A Ridesharing Platform with an Oversupply of Drivers: Different Assigning Rates or Commission Rates for Drivers?
研究了司机过剩时网约车平台如何选择差异化派单率或佣金率的补偿方案，发现过剩程度影响平台选择，且低过剩时派单率方案对平台和低成本司机双赢。
This study examines a ridesharing platform's choice among two quality-dependent compensation schemes for drivers when the market is oversupplied, that is, there are more drivers than riders at a given time. We examine an information asymmetric context in which drivers (high and low cost) have different costs to improve service quality, which is private information. The platform designs a menu of contracts for drivers to reveal their type. In the assigning-differentiation compensation scheme, the platform offers contract options with different assigning rates, that is, the probability of receiving an order assignment, different service-quality levels, but the same commission rate. In the commission-differentiation compensation scheme, the platform offers contract options with different commission rates, different service-quality levels, but the same assigning rate. We find that the oversupply degree, measured as the ratio of the number of drivers to the number of riders, plays a critical role in the platform's choice of scheme. The platform's expected profit is higher under the assigning-differentiation (commission-differentiation) scheme when the oversupply degree is high (low). We also determine the effect of the driver-type distribution on the platform's choice of scheme. When the difference in the drivers’ costs is small, the platform is more likely to choose the commission-differentiation scheme when there are more high-cost drivers. When the cost difference is large, first, the platform is more (less) likely to choose the commission-differentiation (assigning-differentiation) scheme, but then, it is more (less) likely to choose the assigning-differentiation (commission-differentiation) scheme when there are more high-cost drivers. Further, high-cost drivers are indifferent to either compensation scheme, whereas low-cost drivers are better off under the assigning-differentiation (commission-differentiation) scheme when the oversupply degree is low (high). In addition, we find that both the platform and (low-cost) drivers prefer the assigning-differentiation scheme, a win-win outcome under certain conditions. Finally, we show that the platform can provide a subsidy to mitigate potential misalignment with the drivers.