Influence of Human Behavior on Success of Complex Public (PDF)

European Scientific Journal December 2017 edition Vol.13, No.34 ISSN: 1857 – 7881 (Print) e - ISSN 1857- 7431 313 Influence of Human Behavior on Success of Complex Public Infrastructural Megaprojects in Kenya Austin Baraza Omonyo, PhD Centre for Finance & Project Management Nairobi, Kenya Doi: 10.19044/esj.2017.v13n34p313 URL:http://dx.doi.org/10.19044/esj.2017.v13n34p313 Abstract The main objective of this study was to investigate the influence of human behavior on the success of public infrastructural megaprojects in Kenya. The need for this study arose from the thesis that complexity due to human behavior is the main cause of waste and failure that results in infrastructural megaprojects being delivered over budget, behind schedule, with benefit shortfalls, over and over again. The study was designed as multiple-method research, based on virtual constructionist ontology recognizing that complexity is the mid-point between order and disorder. A cross-sectional census survey of 27 completed public infrastructural megaprojects was conducted using two interlinked questionnaires assessing human behavior constructs and project success. A total of 108 respondents made up of project managers, team members and organizational sponsors, participated in this study. Using both descriptive and inferential analysis, the results of this study have confirmed that human behavior significantly influences success of public infrastructural megaprojects. Optimism bias remains the main individual behavior that leads to cost and schedule underperformance in infrastructural megaprojects but loss aversion is the most occurring cognitive bias. In light of this finding, the study recommends that implementing organizations adopt structures that allow for continued business justification, focus on products and give project managers sufficient authority over project resources in line with the postulations of the structural contingency theory. Keywords: Optimism bias, sunk cost effect, megaproject, complexity, human behavior Introduction The International Centre for Complex Project Management (ICCPM) describes complex projects as those characterized by uncertainty, ambiguity, with emergent dynamic interfaces, influenced by significant political or  European Scientific Journal December 2017 edition Vol.13, No.34 ISSN: 1857 – 7881 (Print) e - ISSN 1857- 7431 314 external change, are run over a period which exceeds the product life cycles of the technologies involved or where significant integration issues exist; are defined by effect (benefit and value) but not by solution (product) at inception (Hayes & Bennet, 2011). This description is important in distinguishing complex systems from complicated ones, which have many moving parts that operate in patterned ways. Organizational complicatedness is usually measured based on the number of procedures, vertical layers, interface structures, coordination bodies and decision approvals (Morieux, 2011). Complex systems by contrast are imbued with features that may operate in patterned ways but whose interactions are continually changing. According to Sargut and McGrath (2011), three properties determine the complexity of the environment namely; multiplicity, interdependence and diversity. Several studies linking complexity with project success have confirmed that complexity predominantly determines project success (Meyer, 2014; Hargen & Park, 2013; O’Donnell, 2010; Shermon, 2011, Flyvbjerg, Holm & Buhl, 2004; Vanston & Vanston, 2004). Infrastructural megaprojects are among the most complex category of project (Brady & Davies (2014). These projects are usually large-scale, complex ventures that cost billions of money, take many years to develop and build, involve multiple public and private stakeholders, are transformational, and impact millions of people (Flyvbjerg, 2014). They are “greenfield” in nature as they often create new assets and utilize a variety of delivery models depending on their inherent complexity. In Kenya, megaprojects are increasingly used as the preferred delivery model for goods and services across a range of businesses and sectors. Such projects include the Standard Gauge Railway, the Konza techno-city, the LAPPSET Corridor and the Thika Superhighway, to mention but a few. Inherent complexity in megaprojects is the main source of contextual risk which is usually referred to as typological risk (Omonyo, 2015). The magnitude of this risk increases as we move from an environment of low complexity towards high complexity. The effectiveness of project control is usually affected by typological risk in such a way that as the value of the typological risk increases, exercising project control becomes more difficult. This inability to control complexity has been recognized as a major factor in project failure for a number of years (Williams, 1999). However, complexity remains ambiguous and ill-defined in much of the project management literature (Geraldi, 2008). This could explain why complex infrastructural megaprojects are usually delivered over budget, behind schedule, with benefit shortfalls, over and over again; what Flyvbjerg (2014) characterizes as the “iron law of megaprojects”. According to Kahneman and Tversky (1979) and Lovallo and Kahneman (2003), human behavior is the main  European Scientific Journal December 2017 edition Vol.13, No.34 ISSN: 1857 – 7881 (Print) e - ISSN 1857- 7431 315 explanation for the iron law of megaprojects. It is this thesis that necessitated this study. This study, through a cross-sectional census survey of completed complex public infrastructural megaprojects investigates the influence of human behavior on success of public infrastructural megaprojects in Kenya. The main contributions of this research include: confirming that human behavior has significant negative influence on success of public infrastructural megaprojects; optimism bias remains the main individual behavior associated with cost overruns and schedule delays; loss aversion is the most occurring cognitive bias among the individual systematic biases; and public infrastructural megaprojects in Kenya are delivered within a culture that does not recognize uncertainty, rapid change, emergence, connectedness and dependencies that characterize the context of these projects. For the remainder of this article, I review relevant theoretical and empirical literature which presents an argument for the hypothesis of the study. This is followed by a description of the research design, data analysis and results together with a discussion of those results. The final section provides conclusions from the study and implications for both research and practice. Literature Review and Hypothesis To underscore the importance of complexity in determining project outcomes, Project Management Institute (PMI) published a global practice guide on navigating complexity in 2014. According to this standard, the causes of complexity in projects and programs are grouped into human behavior, system behavior and ambiguity. Of these causes, human behavior is said to be the main explanation for delivering megaprojects with cost overruns, behind schedule, with benefit shortfalls, over and over again (Kahneman & Tversky, 1979; Lovallo & Kahneman, 2003; Flyvbjerg, 2014). Human behavior may be the result of factors such as changing power relationships, political influence, and individuals’ experiences and perspectives (PMI, 2014). These factors may hinder the clear identification of project goals and objectives, thus affecting the project delivery capability. The PMI Practice Guide for Navigating Complexity identifies four main constructs of human behavior namely; individual behavior, group, organizational and political behavior, communication and control, and organizational design and development. A broad description of each of these constructs and therefore, of human behavior, is to be found in the discipline of organization theory. Organization theory describes a body of knowledge that brings together several management and organization theories. The main  European Scientific Journal December 2017 edition Vol.13, No.34 ISSN: 1857 – 7881 (Print) e - ISSN 1857- 7431 316 approaches in organization theory stem from the works of the main schools of management thought namely; classical, human relations, systems, contingency, decision and social action (Mullins, 2007). As a body of knowledge, organization theory studies organizational designs and structures, relationships of organizations with their external environment and behavior of managers and technocrats within organizations. Besides suggesting ways in which organizations can cope with rapid change, organization theory provides a framework of studying organizations to identify the patterns and structures they use to solve problems, maximize efficiency and productivity and meet the expectations of stakeholders. A related (even though widely held as distinct) body of knowledge relates to organization behavior. Organization behavior involves understanding of individual and group behavior, and patterns of structure in order to help improve organizational performance and effectiveness (Mullins, 2007). The theories of organization behavior relate to the understanding, prediction and management of human behavior in organizations (Luthans, 2002). According to Wagner and Hollenbeck (2010), the study of organization theory can be divided into three levels namely; micro, meso and macro. The first level involves the study of individuals in organization, the second level involves the study of work groups and the third level involves the study of how organizations behave. It can be concluded therefore that organization behavior is a subset of organization theory and that each of the levels in the study of organization theory represents the main constructs of human behavior in organizations, namely; the individual, the group and the organization (Mullins, 2007; PMI, 2014). There are several management and organization theories that explain human behavior, some of which are described by Miles (2012). However, for the purposes of this study, three theories were used, namely; agency theory at the micro level, social identity theory at the meso level and structural contingency theory at the macro level. Agency theory, also referred to as principal-agent problem or agency dilemma, relates to risk sharing among groups that are in a contractual relationship. With its roots in behavioral economics, agency theory has been applied extensively in organization behavior (Eisenhardt, 1985; 1988). Agency problem occurs when cooperating parties have different goals and vision of labor (Jensen & Meckling, 1976). As such, this theory is concerned with resolving two problems that can occur in agency relationships-the first arising when the desires or goals of the principal and agent conflict and the second arising when it is difficult or expensive for the principal to verify what the agent is actually doing. The second is the problem of risk sharing that arises when the principal and agent have different attitudes toward risk. For instance, in a cost-plus percentage fee contract, a contractor may have no incentive to  European Scientific Journal December 2017 edition Vol.13, No.34 ISSN: 1857 – 7881 (Print) e - ISSN 1857- 7431 317 reduce costs since the higher the delivery cost, the higher their fee. Likewise, a project manager may see no value in terminating a failing project because of individual benefit. Given the operation of the agency problem, organizations are faced with the problem of integrating the individual and the organization to enable successful delivery of its initiatives and this requires the understanding of both human personality and formal organization. This integration recognizes that individuals behave differently when acting in their organizational role than when acting separately from the organization (Chester, 1938). Thus, agency theory is key in explaining how individual behavior affects key organizational outcomes. This study takes the view that all dysfunctional individual behaviors and cognitive biases such as optimism bias, loss aversion, misrepresentation, etc., arise out of the lack of integration between individual and organizational goals, and also out of their differences in risk taking. Many studies have been conducted linking individual behaviors with project success. For instance, in a study to establish the effect of optimism bias on the decision to terminate failing projects, Meyer (2014) showed that in-project optimism bias is a significant contributor to decision maker’s motivation to continue with a failing project. For post-project optimism bias, the study showed that it is prevalent throughout the project and increases as the project approaches the end. The conclusions of this study are in line with the findings of Lovallo and Kahnemann (2003) whose research concluded that optimism and risk aversion were the main biases in forecasting and risk taking and that these two undermine executives’ decision-making. Mackie and Preston (1998) also found optimism to be among the 21 sources of error and bias in appraisal of transport projects. In a study to identify systematic biases in project failures, Shore (2008) conducted research on 8 large projects and wrote case studies on each failure to demonstrate how organizational and project culture could contribute to those biases. The findings of the study confirmed that there are indeed systematic biases and culture in project failure that are worth exploring. The main premise of this study was the fact that systematic biases are common in the human decision- making process and this provides a fundamental reason why project failure should not be an unexpected result. In a study of the causes of cost overruns in 258 transport infrastructure projects across 20 nations, Flyvbjerg, Holm and Buhl (2004) used Regression Analysis and concluded that underestimation cannot be explained by error and is best explained by strategic misrepresentation, that is, lying, which is a manifestation of agency problem. This is in line with the findings in Bruzelius, Flyvbjerg and Rothengatter (2002) who in a study on improving accountability in megaprojects, argued that differences between forecasts and actual costs could only be explained by the strategic behavior of the project proponents.  European Scientific Journal December 2017 edition Vol.13, No.34 ISSN: 1857 – 7881 (Print) e - ISSN 1857- 7431 318 They identify lack of long-term commitment, rent seeking behavior for special interest groups and the tendency to underestimate in tenders to get proposals accepted, as the main strategic behaviors of project proponents that adversely affect project outcomes. A second set of organization theories that explain human behavior is the social identity theory as attributed to Tajfel (1978). This theory explains the behavior of individuals in groups based on the need to maintain their social identity. According to this theory, people work to achieve and maintain a positive social identity which is based on favorable comparisons made among groups to which a person belongs and groups to which a person does not belong, and if social identity is unsatisfactory, then people strive to leave their current groups and join more favorable groups, or they try to make their current groups more satisfactory (Tajfel & Turner, 1986). Social identity research findings suggest three important consequences for organizations (Miles, 2012), namely; employees select and perform activities that resonate with their social identities, and they tend to support organizations that support their social identities; social identification tends to influence important group outcomes, such as cohesion, cooperation, altruism, and positive evaluations of the group (Turner, 1982, 1984); and, as employees come to increasingly identify with the organization, then the values, ideals, and practices of that organization can be perceived as more unique, distinctive, and positive compared to other organizations. This theory provides key explanation for group behaviors such as groupthink, groupshift, self-organization and tribal mindset. It is at the centre of explaining the evolution of team and project culture. The structural contingency theory is the third theory that explains human behavior particularly at the macro level. This theory stands on the premise that there is no one best organizational structure; rather, the appropriate organizational structure depends on the contingencies facing the organization (Burns & Stalker, 1961; Chandler, 1962). The theory posits that organizations will be effective if managers fit characteristics of the organization, such as its structure, with contingencies in their environment (Donaldson, 2001). Such contingencies could include organizational maturity, culture, opacity, among others. One of the most important concepts in the theory is alignment An organization whose characteristics align with the contingencies in its situation will perform more effectively compared to an organization whose characteristics do not fit with the contingencies in its situation. According to the theory, there are two main contingencies that need to be considered: organizational size and organizational task (Miles, 2012). This theory is critical in explaining the organizational design and development construct.  European Scientific Journal December 2017 edition Vol.13, No.34 ISSN: 1857 – 7881 (Print) e - ISSN 1857- 7431 319 A number of studies have been conducted linking this theory to project outcomes. For instance, in a study involving a critical review of extant literature, Olaniran, Love, Edwards, Olatunji and Mathews (2015) conclude that complex interactions between project characteristics, people, technology, and structure and culture contribute to the occurrence of cost overruns in hydrocarbon megaprojects. In exploring the role of project management maturity (PMM) and organizational culture in perceived performance, Yazici (2009) conducted a survey-based research with 86 project professionals from the manufacturing and service sectors in the United States of America. This study revealed that PMM is significantly related to business performance but not to project performance. According to this study, organizational culture change towards sharing, collaboration and empowerment, is required in order to deal with (overruns) in project time, cost and expectations. In a study of cost and time overruns in public sector projects, Morris (1990) identified bureaucratic indecision and a lack of coordination between enterprises to be among the main causes of cost and time overruns in large public sector projects. Both these factors map onto organizational design and development as an aspect of human behavior. In a similar study, Kaliba, Muya and Mumba (2008) conducted a study on cost escalation and schedule delays in road construction projects in Zambia and found that administrative structures and inexperienced administrative personnel were among the factors that explained cost overruns. In conclusion, the literature reviewed suggests that human behavior can have either positive or negative outcomes depending on the context. For instance, some positive psychologists postulate that optimism could be a very positive force at the workplace as it could motivate project teams to work harder, have high levels of inspiration and set stretch goals (Luthans, 2002). In the same veil, negative psychologists believe that optimism has a downside effect that could lead to dysfunctional outcomes. With this understanding, this study tested a non-directional research hypothesis that: HA1: Human behavior has significant influence on success of public infrastructural megaprojects. Another set of theory relevant to this research study was the project success theory. Project success theory is generally presented as a body of knowledge bringing together various research contributions to the success school of project management. Our review shows that there have been various attempts over the history of project management to define suitable criteria against which to anchor and measure project success (McLeod, Doolin & MacDonell, 2012). The most recognized of these measures is the long established and widely used “iron triangle” of time, cost and quality (Atkinson, 1999; Cooke-Davies, 2002; de Wit, 1988, Ika, 2009; Jugdev, Thomas, & Delisle, 2001). However, the “iron triangle” dimensions are  European Scientific Journal December 2017 edition Vol.13, No.34 ISSN: 1857 – 7881 (Print) e - ISSN 1857- 7431 320 inherently limited in scope (Atkinson, 1999; Ika, 2009; Wateridge, 1998). A project that satisfies these criteria may still be considered a failure; conversely a project that does not satisfy them may be considered successful (Baccarini, 1999; de Wit, 1988, Ika, 2009). The “iron triangle” only focuses on the project management process and does not incorporate the views and objectives of all stakeholders (Atkinson, 1999; Baccarini, 1999; Bannerman, 2008; de Wit, 1988; Jugdev & Muller, 2005; Wateridge, 1998). In recognition that project success is more than project management success and that it needs to be measured against overall objectives of the project thus reflecting a distinction between the success of a project’s process and that of its product (Baccarini, 1999; Markus & Mao, 2004; Wateridge, 1998), researchers have broadened the scope of project success to include three key measures, namely; process success, product success and organizational success (McLeod et al., 2012). Product success involves such criteria as product use, client satisfaction and client benefits. Organizational success criteria incorporates achievement of broader set of organizational objectives involving benefits to the wider stakeholder base (see Shenhar, Dvir, & Levy, 1997; Shenhar, Dvir, Levy & Maltz, 2001; Shenhar & Dvir, 2007; Hoegl & Gemuenden, 2001). This is plausible given that projects are a means of delivering the organization’s strategic objectives. Proponents of this school of thought advocate for inclusion of success criteria such as business and strategic benefits. Research Conceptual Model and Hypothesis Figure 1 illustrates the hypothesized research conceptual model which is based on PMI (2014) and McLeod et al. (2012). According to this model, human behavior as defined by individual behavior, group behavior and organizational design and development, represent independent variable while success of infrastructural megaprojects (defined as process, product and organizational success) was identified as the dependent variable.  European Scientific Journal December 2017 edition Vol.13, No.34 ISSN: 1857 – 7881 (Print) e - ISSN 1857- 7431 321 Method Context and Design This study was operationalized through exploratory, descriptive and explanatory research goals based on Neuman (2003) classification of research goals. To achieve these goals, a post-positivist philosophy emphasizing virtual constructionist ontology (Gauthier & Ika, 2012) was assumed. This philosophy utilizes both interpretivist (Bryman & Bell, 2007) and pragmatist (Goldkuhl, 2012) epistemologies to generate knowledge based on a combination of deductive and inductive approaches. The choice of this philosophical perspective was guided by the social world of complex megaprojects. In this social world, complexity is the midpoint between order and disorder, and megaproject management is neither a practice nor a tool (as is the case with projects implemented in the modern social world) but a rallying rhetoric in a context of power play, domination and control (Gauthier & Ika, 2012). This study was designed to be mixed-method research combining both quantitative and qualitative strategies (Burch & Carolyn, 2016). The mixed-method research provides an epistemological paradigm that occupies the conceptual space between positivism and interpretivism (Tashakkori & Creswell, 2007), the main epistemologies on which the virtual constructionist ontology thrives. To generate data for this study, a cross- sectional census survey design was used. This design entails the collection of data (predominantly by questionnaire or structured interview) on usually quite a lot more than one case and at a single point in time in order to collect a body of quantitative or quantifiable data in connection with two or more variables, which are then examined to detect patterns of association (Bryman & Bell, 2007). Population and Sample This study had as its primary population public sector infrastructural megaprojects implemented by the government of Kenya since 2005. Following Flyvbjerg (2014), the minimum budget for megaprojects included in this study was approximately Ksh. 1 billion. Managers, team members, sponsors and key stakeholders of these projects constituted the population of respondents from whom data was collected. A total of 31 projects were included in this study. For each project, four respondents comprising the project manager, project sponsor and two project team members were surveyed. In total, 108 respondents participated in this study. A total of 27 completed infrastructural megaprojects, representing a response rate of 87.1%, were surveyed as part of this research. Of these projects, 2 were from Kenya Ports Authority, 2 were from Kenya Pipeline Company, 6 were from Kenya Airports Authority, 3 were from Kenya Power and Lighting  European Scientific Journal December 2017 edition Vol.13, No.34 ISSN: 1857 – 7881 (Print) e - ISSN 1857- 7431 322 Company, 1 was from Kenya Electricity Generating Company, 5 were from Kenya Urban Roads Authority, 1 was from Kenya Civil Aviation Authority, 1 was from Geothermal Development Company, with the remaining 6 coming from Kenya National Highways Authority. Instruments and Data Collection Fieldwork for this study utilized two interlinked questionnaires namely, the human behavior assessment questionnaire and the project success questionnaire. The human behavior questionnaire was constructed based on the Practice Guide for Navigating Complexity (PMI, 2014) while the project success questionnaire was developed based on the works of Shenhar and Dvir (2001) and McLeod et al. (2012). Questionnaire survey is hailed to be an efficient data collection mechanism when the researcher knows exactly what is required and how to measure the variables of interest (Neuman, 2003). The human behavior scale comprised a 22-item Likert-type scale with the responses on each item being rated on a 5-point mutually exclusive scale where a rating of 1 denoted a “strongly agree” response, 2 denoted “agree” response, 3 denoted “somewhat agree” response, 4 denoted “disagree” response, while 5 denoted a “strongly disagree” response. A choice of either 1 (strongly agree) or 2 (agree) implied low complexity while a choice of either 4 (disagree) or 5 (strongly disagree) implied high complexity due to human behavior. A choice of 3 (somewhat disagree) implied a neutral and borderline response which did not communicate much on the complexity of projects studied and was therefore dropped from further analysis. The success scale comprised 18 items blending open and closed ended questions on one part and Likert-type questions on the other part. The first part involving closed and open ended questions was meant to assess process success while the Likert-type questions assessed product and organizational success on a scale of 1 (strongly agree) to 5 (strongly disagree). The first phase of data collection involved a pilot study on four projects to test the reliability and validity of the instruments. The results of the pilot study showed that both instruments were reliable with the human behavior scale recording internal reliability of 0.879. The overall internal reliability of the success scale was 0.889, both these values are greater than the cut-off Cronbach’s alpha of 0.7 (Nunnally, 1978). The pilot study results also demonstrated high concept, construct, and external reliability, in the study instruments. The second phase involved using revised study instruments to collect primary data from the remaining 24 projects. Generally, the projects surveyed had a budget at appraisal ranging from approximately Ksh. 1 Billion to Ksh. 40 Billion with 8 of these projects (29.6%) having a budget at appraisal of over Ksh. 10 Billion. The scheduled  European Scientific Journal December 2017 edition Vol.13, No.34 ISSN: 1857 – 7881 (Print) e - ISSN 1857- 7431 323 duration for these projects ranged from 4 months to 72 months with most projects having a scheduled duration of above 20 months. The project locations were spread across several counties in Kenya. All the projects were turnkey, involving a variation of Engineer-Procure-Construct and Design- Build-Transfer delivery arrangements. Data Analysis and Results Collected data was processed and analyzed using Microsoft Access 2010, IBM’s SPSS version 20 and Microsoft Excel 2010. Quantitative data analysis was conducted using both descriptive and inferential statistics. The main descriptive statistics used were the mean, standard deviation, coefficient of variation, indices, skewness, kurtosis and percentages. The inferential statistics used were F-test, t-test, Pearson correlation coefficients, coefficients of determination and tests of significance. Qualitative data analysis was done through expert judgment, scenario mapping and critical thinking. Data presentation was largely through text, figures, tables, numerical values and equations. The results are presented per construct in the sections that follow. Infrastructural Megaproject Success Infrastructural Megaproject success was measured along three constructs namely process, product and organizational success. Process success incorporates the traditional measures of efficiency (delivery within budget and time schedule) and quality. Efficiency was measured using the cost and schedule performance indices with the weighted average of these indices calculated to denote the overall efficiency index for the project. The CPI results show that 14 projects (52%) were delivered over budget, 9 projects (33%) were delivered on budget with the remaining 4 (15%) being delivered under budget. SPI results show that of the 27 megaprojects surveyed, 22 (81%) were delivered behind schedule, 3 (11%) were delivered on schedule while 2 (7%) were delivered ahead of schedule. Simple weighted averages of the CPI and SPI values were calculated to give the Weighted Project Efficiency (WPE) values for each project. Using these values, a total of 4 megaprojects (15%) had efficiency levels greater or equal to 1 (100%). The rest (85%) of the megaprojects were delivered at efficiency levels lower than 100%. As shown in Table 6, the energy sector projects had the lowest relative cost performance (CV=0.42) but had the highest schedule (CV=0.19) and overall efficiency (CV=0.14) performances. The roads sector scored highest on cost performance (CV=0.16) while ports (air and sea) projects scored lowest in both schedule performance (CV=0.47) and overall efficiency (CV=0.31).  European Scientific Journal December 2017 edition Vol.13, No.34 ISSN: 1857 – 7881 (Print) e - ISSN 1857- 7431 324 SECTOR DESCRIPTIVE STATISTICS FOR EFFICIENCY MEASURES CPI SPI WPE MEAN STDEV CV MEAN STDEV CV MEAN STDEV CV Ports 9  n 0.85 0.17 0.20 0.79 0.37 0.47 0.80 0.25 0.31 Energy 7  n 0.97 0.41 0.42 0.78 0.15 0.19 0.88 0.12 0.14 Roads 11  n 0.91 0.15 0.16 0.66 0.19 0.28 0.79 0.12 0.15 Table 1: Project Efficiency by Sector The process success score was determined by adding a score for project quality to the score for project efficiency. The quality score was based on the effect of changes (if any) to the scope baseline and was based on a scale of 1 (no or low impact) to 3 (high impact). The results showed that 6 megaprojects (22%) underwent more than three scope changes, 13 megaprojects (48%) underwent up to 3 scope changes while 8 megaprojects (30%) did not undergo any scope change. Product and organizational success were measured using a 9-item questionnaire of Likert-type scale with respondents being asked to respond to each item based on a 5-point scale (1=strongly disagree, 2=disagree, 3=somewhat disagree, 4=agree, 5=strongly agree). A score of 1 indicated low success score and 5 indicated high success score. Product success measures the effectiveness of the project in delivering a product that meets the customer requirements, improves customer performance, and satisfies customer needs. Organizational success measures the interaction of process and product success to meet organizational objectives, maximize stakeholder value, and enhance organizational innovation capacity to deliver future projects. The results indicate that the projects had a mean product success score of 4.09 with a standard deviation of 0.94, and a mean organizational score of 4.39 with a standard deviation of 0.82. The overall success scores were obtained by taking the simple weighted average of the mean success scores for process, product and organizational dimensions. With the highest score assigned to process, product and organizational dimensions being 8, 5, and 5 respectively, the highest possible mean composite success score was therefore 6. Human Behavior Human behavior was measured using three constructs, namely; individual behavior, group behavior and organizational design and development. Individual behavior was measured using a 7-item Likert type scale assessing cognitive biases in human behavior while group behavior was measured based on a 7-item Likert type scale assessing team commitment,  European Scientific Journal December 2017 edition Vol.13, No.34 ISSN: 1857 – 7881 (Print) e - ISSN 1857- 7431 325 cohesion, co-responsibility, top management support and motivation. Complexity due to organizational design and development was measured using two sets of indicators- alignment, opacity and process maturity as one set, and organization structure, stakeholder engagement and culture as another. Complexity based on alignment, opacity and process maturity was measured using an 8-item Likert type scale. Data on organization structure and stakeholder engagement was collected using a checklist in which the respondents were required to select the statements that applied to their projects. Based on the responses, the items on the individual behavior scale were mapped onto common cognitive biases that have been linked to project failures by past researches and in extant literature. The first item on the scale mapped onto the “framing effect” bias, the second item mapped onto “anchoring” bias, the third and the fourth items mapped onto “optimism bias”, the fifth item mapped onto “misrepresentation/noble lying”, the sixth item mapped onto “resistance to change” bias while the seventh item mapped onto “loss aversion/sunk cost effect” bias. Using the responses for those who either disagreed or strongly disagreed, the results show that loss aversion (sunk cost effect) was the most cited individual behavior exhibited by the projects (48.1%) followed by optimism bias (25.9%), misrepresentation (14.8%), anchoring bias (7.4%) and resistance to change (3.7%). Table 2 summarizes cost and schedule performance for projects exhibiting the identified cognitive biases, with the general result that projects that exhibited optimism bias had most of them delivered with budget overrun and schedule delay. Individual Behavior % of Projects Exhibiting Behavior % Delivered Within Budget % Delivered Within Schedule % Delivered With Budget Overrun and Schedule Delay Anchoring bias 7.4 50 0 50 Optimism bias 25.9 42.9 0 57.1 Misrepresentation 14.8 25 25 50 Resistance to change 3.7 100 0 0 Loss aversion (Sunk Cost effect) 48.1 46.2 15.4 46.2 Table 2: Individual Behaviors and Performance Since individual behavior can collectively define the culture of an organization, the individual behavior systematic biases identified were mapped onto four dimensions of organizational culture using the Competing Values Model (Livari & Huisman, 2007), in order to determine the culture of each project. The dimensions are internal focus, external focus, stability and change. The results show that all projects exhibiting the identified biases mapped onto a project culture that can be characterized as having a preference for an internal focus and stability. These biases were associated  European Scientific Journal December 2017 edition Vol.13, No.34 ISSN: 1857 – 7881 (Print) e - ISSN 1857- 7431 326 with escalation in cost and schedule overrun. Table 3 shows the mapping of the individual behaviors on the competitive values model. Cognitive Bias % of Projects Exhibiting Bias Dimensions of Competing Values Model Implied Internal Focus External Focus Stability Change Anchoring Bias 7.4 Optimism Bias 25.9 Misrepresentation 14.8 Resistance to Change 3.7 Loss Aversion (Sunk Cost Effect) 48.1 Table 3: Cognitive Biases Mapped onto Competing Values Model Analysis of project delivery was conducted based on the responses in the GB scale and summarized as shown in Table 4. Overall, the results show that low complexity (strongly agree/disagree responses) was associated with somewhat better project delivery compared to instances of high complexity (disagree/strongly disagree response). Item in the Scale Responses Strongly Agree/Agree Strongly Disagree/Disagree % of Projects Delivering Within % of Projects Delivering Within Budget Schedule Budget Schedule Senior management team and other key stakeholders were fully committed to the project 48.2 18.5 - - The project had the support, commitment and priority from the organization and functional groups 52.0 20.0 - - The project team was cohesive and always worked towards common goals and objectives 54.2 20.8 - - Contractual terms were well understood by all parties involved 55 25 0 0 The project team members were co- located, co-incentivized and co- responsible for the outputs of their projects 38.9 16.7 33.3 0 The project team members primarily worked face to face (rather than virtually) throughout the life of the project 47.4 10.5 0 0 Team members or stakeholders were able to accept the project information that may have been contrary to their beliefs, assumptions or perspectives 50.0 16.7 25.0 0 Table 4: Cost and Schedule Performance Based on Group Behavior Responses  European Scientific Journal December 2017 edition Vol.13, No.34 ISSN: 1857 – 7881 (Print) e - ISSN 1857- 7431 327 Responses to the first set of items measuring organizational design and development were analyzed and linked to cost and schedule performance as shown in Table 5. Whereas the distinction in cost performance based on complexity levels is not apparent, the results indicate that projects that had low complexity recorded relatively better schedule performance compared to those with high complexity. Item in the Scale Responses Strongly Agree/Agree Strongly Disagree/Disagree % of Projects Delivering Within % of Projects Delivering Within Budget Schedule Budget Schedule Alignment: The project had clearly defined boundaries with other projects and initiatives that were running in parallel 47.8 21.7 100 0 The organization had the right people with the necessary skills and competences as well as the tools, techniques or resources to support the project 54.5 22.7 0 0 There was an effective portfolio management process within the organization to facilitate strategic alignment and enable successful delivery of projects 41.2 17.6 50 0 Opacity: The sponsor or project organization made decisions, determined strategies, and set priorities in a manner that promotes transparency and trust 50.0 20.8 0 0 There was open communication, collaboration and trust among the stakeholders and project team 47.8 13 0 0 Process Maturity: It was feasible to obtain accurate status reporting throughout the life of the project 52.2 21.7 100 0 The client created and ensured the use of common processes across all projects 47.4 21.1 66.7 0 The project manager had the authority to apply internal or external resources to project activities 45.5 18.2 40 10 Table 5: Delivery Based on Alignment, Opacity and process Maturity Data on organization structure showed that the megaprojects studied fall into two main categories following the classification by Shenhar and Dvir (2007). Most of the projects were system projects which produced a