A thesis submitted to the Faculty of Business and Law in fulfillment of the requirements for the degree of. The association analysis yielding the study's key findings indicated that work experience, manageability, anxiety, and cost decision-making have a significant impact on cost overruns due to being overly optimistic, while other factors were not significant.
Background of Study
More recent studies on cost overruns in infrastructure megaprojects show similar findings from the previous literature. For example, Cantarelli and Flyvbjerg (2015) studied the cost overrun in various regions of the world.
Problem Statement
17 The study by Flyvbjerg et al. 2003) recognized and summarized the main causes of megaproject cost overruns in infrastructure as incomplete risk assessment and poor decision-making due to the absence of accountability for the decisions. Further, decision making is affected by similar factors as the problem in this study, which is cost overruns.
Research Gaps in Knowledge
According to Calabretto et al. 2016), strategic decision-making is optimized through a combination of analytical and intuitive decision-making. Decision making also includes the mental effect of the decision maker on the decision made (Oliveira, 2007).
Research Aims and Objectives
General Aim
Specific Objectives
Research Questions
RQ2: What are the key demographic characteristics of project managers that influence decision-making and performance for megaprojects. RQ3: What personality traits do project managers have that can be associated with cognitive bias, decision making and performance.
Research Scope
26 RQ1: What are the cognitive biases in decision-making that can lead to cost overruns in megaprojects. Different theoretical conceptualizations of decision-making processes are also included to contextualize cognitive biases with existing research.
Significance of the Research
Purpose of the Research
The aim of the study is not only to explain how cognitive biases influence decision-making, but also to consolidate cognitive biases as one of the main reasons megaprojects end in cost overruns. In addition, the aim of the study is to generate interest among scientists studying megaprojects, especially since the influence of cognitive biases on day-to-day decision-making is often ignored and most project managers are unaware of such biases.
Structure of the Project
Ultimately, being aware of cognitive biases is enough to avoid falling prey to the phenomenon, especially on mega projects where miscalculations can be costly and damaging to entire economies. The decision to combine cognitive biases and decision-making theories in one chapter is motivated by the close relationship assumed in the current research.
Introduction
Mega Projects and Decision-making
Another factor that contributes to failed mega projects is related to poor project execution. The last reason is related to the actual structure of organizations involved in the implementation of mega projects.
Role of Decisions Made in Mega Projects
It is therefore interesting to critically examine the claims of the Harvard Business Review (2016) about the importance of project phases in decision-making, specifically for mega projects. In other words, Pitsis et al. 2018) suggests that if any of the above factors are absent.
Measuring Performance of Mega Projects
In this regard, project decision-making processes are greatly influenced by different circumstances surrounding specific projects, and this has played a major role in the overall performance of megaprojects. In this regard, while measuring the performance of megaprojects from the cognitive biases perspective, includes areas that cannot be overlooked.
Challenges of Mega Projects
In general, this is a crucial issue of administration that often causes delicate megaprojects – megaprojects to self-destruct as a result of a lack of shared ownership and conviction (Merrow, 2011). The above decision is typically difficult, especially when there are political motives behind the initiation of the megaproject.
Summary
Even when the project meets the expected specifications, there is a high probability that some aspects of the project will be considered unsuccessful. For example, a project may end up providing project benefits to a local population, but at extremely high costs, time and negative environmental impact.
Introduction
Cognitive Biases
- Controllability Bias
- Availability
- Anchoring
- Confirmation Bias
- Cognitive Dissonance
- Dread
- Familiarity
- Hindsight
- Scale
- Representative Bias
- Optimism Bias
- Venturesomeness
Brent (2018) states that "the disinformation effect refers to the impairment of memory for the past that arises after exposure to misleading information." The disinformation effect has a huge negative impact on the decision-making process. It was quite a different case when the question was asked as, "How fast were the cars going when they crashed into each other?" the type of responses received from the latter suggests that the cars were traveling at a slower speed compared to the responses to the first question (Sarah, 2018).
Decision Making
Decision-Making Styles
Rational decision making focuses on the lasting results of the decision and includes enough evidence to support the decision, hence the. 73 rational decision making can be explained by being intentional, investigative and reasonable (Russ, McNeilly and Comer 1996).
Decision Making Theories
It must be observed to ascertain when the causes of the person's behavior are internal or external. The causes of facades are those components that are outside the person who is the subject of perception (McLeod, 2018).
Decision Making and Mega Projects Costs
Where the project is confronted by such a challenge, high-frequency decision-making becomes a necessity. However, the decision is subject to an attraction for the desired goals of the project (John Eweji, 2012).
Risk Decision making in mega projects
Risks define the behavior as well as the decision-making capacity of the project managers (Van de Ven, 2008). The risk management team sometimes makes decisions that sometimes conflict with the work of the project planning team.
Summary
The chapter also discussed the decision-making theories, an addition to decision-making and risks associated in the context of megaprojects, as well as potential costs. The next chapter will discuss in detail the conceptual framework model that guided this study, attempting to demonstrate the association between megaprojects and decision-making based on cognitive biases respectively.
Introduction
Furthermore, different parties involved in the execution of the project may attribute different levels of success or failure. In this conceptual framework, more emphasis is placed on the definition of the variables and constructs being investigated more than whether a project is a success or a failure.
The Conceptual Model/Framework
Independent Variables
First, control bias is one of the cognitive biases identified in the current debate. According to Margaret Rouse (2018), availability bias is close to the factors that touch the physical availability of the project manager.
Mediating Variables
The traits are determined by the society in which he grows up, as well as by the socio-economic structure of the society in which he grows up.
Dependent Variables
Project costs are products of an estimate that is highly dependent on the perceived complexity of the project (Hugo, 2010). About 25% of such projects fail due to a cost overrun that can sometimes amount to as much as 33% of the estimated cost of the project (Berechman, 2011).
Theoretical Framework
On the other hand, evaluation of resources will help to reduce cost overruns. On the other hand, it will facilitate employing both task-oriented and people-oriented project managers in mega-projects.
Application to Research Problem
In fact, the interruption of the failures acted as a mast for the real problems that were common in the equipment. This study is pertinent to the question of the relationship between cognitive biases and risk management.
Synthesis of the Study Constructs
The emphasis is on the multi-level nature of relationships, which is clearly shown in Figure 4.2. The first level is attributed to the demographic characteristics of project managers involved in mega projects.
Summary
Ideally, dependent variables are examined at lower levels of the multilevel model; however, this study suggests hierarchical. Finally, a synthesis of the constructs is detailed to simplify and integrate the extensive discussion in the previous sections.
Introduction
Research Philosophy
Overview of Philosophical Stances
Moreover, it is important that different aspects are reflected using different techniques, but the meaning should be focused in comparison with the facts. Precisely, it is seen to be the opposite of positivism in each of the above constructs as it focuses on multiple and socially constructed realities as opposed to single and objective ones (Myers, 2008).
Justification of the Philosophy Choice
In fact, strict followers of the positivist paradigm maintain that the researcher should ideally be considered independent of the study as is the case in the current investigation. The selection of the positivist paradigm for this research is particularly the need to emphasize facts rather than actual meaning.
Research design
The aforementioned examples demonstrate the accreditation of the research design by other scholars investigating cognitive bias and decision making or related variables. An examination of the research questions posed in the ongoing investigation nevertheless suggests that a quantitative approach is best suited to the investigation.
Research approach
The deductive approach is highly recommended for quantitative research because of the necessity of statistical procedures to test hypotheses formulated from background theory. In particular, the deductive approach's over-reliance on observations to provide conformations about reality has been blamed as a potential weakness.
Target Population
Sampling and Sample Size
Typically researchers in the social sciences work on the basis of a 5% margin of error or 95% confidence level to estimate sample size. Where n is the sample size, p = percentage of project managers who have the characteristic (ie, worked on mega projects, q = 1-p, and d = margin of error.
Questionnaire design
- Types of Questions
- Measurements
- General Structure
- Specifics
- Questionnaire data coding and validation
Although the questions are based on existing studies, they are adapted to the role of the research. The data collected from the specific part of the questionnaire is coded with different weights, as is common with questions on the Likert scale.
Pilot Study
Reliability
All the primary constructs of the current study are checked for reliability using Cronbach's alpha. In addition, the study checked the reliability of the sub-instruments used in the research to ensure that they provide consistent results and minimize errors.
Validity
Nature and Source of Data
Data Analysis
The actual analysis of the data collected from the study participants follows reliability and validity tests. In addition to the analysis of the participant's personal data, descriptive statistics of the main variables under investigation follow.
Ethical Foundations
Chapter Summary
Introduction
Common Bias Testing
Reliability Test Results
Descriptive Statistics
Participant’s general information
Descriptive statistics for main variables
Variance Analysis Using ANOVA
Hypothesis testing
Cost decision-making in mega projects
Project risk decision-making in mega projects
Personality traits on cost decisions
Factors contributing to project cost overrun
Summary
Association Analysis
Normality tests
Linearity test
Multicollinearity Test – Tolerance and VIF
Homoscedasticity verification
Correlation analysis
Regression Analysis
Summary
Testing the combined influence using hierarchical regression
Significance of the Estimated Coefficients
Introduction
Hypotheses
Determinants of Over Optimism in Mega Projects
Research Question 1
Association Analysis
Robustness of the Methodology
Accomplishing the Research Objectives
Generalisability, Applicability and Implications of the Findings
Research Limitations
Contribution to Knowledge of the Research
Recommendations for Further Research
Reliability Tables
Variance Analysis Tables
Normal P-P Plots
Scatter Plots
Correlation Analysis
Hierarchical Regression Tables
