ways a variable can deviate from normality, either

lacking symmetry (called skewness) and pointiness (called kurtosis). Skewed

distributions are not symmetrical and have the majority of the cases clustered in

one side of the scale. The kurtosis measures the degree to which the scores cluster

at the tails of the distribution. The standard tests for normality gives numbers for

the skewness, and if it’s above zero it’s called a positive skewed distribution, with

most of the cases in the left of a histogram, and if it’s below zero it’s a negatively

skewed distribution, with most of the cases to the right. The limit of tolerance used

here is plus or minus 1.96 for skewness. The test also give a number for the

kurtosis, and a positive number represents a pointy distribution, with most cases

concentrated around the mean, and a negative kurtosis indicates a flat distribution.

The limit for kurtosis is plus or minus 3.2. All variables were checked for their

skewness and kurtosis, and if they were above the limit of +/-1.96 or +/-3.2

respectively (Field, 2005), some transformations were necessary in order to bring

the variable to normality without affecting the regressions.

• Univariate outliers: an univariable outlier is a case of an extreme value that

distorts the statistics for a variable (Tabachnick & Fidell, 2007), in some cases

affecting the normality of the distribution. The data were checked for those cases,

and if outliers are found, transformations may be required to minimize the impact

of the outlier on the series.


There were also post-checks performed in the regressions, to verify the quality of the data

and the adequacy of the model. They were:

• Multivariate outliers: multivariate outliers are cases in which the dependent

variable differs considerably from the predicted value of the regression equation

(Tabachnick & Fidell, 2007). They could be caused by misinterpretation or errors

while filling the questionnaire, or by valid cases in which a regression equation

cannot predict the outcome. All multivariate outliers had their influence in the

model checked, using their Malahanobis and Cook’s distance, their leverage

values and covariance ratios. As no case exceeded the recommended value of one

for Cook’s distance, all cases were kept.

• Multicollinearity: is the case where the independent variables of the regression are

highly correlated. If the correlation is too high the regression equation cannot be

properly calculated and the results are unreliable (Field, 2005). All regressions

were checked using the Variance Inflation Factor (VIF), which was around one for

all cases and therefore all cases were acceptable (Field, 2005).

• Homoscedasticity: Another assumption of regression analysis is that the variables

are homoscedastic or that its variance is constant (Tabachnick & Fidell, 2007). In

order to verify the homoscedasticity, the residuals of the regressions were plotted

against the predicted values. Although all regressions were checked, for the

regressions resulting in significant relationships, the plots with the residuals are in

Appendix B.

• Independent errors: this assumption is that any two observations, the residual

terms must be independent or uncorrelated (Field, 2005). The test for this

condition is the Durbin-Watson test, and all regressions were around the

acceptable value of two.

3.8. Summary

In this chapter the research design for this thesis was described. It started from the

philosophical choices of the study, in particular the critical realist standpoint, with the

considerations and implications for theory building. The method chosen for the research,


purely quantitative, was justified based on prior work and stage of development of the

concepts under study, namely contingency theory and project management maturity.

The research question is then defined based on the all that has been discussed thus far and

can be phrased as “What are the factors that influence the impact of project management

maturity on performance?”

The research model uses maturity as independent variable, and the dependent variables

are project and organizational performance. The moderating variables are project novelty,

technology, complexity, pace, and the knowledge of project goals and methods. The

analysis of the data will be carried on using moderated hierarchical regression analysis, as

discussed in the next chapter.


Chapter 4 – Data Analysis

In this chapter the results of the analysis of the data collected are reported.

The descriptive statistics are presented. The independent variable, performance, is

grouped using factor analysis, which enable us to refine the main hypotheses defined in

Chapter 3. The research model and the refined hypotheses are then tested using moderated

hierarchical regression analysis.

4.1. The Sample

The responses were collected via a web survey from October 4th to November 25th, 2013.

The invitations were sent by email to personal contacts from the researcher, posted to

newsgroups and communities related to project management, sent to PMI chapter officers

to be forwarded to their members and to the researcher’s alumni network.

The response is shown in Table 11, including incomplete responses.

Table 11 – Sources and the number of responses

From the 279 responses, 70 did not complete the questionnaire. An analysis of those 70

revealed that two participants were very close to completion: One case had no missing

data, so probably the tool did not recognize the finalization of the survey for some reason.

One other case did not complete the business performance questions, but completed all

other questions up to the project performance questionnaire. For that reason they were

included in the analysis, resulting in 211 valid responses. Missing data for all cases will

be treated further in the analysis.

Source Group size Responses Newsgroups (Yahoo groups, Google Groups, LinkedIn communities, all project management related)

N/A – Public access 21

Alumni network 64 24 PMI Chapters Sent to members of 25 PMI

chapters 28

Personal contacts 623 203 Other social networks (Facebook, twitter, tumblr) N/A – Public access 3 Total 279

Responses


The survey was published on the SurveyMonkey1 platform, which offers a number of

features for tracking the responses, such as separate links for each group and charts.

Because of the nature of the snowball technique, estimating the response rate or the

sampling frame is not possible. The definition of the sample size has to be done based on

the research question and data analysis technique (Saunders et al., 2009).

For the analysis there must be a minimum of five observations per independent variable

(Bryant & Yarnold, 1995), but the recommended number for generalizability is 15 to 20

observations per independent variable (Hair, Anderson, Tatham, & Black, 1998). In our

model, there are nine contingency variables and one for maturity. Therefore, 150 to 200

responses would be necessary. As there were 211 valid responses, they meet the

requirements for generalizability and regression analysis.

The organizations represented in the responses were from many countries as presented in

Table 12. There is a concentration of responses in Brazil, the researcher’s home country,

and Switzerland, where the researcher resides.

Table 12 – Country distribution

What is the country of origin of your organization? Brazil 54 25.6% United States 33 15.6% Switzerland 32 15.2% France 20 9.5% United Kingdom 8 3.8% Canada 7 3.3% China 7 3.3% Germany 7 3.3% Turkey 5 2.4% Sweden 3 1.4% United Arab Emirates 3 1.4% Australia 2 0.9% Denmark 2 0.9% Italy 2 0.9% Korea South 2 0.9% Norway 2 0.9% South Africa 2 0.9% Ukraine 2 0.9% Argentina 1 0.5% Austria 1 0.5% Belgium 1 0.5% Cambodia 1 0.5%

1 http://www.surveymonkey.com


Chile 1 0.5% Croatia 1 0.5% Indonesia 1 0.5% Iran 1 0.5% Israel 1 0.5% Japan 1 0.5% Kuwait 1 0.5% Latvia 1 0.5% Lebanon 1 0.5% Mexico 1 0.5% Netherlands 1 0.5% Portugal 1 0.5% Romania 1 0.5% Did not answer 1 0.5% Total 211

Analyzing the role of respondents, a large number of respondents (45, or 23.1%) had

answered as “other” and entered the textual description of the role. The questionnaire

contained the following pre-defined roles: Project Team Member, Project Manager,

Project Director, Program Manager, Program Director, Sponsor, Line/Department

Manager, CEO/COO, Other. Upon examination of the roles entered as “other”, new roles

were used in the coding when their roles were not exactly a good match to any of the

previous options, and some roles were recoded using existing ones (such as software

developer, which can be recoded as project team member).

The full distribution of roles is in Table 13. A large number of respondents had the role of

project team members (107, or 50.7%), which is compatible with the general distribution

of roles in the project since the survey was not targeted at any specific project role.

Table 13 – Role of respondent distribution

What was your role in the project? Project Team Member 107 50.7% Project Manager 32 15.2% Line/Department Manager 15 7.1% External Consultant 10 4.7% CEO/COO 7 3.3% Program Manager 7 3.3% Program Director 5 2.4% Product Owner 4 1.9% Project Director 4 1.9% Project Controller 3 1.4% Other 2 0.9% Product Manager 2 0.9% Team Leader 2 0.9% Account Executive 1 0.5%


Certified Scrum Master 1 0.5% Construction Manager 1 0.5% Project Coordinator 1 0.5% Project Manager Assistant 1 0.5% Project Planner 1 0.5% Purchasing Manager 1 0.5% QA 1 0.5% Release Manager 1 0.5% Risk Manager 1 0.5% Sponsor 1 0.5% Total 211

In the following sections the independent variables will be checked for normality and

adequacy for the multiple regression analysis.

4.2. Project Management Maturity

The data for project management maturity was collected using a questionnaire derived

from the ProjectFRAMEWORK model (Levin et al., 2013c). The questionnaire offers

maturity levels for the 10 PMBOK knowledge areas, and one additional question

addresses the global maturity in order to complement and triangulate the expected

maturity level of the organization.

The descriptive statistics for the maturity data is presented in Table 14.

Table 14 – Descriptive Statistics for Project Management Maturity

N Minimum Maximum Mean Std. Deviation Skewness Kurtosis Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error

Scope 201 1 3 2.06 .719 -.089 .172 -1.049 .341 Time 203 1 3 2.02 .767 -.042 .171 -1.294 .340 Cost 195 1 3 2.07 .770 -.124 .174 -1.299 .346 Quality 204 1 3 2.16 .691 -.217 .170 -.897 .339 HR 193 1 4 2.45 1.089 .225 .175 -1.254 .348 Communication 206 1 3 1.90 .784 .173 .169 -1.352 .337 Stakeholder 200 1 3 1.92 .715 .111 .172 -1.031 .342 Risk 202 1 4 2.23 .961 .379 .171 -.778 .341 Procurement 165 1 4 1.70 .775 .651 .189 -.776 .376 Integration 186 1 4 2.16 1.000 .456 .178 -.847 .355 Overall maturity 208 1 5 2.47 1.304 .735 .169 -.582 .336 Valid N (listwise) 144

The values for kurtosis and skewness are under the limit of +/-3.2 and +/-1.96 (Field,

2005), demonstrating normality as required for the regression analysis.


The apparent problem is the quantity of missing values, with the option of “I have no

experience in this area” present in the scale. Particularly, in the case of the variables

Procurement and Integration, the number of missing values is of 22% and 11%,

respectively.

Further analysis in Figure 10 shows that 69% of the cases have no data missing, and 93%

of the data points are present.

Figure 10 – Missing data analysis for maturity

One important test for missing values is the Little’s Missing Completely At Random

(MCAR) test in order to check if the missing values occur at random (Field, 2005). The

result is in Table 15.

Table 15 – EM Means for Project Management Maturity

Scope Time Cost Quality HR Communication Stakeholder Risk Procurement Integration Overall Maturity

2.06 2.02 2.07 2.16 2.43 1.91 1.92 2.22 1.68 2.19 2.47

a. Little's MCAR test: Chi-Square = 342.775, DF = 288, Sig. = .015

What this test shows is that the null hypothesis that the missing data is random cannot be

rejected, which is due to the low significance (.015). In this situation, removing the cases

which contain missing values or replacing them with means can introduce bias and

invalidate the results of the analysis (Field, 2005).

Even though any strategy to deal with missing data will deliver results that are worse than

using the real data, some methods can still deliver useful information (Tabachnick &

Fidell, 2007). As the missing data points are relatively low (6.7%), the existing methods

will provide similar results (Tabachnick & Fidell, 2007).


For this dataset, we used SPSS to estimate the missing values using the expectation-

maximization (EM) method. The descriptive statistics for the dataset after estimation is

shown in Table 16

Table 16 – Descriptive statistics for maturity variables after filling missing data


Scope 211 1 3 2.06 .704 -.075 .167 -.977 .333 Time 211 1 3 2.02 .758 -.025 .167 -1.269 .333 Cost 211 1 3 2.07 .753 -.114 .167 -1.252 .333 Quality 211 1 3 2.16 .685 -.241 .167 -.864 .333 HR 211 1 4 2.43 1.061 .268 .167 -1.181 .333 Communication 211 1 3 1.91 .780 .155 .167 -1.349 .333 Stakeholder 211 1 3 1.92 .702 .136 .167 -.967 .333 Risk 211 1 4 2.22 .952 .391 .167 -.764 .333 Procurement 211 1 4 1.69 .699 .750 .167 -.300 .333 Integration 211 1 4 2.17 .971 .420 .167 -.804 .333 Overall maturity 211 1 5 2.47 1.296 .738 .167 -.559 .333 Valid N (listwise) 211

4.2.1. Reliability of the Scale

Testing the maturity variables for reliability of the scale shows a high Cronbach alpha of

0.891. The item-total statistics are presented in Table 17.

Table 17 – Reliability analysis for maturity variables

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Corrected Item-Total Correlation

Cronbach's Alpha if Item Deleted

Scope 21.34 38.758 .601 .883 Time 21.42 38.720 .559 .885 Cost 21.33 37.874 .672 .879 Quality 21.26 39.003 .621 .882 HR 21.05 36.047 .580 .885 Communication 21.49 37.468 .696 .877 Stakeholder 21.54 38.949 .600 .883 Risk 21.24 36.419 .669 .878 Procurement 21.75 40.762 .344 .895 Integration 21.17 34.802 .746 .872 Overall maturity

20.92 31.707 .769 .873

The variables are also highly correlated, with most correlation indexes between 0.3 and

0.4, as shown in Table 18.


Table 18 – Pearson correlation indexes for maturity variables

1 2 3 4 5 6 7 8 9 10 11 1. Scope 1 .457** .393** .561** .412** .489** .394** .376** .258** .470** .446** 2.Time .457** 1 .489** .437** .333** .464** .392** .371** .239** .422** .407** 3.Cost .393** .489** 1 .426** .433** .459** .347** .413** .347** .482** .534** 4.Quality .561** .437** .426** 1 .361** .493** .428** .415** .225** .541** .495** 5.HR .412** .333** .433** .361** 1 .422** .418** .383** .142* .466** .466** 6.Communication .489** .464** .459** .493** .422** 1 .410** .513** .310** .579** .549** 7.Stakeholder .394** .392** .347** .428** .418** .410** 1 .441** .318** .409** .438** 8.Risk .376** .371** .413** .415** .383** .513** .441** 1 .263** .563** .569** 9.Procurement .258** .239** .347** .225** .142* .310** .318** .263** 1 .391** .236** 10.Integration .470** .422** .482** .541** .466** .579** .409** .563** .391** 1 .679** 11.Overall maturity .446** .407** .534** .495** .466** .549** .438** .569** .236** .679** 1 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

For the regression, the independent variable used was a global organizational maturity

indicator calculated as a sum of all 11 indexes.

4.3. Project Context

Descriptive statistics for the context variables are shown in Table 19.

Table 19 – Descriptive Statistics for Context Variables

N Minimum Maximum Mean Std. Deviation Skewness Kurtosis Statistic Statistic Statistic Statistic Statistic Statistic Std.

Error Statistic Std.

Error Project duration (months)

211 0 96 17.74 16.520 2.314 .167 7.269 .333

Project budget (USD) 86 0 2000000000 81761548.85 260957446.512 5.518 .260 36.100 .514 Age of organization (years)

203 1 205 33.85 39.663 2.026 .171 4.186 .340

Novelty 211 1 4 2.06 .967 .547 .167 -.690 .333 Technology 211 1 4 2.29 .730 .076 .167 -.288 .333 Complexity 211 1 4 2.94 .781 -.799 .167 .724 .333 Pace 211 1 4 2.17 .820 -.107 .167 -1.096 .333 Goals 211 1.00 2.00 1.7725 .42021 -1.309 .167 -.288 .333 Methods 211 1.00 2.00 1.6256 .48512 -.523 .167 -1.743 .333 Valid N (listwise) 84

The project budget variable seems to have too many missing values – only 86 valid

responses from the 211 cases, or only 40% valid responses. For that reason the variable

will not be considered. It can be explained by most of the respondents having no

information on the budget of the project, as their role in the project did not have access to

these data.


The other ratio variables, age of organization and project duration, have problems in the

normality, showing skewness of 2.02 and 2.31 respectively, beyond the limit of 1.96

(Field, 2005). The kurtosis of 4.19 and 7.27 are also beyond the limit of 3.2 (Field, 2005).

Those variables can be transformed by recoding them in categories. The categories were

defined using +/- 1 and +/- 3 standard deviations, resulting in six categories, and the

missing variables were replaced with the mean of the series. The descriptive statistics

after transformations are shown in Table 20, and the tests for normality are now

acceptable.

Table 20 – Descriptive Statistics For Ratio Variables After Transformation


Project duration (recoded) 211 2 6 3.55 .846 .882 .167 .364 .333 Age of organization (recoded) 211 3.0 6.0 3.483 .7581 1.416 .167 .937 .333 Valid N (listwise) 211

The other variables for project context are nominal: they are project customer (internal

and external), industry of the project and strategic goal. Their frequencies are in the tables

below.

Table 21 – Frequencies For Project Customer

Frequency Percent Valid Percent Cumulative Percent External (external contract or consumers) 140 66.4 66.4 66.4 Internal (internal user or another department) 71 33.6 33.6 100.0 Total 211 100.0 100.0

Table 22 – Frequencies for Project Strategic Goal

Frequency Percent Valid Percent

Cumulative Percent

Building the Future (R&D, Technology Development, Exploring new ideas – No specific customer in mind) 39 18.5 18.5 18.5

Maintenance/Keep the Lights On Project (Routine maintenance, fixing regular problems) 6 2.8 2.8 21.3

Money-Making Project (selling a product or service to clients) 94 44.5 44.5 65.9 Money-Saving Project (Internal effort of cost reduction) 13 6.2 6.2 72.0 Problem Solving Project (Project focused on a unique narrow problem) 17 8.1 8.1 80.1

Utility/Infrastructure (Acquiring and installing new equipment or software, implementing new methods or new processes) 42 19.9 19.9 100.0

Total 211 100.0 100.0


The projects analyzed were from different industries, with a major concentration in

information technology, software and telecommunications, as the network of the

researcher is strongly linked to those businesses. The full list and their frequencies are in

Table 23.

Table 23 – Frequencies for Project Industry

What was the principal industry of the project? Advertising & Marketing 3 1.4% Agriculture 1 .5% Airlines & Aerospace (including Defense) 6 2.8% Automotive 2 .9% Business Support & Logistics 3 1.4% Construction, Machinery, and Homes 11 5.2% Consulting 9 4.3% Consumer Electronics 10 4.7% E-Commerce 3 1.4% Education 7 3.3% Energy 7 3.3% Entertainment & Leisure 1 .5% Finance & Financial Services 19 9.0% Food & Beverage 4 1.9% Government 13 6.2% Healthcare 6 2.8% Information Technology 33 15.6% Insurance 5 2.4% Manufacturing 7 3.3% Nonprofit 2 .9% Pharmaceuticals 4 1.9% Real Estate 1 .5% Retail & Consumer Durables 6 2.8% Software 18 8.5% Telecommunications 24 11.4% Utilities, Energy, and Extraction 6 2.8% Total 211 100.0%

4.4. Performance

Descriptive statistics are in Table 68, in Appendix B. The first point to address is the

quantity of missing data, due to the option “N/A” present in the questionnaire. From the

descriptive statistics, only 88 cases out of 211 (41%) have no variable missing for

performance.


A detailed analysis using SPSS shows that, even if the percentage of cases and variables

with missing data is high, the number of values missing is low – only 11% – as shown in

Figure 11.

Figure 11 – Missing value analysis for performance variables

The Missing Completely at Random test resulted in

Little’s MCAR test: Chi-square = 3090.867, DF = 3027, Sig. = .181

With a significance value of .181, it is possible to assume the values are missing

completely at random and replace them with the variable means. The full descriptive

statistics after replacement is presented in Table 69 in Appendix B.

The test for normality shows that one variable – The project contributed to stakeholder

value – has Kurtosis beyond the acceptable value of +/-3.2. Upon examination, the

question contains three outliers, as shown in the box plot at Figure 12.

Figure 12 – Boxplot for Stakeholder Value


Upon examining the cases, the reason is that those three cases refer to projects that had

little or no stakeholder value, which is theoretically possible and relevant for the analysis.

In this case it is necessary to perform a transformation of the variable to minimize the

impact of the outliers and bring the variable closer to the normal distribution (Tabachnick

& Fidell, 2007).

The transformation used was a reflected square root of the variable, adequate for this

situation (Tabachnick & Fidell, 2007). The variable was then re-reflected, in order to

maintain the direction for future interpretation.

Table 24 – Descriptive Statistics to The Project Contributed to Stakeholder Value

Before Transformation After Transformation

N Valid 211 211 Missing 0 0

Mean 3.21 1.9187 Std. Deviation .591 .21818 Skewness -1.073 -.177 Std. Error of Skewness .167 .167 Kurtosis 4.827 1.266 Std. Error of Kurtosis .333 .333

4.4.1. Factor Analysis

In order to have a successful factor analysis uncovering underlying scales in the data, the

variables must have some degree of correlation between themselves. The correlation for

the performance variables is above .3 for 30% of the pairs, indicating a good fit for factor

analysis.

The Kaiser-Meyer-Olkin (KMO) test is in Table 25, showing a value of 0.863, above the

0.6 required for factor analysis (Tabachnick & Fidell, 2007), and the Bartlett’s test shows

significance below 0.001, also appropriate for factor analysis.

Table 25 – KMO and Bartlet's test for Performance Variables

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .863

Bartlett's Test of Sphericity

Approx. Chi-Square 3410.625

df 528

Sig. .000


The diagonals of the anti-image correlation matrix all show values above 0.5 (Table 70,

on Appendix B). Table 26 – Rotated Component Matrix for Performance (coefficients above 0.5)

Component 1 2 3 4 5 6 7 8

Eingenvalue 9.160 3.089 2.494 2.041 1.580 1.400 1.123 1.007 % of Variance 27.756 9.361 7.559 6.184 4.789 4.243 3.404 3.05 Cumulative % 27.756 37.117 44.676 50.86 55.649 59.892 63.296 66.346

The project was completed on time or earlier. .542 The project was completed within or below budget. .651 The project had only minor changes. .735 The project improved the customer’s performance. .583 The customer was satisfied. .650 The project met the customer requirements. .705 The customer is using the project result. .701 The customer will come back for future work. .657 The project team was highly motivated and satisfied. .804 The team was highly loyal to the project. .785 The project team had high morale and energy. .867 The team felt that working on this project was fun. .812 Team members experienced personal growth. .657 Team members wanted to stay in the organization. .648 The project was an economic business success. .709 The project increased the organization’s profitability. .804 The project has a positive return on investment. .795 The project increased the organization's market share. .662 The project contributed to stakeholder value. .666 The project contributed to the organization's direct performance.

.584

The project outcome will contribute to future projects.

.522

The project will lead to additional new products. .790 The project will help create new markets. .757 The project created new technologies for future use. .635 The project contributed to new business processes. .542 .579 The project developed better managerial capabilities. .749 Overall, the project was a success. The rate of sales growth of my organization improved as a result of its projects

.795

The profitability of my organization improved as a result of its projects .769


.692


.771

The internal efficiency of my organization improved as a result of its projects .640

The overall business performance of my organization improved as a result of its projects

.651

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 8 iterations.


Factor analysis with varimax rotation was run in the variables for performance. Eight

factors with Eingeinvalue above 1.0 were extracted as seen in Table 26.

The extraction of eight values is justified by its Engeinvalue and the scree plot presented

in Figure 13, where there is a visible drop between factors eight and nine, before the chart

stabilizes (Field, 2005).

Figure 13 – Scree Plot of Performance Factors

The interpretation of the factors are rather straightforward, even if they do not follow

strictly the groupings from the project success model from Shenhar & Dvir (2007) from

which most of the questionnaire was used. All but one of the questions added to reflect

organizational performance were grouped in the second factor. The factors were tested for

reliability using Cronbach alpha, and the summarized results are in Table 27. Table 27 – Reliability Tests for Performance Factors

Factor Cronbach Alpha

1. Impact on team 0.889

2. Organizational performance 0.868

3. Impact on customer 0.794

4. Project financial results 0.878

5. Preparing for the future 0.737


6. Project impact on business 0.793

7. Project efficiency 0.660

8. Internal efficiency 0.653

Detailed reliability tests for the factors can be found in the Appendix B. All factors but

two have the general Cronbach alpha above 0.7, showing good reliability. Two factors

(project efficiency and internal efficiency) have general Cronbach alpha between 0.6 and

0.7, which is not ideal but still acceptable (Field, 2005).

4.5. Review of Research Model and Hypotheses Definition

At this point the research model can be refined to accommodate the operationalization of

the constructs, and allow us to define the hypotheses to be tested in the study.

Organizational Project

Management Maturity

Project Context Novelty, Complexity, Technology, Pace Goals and Methods Customer of the project

Project Performance a: Impact on team b: Organizational Performance c: Impact on Customer d: Project Financial Results e: Preparing for the future f: Project impact on business g: Project Efficiency h: Internal Efficiency i: Overall Performance

H1

H2

Strategic Goal Building the future, maintenance, money saving, money making, problem solving,

H3

Industry

H4

The hypotheses are


performance. The sub-hypotheses are defined for each aspect of performance:

• H1a: Impact on team

• H1b: Organizational performance

• H1c: Impact on customer

• H1d: Project financial results


• H1e: Preparing for the future

• H1f: Project impact on business

• H1g: Project efficiency

• H1h: Internal efficiency

• H1i: Overall performance

The other hypotheses refer to the moderating variables impacting the relationship

hypothesized in H1. They are:


related to performance.


is related to performance

• H4: Industry of the project affects how organizational project management


For each of the hypotheses H2, H3 and H4 it will be tested the sub-hypotheses a..i for

each of the performance factor as described for H1.

4.6. Regression Analysis

The hypotheses H1, H2, H3 and H4 were tested using multiple hierarchical regression

analysis. The variables were all tested for normality, as shown previously in this chapter.

The recommended number of cases for this analysis is above 50 + 8m, where m is the

number of independent variables (Tabachnick & Fidell, 2007). There are 211 cases, which

is above 150 (50 + 10 * 10).

The regressions will be performed for all performance factors which resulted from the

factor analysis (see Table 27). The regression analysis was done on SPSS, using the

performance factors as dependent variables. The independent variables were added in

steps: in the first step, maturity was added as the main predictor; in the second step,

contingency factors were added; in the third and last step, interaction factors of maturity

and contingency were added. The results are shown in the following sections.


4.6.1. Impact on Team

The first performance factor to be analyzed is the impact on team and project context. The

results are in Table 28.

Table 28 – Regression for impact on team

Variable Step 1 Step 2 Step 3 Main effect Maturity .233**** .057 .071 Moderators Age of Organization -.073 -.084

Project Duration -.091 -.044 Customer .097 .098 Methods .166** .150* Goals .159** .110 Pace .028 .042 Complexity .006 .006 Technology .229*** .236** Novelty .081 .063

Interaction Terms Maturity * Age .115 Maturity * Project Duration -.059 Maturity * Customer -.050 Maturity * Methods -.062 Maturity * Goals -.167** Maturity * Pace -.037 Maturity * Complexity -.004 Maturity * Technology .038 Maturity * Novelty -.111

F Change 10.958**** 2.987*** 1.391 F Regression 10.958**** 3.878**** 2.736**** R2 .050 .162 .214 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

Maturity is a predictor of the performance factor, impact on team, with p < 0.001, as

shown in the first step of the regression. This result supports the hypothesis H1a. In the

second step, some contingency factors appear as independent variables influencing the

impact on team performance factor. They are methods, goals and technology.

In the last step of the regression, the interaction term maturity and goals show significance

with p < 0.05, however the F change of the step (1.391) is not significant, with p > 0.1. If

the interaction term is significant, the low F change of the step could be a result of the

high number of variables in the regression. Running the regression with only goals as

contingency factors, the results are different, as seen in Table 29.


Table 29 – Regression for impact on team with reduced terms

Variable Step 1 Step 2 Step 3 Main effect Maturity .223**** .182*** .186*** Moderators Goals .164** .114 Interaction Terms Maturity * Goals -.149** F Change 10.958**** 5.643** 4.539** F Regression 10.958**** 8.422**** 7.223**** R2 .050 .075 .095 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

In this case, the F change for step three is higher (4.539) and significant with p < 0.05,

thus supporting hypothesis H2a.

Goals appear to have a significant relation with impact on team performance factor both

as an independent variable and in the interaction term. When a variable acts as an

independent and a moderator variable simultaneously, it is called a quasi-moderator in the

typology of Sharma et al. (1981).

The coefficient for maturity is positive, whereas the coefficient for the interaction term is

negative. The variable for goals was coded for the regression as:

1 – the goals for the project are not well defined, and

2 – the goals for the project are well defined

Therefore the coefficient can be interpreted as: in projects whose goals are well defined,

the influence of maturity in the performance factor impact on team is stronger than in

projects whose goals are not well defined.

The regression was tested for homoscedasticity, and the scatterplot is in Figure 26 in

Appendix B.

The same test was performed for H3a. As strategic goal is a categorical variable, the

regression analysis used dummy variables. The variable strategic goal of the project was

recoded in six dummy variables, using the value one if the case belongs to that category

or zero otherwise.


To avoid problems with the perfect multicollinearity of such strategy, the variable for

money-making was removed, as it was the most frequent case, according to the

frequencies presented in Table 22 therefore it is treated as baseline group (Field, 2005).

The results are in Table 30. Table 30 – Regression for impact on team and project strategic goal

Variable Step 1 Step 2 Step 3 Main effect Maturity .223**** .233**** .209** Moderators Building the Future .111* .087

Maintenance -.121* -.128* Money-Saving -.092 -.092 Problem Solving -.005 .009 Utility / Infrastructure -.043 -.033

Interaction Terms Maturity * Building the Future -.103 Maturity * Maintenance .102 Maturity * Money-Saving .004 Maturity * Problem Solving .062 Maturity * Utility / Infrastructure .057

F Change 11.010**** 1.672 1.205 F for Regression 11.010**** 3.258*** 2.334*** R2 .050 .087 .114 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

The F change of step 3 is not significant; therefore the project strategic goal is not a

moderating factor for maturity and performance factor impact on team and does not

support hypothesis H3a. To test hypothesis H4a, using industry of the project, the same

procedure can be applied. Dummy variables were created for industries whose

representation in the sample was above 4%, as anything below that would be difficult to

demonstrate statistical significance. The results are presented in Table 31 Table 31 – Regression for impact on team and project industry

Variable Step 1 Step 2 Step 3 Main effect Maturity .223**** .223*** .192* Moderators Telecommunication -.069 -.071

Software .041 .011 Information Technology .012 .010 Government -.025 -.033 Finance -.032 -.020 Consumer Electronics -.004 .043 Construction .048 .071 Consulting -.025 -.027

Interaction Terms Maturity * Telecommunication .062 Maturity * Software -.099 Maturity * Information Technology .000 Maturity * Government -.031 Maturity * Finance .086 Maturity * Consumer Electronics .171** Maturity * Construction -.070 Maturity * Consulting .008

F Change 10.958**** .323 1.399 F for Regression 10.958**** 1.473 1.450 R2 .050 .062 .113 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001


The moderating factor of industry consumer electronics is significant, even though the F

change for the third step is not. If the regression is performed isolating the variable, the

results change and the F change is significant, as shown in Table 33.

Table 32 – Regression for impact on team and project industry

Variable Step 1 Step 2 Step 3 Main effect Maturity .223**** .223*** .197* Moderators Consumer Electronics .000 .047 Interaction Terms Maturity * Consumer Electronics .170** F Change 10.958**** .000 5.825** F for Regression 10.958**** 5.453*** 5.661**** R2 .050 .050 .076 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

The interpretation is that in projects from the consumer electronics industry, the positive

relationship of maturity and performance factor impact on team is stronger than other

industries. The scatterplot of the regression is in Figure 27 in Appendix B. Hypothesis

H4a is supported, having project industry consumer electronic as moderating factor.

4.6.2. Organizational performance

The results of the regression analysis using the performance factor organizational

performance as the dependent variable are shown in Table 33.

Table 33 – Regression for organizational performance

Variable Step 1 Step 2 Step 3 Main effect Maturity .212*** .169** .182** Moderators Age of Organization -.116 -.120

Project Duration -.036 -.061 Customer -.012 .000 Methods -.010 -.006 Goals .089 .130 Pace .040 .040 Complexity -.033 -.035 Technology .199** .176** Novelty -.081 -.068

Interaction Terms Maturity * Age -.033 Maturity * Project Duration .009 Maturity * Customer .098 Maturity * Methods .039 Maturity * Goals .155* Maturity * Pace -.018 Maturity * Complexity .002 Maturity * Technology -.011 Maturity * Novelty .037

F Change 9.796*** 1.279 .563 F for Regression 9.796*** 2.143** 1.388 R2 .045 .097 .121 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001


Maturity has a positive and significant relationship with organizational performance,

supporting hypothesis H1b. However, no interaction terms have shown significant

relationships, neither the F change of the third step of the regression. However, goals

seem to have a significant relationship, with p < 0.1.

A second regression, with only goals as an interaction term, is shown in Table 34.

Table 34 – Regression for organizational performance, with goals as interaction term

Variable Step 1 Step 2 Step 3 Main effect Maturity .212*** .192*** .188*** Moderators Goals .078 .120 Interaction Terms Maturity * Goals .125* F Change 9.796*** 1.259 3.069* F Regression 9.796*** 5.534*** 4.749*** R2 .045 .051 .064 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

The relationship is not significant, with p = .081. The results do not support the

hypothesis (H2b), but they could be interesting for future research. The results for project

strategic goal, using the variable coded as dummy variables, are in Table 35

Table 35 – Regression for organizational performance and project strategic goal

Variable Step 1 Step 2 Step 3 Main effect Maturity .212*** .208*** .133 Moderators Building the Future -.048 -.059

Maintenance -.045 -.044 Money-Saving .077 .090 Problem Solving -.028 .000 Utility / Infrastructure .040 .066

Interaction Terms Maturity * Building the Future -.019 Maturity * Maintenance -.010 Maturity * Money-Saving -.070 Maturity * Problem Solving .132* Maturity * Utility / Infrastructure .152*

F Change 9.842*** .550 1.687 F for Regression 9.842*** 2.081* 1.921** R2 .045 .057 .096 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

The strategic goal of problem solving and utility infrastructure as an interaction term

shows a p-value of .78 and .57, respectively, which indicates a possible statistical

significance.

By running the regression with those terms only, we have the results shown in Table 36.


Table 36 – Regression for organizational performance and project strategic goal

Variable Step 1 Step 2 Step 3 Main effect Maturity .212*** .213*** .111 Moderators Problem Solving -.027 .000

Utility / Infrastructure .041 .066 Interaction Terms Maturity * Problem Solving .138*

Maturity * Utility / Infrastructure .162** F Change 9.842*** .265 3.615** F for Regression 9.842*** 3.434** 3.558*** R2 .045 .047 .079 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

In this case, the value of F for the change of the third step is significant, as the interaction

term utility / infrastructure, thus supporting hypothesis H3b. It can be interpreted as: in

projects in which the strategic goal is utility or infrastructure, the impact of maturity in

organizational performance is stronger than projects with other strategic goals. Problem

solving did not have a statistically significant coefficient, however this could be explained

by the low number of problem-solving projects in the sample – only 17, or 8.1% of the

sample.

The procedure is now repeated for the industry of the project as interaction term, and the

results are in Table 37

Table 37 – Regression for organizational performance and project industry

Variable Step 1 Step 2 Step 3 Main effect Maturity .212*** .194*** .213** Moderators Telecommunication -.033 -.032

Software -.086 -.108 Information Technology .012 .012 Government -.131 -.100 Finance -.198*** -.198*** Consumer Electronics -.072 -.077 Construction -.110 -.111 Consulting -.019 -.008

Interaction Terms Maturity * Telecommunication -.067 Maturity * Software -.087 Maturity * Information Technology -.016 Maturity * Government .144* Maturity * Finance -.009 Maturity * Consumer Electronics -.021 Maturity * Construction -.001 Maturity * Consulting -.024

F Change 9.796*** 1.638 .898 F for Regression 9.796*** 2.571*** 1.778** R2 .045 .063 .059 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001


For the industry of the project, there is significance at p < 0.1 level. Running the

regression again, separating government projects from other industries, we have the

results presented in Table 38.

Table 38 – Regression for organizational performance and government projects

Variable Step 1 Step 2 Step 3 Main effect Maturity .212*** .206*** .166** Moderators Government -.094 -.065 Interaction Terms Maturity * Government .157** F Change 9.796*** 1.925 4.908** F for Regression 9.796*** 5.882*** 5.631**** R2 .045 .054 .075 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

In this case, the F change in the third step is significant. It can be interpreted as: in

projects that are in the government, the positive relationship between maturity and

organizational performance is stronger than in projects from other industries. This result

supports hypothesis H4b. The scatterplot of the regression is in Figure 28 in Appendix B.

4.6.3. Impact on customer

Table 39 below presents the results of the regression analysis using performance factor

project impact on the customer as the dependent variable.

Table 39 – Regression for impact on customer

Variable Step 1 Step 2 Step 3 Main effect Maturity .141** .084 .109 Moderators Age of Organization .012 -.005

Project Duration -.072 -.051 Customer -.047 -.062 Methods .061 -.076 Goals .081 .131 Pace -.009 .000 Complexity .021 .066 Technology -.033 .006 Novelty .064 .035

Interaction Terms Maturity * Age .026 Maturity * Project Duration -.050 Maturity * Customer -.062 Maturity * Methods -.290**** Maturity * Goals .065 Maturity * Pace .101 Maturity * Complexity -.003 Maturity * Technology .041 Maturity * Novelty .215***

F Change 4.244** .563 2.404** F for Regression 4.244** .951 1.671** R2 .020 .045 .143 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001


Maturity has a significant positive relationship with the performance factor impact on the

customer, supporting hypothesis H1c.

The interaction terms with contingency factors methods and novelty show significant

relationship with impact on the customer, however the F change of the third step of the

regression is not significant. A second regression with only these two terms is presented

in Table 40 below.

Table 40 – Regression for impact on customer with reduced terms

Variable Step 1 Step 2 Step 3 Main effect Maturity .141** .086 .118 Moderators Methods .096 .001 Novelty .041 .055 Interaction Terms Maturity * Methods -.240**** Maturity * Novelty .213*** F Change 4.244** .903 8.514**** F Regression 4.244** 2.015 4.702**** R2 .020 .028 .103 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

In the second regression, the F change of the third step is significant with p < 0.001,

supporting the hypothesis H2c.

The coefficient for the interaction term of methods with maturity is negative, while the

coefficient for maturity alone is positive. Similar to the variable goals, the coding for

methods are the following:

1 – the methods for the project are not well defined, and

2 – the methods for the project are well defined.

The negative coefficient means that in projects where the methods are well defined, the

positive relationship of maturity in the project’s impact on customer is weaker than in

projects where the methods are not well defined. The coefficient for novelty is positive. It

means, the higher the novelty of the project, the stronger the positive relationship between

maturity and the project’s impact on the customer. The regression was tested for

homoscedasticity and the scatterplot is in Figure 29 in Appendix B.


Testing for the impact of the project strategic goals, the regression results are in Table 41

below. Table 41 – Regression for impact on customer and project strategic goal

Variable Step 1 Step 2 Step 3 Main effect Maturity .141** .141** .214** Moderators Building the Future -.059 -.053

Maintenance .016 .020 Money-Saving -.111 -.106 Problem Solving .127* .106 Utility / Infrastructure -.083 -.088

Interaction Terms Maturity * Building the Future .000 Maturity * Maintenance -.068 Maturity * Money-Saving -.041 Maturity * Problem Solving -.104 Maturity * Utility / Infrastructure -.056

F Change 4.264** 1.694 .605 F for Regression 4.264** 2.134** 1.428 R2 .020 .059 .073 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

For project strategic goals, the third step F change is not statistically significant, neither

are any of the coefficients of the interaction terms, therefore it can be concluded that the

project’s strategic goal does not moderate the influence of maturity in impact on customer

performance factor, and H3c is not supported.

The results for project industry are shown below in Table 42 below.

Table 42 – Regression for impact on customer and project industry

Variable Step 1 Step 2 Step 3 Main effect Maturity .141** .147** .212** Moderators Telecommunication .134* .136*

Software .095 .051 Information Technology .195*** .201*** Government -.019 -.019 Finance .063 .053 Consumer Electronics .058 .093 Construction .072 .086 Consulting .067 .110

Interaction Terms Maturity * Telecommunication .014 Maturity * Software -.178** Maturity * Information Technology .023 Maturity * Government -.029 Maturity * Finance -.089 Maturity * Consumer Electronics .107 Maturity * Construction -.054 Maturity * Consulting -.090

F Change 4.244** 1.218 1.454 F for Regression 4.244** 1.558 1.524* R2 .020 .065 .118 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001


The results show statistical significance for projects in the software industry, even if the F

change in step three is not significant.

Running the regression analysis testing for software as the only interaction term shows the

results from Table 43 below.

Table 43 – Regression for impact on customer and project industry

Variable Step 1 Step 2 Step 3 Main effect Maturity .141** .144** .179** Moderators Software .036 -.008 Interaction Terms Maturity * Software -.170*** F Change 4.244** .280 5.541** F for Regression 4.244** 2.255 3.383** R2 .020 .021 .047 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

Those results show statistical significance for the value of F change in the third step,

supporting hypothesis H4c with the software project industry as a moderating factor. The

coefficient in this case is negative, which leads to the interpretation: in projects whose

industry is software, the positive relationship between maturity and the impact on

customer is weaker than in other industries. The full discussion of this result is in the next

chapter. The regression was tested for homoscedasticity, and the scatterplot is in Figure

30 in Appendix B.

4.6.4. Project Financial Results

Table 44 below presents the results of the regression analysis using the performance

factor project financial results as the dependent variable.


Table 44 – Regression for project financial results

Variable Step 1 Step 2 Step 3 Main effect Maturity .152** .195** .189** Moderators Age of Organization .017 .027

Project Duration -.019 -.052 Customer -.015 .019 Methods -.023 -.055 Goals -.054 -.009 Pace -.019 -.006 Complexity -.044 -.010 Technology -.001 .015 Novelty -.077 -.063

Interaction Terms Maturity * Age -.082 Maturity * Project Duration .050 Maturity * Customer .075 Maturity * Methods -.067 Maturity * Goals .124 Maturity * Pace .001 Maturity * Complexity .177** Maturity * Technology -.153* Maturity * Novelty .038

F Change 4.958** .314 1.214 F for Regression 4.958** .764 .981 R2 .023 .037 .089 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

Maturity has a significant positive relationship with the project’s financial results,

supporting hypothesis H1d. The F change of third step is not significant, even though one

contingency factor, complexity, was significant at p < 0.05, and technology was very

close to being at the same level, with p = 0.059.

A new regression is presented, with only complexity and technology as contingency

factors in Table 45 below.

Table 45 – Regression for project financial results using reduced interaction terms

Variable Step 1 Step 2 Step 3 Main effect Maturity .152** .167** .148** Moderators Complexity -.057 -.034

Technology -.032 -.013 Interaction Terms Maturity * Complexity .163**

Maturity * Technology -.141** F Change 4.958** .485 3.512** F for Regression 4.958** 1.968 2.614** R2 .023 .028 .060 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001


The F change of the third step is significant, supporting hypothesis H2d. The coefficient

for the interaction term of complexity and maturity is positive, which means the higher

the complexity of the project, the stronger the positive relationship between maturity and

the project’s financial results. However, the coefficient for technology is negative,

meaning that the higher the technology of the project, the weaker the positive relationship

between maturity and the project’s financial results. The scatterplot of residuals show no

problem of homoscedasticity, as shown in Figure 31 in Appendix B.

The regression using project strategic goal as interaction term is shown in Table 46 below.

Table 46 – Regression for project financial results and project strategic goal

Variable Step 1 Step 2 Step 3 Main effect Maturity .152*** .149** .092 Moderators Building the Future -.072 -.054

Maintenance -.054 -.055 Money-Saving .047 .062 Problem Solving -.041 -.009 Utility / Infrastructure .073 .062

Interaction Terms Maturity * Building the Future .107 Maturity * Maintenance .025 Maturity * Money-Saving -.081 Maturity * Problem Solving .144* Maturity * Utility / Infrastructure -.030

F Change 4.982** .739 1.512 F for Regression 4.982** 1.441 1.483 R2 .023 .040 .075 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

The strategic goal with problem solving, as an interaction term has a significance of p =

0.54. A second regression, using only problem solving as the interaction term, is shown

below in Table 47 below.

Table 47 – Regression for project financial results and project strategic goal

Variable Step 1 Step 2 Step 3 Main effect Maturity .152** .149** .113 Moderators Problem Solving -.041 -.009 Interaction Terms Maturity * Problem Solving .138* F Change 4.982** .359 3.577* F for Regression 4.982** 2.662* 2.989** R2 .023 .025 .041 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

In the second regression with only with problem solving as interaction term, the

significance of the F change has p = 0.60, which is not significant enough to reject the

null hypothesis. It could be caused by the low number of projects in the sample whose


strategic target was problem solving – only 17 projects, or 8.1% of the full sample.

Hypothesis H3d is not supported.

Using industry of the project as interaction term for project financial results, we have the

regression coefficients presented in Table 48 below.

Table 48 – Regression for project financial results and industry of the project

Variable Step 1 Step 2 Step 3 Main effect Maturity .152** .144** .084 Moderators Telecommunication .054 .051

Software .054 .053 Information Technology .053 .046 Government -.068 -.063 Finance .091 .093 Consumer Electronics .029 .009 Construction .114 .113 Consulting .080 .069

Interaction Terms Maturity * Telecommunication .163** Maturity * Software .013 Maturity * Information Technology -.043 Maturity * Government .045 Maturity * Finance .042 Maturity * Consumer Electronics -.057 Maturity * Construction .011 Maturity * Consulting .031

F Change 4.958** .812 .857 F for Regression 4.958** 1.268 1.071 R2 .023 .054 .086 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

Telecommunication as an interaction term is statistically significant. As was done in the

other factors, a regression analysis is performed only with telecommunication as

interaction term to verify the significance of F change in the third step as shown in Table

49 below.

Table 49 – Regression for project financial results and industry of the project

Variable Step 1 Step 2 Step 3 Main effect Maturity .152** .152** .103 Moderators Telecommunication .018 .017 Interaction Terms Maturity * Telecommunication .157** F Change 4.958** .069 4.826** F for Regression 4.958** 2.502* 3.308** R2 .023 .023 .046 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

In this case, telecommunication is statistically significant as an interaction term, therefore

supporting hypothesis H4d. The coefficient is positive, consequently the result can be

interpreted as: in projects whose industry is telecommunication, the positive link between


maturity and project financial results is stronger than in other industries. The scatterplot is

in the Figure 32 in Appendix B.

4.6.5. Preparing for the future

Table 50 below presents the results of the regression using the performance factor

preparing for the future as the dependent variable.

Table 50 – Regression for preparing for the future

Variable Step 1 Step 2 Step 3 Main effect Maturity -.030 -.049 -.014 Moderators Age of Organization -.008 -.040

Project Duration -.103 -.081 Customer .037 .051 Methods -.075 -.051 Goals -.090 -.079 Pace .095 .125* Complexity .173** .138* Technology .139* .110 Novelty .202*** .184**

Interaction Terms Maturity * Age .168** Maturity * Project Duration -.084 Maturity * Customer .033 Maturity * Methods .058 Maturity * Goals .042 Maturity * Pace -.058 Maturity * Complexity -.101 Maturity * Technology .025 Maturity * Novelty -.125*

F Change .194 3.835**** 1.931** F for Regression .194 3.473**** 2.819**** R2 -.003 .109 .119 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

From these results there are no significant relationships between maturity and the project

capability to prepare the organization for the future, therefore the hypotheses H1e and

H2e cannot be supported. However it is interesting to see that the second and third step of

the regression present a significant F change. This could be explained by maturity acting

as a moderator of other significant direct relationships present in the second step of the

regression. This could be investigated in future research, and it is not the purpose of this

thesis; therefore no further analysis will be made.


4.6.6. Project impact on business

Below in Table 51 below are the results of the regression using the performance factor

project impact on business as the dependent variable.

Table 51 – Regression for project impact on business

Variable Step 1 Step 2 Step 3 Main effect Maturity .001 -.043 -.049 Moderators Age of Organization .005 .001

Project Duration .038 .039 Customer -.076 -.059 Methods -.045 -.011 Goals .002 -.023 Pace .057 .044 Complexity -.016 -.006 Technology .147* .130 Novelty .107 .128

Interaction Terms Maturity * Age -.001 Maturity * Project Duration -.011 Maturity * Customer .071 Maturity * Methods .067 Maturity * Goals -.028 Maturity * Pace -.075 Maturity * Complexity .085 Maturity * Technology -.016 Maturity * Novelty -.014

F Change .000 1.567 .371 F for Regression .000 1.410 .897 R2 -.005 .019 -.009 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

In this regression analysis, maturity has no significant relationship with the project impact

on business; therefore there is no support for hypothesis H1f, H2f, H3f or H4f.

4.6.7. Project efficiency

Below in Table 52 below are the results of the regression using the performance factor

project efficiency as the dependent variable.


Table 52 – Regression for project efficiency

Variable Step 1 Step 2 Step 3 Main effect Maturity .149** .096 .090 Moderators Age of Organization .104 .092

Project Duration -.183*** -.178** Customer -.025 -.024 Methods .077 .064 Goals .168** .215*** Pace -.051 -.047 Complexity -.097 -.099 Technology .015 .031 Novelty -.062 -.063

Interaction Terms Maturity * Age .002 Maturity * Project Duration -.055 Maturity * Customer -.048 Maturity * Methods -.063 Maturity * Goals .103 Maturity * Pace -.034 Maturity * Complexity -.009 Maturity * Technology -.045 Maturity * Novelty .085

F Change 4.769** 2.754*** .477 F for Regression 4.769** 2.732*** 1.649** R2 .018 .087 .065 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

Maturity is significantly related to project efficiency, supporting hypothesis H1g.

However none of the interaction terms or the F change is significant, so hypothesis H2g is

not supported.

The regression for project strategic goal is shown below in Table 53 below.

Table 53 – Regression for project efficiency and project strategic goal

Variable Step 1 Step 2 Step 3 Main effect Maturity .149** .157** .190* Moderators Building the Future -.023 -.032

Maintenance -.016 -.016 Money-Saving .036 .023 Problem Solving .108 .101 Utility / Infrastructure .034 .030

Interaction Terms Maturity * Building the Future -.053 Maturity * Maintenance -.013 Maturity * Money-Saving .070 Maturity * Problem Solving -.035 Maturity * Utility / Infrastructure -.034

F Change 4.792** .630 .370 F for Regression 4.792** 1.317 .876 R2 .022 .037 .046 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001


The interaction with project strategic goal showed no statistically significant relationship,

therefore, it does not support hypothesis H3g.

Considering industry of the project as interaction term, we have the regression

coefficients presented in Table 54 below.

Table 54 – Regression for project efficiency and industry of the project

Variable Step 1 Step 2 Step 3 Main effect Maturity .149** .144** .221** Moderators Telecommunication -.019 -.017

Software .063 .048 Information Technology -.003 .002 Government .044 .039 Finance -.076 -.087 Consumer Electronics .004 -.026 Construction -.061 -.049 Consulting .106 .082

Interaction Terms Maturity * Telecommunication .027 Maturity * Software -.078 Maturity * Information Technology .000 Maturity * Government -.054 Maturity * Finance -.102 Maturity * Consumer Electronics -.125* Maturity * Construction -.051 Maturity * Consulting .033

F Change 4.769** .764 .814 F for Regression 4.769** 1.204 1.016 R2 .022 .051 .082 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

Industry also did not have any statistically significant relationship, thus hypothesis H4g is

also not supported.

4.6.8. Internal Efficiency

Below in Table 55 are the results of the regression using the performance factor internal

efficiency as the dependent variable.


Table 55 – Regression for internal efficiency

Variable Step 1 Step 2 Step 3 Main effect Maturity .323**** .360**** .350**** Moderators Age of Organization -.068 -.037

Project Duration .078 .061 Customer .204*** .192** Methods -.007 .015 Goals .019 .015 Pace .002 -.011 Complexity .002 -.025 Technology -.029 -.017 Novelty -.024 -.017

Interaction Terms Maturity * Age -.129* Maturity * Project Duration .098 Maturity * Customer -.023 Maturity * Methods .016 Maturity * Goals .004 Maturity * Pace -.007 Maturity * Complexity -.089 Maturity * Technology -.069 Maturity * Novelty -.021

F Change 24.289**** 1.161 .869 F for Regression 24.289**** 3.491**** 2.238*** R2 .104 .149 .182 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

Maturity is significantly related to internal efficiency, supporting hypothesis H1h.

The interaction term, company age, presents a slight significance with p < 0.1. Below in

Table 56 is the regression using only company age as a contingency factor.

Table 56 – Regression for internal efficiency using company age as interaction term

Variable Step 1 Step 2 Step 3 Main effect Maturity .323**** .323**** .316**** Moderators Age of Organization -.007 .022 Interaction Terms Maturity * Age -.157** F Change 24.289**** .012 5.642** F for Regression 24.289**** 12.093**** 10.123**** R2 .104 .104 .128 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

In the regression using company age, the F change of the third step is significant with p <

0.05, supporting hypothesis H2h. The coefficient for the interaction term is negative,

which leads us to the interpretation: the younger the organization, the stronger the positive

relationship between maturity and the project impact on internal efficiency of the

organization. The regression was tested for homoscedasticity, and the scatterplot is in the

Appendix B, in Figure 33.


The regression for project strategic goal as moderating factor is shown below in Table 57.

Table 57 – Regression for internal efficiency and project strategic goal

Variable Step 1 Step 2 Step 3 Main effect Maturity .323**** .332**** .264*** Moderators Building the Future -.024 -.017

Maintenance -.037 -.040 Money-Saving .050 .039 Problem Solving .036 .021 Utility / Infrastructure .105 .113*

Interaction Terms Maturity * Building the Future .058 Maturity * Maintenance .062 Maturity * Money-Saving .079 Maturity * Problem Solving -.040 Maturity * Utility / Infrastructure .065

F Change 24.405**** .777 .658 F for Regression 24.405**** 4.694**** 2.838*** R2 .104 .121 .135 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

The F change for the third step is not statistically significant, nor are any of the interaction

terms, therefore there is no support for project strategic goal being a moderator for

internal efficiency, the hypothesis H3h.

The same analysis performed for industry of the project provides the results presented in

Table 58.

Table 58 – Regression for internal efficiency and industry of the project

Variable Step 1 Step 2 Step 3 Main effect Maturity .323**** .312**** .255** Moderators Telecommunication -.185*** -.187**

Software -.115* -.117* Information Technology .031 .028 Government .052 .052 Finance .053 .034 Consumer Electronics -.019 -.010 Construction .039 .031 Consulting .055 -.064

Interaction Terms Maturity * Telecommunication .063 Maturity * Software .012 Maturity * Information Technology .020 Maturity * Government .023 Maturity * Finance -.088 Maturity * Consumer Electronics .042 Maturity * Construction .034 Maturity * Consulting .228***

F Change 24.289**** 1.915* 1.502 F for Regression 24.289**** 4.496**** 3.134**** R2 .104 .168 .216 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001


In this case, consulting as the project industry has a significant relationship as a

moderating variable. To test the significance of the F change, we run the regression

analysis only with the consulting industry. The results are in Table 59. Table 59 – Regression for internal efficiency and industry of the project with reduced terms

Variable Step 1 Step 2 Step 3 Main effect Maturity .323**** .315**** .282**** Moderators Consulting .066 -.051 Interaction Terms Maturity * Consulting .223*** F Change 24.289**** .990 8.186*** F for Regression 24.289**** 12.639**** 11.445**** R2 .104 .108 .142 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

The value of F change is significant, supporting hypothesis H4h for consulting as the

project industry. The coefficient is positive, so the result can be interpreted as: in

consulting projects, the positive relationship between maturity and performance factor

internal efficiency is stronger than in other industries. The regression was tested for

homoscedasticity, and the scatterplot is in Figure 34 in Appendix B.

4.6.9. Overall Performance

Below in Table 60 are the results of the regression using overall project performance as

the dependent variable. Overall performance is a variable calculated with a sum of all

other performance factors. Table 60 – Regression for overall performance

Variable Step 1 Step 2 Step 3 Main effect Maturity .414**** .307**** .328**** Moderators Age of Organization -.045 -.058

Project Duration -.138*** -.130* Customer .058 .076 Methods .051 .011 Goals .132* .174** Pace .050 .066 Complexity .004 .012 Technology .235**** .243**** Novelty .074 .071

Interaction Terms Maturity * Age .024 Maturity * Project Duration -.036 Maturity * Customer .033 Maturity * Methods -.106 Maturity * Goals .105 Maturity * Pace -.045 Maturity * Complexity .021 Maturity * Technology -.067 Maturity * Novelty .037

F Change 43.167**** 2.618*** .661 F for Regression 43.167**** 6.974**** 3.928**** R2 .171 .259 .281 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001


Using the overall performance as dependent variable, maturity has a significant positive

relationship, supporting hypothesis H1i. The interaction terms show no significant

relationship with performance, not supporting hypothesis H2i.

Below in Table 61 are the results of the regression for project strategic goal as moderating

factor. Table 61 – Regression for overall performance and project strategic goal

Variable Step 1 Step 2 Step 3 Main effect Maturity .414**** .417**** .354**** Moderators Building the Future .000 -.014

Maintenance -.128** -.134** Money-Saving -.033 -.030 Problem Solving .024 .049 Utility / Infrastructure -.021 -.012

Interaction Terms Maturity * Building the Future -.043 Maturity * Maintenance .099 Maturity * Money-Saving -.010 Maturity * Problem Solving .117* Maturity * Utility / Infrastructure .069

F Change 43.373**** .932 1.273 F for Regression 43.373**** 7.994**** 4.968*** R2 .171 .190 .215 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

Here again the strategic goal problem solving has an indication of being statistically

significant. A regression with only problem solving as moderating factor is presented

below in Table 62.

Table 62 – Regression for overall performance and project strategic goal

Variable Step 1 Step 2 Step 3 Main effect Maturity .414**** .415**** .387**** Moderators Problem Solving .024 .049 Interaction Terms Maturity * Problem Solving .108 F Change 43.373**** .146 2.556 F for Regression 43.373**** 21.671**** 15.407**** R2 .171 .172 .182 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

Problem solving as a moderating factor is not significant, having p = 0.111, therefore

there is no support for hypothesis H3i.

Looking at project industry as an interaction term, the regression analysis resulted in the

coefficients presented in Table 63.


Table 63 – Regression for overall performance and industry of the project

Variable Step 1 Step 2 Step 3 Main effect Maturity .414**** .400**** .439**** Moderators Telecommunication -.100 -.100

Software .002 -.051 Information Technology .130* .130* Government -.064 -.060 Finance -.099 -.108 Consumer Electronics -.018 .010 Construction -.004 .001 Consulting .077 .003

Interaction Terms Maturity * Telecommunication .034 Maturity * Software -.201*** Maturity * Information Technology -.041 Maturity * Government .008 Maturity * Finance -.070 Maturity * Consumer Electronics .088 Maturity * Construction -.020 Maturity * Consulting .131*

F Change 43.167**** 1.715* 2.147** F for Regression 43.167**** 6.452**** 4.582**** R2 .171 .224 .288 *.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

From the results of the regression, software projects appeared again as a strong

moderating factor, in this case also the value of F change is significant, supporting

hypothesis H4i. The negative coefficient means this result can be interpreted as: in

software projects, the positive relationship between maturity and overall project

performance is weaker than in other industries. This result will be discussed in the next

chapter.

4.6.10. Summary of Results

In Table 64, a summary of the results is presented.

Table 64 – Summary of results

Performance Factor Hypothesis Supported Contingency Factors Coefficient R2 Impact on team H1a – Maturity as main factor Yes - .223**** .050

H2a – Maturity and project context

Yes Goals -.149** .095

H3a – Maturity and strategic goal

No -

H4a – Maturity and industry of the project

Yes Consumer Electronics

.170** .076

Organizational Performance

H1b – Maturity as main factor Yes - .212*** .045 H2b – Maturity and project context

No -

H3b – Maturity and strategic Yes Infrastructure .162** .079


goal H4b – Maturity and industry of the project

Yes Government .157** .075

Impact on Customer H1c – Maturity as main factor Yes .141** .020 H2c – Maturity and project context

Yes Methods Novelty

-.240**** .213***

.103

H3c – Maturity and strategic goal

No

H4c – Maturity and industry of the project

Yes Software -.170*** .047

Project Financial Results

H1d – Maturity as main factor Yes .152** .023 H2d – Maturity and project context

Yes Complexity Technology

.163** -.141**

.060

H3d – Maturity and strategic goal

No

H4d – Maturity and industry of the project

Yes Telecommunications .157** .046

Preparing for the future H1e – Maturity as main factor No H2e – Maturity and project context

No

H3e – Maturity and strategic goal

No

H4e – Maturity and industry of the project

No

Project impact on business

H1f – Maturity as main factor No H2f – Maturity and project context

No

H3f – Maturity and strategic goal No H4f – Maturity and industry of the project

No

Project Efficiency H1g – Maturity as main factor Yes .149** .018 H2g – Maturity and project context

No

H3g – Maturity and strategic goal

No

H4g – Maturity and industry of the project

No

Internal Efficiency H1h – Maturity as main factor Yes .323**** .104 H2h – Maturity and project context

Yes Age of organization -.157** .128

H3h – Maturity and strategic goal

No

H4h – Maturity and industry of the project

Yes Consulting .223*** .142

Overall Performance H1i – Maturity as main factor Yes .414**** .171 H2i – Maturity and project context

No

H3i – Maturity and strategic goal No H4i – Maturity and industry of the project

Yes Software -.201*** .288

*.p<=0.10; **.p<=0.05; ***.p<=0.01; ****.p<=0.001

Looking at H1, the hypothesis that maturity impacts performance positively, seven of the

nine sub-hypotheses were supported, including the overall performance sub-hypotheses.


Regarding H2, the hypothesis that contingency factors moderate the positive relationship

described in H1, six of the nine sub-hypothesis were supported. Full discussions of the

implications of these results are in Chapter 5.

4.7. Summary

This chapter presented the analysis of the collected data and its results. It began with the

data preparation and the descriptive statistics for all variables. Factor analysis was

performed over the performance variables, to search for underlying groupings of the

variables and to reduce the dimensions of the model. With the results, the research model

was refined, and sub-hypotheses were created. The reliability of the scales was checked

using Cronbach alpha, and finally the hypotheses were tested using multiple hierarchical

regression analysis. In the next chapter, the results of the analysis will be discussed

together with its implications for theory and practice. The limitations of the research will

be discussed, and suggestions for future research will be presented.


Chapter 5 – Discussions and Conclusions

This chapter contains the conclusion of the thesis. The results from the data analysis are

evaluated and explained, with a discussion of the contribution for theory and for

practitioners. The limitations of the research are presented, and topics for future research

are suggested.

5.1. Project management maturity

The concept of maturity, despite having a clear lineage coming from the early work of

Humphrey (1989), is today fragmented with many different models and interpretations of

what maturity in fact is (De Bruin et al., 2005). The landscape for project management

maturity models is not better, being also fragmented and sometimes lacking theoretical

background or purpose (Cooke-Davies, 2007).

This thesis attempted to advance the theory in the topic, first by going to the literature to

understand the origins of the concept of maturity and then by looking at the existing

models with their particularities and benefits. By applying the chosen model in an

empirical study, important insights were uncovered to advance our knowledge of project

management maturity.

5.2. Contingency applied to project management

The study of contingency in organizational theory presents a diverse set of environments,

which interacts with organizational structures to predict high or low performance. As seen

in the literature review, those environments include the rate of change (Burns & Stalker,

1961), use of technology (Woodward, 1958), uncertainty and complexity (Lawrence &

Lorsch, 1967).

Similar factors are used in the study of contingency theory applied to project

management, as seen in the models proposed by Shenhar & Dvir (2007) and by Turner &


Cochrane (1993). Both models were used in this thesis, and which were proven to interact

with organizational structure (in this case, organizational project management maturity) to

predict performance.

In the case of the goals-and-methods matrix, the results show that in projects where the

goals are not well known, the project team increases satisfaction with higher maturity,

which does not happen in projects where the goals are not well known. Also, in projects

where the methods are not well defined, the customer satisfaction increases with project

management maturity, which does not happen when the methods are well defined.

The NTCP matrix has also shown that it moderates the impact of maturity in performance,

in the case of novelty, technology and complexity – but not pace. The impact was

statistically significant for the impact on customer and project financial results.

These results support the use of these contingency models to advance the theory of project

management, using them as an explanatory tool to investigate the effect of project context

in performance.

5.3. Impact of maturity on performance

One clear outcome of this research is the positive link between project management

maturity and performance. Of the nine performance dimensions, seven had a significant

positive relationship, as seen in the data analysis and summarized in Table 65.

Table 65 – Performance factors and link to maturity

Performance Factor Significantly related to maturity

R2 for regression

Impact on Team Yes .05 Organizational Performance Yes .045 Impact on Customer Yes .020 Project Financial Results Yes .023 Preparing for the Future No - Project Impact on Business No - Project Efficiency Yes .018 Internal Efficiency Yes .104 Overall Performance Yes .171


Although this finding merely supports many others found in the literature, and despite it

being an assumption for the main research topic of this thesis, the link between maturity

and performance is not really a consensus, being more an elusive one. Most of the early

research in the topic focused only on project efficiency, and even there the link was not

always obvious. Flowe & Thordahl (1994) found a link between maturity and CPI/SPI,

but only for certain maturity levels and project sizes. Herblseb et al. (1997) found, in

general, a link between maturity and several dimensions of performance, but customer

satisfaction, in certain cases, actually dropped when maturity increased – which also was

the result in another study done by Gibson et al (2006). Ibbs et al. (Ibbs & Kwak, 2000;

Ibbs et al., 2004) found a link between maturity and variation in CPI/SPI, but a very weak

link between maturity and the CPI/SPI. Thomas & Mullally (2008) only found a link

between maturity and intangible measures of value, but none for tangible measures. One

explanation for those different results is that the link, found in this thesis, does not explain

a lot of the variation – with R2 ranging from 0.17 to 0.02. Combined with the fact that

there are several moderating factors at play, also found in the results of this thesis,

different samples could provide different results. The results of this thesis have a

relatively large sample, of 211 respondents, compared the other mentioned studies

(Thomas & Mullaly (2008) used a sample of 50 projects), and also used a more diverse

set of measurements for performance than CPI/SPI, therefore the results could be

considered at least as important as the previous findings to increase our knowledge of this

link.

As a whole, the results of this thesis strongly reinforce the positive relationship between

maturity and performance, and it supports future research interested in further exploring

the dynamics of this relationship.

5.4. Industry of the project

One finding of this thesis is that the industry of the project affects the relationship

between maturity and performance in many different aspects of performance. This finding

deserves a special discussion.

Firstly, to evaluate the generalizability of the results it must be taken into account the fact

that the spread of industries in the sample was considerably high, meaning that any


particular industry had a small representation in the full sample – in the regressions, the

industries used ranged from 4.3% to 15.6% of the sample (9 and 33 cases, respectively).

This is a common problem of including industry in contingency studies (Serrador, 2012).

Nevertheless, the fact that different industries have different approaches and extract

different benefits of project management maturity is recognized in the literature (Cooke-

Davies & Arzymanow, 2003; Cooke-Davies, 2004; Grant & Pennypacker, 2006). The

results of this thesis can be used in further research replicating the research of impact of

maturity in performance for different industries.

5.5. Project management maturity, context and performance

The most important findings of the research are related to the moderating factors of the

link between maturity and performance. The research question this thesis set out to

answer was “What are the factors that influence the impact of project management

maturity on performance?” and it was answered according to the results presented here.

First, the factors are not the same depending on the performance dimension.

Table 66 – Significant contingency factors

Performance Factor Contingency Factors Impact on team Goals

Industry Organizational Performance Project Strategic Goals

Industry Impact on customer Methods

Novelty Industry

Project financial results Complexity Technology Industry

Internal efficiency Age of organization Industry

Overall performance Industry

Therefore the analysis must be made for each performance factor. They are discussed

below.

5.5.1. Impact on team

The impact on team performance factor covers many aspects of the team satisfaction with

the project outcome, such as learning, high morale and motivation to work in the project.

Maturity has a positive relationship with the impact on the team; therefore teams seem to


be more motivated to work on projects in high maturity organizations. However

knowledge of project goals as an interaction term has a negative influence, meaning that

in projects where the goals are well defined, the maturity does not influence the impact on

team to a significant degree. The two regression lines can be seen in Figure 14, where the

steeper blue line represents the regression of maturity and team satisfaction on projects

where the goals are not well defined, and the green line, which is more flat, for the

regressions for projects where they are well defined.

Figure 14 – Regression line for impact on team

The results imply that organizational maturity help teams cope with the uncertainty of

project goals, at least from the perspective of the team member’s own satisfaction with the

project.

Industry was also a moderating factor, more specifically consumer electronics. The

positive coefficient represents a stronger relationship between maturity and impact on

team for the consumer electronic industry, as shown in the regression lines in Figure 15.


Figure 15 – Regression line for impact on team, project industry

5.5.2. Organizational Performance

The factor organizational performance is related to aspects of the performance of the

organization owner of the project, and not performance of the project itself – therefore is a

secondary unit of analysis. It contains measures of sales growth, profitability and market

share. Maturity is correlated with organizational performance, although it is always

important to remember that correlation does not necessarily mean causation (Field, 2005),

and the link between organizational performance and project management maturity is not

supported by the literature as strongly as the link with project performance is, as seen in

the literature review.

The moderating factor found is the project’s strategic goal, particularly the goal utility /

infrastructure. This goal is described in the questionnaire as Acquiring and installing new

equipment or software, implementing new methods or new processes.

According to the results, projects with the goal described as utility / infrastructure has a

stronger link between maturity and organizational performance, as shown in the

regression lines in Figure 16.


Figure 16 – Regression line for organizational performance and project strategic goals

Industry is also a moderating factor for organizational performance, particularly for

government projects. The results have shown that in government projects the positive

relationship between maturity and organizational performance is stronger than in other

industries, as indicated in the regression lines (see Figure 17).

Figure 17 – Regression lines for organizational performance and industry of the project

5.5.3. Impact on Customer

The performance factor impact on the customer is related to the satisfaction of the project

customer, be it internal or external to the organization, with the outcomes of the project.

In this case, the moderating factor discovered was the knowledge of the methods to

achieve the project goals. Similar to the previous case of project goals, the coefficient of


the interaction term with project methods is negative, meaning that when methods are

well defined, the impact of maturity on customer satisfaction is weaker. The regression

lines can be seen in Figure 18.

As maturity is essentially the standardization of methods to manage the projects (Cooke-

Davies et al., 2001), it is natural that for projects in which the methods are not well

known, a high maturity environment can better support the team with the necessary tools

to accomplish the project goals. What the results here imply is that the customer of the

project is sensitive to this support and sees the benefit of maturity in these projects.

Figure 18 – Regression line for impact on customer and goals

The second moderating factor is novelty, which measures how new is the project to the

organization, as opposed to projects that deliver small increments to existing products or

services. There are four levels of novelty, they are:

1. Derivative (Improvement)

2. Platform (A new generation in an existing product line – e.g., new car model)

3. New to the Market (Adopting an existing product to a different market – e.g., first

Personal Computer)

4. New to the World (Product never existed before)

The impact is positive, which in fact is a very interesting finding. It means that the

relationship of maturity and customer satisfaction with the project is stronger in high

novelty projects than in incremental ones. The regression lines can be seen in Figure 19.


Figure 19 – Regression line for impact on customer and novelty

The result is interesting because it contrasts with the view that high maturity

organizations, similar to the mechanistic structure, cannot cope well with changing

environments, and are more likely to succeed in mass-production, slow changing

environments (Burns & Stalker, 1961; Woodward, 1958). In this case, higher maturity

acts as an enabler for high novelty projects. One possible explanation for this result is the

concept of “liability of newness”, which suggests that in extremely turbulent

environments, such as emergent economic sectors, organizations with a higher degree of

formalization and specialization, or more mechanistic structures, perform better than

organic ones (Sine et al., 2006; Stinchcombe, 1965). The results are also aligned with the

information from the CMMI, which states that higher maturity gives the stability to the

organization to cope with change (CMMI Product Team, 2010).

The project’s industry, more importantly the software industry, also demonstrated a

significant moderating relationship between maturity and impact on customer. In this case

the coefficient was negative, meaning that in software projects the impact of maturity in

customer satisfaction is weaker than in other industries. The moderating effect can be

seen in the regression lines in Figure 20.


Figure 20 – Regression line for impact on customer and industry of the project

In fact, the impact is quite strong, which leads the line to revert the slope. It means that in

the analyzed sample, for software projects an increase in maturity actually decreases

customer satisfaction, being the reverse of hypothesis H1. This result is not completely

unknown in the literature, and some studies have revealed similar results (Gibson et al.,

2006), i.e. customer satisfaction decreasing as an effect of an increase in maturity (see

also section 5.4 for a discussion on the generalizability of these results).

5.5.4. Project Financial Results

The performance factor, financial results, are the aspects related to the return on

investment of the project, and if the project was a financial success. The moderating

factors found were complexity of the project and technology.

Complexity is measured by the interactions present in the project. It is measured in four

levels:

1. Component/Material (An element or material in a subsystem)

2. Assembly (A subsystem – Performing a single function)

3. System (A collection of subsystems – Performing multiple functions)

4. Array (System of systems. A widely dispersed collection of systems serving a

common mission)

5.


The interaction is positive, which means in complex projects the relationship of maturity

and performance is stronger than in less complex projects. To illustrate the moderating

influence, the two regression lines are shown in Figure 21.

Figure 21 – Regression line for project financial results and complexity

This finding supports the view that complexity in project environments requires more

formalism to the procedures to manage the project (Shenhar & Dvir, 2007; Shenhar,

2001). According to this view, formalism, structure and bureaucracy ensure proper

integration of the different parts of the project complex environment. This formalism and

structure is provided in high maturity organizations.

The second moderating factor is technology. It is measured by the degree of use of new

technology in the project, using the scale:

1. Low-Tech (No new technology)

2. Medium-Tech (Some new technology)

3. High-Tech (All or mostly new but existing technology)

4. Super-High-Tech (Project will use non-existing technologies at project initiation)

The impact for technology, different from complexity, is negative. The regression line is

shown in Figure 22.


Figure 22 – Regression line for project financial results and technology

This finding is consistent with the general view of contingency theory, in which high

technology require structures closer to the organic profile – preferring informal

communications and procedures and providing more autonomy to the technical personnel

(Burns & Stalker, 1961; Mintzberg, 1979; Shenhar & Dvir, 2007), which is not the

environment provided by high maturity organizations.

The industry of the project, particularly telecommunications, also presented a significant

moderating relationship with a positive coefficient, meaning that in telecommunication

projects the impact of maturity in terms of the financial results is stronger than in other

industries. The regression lines show the moderating effect in Figure 23.

Figure 23 – Regression lines for project financial results and industry of the project


5.5.5. Internal Efficiency

This performance factor is related to the improvements in the internal efficiency of the

organization caused by the project, together with the development of new managerial

capabilities and the creation of new business processes.

The contingency factor found to be significant for internal efficiency was the age of the

organization, and the coefficient is negative. This can be interpreted as in younger

organizations the relationship between maturity and increases in internal efficiency is

stronger than in older organizations. The two regression lines are shown in Figure 24.

Figure 24 – Internal efficiency and age of organization

This result could be also attributed to the liability of newness (Stinchcombe, 1965) in

which it is suggested that new ventures perform better using formal structures than

informal ones (Sine et al., 2006).

5.5.6. Overall Performance

This factor is a sum of all performance factors. The regression has shown that industry

plays the role of moderating variable, with a statistically significant relationship for the

software industry. The coefficient is negative, similar to the results found for the

performance factor, impact on customer. The impact can be analyzed in the plot of the

regression lines in Figure 25.


Figure 25 – Regression lines for overall performance and industry of the project

Once more, as similarly to the performance factor impact on customer, the software

projects have a downward regression line, whereas the other industries have a regression

line with a positive slope. It means that in software projects, the overall performance

diminishes as the maturity increases. This finding has no support in the literature

reviewed, and it could be a result of the small number of software projects present in the

global sample (18 projects, or 8.5% of the sample). Nevertheless, there is a significant

relationship in this sample – the regression analysis using the subsample of 18 projects,

with overall performance as independent variable, presents a negative coefficient for

maturity of -.472, R2 of .222 and p-value of .048. This warrants further analysis, which

can be performed in a future study of the cases present in this sample. Also, see additional

comments on generalizability of these results in section 5.4.

5.5.7. Variance

It is important to notice in the plots for the regression lines that the variance is normally

higher in the left side of the charts for all performance factors. The result is consistent

with previous studies in which maturity correlated with the standard variation of

performance measures (Ibbs et al., 2004), and with the CMMI assumption that low

maturity organizations can have successful projects, but they depend on heroic efforts of

individuals and not on the sustainable capability of the organization to manage successful

projects (CMMI Product Team, 2010). Additionally, the foundation of the CMM was

Shewhart’s work on process control and the search for process capability, in terms of


higher predictability of the process outcomes (CMMI Product Team, 2010; Humphrey,

1989) – therefore is natural to assume that higher maturity levels will have more

predictable outcomes, with a smaller variation of the process.

5.5.8. Discussion on Counterbalancing Contingency Factors

Although the results, when analyzed individually, are plausible and possibly actionable,

when they are analyzed as a group they may seem contradictory and difficult to be used in

practice. It may seem that for projects with high degree of novelty and technology

simultaneously, the impact of maturity in performance will be the result of the

competition between both factors at play.

This finding, however, is not completely new to studies of contingency theory – the

balance of needs for formal and informal structures was recognized in early studies of

contingency, such as the work of Lawrence & Lorsch (1967) who proposed the

segmentation of the organization in subsystems to cope with those different environments,

to more recent studies suggesting the need to balance flexibility and firmness (Tatikonda

& Rosenthal, 2000). Shenhar & Dvir (2007) also explored the need to balance firmness

and flexibility in a project with high-tech/high-complexity contexts, while another study

has shown how projects can fail if this balance is not achieved (Sauser et al., 2009).

5.6. Contributions to Theory

This thesis set out to investigate the relationship between maturity and performance and

look for moderating factors to this relationship using a contingency view. As stated in the

literature review, there is a gap in current knowledge of the dynamics of maturity and

performance in different contexts.

The thesis reduced the gap, by presenting evidence that there are factors that moderate the

relationship. This evidence contributes toward further application of contingency theory

to project management – in which different organizational structures are adequate for

different project contexts, in this case, adding project management maturity as an aspect

of organizational structure.


Also, this thesis has presented empirical evidence that the construct of project

management maturity can be compared to the continuum of mechanistic and organic

structures defined by Burns & Stalker (1961), as high maturity organizations share

similarities with mechanistic structures.

As for the contingency factors, this thesis also reinforced the value of using of

contingency models such as the NTCP (Shenhar & Dvir, 2007) and the goals-and-

methods matrix (Turner & Cochrane, 1993) in more studies of project management, as

they are a good representation of different project contexts.

5.7. Contributions to Practice

For practitioners who are in the process of evaluating, assessing or planning initiatives to

adopt project management maturity models, the results here presented can be useful to

recognize the value of maturity in the context of the projects of the organization.

Furthermore, it can guide the initiative in customizing the implementation of maturity

models depending on the context of the projects. Looking at the medium and long term,

new maturity models or improved versions of the current ones could make the evaluation

of project contexts and the tailoring to the organization context as a core part of the

model.

5.8. Limitations of the Research

Any research has its limitations. They are important, as they limit the generalizability of

the results, but they also serve to indicate areas for future research. All the results

presented in this thesis must be considered together with the limitations presented here.

First, all problems related to self reported data are relevant to the results reported here. As

described in the methodology chapter, the most common problems are: common method

variance, consistency motif and social desirability (see 3.4.1). Also, despite the efforts to

make the questionnaire simple, some respondents may not have all the necessary

knowledge or information to answer the questions correctly – particularly since many

respondents were team members of the project, and not full-time project managers.

However, as also explained in the methodology chapter, the benefits of self-reported


questionnaires outweigh the mentioned problems; therefore they do not invalidate the

results.

One other limitation was that, in the case of some nominal variables used in the analysis

such as industry and project strategic goal, the cases were too spread among different

values – therefore there were few cases for each value. Having a low number of cases can

influence the regression on both sides – either inflating the statistical significance or

deflating them. It is a limitation of the research and to the generalizability of the results

using those variables, which future studies can address.

One other limitation is the relatively low numbers for the regressions R2 values. The range

was between .171 and .02. However, regressions with low p values for significance are

relevant even if they present low coefficients for R2 (Cooper & Schindler, 2006). In

addition, the topic of project performance is extremely complex (Jugdev & Müller, 2005;

Shenhar et al., 1997), and hardly any factor would alone account for a an excessive part of

the variability, giving the amount of research existing on project success factors (Cooke-

Davies, 2002).

Finally, the quantity of missing values are also a limitation of the research, in the sense

that the way they were handled could affect the correlations, either inflating or deflating

them (Tabachnick & Fidell, 2007). As explained in the data analysis, the percentage of

missing values was low, however this information must be considered when analyzing the

results.

5.9. Opportunities for Future Research

As for future research that can be conducted based on the results of this thesis, one could,

for instance can enlarge this study and use other contingency factors as moderating

variables. Some variables that were not possible to be analyzed in this study are project

budget, country, region, autonomy of the organization and any other aspect used in

contingency studies, in order to understand more the impact of context in maturity.


Also, future studies can investigate further the role of the industry of the project and the

strategic goal of the project, as in this thesis there not a high enough number of cases for

each industry or strategic goal.

Another possibility would be to perform similar investigations using other maturity

models, for instance, more comprehensive models, such as OPM3 (PMI, 2013b), to verify

if different approaches to maturity can provide different results. Also, other frameworks

for project performance can be used, such as the project success framework from Turner

& Zolin (2012). The use of other measures would allow a data triangulation of the results

in order to investigate the generalizability of the findings (Tashakkori & Teddlie, 1998).

For the success factor preparation for the future, the correlation with maturity was not

found to be statistically significant. However there was a significant relationship with the

interaction term maturity with age of the organization and maturity with novelty. This

could be investigated further, to understand what variables are acting as moderators and

main factors, possibly with a qualitative analysis.

As this study was purely cross-sectional, a similar longitudinal study can yield interesting

results. The concept of fit, or the congruence between structure-context predicting

performance, is not a static but a dynamic and always changing one (Mullaly & Thomas,

2009), therefore the study of fit between maturity and context during a period of time as

an organization starts to adopt a maturity model can provide important results.

5.10. Summary

In this chapter, the results from the data analysis were discussed and explained according

to the theory and literature. The limitations of the research were presented, and topics for

future research were suggested.

The research question this thesis set out to answer was “What are the factors that

influence the impact of project management maturity on performance?”

The answer, according to the results, is that different factors influence different

performance dimensions.


Table 67 – Factors influencing the impact of project management maturity on performance

Performance Factor Positively impacted by Project Management Maturity

Contingency Factors

Impact on team Yes Goals Industry

Organizational performance Yes Project Strategic Goals Industry

Impact on customer Yes Methods Novelty Industry

Project financial results Yes Complexity Technology Industry

Preparing for the future No Project impact on business No Project efficiency Yes No moderating factor Internal efficiency Yes Age of organization

Industry Overall performance Yes Industry

The factors are listed in Table 67, which also shows performance dimensions that are not

influenced by maturity and performance dimensions that do influence maturity but are not

moderated by any factor – answering the research question and hopefully advancing

knowledge in this important area of project management theory.


References

Andersen, E. S., & Jessen, S. A. (2003). Project maturity in organisations. International

Journal of Project Management, 21(6), 457–461.

Artto, K., Martinsuo, M., Dietrich, P., & Kujala, J. (2008). Project strategy: strategy types

and their contents in innovation projects. International Journal of Managing Projects

in Business, 1(1), 49–70.

Bahli, B., Sidenko, S., & Borgman, H. (2011). Can project management maturity endure

project risk? A strategic perspective of IS project success. In ICIS 2011 Proceedings

(pp. 1–9). Shangai.

Becker, J., Knackstedt, R., & Pöppelbuß, J. (2009). Developing maturity models for IT

management. Business & Information Systems Engineering, 1(3), 213–222.

Betts, S. (2011). Contingency theory: science or technology? Journal of Business &

Economics Research, 1(8), 123–130.

Bisbe, J., & Otley, D. (2004). The effects of the interactive use of management control

systems on product innovation. Accounting, Organizations and Society, 29(8), 709–

737.

Bollinger, T., & McGowan, C. (1991). A critical look at software capability e valuations.

IEEE Software, 8(4), 25–41.

Bollinger, T., & McGowan, C. (2009). A critical look at software capability evaluations:

an update. IEEE Software, 26(5), 80–83.

Bredillet, C. (2008). Exploring research in project management: Nine schools of project

management research (part 5). Project Management Journal, 39(2), 2–4.

Bredillet, C., Anbari, F. T., & Turner, J. R. (2008). Perspectives on research in project

management. In Academy of Management Conference 2008. Anaheim, CA.

Bryant, F. B., & Yarnold, P. R. (1995). Principal-components analysis and exploratory

and confirmatory factor analysis. In L. G. Grimm & P. R. Yarnold (Eds.), Reading


and understanding multivariate statistics (1st ed., pp. 99–136). Washington, DC:

American Psychological Association.

Burns, T., & Stalker, G. M. (1961). The management of innovation. London, England:

Tavistock.

Camp, R. C. (1989). Benchmarking: The search for industry best practices that lead to

superior performance. Milwaukee, Wisconsin: ASQC Quality Press.

Camp, R. C. (1995). Business process benchmarking (1st ed.). Milwaukee, Wisconsin:

ASQC Quality Press.

Carson, S., Madhok, A., Varman, R., & John, G. (2003). Information processing

moderators of the effectiveness of trust based governance of interfirm R&D

collaboration. Organization Science, 14(1).

Chenhall, R. H., & Langfield-Smith, K. (2007). Multiple perspectives of performance

measures. European Management Journal, 25(4), 266–282.

Cheung, Y., Zolin, R., Turner, J. R., & Remington, K. (2010). Agree or disagree? Level

of alignment between project manager and stakeholders on performance indicators.

In Proceedings of the 7th Annual Project Management Australia Conference, 23-26

August 2010, Brisbane Convention & Exhibition Centre. Queensland, Australia.

Cleland, D. I., & Ireland, L. R. (2006). Project management maturity. In Project

Management: Strategic Design and Implementation (5th ed., pp. 243–261). New

York City: McGraw-Hill Osborne Media.

CMMI Product Team. (2000). CMMI-SE/SW/IPPD, V1.02, Staged. Software Engineering

Institute, Carnegie Mellon University.

CMMI Product Team. (2010). CMMI® for development, version 1.3. Software

Engineering Institute, Carnegie Mellon University. Retrieved from

http://www.sei.cmu.edu/library/abstracts/reports/10tr033.cfm


Conway, J. M., & Lance, C. E. (2010). What reviewers should expect from authors

regarding common method bias in organizational research. Journal of Business and

Psychology, 25(3), 325–334.

Cooke-Davies, T. J. (2002). The “real” success factors on projects. International Journal

of Project Management, 20(3), 185–190.

Cooke-Davies, T. J. (2004). Measurement of organizational maturity. Innovations -

Project Management Research 2004, 1–19.

Cooke-Davies, T. J. (2005). The executive sponsor – the hinge upon which organisational

project management maturity turns? In PMI Global Congress Proceedings.

Edinburgh, Scotland.

Cooke-Davies, T. J. (2007). Project management maturity models. In J. K. Pinto & P. W.

G. Morris (Eds.), The Wiley Guide to Project Organization & Project Management

Competencies (pp. 290–311). Hoboken, New Jersey: John Wiley & Sons, Inc.

Cooke-Davies, T. J., & Arzymanow, A. (2003). The maturity of project management in

different industries. International Journal of Project Management, 21(6), 471–478.

Cooke-Davies, T. J., Schlichter, J., & Bredillet, C. (2001). Beyond the PMBOK guide. In

Proceedings of the 32nd Annual Project Management Institute 2001 Seminars and

Symposium, Nashville, TN. Newtown Square, PA: Project Management Institute.

Cooper, D. R., & Schindler, P. S. (2006). Business research methods (9th ed.). New York,

New York, USA: McGraw-Hill/Irwin.

Crawford, J. K. (2006). The project management maturity model. Information Systems

Management, 23(4), 50–58.

Crawford, J. K. (2007). Project management maturity model (2nd ed.). Boca Raton, FL:

Auerbach Publications.

Crosby, P. B. (1979). Quality is free: The art of making quality certain (1st ed.). New

York City: McGraw-Hill Book Company.


Crosby, P. B. (1996). Quality is still free. New York City: McGraw-Hill.

Curtis, B., Miller, S. A., & Hefley, W. E. (2001). People Capability Maturity Model (P-

CMM) Version 2.0 (CMU/SEI-2001-MM-001). Software Engineering Institute,

Carnegie Mellon University.

Czaja, R., & Blair, J. (2005). Designing surveys (2nd ed.). Thousand Oaks, CA: SAGE

Publications.

Davila, T. (2000). An empirical study on the drivers of management control systems’

design in new product development. Accounting, Organizations and Society, 25(4-5),

383–409.

De Bruin, T., Freeze, R., Kaulkarni, U., & Rosemann, M. (2005). Understanding the main

phases of developing a maturity assessment model. In Australasian Conference on

Information Systems. Sidney, Australia.

DeMarco, T., & Lister, T. (1999). Peopleware  : productive projects and teams (2nd ed.).

New York, NY: Dorset House Publishing.

Deming, W. E. (1982). Out of the crisis. Cambridge, Massachusetts: MIT, Center for

Advanced Educational Services.

Denison, D. R., & Mishra, A. K. (1995). Toward a theory of organizational culture and

effectiveness. Organization Science, 6(2), 204–223.

Dooley, K., Subra, A., & Anderson, J. (2001). Maturity and its Impact on new product

development project performance. Research in Engineering Design, 13, 23–29.

Drazin, R., & Ven, A. Van de. (1985). Alternative forms of fit in contingency theory.

Administrative Science Quarterly, 30(4), 514–539.

Dunlap, W. P., & Kemery, E. R. (1987). Failure to detect moderating effects: Is

multicollinearity the problem? Psychological Bulletin, 102(3), 418–420.

Edmondson, A. C., & Mcmanus, S. E. (2007). Methodological fit in management field

research. Academy of Management Review, 32(4), 1155–1179.


Electronic Industries Alliance. (2002). Systems Engineering Capability Model (EIA/IS-

731.1) (Vol. 1). Washington, DC.

Fayol, H. (1919). General and industrial management. London: Pitman.

Field, A. (2005). Discovering statistics using SPSS (2nd ed.). London: Sage.

Flowe, R., & Thordahl, J. (1994). A correlational study of the SEI’s capability maturity

model and software development performance in DoD contracts. Air Force Institute

of Technology, Wright-Patterson Air Force Base, OH.

Fraser, P., Moultrie, J., & Gregory, M. (2002). The use of maturity models/grids as a tool

in assessing product development capability. In IEEE International Engineering

Management Conference (Vol. 1, pp. 244–249). IEEE.

Friedrich, R., Schlichter, J., & Haechk, B. (2003). The History of OPM3. In PMI Global

Congress EMEA Proceedings. Newtown Square, Pennsylvania: Project Management

Institute.

Füssinger, E. (2006). Maturities of project-oriented companies of about 15 project-

oriented nations. In ICEC-IPMA 2006 (pp. 1–7). Retrieved from

http://www.wu.ac.at/strategy/institutes/pmg/fs/pub/ef-ipma-2005.pdf

Galbraith, J. R. (1973). Designing complex organizations (1st ed.). Reading, MA:

Addison-Wesley Publishing Company.

Galbraith, J. R. (1977). Organization design. Reading, MA: Addison-Wesley Publishing

Company.

Galin, D., & Avrahami, M. (2006). Are CMM program investments beneficial? Analyzing

past studies. IEEE Software, 23(6), 81–87.

Gareis, R. (2004). Management of the project-oriented company. In P. Morris & J. Pinto

(Eds.), The Wiley guide to managing projects (pp. 123–143). Hoboken, New Jersey:

John Wiley & Sons, Inc.


Gareis, R., & Füssinger, E. (2007). Final report: Analysis and benchmarking of the

maturities of project-oriented nations. Vienna, Austria: PROJEKTMANAGEMENT

Group, Vienna University of Economics and Business Administration.

Gibson, C. F., & Nolan, R. L. (1974). Managing the four stages of EDP growth. Harvard

Business Review, 52(1), 76–88.

Gibson, D. L., Goldenson, D. R., & Kost, K. (2006). Performance results of CMMI-based

process improvement. Pittsburgh, Pennsylvania: Software Engineering Institute.

Grant, K. P., & Pennypacker, J. S. (2006). Project management maturity: an assessment of

project management capabilities among and between selected industries. IEEE

Transactions on Engineering Management, 53(1), 59–68.

Greiner, L. E. (1972). Evolution and revolution as organizations grow. Harvard Business

Review, 50(4), 37–46.

Guba, E. G., & Lincoln, Y. S. (1994). Competing paradigms in qualitative research. In

Handbook of Qualitative Research (pp. 105–117). Thousand Oaks, CA: Sage.

Gupta, A. K., & Govindarajan, V. (1984). Business unit strategy, managerial

characteristics, and business unit effectiveness at strategy implementation. Academy

of Management Journal, 27(1), 25–41.

Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate Data

Analysis (5th ed.). Englewood Cliffs, NJ: Prentice-Hall, Inc.

Hanisch, B., & Wald, A. (2012). A bibliometric view on the use of contingency theory in

project management research. Project Management Journal, 43(3), 4–23.

Harter, D., & Krishnan, M. (2000). Effects of process maturity on quality, cycle time, and

effort in software product development. Management Science, 46(4).

Hartman, F., & Skulmoski, G. (1998). Project management maturity. Project

Management, 74–78.


Henderson, R. M., & Clark, K. B. (1990). Architectural innovation: the reconfiguration of

existing product technologies and the failure of established firm. Administrative

Science Quarterly, 35(1), 9–30.

Herbsleb, J., Zubrow, D., Goldenson, D., Hayes, W., & Paulk, M. C. (1997). Software

quality and the capability maturity model. Communications of the ACM, 40(6), 30–

40.

Hillson, D. (1997). Towards a risk maturity model. International Journal of Project and

Business Risk Management, 1(1), 35–45.

Hillson, D. (2003). Assessing organisational project management capability. Journal of

Facilities Management, 2(3), 298–311.

Humphrey, W. S. (1988). Characterizing the software process: A maturity framework.

IEEE Software, 5(2), 73–79.

Humphrey, W. S. (1989). Managing the software process. Pittsburgh, Pennsylvania:

Addison-Wesley Publishing Company.

Humphrey, W. S., & Curtis, B. (1991). Comments on “A critical look.” IEEE Software,

8(4), 42–46.

Humphrey, W. S., Sweet, W. L., Edwards, R. K., LaCroix, G. R., Owens, M. F., & Shulz,

H. P. (1987). A method for assessing the software engineering capability of

contractors. Software Engineering Institute. Retrieved from

http://www.sei.cmu.edu/reports/87tr023.pdf

Ibbs, C. W., & Kwak, Y. H. (2000). Assessing project management maturity. Project

Management Journal, 31(1), 32–43.

Ibbs, C. W., Reginato, J., & Kwak, Y. H. (2004). Developing project management

capability: Benchmarking, maturity, modeling, gap analyses, and ROI studies. In P.

Morris & J. Pinto (Eds.), The Wiley guide to managing projects (pp. 1214–1233).

Hoboken, New Jersey: John Wiley & Sons, Inc.


Iqbal, S. (2012). Organizational maturity: Managing programs better. In G. Levin (Ed.),

Program management: a Life cycle approach (pp. 457–492). Boca Raton, FL:

Auerbach Pub.

Jiang, J. J., Klein, G., Hwang, H.-G., Huang, J., & Hung, S.-Y. (2004). An exploration of

the relationship between software development process maturity and project

performance. Information & Management, 41(3), 279–288.

Jochem, R., Geers, D., & Heinze, P. (2011). Maturity measurement of knowledge-

intensive business processes. The TQM Journal, 23(4), 377–387.

Jugdev, K., & Mathur, G. (2012). Classifying project management resources by

complexity and leverage. International Journal of Managing Projects in Business,

5(1), 105–124.

Jugdev, K., & Müller, R. (2005). A retrospective look at our evolving understanding of

project success. Project Management Journal, 36(4), 19–31.

Jugdev, K., & Thomas, J. L. (2002a). Blueprint for value creation: Developing and

sustaining a project management competitive advantage through the resource based

view. In Proceedings of the 2nd PMI Research Conference 2002, Seattle, WA.

Newtown Square, PA: Project Management Institute.

Jugdev, K., & Thomas, J. L. (2002b). Project management maturity models: The silver

bullets of competitive advantage. Project Management Journal, 33(4), 4–14.

Jung, H.-W., & Goldenson, D. R. (2009). Evaluating the relationship between process

improvement and schedule deviation in software maintenance. Information and

Software Technology, 51(2), 351–361.

Kaplan, R., & Norton, D. (1996). The balanced scorecard. Boston: Harvard Business

School Press.

Kerzner, H. (2005). Using the project management maturity model: Strategic planning for

project management (2nd ed.). Hoboken, New Jersey: John Wiley & Sons, Inc.


King, J. L., & Kraemer, K. L. (1984). Evolution and organizational information systems:

an assessment of Nolan’s stage model. Communications of the ACM, 27(5), 466–

475.

King, W. R., & Teo, T. S. H. (1997). Integration between business planning and

information systems planning: Validating a stage hypothesis. Decision Sciences,

28(2), 279–308.

King, W. R., & Teo, T. S. H. (2000). Assessing the impact of proactive versus reactive

modes of strategic information systems planning. Omega, 28(6), 667–679.

Kujala, J., & Artto, K. (2000). Criteria for project performance in business context.

Project Management, 6(7), 46–53.

Kwak, Y. H., & Ibbs, C. W. (1998). Benchmarking project management organizations.

PM Network, (February), 49–53.

Kwak, Y. H., & Ibbs, C. W. (2002). Project management process maturity model. Journal

of Management in Engineering, 18(3), 150–155.

Lawrence, P. R., & Lorsch, J. W. (1967). Differentiation and integration in complex

organizations. Administrative Science Quarterly, 12(1), 1–47.

Lee, L. S., & Anderson, R. M. (2006). An exploratory investigation of the antecedents of

the IT project management capability. E-Service Journal, 5(1), 27–42.

Levin, G., Artl, M., & Ward, J. L. (2010a). PortfolioFRAMEWORK®. Arlington, VA:

ESI International.

Levin, G., Artl, M., & Ward, J. L. (2010b). ProgramFramework®. Arlington, VA: ESI

International.

Levin, G., Artl, M., & Ward, J. L. (2013a). PortfolioFRAMEWORK®. Arlington, VA:

ESI International.

Levin, G., Artl, M., & Ward, J. L. (2013b). ProgramFRAMEWORK®. Arlington, VA:

ESI International.


Levin, G., Artl, M., & Ward, J. L. (2013c). ProjectFRAMEWORK®. Arlington, VA: ESI

International.

Levin, G., Hill, G., Defilippis, P., Ward, J. L., & Shaltry, P. (1999).

ProjectFRAMEWORK®. Arlington, VA: ESI International.

Lubianiker, S., & Levin, G. (2001). Using the web for next generation project

management assessments. In Proceedings of the Project Management Institute

Annual Seminars & Symposium. Nashville, Tenn.: Project Management Institute.

Mature. (2013). Merriam-Webster.com. Retrieved June 10, 2013, from

http://www.merriam-webster.com/dictionary/mature

Mettler, T., & Rohner, P. (2009). Situational maturity models as instrumental artifacts for

organizational design. In Proceedings of the 4th International Conference on Design

Science Research in Information Systems and Technology - DESRIST ’09 (p. 1).

New York, New York, USA: ACM Press.

Mintzberg, H. (1979). The structuring of organizations. Englewood Cliffs, NJ: Prentice-

Hall, Inc.

Morris, P. (2000). Researching the unanswered questions of project management. In

Proceedings of PMI Research Conference (pp. 87–101). Newtown Square, PA:

Project Management Institute.

Mullaly, M. (2006). Longitudinal analysis of project management maturity. Project


Mullaly, M., & Thomas, J. L. (2009). Exploring the dynamics of value and fit: Insights

from project management. Project Management Journal, 40(1), 124–135.

Mullaly, M., & Thomas, J. L. (2014). The value of project management: Rethinking

project management maturity and fit. In J. R. Turner (Ed.), Gower Handbook of

Project Management (5th ed.). Aldershot, England: Gower Publishing Limited.

Müller, R., & Turner, J. R. (2007). Matching the project manager’s leadership style to

project type. International Journal of Project Management, 25(1), 21–32.


Nahm, A. Y., Vonderembse, M. A., & Koufteros, X. A. (2003). The impact of

organizational structure on time-based manufacturing and plant performance.

Journal of Operations Management, 21, 281–306.

O’Hara, S., & Levin, G. (2000). Using metrics to demonstrate the value of project

management. In Proceedings of the Project Management Institute Annual Seminars

& Symposium. Houston, Texas: Project Management Institute.

Ofner, M. H., Huener, K. M., & Otto, B. (2009). Dealing with complexity: A method to

adapt and implement a maturity model for corporate data quality management. In

AMCIS 2009 Proceedings (p. 491). Retrieved from

http://works.bepress.com/boris_otto/6/

OGC. (2009). Managing successful projects with PRINCE2. London, England.

OGC. (2010a). Portfolio, programme and project management maturity model (Version

2.1). Buckinghamshire, UK: Office of Government Commerce. Retrieved from

http://www.p3m3-officialsite.com/P3M3Model/Model_mhtry.aspx

OGC. (2010b). PRINCE2 maturity model (Version 2.1). Buckinghamshire, UK: Office of

Government Commerce. Retrieved from http://www.p3m3-

officialsite.com/P3M3Model/Model_mhtry.aspx

Pasian, B. L. (2011). Project management maturity: a critical analysis of existing and

emergent contributing factors. Retrieved from

http://epress.lib.uts.edu.au/dspace/handle/2100/1258

Pasian, B. L., Sankaran, S., & Boydell, S. (2011). Factors for designing a second

generation of project management maturity models. In Proceedings of the Project

Management Institute Global Congress. Dallas. Retrieved from

http://epress.lib.uts.edu.au/research/handle/10453/19344

Pasian, B. L., Sankaran, S., & Boydell, S. (2012). Project management maturity: a critical

analysis of existing and emergent factors. International Journal of Managing

Projects in Business, 5(1), 146–157.


Paulk, M. C., Curtis, B., Chrissis, M. B., & Weber, C. V. (1991). Capability maturity

model for software. Pittsburgh, Pennsylvania: Software Engineering Institute.

Paulk, M. C., Curtis, B., Chrissis, M. B., & Weber, C. V. (1993). Capability maturity

model for software version 1.1. Pittsburgh, Pennsylvania: Software Engineering

Institute.

Pennypacker, J. S. (2005). Project portfolio management maturity model. Havertown,

Pennsylvania: Center for Business Practices.

Pennypacker, J. S. (2006). Project management maturity - A benchmark of current best

practices. Havertown, Pennsylvania: Center for Business Practices.

Pennypacker, J. S., & Grant, K. P. (2003). Project management maturity: An industry

benchmark. Project Management Journal, 34(1), 4–11.

Perrow, C. (1967). A framework for the comparative analysis of organizations. American

Sociological Review, 32(2), 194–208.

Pich, M., & Loch, C. (2002). On uncertainty, ambiguity, and complexity in project

management. Management Science, 48(8), 1008–1023.

Piney, C. K. (2004). Project management maturity case study: What you can do before

OPM3TM. In Proceedings of the PMI Global Congress 2004. Newtown Square, PA:

Project Management Institute.

Pinto, J., & Slevin, D. (1988). Project success: definitions and measurement techniques.

Project Management Journal, 19(1), 67–72.

PMI. (2008). Organizational project management maturity model: Knowledge foundation

(2nd ed.). Newtown Square, Pennsylvania: Project Management Institute.

PMI. (2013a). A guide to the project management body of knowledge (5th ed.). Newtown

Square, PA: Project Management Institute.

PMI. (2013b). Organizational project management maturity model: Knowledge

foundation (3rd ed.). Newtown Square, Pennsylvania: Project Management Institute.


Podsakoff, P., & Organ, D. (1986). Self-reports in organizational research: Problems and

prospects. Journal of Management, 12(4), 531–544.

Pöppelbuß, J., & Röglinger, M. (2011). What makes a useful maturity model? A

framework of general design principles for maturity models and its demonstration in

business process management. In ECIS 2011 Proceedings.

Prado, D. (2008). Maturidade em gerenciamento de projetos. Nova Lima: INDG.

Prananto, A., McKay, J., & Marshall, P. (2003). A study of the progression of e-business

maturity in Australian SMEs: some evidence of the applicability of the stages of

growth for e-business model. In PACIS 2003 Proceedings (pp. 68–80).

Radice, R. A., Harding, J. T., Munnis, P. E., & Phillips, R. W. (1985). A programming

process study. IBM Systems Journal, 24(2), 91–101.

Remenyi, D., Williams, B., Money, A., & Swartz, E. (1998). Doing research in business

and management. London, England: SAGE Publications.

Röglinger, M., Pöppelbuß, J., & Becker, J. (2012). Maturity models in business process

management. Business Process Management Journal, 18(2), 328–346.

Rousseau, D. M., Manning, J., & Denyer, D. (2008). Chapter 11: Evidence in

management and organizational science: Assembling the field’s full weight of

scientific knowledge through syntheses. The Academy of Management Annals, 2(1),

475–515.

Rudestam, K. E., & Newton, R. R. (2007). Surviving your dissertation (3rd ed.).

Thousand Oaks, CA: SAGE Publications.

Saunders, D. R. (1956). Moderator Variables in Prediction. Educational and

Psychological Measurement, 16(2), 209–222.

Saunders, M., Lewis, P., & Thornhill, A. (2009). Research methods for business sudents

(5th ed.). Essex, England: Pearson Education Limited.


Sauser, B. J., Reilly, R. R., & Shenhar, A. J. (2009). Why projects fail? How contingency

theory can provide new insights – A comparative analysis of NASA’s Mars Climate

Orbiter loss. International Journal of Project Management, 27(7), 665–679.

Schlichter, J. (2001). PMI’s organizational project management maturity model:

Emerging standards. In Proceedings of the 32nd Annual Project Management

Institute 2001 Seminars and Symposium, Nashville, TN.

Schlichter, J., McEver, J., & Hayes, R. E. (2010). Maturity frameworks for enterprise

agility in the 21st century. In PMI Global Congress Proceedings. Newtown Square,

Pennsylvania: Project Management Institute.

Scott, W. R. (2004). Reflections on a half-century of organizational sociology. Annual

Review of Sociology, 30(1), 1–21.

Serrador, P. (2012). The planning phase impact on project success. Skema Business

School.

Shao, J., Müller, R., & Turner, J. R. (2012). Measuring program success. Project


Sharma, S., Durand, R., & Gur-Arie, O. (1981). Identification and analysis of moderator

variables. Journal of Marketing Research, 18(3), 291–300.

Shenhar, A. J. (2001). One size does not fit all projects: Exploring classical contingency

domains. Management Science, 47(3), 394–414.

Shenhar, A. J., & Dvir, D. (2007). Reinventing project management: the diamond

approach to successful growth and innovation. Boston, MA: Harvard Business Press.

Shenhar, A. J., Levy, O., & Dvir, D. (1997). Mapping the dimensions of project success.

Project Management Journal, 28(2), 5–13.

Shenhar, A. J., & Stefanovic, J. (2006). Operational excellence won’t do it–toward a new

project management maturity model. In PMI Research Conference 2006

Proceedings (pp. 16–19). Montreal, Canada.


Shewhart, W. A. (1939). Statistical method from the viewpoint of quality control.

Washington, DC: Graduate School of the Department of Agriculture.

Sine, W. D., Mitsuhashi, H., & Kirsch, D. a. (2006). Revisiting Burns and Stalker: Formal

structure and new venture performance in emerging economic sectors. Academy of


Skulmoski, G. (2001). Project maturity and competence interface. Cost Engineering,

43(6), 11–18.

Smith, H., & Fingar, P. (2004). The third wave - process management maturity models.

Business Process Trends, (7). Retrieved from

http://www.bptrends.com/publicationfiles/07-04 COL Maturity Models- Smith-

Fingar.pdf

Stinchcombe, A. (1965). Social structure and organizations. In J. March (Ed.), Handbook

of organizations (pp. 142–193). Chicago: Rand McNally.

Suares, I. (1998). A real world look at achieving project management maturity. In

Proceedings of the 29th Annual Project Management Institute 1998 Seminars and

Symposium. Newtown Square, PA: Project Management Institute.

Subramanian, G. H., Jiang, J. J., & Klein, G. (2007). Software quality and IS project

performance improvements from software development process maturity and IS

implementation strategies. Journal of Systems and Software, 80(4), 616–627.

Tabachnick, B. B., & Fidell, L. S. (2007). Using multivatiate statistics (5th ed.). Boston,

MA: Pearson Education.

Tashakkori, A., & Teddlie, C. (1998). Mixed methodology - combining qualitative and

quantitative approaches. Thousand Oaks, CA: SAGE Publications.

Tatikonda, M. V, & Rosenthal, S. R. (2000). Successful execution of product

development projects: Balancing firmness and flexibility in the innovation process.

Journal of Operations Management, 18(4), 401–425.

Taylor, F. J. (1911). The principles of scientific management. New York: Harper.


Teague, J. A., & Cooke-Davies, T. J. (2007). Developing organizational capability:

Pointers and pitfalls. In PMI Global Congress EMEA Proceedings. Budapest,

Hungary: Project Management Institute.

Thomas, J. L., & Mullaly, M. (2008). Researching the value of project management.

Newtown Square, PA: Project Management Institute.

Thompson, J. D. (1967). Organizations in action. New York, New York, USA: McGraw-

Hill.

Tracey, M., Vonderembse, M. A., & Lim, J.-S. (1999). Manufacturing technology and

strategy formulation: keys to enhancing competitiveness and improving

performance. Journal of Operations Management, 17(4), 411–428.

Turner, J. R., Anbari, F. T., & Bredillet, C. (2013). Perspectives on research in project

management: the nine schools. Global Business Perspectives, 1(1), 3–28. Retrieved

from http://link.springer.com/10.1007/s40196-012-0001-4

Turner, J. R., & Cochrane, R. A. (1993). Goals-and-methods matrix: coping with projects

with ill defined goals and/or methods of achieving them. International Journal of

Project Management, 11(2), 93–102.

Turner, J. R., & Zolin, R. (2012). Forecasting success on large projects: Developing

reliable scales to predict multiple perspectives by multiple stakeholders over multiple

time frames. Project Management Journal, 43(5), 87–99.

Turner, J. R., Zolin, R., & Remington, K. (2009). Monitoring the performance of complex

projects from multiple perspectives over multiple time frames. In Proceedings of the

9th International Research Network of Project Management Conference (pp. 11–13).

Berlin: IRNOP.

Venkatraman, N. (1989). The concept of fit in strategy research: Toward verbal and

statistical correspondence. Academy of Management Review, 14(3), 423–444.


Venkatraman, N., & Ramanujam, V. (1986). Measurement of business performance in

strategy research: a comparison of approaches. Academy of Management Review,

11(4), 801–814.

Ward, P. T., & Duray, R. (2000). Manufacturing strategy in context: environment,

competitive strategy and manufacturing strategy. Journal of Operations

Management, 18(2), 123–138.

Weber, M. (1968). Economy and society, an outline of interpretive sociology. (G. Roth &

C. Wittich, Eds.). New York: Bedminster.

Woodward, J. (1958). Management and technology. London: Her Majesty’s Stationary

Office.

Yazici, H. (2009a). Does project maturity matter for organizational success? In Industrial

Engineering Research Conference (pp. 356–361).

Yazici, H. (2009b). The role of project management maturity and organizational culture in

perceived performance. Project Management Journal, 40(3), 14–33.

Zhu, Q., & Sarkis, J. (2004). Relationships between operational practices and

performance among early adopters of green supply chain management practices in

Chinese manufacturing enterprises. Journal of Operations Management, 22(3), 265–

289.


Appendix A – Questionnaire

A contingency view on the effect of project management maturity on

perceived performance

This questionnaire is part of a research project to investigate the impact of project

management maturity in performance, situated in different contexts.

The questionnaire contains two parts. The first part measures the organizational maturity

of project management in your organization. The second part, the nature of the last

projects you participated and how successful the project was.

Information obtained from you will be held in strict confidence. No reference will be

made to specific individuals or names of organizations in future reports. Participation is

voluntarily. The overall summary of the results will be shared with you if you indicate so

in the questionnaire.

It contains 27 questions. Please answer them to the best of your knowledge of your

organization and your last completed project.

Thank you for your time

Luciano Torres, PhD candidate

Skema Business School

Part 1 – Maturity

Q1.1. Which of the following best describes your view of your organization project scope

management?


1. Projects may or may not end up meeting customer needs. Many projects originate

because individuals just decide to do them; other projects begin when dictated by

management.. A requirements management plan, scope management plan, and a

scope statement are not prepared. Many project managers do not prepare work

breakdown structures (WBSs). Scope creep is a problem in project execution.

Scope verification is limited. Formal project acceptance may not be sought.

2. Organization is working to ensure that because all projects are based on needs and

requirements, the requirements are specified accurately. There is a focus on

establishing and maintaining agreement among the project team, including the

customer and suppliers, with respect to the requirements. Problems in meeting

commitments are identified when they arise. Requirements are baselined, and the

content is controlled.

3. A requirements management plan and a requirements traceability matrix are

developed as part of collect requirements. A written scope statement, WBS, scope

and a scope management plan are prepared. A scope validation process ensures

deliverables are accepted by the customer and fulfills project objectives, bringing

value to the business. A scope change control system is implemented.

4. I have not been exposed to this/or I do not have experience in this area.

Q1.2. Which of the following best describes your view of your organization project time

management?

1. Project management software is just beginning to be implemented in the

organization and is used to list specific tasks to be performed. Resource planning

is ad hoc. Generally, project schedules are developed based on end dates imposed

by customers or project sponsors. A schedule baseline is not established, and a

schedule management plan is not part of the overall project management plan.

2. Organization is committed to project time management. A project schedule is

prepared, issued, and baselined. Changes to the schedule that affect commitments

by stakeholders are resolved. Schedule control involves monitoring performance


regularly to detect deviations from the plan. The organization has adopted

standard project management software that is used for schedule development and

tracking. Project managers and team members receive training in the use of this

software.

3. The project schedule is approved according to the project management

methodology and serves as a baseline for use in measuring and reporting schedule

performance. Crashing, fast-tracking, and leveling techniques are used as required.

A schedule management plan is prepared and followed.


Q1.3. Which of the following best describes your view of your organization project cost

management?

1. The focus of project management is on the project schedule, not costs. Costs are

not formally managed and tend to exceed available budget. Cost estimating is not

coordinated with activity resource estimating or activity duration estimating. Cost

reporting is also done in an ad hoc way. There is no formal project budget. A cost

management plan is not prepared.

2. Organization is committed to preparing and using cost estimates. Project costs are

tracked, and corrective actions are taken as required The WBS serves as the basis

for the cost estimate. A project budget is developed based on allocating elements

of the project cost estimate to individual work items. Cost control – that is,

monitoring performance to detect variances from the plan – is exercised.

3. Project cost management activities are planned, scheduling and cost estimating is

coordinated. The cost baseline serves as a time-phased budget to measure and

monitor project cost performance. Earned value analysis is used for performance

measurement and forecasting.



Q1.4. Which of the following best describes your view of your organization project

quality management?

1. Quality management planning is not accomplished at the project level. Quality

assurance programs and policies aimed at management by projects are virtually

nonexistent. Quality control is conducted on an ad hoc basis. Rework is expected

because project specifications often are poorly defined at the outset of a project.

2. The emphasis in quality management is on the product or service of the project

and not the process by which the project is executed. Quality is considered to be

inspected into the product or service rather than being designed in during the

planning phase. Quality control consists of inspection activities.

3. Project quality management addresses project management processes, as well as

the product or service delivered. The organization has a quality policy for project

management. The organization emphasizes the importance of quality

improvements. Tools and techniques for quality management planning are used

regularly. Quality assurance activities are performed routinely, with audits

providing a structured review to address lessons learned. Unanticipated rework is

minimal.



human resources management?

1. Project managers who are successful are heroes and are rewarded individually.

Project managers are assigned on an ad hoc basis. The project team is staffed

based on the availability of individuals. Project management is not a recognized

practice.


2. Organization is being structured for effective assignment of project team

members. It is using a project matrix organization, where project managers and

functional managers are working out respective roles and responsibilities. A

resource assignment matrix is prepared for each project. Management recognizes

the need to identify the training required for project members.

3. Team-building training emphasizes the temporary nature of project management,

the dual reporting that exists in the matrix structure, and the importance of

communication skills; team development occurs throughout each project.

Information is collected throughout the organization to determine a resource

productivity/utilization factor to support resource planning and the development of

future metrics.

4. Project management is established as a core professional competency. The

organization maintains a current inventory of project management knowledge,

skills, and competency profiles for project personnel. Personal development plans

are prepared for project team members.. Mentoring is considered essential to help

develop project managers.


communications management?

1. Project communication tends to be informal, unstructured, and limited. Project

performance reports are prepared, and project performance reviews are held, only

as needed or when requested by project sponsors, the contract, or customers.

2. Organization is committed to project communications management. The

organization has processes in place, as well as tools and techniques that facilitate

collecting and analyzing project data and preparing management reports. Project

information is recorded and distributed for individual projects.

3. Communication management activities are well defined and carried out by all

team members. Management reviews are held regularly on each project, and


standard performance reports are prepared. A project management information

system. Performance reports provide the level of detail required by stakeholders as

documented in the project communication plan.



stakeholder management?

1. Stakeholder identification is not performed so the interests and influence of the

project stakeholders are not known or documented. There is limited interest in

communicating or in working with stakeholders to anticipate their needs or to

address and respond to any issues they may have.

2. Organization is committed to project stakeholder management. The organization

has processes and procedures in place, as well as tools and techniques that

facilitate stakeholder engagement. Project teams consistently document the plan to

engage stakeholders as part of the project management plan. Stakeholders’

expectations are managed ensuring they understand the project goals and risks and

that they are active supporters of the project.

3. Stakeholder engagement, including identification of stakeholders, are well defined

and carried out by all team members. Data are collected, and regular reviews are

held of our stakeholder engagement approaches in order that we can continue to

improve in this area.


Q1.8. Which of the following best describes your view of your organization project risk

management?


Appendix A No formal risk management process is in place. Risk identification, analysis,

response planning, and monitoring and controlling are not performed.

Appendix B Risks are identified and analyzed, and risk responses are planned. Risk

qualification and/or quantification is performed to evaluate risks and risk interactions,

to assess the range of possible project outcomes, and to determine which risk events

warrant response. The risk management process is continual throughout each project.

Appendix C Risk management is a continuous activity, with risk identification addressing

both threats and opportunities. A risk management plan is prepared, and the

organization begins to develop a risk management capability and culture of dealing

openly with risk. Risk communication is stressed. Contingency planning is an integral

part of risk management planning. Specific estimates are made of needed contingency

and management reserves.

Appendix D To make informed decisions, the organization promotes a risk management

culture that is characterized by direct and open communication with stakeholders

regarding project risks, their impact, and the inevitable trade-offs associated with

various risk responses. Risk management is such an integral part of project

management that they are not viewed as separate and distinct activities; rather, they

are viewed as one.

Appendix E I have not been exposed to this/or I do not have experience in this area.


procurement management?

1. Procurement is not considered part of project management; it is to be handled by

the procurement function in the organization. Accordingly, project procurement

planning is ad hoc.

2. Organization is implementing processes for project procurement management.

Procurement management plans are established. Projects use documented


processes to select sellers and to manage their contracts. Contract administration is

the responsibility of the project team under the overall guidance of the project

manager. Project teams track the performance of sellers. Ongoing communication

is maintained with sellers because they are considered members of the project

team. Commitments are agreed upon, and any changes are implemented according

to a contract change control procedure.

3. Project procurement activities are based on two perspectives—that of the buyer

and that of the seller. Specific processes are in place for either perspective. Either

process requires that the project manager work in partnership with the

procurement or contracting department.



integration management?

1. The organization may provide forms or checklists for use on project activities but

offers little guidance or training in conducting those activities. A project schedule

is sometimes believed to constitute a project management plan. There is limited

emphasis on formal project initiation or closeout activities.

2. Organization has developed an essential project management methodology that

contains process for project management within a project management life cycle.

The methodology provides a structured, repeatable, customizable approach to

guide the project team, with standard practices, techniques, terminology and tools.

The planning and tracking of new projects are based on experience with similar

projects. The commitment to project management is further evidenced by the

identification of a function whose major purpose is the development, refinement,

and institutionalization of the project management methodology.

3. Organization applies its standard project management methodology for all projects

tailoring by criteria such as complexity of requirements, size and duration of work


effort, cost, risk, and strategic value. Project reviews are conducted at a frequency

that depends on the project classification. Each project has a governance board to

oversee its progress and to conduct stage gate reviews. Best practices are shared,

and a project management information system (PMIS) is established. Project

management plans are prepared regularly. Assumptions and constraints from

project management plans are documented. Change management is an integral

part of project management. An established change control process for scope, cost,

and schedule is developed. The baseline changes only intermittently and in

response to approved scope changes. Project management plans, though,

continually change and are reviewed regularly as more detail is available.

Planning for project closeout and transition to the customer begins during the

project planning phase. Project records are prepared for archiving lessons learned.

4. Organization recognizes and supports project management at all levels because

projects are viewed as essential to the growth of the organization. The PM

methodology is ingrained in the organization and used consistently. The PMO’s

focus is on project management improvement. Best practices in project

management are established and followed Metrics are established that focus on

strategic alignment, monitoring and controlling risks and opportunities, and

ensuring the project’s outcomes are as expected according to its business plan. In

addition, the organization has developed and uses integrated systems, available to

all project stakeholders. Partnering is fostered at all levels, both internally and

externally.



management maturity?

1. Projects are managed in an ad hoc fashion, and no formal project management

methodology exists. Performance is inconsistent. Organization may complete

projects successfully, but many are accomplished through the heroic efforts of a

few persons. Project cost overruns and schedule delays are common. People


working on projects either struggle with the organization’s existing processes or

tend to invent a process as they work on the project.

2. Policies are established for project management processes, responsibilities are

identified for each process, resources are allocated and obtained to perform the

process, personnel performing specific roles are appropriately trained, and the

processes are documented. Processes are repeatable across projects. Management

reviews the status of each process and based on the results of the reviews, takes

corrective action as appropriate.

3. Organization is motivated to gain a competitive advantage through its

management of projects. The organization is able to improve its ability to predict

the performance of its projects and capitalizes on prior success by adapting and

enhancing its project management methodology for deployment throughout the

organization.

4. Project management is an integral part of each person’s responsibilities. Practices

are well understood and followed, support for project management processes

exists throughout the organization, and project management teams and functional

organizations understand how projects relate to, and are integrated with, the

ongoing operations of the organization. Each project has a governance board to

ensure projects support and link to the organization’s strategy.

5. Organization’s project management methodology operates routinely, and projects

meet schedule, cost, technical, and quality requirements. Continuous project

management process improvement is established and maintained. A project

portfolio management system is used to ensure that projects are selected and

continued according to strategic organizational goals and objectives. Project team

performance incentives rewards both individual and team accomplishments.



Part 2 – Contingency

Answer the following questions about your last completed project in our organization.

Q2.1. In regards to novelty, which of the following would best describe your project?

1. Derivative (Improvement)

2. Platform (A new generation in an existing product line – e.g., new car model)

3. New to the Market (Adopting an existing product to a different market – e.g., first

PC)

4. New to the World (Product never existed before)

Q2.2. In regards to technology uncertainty, which of the following would best describe

your project?

1. Low-Tech (No new technology)

2. Medium-Tech (Some new technology)

3. High-Tech (All or mostly new but existing technology)

4. Super-high-tech (Project will use non-existing technologies at project initiation)

Q2.3. In regards to complexity, which of the following would best describe your project?

1. Component/Material (An element or material in a subsystem)

2. Assembly (A subsystem – Performing a single function)

3. System (A collection of subsystems – Performing multiple functions)

4. Array (System of systems. A widely dispersed collection of systems serving a

common mission)

Q2.4. In regards to pace, which of the following would best describe your project?

1. Regular (Delays not critical)

2. Fast/competitive (Time to market is a competitive advantage)

3. Time-critical (Completion time is critical to success, window of opportunity)


4. Blitz (Crisis project)

Q2.5. In regards to the goals of the project, which of the following would best describe

your project?

1. The goals of the project are well defined

2. The goals of the project are not well defined

Q2.6. In regards to the methods to achieve the project goals, which of the following

would best describe your project?

1. The methods are well defined

2. The methods are not well defined

Part 3 – Project classification

Q.3.1. What was your role in the project?

Project Team Member, Project Manager, Project Director, Program Manager, Program

Director, Sponsor, Line/Department Manager, CEO/COO, Other (please specify)

Q.3.2. What was the principal industry of the project?

Pharmaceuticals, Consumer Electronics, Telecommunications, Information Technology,

Financial Services, Automobile, Defense, Energy, Software, Manufacturing, Advertising,

Entertainment, Health Care, Insurance, Construction, Travel, Consulting, E-Commerce,

Other: ______

Q3.3. What was the customer of your project?

1. Internal (internal user or another department)

2. External (external contract or consumers)


Q3.4. What was the strategic goal of the project?

1. Money-Making Project (selling a product or service to clients)

2. Money-Saving Project (Internal effort of cost reduction)

3. Utility/Infrastructure (Acquiring and installing new equipment or software,

implementing new methods or new processes)

4. Maintenance/Keep the Lights On Project (Routine maintenance, fixing regular

problems)

5. Building the Future (R&D, Technology Development, Exploring new ideas – No

specific customer in mind)

6. Problem Solving Project (Project focused on a unique narrow problem)

Q3.5. Project duration in months: ___

Q3.6. Project budget in dollars: ___

Q4. Project Performance

Please respond to each of the following statements about your last completed project.

Indicate to which degree do you agree or disagree with the statement by marking one

response for each item

Stro

ngly

D

isag

ree

Dis

agre

e

Agr

ee

Stro

ngly

A

gree

N/A

The project was completed on time or earlier.

The project was completed within or below budget.

The project had only minor changes. The project improved the customer’s performance.

The customer was satisfied.


The project met the customer requirements. The customer is using the project result. The customer will come back for future work.

The project team was highly motivated and satisfied.

The team was highly loyal to the project. The project team had high morale and energy.

The team felt that working on this project was fun.

Team members experienced personal growth.

Team members wanted to stay in the organization.

The project was an economic business success.

The project increased the organization’s profitability.

The project has a positive return on investment.

The project increased the organization's market share.

The project contributed to stakeholder value. The project contributed to the organization's direct performance.

The project outcome will contribute to future projects.

The project will lead to additional new products.

The project will help create new markets. The project created new technologies for future use.

The project contributed to new business processes.

The project developed better managerial capabilities.

Overall, the project was a success.

Q5. Business Performance

Please respond to each of the following statements about your organization. Consider all

projects from your organization, not only the last one. Indicate to which degree do you

agree or disagree with the statement by marking one response for each item.


Strongly

Disagree

Disagree

Agree

Strongly

Agree

N/A





The internal efficiency of my organization

improved as a result of its projects

The overall business performance of my

organization improved as a result of its

projects

Q6. How old is your organization, in years? __

Q7. What is the country of origin of your organization? __


Appendix B – Additional analysis figures and charts

Table 68 – Descriptive statistics for performance questions before treatment for missing values

N Minimum Maximum Mean Std. Deviation Skewness Kurtosis

Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic

Std. Error

The project was completed on time or earlier. 208 1 4 2.77 .902 -.335 .169 -.629 .336

The project was completed within or below budget. 187 1 4 2.87 .795 -.209 .178 -.524 .354

The project had only minor changes. 202 1 4 2.44 .851 -.090 .171 -.643 .341 The project improved the customer’s performance. 194 1 4 3.29 .669 -.735 .175 .758 .347

The customer was satisfied. 197 2 4 3.30 .586 -.174 .173 -.581 .345 The project met the customer requirements. 199 2 4 3.29 .563 -.044 .172 -.523 .343

The customer is using the project result. 195 2 4 3.42 .607 -.529 .174 -.612 .346 The customer will come back for future work. 174 1 4 3.41 .618 -.686 .184 .289 .366

The project team was highly motivated and satisfied. 201 1 4 2.85 .865 -.550 .172 -.207 .341

The team was highly loyal to the project. 205 1 4 3.08 .746 -.564 .170 .180 .338

The project team had high morale and energy. 203 1 4 2.94 .797 -.537 .171 .045 .340

The team felt that working on this project was fun. 202 1 4 2.75 .829 -.292 .171 -.398 .341

Team members experienced personal growth. 201 1 4 3.14 .724 -.625 .172 .386 .341

Team members wanted to stay in the organization. 198 1 4 3.01 .740 -.623 .173 .541 .344

The project was an economic business success. 179 0 4 2.94 .747 -.725 .182 1.235 .361

The project increased the organization’s profitability. 173 0 4 3.01 .774 -.619 .185 .710 .367

The project has a positive return on investment. 166 0 4 3.02 .805 -.809 .188 .951 .375

The project increased the organization's market share. 152 0 4 2.88 .829 -.551 .197 .304 .391

The project contributed to stakeholder value. 179 0 4 3.21 .642 -.989 .182 3.654 .361

The project contributed to the organization's direct performance. 186 1 4 3.22 .679 -.609 .178 .514 .355

The project outcome will contribute to future projects. 196 1 4 3.35 .651 -.730 .174 .518 .346

The project will lead to additional new products. 183 1 4 2.98 .845 -.456 .180 -.450 .357

The project will help create new markets. 176 0 4 2.70 .859 -.143 .183 -.371 .364

The project created new technologies for future use. 185 0 4 2.72 .942 -.119 .179 -.778 .355

The project contributed to new business processes. 189 1 4 2.81 .866 -.215 .177 -.699 .352

The project developed better managerial capabilities. 190 0 4 2.73 .860 -.398 .176 -.149 .351

Overall, the project was a success. 202 1 4 3.14 .585 -.182 .171 .476 .341 The rate of sales growth of my organization improved as a result of its projects

162 1 4 2.91 .700 -.100 .191 -.403 .379


The profitability of my organization improved as a result of its projects 181 1 4 2.98 .683 -.078 .181 -.530 .359


182 1 4 3.06 .614 -.323 .180 .746 .358

The market share of my organization improved as a result of its projects 161 1 4 2.89 .704 -.068 .191 -.459 .380


188 1 4 2.86 .735 -.093 .177 -.472 .353


186 1 4 2.90 .675 .011 .178 -.533 .355

Valid N (listwise) 88

Table 69 – Descriptive statistics for performance questions after treatment for missing values

N Minimum Maximum Mean Std. Deviation

Skewness Kurtosis

Statistic Statistic Statistic Statistic Statistic Statistic Std. Error

Statistic Std. Error

The project was completed on time or earlier. 211 1 4 2.77 .895 -.338 .167 -.595 .333

The project was completed within or below budget. 211 1 4 2.87 .749 -.222 .167 -.203 .333

The project had only minor changes. 211 1 4 2.44 .833 -.092 .167 -.536 .333 The project improved the customer’s performance. 211 1 4 3.29 .641 -.766 .167 1.086 .333

The customer was satisfied. 211 2 4 3.30 .566 -.180 .167 -.407 .333 The project met the customer requirements. 211 2 4 3.29 .546 -.045 .167 -.372 .333

The customer is using the project result. 211 2 4 3.42 .584 -.550 .167 -.414 .333 The customer will come back for future work. 211 1 4 3.41 .561 -.754 .167 .988 .333

The project team was highly motivated and satisfied. 211 1 4 2.85 .844 -.563 .167 -.067 .333

The team was highly loyal to the project. 211 1 4 3.08 .735 -.573 .167 .273 .333 The project team had high morale and energy. 211 1 4 2.94 .781 -.548 .167 .165 .333

The team felt that working on this project was fun. 211 1 4 2.75 .811 -.299 .167 -.281 .333

Team members experienced personal growth. 211 1 4 3.14 .706 -.640 .167 .555 .333

Team members wanted to stay in the organization. 211 1 4 3.01 .717 -.642 .167 .773 .333

The project was an economic business success. 211 0 4 2.94 .688 -.786 .167 1.988 .333

The project increased the organization?s profitability. 211 0 4 3.01 .700 -.683 .167 1.522 .333

The project has a positive return on investment. 211 0 4 3.02 .714 -.910 .167 2.016 .333

The project increased the organization's market share. 211 0 4 2.88 .703 -.647 .167 1.584 .333

The project contributed to stakeholder value. 211 0 4 3.21 .591 -1.073 .167 4.827 .333

The project contributed to the organization's direct performance. 211 1 4 3.22 .638 -.648 .167 .985 .333

The project outcome will contribute to future projects. 211 1 4 3.35 .628 -.757 .167 .787 .333

The project will lead to additional new products. 211 1 4 2.98 .787 -.489 .167 -.056 .333

The project will help create new markets. 211 0 4 2.70 .784 -.156 .167 .156 .333


The project created new technologies for future use. 211 0 4 2.72 .882 -.127 .167 -.461 .333

The project contributed to new business processes. 211 1 4 2.81 .820 -.227 .167 -.427 .333

The project developed better managerial capabilities. 211 0 4 2.73 .816 -.419 .167 .168 .333

Overall, the project was a success. 211 1 4 3.14 .573 -.186 .167 .630 .333 The rate of sales growth of my organization improved as a result of its projects

211 1 4 2.91 .613 -.114 .167 .388 .333

The profitability of my organization improved as a result of its projects 211 1 4 2.98 .632 -.084 .167 -.117 .333


211 1 4 3.06 .570 -.348 .167 1.341 .333

The market share of my organization improved as a result of its projects 211 1 4 2.89 .614 -.077 .167 .336 .333


211 1 4 2.86 .694 -.099 .167 -.160 .333


211 1 4 2.90 .633 .012 .167 -.198 .333

Valid N (listwise) 211


Table 70 – Performance questions anti-image correlation diagonals (Measure of Sample Adequacy)

Q4.1 .927a Q4.9 .921a Q4.17 .888a Q4.25 .657a

Q4.2 .843a Q4.10 .860a Q4.18 .818a Q4.26 .754a

Q4.3 .764a Q4.11 .850a Q4.19 .910a Q4.27 .943a

Q4.4 .917a Q4.12 .917a Q4.20 .898a Q5.1 .839a

Q4.5 .852a Q4.13 .883a Q4.21 .835a Q5.2 .883a

Q4.6 .832a Q4.14 .939a Q4.22 .715a Q5.3 .889a

Q4.7 .856a Q4.15 .894a Q4.23 .730a Q5.4 .803a

Q4.8 .851a Q4.16 .869a Q4.24 .761a Q5.5 .796a

Q5.6 .877a

Table 71 – Reliability test for impact on team

Item-Total Statistics Scale Mean if

Item Deleted Scale Variance if Item Deleted


Squared Multiple Correlation


The project team was highly motivated and satisfied. 14.88 9.302 .782 .633 .857

The team was highly loyal to the project. 14.65 10.217 .703 .565 .870

The project team had high morale and energy. 14.81 9.490 .813 .712 .852

The team felt that working on this project was fun. 15.00 9.720 .741 .581 .864

Team members experienced personal growth. 14.60 11.015 .550 .324 .892

Team members wanted to stay in the organization. 14.72 10.449 .646 .423 .879

Table 72 – Reliability tests for organizational performance







11.73 5.250 .634 .417 .854


11.73 5.023 .706 .528 .836


11.68 4.958 .760 .589 .823


11.57 5.396 .669 .452 .846


11.73 5.066 .690 .481 .840


Table 73 – Reliability tests for impact on customer






The project improved the customer’s performance. 13.45 3.390 .555 .326 .762

The customer was satisfied. 13.46 3.403 .638 .506 .735 The project met the customer requirements. 13.48 3.502 .645 .495 .735

The customer is using the project result. 13.32 3.631 .493 .286 .781

The customer will come back for future work. 13.34 3.490 .553 .335 .762

Table 74 – Reliability tests for project financial results






The project was an economic business success. 6.02 1.723 .676 .458 .840

The project increased the organization’s profitability. 5.96 1.604 .746 .569 .775

The project has a positive return on investment. 5.95 1.565 .753 .578 .768

Table 75 – Reliability tests for preparing for the future






The project will lead to additional new products. 8.10 4.410 .565 .336 .658

The project will help create new markets. 8.34 4.329 .560 .329 .660

The project created new technologies for future use. 8.33 4.211 .525 .278 .681

The project contributed to new business processes. 8.25 4.653 .470 .222 .710

Table 76 – Reliability tests for project impact on business






The project increased the organization's market share. 9.69 2.625 .617 .421 .741

The project contributed to stakeholder value. 9.40 3.001 .658 .448 .718


The project contributed to the organization's direct performance.

9.36 3.006 .628 .398 .731

The project outcome will contribute to future projects. 9.27 3.161 .530 .302 .777

Table 77 – Reliability tests for project efficiency






The project was completed on time or earlier. 5.33 1.815 .505 .297 .516

The project was completed within or below budget. 5.26 1.964 .544 .317 .472

The project had only minor changes. 5.66 2.137 .375 .143 .688

Table 78 – Reliability tests for internal efficiency






The project contributed to new business processes. 5.56 1.717 .474 .264 .543

The project developed better managerial capabilities. 5.67 1.668 .562 .321 .412


5.54 2.249 .366 .148 .673

Figure 26 – Scatterplot for impact on team regression


Figure 27 – Scatterplot for impact on team and project industry

Figure 28 – Scatterplot for organizational performance and project industry

Figure 29 – Scatterplot for impact on customer regression


Figure 30 – Scatterplot for impact on customer and project industry regression

Figure 31 – Scatterplot for project financial results regression

Figure 32 – Scatterplot for project financial results and project industry regression


Figure 33 – Scatterplot for internal efficiency, using age of organization as moderator

Figure 34 – Scatterplot for internal efficiency and industry of the project as moderator

a contingency view on the effect of project management maturity on perceived performance

Documents

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