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International Journal of Project Management 31 (2013) 307–312www.elsevier.com/locate/ijproman

A comparative study of the measurements of perceived risk amongcontractors in China

Shaokai Lu a,⁎, Hong Yan b

a School of Economics and Management, Southwest Jiaotong University, Chinab Department of Logistic, Hong Kong Polytechnic University, Hong Kong

Received 4 April 2012; received in revised form 31 May 2012; accepted 7 June 2012

Abstract

Due to the unique character of construction projects, perceived risk is widely used to quantify risks in the construction industry. This studyinvestigates the two main types of measurement of perceived risk used in construction projects: direct measurement and expected utility-basedmeasurement. Project managers from contract firms in China assess 15 independent risks using three different strategies: direct measurement, riskprobability and potential impact. The last two are combined to create the expected utility-based measurement. The results show that the riskranking order obtained from the direct measurement strategy is significantly different from that obtained from the expected utility-basedmeasurement. Moreover, the former measurement is in general a better predictive indicator of relative managerial input than the latter. Based onthese conclusions, some suggestions are presented for better risk management and cooperation in the construction industry.© 2012 Elsevier Ltd. APM and IPMA. All rights reserved.

Keywords: Perceived risk; Measurement; Risk management; Contractor

1. Introduction

It is widely acknowledged that riskmanagement is an essentialpart of construction projects (Project Management Institute,2004). Although there has been a large amount of researchdevoted to developing methods to identify, analyze and evaluatethe relative importance of risks associated with particularpolitical, geographic, economic, environmental, regulatory andcultural factors (e.g., Ashley and Bonner, 1987; Chua et al., 2003;Dikmen et al., 2007; Khattab et al., 2007), most of these analysesare heavily dependent on the risk perceptions of the topmanagement of construction companies (Ramgopal, 2003). Inthese studies, a few individuals' perceptions of risk may have asignificant effect on the validity and reliability of the results.Therefore, our understanding of how construction groups orindividuals perceive risk is still very limited. Although

⁎ Corresponding author at: School of Economics and Management, SouthwestJiaotong university, 610031, Chengdu, Sichuan, China. Tel.: +86 028 87603604;fax: +86 028 87603822.

E-mail address: shaokailu@163.com (S. Lu).

0263-7863/$36.00 © 2012 Elsevier Ltd. APM and IPMA. All rights reserved.doi:10.1016/j.ijproman.2012.06.001

psychologists have studied the factors that influence perceivedrisk and its components (Weber and Hsee, 1998; Weber et al.,2002), this psychological perspective has seldom been applied toa construction project context.

This topic is of particular importance in developing countrieslike China, where rapid economic growth has led to a boom ininfrastructure building, but where few professionals in theconstruction industry have sufficient training in risk manage-ment. For example, Fung et al. (2012) state that safetyprofessionals make risk assessments based on their personalexperiences rather than on systematic processes. Obviously, tosurvive in today's construction market, contractors must shouldera high degree of risk. Although China's rapid economic growthprovides many opportunities for overseas construction compa-nies, foreign participants usually cooperate with local contractors,in part to reduce the risk level. Thus, understanding how risk isperceived by contractors in China not only provides usefulguidelines for effective cooperation, but also helps to establishbetter cooperation between local partners and international firms.

This study conducts pioneering research on how contractorsin China perceive risk and develops helpful suggestions for

308 S. Lu, H. Yan / International Journal of Project Management 31 (2013) 307–312

more effective risk management. The rest of the paper isorganized as follows. In the next section, the primary riskranking or risk evaluating methodologies for constructionprojects are reviewed. Using a list of 15 independent riskfactors associated with construction projects, 76 projectmanagers from contractors in China are invited to assess theserisk factors using different methodologies. A series ofregression models are then established to investigate whichmeasurement is a better indicator of relative managerial input.Based on these findings, practical applications are proposedand discussed in the conclusions.

2. Risk measurements for construction projects

Risk in the construction industry is often defined as thepresence of potential events that influence the objectives of aproject as a consequence of uncertainty (Project ManagementInstitute, 2004. Although risk can be an opportunity for bothgains and losses (Dias and Ioannou, 1995), most riskassessment efforts in the construction field are focused onrisks associated with the possibility of loss. According to theestablished literature on construction risk management, theanalysis and assessment of risk are two vital steps in theformulation of an effective response (Kangari, 1995; Smith,1999). Due to the lack of data and the unique nature ofconstruction projects, most measurements for ranking therelative importance of risk factors depend upon the pro-fessionals' subjective perceptions. Existing systems can beclassified into two types.

Traditional risk assessment for construction projects evalu-ates perceived risk directly by soliciting experts' opinions withquestions such as “How risky do you think this factor is?” (e.g.,Choi et al., 2010; Ghosh and Jintanapakanont, 2004). Varioustypes of numerical rating scales are applied to help pro-fessionals to quantify the answers. The mean scores of theinvestigated risk factors are then used to rank their relativeimportance.

Another type of risk assessment is based on the expectedutility theorem (Tversky and Kahneman, 1992). In this type ofmeasurement system, the respondents, experts in the construc-tion industry, are required to provide rough assessments of theprobability of occurrence and the degree of impact associatedwith a particular risk factor. Numerical scales are used to scoreeach risk factor in terms of its impact and the probability ofoccurrence, and the product of these two values is often takenas the basis for a rank ordering of risks (e.g., Chan et al., 2011;El-Sayegh, 2008).

Although the above types of risk measurement are based onindividuals' subjective estimates, Ward (1998) states that thequantification of the risk categories in the expected utility-based approach makes the classification of risks less subjective.Moreover, if statistically significant numbers of experts aresampled, the expected utility measures should provide objec-tive baselines for risky decisions (e.g., Fu et al., 2012; Shen etal., 2001; Zou et al., 2007). A similar position is held by Baloiand Price (2003), who further state that individual knowledge,experience, intuitive judgment and rules of thumb should be

used to facilitate the risk assessment of a particular project.Given the above discussion, we classified existing measure-ments of perceived risk into the two main types of directmeasurement and expected utility-based measurement.

Some studies analyze perceived risk using methods such asthe analytic hierarchy process or the fuzzy set theory to assessthe risk degree of a project (Dikmen et al., 2007; Gunhan andArditi, 2005). However, a number of studies in the behaviorfield indicate that the human perceptions may have a systematicbias against objective truth (Kahneman and Tversky, 1979;Loewenstein et al., 2001). Differences in risk behaviors may becaused by differences in the way risk is perceived (Kutsch andHall, 2010). For example, some people may assess the riskinessof an event based on the expected utility. That is, they trulyassess risk with a cross-consideration of probability and impact.Other people may have a different subjective impression ofriskiness and focus only on the impact of potential loss. Indeed,although the expected utility-based measurements are widelyrecommended in most recent studies, many scholars use onlyprobability measurements to quantify risk levels (Lam et al.,2007).

Understanding how risk is perceived is of critical importancebecause managerial strategies are based on the results of therisk assessment. On the one hand, any deviation between thesubjective judgment of risk and the truth may cause seriousadverse outcomes. On the other hand, differences in the wayrisk is perceived may also provide opportunities for collabora-tion. When the results of risk assessment differ and the groupsinvolved can determine the nature of these differences, it ispossible to generate tradeoffs that make increase the satisfac-tion of all of the parties involved (Deutsch, 1973; Weber andHsee, 1998).

3. Research methodology

We conduct two investigations to explore how contractors inChina perceive risk and to determine which of the twomeasurement types most accurately predict the relativemanagerial input. In the following sections, we discuss eachstudy in detail.

3.1. Study 1

3.1.1. ProcedureThis study uses a questionnaire to investigate the risk

perception of contractors in China. The questionnaire consistsof 15 risk factors associated with construction projects. Theserisk elements are culled from four primary sources: a literaturereview (Zou et al., 2007); the expertise of the research team;structured interviews with five senior managers of contractfirms; and further review by three industrial representatives.The final list of risks is refined and an agreement is reachedregarding the exact terms and nomenclature of elementdefinitions. Table 1 presents the descriptions of the riskfactors.

Each respondent sees each of the 15 risks three times inrandom order. The first time they see an element they are asked

Table 1The descriptions of risk factors.Adapted from Zou et al. (2007).

Risk factors Descriptions

1. Lack of insurance Not buying insurance for major equipment and employees2. Lack of professionals A majority of Chinese contractors lack high-quality staff to manage large projects3. Defective materials Defective construction materials that do not meet the building requirements4. Poorly trained laborers Poorly trained laborers may lead to poor quality outcomes for the project5. Inflation The price of construction materials rises with inflation6. Amphibolous contract The clauses of the contract are hard to understand because of the loose or awkward way in which they are worded7. Design variations Design variations may result from issues such as changes by the client or defective designs8. Government bureaucracy Excessive approval procedures in government departments9. Inaccurate cost estimate Many unforeseen factors may occur in construction activities causing the estimated cost to deviate from the real cost10. Poor communication Lack of effective communication among project partners11. Unavailability of funds Client's poor management of funding in the development of construction projects12. Long term of investment Some project delivery approaches, such as BT, require the contractor to make large endowments in advance13. Deregulation of safety Poor safety awareness of project managers and inadequate safety measures14. Theft Employees may steal construction materials and equipment15. Pollution Dust, harmful gases, noise, solid and liquid wastes, etc.

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to assess its riskiness using their subjective judgment. Thesecond time they see it they are asked to assess the probabilityof occurrence, and the third time to rate the degree of impact.Using the research team's experience, the literature review andstandard industrial practices (Gibson and Walewski, 2004), wedevelop numerical scales to help respondents quantify theiranswers. Table 2 gives the probability division for thelikelihood of occurrence, the relative impact definitions, andthe numerical scale for subjective measures used in theresearch.

The 76 participants are all project managers employed bycontractors and enrolled in a part-time training program at amidsize university in southern China. According to Chineselaw, these project managers must attend the training program tomaintain their professional qualifications. In constructionprojects, the project manager is solely responsible for theoverall management. Therefore, the opinions of projectmanagers broadly represent the views of the contractor firms,even though the project managers are individuals.

A total of 46 (61%) respondents have greater than or equalto 10 years of construction related experience and 63 (83%) arehigh-ranking or top-post project managers. All of the projectmanagers are male; 5% have a high school diploma, 38% havean associate's degree, 50% have a bachelor's degree, and theothers have a graduate degree. In addition, all respondents arefrom first class Chinese firms. These contractors have morethan 1000 employees on average, and the accumulative

Table 2Numerical scales used in Study 1.

Probability — occurrence Relative im

About 5% — very low chance 1 — NegliAbout 20% — low chance 2 — MinoAbout 50% — medium chance 3 — ModeAbout 70% — high chance 4 — SigniAbout 90% — very high chance 5 — Extre

proportion of state-owned and collective owned companies is92%. Thus, the results can be treated as representative ofexperienced and highly-ranked contractors.

3.1.2. ResultsThe statistical means of the assessments of the 15 risk

elements using direct measurement, probability, impact, thecalculated expected utility-based measurement, and the corre-sponding rankings are shown in Table 3.

With the help of SPSS 18.0, the Spearman rank correlationcoefficient (rs) is used to measure the agreement between therankings in the risk importance indexes generated by the variousrisk assessment measurements. The null hypothesis is that thereis no significant relationship between the two groups of data. Asthe results in Table 4 show, the two major risk indicators ofdirect measurement and expected utility-based measurementhave significantly different risk importance indexes (rs=0.28,pN0.05). For example, “Government bureaucracy” is rankedthirteenth in the direct measurement index, whereas it is rankedthird in the expected utility-based measurement index. In sum,even when identical risks are evaluated by the same re-spondents, different measurement criteria may produce variousrankings of risk assessment. Moreover, it is also shown that thedirect measurement is significantly related to the impactmeasurement (rs=0.87, pb0.001), whereas it is insignificantlyrelated to probability assessment (rs=0.15, pN0.05).

pact Direct measurement

gible consequence 1 — Very low riskr consequence 2 — Low riskrate consequence 3 — Medium riskficant consequence 4 — High riskme consequence 5 — Very high risk

Table 3Means and rankings by various measurements.

Risk factors Direct measurement Probability Impact Expected utility-basedmeasurement

Mean Rank Mean Rank Mean Rank Mean Rank

1. Lack of insurance 2.83 12 0.20 11 2.68 13 0.52 142. Lack of professionals 2.92 9 0.33 7 3.08 7 1.08 63. Defective materials 3.87 1 0.19 13 3.74 2 0.65 104. Poorly trained laborers 3.13 8 0.24 9 2.87 12 0.71 95. Inflation 3.53 3 0.50 1 3.13 6 1.61 26. Amphibolous contract 3.17 7 0.21 10 3.15 5 0.65 107. Design variations 3.49 4 0.15 15 3.64 3 0.54 138. Government bureaucracy 2.75 13 0.44 3 3.00 9 1.48 39. Inaccurate cost estimate 2.91 10 0.36 6 2.96 11 1.12 510. Poor communication 2.89 11 0.20 11 2.98 10 0.61 1211. Unavailability of funds 3.79 2 0.46 2 3.91 1 1.84 112. Long term of investment 3.42 5 0.39 4 3.40 4 1.41 413. Deregulation of safety 3.40 6 0.28 8 3.04 8 0.88 814. Theft 2.68 14 0.37 5 2.25 15 0.89 715. Pollution 2.57 15 0.18 14 2.60 14 0.50 15

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3.2. Study 2

3.2.1. ProcedureThe following investigation examines which measurement

of perceived risk most accurately predicts the relativemanagerial input of contractors. A simplified version of thequestionnaire from Study 1, with only eight risks, is used tocollect data. In this questionnaire participants are asked toevaluate the real efforts they input into each risk factor, using a5-point Likert scale (where “1” indicates “nearly no input” and“5” indicates “input as much as possible”). Of the 38 projectmanagers from various contractor firms in this study, 30 (79%)respondents have greater than or equal to 10 years ofconstruction related experience and 22 (58%) are high-ranking or top-post project managers.

3.2.2. ResultsFollowing Weber and Hsee (1998), we fit the following

regressions from individual levels. As we focus on themeasurements used in the construction field, the modelregresses on the managerial input on the direct measurementand expected utility-based measurements associated with aparticular respondent. Table 5 shows the means of thecoefficients of the regression models.

As the above regression models have the same number ofpredictor variables, their adjusted R2-values can be directlycompared as measures of model fit. It is clear that the direct

Table 4Spearman rank correlation test for the risk importance indexes by variousmeasurements.

Measurement 1 2 3 4

1. Direct measurement 1.002. Probability 0.15 1.003. Impact 0.87 ⁎⁎⁎ 0.14 1.004. Expected utility-based measurement 0.29 0.91 ⁎⁎⁎ 0.31 1.00

⁎⁎⁎ Refers to no significant disagreement on the ranking at the 0.001 level.

measurement models account for an average of 52% of thevariance in the managerial input across all 38 respondents, witha range from 36% to 87%. The expected utility-basedmeasurement models, in contrast, account for an average of15% of the variance in the managerial input across respondents,with a range from −7% to 43%. Furthermore, the standardizedregression coefficients of the direct measurement models aresignificantly higher than those of the expected utility-basedmeasurement models (t=5.40, pb0.001). It can therefore beinferred that Chinese contractors primarily depend upon directmeasurement strategies to deal with risks rather than theexpected utility-based approach.

4. Discussion

4.1. Theoretical implications

Perceived risk is a widely used strategy for risk assessmentin the construction field as construction projects often lack thedata necessary for objective assessment. Thus, understandingthe way risk is perceived is vitally important as it forms thebasis for managers' risk management strategies. Variations inthe perception or understanding of risk may lead to seriousoutcomes. Unfortunately, our present study shows that the riskranking orders obtained by the two main types of measurementsof perceived risk, direct measurement and expected utility-based measurement, are significantly different. The resultsobtained by the former are a better reflection than the latter ofthe real managerial input of contractors.

It is should not be surprising that the behavior of contractorsdoes not reflect probability variations, whereas the expectedutility-based measurement does take probability into account.Existingmodels of risk as emotional assessments (Loewenstein etal., 2001; Slovic et al., 2004) point out that real actions in riskysituations are significantly affected by the mental imaginings ofdecision makers, and that such imaginings are not significantlyaffected by probabilities. For instance, Viscusi and Magat (1987)

Table 5Mean regression coefficients for managerial input as a function of perceived risk.

Measurement Managerial inputModel (mean)

Intercept term a Regression weight b Adjusted R2 (F-value)

Direct measurement 2.25 0.61 0.52 (10.28)Expected utility-based measurement 1.57 0.25 0.15 (1.54)

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find that people are willing to pay considerably more to reducethe risk of skin poisoning from an insect spray from 5 in 10,000 to0, than to reduce the risk from 15 in 10,000 to 5 in 10,000.

4.2. Managerial implications

These findings can be used to help contractors form moreeffective managerial risk strategies. First, it seems that mostChinese contractors depend on their gut feelings to deal withrisk. This should not be surprising as most Chinese projectmanagers lack training and skills in risk management, althoughthey have to deal with risks in practice. As a result, thesecontractors may pay more attention to risks with potentiallyserious effects, but ignore other risks that have a high probabilityof occurrence. As the expected utility-based measurement canprovide a relatively objective benchmark for risk assessment, it issuggested that Chinese contractors should be encouraged todevelop a more holistic review of risk and not to underestimatethe role of probabilities.

In addition, rating risk at an early stage is of criticalimportance for contractors who are deciding whether toundertake a particular project or participate in a partnership.Effective managerial risk strategies are absolutely necessary toattract more contractors to a project, or to get better cooperationwith them. Based on our findings and on other recent studies (Keet al., 2010), establishing a risk-sharing mechanism would be ofmore value to Chinese contractors than reducing the likelihood ofparticular occurrences associated with risks. As potential lossesplay a vital role in ranking the perceived risk for Chinesecontractors, such a risk-sharing mechanism might help groupmembers feel safer in risky situations (Weber and Hsee, 1998).

Different approaches to risk assessment among contractorsmay also provide opportunities for better collaboration betweenvarious construction companies. According to our findings, theranking orders of risks are significantly different between thedirect measurement and the expected utility-based measure-ment strategies. Suppose there are two risk factors, one with ahigher than expected utility and a lower impact, and the otherwith a lower than expected utility and a higher impact. It can beinferred that a construction firm that uses the direct measure-ment to assess risk factors would be likely to take responsibilityfor the first risk factor, whereas another firm using the exceptedutility strategy would be likely to prepare for the second. This isconsistent with findings that there are significant differencesbetween the way that Chinese and Canadian project managersassess risks (Camprieu et al., 2007). Differences in riskperception may therefore provide opportunities for tradeoffsthat satisfy all parties.

5. Conclusion

Most risk assessments of construction projects are based onperceived risk. In this study, we focus on the risk perceptionbehavior of contractors in China. The findings show that therisk ranking order obtained by the direct measurement strategyis significantly different from that obtained by the expectedutility-based measurement strategy. Furthermore, the directmeasurement is a better predictive indicator of the relativemanagerial input than the utility-based measurement. Under-standing how risk perception affects risk assessment has thepotential to become a valuable component of courses on strat-egies of risk management and on risk allocation negotiations.

This study provides new insight into risk management in theconstruction field. However, future research is needed to fullyunderstand the variation in risk perception across differentcontexts. First, our research focuses on contractors in theChinese construction industry, and the findings should be takenas preliminary results. More research needs to be conducted toconfirm the differences within and across industries andcountries. Second, more recent studies in psychology suggestthat the identification of risks in various domains may lead todifferent cognitive processes and emotional responses (Qin andHan, 2009). As risks in construction projects have differentimpacts on multiple domains including the economic, socialand safety domains (Fung et al., 2010), it is necessary toinvestigate whether there are some differences in the way riskfactors are perceived across domains. Third, more work isnecessary to improve the accuracy of risk assessment. Althoughthe expected utility-based measurement strategy may provide amore objective view, the assessment of risk is still mainlydependent on the subjective feelings of project managers. Tomake these evaluations more valid, more analysis of data fromhistorical documents (Fung et al., 2012) is needed.

Acknowledgements

We thank the participants of the research at the ShenzhenUniversity of China. This research was supported by NSFC(70902037 and 71090402), the Hong Kong PolytechnicUniversity research grant J-BB7D, and the Hong Kong GRFgrant PolyU 5515/10H.

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