INCENTIVE MECHANISMS OF AN EXPERIMENTAL RESOURCE-SHARING PLATFORM CONSIDERING REPUTATION EFFECTS FOR MEGAPROJECTS

Abstract

Participating in megaproject experimental tasks would significantly improve the laboratories’ industry influence and future competitiveness. Thus, this paper introduces the long-term reputation effects of the incentive model of an experimental resource-sharing platform for megaprojects, which could motivate them to consider future benefits and improve their current efforts. The aim is to incentivize laboratories’ resource-sharing behavior more effectively and to increase the amount of resources shared by these laboratories on the platform, thus guaranteeing the long-term sustainability of the platform. It constructs the incentive model by combining dual implicit and explicit incentive mechanisms. It analyses the incentive mechanism of a reputation effect on laboratories compared with the pure explicit mechanism so that the primary conditions for reputation incentives can be obtained to achieve Pareto improvement. Finally, the proposed method is validated in combination with data simulation. The results show that although dual implicit and explicit incentive mechanisms could reduce the information asymmetry between the two sides and increase the efforts of laboratories and the benefits of the platform, the platform should not blindly increase the intensity of these incentives and need to consider the influence of the subsidies of these laboratories’ upfront inputs, the degree of sharing and their informatization capabilities.