Incentive Mechanisms in Mega Project-Risk Management Considering Owner and Insurance Company as Principals
Publication: Journal of Construction Engineering and Management
Volume 146, Issue 10
Abstract
In mega projects, the stakeholders may be exposed to significant on-site construction risk, especially the owners and insurance companies who take the most responsibility for the risk loss. It is difficult for insurance companies to diversify their risks by undertaking enough similar policies, and participating in on-site risk management has become an important method of active risk control. Based on the principal-agent relationship between the owner, insurance company, and contractor, this paper establishes incentive mechanisms for risk management considering the common agency and exclusive agency models. The results show that an insurance company’s involvement in the common agency model creates external effects that can improve the utility of both the owner and the insurance company. The owner is then willing to provide a higher incentive coefficient, and the contractor’s nonrisk and risk management efforts increase accordingly. From the owner’s perspective, the influence of the participants’ characteristics and external uncertainties on the incentive strategy are discussed. The results recommend that it is better for the owners and insurance companies to jointly establish a good cooperative relationship and build the incentive mechanism. The spillover effect has a positive effect on the cooperation between the two parties, while the impact of the uncertainty in risk management output on the cooperative relationship is negative. This paper contributes to the body of knowledge for understanding the on-site risk management considering stakeholders’ participation and provides a practical mode for owners and insurance companies to implement active risk management in mega projects, thus achieving better risk governance of mega projects.
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Data Availability Statement
All data generated or analyzed during the study are included in the published paper. Information about the Journal’s data-sharing policy can be found here: http://ascelibrary.org/doi/10.1061/(ASCE)CO.1943-7862.0001263.
Acknowledgments
This work was supported by National Natural Science Foundation of China (Nos. 51978164, 71871113, 71971100, and 71671078), and the program A&B for Outstanding Ph.D. candidate of Nanjing University (Nos. 201701B010, 201801A001, and 201802A016).
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Received: Jul 8, 2019
Accepted: May 18, 2020
Published online: Jul 24, 2020
Published in print: Oct 1, 2020
Discussion open until: Dec 24, 2020
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