Problem definition: We study the use of nonmonetary incentives based on reciprocity to facilitate capacity sharing between two service providers that have limited and substitutable service capacity. Academic/practical relevance: We propose a parsimonious game theory framework, in which two firms dynamically choose whether to accept each other’s customers without the capability to perfectly monitor each other’s capacity utilization state. Methodology: We solve the continuous-time imperfect-monitoring game by focusing on a class of public strategy, in which firms’ real-time capacity-sharing decision depends on an intuitive and easy-to-implement accounting device, namely the current net number of transferred customers. We refer to such an equilibrium as a trading-favors equilibrium. We characterize the condition in which capacity sharing takes place in such an equilibrium. Results: We find that some degree of efficiency loss (as compared with a central planner’s solution) is necessary to induce reciprocity. The efficiency loss is small when the two firms have similar traffic intensity even if they are different in service-capacity scale, whereas the efficiency loss can be considerably large when the two firms have significantly different traffic intensities. The trading-favors mechanism, surprisingly, can outperform the perfect-monitoring benchmark when the two firms exhibit high asymmetry in terms of service-capacity scale or traffic intensity because the smaller firm tends to deviate from collaboration. Managerial implications: Firms should consider engaging in nonmonetary reciprocal capacity sharing if regulations, transaction costs, or other market and operational frictions make it difficult to use a capacity-sharing contract based on monetary payments. The trading-favors collaboration can improve the firms’ payoff close to the centralized upper bound when the firms have similar traffic intensities. However, when their traffic intensities are highly different, firms are better off with a monetary-payment contract to induce more capacity sharing and are worse off investing in increasing their visibility to each other’s real-time available capacity, namely investing in perfect monitoring.
Ph.D. in Operations and Management Science
Bachelor of Science in Mathematics
Dr. Hu is an associate professor in the area of Innovation and Information Management. Her expertise includes online retailing operations, such as dynamic pricing and logistics, revenue management, and sharing economy operations. Her research on these topics has been published in top-tier research journals including Management Science, Operations Research, and Manufacturing and Service Operations Management.
She received her Ph.D. from the Stern School of Business at the New York University, and obtained her Bachelor of Science in Mathematics from the Peking University. Before joining the University of Hong Kong, she taught at the University of Oregon.
- Operations Management
- Dynamic Decision Making
- Revenue Management and Dynamic Pricing
- Online Retailing and Platforms
- Models of Incentives and Strategic Interactions
- Xu Min, Wei Chi, Xing Hu, and Qing Ye, 2023. Set a Goal for Yourself? A Model and Field Experiment with Gig Workers, forthcoming in Production and Operations Management
- Xing Hu and René Caldentey, 2023, Trust and Reciprocity in Firms’ Capacity Sharing. Manufacturing & Service Operations Management, 25(4):1436-1450
- Xing Hu, Zhixi Wan, and Nagesh Murthy, 2017, Dynamic Pricing of Limited Inventories with Product Returns. Manufacturing & Service Operations Management, 21(3):501-518
- Shanshan Hu, Xing Hu, and Qing Ye, 2017, Optimal Rebate Strategies under Dynamic Pricing. Operations Research, 65(6), pp. 1546-1561
- Xing Hu, René Caldentey, and Gustavo Vulcano, 2013, Revenue Sharing in AirlineAlliances, Management Science, 59:1177-1195
(Second Place of the AGIFORS Anna Valicek Paper Competition, 2011)
- Lundquist College of Business Research Award (2015)
- Second Place of the AGIFORS Anna Valicek Paper Competition, 2011