This article explores the impact of risk attitude on the assessment of project uncertainty encompassing both risk and opportunity and expected project performance. A survey was conducted involving project risk management experts from the construction 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 authors’ 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.
AladağH.IsikZ. (2018). The effect of stakeholder-associated risks in mega-engineering projects: A case study of a PPP airport project. IEEE Transactions on Engineering Management, 67(1), 174–186.doi:10.1109/TEM.2018.2866269
2.
AtkinsonR. (1999). Project management: Cost, time and quality, two best guesses and a phenomenon, its time to accept other success criteria. International Journal of Project Management, 17(6), 337–342.doi:10.1016/S0263-7863(98)00069-6
3.
AvenT. (2016). Supplementing quantitative risk assessments with a stage addressing the risk understanding of the decision maker. Reliability Engineering & System Safety, 152, 51–57.doi:10.1016/j.ress.2016.03.003
4.
AvenT. (2017). Improving risk characterisations in practical situations by highlighting knowledge aspects, with applications to risk matrices. Reliability Engineering & System Safety, 167, 42–48.doi:10.1016/j.ress.2017.05.006
5.
BakhshiJ.IrelandV.GorodA. (2016). Clarifying the project complexity construct: Past, present and future. International Journal of Project Management, 34(7), 1199–1213.doi:10.1016/j.ijproman.2016.06.002
6.
Bosch-RekveldtM.JongkindY.MooiH.BakkerH.VerbraeckA. (2011). Grasping project complexity in large engineering projects: The TOE (Technical, Organizational and Environmental) framework. International Journal of Project Management, 29(6), 728–739.doi:10.1016/j.ijproman.2010.07.008
7.
BrowningT. R. (2014). A quantitative framework for managing project value, risk, and opportunity. IEEE Transactions on Engineering Management, 61(4), 583–598.doi:10.1109/TEM.2014.2326986
8.
ChanA. P. C.ScottD.ChanA. P. L. (2004). Factors affecting the success of a construction project. Journal of Construction Engineering and Management, 130(1), 153–155.doi:10.1061/(ASCE)0733-9364(2004)130:1(153)
9.
ChapmanC.WardS. (2004). Why risk efficiency is a key aspect of best practice projects. International Journal of Project Management, 22(8), 619–632.doi:10.1016/j.ijproman.2004.05.001
10.
ChapmanC.WardS. (2011). How to manage project opportunity and risk. John Wiley & Sons.
11.
DaoB.KermanshachiS.ShaneJ.AndersonS.HareE. (2017). Exploring and assessing project complexity. Journal of Construction Engineering and Management, 143(5), 04016126.doi:10.1061/(ASCE)CO.1943-7862.0001275
12.
DenicolJ.DaviesA.KrystallisI. (2020). What are the causes and cures of poor megaproject performance? A systematic literature review and research agenda. Project Management Journal, 51(3), 328–345.doi:10.1177/8756972819896113
13.
DikmenI.BirgonulM. T. (2006). An analytic hierarchy process based model for risk and opportunity assessment of international construction projects. Canadian Journal of Civil Engineering, 33(1), 58–68.doi:10.1139/l05-087
14.
DikmenI.BudayanC.Talat BirgonulM.HayatE. (2018). Effects of risk attitude and controllability assumption on risk ratings: Observational study on international construction project risk assessment. Journal of Management in Engineering, 34(6), 04018037.doi:10.1061/(ASCE)ME.1943-5479.0000643
15.
EkiciA.Önsel EkiciŞ. (2019). Understanding and managing complexity through Bayesian network approach: The case of bribery in business transactions. Journal of Business Research.doi:10.1016/j.jbusres.2019.10.024
16.
FidanG.DikmenI.TanyerA. M.BirgonulM. T. (2011). Ontology for relating risk and vulnerability to cost overrun in international projects. Journal of Computing in Civil Engineering, 25(4), 302–315.doi:10.1061/(ASCE)CP.1943-5487.0000090
17.
FloricelS.MichelaJ. L.PipercaS. (2016). Complexity, uncertainty-reduction strategies, and project performance. International Journal of Project Management, 34(7), 1360–1383.doi:10.1016/j.ijproman.2015.11.007
18.
GalliB. J. (2018). The future of economic decision making in project management. IEEE Transactions on Engineering Management, 67(2), 396–413.doi:10.1109/TEM.2018.2875931
19.
GeNIe 2.0. The decision systems laboratory, GeNIe and SMILE.
20.
GeraldiJ.MaylorH.WilliamsT. (2011). Now, let’s make it really complex (complicated): A systematic review of the complexities of projects. International Journal of Operations & Production Management, 31(9), 966–990.
21.
HanS. H.DiekmannJ. E.OckJ. H. (2005). Contractor’s risk attitudes in the selection of international construction projects. Journal of Construction Engineering and Management, 131(3), 283–292.doi:10.1061/(ASCE)0733-9364(2005)131:3(283)
22.
HeQ.LuoL.HuY.ChanA. P. C. (2015). Measuring the complexity of mega construction projects in China—A fuzzy analytic network process analysis. International Journal of Project Management, 33(3), 549–563.doi:10.1016/j.ijproman.2014.07.009
23.
HillsonD. (2002). Extending the risk process to manage opportunities. International Journal of Project Management, 20(3), 235–240.doi:10.1016/S0263-7863(01)00074-6
24.
JaafariA. (2001). Management of risks, uncertainties and opportunities on projects: Time for a fundamental shift. International Journal of Project Management, 19(2), 89–101.doi:10.1016/S0263-7863(99)00047-2
25.
KahnemanD.TverskyA. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–292.doi:10.2307/1914185
26.
KelangathS.DasP. K.QuigleyJ.HirdarisS. E. (2011). Risk analysis of damaged ships—A data-driven Bayesian approach. Ships and Offshore Structures, 7(3), 333–347.doi:10.1080/17445302.2011.592358
27.
KjaerulffU. B.AndersL. M. (2008). Bayesian networks and influence diagrams: A guide to construction and analysis. Springer-Verlag.
28.
KottasA. T.MadasM. A. (2018). Comparative efficiency analysis of major international airlines using data envelopment analysis: Exploring effects of alliance membership and other operational efficiency determinants. Journal of Air Transport Management, 70, 1–17.doi:10.1016/j.jairtraman.2018.04.014
29.
LessardD.SakhraniV.MillerR. (2014). House of project complexity—Understanding complexity in large infrastructure projects. Engineering Project Organization Journal, 4(4), 170–192.doi:10.1080/21573727.2014.907151
30.
LuoL.HeQ.XieJ.YangD.WuG. (2017). Investigating the relationship between project complexity and success in complex construction projects. Journal of Management in Engineering, 33(2), 04016036.doi:10.1061/(ASCE)ME.1943-5479.0000471
31.
MarquesG.GourcD.LaurasM. (2011). Multi-criteria performance analysis for decision making in project management. International Journal of Project Management, 29(8), 1057–1069.doi:10.1016/j.ijproman.2010.10.002
32.
MirzaE.EhsanN. (2017). Quantification of project execution complexity and its effect on performance of infrastructure development projects. Engineering Management Journal, 29(2), 108–123.doi:10.1080/10429247.2017.1309632
33.
OlssonR. (2007). In search of opportunity management: Is the risk management process enough?International Journal of Project Management, 25(8), 745–752.doi:10.1016/j.ijproman.2007.03.005
34.
PadalkarM.GopinathS. (2016). Six decades of project management research: Thematic trends and future opportunities. International Journal of Project Management, 34(7), 1305–1321.doi:10.1016/j.ijproman.2016.06.006
35.
PineyC. (2003). Applying utility theory to risk management. Project Management Journal, 34(3), 26–31.doi:10.1177/875697280303400304
36.
QaziA.DikmenI. (2019). From risk matrices to risk networks in construction projects. IEEE Transactions on Engineering Management, 1–12.doi:10.1109/TEM.2019.2907787
37.
QaziA.DikmenI.BirgonulM. T. (2019). Mapping uncertainty for risk and opportunity assessment in projects. Engineering Management Journal, 32(2), 86–97.doi:10.1080/10429247.2019.1664249
38.
QaziA.QuigleyJ.DicksonA.KirytopoulosK. (2016). Project complexity and risk management (ProCRiM): Towards modelling project complexity driven risk paths in construction projects. International Journal of Project Management, 34(7), 1183–1198.doi:10.1016/j.ijproman.2016.05.008
39.
QureshiS. M.KangC. (2015). Analysing the organizational factors of project complexity using structural equation modelling. International Journal of Project Management, 33(1), 165–176.doi:10.1016/j.ijproman.2014.04.006
40.
RuanX.YinZ.FrangopolD. M. (2015). Risk matrix integrating risk attitudes based on utility theory. Risk Analysis, 35(8), 1437–1447.doi:10.1111/risa.1240025958890
41.
SA. (2009). Risk management: Principles and guidelines (AS/NZS ISO 31000: 2009). Standards Australia.
42.
SigurdssonJ. H.WallsL. A.QuigleyJ. L. (2001). Bayesian belief nets for managing expert judgement and modelling reliability. Quality and Reliability Engineering International, 17(3), 181–190.doi:10.1002/qre.410
43.
TarounA. (2014). Towards a better modelling and assessment of construction risk: Insights from a literature review. International Journal of Project Management, 32(1), 101–115.doi:10.1016/j.ijproman.2013.03.004
44.
TatikondaM.RosenthalS. R. (2000). Technology novelty, project complexity, and product development project execution success: A deeper look at task uncertainty in product innovation. IEEE Transactions on Engineering Management, 47(1), 74–87.doi:10.1109/17.820727
ThoméA. M. T.ScavardaL. F.ScavardaA.ThoméF. E. S. (2016). Similarities and contrasts of complexity, uncertainty, risks, and resilience in supply chains and temporary multi-organization projects. International Journal of Project Management, 34(7), 1328–1346.doi:10.1016/j.ijproman.2015.10.012
47.
ToorS. R.OgunlanaS. O. (2010). Beyond the ‘iron triangle’: Stakeholder perception of key performance indicators (KPIs) for large-scale public sector development projects. International Journal of Project Management, 28(3), 228–236.doi:10.1016/j.ijproman.2009.05.005
48.
WangC. M.XuB. B.ZhangS. J.ChenY. Q. (2016). Influence of personality and risk propensity on risk perception of Chinese construction project managers. International Journal of Project Management, 34(7), 1294–1304.doi:10.1016/j.ijproman.2016.07.004
49.
WangJ.YuanH. (2011). Factors affecting contractors’ risk attitudes in construction projects: Case study from China. International Journal of Project Management, 29(2), 209–219.doi:10.1016/j.ijproman.2010.02.006
50.
WardS.ChapmanC. (2003). Transforming project risk management into project uncertainty management. International Journal of Project Management, 21(2), 97–105.doi:10.1016/S0263-7863(01)00080-1
51.
WilliamsT. (2017). The nature of risk in complex projects. Project Management Journal, 48(4), 55–66.doi:10.1177/875697281704800405
52.
YazdaniM.AbdiM. R.KumarN.Keshavarz-GhorabaeeM.ChanF. T. S. (2019). Improved decision model for evaluating risks in construction projects. Journal of Construction Engineering and Management, 145(5), 04019024.doi:10.1061/(ASCE)CO.1943-7862.0001640
53.
ZhangL.QianQ. (2017). How mediated power affects opportunism in owner–contractor relationships: The role of risk perceptions. International Journal of Project Management, 35(3), 516–529.doi:10.1016/j.ijproman.2016.12.003
