Abstract:
Construction projects are prone to risk of unparalleled degree due to their size, complexity, resource utilization, safety hazard and dynamic nature. If not managed properly, risk may increase the possibility of project failure. For effective management, it is vigorously assessed using qualitative, quantitative and simulation-based methods. The metrological underpinning of risk is based on well-grounded Expected Utility Theory which stands as a de facto standard of risk quantification. This theory, however intuitive and logical, is a normative way of measuring risk and has been criticized for its averaging method. Also, it does not reflect the behavioural tendencies of decision makers into realistic risk quantification. To improve upon the state of art, Prospect Theory (PT) was proposed which better captures the intricacies of human nature into risk quantification. It uses the concept of probability weighting function to truly reflect the significance of risky prospect. It has been extensively used in financial decision making, giving birth to a new field of Behavioural Economics. However, the construction industry lacks the applications of PT and still resorts to conventional methods.
This study aims at investigating the prospect weights to better quantify risk in construction projects by rationalizing the over- and under-estimating pattern of decision makers in the face of threats and opportunities respectively. In doing so, a detailed scenario-driven, semi-structured, interview-based data collection is performed engaging senior project management professionals from construction industry of Pakistan. It is revealed that on average construction professionals underestimate the opportunities by 7.5% and overestimate the threats by 8%. Factoring these findings into the development of response strategies will result into realistic and effective contingencies, and justified resource allocation. The body of knowledge will benefit from this novel development of rationalizing factor which may trigger more research into better measurement of risk.
