Management Science and Information Systems' Seminar（2017-14）
Topic:The Incentive Game under Target Effect in the Ride-Sharing Market: Theory and Evidence
Speaker:Weiming Zhu, IESE Business School, University of Navarra
Time:Thursday, December 7th, 13:30--15:30
Place:Room K02, Guanghua Building 2
To wield a flexible self-scheduled supply to match the ever-changing demand and maintain market shares, ride-sharing platforms such as Uber and Didi strive to keep more registered drivers active on the road, especially during peak hours in which the demand tends to be the highest. Usually, platforms offer monetary bonuses to incentivize drivers to work longer. However, drivers’ responses to bonuses can be affected by the target effect, which if present may undermine the effectiveness of such bonus schemes. In this paper, we provide a theoretical and empirical analysis of the impact of monetary bonuses on ride-sharing drivers’ working hours as well as the platform’s best incentive strategy under the target effect. We first model driver’s decision-making process and platform’s optimization problem respectively, and show that driver’s working hours do not increase monotonically with the bonus rate under the target effect. Then, utilizing comprehensive datasets from a leading ride-sharing platform, we provide reduced-form evidence on the existence of driver’s target-setting behavior. Furthermore, we structurally probe the existence of the income target and break down drivers’ targets depending on their characteristics. Finally, through counterfactual analysis, we find that the optimal bonus strategy improved the capacity level during peak hours by more than 12% within the current budget limit.
Weiming Zhu joined IESE Business School in Sep 2016. He received his PhD from University of Maryland. His research interests are empirical operations management, operations - finance interface, the sharing economy, economics of operations management and risk management in supply chains.
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