Investigating Factors That Affect Project Manager

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of information available, the amount of training they have received, and their level of .... concern. It is clear that project managers should do more to build their competence in ..... environments, thereby enabling effective assessment of progress. .... megaproject teams are characteristically multicultural and widely dispersed.
Investigating Factors That Affect Project Manager Decisions on Oil and Gas Megaprojects, and How They Impact the Realisation of Strategic Value

by

EWEJE, John Ajibola A thesis submitted for the degree of Doctor of Philosophy in Strategy, Program and Project Management

Skema Business School, Lille, France July 2010 Skema Business School

EWEJE, J. A, PhD 2010

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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.

EWEJE, John A. July 2010

Skema Business School

EWEJE, J. A, PhD 2010

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Acknowledgment Greatest acknowledgment to Jesus, my Rock—you are the ultimate encourager, and your provision of grace through the challenges of this research has been awesome. Thank you very much.

To the most pleasant treasures God has given me: first, my wife, Opeyemi—your desire was always for me to have and give the best. Thank you for your sacrificial love. You are the best! To my children, Lolu, Ibukun, and Itunu—you are the best any parent can ask for. Your loving support through this study has made me a proud father. My sister, Dr Lucy Kehinde – your words were always kind and encouraging. Thank you for your suggestions, and being there.

To my supervisors, Professors Rodney Turner and Ralf Muller—your teamwork has been excellent. Thank you so much for your thoughtfulness, frankness, and guidance. Your thoroughness was a great benefit; the quality of your knowledge is inspiring. I feel privileged to have worked with both of you.

To Professor Christophe Bredillet—you are a wonderful encourager. Many thanks for all your support in helping me finish this journey successfully.

To Nadine Sauze—you were patient and always ready to help. Thank you for the strong sustenance you provided behind the scenes.

I am successful only because all of you were there for me!

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Table of Contents CERTIFICATE OF AUTHORSHIP/ORIGINALITY ............................................ 2 Acknowledgment ...................................................................................................... 3 List of Illustrations and Tables ............................................................................... 6 Figures...................................................................................................................... 6 Tables ....................................................................................................................... 7 Abstract .................................................................................................................... 10 CHAPTER ONE: Introduction .............................................................................. 12 1.1 Background and Research Questions .......................................................... 12 1.2 Research Perspective and Approach ........................................................... 20 1.3 Summary of Research Outcome ................................................................... 21 1.4 Thesis Layout ................................................................................................. 22 CHAPTER TWO: Literature Review .................................................................... 24 2.1 Understanding the Nature of Oil and Gas Megaprojects .......................... 24 2.2 Strategy, Organisational Performance, and Megaprojects Management 43 2.3 Decision Making on Megaprojects ............................................................... 58 CHAPTER THREE: Research Design ................................................................... 85 3.1 Research Philosophy ..................................................................................... 85 3.2 Data Gathering Strategy ............................................................................... 87 3.3 The Qualitative Approach ............................................................................ 87 3.4 The Quantitative Approach .......................................................................... 89 3.5 Design of the Quantitative Survey................................ ..... ............................90 3.6 Data Analysis Approach ............................................................................... 96 CHAPTER FOUR: Research Definition: The Early Qualitative Input .......... 103 4.1 Background and Approach ........................................................................ 103 4.2 Results and Implications for Defining the Study ..................................... 104 CHAPTER FIVE: Data Presentation ................................................................... 108 5.1 Quantitative Data Preparation ................................................................... 108 5.2 Data Grouping ............................................................................................. 110 5.3 Identification of Underlying Structure within Model Constructs...... . ....110 5.4 Securing Goodness of Measures ................................................................ 124 CHAPTER SIX: Data Analysis and Interpretation ........................................... 129 6.1 Data and Sample Characterisation ............................................................... 129 6.2 Tests of Hypotheses ....................................................................................... 132 6.3 Summary of Hypothesis Tests ...................................................................... 169

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CHAPTER SEVEN: Conclusions and Recommendations ............................... 173 7.1 Research Overview ..................................................................................... 174 7.2 Knowledge Advancement Provided by the Research.............................. 179 7.3 Implications for Practitioners ..................................................................... 180 7.4 Theoretical Implications ............................................................................. 188 7.5 Summary of Recommendations ................................................................. 194 7.6 Limitations of the Study ............................................................................. 195 7.7 Opportunities for Further Research ........................................................... 196

APPENDICES:...........................................................................................................199 APPENDIX C3: APPENDIX TO CHAPTER 3 ................................................... 199 APPENDIX C3-A: Preliminary Research Data-Gathering Interview ................ 200 APPENDIX C3-B: General Survey Design Principles ......................................... 202 APPENDIX C3-C: Web Survey Design Principles .Error! Bookmark not defined. APPENDIX C3-D: Outline of Construct Operationalisation ............................. 204 APPENDIX C3-E: Pretest Evaluation Questions ................................................. 205 APPENDIX C3-F: Introductory Cover Note Issues with the Survey ................ 206 APPENDIX C3-G: Main Research Questionnaire: Web Survey . Error! Bookmark not defined. APPENDIX C5: APPENDIX TO CHAPTER 5 ....... Error! Bookmark not defined. Table 1A: ANOVA Results – Comparing Company Types ............................... 225 Table 1B: ANOVA Results – Comparing Project Cost ........................................ 225 Table 1C: ANOVA Results – Comparing Respondee Project Roles .................. 226 Table 2: Subvariables Eliminated as a Result of Factor Analysis....................... 227 Table 3: Descriptive Statistic of Construct Variables .......................................... 228 Table 4: Pearson Correlation Table of Constructs ............................................... 229 Table 5: Scores for Underlying Variables of Controllability .............................. 229 Figure 1A: P-P Plots of Variables That Did Not Satisfy Required Skewness and Kurtosis Conditions ...................................................................... 230 Figure 1B: Some P-P Plots of Standardised Residuals from Regressions......... 231 Figure 2: Controllability and Use of Information................................................ 231 Bibliography .......................................................................................................... 232

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List of Figures and Tables Figures Figure 1.1: Capital Expenditure Profile for the Oil and Gas Industry...........13 Figure 2.1: The Opportunity and Project Realisation Process.........................27 Figure 2.2: Typical Megaproject Organisational Setup....................................33 Figure 2.3: Strategy Management Process.........................................................45 Figure 2.4: Megaproject Challenges Mapped to Strategy Implementation Barriers.......................................................................................52 Figure 2.5: Learning Feedback in the Decision Process....................................61 Figure 2.6: Conceptual Model of Stakeholder Influence on Megaproject Decision Making....................................................................................................80 Figure 2.7: Decision Process for Problem Solving............................................67 Figure 2.8: The Three Factors That Determine Outcomes...............................74 Figure 2.9: The Research Model...........................................................................83 Figure 3.1: The Research Model as Tested..........................................................103 Figure 5.1A/B: Research Model – Post Factor Analysis...................................123 Figure 6.1: Regression Model for H1 (Information Feed vs. Strategic Value)......................................................................................................................128 Figure 6.2: Regression Model for H2 (Interpretation vs. Strategic Value)......................................................................................................................129 Figure 6.3: Regression Model for H3A (Controllability vs. Information Feed)........................................................................................................................129 Figure 6.4: Regression Model for H3B (Controllability vs. Interpretation factors).....................................................................................................................129 Skema Business School

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Figure 6.5: Regression Model for H4 (Decision Practice vs. Strategic Value)....................................................................................................................130 Figure 6.6: Regression Model for H5 (Decision Implementation vs. Long-term strategic value).................................................................................130 Figure 6.7: Regression Model for H7 (Context Factors Moderating Information Feed vs. Long-term strategic value)...........................................130 Figure 6.8: Social Influence Environment around Projects..........................153 Figure 6.9: Typology of Specification Variables.............................................157 Figure 6.10A/B: Research Model – Post Analysis...........................................166

Tables Table 2.1: Description of the Opportunity and Project Realisation Phases....................................................................................................................28 Table 2.2: Some Success and Failure Factors Related to Megaproject Execution...............................................................................................................37 Table 2.3: Stakeholder Mapping for Oil and Gas Megaprojects...................41 Table 2.4: Mapping of Sustainable Development Measure to Success Dimensions...........................................................................................................49 Table 2.5: Examples of Chance Events Affecting Megaprojects...................78 Table 2.6: Factors Affecting Decision Making.................................................82 Table 4.1: Characteristics of Interviewees.......................................................101 Table 4.2: Summary of Semi-Structured Interviews to Define the Research and Scope............................................................................................102 Table 5.1: Results of KMO Test.........................................................................108 Table 5.2: Factor Loading – Information Feed Construct..............................110 Skema Business School

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Table 5.3: Factor Loading – Interpretation Construct.....................................112 Table 5.4: Factor Loading – Controllability Construct....................................113 Table 5.5: Factor Loading – Decision Making Practice Construct.................115 Table 5.6: Factor Loading – Decision Implementation Construct.................116 Table 5.7: Factor Loading – Long-term strategic value Construct................118 Table 5.8: Factor Loading – Senior Management Drivers Construct............120 Table 6.1: Descriptive Statistics for Long-term strategic value Variable......127 Table 6.2: Regression Result of Information Feed (IV) and Strategic Value (DV).............................................................................................................132 Note: IV = Independent Variable; DV = Dependent Variable

Table 6.3: Regression Result of Interpretation Factors (IV) and Long-term Strategic value (DV).........................................................................137 Table 6.4: Regression Result of Labelling Factors (IV) and Strategic Value (DV).............................................................................................................140 Table 6.5: Regression Result of Controllability factors (IV) and Information Feed (DV).........................................................................................143 Table 6.6: Regression Result of Controllability Factors (IV) and Interpretation (DV)..............................................................................................146 Table 6.7: Regression Result of Decision Practice Factors (IV) and Long-term Strategic value (DV).........................................................................149 Table 6.8: Regression Result of Decision Implementation Factors (IV) and Long-term Strategic value (DV)............................................151 Table 6.9: Hierarchical Regression Result for “Information on Corporate Performance” (IV), “Senior Management Drivers” (Moderating Variables), and Long-term Strategic value (DV)......................158

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Table 6.10: Hierarchical Regression Result for “Information Timeliness” (IV), “Senior Management Drivers” (Moderating Variables), and Long-term Strategic value (DV)..............................................159 Table 6.11: Hierarchical Regression Result for “Information on Stakeholder Pulse” (IV), “Senior Management Drivers” (Moderating Variables), and Long-term Strategic value (DV).......................159 Table 6.12: Hierarchical Regression Result for “Project Performance Information” (IV), “Senior Management Drivers” (Moderating Variables), and Long-term Strategic value (DV)..............................................160 Table 6.13: Summary of Hierarchical Regression Results for “Information Feed” Variables (IV), “Senior Management Drivers” (Moderating Variables), and Long-term Strategic value (DV).......................160 Table 6.14: Overview of How Research Hypotheses Were Supported...165 Table 7.1: Summary of the Results of Hypotheses Tests......................167 Table 7.2: Most Influential Factors Affecting Megaproject Managers’ Decision Making...................................................................................................172 Table 7.3: Most influential factors impacting long-term strategic value .....1??

Table 7.4: Summary of Standardised Regression Coefficients (beta)............185

`

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Abstract The performance history of megaprojects has been inconsistent. More than half of industry executives in a survey by Wilczynski et al. (2006) were dissatisfied with their companies’ overall project performance. The primary reasons for their dissatisfaction were the failure to realise anticipated benefits and, in some cases a loss of business opportunities expected after the project’s execution. Most of these executives were from the oil and gas industry. Various studies have established that the root cause of almost all project failures can be traced back to human error or poor judgment (Wilson 1998; Johnson 2006; Rombout and Wise 2007), and poor judgment in turn can often be traced back to the way in which decisions were made (Hammond et al. 1998). A literature survey found that, while decision making has been widely studied within the field of organisational management, there have been few studies of decision making within the oil and gas industry, especially on projects.. This study investigated the relationship between project manager decision making and strategic success of megaprojects. The research methodology applied was largely a post-positivist (quantitative) approach that included a worldwide survey and parametric analysis. The study determined that, in addition to having influence or control over substantial corporate resources, project managers are definitely major influencers of corporate strategy, especially through their decisions, as suggested by Brower & Gilbert (2007). It also confirmed that decision making by project managers takes place primarily as a risk-based process, in accordance with the prospect theory (Kahnemann 1992; Tversky and Kahnemann 2004). Clearly, the impact of decisions that project managers make while executing a megaproject last far beyond the project execution life cycle (short-term) to affect the longer-term value of the asset delivered by the project. Of all the factors identified as critical to decision making, those that relate to stakeholder Skema Business School

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relationship management, especially external stakeholders, stand out as the most influential. This underscores the need for project managers to promote a win-win perspective on stakeholder management. It was found that the extent to which project managers feel persuaded that they have adequate resources does influence their decisions, hence the outcomes. When viewing megaprojects in light of long-term value, this study demonstrated that the value added is insignificant when project managers strongly canvass for authority and allow timing to be a predominant driver of decisions. Also an observed heavy demand for project efficiency (i.e., low cost and timely completion) by most corporate managers was found to affect project managers’ commitment to project Health Safety and Environment (HSE) management. Project managers should do more to build their competence in making value-creating decisions. The study recommends possible actions that project managers, project sponsors, and corporate managers in the oil and gas industry could take to maximize the long-term value of their megaproject investments.

KEY WORDS: Megaprojects, Program Management, Decision Making, Strategic Value

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CHAPTER ONE Introduction This introductory chapter describes the background and motivation for the present research. The research questions and an overview of the methodological approach and outcomes are also presented. The last section of the chapter outlines the remainder of the thesis. The primary purpose of this research was to investigate the extent to which project manager decisions underpin the strategic success of megaprojects in the oil and gas industry. Another important goal was to detect which factors can affect project managers’ decisions in ways that may compromise strategic objectives. Consequently, the study sought to identify decision traps and provide a basis for understanding how the decision framework applied by these project managers on oil and gas megaprojects could be better structured to enable a more tangible realisation of strategic value. The unit of analysis is the decision-making framework of the megaproject manager.

1.1 Background and Research Questions Setting the Scene: World Energy Needs and Megaprojects in the Oil and Gas Industry The International Energy Agency (IEA) estimates that about US $20 trillion will be needed to pursue forecasted world energy growth through 2030 (international energy agency 2007). Production of oil and gas is expected to grow by 40% and 66%, respectively, over this period, despite the increasing share of energy obtained from non-fossil sources. In response to the opportunity offered by this scenario, many large oil and gas companies are making major capital expenditures, in some cases investing up to 90% of their annual profits (Royal Dutch Shell 2008; Exxon Mobil 2008; BP 2007; Chevron 2008). For example, in 2005 alone five major oil and gas companies (BP, Shell, Skema Business School

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Chevron, PetroChina, and Petrobras) invested a collective sum of $71.7 billion as capital expenditure, much of which was on megaprojects (EIG 2007) valued at over $3 billion each (McKenna et al. 2006). In 2007 Shell on its own invested about $25 billion (Royal Dutch Shell, 2007) in capital projects, and today there exist individual projects costing as much as US $20 billion. A capital expenditure profile for the oil and gas industry over the years 2005-2010 is shown in Figure 1.1.

Figure 1.1: Capital Expenditure Profile for the Oil and Gas Industry Legend: NOC = National Oil Company; E&P = Exploration and Production

The values of some of these megaprojects represent a substantial percentage of the entire annual budget of some of the smaller nations where these hydrocarbon resources are located. Thus the financial and sociopolitical stakes of these megaprojects are so huge that they can both endanger the survival of corporations and threaten the economic stability of the countries involved (Miller & Lessard 2000). To achieve satisfactory benefits from these huge capital projects, oil and gas companies need a long-term presence in the investment locations; they need the cooperation of the host governments and communities where the resources are located, but they cannot make substantial Skema Business School

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funding commitments as the host countries would like before recouping some of their investment. Incidentally, the later is a tension that international oil and gas companies in particular grapple with when they invest in most developing regions. Megaprojects are programs (Miller & Lessard, 2000; Strupples, 2000; Jaafari, 2004) that integrate strategically aligned and logistically combinable projects under a single management responsibility. Hence megaprojects have been approached in this study from a program management paradigm, with a focus on benefit management. Megaproject managers are individually responsible for corporate resources worth as much as $20 billion. Along with this

enormous

investment,

the

characteristically

long-term

nature

of

megaprojects (typically lasting 5 to 12 years) makes them vulnerable to both uncertainty and ambiguity, underscoring the need for application of a strategic decision management paradigm in their management rather than the tactical short-term efficiency approach of traditional project execution (Thiry 2004; PMI 2008). Indeed, other scholars have already noted parallels between aspects of corporate and megaproject management (Jaafri, 2004). Megaproject performance in the oil and gas industry has been a mixed bag, with several studies showing little improvement over the last 10 to 15 years (Merrow 1988, 2003; Fayek et al. 2006). Reported underperformances have included disappointing levels of benefit in financial return, operational performance, social acceptability, and regulatory compatibility (Miller & Lessard 2000; Flyvbjerg 2003), in some cases jeopardising current and future business opportunities (Gizitdinov & Kim 2008). These reports suggest that up to 40% of megaprojects underperform with regard to strategic benefits, and that costs can balloon to as much as nearly double the original estimates. Although megaprojects are unavoidably associated with companies’ long-term strategies, most of them are also expected to achieve short-term

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goals, in terms of remaining within budget and on schedule (Merrow 1988, 2003; Miller & Lessard 2000). This study confirmed those expectations to be the case within the oil and gas industry. The distractions generated by this burden for efficiency (short-term benefits) have the potential to jeopardise realisation of long-term strategic objectives (i.e., corporate effectiveness), the principal reason for sanctioning megaprojects in the first place (Thomas et al. 1993). To appreciate the foregoing phenomenon and understand how long-term value can be put at risk, the first research question is proposed:

Q1: What are the predominant long-term strategic objectives of oil and gas companies conducting megaprojects, and can we determine whether the companies’ approach to megaproject execution will enable realisation of these objectives? This question was addressed both through the literature review (see section 2.3 below) and by analysing the results of the worldwide survey conducted in support of the research (see chapter 5).

Decision Making: Are Megaprojects Challenged? Project management involves strategic decisions of great significance (Virine & Trumper 2007; Turner 2009). Various studies have established that the root cause of almost all project failure can be traced back to human error or poor judgement (Wilson 1998; Johnson 2006; Rombout and Wise 2007), and that poor judgment in turn can often be traced back to the way in which decisions were made (Hammond et al. 1998). It can be inferred, therefore, that the cost of poor project decisions can be very high. To take an example from another sector (the extremely high-tech and safety-conscious US medical industry): annually, 44,000 to 98,000 deaths occur due to medical decision errors, which is approximately 1.8 to 4.0 percent of the 2.4 million deaths that the Centres for Disease Control (CDC) reported in 1999 (Kohn, Corrigan, & Donaldson 2000). Skema Business School

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Poor decision making can also be fatal to business ventures, but the literature contains very little analysis of the impact of project decisions on oil and gas projects. Megaprojects are different from the better-known traditional projects that are typically much smaller in scope and complexity. Megaprojects are inherently complex and contain a high level of potential for systemic reaction (Williams 2004; Girmscheid & Brochmann 2008), that can put desired outcomes at risk. Some earlier works have found that, when management action places the top priority on efficiency of performance (e.g., staying on schedule), decision quality can be impaired (Chu & Spires 2001). Given that megaprojects are of major strategic importance and are underpinned by decisions, one would expect megaproject managers to have keenly developed decision-making capacities. Unfortunately this is not the case, as many project managers presume that their decision-making capabilities are above average (Massey et al. 2006) and, consequently, care little about improving the quality of their decisions (Capen 1976; Rose 1987; Goodwin & Wright 2004; Virine & Trumper 2008). The combination of poor decisions and project managers’ negative attitude toward improving their decision skills (Wilson 1998; Johnson 2006; Rombout and Wise 2007) provides a credible explanation for the very slow pace of improvement in project performance as observed by Merrow (1988, 2003). This study contributes further evidence regarding the connection between project manager decisions and ultimate project performance. Organisational behaviour theory suggests that the ability of a person within an organisation to influence its strategic direction is a function of the size of resource allocation that he or she is able to control or influence (Brower & Gilbert 2007), and not necessarily his or her seniority level within the organisation. As noted earlier, senior project managers have great influence on corporate resource allocation, and thus their ability to significantly influence Skema Business School

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corporate strategic direction should not be underestimated. One might tend to conclude that the decision-making process of these megaproject managers can be leveraged in support of realising long-term strategic objectives. These discussions informed a second research question:

Q2: How strong is the relationship between the project manager’s decision making and the strategic value realised from megaprojects? Strategic value is here defined as a measure of the extent to which a megaproject contributes to realisation of corporate strategic objectives. Company reports show that the customer bases and therefore the strategic objectives of most oil and gas companies are similar; hence the study was focused on strategic objectives that are common to these companies. Compared to the execution phase, a greater number of academic studies have examined the initiation and front-end engineering phases of megaprojects, including decision making related to these phases (e.g., Flyvberg 2003; Olsson 2006; Olsson & Samset 2006). The present research was therefore focused on decision management during the execution phase, which includes such project activities as detailed design, construction, commissioning, and startup activities.

Conceptual Approach This study has drawn from theories in organisational behaviour, decision making, and program management as a basis to understand megaproject managers’ decision-making framework and establish the strength of the relationship between this decision making and the realisation of corporate strategic objectives. A literature survey found that, while applications of these theories have been extensively studied within the context of corporate organisational behaviour, less application has been made to the world of project Skema Business School

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management. Some highlights of the literature review conducted in support of the conceptual framework of this research follow: 1) Making decisions can be considered the most important job of the megaproject manager, just as for any executive in the business organisation (Hammond et al. 1998). 2) There

is

a

positive

correlation

between

business/organisational

performance and good decision-making practice (Thomas et al. 1993; Lamb et al. 1999; Jinkman et al. 2000; Simpson et al. 2000; Begg et al. 2001; Mackie et al. 2007); in our context the same correlation can be made between good decision making and project performance. 3) Strategy is realised through consistency of decision making and action (Mintzberg 1978; Neely et al. 1994). 4) Quantity and quality of available information are correlated to decisionmaking quality (Thomas et al. 1993). 5) The failure of most megaprojects is also attributable to pressures from socioeconomic and socio-political factors surrounding them (Miller & Lessard, 2000). These pressures could influence the project manager psychologically and ultimately affect decision-making. 6) Decision outcomes in most real-world situations depend not only on the quality of the decision process, but also on the level of implementation effort, chance (Russo & Schoemaker 2002; Mackie et al. 2007) and contextual factors related to the decision makers, the business organization, and the project opportunity. Hence decision quality cannot be judged solely by outcomes. 7) Expectations of individuals based on their stereotypes or emotions can colour how they experience their environment, and thus affect their decision making and the resulting outcomes (Ariely 2008).

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8) The amount of emphasis that decision makers give to the different types of information available, the amount of training they have received, and their level of experience can all affect their interpretation of challenging situations, resulting in differing decisions (Dutton & Duncan 1987). 9) Similarly, the label or meaning that a project executive gives to a challenge (e.g., whether it is viewed as a threat or an opportunity, a potential gain or a likely loss) affects the decisions that the project executive eventually makes in response to the challenge (Thomas et al. 1993; Fredrickson 1985; Thomas & McDaniel 1990). 10) The best indicators of decision quality are the quality of the rule(s) or process(es) guiding commitment to decisions (Russo & Schoemaker 2002; Mackie et al. 2007) and how consistently they are applied (Neely et al. 1994; Mintzberg 1978). 11) Poor knowledge of the macro-environment (or of changes in the macroenvironment)

by

decision

makers,

sometimes

referred

to

as

“incognisance” (Spetzler et al. 2005), is a large contributor to megaproject underperformance (Merrow 1988). To study the impact of decision making on the achievement of strategic objectives, we must examine the anatomy of decision making and management. The work of Turner (2009), Russo & Schoemaker (2002), and Thomas et al. (1993) indicate that high-level elements of decision management anatomy include the process of problem identification and arriving at solution alternatives; the influence of decision criteria; and the implementation of the chosen alternative. Evaluating the impact of project manager decisions on achievement of strategic objectives entails studying the influence of each of these aspects of decision making. The third research question arises from this understanding:

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Q3: Which decision-making, decision practice, or implementation factors most significantly impact achievement of strategic objectives during the execution of oil and gas megaprojects? What are the sources of these factors that affect project decisions and outcomes?

One major intent of this study is to develop decision-making recommendations that could benefit practitioners. The fourth research question emphasizes this effort to make study outcomes practically useful to megaproject managers:

Q4: How could existing frameworks for project manager decision making on oil and gas megaprojects be enhanced so as to achieve better strategic outcomes?

1.2 Research Perspective and Approach A qualitative (semi-structured interview) approach was used early in the research to define the study direction and scope so as to make the research more relevant to practitioners. This early qualitative venture resulted in a change of the primary research theme from performance management to decision making. For the main empirical study of project manager decision making however, a post-positivist (quantitative) approach was applied, including a worldwide survey and parametric analysis testing all hypotheses. The research was approached from the viewpoint of the megaproject manager, focusing on long-term strategic value as the dependent variable. The empirical enquiries were limited to the execution phase of megaproject delivery process, which is where projects managers actually take the lead (i.e., taking Skema Business School

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over from the front-end development managers).

Value is envisioned and

planned into the project at the front-end phase, but execution is when the value develops from vision to reality. The unit of analysis is the decision-making framework of the megaproject manager. As determining direct causality between decision-making factors and strategic success of megaprojects would be very difficult methodologically, this study instead seeks to establish relationships between decision making and project success by investigating the statistical significance and strength of correlations.

1.3 Summary of Research Outcomes The research confirmed the existence of a significant relationship between a project manager’s decision making and corporate Long-term Strategic value. It also reinforced the view of Brower & Gilbert (2007), in their work on organisational management theory, that influencers and controllers of resource allocation—in this case, project managers—have a major influence on corporate strategic direction. This observed decision making/value relationship offers potential applications that are often not optimally exploited. The study identified some important attributes and behaviours that would make project managers better equipped for their decision responsibilities. At least two-thirds of all the decision-making factors studied that significantly affected Long-term Strategic value had to do with cultivating relationships. This finding underscores the fundamental importance of relationship

management

within

the

overall

scheme

of

megaproject

management. The need for good quality and quantity of information feed was apparent; however, the type of information most significant to success is information focused on the external environment and associated stakeholders. A heavy demand for project efficiency (i.e., remaining within budget and on schedule) by corporate managers was found to affect project managers’ Skema Business School

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commitment to HSE. The study recommends actions that corporate managers and project sponsors in the oil and gas industry could take to address this concern. It is clear that project managers should do more to build their competence in making value-creating decisions. The presumption that project managers have above-average decision-making capabilities is documentably false. The study provides some guidance on creating a decision-making framework tailored towards the peculiarities of each megaproject. This research reinforces the prospect theory (Kahnemann 1992; Tversky and Kahnemann 2004) by confirming that decision making fundamentally takes place as a riskbased process for the decision-maker. Details of analysis, outcomes, and discussion are presented in chapters 4 and 5.

1.4 Thesis Layout Chapter 2 of this thesis presents the underlying theoretical framework, which is rooted in strategy, organisational performance, program management, and decision-making theories. Key aspects of these theories are interlinked with the practice of megaproject management. Section 2.3 includes seven hypotheses related to the research questions earlier presented in this introductory chapter. Chapter 3 discusses the research approach, applied methodology, and data analysis techniques in detail. Chapter 4 presents outcomes of the semi-structured interviews (quantitative) done at the very early stages of the research to help define scope and refine direction. Chapter 5 then presents an overview of the raw data collected from the survey, and how they were prepared for further analysis. It discusses the extraction of underlying factors within each construct and the reliability of scales, including the construct and content validity of the data.

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Chapter 6 then describes the tests conducted to investigate each hypothesis, the outcomes of analysis, and some discussion of the results. Practical implications of the research for megaproject managers are introduced here. Finally, chapter 7 summarises study outcomes with relation to the research questions and associated hypotheses. It discusses both practical and theoretical implications of the research, including its limitations and opportunities to extend further the knowledge gained from this research.

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CHAPTER TWO Literature Review This chapter summarises the literature review that supports the present study. The literature review focused on theories relevant to important aspects of megaprojects, including strategy, organisation performance, and decision making. (Note that hereafter, for simplicity, “project” will be used as a synonym for “megaproject” unless indicated otherwise.)

2.1 Understanding the Nature of Oil and Gas Megaprojects Introduction As Cicmil & Hodgson (2006) explained, project management emerged as a formal practice in the 1940s through various major projects, including some in the US oil and chemical industries. The main industries that championed its development, however, were the defence, aeronautics and space sectors (Harrison 1981). Projects are the means for introducing unique deliverables with the intent to change the dynamics of the environment into which the deliverable is introduced. The Project Management Institute body of knowledge (PMBok) (2008) presents projects as a means by which organisations achieve strategic plans. Project management is therefore about managing people and non-human resources to deliver the desired change or results (Turner 2009). The change to be addressed by a project should be aligned with the strategy of its sponsoring organization(s), which could be to address a desired shift in the organisation’s market position, improve operational efficiency, or enhance product or service value for customers.

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The Project Management Institute (PMI), the most widespread professional body of project practitioners, views project management as “the application of knowledge, skills, tools and techniques to project activities to meet project requirements” (PMBok, 2008). This may appear to be a narrower definition than that of Turner (2009), as it tends to emphasise the presence of competence and tools to determine if project management is actually taking place. In actuality, there are many occasions when people lacking the knowledge, skills, and tools that lead to good project management are nevertheless placed in charge of projects. Each project has its own differentiating characteristics, such as complexity, duration, cost, resource requirements, quality demands, and the types and interests of stakeholders to be addressed. The way in which these features combine within a project is important to determining a management approach for the project—for example, whether to apply traditional project management or program management principles. As the name depicts, the traditional project management approach is used for small or medium-sized projects that are more focused on tactical or operational requirements. In this type of project, which Santana (1990) calls “Normal,” one engineering discipline usually dominates, a single contractor is involved, and the project is performed in a short time frame. The program management approach, in contrast, is applied to very large projects initiated for strategic purposes. As a key strategic response by the oil and gas industry to steeper world demand for energy and to shareholder demands for better long-term value, asset deliveries are now packaged as large projects (Miller & Lessard 2000) or, as they are usually called, megaprojects (Strupples 2000; Jaafari 2004). For example, most of the largest multinational oil and gas companies have being reinvesting much of their annual profits (up to 90% in some cases) as capital expenditures in recent years (Royal Dutch Shell 2008; Exxon Mobil 2008; BP Skema Business School

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2007; Chevron 2008). Most of this capital expenditure is on the delivery or acquisition of large oil and gas assets. As noted by Miller & Lessard (2000) and Grun (2004), the literature contains relatively little research specifically on megaprojects or the application of conceptual theories to megaprojects.

What Are Megaprojects? Various organisations and professional bodies have proposed different methods to categorise projects, such as by size or complexity (PMI 2008; Crawford et al. 2002). Megaprojects have been defined as projects of significant cost (e.g. costing US $1 billion or more) that attract a high level of public attention or political interest. The public and political interests result because megaprojects

exert

substantial

direct

and

indirect

impacts

on

local

communities, on the environment, and even on national budgets (Miller & Lessard 2000; Flyvberg 2003; Fiori & Kovaka 2006; Flyveberg 2007; ECC 2007). Reinhards (1989) uses a resource requirement point of view, defining megaprojects as “any project, which requires 2 million engineering man-hours and 15 million field man-hours.” Screbowski (2004) observed that the average implementation time for oil and gas megaprojects (from resource discovery to first production of oil or gas) is about 6 years. Combining the definitions by Screbowski and Reinhards, a typical oil and gas megaproject would put about 4,500 men to work every day for six years—a huge undertaking indeed! Compared to traditional and tactical projects, megaprojects are considerably more vast in scope, complexity of project activities and milestones, and expectations of strategic benefits; they also can have very cumbersome owner or stakeholder structures (Grun, 2004).

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Oil and gas megaprojects in particular are typically characterized by high complexity and uncertainty, with various technical, social, environmental, and commercial issues in play simultaneously. More than 90% of these projects involve the exploration, production, or refining of hydrocarbons. Many oil and gas megaprojects are now executed in very difficult environments; in most cases the locations lack adequate basic infrastructure. By nature of their surface or subsurface impacts and their land taking requirements, oil and gas megaprojects also have substantial direct and indirect impacts on the environment. From an environmentalist perspective, megaprojects have been defined as projects that transform landscapes rapidly, intentionally, and profoundly in very visible ways, requiring coordinated application of capital and state power (Gellert & Lynch 2003). The stakes involving megaprojects are so huge that they can endanger the survival of corporations and threaten the economic stability of the countries involved (Miller & Lessard 2000; Gellert & Lynch 2003). Figure 2.1 illustrates the typical project process followed by most oil and gas organisations. The process is decision-based, but heavily biased towards the front end of projects where the most value could be created for the project. It shows four decision stage-gates where the project delivery effort and challenges are reviewed. Each review includes a “go” or “no-go” decision whether to advance the project to the next stage. The execution phase, the focus of this study, is the point where project managers take over leadership of the project. Figure 2.1: The Opportunity and Project Realisation Process (adapted from Webb 2003 and Walkup & Ligon 2006).

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Though more decision-driven, the opportunity and project realisation process is in alignment with the traditional project phasing as suggested in most project management literature (e.g., PMBok 2008; APMBok 2006). A more detailed description of the process and associated deliverables appears in Table 2.1. Cooke-Davies (2004), however, noted that, although process is of fundamental importance to project execution, successful implementation of project management processes does not necessarily translate to a successful project.

Table 2.1: Description of the opportunity and project realisation phases (with some adaptation from Webb 2003; Walkup & Ligon 2006)

Phase (and Objective)

Key Deliverables

Identify Generate ideas and verify project alignment with business strategy. Establish potential value and decide whether to resource. Assess Identify and evaluate potential development options and their outcomes, in order to understand or determine the potential value of an opportunity and its alignment with the business strategy.

• Project charter describing value potential of the opportunity, and how it could be framed in context of business strategy, including regret risks and stakeholder mapping

Select Evaluate and select the preferred project development option, and make clear why the other options are less favourable.

• A field development plan (definition of a preferred option with budget estimate) • Preliminary basis for design • Updated plan for next and subsequent phases including funding and resource requirements

Define Finalise project scope, cost, schedule and get project funded.

• Final basis for design • Project specifications – completed engineering definition, estimate for funding appropriation, execution plan, and schedule. • Business case • Updated plan for next and subsequent phases including funding and resource requirements • Funding request or final investment decision

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and

• A valuation report documenting known data and identifying a range of options aligned with business strategy, ranked by cost and uncertainty • Plan for next and subsequent phases including funding, resource requirements, and value assurance requirements

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Phase (and Objective)

Key Deliverables

Execute Build an operating asset consistent with scope, cost, and schedule.

• Completed asset, commissioned and ready for handover

Operate Start-up, operate, and evaluate asset to ensure performance to specifications and maximum return to shareholders.

• Post-investment review • Beneficial operation of the asset

The Megaproject Execution Phase The megaproject execution phase is initiated just after the fourth stage gate shown in Figure 2.1, at the point of a final investment decision to fund the project, and concludes with the successful handover of the end product to the client organization, including the contractual closeout of the project, documentation of lessons learned, and archiving of the project documents. Due to the long execution time frame and the huge dynamics present in their socioeconomic and political environment, megaprojects are often captive to strong change influences (Jaafari and Schub 1990; Morris and Hough 1987). The project experiences its greatest vulnerability to uncertainties, which are largely unpredictable events during the execution phase. On the other hand, most oil and gas organisations tend to have elaborate structures for managing risks, which are the known probable events that could jeopardise corporate and project objectives should they occur. So the project manager must manage alignment of project and corporate objectives and implement appropriate changes to project strategy. Jaafari (2004) suggested the following strategies to realise this desired alignment: •

Applying a decision-making process or method agreed upon by the project team and decision board;

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Proactiveness – with focus on problem anticipation and resolution, and with an effective risk management system in place;



Employing an integrated information management system to provide feedback on project performance and the internal and external environments, thereby enabling effective assessment of progress.

The Program Management Paradigm and Megaprojects Programs are defined as a collection of change actions (projects and operational activities) purposefully grouped together to realize strategic and/or tactical benefits (Murray-Webster and Thiry 2000; Turner 2009). Program management, therefore, is the coordinated planning, management, and execution of multiple, related projects directed toward the same strategic, business, or organizational objectives, so that together they generate benefits beyond those that would have resulted from their separate execution (PMI 2008; APM 2006, Gartner 2009). This description precisely defines the basic nature of oil and gas megaprojects, which are essentially programs that integrate strategically aligned, commercial viable, and logistically combinable projects under a single management umbrella. Program management is directed at strategic effectiveness and tactical efficiency, as it provides a means to bridge the gap between project delivery and organisational strategy (Lycett et al. 2004). It is a management process that addresses both decision making and decision implementation (Thiry 2004). Though characterized by high cost and uncertainty, megaprojects also promise attractive long-term financial and other outcomes (Buckley 1998; Miller & Lessard 2000; Flyvberg 2003). Program management should enable strategy adaptation. In a program management context, strategy is the organization’s response to external or internal pressures to change (Jaafari 2004) due to

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influences

of

uncertainty

and

complexities

that

could

threaten

its

competitiveness (Kaplan and Norton 1992; Partington 2000). Compared to traditional projects, which are much smaller in scope, megaprojects are significantly less predictable in both time and scope (Cooke-Davies, 2002), demand substantial irreversible commitments, have high probabilities of failure, and often have a skewed reward structure when successful (Miller and Lessard 2000) making their management a daunting challenge. Megaproject managers, however, could easily be drawn into the traditional mode of project delivery because most of them have more experience and training at this level. The traditional project management paradigm is based on a performance culture that has proven effective mainly in delivering short-term, tactical-level deliverables (Thiry 2004). In this mode, the focus is more on project efficiency issues, especially time and cost (Shenhar & Dvir 2007) rather than on how to attain the best overall project results (Halman and Braks 1999; Asrilhant et al. 2006). Approaching megaprojects in this way negates the longer-term strategic views that inform the initiation of these projects in the first place. From inception to initial revenue generation, megaprojects typically take 5 to 12 years (Reinhards 1989; Screbowski 2004), while their operational life span would typically be between 20 to 25 years. The long-term nature of megaproject execution make these projects very vulnerable to uncertainties and ambiguities, underscoring the need for a strategic decision-making paradigm in their management, rather than the tactical short-term efficiency view of traditional project execution (Thiry 2004, PMI 2008). To develop an effective decisionmaking paradigm, it is necessary to obtain and process the right information so as to reduce ambiguity (Thiry 2004). Applying the program management paradigm to megaprojects can help to address this need, as this broader paradigm entails benefit management, stakeholder management, and ensuring Skema Business School

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effective governance (Jaafari 2004; PMI 2006). Megaproject managers are essentially program managers; as such, they are responsible for guiding the megaproject through the ambiguities of strategy and its emergence, providing leadership, and managing socio-cultural and political issues involving other parts of the organisation (Thiry 2004) and external stakeholders.

Governance of Megaprojects The management of megaprojects is especially challenging because of their complex governance structure. Pinto (2006) defined governance as the use of systems, structures of authority and processes to allocate resources and coordinate or control activity in a project. He suggested that an important means of governance is a top-down approach that involves senior management oversight. Inadequate governance on megaprojects exposes them to high probability of failure (Miller & Hobbs 2005). Aside this, Miller & Hobbs (2005) also claimed that the complexity of megaprojects demands the application of a governance regime different from that used on traditional projects. Governance structures must be flexible enough to adapt to the varying dynamics of the project and business environment in view of the protracted execution time often required. Governance regimes for megaprojects are time-dependent and selforganizing. Ownership

and

funding

are

typically

multi-organisational,

and

the

organisations in most cases will have differing ambitions for the project. Megaproject managers and sponsors therefore face the problem of satisfying the various interests of project owners, ruling out the possibility of delivering the projects solely under the governance influence of a single sponsor (Grun 2004). Invariably, therefore, megaprojects tend to exercise varying levels of independence from all their owner-organisations to establish its own unique organisational identity. Megaprojects can rarely be treated within the context of Skema Business School

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a single organization (Miller Hobb 2005). Grun (2004) called the megaproject a “project-specific company” shared by all the project owners. Jessop (1997) presented governance as a complex process that aims at steering multiple organisations, which are autonomous but linked in projects through various forms of reciprocal interdependencies. Joint governance, as required on megaprojects, should include sharing of risks across the various owner (and contractor) organisations to enable greater adaptability of the project to environmental dynamics and more objective sharing of returns on its investment (Miller & Lessard 2000). Governance structure as largely practiced on oil and gas megaprojects is in three layers, with the joint venture (investing) partners and executive board of the implementing company (jointly providing strategic direction) at the top, supported by the sponsor or decision executive (the accountable executive representing the joint venture partners on the project) and the project director who directly manages the project team, including the project’s day-to-day implementation. A typical governance structure is illustrated in Figure 2.2. As some megaproject managers confirmed in interviews, motivations of the investing partners are not always complementary, making governance and funding of the projects more challenging. McKenna et al. (2006) noted that governance and managerial complexity of oil and gas projects could become a factor impeding the path to energy independence for much of the world.

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Figure 2.2: Typical Megaproject Organisational Setup

Joint Venture Partners

Corporate Executive & Board Project Sponsor or Decision Executive

Decision Board

(Mega)Project Director/ Opportunity Manager Support Organisation

Operations Readiness Manager

Project Manager

(HR, Fin., External Affairs, etc)

(Project 1)

Project Manager (Project 2)

Project Team 1

Project Team 2

Project Manager (Project 3)

Project Team 3

Project Services Manager

Project Services Team

Contractors and Sub-contractors Figure 2.3: Typical Megaproject Organisational Set-up

The Challenge of Megaproject Execution More than half of the industry executives surveyed by Wilczynski et al. (2006) were dissatisfied with their company’s overall project performance, and most of these executives were from the oil and gas industry. Over 40% of their projects were plagued with costly budget and schedule overruns. Both producers and contractors complained about the same issues: insufficient collaboration on project planning, inadequately robust risk management, performance risk, and human resource difficulties, all of which lead to wrong and costly judgments. They also observed that many traditional ways of doing business do not apply to these projects of enormous size and complexity.

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Along with the governance issues frequently posed by multiple (joint venture) sponsorship, challenges also arise from the unavoidable use of multiple contractors and execution across multiple locations. Oil and gas megaproject teams are characteristically multicultural and widely dispersed geographically, often resulting in communication difficulties and cross-cultural conflicts that can in turn lead to ineffective decision making by project leadership. As has been noted, megaprojects, just like smaller initiatives, are executed under high schedule and cost pressure (Merrow 1988, 2003; Miller & Lessard 2000). It has also been established that time pressure impairs decision quality (Chu & Spires, 2001). The early stages of this study included interviews with seven very experienced (average of 20 years) project managers who were asked about the most nagging strategic issues faced by megaprojects in the execution phase. Their responses were also classified applying the PEST risk classification framework (OGC, 2006), in which PEST stands for political (including legal and regulatory issues), economic, social, and technological categories. Issues identified by the interviewees included contracting and procurement management (E); government relations management (P/S), in that the decision mechanisms of host governments are often unclear (see McKenna et al. 2006) and can lead to significant complications; host community relations management (P/S); joint venture interface management (E/P); health, safety, security, and environmental matters (S/T); multi-location management of fabrication and facilities integration (P/S/T); local content implementation (S); project governance (P/S); managing the project team

(and team members’

individual aspirations and job satisfaction issues) so as to maintain cohesion (P/S); and multi-cultural leadership within the project (P/S). These issues align closely with what Miller & Lessard (2000) identified as the top failure factors in large projects. Most of the challenges are political (P) and social (S), and many Skema Business School

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of them involve relations with stakeholders who have the potential to affect decision making. Technical risks seem to be less challenging. A study of strategic projects in the UK oil and gas sector (Asrilhant et al. 2007) revealed that project managers paid the least attention to certain factors that were among those most significant to project success. For example, managers showed much more concern about financial, technological, and environmental indicators, whereas understanding the external environment (i.e., sociopolitical and economic issues) and its impact on the project, a more significant factor affecting project success, received less attention. Note further that sociopolitical and economic issues are frequently even more pressing in less stable political environments (where most of the currently active oil and gas megaprojects are located) than in the UK.

Overall, effective governance

remains a major challenge in many megaprojects, while factors related to technical content and project economics pose fewer difficulties (Miller & Lessard 2000).

History of Megaproject Performance Megaproject performance in the oil and gas industry has seen little improvement over the last 20 years (Merrow 1988, 2003; Fayek et al. 2006), with many executives in the industry remaining dissatisfied with their companies’ overall

project

performance

(Wilczynski

et

al.

2006).

Reported

underperformance includes inadequate benefits in numerous areas, such as financial gain,, facility performance or integrity, social acceptability, and environmental compatibility, (Merrow 1988, 2003; Miller & Lessard 2000; Grun 2004; Fayek et al. 2006); in some cases, current and future business opportunities

are

compromised

as

a

result

(Bloomberg.com,

2008).

Megaprojects are not only giant in size, but also giant in risks and in the potential for corporate disaster (Grun 2004), as illustrated by cases like the Skema Business School

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Eurotunnel railway and the 1994 Lillehammer (Norway) and 2000 Sydney Olympic Games. Of course, there remains an irresistible pull to take on megaprojects of compelling social and economic value despite the enormous costs involved in undertaking this event. A fair assessment of megaproject performance requires some type of balanced, objective scorecard. Table 2.2 highlights some success and failure factors distilled from the surprisingly limited literature on megaproject execution.

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Table 2.2: Some success and failure factors related to megaproject execution

SUCCESS FACTORS

FAILURE FACTORS

• Clearly defined project – including implementation requirements

strategy, and

regulatory

facilities

to

be

its

own

project

environment; e.g., its execution overwhelms the local infrastructure or natural environment • Resourcing the project creates labor shortages,

• Partnering

system interests

that of

recognises stakeholders,

provides for disciplined communication, and measures team effectiveness

• Shenhar & Reiner 1996 • Higgs 2001 • Merrow 2003

stance

2006

depleting the available supply of capable

• Palmer & Mukherjee 2006

contractors

• Anderson, Douglass, & Kaub

• Political opportunism created by the volume of

2006 • Flyvberg 2003, 2007

money suddenly available

• Adoption of a relatively risk-averse

• Once derailed, megaprojects are very difficult

• Lawrence & Scalan 2007

to put right

• Project schedules driven by data rather than

destabilizes

• Dinsmore & Cooke-Davies

delivered.

common

• Project

REFERENCES

by

arbitrary

or

politically

• Failure to

define scope sufficiently

for

contractors

established end dates • Success framework defined for project is strongly linked with the long-term strategic goals of the enterprise. E.g. performance management system is based on a robust approach like the

• Underestimation of the impact of human factors • Ineffective executive sponsor • Inadequate communication, especially with stakeholders outside the project team

Balanced Scorecard System or the • Inappropriate

Diamond Approach. • Execution by an integrated team • Generously

staffed

personnel;

resources

with

development

resource planning

inadequate

performance

project should remain in view of project owner

optimised

in

support of corporate strategy • Improved

or

monitoring metrics. Business case for the

and

career

for

teams;

mentoring for younger professionals • Use of local contractors and indigenous employees • Good communication channels between

manager. • Inappropriate project organisational structure • Insufficient empowerment of knowledgeable people and project leaders • Reluctance of project personnel to change • Leaving implementation of best practices and lessons learned in the hands of project team members, with inadequate supervision or monitoring

project team, senior management and suppliers; including sharing and aligning of goals across board.

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• Poor matching and application of processes and tools

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A study of some Canadian megaprojects (most of which were oil or gas projects) indicated that all were at least 100% over-budget and had delays of 25% to 200% relative to the original schedule (Fayek et al. 2006). A crossindustry cost performance benchmark of megaprojects found overruns of 30% to 700% (Miller & Lessard 2000). It appears that, during the execution phase, attention is most often shifted from strategic issues to the simultaneous chasing of the triple-constraint targets: time, cost, and quality. This shift may be catalysed by the short-term efficiency views of most stakeholders and the heavily technical mindset of many project managers. The media also tend to focus much of their reporting on whether megaprojects are meeting time and cost goals. These priorities may partly explain Miller and Lessard’s (2000) observation that, despite reported overruns, most megaprojects tend to display more concern for efficiency (focus on short-term cost, time, and quality) than for effectiveness (focus on longer-term project and business objectives). While cost and schedule overruns should certainly be taken seriously, they are often symptomatic of more fundamental project issues that present a greater threat of long-term strategic value erosion. Behavioural factors of project managers, especially the conservative reluctance of project managers to adopt new ways to manage projects, have also been found to be a key factor in large project failures (Capen 1976; Rose 1987; Goodwin & Wright 2004; Virine & Trumper 2008; Lawrence & Scalan 2007). This concern is quite relevant to oil and gas megaprojects, as they are typically managed by older and more experienced project managers who have generally gained their prior experience on smaller, traditional projects and are prone to adopt conservative management characteristics.

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Stakeholder Constellation on Oil and Gas Megaprojects Stakeholder (i.e., customer and shareholder) satisfaction is a critical key to enterprise performance (Cokins 2005; Neely, 1999). Customer satisfaction was found to cause increases of up to 11.5% in the net present value accruable to a typical Swedish enterprise (Anderson et al. 1994, quoted by Neely 1999). Megaproject stakeholders are numerous, and so is the diversity of objectives that need to be managed. The most significant turbulences in oil and gas megaprojects are rooted in stakeholder management issues (Miller & Lessard 2000). Main stakeholders in the oil and gas industry include the major oil companies and national governments as resource holders, local communities as hosts of the physical project assets, non-governmental organisations (NGO) interested

in

assuring

sociocultural

and

environmental

sustainability,

employees (including the project team) seeking personal well-being, and shareholders demanding acceptable profit levels. With such a constellation of stakeholders, it is inevitable that the project will attract high socioeconomic and political interest, in addition to industrial and public attention. Clarity as to the various stakeholders’ strategic goals and how they are linked to the project is therefore essential. Also megaproject managers must be aware of strategic risks to which the project may be exposed. Broadly speaking and aside employees of the executing company, project stakeholders can be grouped as internal (shareholders) or external (customers) to the project. An elaboration of the stakeholder types follows. Shareholders are people who own, or have a sense of ownership regarding, the project and so expect some form of reward from its execution. Usually the term is applied specifically to those who own stock in the corporation; however, oil and gas megaprojects have other non-financial contributors who frequently approach the project with similar expectations. For

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instance, the host country and local communities who own the land or have to live with the inconveniences of the project often expect some form of compensation or reward. Main contractors whose assistance is required in order to deliver the project often fall in this category also. Many disruptions of oil and gas megaprojects are the result of conflicts with these non-stock-owning shareholders or institutions. Customers are the persons or businesses that purchase an organisation’s products. For projects, they are the recipients or users of project deliverables and the commercial hydrocarbon products obtained. However, megaproject managers and sponsors should also regard host communities as important customers as well since they pay for the production as well, through the environmental and other impacts of the hydrocarbon product generation processes and associated waste disposal. Following numerous project disruptions, the extension of obligatory customer rights to host communities is beginning to gain more attention from oil and gas companies. Competitors are rivals or entities that pose a threat to an organisation’s market position or profitability. The management strategy for dealing with competitors usually involves subtle arm’s-length cooperation, sometimes accompanied by aggression. Table 2.3 lists and classifies the main stakeholders involved in typical oil and gas megaprojects. Agitations by host communities and governments around the world have been a major headache and source of disruption for oil and gas megaprojects. The local complaints seem to point to a perceived misalignment between how the company views them and how they think they should be viewed. Of the 60 megaprojects studied by Miller & Lessard (2000), 40%

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performed badly due to exogenous and endogenous shocks. Exogenous shocks are turbulences from political, macroeconomic, and social events involving host communities, local industries, and governments (while endogenous shocks result from a breakdown of internal relationships with partners or contractors). These are high-risk issues that should be considered significant to decision making with regard to megaproject execution.

Table 2.3: Stakeholder mapping for oil and gas megaprojects

List of Stakeholders INTE RNAL

E X T E R N A L

For example, wrongly treating a stakeholder as a competitor when that entity would be more appropriately viewed as a customer can result in undesirable backlash, such as the unfriendly reactions that international oil companies (IOC) sometimes experience from indigenous governments and other host entities. About 25% of the project managers surveyed in this study Skema Business School

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reported experiencing very reluctant support or, worse, outright antagonism from host governments and communities.

History is a lantern at the stern of a ship, revealing only where it has been, casting only a dim light on the course ahead. –Samuel Taylor Coleridge

2.2 Strategy, Organisational Performance, and Megaproject Management

Introduction The literature presents strategy as a series of goal-directed decisions and actions that match an organisation’s skills and resources with opportunities and threats in its internal and external environment (Coulter, 2000). Strategy is an organisation’s way of charting its overarching course of action to deal with the challenges of its business environment and realise its goals. Strategic objectives or goals typically remain unchanged over a long period of time, though the strategic directions used to realise those goals could change depending on dynamics within the business environment. Such change, referred to as “strategy evolution” (Coulter 2000; Neely 1999), is a normal occurrence in oil and gas megaprojects. Two major types of strategy are identified: deliberate strategies, which are planned, and emergent strategies, which are responses to unplanned inputs (Mintzberg and Waters 1994). Emergent strategies are usually based on leaders’ intuitive decision and their ability to negotiate (Thiry 2004). The need for an emergent strategy often arises suddenly, allowing little time for robust thinking. The personal experience of managers and the extent to which they share the corporate vision Skema Business School

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and objectives will therefore play a key role in decisions that deal with emergent input. Due to high external interest and long time frames, megaprojects are often forced to adapt to changing dynamics in their direct environment or that of key stakeholders. Sometimes these dynamics could be conflicting. Emergent strategy management can thus become the greatest challenge in megaproject decision making. Organisations can consciously decide to be defensive or offensive in their strategy (Thomas & McDaniel 1990). Organisations with offensive strategies view their external environment as presenting opportunities of which the organisation can take advantage through quality service and innovation (Miles 1982). Scholars argue that an offensive approach to strategy is required for an organisation to create a competitive edge for itself (Irwin 1995). Those with a defensive approach however tend to see their external environment as a threat against which they need to defend themselves (Thomas & McDaniel 1990). The philosophy of leaders with a defensive strategy approach will typically emphasize value protection. In most oil or gas projects, risk analysis is largely focused on the identification of threats more than of opportunities, and project managers are often more sensitive to the presence of stakeholder objectives that could derail project goals or the project manager’s personal ambitions. Even the structure of the of the opportunity and project realisation process (see Figure 2.1) emphasises that the project execution phase should be about realising and protecting the value created during the project front-end phase. Hence managers tend to take a defensive approach to their project execution strategy. Brower & Gilbert (2007) suggested that the ability of a person within an organisation to influence its strategic direction is a function of the amount of resource allocation he or she is able to control or influence, and not necessarily his or her seniority level or role in formulating the strategy. Corporate Skema Business School

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executives not aware of this pattern are often surprised at the impact some supposedly lower people in the organisation have had on strategic success or failure in their businesses. As influencers and controllers of substantial corporate resources ranging from several hundred million to billions of US dollars, megaproject managers should not be underestimated as to their ability to substantially influence corporate strategic direction. To effectively address the phenomenon of strategy evolution, managers and other potential influencers of strategy within the organisation should possess a working knowledge of corporate governance and strategy, and of the relationship between them (Klein 1989). Unfortunately, not many businesses have processes to effectively manage performance while dealing with strategy evolution (Coulter 2000).

Strategy evolution reveals the non-linearity of the strategy

management process (Sull 2007), in the sense that strategy management does not consist simply of formulation followed by execution but, rather, involves an iterative (loop) process consisting of four major steps as illustrated in Figure 2.3. Megaproject manager ultimately impact long-term strategic value. In the context of this study strategic value is defined as a measure of the extent to which a megaproject contributes to realisation of corporate strategic objectives. Figure 2.3: Strategy Management Process (Sull 2007)

1 Making sense of Sense of aa situation based on strategy

4

Strategy revision based Revision based on new information

Making choices Choices about action

2

Making the choices happen

3 Skema Business School

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Strategy in the Oil and Gas Industry The following overview of the business landscape in the oil and gas industry should help to illustrate the challenges faced by oil and gas companies and the strategies they apply in order to improve their competitive position. Business Landscape: The Oil and Gas Industry Global oil capacity currently declines by about 4% (more than 1 million barrels of oil per day - MMbopd) each year (Skrebowski 2004), yet oil and gas demand is growing as fossil fuels remain the world’s primary energy source. Though dependence on fossil fuels is expected to decrease globally over the next 25 years in response to global climate campaigns, it is estimated that the increasingly steep demand for energy will still result in a net increase in the volumetric requirement of fossil fuels (RDS 2008). Access to “easy oil” reserves has become very rare today for two reasons: (1) the technical, socioeconomic, and sociopolitical challenges involved in exploiting recent discoveries are becoming increasingly intense; (2) most of the easily accessible known reservoirs are largely depleted and now require secondary or tertiary recovery support. Already most new capital projects now take place in regions that are politically and socially unstable, such as West Africa, parts of Eastern Europe, and the Middle East (Mandil 2006; Wilczynski et al. 2006). The International Energy Agency (IEA) has forecasted that oil requirements will increase by more than 40% and gas demand by 66% between now and 2030 (international energy agency 2006 2007), so more aggression with exploration and production can be expected as companies seek to take advantage of this demand and supply imbalance. We can thus expect the continued emergence of huge, multi-billion dollar projects where opportunities present themselves. And we can expect the industry to face intense challenges as it seeks to manage this growing portfolio of megaprojects and meet market Skema Business School

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demands in line with corporate strategy. Industry leaders have identified three key challenges (The Economist 2007) that should inform megaproject strategy: •

Greater complexity of risk management, due to local geopolitics, infrastructure issues, and increasing emphasis on environmentally responsible and sustainable development approaches to industrial operations and project delivery.



Managing the relative maturity of the emerging markets compared to the more developed countries.



Understanding the makeup of customers and other important stakeholders in these emerging world markets.

For example, in Nigeria where joint ventures between international oil companies and the government’s national oil company are common, effectiveness of project delivery is increasingly becoming a key criterion as the government decides which projects can most benefit from its limited joint venture funds. To be successful, project managers can no longer rely on traditional approaches to project management; they need a working knowledge of their project environment, the place of their industry in local and regional politics, and how to integrate this knowledge into project plans and implementation. Project leaders will be operating within societies that seek an influential voice in the execution and operation of projects built in their environment. Ability to respond proactively to these increasing social risks is now an essential skill not only for business leaders (WEF 2007), but also for megaproject managers.

Overview of Strategy within the Oil and Gas Industry The long-term strategic priorities of the major oil and gas companies reviewed can be summarised as creating value for stakeholders through competitive Skema Business School

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investments that deliver growth; growing people and technology; and becoming the preferred business partner with resource holders (BP 2008; Chevron 2008; Royal Dutch Shell 2008; ExxonMobil 2008). Interestingly, all of these major players in the oil and gas industry have adapted a sustainable development (SD) approach to strategy realisation. SD is seen as entailing the capacity to endure as a business organisation (BP 2008) and is guided by the principle that present needs should be met without compromising the ability of future generations to meet their own needs (WCED 1987; ARIC 2000). This approach represents the energy industry’s attempt to respond to societal objections to its historically negative socioeconomic and political impacts. Sustainable development is about integrating the environment, society, and economy in a balanced way so as to promote equality and justice through empowerment and a sense of global citizenship (ARIC 2000). All of the major industry players identified SD as a core business value for strategic projects; hence the extent to which SD goals are achieved will be a direct reflection of the worth (value) of their strategies to stakeholders. As stated by Thiry (2004), the ultimate measure of project benefits is neither completion of project deliverables nor the implementation of a strategic plan, but the impact of both items (strategic plan implementation and deliverables) on the business organization and its stakeholders. Specific SD goals identified as common among major oil/gas companies include: ! earning the admiration of key stakeholders ! making significant positive socioeconomic contributions to society ! economic profitability of the business ! health, safety, and environmental responsibility These SD objectives are aligned with the well-tested Balanced Score Card for organisational performance evaluation (Norton & Kaplan 1996), as well as with the Diamond Approach that Shenhar and Dvir (2007) suggest for projects. The

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Diamond Approach presents five main success dimensions: project efficiency; impact on customers and users; impact on the project team; business and direct organisational success; and preparing for the future. Table 2.4 presents a mapping of SD elements against the success dimensions suggested by the Diamond approach.

Table 2.4: Mapping of Sustainable Development Measures to Success Dimensions Relationship to Success Dimension (Diamond Approach) Project

Impact on

Impact

Business and

Preparing

Efficiency

customers/

on

direct

for the

users

project

organisation

future

team

al success

SD Measures 1

Economic profitability

2

Health,

safety,





and

environmental



















responsibility 3

Ensuring

value

to

customers and joint venture partners 4

Ensuring value to host governments

and

communities

Megaproject Strategy Recent studies have demonstrated that large, long-term projects (lasting more than three years) are significantly less predictable than shorter projects in terms of time and scope (Cooke-Davies, 2002). These studies expose the failure of project management to effectively respond to emerging dynamics in the business and sociopolitical environment (Murray-Webster & Thiry 2000), as well as weaknesses in integrating strategic intentions into these extensive Skema Business School

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projects (Thiry 2004). Several authors (Frame 2002; Cooke-Davies 2002; Thomas et al. 2000; Kendall 2001, Morris 1997) have consequently advocated the evolution of the role of project manager toward a more business-focused function as a means of mitigating this gap. Megaprojects are means of strategy realisation, and are thus strongly dependent on decision making (Sull, 2007). High-consequence decisions are unavoidable in projects of this size (Klein, 1989). Megaproject managers are particularly important elements within the decision chain, because their views and their interpretation of strategic issues directly influence both how the project team responds (downward influence) and how senior management perceives such challenges (upward influence). The APMBok (Association for Project Management body of knowledge) industry guide presents “project strategy” as a comprehensive definition of how a project will be developed and implemented (APMBoK 2006). This definition would appear to encompass both the project feasibility assessment and execution plan. While the PMIBoK (2008), a complementary industry standard, does not specifically mention the phrase “project strategy,” it does talk about synonymous deliverables, referred to as the project charter and execution plan. PMIBoK (2008) suggests that a project is linked to organisational strategy via its charter document and then kept so linked using a management tool called the “requirement traceability matrix.” This matrix, if properly used, can help to create and maintain a link between the project scope and the business

objectives through the project management life cycle

(PMIBoK 2008). Project strategy should be unique to a specific project and should indicate clearly how the project team intends to deliver the aspirations of its stakeholders and realise associated corporate strategy. Aside from describing the overall strategy by which the project will be realised, project execution strategy should also highlight the following: •

the link between the project and corporate strategy,

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processes and procedures to be applied in its execution,



identification of key issues to be addressed together with all critical activities associated with them, and



an outline of decisions to be made at various stages in support of the defined strategic objectives. The project strategy document is typically available at the beginning of

the execution phase, and it should be adapted as necessary in accordance with relevant dynamics in the project’s environment (Jaafari 2004). For most oil and gas megaprojects these dynamics include the influence of external stakeholders as well as the complex requirements often imposed by legal, environmental, social, safety, and fiscal regulations (Hobbs & Miller 1998). Decisions throughout a project’s life should be judged in terms of their overall impact on the business and strategic objectives that the project is required to deliver. Thus, intermediary objectives such as minimising delays or cost changes should be viewed as important in expediting project implementation, but not as the ultimate criterion for decision making (Morris & Pinto 2004) or judging success (Muller & Jugdev 2005; Shenhar & Dvir 2007). It appears that many company executives have failed to grasp this principle. Thus project managers could, in many cases, make significant contributions to their company by helping company executives understand this concept, and by keeping them and project team members focused on what really matters to ultimate project success. As Napier & McDaniel (2006) concluded, much of the power of senior leaders who are ultimately responsible for high-commitment decisions is bound up in what they choose to pay attention to. Project managers should realise that, in various ways, they can influence what their superiors pay attention to.

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The Challenge of Strategy Implementation and Organisational Performance Effective strategy management includes a proper balance among external challenges (i.e., competition for value), internal challenges (such as operational, resource, and organisational competence), and the future of the organisation within its business environment (Coulter 2000). Studies by Norton & Kaplan (2005) show that only about 10% of formulated strategy is effectively implemented. Paladino (2007) advanced four reasons for this undesirable situation: 1)

Vision barrier: poor awareness of corporate vision among employees. Only about 5% of employees have a proper understanding of company strategy and objectives.

2)

Management barrier: interface and integration issues between financial, operations, people and customer objectives or measures.

3)

Resource barrier: poor link between corporate budget/funding, organisational setup, and strategy. For large projects, this could mean a difference in viewpoint between senior business managers and project managers on what the critical performance or success factors should be.

4)

People

barrier:

ineffective

connection

between

people

incentives and strategy. Paladino’s scheme closely parallels the four megaproject challenge areas that McKenna et al. (2006) identified among both oil and gas producing companies and their EPC (Engineering, Procurement, and Construction) contractors, as shown in Figure 2.4.

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Figure 2.4: Megaproject Challenges Mapped to Strategy Implementation Barriers Large Project Challenges

Strategy Implementation Barriers

Project Planning

Vision Barrier

Risk Management

Management Barrier

Performance Management

Resource Barrier

Human Resource Management

People Barrier

One of the classic causes of catastrophic failures on megaprojects, as revealed in various studies reported by Shenhar et al. (2001), is “unrecognised changes” in the project environment. The issues go unrecognised mainly because of poor quality of the project’s information management value chain. Summarising a review of the debate on project success, Cicmil & Hodgson (2006) also suggested that a range of social and behavioural factors are largely responsible for project failures. Having the best understanding possible about the project environment and stakeholder ambitions is thus crucial. Strategy development and project management should enable this understanding, creating and maintaining the delicate balance between short- and long-term profits (delivering customer value), funding (shareholder value creation), and stakeholder satisfaction in general (Cokins 2005). Paladino (2007) presents five principles to deal with these strategy implementation barriers earlier discussed. The summary of these principles below contains some language adaptations for the project environment. 1) Deploy a Corporate Performance Manager or Officer. This is considered the core and starting point for implementing the other four principles. This staff member should report directly to the CEO. On large projects this Skema Business School

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person could be a dedicated and adequately empowered person who reports directly to the project manager (e.g. projects or program management office manager). 2) Refresh and Communicate Strategy. Management team members and key staff responsible for facilitating implementation need to be aware of relevant changes within the internal and external environment, and to consider these changes in updating and communicating strategy. 3) Cascade and manage strategy: The manager must be able to translate the business strategy into objectives and measures and communicate them in ways that effectively direct team members’ actions. For instance, the project manager’s ability to effectively establish and communicate the link between corporate strategy and project objectives; and then assign project roles in ways that both tap the strength and motivate team members is a key to project success. 4) Improve performance: Many poor decisions, resulting in unacceptable project performance, can be traced to inaccurate understanding of stakeholder needs and expectations, or to the inability to respond to changes in stakeholder needs (Carver and Scheier 1990; Kubr 1996; Standish 1996; Hartman and Ashrafi 2002). To effectively manage stakeholders, one must maintain good information on their perception of the project and their needs. This researcher has found project managers to be often seriously lacking in this regard. 5) Manage and leverage knowledge: Knowledge is a key asset for any business organisation to stay competitive. Project management practioners have long agreed that identifying and applying “lessons learned” will enable improvement in project delivery. Unfortunately, developing a strategy to identify and apply these lessons in support of subsequent decisions remains difficult.

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Challenge Labelling and Strategic Performance Managers exposed to identical challenges may respond in very different ways depending on how each one interprets the challenge (Dutton & Jackson 1987; Thomas & McDaniel 1990; Thomas et al. 1993). Factors responsible for these variations in response can be found within the cognitive processes of an organisation’s members and the contextual features of that organisation itself (Thomas & McDaniel 1990). They may include a priori theories, beliefs, structures, and procedures (Hall 1984). The manager’s belief system or knowledge base could influence whether that manager classifies a particular strategic issue as a threat or an opportunity (Dutton & Jackson 1987; Thomas & McDaniel 1990; Thomas et al. 1993), and thus how the manager reacts to it. An “opportunity” is a positive situation in which gain is likely and over which the decision-maker has a significant level of control (Dutton & Jackson 1987), and therefore confidence of action (Taylor 1989). Better decisions and outcomes are more likely under these conditions. Managers who identify a situation as a threat rather than an opportunity tend to be overly concerned about efficiency, and therefore implement cost or activity restrictions (Thomas et al. 1993). These responses could in turn distort information processing and decision making.

The Effect of Time on Strategic Decisions As the strategy loop theory (Sull 2007) illustrates, strategy realisation and consequent value are fundamentally about decision making, and timing of decisions is often essential (Chu & Spires 2001). Projects are fast-paced environments, often characterized by tight time pressures and the influence of other social or political dynamics. It has been demonstrated that time constraints impair decision performance in organisations (Eisenhardt 1989; Chu Skema Business School

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& Spires, 2001). When project managers are under time constraints, they will tend to process information faster, process less information and be less rigorous in applying appropriate decision criteria. These attributes have all been identified as especially characteristic of threat-labelling decision makers (Thomas et al. 1993). Eisenhardt (1989) suggested important characteristics of business executives whose decision making in fast-paced situations was positively correlated with strong firm performance: 1) They use more, not less information. 2) They develop more, not fewer alternatives. 3) They emphasize input from experienced counsellors (who could be experts, personal coaches, or mentors). 4) They give attention to active conflict resolution because they recognise that conflicts create interruptions in the decision process (Mintzberg et al. 1976). 5) They integrate strategic decisions with one another and with tactical plans. They do not see each challenge requiring a decision as independent of previous or other pending decisions. It is noteworthy that the decision outcomes pursued in most fast-paced organisation tend to be concerned with the near term. Nonetheless, megaproject managers who have to deal with longer term issues can considerably benefit from adapting and operationalising the outcome of this study by Eisenhardt (1989).

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"The roads we take are more important than the goals we announce. Decisions determine destiny." -- Frederick Speakman

2.3 Decision-Making on Megaprojects The ability to make good decisions is critical to an organisation’s success (Skinner 2001) and especially to its long-term survival. As in organisational management, decision making also underpins management of large projects (Bruzelius et al. 2002). Good decision making should minimise surprises, or mismatches between what actually happens and what was expected to happen (Gharajedaghi 1999). Reasons why mismatches between expectation and actual events can occur following decisions include (Gharajedaghi 1999; Russo & Schomaker, 2002): •

Inadequate information, unreliable information , or processing problems due to information overload



Poor implementation



Changes in the physical, social, or business environment after the decision was made. The change could be completely unanticipated, making it a “chance” issue. Unfortunately, the time pressure on most projects may give little time to adjust to these changes.



The decision itself may be fundamentally flawed, perhaps as a result of the decision-making approach or process. For example, there could be conflicts between the project manager’s ambition or goals and that of the sponsoring organisation. A good decision-making approach will balance corporate objectives with the decision maker’s preferences and key stakeholder demands. Ideally, decisions should be proactive in order to maximise the chances

of achieving the desired objectives (Jaafari 1999). Studies by Wilson (1998), Skema Business School

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Johnson (2006), Rombout and Wise (2007) as quoted by Virine & Trumper (2008) have established that the root cause of almost all project failure can be traced back to human error or poor judgment, while Hammond et al. (1998) suggested that poor judgment can often be traced back to the way in which the decisions were made. Project decisions themselves are influenced by the dynamics in their environment. Jaafari (2004) broadly categorised these dynamics into six types: •

market/ external business dynamics;



sponsoring organisation dynamics;



social, environmental, and political dynamics;



technological factors and innovation;



legal, ethical, and due diligence requirements; and



project team and implementation dynamics.

The extent to which decision makers are aware of these dynamics within their environment and the magnitude of management attention given to associated risks, are usually good predictors of decision outcomes. Research has shown that the type of strategic decision management process required in projects should link decision making to anticipated results (value), and to the availability of resources in support of implementation (Hartman & Ashrafi 2002). Minimising the gap between expectations and actual outcomes is especially

important

in

megaprojects,

because

of

their

considerable

uncertainties that can significantly impact business performance. Some oil and gas companies are already beginning to see the ability to make right decisions as a principal indicator of project management competence. Hammond et al. (1998) concluded that decision making is the most important job of any executive or manager. What really matters, however, is not how many decisions Skema Business School

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or what percentage of total decisions a leader gets right, but how many of the important ones he or she gets right (Tichy & Bennis 2007). Ability to identify and focus on the critical things is therefore a key to megaproject success. Interestingly, however, many project managers tend to presume that their decision-making capabilities are above average (Massey, Robinson, & Kaniel 2006) and, therefore, see little value in taking steps to improve their skill in this area (Goodwin & Wright 2004). This situation should be of major concern to senior executives, given the unsatisfactory outcomes of many projects (see Wilczynski et al. 2006). About 30% of interviewees in the initial problem-defining stage of this study indicated a desire to improve their ability to better understand the likely outcome of future events more accurately. Their implicit goal is to make decisions today that will remain effective tomorrow, thereby preventing future project turbulence and improving venture value. Unfortunately, many project managers seem not to recognise yet that enhancing the quality of their decisionmaking is fundamental to understanding future trends. Hastie and Dawes (2001) indicated that decision-making skills can be improved with training in addition to learning from experience. It is hoped that this study will help project managers to better appreciate the value that enhanced decision-making skills can add to the performance of their projects and organisations (Virine & Trumper 2008; Thomas et al. 1993). This research addresses decision-making from the point of view of the megaproject manager overseeing the project execution phase.

Decisions and Decision Makers In a general sense, a decision is a position, opinion, or judgment reached after consideration (Miller 2009). It is a cognitive phenomenon, the outcome of a complex process of deliberation that includes an assessment of potential Skema Business School

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consequences and uncertainties (Muller et al. 2008). Skinner (2001) defined decision making as a conscious, irrevocable allocation of resources with the purpose of achieving a desired objective, indicating that it involves thinking, judgment, and deliberate action. The term “irrevocable” does not mean that decisions can never be changed, but that changing one’s mind later will require another decision and, probably, additional resources. For the purposes of this study, a “decision” has occurred once it has been communicated in some way and accepted for implementation. Basic elements of a decision process include information seeking and acquisition, ascription of meaning (interpretation), applying decision criteria, and subsequent implementation actions (Giola & Chittipeddi 1991; Russo & Schoemaker 2002).

“A problem well stated is a problem half solved” - John Dewey

A decision maker has been defined as anyone with the authority to allocate the needed resources toward realisation of a decision (Skinner 2001). To varying degrees, project managers influence, control, and allocate project resources, which in the case of megaprojects could amount to billions of dollars. Sterman (2000) said that decisions will always generate feedback; decision makers thus need to be aware that their decisions put into effect new policies that can alter a system. Sterman further observed that elements or people within the system will naturally react to correct perceived new imbalances resulting from the decision. Consequently, Sterman suggested that decision makers need to learn from history and experience in order to improve future decisions. He proposed the double-loop learning model (see Figure 2.5) for this purpose.

Sterman’s model suggests that the main factors that influence

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decisions are related to information and organisational characteristics (structure, strategy, and decision rules).

Fig. 2.5: Learning feedback in the decision process (Sterman 2000)

Stakeholder Decision-Making Influences in Oil and Gas Megaprojects The project manager’s perception of stakeholders’ attitudes can affect decision making. The attitude of stakeholders in turn depends on how they (the stakeholders) perceive that the project pursuits are addressing their needs and helping to mitigate their risk exposures. Figure 2.6 conceptually illustrates how decisions by each of the internal and external stakeholder groups in Table 2.3 are transmitted to the project. Stakeholder decisions could either be transmitted directly to the project or indirectly through another stakeholder group that has direct influence on the project. Note that not every stakeholder can directly influence the megaproject; the most direct influencers are people internal to the project and sponsor organisation(s), including the project manager.

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Figure 2.6: Conceptual Model of Stakeholder Influence on Megaproject Decision Making

Stakeholders internal to project

Feedback Loop

+ve

Mega Project

Strategic Value?

0

(Decisions & implementation)

Stakeholders external to project and sponsor company, but internal to host country

Stakeholders external to project, but internal to sponsor company

Feedback Loop

-ve

Direct inf luence Indirect inf luence

Stakeholders external to project and sponsor company, and also outside host country

Decision Makers in Oil and Gas Projects Three essential players in the oil or gas megaproject organisation that drive decisions in the execution phase: the project sponsor, the project manager, and the asset operator representing the customer(s). It is therefore important to monitor these people’s views of project performance (Harpham 2000). The sponsors represents the voice of the company’s executives and board and should champion integration of joint venture partners, if any are involved. This is a governance role with primary accountability for protecting the business case for the project, steering the project, and ensuring availability of resources (Crawford et al. 2008; PMI 2008). The works of Kloppenborg, Shriberg, and Venkatraman (2003) and Kloppenborg and Tesch (2004) show correlations between sponsor behaviour and certain project success indicators, including “meeting agreed requirements” (i.e., benefits), “customers’ perception of success,” and “the firm’s future.” Skema Business School

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The project manager is accountable for delivery of the integrated scope of the entire project or a substantial part of it, including responsibility for protecting the business case and strategic value. Megaproject managers in the oil industry typically have a technical education, are of middle to senior management cadre in the organisation, and have over 15 years of project management experience. In this study, more than 95% of the managers surveyed had an engineering background, and their average project management experience was 20 years. The asset operator represents the interests of those who will run the project, once executed, so as to realise its stated long-term objectives. Their responsibility includes facilitating social acceptance of the asset and its operations among the local community of people who must live with it over its life span. Asset operators have a high-profile representation on most project teams. Collectively, these three people are accountable for ensuring that all stakeholders keep project strategic objectives and performance in view throughout its execution phase. They are also responsible for monitoring if and when the project will deliver the forecasted benefits and if necessary, recommending continued management support, curtailment, or abandonment of the project.

Decision Theory Decision theory has its root in classic economic theory, with its assumption that people make decisions to maximise utility based on selfinterest and rationality (Skinner 2001; Mackie et al. 2007)—the so-called expected utility or normative decision theory. A later theory that looks at the other side of the coin is known as descriptive decision theory, which considers actual human behaviour in the decision process. These two theories Skema Business School

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fundamentally govern decision making and are further explained in the following two sections.

Expected Utility (Normative Decision) Theory Expected utility theory has been the predominant model for normative decision making (Tversky & Kahnemann 1992) as applied in the field of economics and organizational management. It is considered idealistic because it focuses on how people should make decisions (Mackie et al. 2007) rather than how they actually make decisions (Skinner 2001). Choices are made rationally based more on the results of analysis, which is assumed to be reasonably rigorous and not simply made based on the intuition of the decision maker (Virine & Trumper 2008). The theory exalts self-interest and rationality as supreme and, as a consequence, does not adequately allow for the impact of chance events or other intervening factors that make real-life decisions reference-dependent (Kahnemann 2002). The presence of uncertainty therefore poses a major challenge to effective application of this theory, especially to project execution. Schedule and other pressures that compound the uncertainty level of a project rarely leave project managers with the time and patience needed to follow this decision-making model rigorously and consistently. Nonetheless, expected utility theory has been applied in practice with some level of success, including some cases in the oil and gas industry (Mackie et al. 2007). Most of its application in the oil and gas industry, however, relates to choosing alternatives for exploration and appraisal—the front-end or preexecution phase of oil and gas projects. Technical people in the oil industry (including project managers) have been observed to exhibit a predominantly normative approach to decision making, thereby weakening their ability to deal with uncertainty (Capen 1976; Parkin 1996; Mackie et al. 2007). This tendency could inadvertently lead to a Skema Business School

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frequent rationality-irrationality tension and consequently poor or inconsistent decision outcomes within the complex situations characteristic of megaprojects. Real-life decision making is rarely rational though. Incidentally, as project management in the oil and gas industry is dominated by technical people, probably more than a few project managers are struggling with the impact of this tension between rational decision models and unpredictable uncertainties. Inconsistency in decision-making approach can cause projects to change directions unnecessarily and can lead to failure (Virine & Trumper 2008). Mintzberg’s (1978) finding that strategies are more effectively realised through consistent decision making and action reinforce the likelihood that considerable long-term strategic value may have been lost to poor judgment on many oil and gas projects. Project managers’ consistency in decision making approach may be further hampered when the authority they are allowed to exercise does not match the huge project delivery responsibilities given them, increasing their exposure to unfriendly external influences and undermining their ability to implement what they may consider the best decisions. For this reason Turner (2004) recommended empowerment of the project manager so that he or she has wide flexibility to make choices. Game theory, another normative decision theory was invented by John von Neumann and Oskar Morgenstern in 1944. It is the study of the ways in which strategic interactions among economic agents produce outcomes with respect to the preferences (or utilities) of those agents, where the outcome(s) in question might not have been intended by any of the agents involved (Ross 2010). The theory attempts to mathematically capture behavior in strategic situations, in which an individual's success in making choices depends on the choices of others (Wikipedia, “Game Theory”). Game theory today is a sort of umbrella theory for the rational (or normative) side of social science (Aumann 1987). As a descriptive theory tool it has come under recent criticism majorly

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because the assumptions made by game theorists are often violated (Wikipedia, “Game Theory”). Contrary to observed typical human practice, game theorists assume players would always act in a way to directly maximize their wins. The Prisoner’s Dilemma article (Poundstone 1992) presents a good illustration demonstrating that one player pursuing his own self-interest leads both players to be worse off than had they not pursued their own self-interests. Real Options, which originates from the corporate finance world is a normative theory that is thought to hold the potential of yielding new insights on strategic decision-making under uncertainty (Ruer & Tong 2007). A real option can be defined as the right — but not the obligation — to undertake some business decision (Wikipedia, “Real Option Analysis”). As it attempts to predict the future, the quality of the output will consequently only ever be as good as the quality of the inputs. Like game theory, there is much debate about its merit because many of the assumptions underlying it do not hold in strategic contexts for resource development and deployment, hence it is yet to take root in practice (Ruer & Tong 2007). This currently low levels of development of the game and real-options theories as possible means of addressing decisionmaking under uncertainty do not make them attractive as basis for this study.

Descriptive Decision Theory Descriptive decision theory deals with how people actual make decisions. It postulates that people tend to make decisions to satisfy their most important needs, even if they do not have all the required information and if their choice is not the optimal solution (Mackie et al. 2007). When people must make decisions amidst uncertainty, they simplify the challenges by relying on heuristics or rules of thumb (Kahnemann et al. 1982) that are largely rooted in acquired knowledge, preconceptions, and past experiences. This is a more intuitive and natural approach. For example, 77% of project managers who Skema Business School

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participated in this study said that intuition plays an important or very important role in their process of making strategic decisions. Intuitions are hunches fueled by past experiences, personal preferences, and biases; they can produce both good and bad results (Kahnemann 2002). More recent experimental research by Ariely (2009) further underscores irrationality as the real invisible hand that drives human decision making. Descriptive theory forms the basis for the theory of bounded rationality (Simon 1976; Kahnemann 2002) and for prospect theory (Kahnemann & Tversky 1979; Tversky & Kahnemann 2004). These two offshoots of descriptive theory recognise humans’ inability to be rational most of the time and postulate that inductive thinking is more natural (Arthur 1994; Tversky & Kahnemann 2004; Kahnemann 2002; Ariely 2009). The present study relies on the application of prospect theory, which is elaborated below.

Prospect Theory Prospect theory explains decision making under risk (Tversky & Kahnemann 2004), which more realistically reflects the environment in which megaproject managers work. The theory distinguishes two phases in the decision process: framing and valuation. The framing phase consists of a preliminary analysis of the prospects offered by the challenge to the decision maker, leading to a representative construction of his or her perception of the challenge, associated contingencies, and possible outcomes of the decision (Kahnemann 1992). A heuristic simplification of the risk or challenge takes place so that the decision maker can make meaningful sense of it. During this phase, the quantity, quality, and timeliness of information (i.e., informationfeed characteristics) available to the decision maker, along with the person’s past experiences and knowledge, will affect how that person models the possible prospects (i.e., outcomes) of the process. Information timelines have Skema Business School

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been hypothesized as a factor due to the time pressure that most project managers experience. The works of Hwang (1994), Finucane et al. (2000), and Kahnemann (2002) suggest that time pressure impairs decision making, while Greer & Kroop (1983) observed that good information suffers degradation when not delivered on time for optimum usefulness. The decision maker’s level of multi-tasking (Gilbert 2002), unpleasant body timing (Bodenhausen 1990), and mood (Bless et al. 1996) can also be influential factors. After framing comes the valuation phase, in which the decision maker assesses the value of each prospect based on an “opportunity-threat” or a “gainloss” labeling principle (Thomas et al. 1993; Tversky & Kahnemann 2004) and chooses accordingly. Building on prospect theory, Bateman & Zeilhaml (1989) found that the way in which required decisions are presented influences the behavior and risk preference of the decision maker. Specifically, when required decisions are phrased so as to emphasise the prospect of gain, decision makers tend to be more risk-averse; when they emphasise the prospect of loss, decision makers tend to be more risk-seeking, even when the expected values of the alternatives are equal (Kahnemann & Tversky 1979; Bowman 1982; Neale 1983).

Good decision making is the key to good outcomes. … Don’t let uncertainty paralyze you … evaluate decisions not just on the results, but on how they are made. – Robert Rubin

Modelling Decisions The basic elements of a decision process include information seeking (scanning), ascription of meaning (interpretation), applying decision criteria, and subsequent implementation action (Giola & Chittipeddi 1991; Thomas et al. 1993; Russo & Schoemaker 2002). Decision models reviewed (Thomas et al. 1993; Druker 2001; Virine & Trumper 2008; Turner 2009) were all congruent with this description. Turner’s “ten-step problem-solving cycle” model, however, was preferred as a reference model for this research, as it best suits Skema Business School

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the context of the project environment. This study complements Turner’s model with aspects of the model developed by Thomas et al. (1993). The model distinguishes three parts of decision making: preparing for and making the decision (i.e., information-feed and interpretation activities), making the decision (decision practice/quality activity), and implementing the decision. The adapted Turner model is presented in Figure 2.7.

Figure 2.7: Decision Process for Problem Solving (adapted from Turner (2009)) Gather data Perceive problem Information-Feed (Scanning)

Define and contextualise problem Generate and evaluate solutions

Interpretation Select cause of action/solutions

Influenced by •Chance Events •Contextual variables

Decision Quality (Practice)

Apply decision criteria Communicate cause of action

Implementation Implementation & monitor

The key processes have been grouped in line with the model applied by Thomas et al. (1993): information feed (scanning), interpretation, application of decision criteria, and implementation. Each of these is further discussed below.

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Information Feed in Decisions Information feed (or scanning) involves searching external (Milliken 1990, Coulter 2000) and internal (Cowan 1986; Thomas et al. 1993) environments to identify important issues or events that could affect the organization and its objectives. It is a key element of the decision process (Giola & Chittipeddi 1991) enabling managers to formulate expectations about the future (Greer & Kroop 1983). Information feed should be approached based on what information is desired, what can be obtained, and the type and classifications of information available. In general, top decision makers will have access to far more information than they can deal with (Mintzberg 1973; Thomas et al. 1993). They thus tend to select the information they consider most useful. Decision makers who use more information tend to be more comfortable in dealing with ambiguity and uncertainty (Milliken 1990), and consequently are more proactive and more positive in labelling their challenges as either threats or opportunities (Thomas & McDaniel 1990). A lackluster attitude about decision making could impair a manager’s approach to seeking information to guide the decision (Capen 1976; Rose 1987; Goodwin & Wright 2004; Massey, Robinson, & Kaniel 2006; Virine & Trumper 2008), thus increasing the potential for wrong judgment. Kurdi (2003) found that most managers spend about 75% of their decision time gathering information and working toward conclusions, but without adequately addressing what information they really need, why they need it, and how best to apply it to understanding the challenges they face (i.e., the framing phase). Onlyt about 12% of decision time is spent on framing. Similarly, managers paid very little attention (just about 13%) to learning from their prior decision experiences. Under this scenario, information overload and irrelevance are

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imminent; the decision maker’s attention is likely to be hijacked by items that should be of lower priority, resulting in questionable project decisions. Information feed focused on the external environment in particular is most influential in facilitating positive labelling of challenges (Thomas et al. 1993; Coulter 2000). Dutton & Jackson (1987) found that those who have confidence in their labelling tend to project positive outcomes with expectations of gain or opportunity rather than loss or threat. They also tend to have a greater amount of control of organizational or project direction. Flyvberg (2007) determined that inadequate, unreliable, or misleading information is a key challenge in many megaprojects. For example, poor or inadequate knowledge of the macro-environment (or of changes in the macroenvironment) by decision makers, otherwise known as “incognisance” (Spetzler et al. 2005), is a large contributor to megaproject underperformance (Merrow 1988). De Bruijn & Leijten (2007) found that conflicts between decision making, policy, and planning have major impact on decision outcomes. Both of these types of problems are traceable to weak links in the information-feed chain. Early detection of system disturbances is enhanced through good and timely information feed (Jackson & Dutton 1998) that allows for proactive responses. Less timely information is generally considered inferior because the manager expects that the information will contain a greater amount of error (Greer & Kroop 1983). On the other hand, decision makers tend to use less information when they believe they are adequately conversant with their business environment or situation than when they feel they have less understanding (Thomas & McDaniel 1990). Decision makers may sometimes not be right in this self-assessment, however. The quality and quantity of information available to decision makers in business organisations is correlated with the quality of their decisions (Thomas et al. 1993; O'Reilly 1982). Also, certain information characteristics such as its level of external focus were found to have a significant Skema Business School

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correlation with strategic performance (Thomas et al. 1993; Lamb et al. 1999; Jinkman et al. 2000; Simpson et al. 2000; Begg et al. 2001; Mackie et al. 2007). As project management is similarly underpinned by decisions, one can expect that the quality of information feed to the project manager (as a key decision maker) will influence project performance and resulting long-term strategic value. This discussion informed two hypotheses for the study: H1.A: The information feed in support of the project manager’s decisions on oil and gas megaprojects will have significant influence on the level of derivable longterm strategic value. H1.B: The magnitude of external focus on information feed in support of the project manager’s decisions on oil and gas megaprojects will significantly correlate with the long-term strategic value realised.

Interpretation Interpretation is the process of making meaning of incoming information by fitting it into some structure for understanding and action (Giola 1986). The framework for this structure would be based on an understanding of the ambitions of key stakeholders (i.e., their goals and objectives) and of risks related to the project. Interpretation could be at the individual, group, or organisational level (Thomas & McDaniel 1990). This study approaches interpretation at the individual level of the project manager, where prior knowledge and the decision maker’s mental structure help to create the needed framework to reduce ambiguity and provide meaning (Ramaprasad & Mitroff 1984). A robust interpretation framework would represent the ambitions of all key stakeholders in the project (Schoemaker & Russo 2001) and would allow their integration in the decision process. Goals and objectives are critical to communicating decisions (Muller 2003), so they should be clearly outlined as part of a strategic decision-making process (Carver and Scheier 1990). Morris Skema Business School

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and Hough (1987) and Turner (2004) also presented clarity of objectives as an important success factor. How a business leader or CEO (or megaproject manager) interprets an issue will affect the solution alternatives considered (Billings et al. 1980), the resources commited to resolving the issue (Staw & Ross 1978), and the eventual outcomes. Project managers will ascribe labels such as “threat” or “opportunity” to challenges (Milliken 1990; Thomas & McDaniel 1990; Dutton & Duncan 1987) within the framework of stakeholder goals and objectives and the project risks to which they feel exposed. This is the core of the interpretation process, which is also expected to ultimately influence decision outcomes (Thomas et al. 1993). The foregoing discussion leads to proposition of the following hypothesis:

H2.A: The project manager’s interpretation of project challenges will have significant influence on the level of derivable long-term strategic value from an oil or gas megaproject.

A decision maker who ascribes an opportunity label to a challenge is projecting a positive outcome with expectations of gain (Jackson & Dutton 1988). This positive perspective is correlated with the decision maker’s sense of control (Taylor 1989), and therefore with his or her confidence of action. Conversely, a threat-labelling decision maker projects a negative outcome of loss and is characterised by a feeling of relatively little control over the challenge. Threat labelling leads to an unusually high concern about efficiency (rather than effectiveness) and, in turn, to financial and activity restrictions that could distort information processing and decision making (Thomas et al. 1993). Ultimately, long-term strategic value suffers. The type of challenge label chosen

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can also affect the decision maker’s level of risk-averseness, sense of control within the project environment, and commitment to dealing with issues, thereby affecting what action is taken (Thomas et al. 1993; Kahnemann & Tversky 2000). This reflection leads to the next set of hypotheses:

H2.B: The project manager’s labelling of challenges experienced in the megaproject environment will significantly correlate with the level of long-term strategic value achieved.

Sense of Control (Controllability) The Merriam-Webster Dictionary defines controllability as the “ability to exercise restraining or directing influence over” someone, something, or a situation. The extent to which a business leader or project manager feels in control of strategic issues is an important influence on how information gathering towards decision support and interpretation will be approached (Thomas, Clark, & Giola 1993; Kahnemann 2002; Muller 2003). Several studies have argued that most people see threats as uncontrollable, and that opportunities are characterised by a high degree of controllability (McCrae 1984; Thomas & McDaniel 1990). Based on the works of Morris & Hough (1987), Miller & Lessard (2000), and initial interviews of some project managers during the early stages of this study, the ten areas of greatest challenges on megaprojects were identified: 1. Contracting and procurement management; 2. Government relations management (as McKenna et al. [2006] noted, the decision mechanisms of host governments are often unclear and can lead to significant complications); 3. Host community relations management; Skema Business School

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4. Joint venture interface management; 5. Health, safety, security, and environmental matters; 6. Multi-location

management

of

fabrication

and

facilities

integration; 7. Implementation of local content policies; 8. Project governance; 9. Managing the core project team (individual aspirations, job satisfaction, etc.), including attaining cohesion within the broader team; and 10. Impact of multi-cultural leadership within the project. The perceived significance of risk in each of these challenge areas should be a reflection of the extent to which managers feel they are in control of the project. This perception can be expected to influence the manager’s desires regarding the type and quantity of information needed, and his or her perception of barriers to adopting viable decision alternatives. The hypothesised influence of the project manager’s sense of controllability on the decision process is expressed in the following hypotheses: H3.A: The project manager’s perception of his or her level of controllability will significantly influence information feed on the project. H3.B: The project manager’s perception of his or her level of controllability will significantly influence his or her interpretation of challenges that require decisions.

"Stay committed to your decisions, but stay flexible in your approach." -- Tom Robbins

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Decision Quality and Implementation Most people and organisations judge the quality of decisions by their outcomes (Mackie et al. 2007), as these are generally the most visible and objective criteria available. This tendency has fostered the belief that a good outcome implies that a good decision process has been applied, and vice versa (Mackie et al. 2007). However, given the many factors that influence actual outcomes, this simplistic view becomes flawed. Russo & Schoemaker (2002) propose three influencers of decision outcomes in his model (see Figure 2.8): 1) Deciding (applying a decision criterion and settling on an option), 2) Doing (implementation and managing the factors under one’s control), and 3) Chance (uncontrollable factors, unanticipated events).

Figure 2.8: The Three Factors That Determine Outcomes (adapted from Russo & Schoemaker 2002)

Russo & Schoemaker (2002) demonstrated, that no matter how perfect the information feed and its interpretation may be, the eventual outcome of any Skema Business School

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decision will still be affected by how it is implemented, by contextual issues in the internal and external project environment, and by chance events. While the project manager endeavors to move as many factors as possible under his or her control by applying risk management or related techniques, chance events cannot be readily controlled. Due to this very unpredictable play of pure chance, a good process, even when excellently implemented, cannot guarantee a good outcome 100 percent of the time. Baucells & Rata (2006) advocated that judgements of decision quality should be based on knowledge of goals and objectives and on information available at the point of decision, not solely by the eventual outcome. They also argue that good decision quality is not necessarily equal to good decision outcome; in fact, they content too much focus on decision outcome is hindering growth in good decision making. Others, such as Russo & Schoemaker (2002) and Mackie et al. (2007), also point out that the best means to assure a good decision outcome is a good thinking and decision process followed by good implementation. The decision process of an organisation implicitly expresses its decision commitment practice. Rarely will a single decision achieve long-term strategic success. As the marker of a successful investment fund is good and consistent investment, so a corporation’s long-term success is marked by good decisions based on consistently applied decision criteria (Tichy & Bennis 2007). This perspective again corroborates Mintzberg’s (1978) conclusion that strategy is best realised by consistency of decision and action, with due consideration of risk exposures. Consequently decision quality within this research is being investigated from a process point of view rather than an outcome-based perspective. Ability to implement decisions will be closely tied to resource availability, to the authority that the project manager perceives he or she has in applying the resources, and to how cumbersome the applicable processes are. Based on the foregoing considerations, the following hypotheses have been

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proposed regarding the relationship between decision quality, implementation action, and long-term strategic value of megaprojects: H4:

The decision quality (practices on how commitments are made to decisions) of project managers will have significant influence on the level of derivable longterm strategic value from an oil or gas megaproject.

H5.A: The project manager’s decision implementation approach will have significant influence on the level of derivable long-term strategic value from an oil or gas megaproject. H5.B: The extent to which the project manager feels adequately provided with required project resources will have a significant influence on the level of long-term strategic value achieved. H5.C: The level of authority that the project manager believes he or she has been granted will have a significant influence on the level of long-term strategic value achieved.

Contextual Influences on Project Decisions Organisational (Thomas et al. 1993), personal, and project characteristics (Muller et al. 2008) are contextual factors that can have moderating influences on decision making; for instance, they may affect the project manager’s approach to information feed and how challenges may eventually be classified as threats or opportunities (Ford 1985). In particular, what the project manager perceives to be important to senior management (an organisational context) should influence his or her own management priorities, and hence his or her decisions. Perception of senior managemetn priorities also informs the project manager’s perception of how he or she may be measured, and hence his or her behaviour.

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Literature on organisational behaviour and decision making infers that experience also plays an important role in decisions (Kahnemann 2000), and that it can be positively related to decision outcomes (Dane & Pratt 2007). So the project manager’s professional experience (a personal context) could also be expected to influence the information framework adopted on the project, and thereby to impact strategic outcomes. These two contextual concepts (perception of senior management, and project manager experience) are framed in the next two hypotheses: H6.A: Organisational context - The project manager’s perception of senior management drivers will significantly influence the relationship between information feed and long-term strategic value from oil and gas megaprojects. H6.B: Personal context - The project manager’s professional experience will significantly influence the relationship between information feed and long-term strategic value from oil and gas megaprojects. Due to time limitations, this study tested hypotheses predicting contextual influences on the relationship between information feed and strategic value only. Other possible hypotheses relating to contextual influences on relationships among interpretation, decision practice/quality, and decision implementation have been left for future studies.

The Impact of Chance Events The long duration of megaprojects makes them captive to the dynamics or uncertainties in their environment, which sometimes come as major surprise events. These events can take the form of political, socioeconomic, regulatory, safety-related, meteorological, or geological issues. For the purpose of this study, all of these uncertain occurrences are described as chance events. Miller & Lessard (2000) identify them as unexpected turbulent events that can have

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their roots in endogenous or exogenous issues. It is argued then that the closest to a guarantee of a good outcome is a good thinking/decision process followed by good implementation (Russo & Schoemaker, 2002; Mackie et al, 2007), as chance could significantly interfere with logical outcomes. Examples of chance events that could affect megaprojects are listed in Table 2.5.

Table 2.5: Examples of Chance Events Affecting Megaprojects (adapted from Miller & Lessard 2000)

Chance Event Type

Examples

Exogenous Events Sociopolitical and macroeconomic

Financial crises (country or world) Unexpected major legislation Abrupt changes in input prices (of oil, gas, or other commodities)

Unexpected natural event or discovery

Bad weather, unforeseen geology

Direct opposition to the project

Court challenges by pressure groups

Discovery of valuable natural resources

Organised community opposition Sovereign behaviour

Rule change by regulators Refusal to grant permits Expropriation battles Granting of competing concessions

Endogenous Events Coalition unraveling

Withdrawal or bankruptcy of major partners Opportunistic moves Difficulties experienced by one of the partners

Uncontrollable interactions

Unexpected consequences of strategy Social deadlocks Accidents or long-lasting strikes Contractor bankruptcy Problems with unfamiliar technology and sites

Ramp-up

Forecasts proven wrong Internal expropriation

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Such issues can bring projects to a sudden halt, often without warning, or require restructuring or reassessment of their viability (Miller & Lessard 2000; Remington and Pollack 2007). History does not provide adequate capacity to predict a political upheaval or a tornado, which is why they and others are treated as “pure chance” events in this study. The IMEC (international program in the management of engineering and construction) research reported by Miller & Lessard (2000) observed that the surprise element of chance events introduces misalignments between original objectives and current realities; in addition, strategies that deal with known risks are not necessarily effective with chance events, and sometimes are even a hindrance. The strength and effectiveness of governance is crucial for successfully steering a project through chance events (Miller & Lessard 2000). Chance events are here envisaged not as a primary factor, but as a factor with moderating influence on decision outcomes. Two hypotheses are proposed to investigate the significance of chance events on megaproject objectives: H7.A: Chance factors will significantly influence the relationship between information feed and long-term strategic value of oil and gas megaprojects. H7.B: Chance factors will significantly influence the relationship between project managers’ interpretation quality and long-term strategic value of oil and gas megaprojects.

Again due to time limitations, Hypotheses H7.A and H7.B have also been limited to testing the effects of chance events on the relationships among information feed, interpretation, and long-term strategic value. Other potential hypotheses, not be tested in this study, include the influence of chance events on relationships among decision practice/quality, decision implementation action, and long-term strategic value.

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Summary of Factors Affecting Decision Making by Sponsors and the Project Manager As in a typical organization (Thomas et al. 1993), decision management on megaprojects is hypothesised to be based on the following process elements: information feed (scanning), interpretation (making meaning out of the information), applying decision criteria, and implementation action. The quality of risk management and the extent to which the project manager feels in control of the project will also influence the manager’s approach to information feed and interpretation of project challenges. Chance and contextual factors are hypothesised as moderating factors. Table 2.6 summarises the factors, identified in this literature review and the early interviews conducted in this study, that can influence decision making by project managers. These factors have been grouped under the elements of the decision process discussed earlier in this section. These process elements form the underlying constructs of the theoretical framework for this study. The associated research model summarising the hypotheses presented earlier follows in Figure 2.9. The research model proposed finds some support in Turner’s (1999) problem-solving cycle (see Figure 2.6), and in the organizational sense-making model used by Thomas et al. (1993).

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Table 2.6: Factors Affecting Decision Making Factors Affecting Decision-Making Factors Affecting

Reference

Factors affecting INFORMATION-F EED Information source and quality, including a lack of awareness of

Thomas, Clark & Giola (1993), Kahneman (2002);

changing external environment

Khosrowpour 2000

Information quantity (inadequate or overload, or contested

Skinner 2001, De Bruijn & Leijten 2007

information due to varied interest in the project Information timeliness

Virine & Trumper 2008, Finucane et al. 2000

Factors affecting INTERPETATION Positive-gain interpretation of challenges

Thomas, Clark & Giola (1993), Kahneman (2002)

Clarity of stakeholder goals for the project (sponsors, host govt and

Russo & Schomaker, 2002; Khosrowpour 2000

communities, etc.) Factors affecting CONTROLLABILITY Perception of project risks &controllability over the challenge at

Thomas, Clark & Giola (1993); Kahneman (2002); Steman

hand

2000; De Jong 2008

Factors affecting DECISION QUALITY Organisation Decision and Project Governance rules

Steman 2000, Virine & Trumper 2008,

Project system set-up and design, including Clarity of procedures

Steman 2000

Quality and structure of decision-making support system

Steman 2000

Factors affecting DECISION IMPLEMENTATIO N Project resourcing Project manager authority (Ascribed authority to manage as perceived) Factors affecting LONG-TERM STRATEGIC VALUE Strategic objectives: Corporate, stakeholder and project

Annual reports: RoyalDutch Shell, ExxonMobil, Chevron, BP

Quality of relationship with the host government and communities

Dalia 2008, De Jong 2008

Factors affecting CHANCE EVENTS Surprise policy and regulatory changes Unusual weather effects CONTEXTUAL FACTORS Age, professional experience and educational background

Thomas, Clark & Giola 1993; Virine & Temper 2008; Russo & Schomaker, 2002

Perception of senior management priority Organisational structure and culture

Khosrowpour 2000

Socio-economic condition of the host environment Schedule and cost pressure Acreage concession contract type (e.g. Joint venture, production sharing contract, etc.) Execution contracting strategy Leader’s mental model and leadership style

Steman 2000, Tuilett 1996

Active Inertia – tendency to unquestionably follow established

Coulter 2000, Kahneman 2002

behavioural patterns without considering the current context of application (negative effect of historical success)

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Figure 2.9: The Research Model

Chance Factors(Sudden changes) • Policy, Business Environment, Meteorological, Social concerns, etc

Controllability factors • QualityofRisk Management

Information-feed factors • Information Quantity • Information Quality • Information Timeliness

H1 H6

H3 Interpretationfactors • Goal/ Objective Clarity • Challenge Labelling Decisions Practice Quality) ( factors • Decision Process/ Criteria • Consistency of process application Decision Implementationfactors •Authority Scope •Resource Availability •Implementation Process

Long-term strategic Value Indicators

H2

H4 H7 H5

• Profitability Value to Customer & Venture • Partners • Health, Safety & Environment management • Natural Resources Management • Impact on Government and Host Communities • External Stakeholder Satisfaction

Context Factors • Experience • Senior ManagementPriorities DEPENDENT VARIABLES

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INDEPENDENT VARIABLE

CHAPTER THREE Research Design This chapter presents the research philosophy and methodological approach, including the data acquisition and data analysis techniques applied.

3.1 Research Philosophy This research focused on investigating the extent to which decisionmaking underpins the strategic success of megaprojects in the oil and gas industry. Previous research related to the study of decision making has been conducted from both descriptive theory (e.g., Kahnemann 2002; Thomas et al. 1993) and a normative or strictly rational (e.g., Skinner 1999) point of view. While the benefits of both views are acknowledged, the outcome of the literature survey inclines this research towards the descriptive approach. Methodologies applied in earlier studies within the organisation management field to investigate the relationship between decisions and strategy have been largely quantitative. For the oil and gas industry, project management is both a technical and society management experience, with most practitioners having core engineering or science backgrounds. This technical professional preparation however tends to make most project managers in the industry more comfortable with objectivity than subjectivity, preferring to work with facts and causality rather than searching for a systemic way to interpret what is happening. This preference aligns with the quantitative and methodological approach of many organisation management researchers on the theme of this research. In general, however, the research and practice of project management in the industry deals with both quantitative and qualitative data.

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Evaluating the strategic performance of megaprojects requires analysis of already-completed megaprojects and investigation into historical experiences of the megaproject managers. The hypothesis testing also required that relationships between various decision-related constructs, project context, and project performance be investigated. To interpret this combination of an existing phenomenon and objective facts, a “post-positivist” ontological position which lies within the continuum between positivism (completely objective) and interpretivism or phenomenology (completely subjective) has been adopted. On one extreme, positivists are concerned with establishing and explaining the fundamental patterns of relationships in social life, usually through acquisition of hard data; on the other extreme, interpretivists seek to establish the motivations and actions that lead to these patterns of behavior (Baker 2001, Blaikie 2000)—that is, they seeking meanings from observed patterns. Post-positivists fall within the two extremes, believing that we each construct our view of the world based on our perceptions of it, and that all observations are theory-laden (Trochim 2006). Because perception and observation are fallible, our constructions must be imperfect and therefore not fully objective. Hence the best hope for achieving objectivity is to triangulate across multiple fallible perspectives (Trochim 2006), especially if generalisation of findings is intended. For this study which is exploratory however, generalisation to megaprojects outside the oil and gas industry is not currently sought. Hence triangulation was not pursued. Following from the ontological stance above, the logical epistemological approach chosen for extracting information and testing for truth is therefore quantitative.

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3.2 Data Gathering Strategy Data gathering for the research phases applied a mixed-method approach as follows: •

Research

scope

definition:

qualitative

approach;

semi-structured

interviews were used at the very early research stage to gather data, so as to better define the research boundary and enable usefulness to practitioners. •

Hypothesis testing: quantitative approach; data gathering was achieved via a worldwide, web-based survey.

Details on both approaches as applied in this study are discussed in the following sections.

3.3 The Qualitative Approach To gain preliminary insight into the challenges that megaproject managers face and to assist in determining the study scope, some semistructured qualitative interviews were conducted at the beginning of this research. These interviews also provided an opportunity to structure the research for practical applicability within the industry. The interviewees were senior project executives in the oil and gas industry with many years of experience. The questionnaire template is presented in Appendix C3-A. As the questionnaire indicates, the original research theme for these interviews was performance management, but the interviews guided subsequent research toward a greater focus on decision making. Details of this development are provided in chapter 4.

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3.4 The Quantitative Approach The study also sought to test hypotheses arising from literature survey based on the research philosophical stance discussed in section 3.1 above. The hypotheses demanded the use of historical data from practitioner experiences while executing megaprojects; in addition, generalisation of research outcomes is limited to the oil and gas industry. Hence the quantitative approach of probabilistic sample surveying that would require minimal interference with the research objects was adopted (Cooper & Schindler 2006). Surveys can be designed to obtain answers to relevant questions from persons representative of the population of interest, in order to determine attitudes and opinions so that behaviours can be understood or predicted (Baker 2001). Other

data

gathering

approaches

such

as

observation

and

experimentation (Baker 2001) were difficult to apply to this context due to time constraints, especially considering that the execution phase of a megaproject usually lasts at least three years. The time frame and resources available for this research did not support such ethnographic-style approach. Also, an observational approach would have minimised the number of megaprojects that could be analysed, hence jeopardising the desired opportunity for generalisation of results within the industry. Implementing the survey involved contacting respondents individually and persuading them to provide the data requested (Czaja & Blair 2005). The researcher personalised each request for survey participation and believes that this strategy helped significantly to elevate the response rate. Since the study had a fairly narrow focus (i.e., extremely large projects in a single industry) securing access to an adequate sample audience posed a challenge. Considerations for the survey design, including determination of the sample size, sample frame, and questionnaire content, are discussed in the next section.

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3.5 Design of the Quantitative Survey Dealing with Potential Survey Errors Much of the literature on maximising value from research surveys emphasises the central importance of reaching and securing the cooperation of the right people (Czaja & Blair 2005) who will provide the required research data. A successful strategy includes a careful selection of the survey sample(s), along with efforts to ensure that the questionnaire is sensitive to issues of length, complexity, wording, and layout (Sekaran 2003; Czaja & Blair 2005; Baker 2003; Cooper & Schindler 2006; Teddlie & Yu 2007). Potential error types and associated mitigation steps identified from the experiences of other researchers were considered in designing this methodology. They are listed below: 1) Sampling error (Sekaran 2003; Baker 2001) arising from: •

Inadequate coverage of target population



Targeting the wrong population

2) Surveying error (Roscoe et al. 1975; Czaja & Blair 2005; Cooper & Schindler 2006), which could stem from: •

Selecting or crafting inappropriate questions (e.g., poor wording or lack of sufficient context)



Inappropriate order and transitioning of questions



Non-response errors from the target population—i.e., failure to obtain replies from some members of the sample frame who may have altered the distribution of answers received had they responded (Dillman et al. 1999)



Analysis error—usually due to poor questionnaire structuring and scaling, with the consequence that results do not adequately lend themselves to the statistical analysis required to test the hypotheses

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Strategies adopted within this research to minimise these errors are discussed in greater detail below.

Sample Framing for the Survey Sample framing—that is, careful selection of the population to be targeted by the survey—is of fundamental importance (Sekaran 2003; Cooper & Schindler 2006). Careful determination of the sample frame is also important in order to avoid measurement errors. The primary target population was the project managers of oil and gas megaprojects employed by the exploration and production companies who own or sponsor these megaprojects. Depending on the company, members of the sample frame may be organisationally designated as: •

Project

directors

(or

senior

project

managers)

with

execution

responsibility over entire megaprojects or a large part thereof; or •

Project managers (or senior project engineers), who typically report to project directors and who have responsibility for a substantial aspect of a megaproject. Some of these managers may also have responsibility for overseeing the activities of the project management office, otherwise known as “project services” in many parts of the oil industry. Activities of this office may include cost management, planning, contract procurement and management, and liaison roles. These staff may be designated with titles such as project services manager, senior project services engineer, or contracts and procurement manager.

While the study’s primary focus was on oil and gas production companies, it was recognised that the views of project executives in the Skema Business School

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engineering and construction companies contracted to execute oil and gas megaprojects would provide a more robust understanding of the decisionmaking challenges of the primary population. In addition, it is presumed that the long-term strategic objectives of the client and contractor should be in substantial alignment. Contractor project managers were therefore also invited to participate in the survey, which contained an item designed to distinguish these participants from the primary population of interest. Only about 6% of actual respondents fell into this category. Responses from practitioners tagged as ”project engineers” were also accepted. In most organisations these staff report to the senior project engineers, and are responsible for subsets of the megaproject (usually no more than $300 million in value). They are not key decision makers on the overall project, but their decisions on the component for which they are responsible could significantly impact core project or corporate objectives. Participation in the survey was solicited using the following platforms: •

Direct and indirect contacts secured within oil and gas producing companies



Project Management Institute special interest groups such as Oil and Gas, Consulting, Metrics, and Research Community. They were asked to publish the survey invitation in their periodic newsletters.



Some Project Management Institute chapters in Europe and the Middle East



Oil and gas professional communities on the web (e.g., LinkedIn, Plaxo)



Prominent oil and gas journals (to reach their corporate members)



Major Projects Association



European construction industry

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Managers of some oil and gas industry forums

Because of the combination of direct and indirect broadcast by which the survey was deployed, it cannot be objectively verified that all members of the sample frame had equal opportunity to access the survey. Also, the actual number of people who received an invitation cannot be quantitatively determined.

Sample Sizing Population sizing was premised on the existence of six so-called “supermajors”—that is, the largest non-state-owned oil and gas companies. They are responsible for up to 37% (approximately $84 billion) of capital expenditure on all ongoing oil and gas megaprojects in the industry (Energy Intelligence Report 2008). These companies include ExxonMobil (XOM), Royal Dutch Shell (RDS), British Petroleum (BP), Chevron Corporation (CVX), ConocoPhillips (COP), and Total S.A. (TOT). Each super-major typically plans to have between 6 and 10 active megaprojects at any one time (Royal Dutch Shell 2007; BP 2007; Chevron 2004). If an average of eight is assumed, then these super-majors will have about 48 active project among them at any time, each of them costing between $1 billion and $20 billion in US dollars. Publicly available data indicate that, on average, an international or big national oil company could have a total population of about 100 to 125 project directors and senior project managers. For example, BP is reported to have had about 125 project personnel of this type in 2007 (Frontier Magazine, April 2007). Typically about 25% to 30% of these could be of project director status. An average population of 110 project managers per major oil and gas company has been assumed for this research. Due to the similarity of roles and influence, project directors and senior project managers have been treated as a single population in this research. Skema Business School

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The sample frame for the research has therefore been estimated as: •

Project directors + senior project managers + project managers: 110 per company x 6 companies = 660 Now that the sample population has been identified, the sample size

must be determined so as to provide adequate confidence in the representativeness of the research results (Sekaran 2003). The G* Power (version 3.0.10) statistical software by Buchner et al. was used to calculate the sample size range that would enable achievement of the required confidence level based on the following predetermined criteria: •

Error probability = 0.05; this is also referred to as the desired significance level for the test (Field 2009), confidence level, or probability of a type-1 error (Cooper & Schindler 2006).



Medium effect size = 0.3; i.e., the standardised objective measure of the magnitude of observed effect (Field 2009).



Statistical power between 0.6 and 0.8. This is the probability that the statistical test will yield statistically significant results (Cohen 1988), or the ability of a test to detect a predetermined effect size, which is 0.3 in this study (Field 2009). A statistical power of 0.8 or greater is normally desired (Field 2009; Cooper & Schindler 2006) to minimise the probability of a type II error, however, for some previous studies 0.6 has been used (Cohen 1988; Hair et al. 1998).

Applying these criteria yielded an estimated target sample size of between 52 (statistical power 0.6) and 82 (statistical power 0.8), based on a two-tailed test of hypotheses. The sample size requirement would be smaller if based on a onetailed test.

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Questionnaire Design A web-based survey was used for data collection. In the process of survey design, antidotes to the potential errors mentioned in earlier sections were garnered from the experience of previous studies. These were considered within the questionnaire design and are summarised in Appendices C3-B (General Survey Design Principles) and C3-C (Web Survey Design Principles). As a show of appreciation, participants were offered the chance to request a summary of responses to the survey, and about 40% of respondents did so. The web survey platform utilised was eSurveypro.com, provided by the Romanian

company

Outside

(www.esurveyspro.com/Default.aspx).

The

Software company

provided

Inc. excellent

customer support in the course of setting up and conducting the survey. While the number of people who received the request to participate in the survey cannot be precisely determined because of some secondary invitations, the number of people who accessed the survey online was easily determined. Following are relevanat survey sample data: •

Number of personal invitations sent out by researcher: 165



Number of invitations to industry and professional associations: 5



Number of people who accessed the survey: 107 (or about 65% of the number of personal invitations)



Number of people that successfully completed the questionnaire: 69 (or 42% of the number of personal invitations)

The personalisation of invitations to participants doubtless enhanced the response rate. Many of the respondents felt personally obliged to inform the researcher when they had completed the survey or when they get to it, even if they did not have a prior personal relationship with him.

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Construct Operationalisation The constructs in the research model were operationalised based on the outcome of literature review. The outline of how the constructs were operationalised is included as Appendix C3-D. Questions have been extracted from previously tested questionnaires relevant to the constructs as much as possible. Where such tested questions could not be found, they were generated by the researcher in accordance with outcomes of the literature survey. Insofar as possible, the questions were arranged within the questionnaire in groups corresponding to the constructs; however, in a few cases this rule was not observed where a deviation appeared to create a better flow of questions. A 5point Likert scale was used for all quantitative measures; in addition, respondents were given several opportunities to qualitatively express supplemental views.

Questionnaire Pretest (and Survey with Finalised Questionnaire) Ten project managers were invited to participate in the pretest, and five of them responded. The pretest questionnaire included an opportunity to capture respondent opinion of the questionnaire at the end, as suggested by other researchers (Narins 1999; Muller 2003). The questions extracting respondents’ opinion of the questionnaire are included as Appendix C3-E. Responses to the pretest resulted both in a reduction of the total number of questions and the inclusion of new questions to gather data considered useful. The pretesting occurred in March and April 2009, and the responses obtained were not considered in the eventual data analysis for this research. The finalised questionnaire was made available for public participation in late May 2009 for about four months early and was closed by early October 2009. Because of the limited sample population, substantial personal solicitation was required to achieve a viable sample size; simply posting announcements on Skema Business School

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public websites did not yield good participation. The cover note and complete questionnaire are included in Appendices F and G, respectively.

3.6 Data Analysis Approach The following five-step strategy was followed to analyse the research data obtained: Data Preparation This step included checking for missing data using the Little’s MCAR method (Field 2009) and deleting blank cases, including cases with only a few questions answered. Outliers with values greater than two standard deviations were identified using box plots (Field 2009). As appropriate for analysis, missing values were assigned the mean value of the variable concerned (Sekaran 2003). Overall, respondents included project directors, project managers, senior project managers, project services managers, contract and procurement managers, and project engineers. No responses were received from the petrochemical industry or from independent oil and gas companies. Testing of means using the ANOVA method was carried out to determine whether data from any of the potential response groupings can be combined. The response groupings that had their means tested for significant differences were: •

Between national oil companies (NOCs), international oil companies (IOCs), oil and gas service companies, and entities other than oil and gas companies (i.e., contractors).



Between project directors, project managers, senior project engineers, project services managers, and project engineers.

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Two modes of project cost were observed in the data, less than $2.5 billion and less than $2.5 billion. These were also checked for any differences in mean.

Purification of Constructs An internal consistency check of the variables extracting data for each construct, was done. The aim was to examine how consistently the variables making up each construct expressed the same concept (Vaus 2002), and consequently to exclude those that appeared inconsistent (Churchill 1979). The measurement scale for each construct can thus be “purified” and better enhanced. The item-item and item-total correlations and Cronbach alphas of all variables within each construct were calculated (see Appendix C5, Table 3). Items with item-to-item correlations less than 0.3 and those that produced a substantial or sudden drop in item-to-total correlation (or alpha less than 0.5) were considered for elimination (Churchill 1979). Outliers were proposed for exclusion as described in the step on data preparation above, but subject also to other logical considerations.

Securing Goodness of Measures Reliability tests are required to ascertain how consistently an instrument measures the concept of attention (Sekaran 2003). Similarly, construct validity tests were done to ascertain that the instrument is actually measuring the particular concept intended (Vaus 2002; Sekaran 2003; Field 2009). Achieving this validity is important to enable generalisation of findings within the sample population (Sekaran 2003). To determine internal consistency or reliability of measure (scale reliability) for each of the constructs, the Cronbach coefficient alpha statistics were computed (Churchill 1979, referencing Nunnally 1967). This step was in Skema Business School

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iteration with step on purification of constructs described above. Reliability statistics was done at the sub-scale level as recommended by Field (2009) for multiple-factor constructs as dealt with in this research. A minimum overall scale alpha exceeding 0.6, considered as a minimum for reliability is desired for each construct (Field 2009). As a prerequisite for parametric analysis, normality of data distribution for all constructs was tested by obtained skewness and kurtosis values, and validated using p-p plots (Cooper & Schindler 2006; Field 2009). Checking for construct validity was approached in three ways: •

The extent to which each construct is independent of others in the model—i.e., there should be low correlations between any particular measure and other measures in the model that are supposedly not measuring the same concept, thus fulfilling the condition for discriminant validity (Churchill 1979). Field (2009) suggests that predictor correlations should be less than 0.8. Co-linearity tests were implemented during regression analysis as a confirmatory check for discriminant validity.



Second, construct validity was achieved by comparing the overall scale reliability of the construct with similar constructs measured in other studies (Churchill 1979; Muller 2003). For example, the historical Cronbach alpha for the “Scanning (Information feed)” measurement scale was between 0.73 and 0.88, and the Cronbach alpha for “Interpretation” was between 0.72 and 0.91 (Thomas et. Al. 1993).



Finally, the variables were checked to see if they would produce results that are consistent with well-established theories (Vaus 2002). For example, a theory based on the results of various studies that

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indicated a positive correlation between decision-making practices and business performance (Thomas et al. 1993; Lamb et al. 1999; Jinkman et al. 2000; Simpson et al. 2000; Begg et al. 2001; Mackie et al. 2007) would predict that– information management, interpretation, decision-commitment,

and

implementation

practices

should

positively correlate with strategic value in some form.

Feel for the Data (Qualitative and Quantitative) Following the step explained above, descriptive statistical analysis was carried out to provide a general sense of data dispersion. Details are discussed in chapter 5.

Hypothesis Testing There are eight constructs to be tested, of which one (long-term strategic value) is the principal criterion variable hypothesised to be dependent on the other variables. After establishing normal distribution of the data and the independence of each observed case, and having used an interval measurement scale for each construct, the next logical step was to use parametric tests for the hypotheses (Cooper & Schindler 2006; Field 2009). The homogeneity of variance as a condition for using parametric tests was checked prior to further analysis. The research hypotheses as stated in chapter 2 essentially entailed (1) checking for relationships (i.e., correlations) and determining the outcomes of a dependent variable based on the effect of other (independent, moderating, or intervening) variables. The hypothesis tests chosen to meet these requirements were “Correlation Analysis,” “Multivariate Regression Analysis,” and “Moderated Hierarchical Regression Analysis” (Sharma et al. 1981; Field 2009). The following conditions

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for applying multivariate (or multiple) regression analysis were satisfied (Vaus 2002; Osbourne & Waters 2002; Field 2009): 1) Dependent variables are measured on the interval scale 2) The measurement scale should be reliable 3) Independent (predictor) variables are predominantly measured on the interval scale 4) Data should be free of outliers that can distort results 5) Variables are normally distributed 6) Dependent and independent variables should have approximately linear relationships 7) Multi-collinearity, or the degree to which the predictor variables are inter-correlated (Grimm & Yarnold 1995), must be absent 8) Homogeneity of variance errors (i.e. homoscedasity) should exist across all levels of independent variables Assumptions (1) through (8) have already been satisfied within the first data analysis steps described above. Item (7), the multi-collinearity diagnostic, was done by calculating statistics for variable inflation factor (VIF) and tolerance measures. As a rule of thumb, tolerances ≤ 0.2 and VIF ≥ 5 are indicative of multi-collinearity problems (Vaus 2002; Field 2009). For regression purposes, a typical rule-of-thumb sample size that reflects about 10 to 15 cases per predictor variable is recommended (Roscoe 1975; Field 2009); 20 cases per predictor are considered ideal, while 5 cases per predictor should be the minimum (Tabachnick & Fidell 1989). Regressions done in this study did not exceeded 6 predictors at any one time. This was achieved by testing the research model in sections (parts), rather than as a whole. The research model as finally prepared for testing based on this is shown in Figure Skema Business School

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3.1. Hence, to attain an availability of 10 cases per predictor, a total number of 60 cases would be satisfactory. This number is within the band of the sampling requirement as defined in section 3.5 (on sampling). The number of viable cases obtained from the web survey exceeded this number.

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Figure 3.1: The Research Model as Tested DEPENDENT VARIABLES

INDEPENDENT VARIABLES

H1

Information-feed factors

Long-term strategic value indicators *

H2

Interpretation factors

Long-term strategic value indicators *

Information-feed factors

H3

Controllability factors Interpretation factors

H4

Decisions practice (quality) factors

H5

Decision implementation factors

H6

Information-feed factors

Long-term strategic value *

Long-term strategic value *

Long-term strategic value *

Interpretation factors

Chance Factors

H7

Long-term strategic value *

Information-feed factors

Context factors

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CHAPTER FOUR Research Definition: The Early Qualitative Input This chapter summarises the basis for and the outcome of some semistructured interviews held to clarify the scope of this study and make it more relevant to project management practitioners. The interview results were not extensively analysed beyond providing preliminary input for research directions.

4.1 Background and Approach This study was initially premised on a performance management theme, with the intention of investigating the influence of performance management on the level of strategic value eventually realised from oil and gas megaprojects. Semi-structured interviews were conducted to gain preliminary insight into the challenges that project managers actually experience, and to guide the research so that it might yield information relevant to project management practitioners within the industry. Seven project executives with a collective project experience of more than 130 years were interviewed. Their experiences included oil and gas (five interviewees) mineral extraction (one interviewee) and government infrastructure (one interviewee) projects. More specific information appears in Table 4.1.

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Table 4.1: Characteristics of Interviewees Interviewee ID

Current Work Industry Type

Location

Years of Project Management Experience

1

Oil and Gas

Europe

20

2

Oil and Gas

Europe

14

3

Oil and Gas

Europe

30

4

Oil and Gas

Europe

33

5

Oil and Gas

Middle East

20

6

Government

USA

20

USA

5

Infrastructure 7

Mineral Extraction

The interviewed oil and gas project managers, though four of them are now based in Europe, also have experience in other regions of the world. Interviewees were assured that their anonymity and that of their company would be protected. The interview questionnaire (reproduced in Appendix 4.A) consisted of six questions that were pretested on two project managers and refined prior to the main interviews. The seven interviews were conducted in April and May 2007 and lasted between 30 and 60 minutes each. Six of them were digitally recorded (with permission); at the seventh interview, notes were taken by hand. Post-interview notes from the recordings were not validated with interviewees, since the original digital recordings were available and because the interviews were not intended for use in testing research hypotheses.

4.2 Results and Implications for Defining the Study The seven semi-structured interviews were characterized by an overriding theme: the fundamental importance of good decision making in Skema Business School

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performance management. As a review of existing research found many studies on performance management, this researcher decided to modify the study theme to decision management, a related area that seemed to offer a wider opportunity to contribute new knowledge. The interviewees also provided insight into the most nagging strategic issues faced by megaprojects in the execution phase. The issues they identified included contracting and procurement management; government relations management; host community relations management; joint venture interface management; health, safety, security, and environmental matters; multilocation management of fabrication and facilities integration; local content implementation; project governance; managing the project team (e.g., team members’ individual aspirations and job satisfaction); attaining cohesion within the broad team; and multi-cultural leadership within the project. They assessed technical risks as generally less challenging. The insights that these interviewees provided helped to identify some factors that may impact project decision making. A summary of key interview findings and the decision-making factors they implied appears in Table 4.2. Many of these factors were later corroborated by the subsequent literature review and influenced the design of the questionnaire used to gather data for hypotheses testing.

Table 4.2: Summary of Semi-Structured Interviews To Define the Research Scope SUMMARY OF FINDINGS

IMPLIED

FACTORS

THAT

COULD

AFFECT DECISION MAKING BY THE PROJECT MANAGER 1. The focus of most project leaders is more

on

Skema Business School

getting

the

project

• Mental model: Ability to create a balance between

EWEJE, J. A, PhD 2010

Megaproject

efficiency

and

Page 105

SUMMARY OF FINDINGS

IMPLIED

FACTORS

THAT

COULD

AFFECT DECISION MAKING BY THE PROJECT MANAGER mechanically

completed

than

on

ultimate long-term strategic value to be realised for the company.

effectiveness performances. • Senior

management

priorities

and

governance policies: how strongly do they drive the project manager’s focus?

2. While most cost and schedule overruns

• Could

problems

from

the

business

are publicly reported to be largely due

environment and external stakeholders

to

sometimes have roots in problems internal

uncertainty

in

the

business

environment and external stakeholders, most

interviewees

internal

to

the

believed project

issues

and

the

to the project/business organisation? • Adequacy of the project system to identify risks

and

opportunities

present

in

a

sponsoring organisation(s) were their

project’s external environment in a timely

main

manner, and to effectively manage them.

performance

challenges

with

respect to cost and schedule. 3. Quality of project sponsorship is key to success.

Project manager’s perception of the level of commitment and support from the project sponsor.

4. The (often irrational)

influence of

politics on project outcomes should not

Quality of the sociopolitical environment around the megaproject.

be underestimated. 5. There

are

of

The level of clarity that the project manager

nonalignment of strategic objectives

(and his or her team) has about goals,

among

objectives and drivers of critical stakeholders.

joint

often

evidences

venture

partners,

contractors, and the project team. 6. There is a high tendency to bypass performance management systems in

Applying inconsistent decision-making rules or processes.

the field, especially on megaprojects. 7. The impacts of cultural diversity in

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Project

EWEJE, J. A, PhD 2010

team

cohesion:

are

diversity,

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SUMMARY OF FINDINGS

IMPLIED

FACTORS

THAT

COULD

AFFECT DECISION MAKING BY THE PROJECT MANAGER project leadership and supervision can

inclusiveness,

and

sociocultural

be tangible.

understanding present within the project team?

8. Implementing

(LC)

Understanding the impact of sociocultural,

requirements on projects often has

sociopolitical, and socioeconomic conditions

greater cost and time impact than

of the project environment, and their potential

anticipated; LC should therefore be

impact on strategic objectives for the project.

realistically project

local

content

integrated

budgeting

within

and

the

schedule

planning. 9. 30% of project managers want to improve their ability to qualitatively

• Desire of project managers for improving decision quality doe not appear high.

predict future project performance;

• How is information relevant to decision

they would like to see more scenario

making managed (with regard to quantity,

analysis

quality, and timeliness)? How much control

practiced

in

megaproject

management.

over

the

environment

around

the

megaproject does the project manager have? • What is the quality of project managers’ decision-making skills? 10. Integrating the main contractors as part of the owner project team has positive

Quality of relationships between project teams and main contractor(s)

impacts.

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CHAPTER FIVE Data Presentation This chapter discusses details of the acquired research data and how it was prepared for analysis. It covers the data purification process, reliability and validity testing, and factor analysis to verify the underlying structure of the research constructs.

5.1 Quantitative Data Preparation A total of 107 responses to the web survey were received between May and October 2009. Of these, 69 were fully or almost fully completed and others were partially completed to varying degrees. Respondents included employees of national or international oil and gas producers (85%), major service providers to the oil and gas industry (7%), and companies other than oil and gas producers (8%). Most of the respondents not from oil and gas companies completed only the introductory part of the questionnaire fully, perhaps because they felt their experiences did not equip them to answer questions in other parts of the survey. With one exception, these responses (N=8), in addition to others with incomplete responses (N=30), were eliminated. The only non-oil and gas response retained was from a major real estate development project. An ANOVA test of means showed that the real estate project manager’s response contained very similar characteristics in size, cost, and complexity to those in the oil and gas industry (see Appendix C5, Table 1A). The eventual sample size of 69 respondents consisted of the following: 94% from oil and gas producing companies, 5% from oil and gas contractors, and 1% from other companies. This number of useful cases, 69, exceeds the minimum requirement of 5 cases per variable for regression (Tabachnick & Fidell 1989, Field 2009), as

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between 7 and 20 cases per regression variable were achieved in statistical analysis. The SPSS software, version 17.0, was used for statistical analysis. A missing value analysis conducted showed there were 0-6% missing values across all the questions, not including a few questions (about 3% of the total data) that were optional. Little’s MCAR test (a chi-square test) was conducted on all construct variables in the model to check the characteristics of missing data, and if there would be any need for data imputation (Field 2009). There are two possibilities to characterise missing data, MCAR (Missing Completely At Random) or MAR (Missing At Random). Little’s test assumes the hypothesis that the missing values are MCAR; hence a p>0.05 significance in the test would confirm MCAR characteristics, with MAR being the conclusion otherwise. When MCAR is true, missing values are considered randomly distributed across all observations; if missing values are characterised as MAR, data imputation is then required. The p values on Little's MCAR test for all constructs in this study were above 0.05, so the missing data were assumed to be randomly distributed and no data imputation was carried out. All but a few of the survey questions were asked with positive inclination; scores on the negatively inclined questions were reversed prior to analysis. In some further analysis, however (e.g., factor analysis and regressions), missing values in retained cases were replaced with variable means (Sekarian 2003; Field 2009). Outliers with values over two standard deviations were identified using box plots and extreme value analysis (Field 2009). The outlier cases were few, constituting about 7% of data. Following data cleansing by scale reliability analysis and reduction by factor analysis, no logical reason was found to remove any of the outlier cases.

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5.2 Data Grouping In order to obtain confidence that data from the various grouping of respondents outlined in section 3.6 could be combined, a comparison of means scores using the ANOVA method was carried out. Data groups checked included: Industry types represented, observed statistical modes of project costs, and the roles of respondents on projects. Results at the construct level indicated no significant differences in mean within each of the groups mentioned above (i.e., all were F>0, p>0.1); hence all responses were analysed as a single data set (see Appendix C5, Tables 1A–1C).

5.3 Identification

of

Underlying

Structure

within

Model

Constructs Based on the literature survey and interviews, variables were hypothetically associated with each construct as discussed in chapter 2. Prior detailed analysis, factor analysis (FA) was done on each construct to either validate or enhance the structure of hypothesised associations, and to eliminate redundant data. Between 25% and 100% of variables within the various constructs had correlations greater than 0.3, and all these were significant at p