Project Management VII Research Paper

1School of Business Administration, American University of Sharjah, Sharjah, United Arab Emirates

Corresponding Author: Abroon Qazi, American University of Sharjah, Sharjah, United Arab Emirates. Email: aeroactuary@ gmail. com

Project Management Journal 2021, Vol. 52(2) 192–209

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Impact of Risk Attitude on Risk, Opportunity, and Performance Assessment of Construction Projects

Abroon Qazi1, Abdelkader Daghfous1, and M. Sajid Khan1

Abstract This article explores the impact of risk attitude on the assessment of project uncertainty encompassing both risk and opportu- nity and expected project performance. A survey was conducted involving project risk management experts from the construc- tion industry, and the data collected were analyzed using various statistical techniques. The findings reveal that the impact of risk attitude significantly varies across multiple dimensions of project uncertainty and project performance. To the best of the au- thors’ knowledge, the association between risk attitude and project uncertainty and expected performance assessments has never been explored while capturing multiple dimensions of project uncertainty and performance.

Keywords construction, opportunity, performance, risk, risk attitude, uncertainty

Article

Introduction Project risk management has been extensively explored in the literature on project management due to the significant influ- ence of risk management on the outcome of projects (Aladağ & Isik, 2018; Galli, 2018; Williams, 2017; Yazdani et al., 2019). Project complexity is considered a key driver of project uncer- tainty that can negatively influence key project objectives such as time, cost, and quality (Floricel et al., 2016; Luo et al., 2017; Mirza & Ehsan, 2017; Thamhain, 2013). Besides time, cost, and quality, it is important to focus on long- term project perfor- mance criteria such as organizational benefits (organizational learning, increased earnings, increased market share, etc.) and stakeholders’ benefits (social and environmental impact, stake- holders’ profits, sustainability, etc.) (Atkinson, 1999; Marques et al., 2011; Toor & Ogunlana, 2010). Project complexity is generally categorized into distinct dimensions such as organi- zational, technical, and environmental complexity (Bosch- Rekveldt et al., 2011). Several studies have focused on operationalizing project complexity and evaluating the impact of project complexity on project performance (Dao et al., 2017; He et al., 2015; Qazi et al., 2016; Qureshi & Kang, 2015).

Although project uncertainty can yield both a positive out- come (opportunity) and a negative outcome (risk), the negative connotation of uncertainty is generally adopted in the literature (Taroun, 2014). For example, risk matrices are widely used by construction project managers to establish the risk exposure of individual risk factors and to select suitable strategies for miti- gating critical risk factors (Qazi & Dikmen, 2019). A few

studies have conceptualized project uncertainties as both risks and opportunities (Browning, 2014; Dikmen & Birgonul, 2006; Hillson, 2002; Olsson, 2007; Qazi, Dikmen, Birgonul, 2019). Ignoring opportunities and focusing exclusively on the down- side potential of an uncertainty could lead to selecting subopti- mal strategies. Despite the importance of modeling and managing both risks and opportunities within a unified frame- work, there is limited application and adoption of such frame- works (Padalkar & Gopinath, 2016; Taroun, 2014).

The assessment of project uncertainties and expected perfor- mance is subjective in that not all uncertainties and perfor- mance criteria can be quantified (Dikmen et al., 2018). Further, different stakeholders may have different preferences (Ruan et al., 2015). An individual’s risk attitude represents their pref- erence specific to a situation involving uncertainty. Risk- seeking individuals are inclined to pursue projects with greater uncertainty, whereas risk- averse individuals prefer to avoid uncertainty. Generally, project managers are assumed as risk- neutral (Taroun, 2014), seeking to maximize the expected mon- etary value. In the case of risk- seeking and risk- averse decision makers, expected utility theory is utilized for establishing the

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decision maker’s risk attitude and optimizing decisions involv- ing uncertainty (Kahneman & Tversky, 1979).

One of the main limitations of existing studies in the litera- ture on project risk management is their limited focus on estab- lishing the association between risk attitude and project uncertainty and expected performance assessment. Ignoring the influence of risk attitude on project uncertainty and expected performance assessment can lead to overlooking critical risks and opportunities, and overestimating noncritical risks and opportunities, thereby selecting suboptimal strategies for man- aging project uncertainty. Related to this, Dikmen et al. (2018) conducted a survey in order to investigate the impact of project managers’ risk attitude on risk assessment and controllability assumptions with respect to controlling different project risks. They found evidence of a moderate association between risk attitude and risk ratings assigned by project managers involved in international construction projects in Turkey. The findings reported by Dikmen et al. (2018) provide useful insights into understanding the limitations of existing risk management tools and techniques; however, it is equally important to estab- lish how risk preferences could influence the assessment of project opportunity and expected project performance. Further, the impact of risk attitude on the assessment of uncertainty associated with multiple dimensions of project complexity and project performance could help in identifying critical dimen- sions and establishing better forecasts.

To the best of the authors’ knowledge, the impact of risk attitude has never been explored on the assessment of project complexity- driven risk and opportunity, and expected project performance, including both short- term and long- term perfor- mance criteria. We establish the association between risk atti- tude and expected project performance and uncertainty assessment through a survey and assess the strength of associa- tion using statistical techniques, including Spearman’s correla- tion analysis, analysis of variance (ANOVA), and Bayesian belief networks (BBNs). The use of three different techniques helps in validating and triangulating the results. The remainder of the article is organized as follows: A brief overview of the relevant literature is presented first, followed by a detailed description of the methodology adopted. Subsequently, the results of the study are presented, and the implications of the results are discussed. Finally, conclusions and directions for future research are presented.

Literature Review Project complexity and uncertainty pose major challenges to project managers, as these can significantly influence project performance. Another challenge is associated with the dynamic nature of complexity and uncertainty- related factors over the life cycle of a project (Geraldi et al., 2011). Several frameworks conceptualizing project complexity and uncertainty have been proposed in the literature, and techniques are introduced for operationalizing complex interactions among complexity, uncertainty, and performance in projects (Qazi et al., 2016;

Thomé et al., 2016). However, none of these frameworks has captured the influence of risk attitude on the assessment of project complexity, uncertainty, and performance.

Project complexity and uncertainty have been conceptual- ized in different ways in the literature (Geraldi et al., 2011; Thomé et al., 2016). For example, uncertainty is considered as one of the dimensions of complexity, which is deemed as the main driver of project risk (Thomé et al., 2016). However, risk has also been conceptualized one of the dimensions of com- plexity (Bosch- Rekveldt et al., 2011). Studies focusing on the complexity–uncertainty–performance triad seek to explore the association across multiple factors associated with the triad (Chan et al., 2004; Lessard et al., 2014; Tatikonda & Rosenthal, 2000). We follow the framework proposed by Qazi et al. (2016), which conceptualizes project complexity as the main driver of project uncertainty and project performance. However, their framework exclusively adopts the negative connotation of uncertainty (risk), thereby ignoring the upside potential of uncertainty (opportunity).

Bosch- Rekveldt et al. (2011) introduced the technical, organizational, and environmental (TOE) framework for char- acterizing project complexity in large engineering projects, which has been extensively used in the literature for assessing different dimensions of project complexity (Qazi et al., 2016; Qureshi & Kang, 2015). Project complexity and uncertainty have also been conceptualized through other frameworks (Geraldi et al., 2011; Thomé et al., 2016). However, the frame- work proposed by Bosch- Rekveldt et al. (2011) comprehen- sively captures all relevant aspects of project complexity associated with large engineering projects. Their framework also operationalizes the assessment of project complexity along different dimensions, such as TOE complexity (Bakhshi et al., 2016; Padalkar & Gopinath, 2016). Further, the frame- work helps in distinguishing between low- and high- complex- ity projects with respect to various project characteristics. Since our study involves generating standardized project com- plexity scenarios and soliciting expert opinion on associated project uncertainty and performance assessment, we utilize the framework proposed by Bosch- Rekveldt et al. (2011) and con- ceptualize project complexity along three dimensions, namely TOE complexity.

Atkinson (1999) advocated the need for considering addi- tional project performance criteria besides cost, quality, and time such that long- term benefits to the organization and stake- holders could be exclusively captured. This conceptualization of project performance could justify the rationale behind under- taking complex projects and embracing complexity, because although complex projects generally result in cost and time overruns (Denicol et al., 2020; Williams, 2017), the strategic importance of such projects outweighs other conventional per- formance criteria. In this article, we adopt this notion of embracing a wide range of performance dimensions and con- ceptualize project performance as the overall performance of a project encompassing both short- term and long- term perfor- mance criteria.

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Although project uncertainty is generally conceptualized as a future event with the potential of negatively influencing proj- ect objectives (Fidan et al., 2011), uncertainty could yield pos- itive outcomes as well. We conceptualize uncertainty as a variable that can positively and negatively influence project objectives (SA, 2009). Risk is the downside potential of uncer- tainty, whereas opportunity is the upside potential of uncer- tainty (Qazi et al., 2019). A few studies focusing on conceptualizing both risk and opportunity in projects have pro- posed a comprehensive uncertainty management process (Chapman & Ward, 2004, 2011; Dikmen & Birgonul, 2006; Hillson, 2002; Jaafari, 2001; Olsson, 2007; Qazi et al., 2019; Ward & Chapman, 2003). For example, Hillson (2002) advo- cates that:

“ ...“Uncertainty” is the overarching term, with two variet- ies: “risk” referring exclusively to a threat, i.e. an uncertainty with negative effects; [and] “opportunity” which is an uncer- tainty with positive effects.” (p. 235) [Hence,] “a single extended risk management process can effectively handle both opportunities and threats, and that there is therefore no need for a separate process focused exclusively on opportunities.” (p. 239)

The impact of risk attitude on different facets of project management has been investigated in several studies (Denicol et al., 2020; Taroun, 2014). Han et al. (2005) conducted an experimental study to investigate the risk behavior of contrac- tors in making a bid decision for international construction projects. They found the participants in their study to exhibit a weak risk- seeking behavior in profit situations and a strong risk- seeking attitude in loss situations. Zhang and Qian (2017) found that the performance risk perceptions of contractors have a positive impact on their tendency to become opportunist. Wang and Yuan (2011) investigated factors influencing con- tractors’ risk attitudes in Chinese construction projects and found knowledge and experience and personal perception as critical factors. Taroun (2014) also argues that human factors, intuition, professional experience, and personal judgment must be considered during risk assessment. Wang et al. (2016) exam- ined the antecedents of risk perception of Chinese construction project managers from the perspective of individual personality traits and concluded a strong association between risk attitude and individual characteristics.

Aven (2016) argues that the conventional risk assessment process must be supplemented with a stage capturing the deci- sion maker’s risk understanding as risk perception. Equally important are the underlying assumptions, which significantly influence the outcome of the risk- assessment process. Therefore, risk assessment must reflect the strength of knowl- edge and underlying assumptions related to the probability and impact ratings (Aven, 2017). Denicol et al. (2020) argue that behaviors in the front end and during execution of complex projects significantly influence project performance, and tech- nological novelty and complexity of projects can significantly increase project uncertainty yielding suboptimal project performance.

Although the drivers of risk attitude and their impact on the decision- making process have been explored in the literature, how risk attitude influences the assessment of project uncer- tainty and expected project performance remains unexplored. It is, therefore, important to establish the association between risk attitude and project uncertainty and expected performance assessment. This may help in establishing the effects of risk attitude on various factors in project uncertainty assessment and identifying critical factors. Dikmen et al. (2018) investi- gated the impact of project managers’ risk attitude and their controllability (of project risk) assumptions on the risk assess- ment of international construction projects. They found a mod- erate correlation between risk attitude and risk ratings, and risk attitude and controllability assumptions. Their findings raise a serious concern about the validity of existing risk management frameworks and processes, which fail to account for the impact of risk attitude on risk assessment. However, their framework does not capture multiple facets of project complexity and per- formance, and the upside potential of project uncertainty. Building on the theoretical framework of Dikmen et al. (2018), this article aims to investigate the impact of the project manag- ers’ risk attitude on multiple factors, including the assessment of project complexity- driven risk and opportunity, and both short- term and long- term project performance criteria.

Research Methodology A structured questionnaire was designed to collect data for test- ing the following hypotheses.

Hypothesis 1: Risk- seeking individuals tend to assign higher ratings to controllability of both project risk and opportunity and performance criteria in comparison with risk- averse and risk- neutral individuals.

Hypothesis 2: Risk- seeking individuals tend to assign a lower (higher) rating to the overall project risk (opportunity) in com- parison with risk- averse and risk- neutral individuals.

Hypothesis 3: Risk- seeking individuals tend to assign lower (higher) risk (opportunity) ratings across different dimensions of project complexity in comparison with risk- averse and risk- neutral individuals.

Hypothesis 4: Risk- seeking individuals tend to assign higher ratings to project performance criteria in comparison with risk- averse and risk- neutral individuals.

These hypotheses are indicated as cause–effect relationships across different themes shown in Figure 1. The four associa- tions investigated in this article are represented by arrows with a solid line. Other cause–effect relationships, represented by arrows with a dashed line, and their mutual interactions are worth investigating. However, the purpose of this article is to exclusively investigate the direct impact of decision makers’

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risk attitude on project uncertainty and expected performance assessments, and the associated controllability assessment.

Two experts with considerable experience of managing uncertainties associated with international construction projects were involved in testing and refining the pilot survey. The revised questionnaire was structured into three parts (see Appendix). Part 1 was designed to collect general information about the respondents such as their type of organization, qualifi- cation, and experience in managing complex international con- struction projects as well as uncertainties associated with such projects. Part 2 was further structured into two sections. The first section required respondents to provide their assessment of the level of controllability for risk and opportunity associated with each dimension of complexity (TOE complexity) as well as for each project performance criterion (time, cost, quality, long- term organizational benefits, and stakeholders’ benefits). The

second section included five different project scenarios specific to the three dimensions of project complexity (Table 1).

Two project scenarios represented the extreme levels of project complexity (scenarios 1 and 5), whereas each of the other scenarios represented a project with a high level of com- plexity relevant to each complexity dimension. Each dimension of project complexity was assigned a high/low complexity level rather than using a comprehensive scale, as this makes it easier for a respondent to conceptualize different scenarios (Dikmen et al., 2018). The respondents were provided with the frame- work proposed by Bosch- Rekveldt et al. (2011) to help them distinguish between the two levels of project complexity (Table 4 in Bosch- Rekveldt et al., 2011). For example, a project with high technical complexity generally involves a huge number of distinct interrelated tasks and makes use of new technology among other elements of technical complexity.

Table 1. Project Complexity Scenarios

Project Complexity Scenario Technical Complexity Organizational Complexity Environmental Complexity

Scenario 1 High High High Scenario 2 High Low Low Scenario 3 Low High Low Scenario 4 Low Low High Scenario 5 Low Low Low

Figure 1. Framework manifesting research hypotheses. Note: The cause–effect relationships represented by arrows with a solid line are investigated in this article.

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In Part 2 of the questionnaire, each respondent was required to provide the overall assessment of risk and opportunity asso- ciated with each project scenario as high, medium, or low. Further, the assessment of risk and opportunity associated with each dimension of project complexity and project performance was solicited in the same section.

Part 3 of the questionnaire was designed following the approach adopted by Dikmen et al. (2018) in that a coin- flipping game was designed and each respondent was asked to choose between receiving a certain amount and playing the game. If the certain amount selected was less (greater) than the expected value of the outcomes of the game, the decision maker would be classified as risk- averse (risk- seeking), whereas the indiffer- ence between the two options would identify the respondent as risk- neutral. Other approaches related to establishing the risk attitude of a decision maker involve developing a complete utility function and conducting psychometric tests (Piney, 2003; Ruan et al., 2015), which are both time consuming and less relevant to the scope of this study. As the purpose of this study was to differentiate between individual risk preferences, it was deemed appropriate to utilize a simple game (Han et al., 2005) rather than establishing the complete utility function of each respondent.

Judgmental sampling was used for data collection due to the specialized nature of this study (Dikmen et al., 2018). Project managers with expertise in managing complex international projects and associated uncertainties were selected for this research project. As the study involved assessing standardized project scenarios without contextualizing specific regional and

cultural risk management practices, it was deemed appropriate to choose respondents globally. Initially, a limited number of experts were approached using the academic and industrial net- work of researchers, and afterward, the snowballing approach (Qazi et al., 2016) was utilized for selecting suitable respondents. In total, 142 respondents were sent the questionnaire electroni- cally and 73 usable responses were received resulting in a response rate of 51.4%, which is considered an acceptable rate in the literature on construction project management (Dikmen et al., 2018). The profile of the respondents is shown in Table 2.

Questionnaire Findings The data were analyzed using SPSS 26. The statistics for the overall risk and opportunity level assigned to each project sce- nario are presented in Table 3. The highest level of risk and opportunity was assigned to scenario 1 owing to its overall highest complexity level. However, the associated risk profile (distribution) was positively skewed, whereas the opportunity profile was symmetric, reflecting the bias associated with the upside and downside potential assessment of uncertainty. There was a consistent decreasing shift observed in the opportunity profile with the change in the project complexity scenario, whereas the change in the risk profile was relatively stable. For scenarios 3, 4, and 5, none of the respondents indicated a high level of opportunity, whereas scenario 5 was considered as a low- risk scenario owing to its lowest complexity level.

The controllability assessment of risk and opportunity asso- ciated with each dimension of complexity and project

Table 2. Profile of Respondents

Attribute Category Number Percentage

Type of organization Contractor 31 42.5 Consultant 20 27.4 Client 22 30.1

Experience in managing complex international construction projects

Less than 5 years 4 5.5 5–10 years 10 13.7 11–15 years 30 41.1 More than 15 years 29 39.7

Experience in managing project uncertainties (risks and opportunities)

Less than 5 years 4 5.5 5–10 years 18 24.7 11–15 years 27 36.9 More than 15 years 24 32.9

Country United Arab Emirates 16 21.9 Saudi Arabia 14 19.2 United Kingdom 10 13.7 South Korea 9 12.3 United States 7 9.6 Australia 6 8.2 Oman 5 6.8 Canada 4 5.5 Turkey 2 2.7

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performance for both short- term (time, cost, and quality) and long- term (organizational and stakeholders’ benefits) perfor- mance criteria is presented in Table 4. Among the three dimen- sions of complexity, environmental complexity was considered as a major concern owing to the limited controllability of the associated risk and opportunity. The overall upside potential of technically complex projects was significantly higher in com- parison with other dimensions of complexity. In terms of the short- (long-) term project performance criteria, cost (organiza- tional benefits) was considered as the least controllable attri- bute. The distributions for time, quality, and organizational benefits seemed relatively symmetric in comparison with the negatively and positively skewed distribution of cost and stake- holders’ benefits, respectively.

Association Between Risk Attitude and Controllability Assessment of Project Uncertainty and Expected Performance Spearman’s correlation analysis was performed in order to assess the impact of risk attitude on controllability assessment of project uncertainty and expected performance (Table 5). There was a significant negative correlation observed between risk attitude and controllability assessment of risk and opportu- nity associated with each dimension of project complexity. This implies that risk- seeking individuals would tend to assign a higher controllability rating to both project risk and opportunity in comparison with risk- neutral and risk- averse individuals. A strong correlation was observed in the case of technical risk, organizational opportunity, and environmental opportunity. In terms of the short- term project performance criteria (time, cost,

and quality), a relatively weak correlation was observed, whereas organizational and stakeholders’ benefits were found to be strongly and moderately correlated with risk attitude, respectively.

In addition to testing the first hypothesis using Spearman’s correlation analysis, one- way ANOVA was also performed. All requirements of ANOVA were examined for each dimension of project complexity and project performance, including the test- ing of the normal distribution of the data and homogeneity of variances at the 5% significance level (Dikmen et al., 2018). The homogeneity of variances was violated in five instances. Therefore, the Welch–Satterthwaite correction (Dikmen et al., 2018) was used and the results of ANOVA were obtained (Table 6). The means across different risk attitudes were found to be significantly different. Games–Howell post- hoc analysis (Kottas & Madas, 2018) was performed to examine the differ- ence of mean ratings between risk- seeking and risk- neutral/ risk- averse individuals. Results corresponding to all dimen- sions of uncertainty and performance were found to be signifi- cantly different except in the case of cost and stakeholders’ benefits, where similar results were found for risk- neutral and risk- seeking individuals.

Association Between Risk Attitude and Assessment of Overall Project Risk and Opportunity The correlation between risk attitude and assessment of overall project risk and opportunity for all project scenarios was ana- lyzed using Spearman’s correlation test (Table 7). There was a positive (negative) correlation observed between risk attitude and project risk (opportunity) assessment indicating that risk- averse individuals assigned a higher (lower) rating to project risk (opportunity) in comparison with risk- seeking and risk- neutral individuals. A strong correlation was observed in the case of four scenarios for project risk, whereas a moderate cor- relation was found in the case of scenario 5. In the case of proj- ect opportunity, there was no statistically significant correlation found in the case of scenarios 3 and 5, whereas a moderate correlation was observed for scenarios 1 and 2.

One- way ANOVA was also performed for hypothesis 2. After verifying the normal distribution of the collected data, the

Table 3. Number of Respondents Who Assigned a Certain Risk/ Opportunity Level to a Given Project Complexity Scenario

Risk/Opportunity Level

Scenario

1 2 3 4 5

Low 9/21 21/28 21/64 24/55 49/65 Medium 35/32 30/39 35/9 22/18 24/8 High 29/20 22/6 17/0 27/0 0/0

Table 4. Number of Respondents Who Assigned a Certain Controllability Level to Various Project Uncertainty and Performance Dimensions

Project Uncertainty and Performance Controllable Partially Controllable Uncontrollable

Technical risk (opportunity) 13 (12) 36 (48) 24 (13) Organizational risk (opportunity) 13 (19) 34 (24) 26 (30) Environmental risk (opportunity) 5 (13) 35 (34) 33 (26) Time 17 39 17 Cost 17 30 26 Quality 20 33 20 Organizational benefits 19 32 22 Stakeholders’ benefits 20 42 11

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homogeneity of variances specific to different risk attitudes was examined at the 5% significance level (Dikmen et al., 2018). The homogeneity of variances was satisfied for scenario 1 (risk), scenario 3 (opportunity), and scenario 5 (opportunity) only. Therefore, the Welch–Satterthwaite correction was used, and the results of ANOVA were obtained (Table 8). The means across different risk attitudes were found to be significantly dif- ferent for all scenarios except scenarios 3, 4, and 5 (opportu- nity). Games–Howell post- hoc analysis was performed to examine the difference of mean ratings between risk- seeking and risk- neutral/risk- averse individuals. Results corresponding to all scenarios were found to be significantly different, except for scenarios 2, 3, 4, and 5 (opportunity), and scenario 5 (risk) with similar results observed across risk- neutral and risk- seeking individuals.

Association Between Risk Attitude and Assessment of Project Complexity-Driven Risk and Opportunity The correlation between risk attitude and assessment of project complexity- driven risk and opportunity for all project scenarios was analyzed using Spearman’s correlation test (Table 9). In general, there was a positive (negative) correlation observed between risk attitude and project complexity- driven risk (opportunity) assessment, indicating that risk- averse individu- als assigned a higher (lower) rating to complexity- driven risk (opportunity) in comparison with risk- seeking and risk- neutral individuals. A very strong correlation was observed in the case of scenario 1 (technical risk and opportunity, organizational risk, and environmental opportunity), scenario 2 (technical opportunity), scenario 3 (organizational risk), and scenario 4 (environmental opportunity). For scenarios 3 and 5, a nonsig- nificant correlation was found between risk attitude and

environmental opportunity, whereas a moderate correlation was observed between risk attitude and organizational opportu- nity. Opportunity was relatively strongly correlated with risk attitude than risk itself in the case of scenario 1.

One- way ANOVA was also performed for hypothesis 3. After verifying the normal distribution of the collected data, the homogeneity of variances specific to different risk attitudes was examined at the 5% significance level. The homogeneity of variances was violated in 12 instances. Therefore, the Welch– Satterthwaite correction was used, and the results of ANOVA were obtained (Table 10). The means across different risk atti- tudes were found to be significantly different in all scenarios except those highlighted in Table 10. Games–Howell post- hoc analysis was performed to examine the difference of mean rat- ings between risk- seeking and risk- neutral/risk- averse individ- uals. Results significantly varied across different dimensions of complexity, uncertainty, and scenarios, implying the impor- tance of capturing all factors including the decision maker’s risk attitude in risk assessment.

Association Between Risk Attitude and Assessment of Expected Project Performance The correlation between risk attitude and assessment of expected project performance for all project scenarios was ana- lyzed using Spearman’s correlation test (Table 11). In general, there was a negative correlation observed between risk attitude and expected project performance assessment, indicating that risk- averse individuals assigned a lower rating to expected project performance in comparison with risk- seeking and risk- neutral individuals. A relatively stronger correlation was observed for time and cost across all project scenarios. For

Table 5. Correlation Between Risk Attitude and Controllability Assessment of Project Uncertainty and Expected Performance

Technical Risk (Opportunity)

Organizational Risk (Opportunity)

Environmental Risk (Opportunity) Time Cost Quality

Organizational Benefits

Stakeholders’ Benefits

Risk attitude −.745** (−.585**)

−.550** (−.653**)

−.408** (−.686**)

−.381** −.302** −.350** −.625** −.576**

Note. Significance level **.01; *.05.

Table 6. ANOVA Results for Controllability Assessment of Project Uncertainty and Expected Performance

Project Uncertainty and Performance F Value Significance Level

Technical risk/opportunity 39.882/17.565 .000/.000 Organizational risk/opportunity 20.244/42.314 .000/.000 Environmental risk/opportunity 9.237/37.613 .001/.000 Time 11.713 .000 Cost 3.884 .029 Quality 13.138 .000 Organizational benefits 30.204 .000 Stakeholders’ benefits 20.290 .000

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project scenarios 2 and 3, the correlation associated with stake- holders’ benefits was found to be statistically nonsignificant.

We used ANOVA to test hypothesis 4. After verifying the normal distribution of the collected data, the homogeneity of variances specific to different risk attitudes was evaluated at the 5% significance level. The homogeneity of variances was vio- lated in 16 instances. Therefore, the Welch–Satterthwaite cor- rection was used, and the results of ANOVA were obtained (Table 12). The means across different risk attitudes were found to be significantly different in all scenarios except scenario 2 (stakeholders’ benefits), scenario 3 (quality and stakeholders’ benefits), scenario 4 (cost, quality, and organizational benefits), and scenario 5 (quality). Games–Howell post- hoc analysis was performed to examine the difference of mean ratings between risk- seeking and risk- neutral/risk- averse individuals. Results significantly varied across multiple performance criteria and scenarios. This signals the importance of capturing perfor- mance criteria encompassing both short- term and long- term criteria, and the decision maker’s risk attitude in risk assessment.

Stochastic Modeling of Association Between Risk Attitude and Project Uncertainty and Expected Performance Assessment Bayesian networks is a statistical technique that helps in cap- turing interdependencies across uncertain variables (Kelangath et al., 2011; Sigurdsson et al., 2001). Different techniques can be used for exploring interdependency modeling of uncertain variables such as artificial neural networks, social network

analysis, analytical network process, structural equation mod- eling, causal loop diagrams, and others (Qazi et al., 2016). However, the key benefit of using BBNs is the ability to visual- ize the propagation impact of uncertainties in a network setting, which can provide key insights to decision makers in prioritiz- ing critical uncertainties. Bayesian networks can be developed using expert judgment and data. Data- driven BBN models are developed using a data set without relying on expert judgment for developing a causal network structure and eliciting proba- bility values. The development of data- driven discrete BBN models involves discretizing a data set and establishing states for each uncertain variable, developing a network structure, and populating the model with probability values. Various soft- ware packages are available for developing data- driven mod- els, such as AgenaRisk, Hugin, Netica, GeNIe, and others (Qazi & Dikmen, 2019). Several algorithms can be used for develop- ing a causal structure and extracting probability values from a data set, such as Naive Bayes, Greedy Thick Thinning, PC, Bayesian search, and others (Kelangath et al., 2011). For a detailed description of the mechanics of BBNs and related algorithms, interested readers may consult Ekici and Önsel Ekici (2019), Kelangath et al. (2011), and Kjaerulff and Anders (2008).

The main purpose of exploring a BBN model was to triangu- late the findings from the ANOVA and Spearman’s correlation- based analyses. The proposed model can be developed further to investigate the cause–effect relationships and synergistic effects among risk attitude, controllability assumptions, and the assessment of project risk and opportunity, and expected

Table 7. Correlation Between Risk Attitude and Assessment of Overall Project Risk and Opportunity

Risk (Opportunity)

Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5

Risk attitude .723** (−.601**)

.636** (−.427**)

.777** (−.042)

.684** (−.217*)

.591** (−.090)

Note. All nonsignificant values appear in bold. Significance level **.01; *.05.

Table 8. ANOVA Results for Assessment of Overall Project Risk and Opportunity

Project Scenario Uncertainty F Value Significance Level

1 Risk 40.299 .000 Opportunity 30.781 .000

2 Risk 33.068 .000 Opportunity 11.618 .000

3 Risk 54.417 .000 Opportunity .665 .519

4 Risk 55.904 .000 Opportunity 1.635 .208

5 Risk 20.705 .000 Opportunity .604 .551

Note. All nonsignificant values appear in bold.

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project performance that are not investigated in this article (Figure 1). In order to establish the stochastic interaction between the decision maker’s risk attitude and corresponding uncertainty and expected performance assessments, we

developed a data- driven BBN model in GeNIe 2.0. We fol- lowed the approach proposed by Qazi and Dikmen (2019) in utilizing the survey data for capturing interactions across uncer- tain variables.

The network structure of the model conformed to the theoretical framework of this study in order to establish the impact of risk attitude on project uncertainty and expected performance assessments (Figure 1). The model compris- ing nodes (uncertainty and performance attributes) and arcs (relationships between interconnected nodes) is shown in Figure 2. For an overall uncertainty (risk and opportu- nity) assessment relative to different project scenarios, the same three states (low, medium, and high) were used in the model as of the survey questionnaire (see Appendix). Similarly, in the case of controllability assessment, uncon- trollable, partially controllable, and controllable states were introduced in the model for a rating of 1, 2, and 3, respectively.

The probability distributions shown in Figure 2 reflect the variation of attributes specific to different decision makers (project risk managers). For example, 44% of the respon- dents were risk- averse, 33% were risk- seeking, and 23% were risk- neutral. The probability distribution of overall project risk assessment was positively skewed for scenario 1, whereas it was negatively skewed for scenario 5. In the case of overall project opportunity assessment, all distributions were negatively skewed except scenario 1, which was sym- metric. In order to establish the impact of risk attitude on uncertainty and performance attributes shown in Figure 2, we changed the risk attitude to all three states (risk- seeking, risk- averse, and risk- neutral) one by one and calculated the variation across the probability distribution of each attribute

Table 9. Correlation Between Risk Attitude and Assessment of Project Complexity- Driven Risk and Opportunity

Risk Attitude

Scenario 1 Technical risk (opportunity) .877** (−.903**) Organizational risk (opportunity) .885** (−.771**) Environmental risk (opportunity) .674** (−.892**)

Scenario 2 Technical risk (opportunity) .761** (−.828**) Organizational risk (opportunity) .220* (−.170) Environmental risk (opportunity) .279** (−.268*)

Scenario 3 Technical risk (opportunity) .425** (−.037) Organizational risk (opportunity) .881** (−.609**) Environmental risk (opportunity) .176 (−.111)

Scenario 4 Technical risk (opportunity) .270* (−.210*) Organizational risk (opportunity) .468** (−.187) Environmental risk (opportunity) .697** (−.885**)

Scenario 5 Technical risk (opportunity) .310** (−.205*) Organizational risk (opportunity) .386** (−.429**) Environmental risk (opportunity) .443** (−.154)

Note. All nonsignificant values appear in bold. Significance level **.01; *.05.

Table 10. ANOVA Results for Assessment of Project Complexity- Driven Risk and Opportunity

Project Scenario Uncertainty F Value Significance Level

1 Technical risk/opportunity 78.037/130.927 .000/.000 Organizational risk/opportunity 94.803/142.714 .000/.000 Environmental risk/opportunity 27.376/220.006 .000/.000

2 Technical risk/opportunity 46.218/180.913 .000/.000 Organizational risk/opportunity 2.030/1.490 .144/.238 Environmental risk/opportunity 5.495/3.564 .008/.038

3 Technical risk/opportunity 10.046/.241 .000/.787 Organizational risk/opportunity 102.859/28.544 .000/.000 Environmental risk/opportunity 4.224/.781 .021/.465

4 Technical risk/opportunity 4.319/1.603 .019/.214 Organizational risk/opportunity 11.621/2.343 .000/.109 Environmental risk/opportunity 35.055/141.136 .000/.000

5 Technical risk/opportunity 4.530/1.604 .016/.214 Organizational risk/opportunity 8.623/8.236 .001/.001 Environmental risk/opportunity 10.472/.974 .000/.386

Note. All nonsignificant values appear in bold.

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with reference to its probability distribution shown in Figure 2. With the evidence of the decision maker’s risk atti- tude fed into the model, the change in the probability of over- all project risk and opportunity assessment was examined across different project scenarios (Table 13). The highest change in the high state probability was observed in the case of project scenario 1 for both risk- averse and risk- seeking individuals. For risk- neutral managers, the maximum change in the high state probability was noticed in the case of project scenarios 3 (risk) and 2 (opportunity).

The change in the probability of uncertainty controllabil- ity assessment was examined given the change in the deci- sion maker’s risk attitude (Table 14). For risk- averse managers, the highest negative change representing an over- all increase in the probability of uncontrollability assessment was observed in the case of technical complexity (risk) and organizational complexity (opportunity). For risk- seeking individuals, technical complexity yielded the highest

positive change with respect to project risk, whereas environ- mental complexity significantly contributed to project oppor- tunity in terms of increasing the probability of controllability assessment.

The change in the probability of performance controllability assessment was evaluated given the change in the decision maker’s risk attitude (Table 15). For risk- averse managers, the highest negative change representing an increase in the proba- bility of uncontrollability assessment was observed in the case of organizational benefits, whereas risk- neutral decision mak- ers found quality to be least controllable. For risk- seeking indi- viduals, organizational benefits yielded the highest decrease in the probability of uncontrollability assessment, whereas quality was associated with the highest positive change in terms of increasing the probability of controllability assessment.

Discussion of Findings The results presented in the previous section provide unique insights into the association between decision makers’ risk attitude and corresponding assessments of expected project performance and project uncertainty. Rather than focusing on the negative connotation of uncertainty alone and assessing project complexity in general (Dikmen et al., 2018), this study treated uncertainty as both risk and opportunity, and contextualized different dimensions of project complexity. Hypothesis 1 was validated in that risk- seeking individuals in general assigned higher ratings to controllability of both project risk and opportunity and project performance in com- parison with risk- averse and risk- neutral individuals. However, the ratings significantly varied across different dimensions of project complexity and project performance. For example, a stronger association was observed in the case of technical complexity and long- term performance criteria. The same observation was validated using the BBN model (Figure 2) as shown in Tables 14 and 15.

Hypothesis 2 was tested using a triangulation method, including ANOVA, Spearman’s correlation, and BBN analy- ses. It was found that risk- seeking individuals were inclined toward assigning a lower (higher) rating to the overall project risk (opportunity) in comparison with risk- averse and risk- neutral individuals. However, the ratings significantly varied across different project scenarios, implying that the level of project complexity influenced the strength of ratings across project managers. The results were cross- validated using the BBN analysis (Table 13). Our results imply that the required rigor of risk assessment is contingent on the complexity level and strategic importance of a project. The variation in uncer- tainty assessment was nonsignificant in the case of low- complexity project scenarios. Importantly, we note the variation of association between risk attitude and the upside (opportunity) and downside (risk) potential of uncertainty. The strength of correlation was relatively stronger in the case of negative connotation of uncertainty (risk), implying that opportunities are not considered at par with risks and the

Table 11. Correlation Between Risk Attitude and Assessment of Expected Project Performance

Risk Attitude

Scenario 1 Time −.749** Cost −.711** Quality −.581** Organizational benefits −.658** Stakeholders’ benefits −.552**

Scenario 2 Time −.673** Cost −.709** Quality −.579** Organizational benefits −.339** Stakeholders’ benefits −.106

Scenario 3 Time −.889** Cost −.874** Quality −.263* Organizational benefits −.733** Stakeholders’ benefits −.123

Scenario 4 Time −.498** Cost −.244* Quality −.234* Organizational benefits −.050 Stakeholders’ benefits −.836**

Scenario 5 Time −.507** Cost −.504** Quality −.147 Organizational benefits −.306** Stakeholders’ benefits −.487**

Note. All nonsignificant values appear in bold. Significance level **.01; *.05.

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Table 12. ANOVA Results for Assessment of Expected Project Performance

Project Scenario Project Performance Criterion F Value Significance Level

1 Time 46.557 .000 Cost 42.830 .000 Quality 18.677 .000 Organizational benefits 27.198 .000 Stakeholders’ benefits 15.635 .000

2 Time 35.146 .000 Cost 47.539 .000 Quality 34.020 .000 Organizational benefits 5.010 .012 Stakeholders’ benefits 2.460 .099

3 Time 103.714 .000 Cost 108.562 .000 Quality 3.074 .057 Organizational benefits 54.142 .000 Stakeholders’ benefits .580 .565

4 Time 14.664 .000 Cost 3.125 .056 Quality 2.560 .089 Organizational benefits .578 .566 Stakeholders’ benefits 74.052 .000

5 Time 15.263 .000 Cost 14.741 .000 Quality .884 .421 Organizational benefits 4.944 .013 Stakeholders’ benefits 11.044 .000

Note. All nonsignificant values appear in bold.

Figure 2. A Bayesian belief network developed in GeNIe 2.0 representing the association between risk attitude and project uncertainty and expected performance assessment. Note: L, M, and H represent the low, medium, and high states, respectively, whereas U, P, and C represent the uncontrollable, partially controllable, and controllable states, respectively.

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negative connotation of uncertainty prevails in practice (Taroun, 2014).

Hypothesis 3 was validated in that risk- seeking individuals generally assigned lower (higher) risk (opportunity) ratings across different dimensions of project complexity in comparison with risk- averse and risk- neutral individuals. However, there was a strong moderating effect of project scenario (complexity) on the association between risk attitude and complexity- driven uncer- tainty assessment. Even for high- complexity project scenarios, risk- seeking individuals assigned significantly lower risk ratings manifesting the controllability bias, whereas risk- averse individu- als assigned relatively higher ratings to low- complexity project scenarios. The ratings were reversed in the case of opportunity assessment of projects. A stronger association was observed in the case of technical complexity- driven risk and opportunity assessment.

Hypothesis 4 was partially validated in that risk- seeking indi- viduals generally assigned higher ratings to certain project perfor- mance criteria in comparison with risk- averse and risk- neutral individuals. The difference in ratings was significant in terms of time, cost, and organizational benefits. Further, the level of project complexity (project scenario) greatly influenced the strength of correlation between risk attitude and expected performance assess- ment. Short- term performance criteria (time, cost, and quality) received a higher variation in ratings in comparison with long- term criteria (organizational and stakeholders’ benefits), implying that short- term performance criteria are generally prioritized over long- term criteria (Atkinson, 1999; Taroun, 2014).

The results of this study specific to the association between risk attitude and project risk (controllability) assessment gener- ally support the findings of Dikmen et al. (2018). This study extends the theoretical framework proposed by Dikmen et al. (2018) to encapsulate different dimensions of project complexity and project performance, and the positive connotation of

uncertainty (opportunity). In line with the findings of Dikmen et al. (2018), we argue that the project manager’s risk attitude (significantly) influences the risk assessment of a project. In addi- tion, risk attitude also influences project opportunity assessment and project performance expectations. These findings raise a seri- ous concern about the validity of uncertainty and expected perfor- mance assessments of projects performed by project managers. It implies that project managers tend to ignore the actual risk and opportunity exposure of projects and benchmark unrealistic tar- gets for various performance criteria. The resulting errors cascade across all stages of the uncertainty management process, yielding suboptimal strategies for managing risks and opportunities.

The main contribution of this study relates to establishing the strength of association between risk attitude and the assessment of project complexity- driven uncertainty (encompassing both risk and opportunity). The strength of association significantly varies across different dimensions of project complexity and the upside and downside potential of uncertainty as well. A similar variation is observed in the case of association between risk attitude and the assessment of multiple dimensions of project performance. These findings provide useful insights to project managers for identify- ing critical dimensions of project complexity and project perfor- mance that are strongly correlated to the decision maker’s risk attitude and therefore, a comprehensive uncertainty assessment process must be adopted for such critical factors. The hypotheses investigated in this study can be extended to examining the inter- action effects among risk attitude, controllability assumptions, and project uncertainty and expected performance assessments. Further, a multitude of project risks and opportunities can be explored to better identify the critical risks and opportunities that are significantly influenced by the decision maker’s risk attitude.

Our study suggests that there is a need to develop a robust uncertainty management process in order to reduce the influence of risk attitude on the assessment of project uncertainty and

Table 13. Change in the Probability of Overall Project Risk/Opportunity Assessment with Respect to Risk Attitude (High, Medium, Low)

Project Scenario Risk- Averse Risk- Neutral Risk- Seeking

1 (.37, −.25, −.12)/(−.27, .13, .14) (−.16, .16, 0)/(.02, −.13, .11) (−.38, .22, .16)/(.34, −.06, −.28) 2 (.23, .05, −.28)/(−.08, −.13, .21) (0, −.11, .11)/(.21, −.23, .02) (−.28, 0, .28)/(−.04, .32, −.28) 3 (.26, .02, −.28)/(0, 0, 0) (−.17, .33, −.16)/(.01, −.06, .05) (−.22, −.26, .48)/(0, .04, −.04) 4 (.22, .10, −.32)/(0, −.09, .09) (.09, −.11, .02)/(.01, −.01, 0) (−.36, −.04, .40)/(0, .12, −.12) 5 (0, .31, −.31)/(0, −.02, .02) (0, −.20, .20)/(0, −.05, .05) (0, −.28, .28)/(0, .05, −.05)

Note. All notable changes in the probability values appear in bold.

Table 14. Change in the Probability of Uncertainty Controllability Assessment with Respect to Risk Attitude (Controllable, Partially Controllable, Uncontrollable)

Project Complexity Dimension Risk- Averse Risk- Neutral Risk- Seeking

Technical (−.17, −.18, .35)/(−.17, .01, .16) (−.11, .31, −.20)/(−.11, .16, −.05) (.31, 0, −.31)/(.27, −.11, −.16) Organizational (−.17, 0, .17)/(−.25, −.01, .24) (−.05, −.05, .10)/(.04, −.15, .11) (.27, .03, −.30)/(.31, .08, −.39) Environmental (−.07, −.07, .14)/(−.17, −.09, .26) (−.06, −.06, .12)/(−.16, .17, −.01) (.13, .14, −.27)/(.35, −.01, −.34)

Note. All notable changes in the probability distributions appear in bold.

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expected performance. One possible solution is to archive a data- base of risk registers and risk matrices for all projects undertaken by an organization and to establish how different project manag- ers within the organization tend to overestimate or underestimate project uncertainty and expected performance. Further, standard- ized expert judgment frameworks must be utilized to calibrate experts with respect to their risk attitude and biases/assumptions related to the assessment of project risk and opportunity (Dikmen et al., 2018). The influence of risk attitude can also be established through designing standardized project scenarios as presented in this study and conducting experimental workshops within the organization. This will help in understanding how experts (project managers) perceive different project scenarios and over- (under) estimate the overall project risk (opportunity) and expected proj- ect performance. The over- (under) rating factor can be deter- mined for each expert for future adjustment of estimates provided by the expert.

Conclusions The main objective of this article was to establish the impact of risk attitude on project risk, opportunity, and expected perfor- mance assessments. Four hypotheses were developed, and a questionnaire was designed using standardized project scenar- ios such that project risk managers involved in international construction projects could provide their assessment of those project scenarios consistently. The data were analyzed using statistical techniques, including Spearman’s correlation analy- sis, ANOVA, and BBNs. The selection of three different tech- niques helped in validating the results.

Uncertainty and performance assessments of projects pro- vide vital information for identifying key projects within the organization and allocating resources to those projects. Organizations utilize various types of uncertainty management tools, such as risk registers and risk matrices in order to assess the exposure of uncertainties across a project and to manage critical risks and opportunities. However, the ratings used in these tools are assumed as the correct representation of actual risk and opportunity exposure without accounting for the sub- jective judgment of subject matter experts, which is mainly influenced by their risk attitude.

The findings revealed that the project managers’ risk atti- tude not only influenced the risk assessment of projects but

also affected the opportunity assessment of projects. Further, there was a strong association found between risk attitude and expected performance assessment of projects. The asso- ciation was relatively stronger in the case of risk assessment in comparison with opportunity assessment. Similarly, not all dimensions of project complexity yielded the same strength of association between risk attitude and the corresponding risk and opportunity assessment. In the case of performance criteria, the assessment of short- term criteria (time, cost, and quality) was strongly associated with risk attitude. All asso- ciations were strongly moderated by the level of project com- plexity. The variation in the uncertainty and expected performance assessment with the change in risk attitude was significant in the case of project scenarios with high complexity.

This study contributes to the literature on project risk man- agement in establishing the extent to which risk managers underestimate (overestimate) overall project opportunity (risk) and expected project performance owing to their risk attitude. The findings necessitate developing a comprehensive uncertainty assessment framework that can capture the risk attitude of experts involved in assessing project uncertainty. Further, risk management tools such as risk registers and risk matrices must be tailored to explicitly account for such devi- ations from the actual risk and opportunity exposure of proj- ects and to adjust the assessment of experts using appropriate factors.

This study has several limitations. The survey was exclusively conducted in the construction industry. The influence of an individual’s experience and biases, such as overconfidence and anchoring on project uncertainty assessment, was not investigated. The interaction effects of multiple factors were not modeled. This study can be developed along different lines of inquiry. The same sur- vey can be conducted across different industries and results can be compared whether there are similarities/ differences across industries. Case studies can be con- ducted in order to establish the influence of risk attitude on uncertainty assessment of projects within the same organization. Future work may focus on investigating the influence of different biases besides risk attitude on proj- ect uncertainty and expected performance assessments. The interaction effects of different factors can be explored

Table 15. Change in the Probability of Performance Controllability Assessment with Respect to Risk Attitude (Controllable, Partially Controllable, Uncontrollable)

Project Performance Criterion Risk- Averse Risk- Neutral Risk- Seeking

Time (−.11, .04, .07) (−.11, −.06, .17) (.21, .01, −.22) Cost (−.13, .05, .08) (0, −.06, .06) (.17, −.04, −.13) Quality (−.11, .06, .05) (−.22, .02, .20) (.29, −.13, −.16) Organizational benefits (−.25, .03, .22) (.09, −.08, −.01) (.27, .02, −.29) Stakeholders’ benefits (−.27, .14, .13) (.13, −.11, −.02) (.25, −.12, −.13)

Note. All notable changes in the probability distributions appear in bold.

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using artificial intelligence techniques such as BBNs. A methodology may be designed for establishing adjustment factors in order to account for the influence of risk atti- tude on the assessment of project uncertainty and expected performance.

Appendix: Questionnaire

Key Terms and Definitions Uncertainty is the overarching term conceptualized in the simple “lack of certainty” sense, with two varieties: risk, referring exclu- sively to a threat, that is, an uncertainty with negative effects; opportunity, which is an uncertainty with positive effects.

Project complexity is the property of a project, which makes it difficult to understand, foresee, and keep under control its overall behavior, even when given reasonably complete infor- mation about the project system.

Controllability of a(n) risk (opportunity) represents the level of its manageability as to how much capable a decision maker/ firm is in terms of managing the risk/opportunity.

Short- term project performance criteria (completion time, cost, and quality) can be assessed at the completion stage of a project, whereas long- term benefits, including organizational benefits (strategic goals, increased profits, organizational learn- ing, and reduced waste) and stakeholders’ benefits (social and environmental impact, satisfied users, economic impact, and profits for contractors/suppliers) are assessed at the postdeliv- ery stage of a project.

Part I: Respondent’s Profile

1. Your organization type: a. Client b. Contractor c. Consultant d. Other (please specify):

5. Educational background: a. College (or higher secondary schooling) b. Bachelor’s degree c. Master’s degree d. Doctorate degree

5. Your experience in managing complex international construction projects: a. Less than 5 years b. 5–10 years c. 11–15 years d. More than 15 years

5. Your experience in managing uncertainties (risks and opportunities) associated with complex international construction projects: a. Less than 5 years b. 5–10 years c. 11–15 years d. More than 15 years

Part II: Uncertainty (Risk/Opportunity) Ratings Following are the details of the categories of complexity and the scales used for assessing different uncertainty/complexity factors.

Table A1. Subcategories of Project Complexity and Related Uncertainties (Bosch- Rekveldt et al., 2011)

Technical Complexity and Uncertainty

Organizational Complexity and Uncertainty

Environmental Complexity and Uncertainty

Goals (number, alignment, clarity)

Size (project duration, project team, site area, locations)

Stakeholders (number, different perspectives, political influence, dependencies)

Scope (largeness, uncertainties, quality requirements)

Resources (resource and skill availability, contract types)

Location (weather, experience in the country, remoteness of location)

Tasks (number, variety, interdependencies)

Project team (different cultures, nationalities)

Market conditions (level of competition, stability of environment, internal strategic pressure)

Experience (newness of technology, experience with the technology)

Trust (project team, contractor)

Table A2. Rating Scale Used for Assessing the Uncertainty Exposure and Performance Achievement

Uncertainty (Risk/Opportunity) Exposure and Performance Achievement Rating

Very low 1 Low 2 Moderate/expected 3 High 4 Very high 5

Table A3. Rating Scale Used for Assessing the Controllability of Project Uncertainty and Performance

Uncertainty (Risk/Opportunity) and Performance Controllability Rating

Uncontrollable 1 Partially controllable 2 Controllable 3

Please assign a controllability rating (Table A3) to each uncer- tainty and performance attribute given in the table below.

Uncertainty and Performance Attribute Rating (1–3)

Uncertainty Risk/Opportunity

(Continued)

Project Management Journal 52(2)206

Uncertainty and Performance Attribute Rating (1–3)

Technical uncertainty Organizational uncertainty Environmental uncertainty Performance Attribute Completion time Cost Quality Long- term organizational benefits Long- term stakeholders’ benefits

Scenario 1: There is a project with the details of the complexity shown below (Table A1).

Technical Complexity

Organizational Complexity

Environmental Complexity

High High High

a. Please evaluate the risk/opportunity level of this project based on your experience. Level of Risk

Low Medium High

Level of Opportunity Low Medium High

b. Please assign a rating (Table A2) to each uncertainty and performance attribute given in the table below.

Uncertainty and Performance Attribute Rating (1–5)

Uncertainty Risk/Opportunity Technical uncertainty Organizational uncertainty Environmental uncertainty Performance Attribute Completion time Cost Quality Long- term organizational benefits Long- term stakeholders’ benefits

Scenario 2: There is a project with the details of the complexity shown below (Table A1).

Technical Complexity

Organizational Complexity

Environmental Complexity

High Low Low

a. Please evaluate the risk/opportunity level of this project based on your experience. Level of Risk

Low Medium High

Level of Opportunity Low Medium High

b. Please assign a rating (Table A2) to each uncertainty and performance attribute given in the table below.

Uncertainty and Performance Attribute Rating (1–5)

Uncertainty Risk/Opportunity Technical uncertainty Organizational uncertainty Environmental uncertainty Performance Attribute Completion time Cost Quality Long- term organizational benefits Long- term stakeholders’ benefits

Scenario 3: There is a project with the details of the complexity shown below (Table A1).

Technical Complexity

Organizational Complexity

Environmental Complexity

Low High Low

a. Please evaluate the risk/opportunity level of this project based on your experience. Level of Risk

Low Medium High

Level of Opportunity Low Medium High

b. Please assign a rating (Table A2) to each uncertainty and performance attribute given in the table below.

Uncertainty and Performance Attribute Rating (1–5)

Uncertainty Risk/Opportunity Technical uncertainty Organizational uncertainty Environmental uncertainty

(Continued)

(Continued)

Qazi et al. 207

Uncertainty and Performance Attribute Rating (1–5)

Performance Attribute Completion time Cost Quality Long- term organizational benefits Long- term stakeholders’ benefits

Scenario 4: There is a project with the details of the complexity shown below (Table A1).

Technical Complexity Organizational Complexity

Environmental Complexity

Low Low High

a. Please evaluate the risk/opportunity level of this project based on your experience. Level of Risk

Low Medium High

Level of Opportunity Low Medium High

b. Please assign a rating (Table A2) to each uncertainty and performance attribute given in the table below.

Uncertainty and Performance Attribute

Rating (1–5)

Uncertainty Risk/Opportunity Technical uncertainty Organizational uncertainty Environmental uncertainty Performance Attribute Completion time Cost Quality Long- term organizational benefits Long- term stakeholders’ benefits

Scenario 5: There is a project with the details of the complexity shown below (Table A1).

Technical Complexity

Organizational Complexity

Environmental Complexity

Low Low Low

a. Please evaluate the risk/opportunity level of this project based on your experience.

Level of Risk Low Medium High

Level of Opportunity Low Medium High

b. Please assign a rating (Table A2) to each uncertainty and performance attribute given in the table below.

Uncertainty and Performance Attribute Rating (1–5)

Uncertainty Risk/Opportunity Technical uncertainty Organizational uncertainty Environmental uncertainty Performance Attribute Completion time Cost Quality Long- term organizational benefits Long- term stakeholders’ benefits

Part III: Measuring Risk Attitude A coin- flipping game is offered to you. If the coin shows head, you will get US$100, and if the coin turns tail, you will get nothing. What is the minimum certain amount of money you would accept to leave the game? Your answer: Note: This coin- flipping game is played only once—the game will be stopped after one flip.

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Author Biographies

Dr. Abroon Qazi is currently an Assistant Professor with the School of Business Administration at the American University of Sharjah, United Arab Emirates. Dr. Qazi is a qualified actu- ary (Fellow Society of Actuaries), a Chartered Enterprise Risk Analyst, a Chartered Engineer, and a Project Management Professional (PMP)® certification holder. He has worked as an operations manager in the aviation industry for 10 years and has taught courses on operations management and decision sci- ence at institutions such as Strathclyde Business School and the State University of New York, Korea. His research interests include operations management and risk management with an emphasis on modeling and managing enterprise- wide risks. His research has appeared in European Journal of Operational Research, Risk Analysis, International Journal of Production Economics, International Journal of Project Management, and others. He can be contacted at aeroactuary@ gmail. com.

Dr. Abdelkader Daghfous received his PhD degree in Technology and Innovation Management from The Pennsylvania State University, University Park, Pennsylvania, USA. He is currently a Professor of Supply Chain Management in the School of Business Administration at the American University of Sharjah. His current research addresses the issues of collaborative innovation, knowledge- enabled risk manage- ment in supply chains, and sustainability- oriented innovation in the healthcare supply chain. His research has been published in journals such as Research Policy, Omega, Technovation, and the Journal of Knowledge Management and he has presented papers in various local and international conferences for aca- demics and practitioners. He can be contacted at adaghfous@ aus. edu.

M. Sajid Khan is Professor of Marketing at the American University of Sharjah where he teaches marketing strategy. He is also a Fellow of The Chartered Institute of Marketing, England. His recent research revolving around drivers of cus- tomer satisfaction and loyalty behavior, risk perceptions, and the adoption of smart technologies has appeared in outlets such as Electronic Markets, Industrial Marketing Management, Risk Analysis, Journal of Strategic Marketing, Marketing Intelligence & Planning, Sustainability, and Journal of Retailing and Consumer Services, among others. He received his PhD in consumer behavior from The University of Manchester in the United Kingdom. He can be contacted at mskhan@ aus. edu.

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