问题定义：我们研究了拼车平台针对容量和利润最大化问题的最佳奖金设置决策，其中司机设置每日收入目标。 

学术和实践相关性：共享经济公司一直在提供金钱奖励，以激励司机自主安排更长工作时间。 我们在拼车行业的背景下研究了货币奖励计划的有效性，该行业的司机高度多样化并设定了收入目标。 

研究方法：我们将驾驶员的决策过程和平台的优化问题建模为主从博弈（Stackelberg game）。 然后，利用从领先的拼车平台获得的综合数据集，我们开发了一种新的经验策略，通过简化形式和结构分析提供关于司机收入目标行为存在的证据。 此外，我们通过使用从估计结果得出的异质驱动因素的特征，进行反事实分析，以计算不同场景的最佳奖金率。

研究结论：我们的理论模型表明，在目标效应下，司机的工作时间不会随着奖金率单调增加，并且平台可能不会将其所有预算用于奖金以最大化容量或利润。 我们凭经验证明司机从事收入目标行为，此外，我们估计异质司机的收入目标。 通过反事实分析，我们说明了当平台面临不同的驱动程序组成和市场条件时，最佳奖金计划如何变化。 我们还发现，与平台之前的奖金设置相比，最优奖金策略将高峰时段的容量水平提高了 26%，使每月总利润增加了 430 万美元。 

管理意义：开发灵活的、自行安排的司机供应，以适应不断变化的需求并保持拼车平台的市场份额具有挑战性。 在提供奖金以激励司机延长工作时间时，司机的收入目标行为可能会削弱此类奖金计划的有效性。 该平台需要了解驾驶员在金钱奖励方面的行为偏好的异质性，以设计有效的奖金策略。

Abstract

Problem definition: We study a ridesharing platform’s optimal bonus-setting decisions for capacity and profit maximization problems in which drivers set daily income targets. 

Academic and Practical Relevance: Sharing-economy companies have been providing monetary rewards to incentivize self-scheduled drivers to work longer. We study the effectiveness of the monetary bonus scheme in the context of the ridesharing industry, where the drivers are highly heterogeneous and set income targets. 

Methodology: We model a driver’s decision-making processes and the platform’s optimization problem as a Stackelberg game. Then, utilizing comprehensive datasets obtained from a leading ridesharing platform, we develop a novel empirical strategy to provide evidence on the existence of drivers’ income-targeting behavior through a reduced-form and structural analysis. Furthermore, we perform a counterfactual analysis to calculate the optimal bonus rates for different scenarios by using the characteristics of heterogeneous drivers derived from the estimation outcomes. 

Results: Our theoretical model suggests that the drivers’ working hours do not increase monotonically with the bonus rate under the target effect and that the platform may not use all its budget on bonuses to maximize capacity or profit. We empirically demonstrate that the drivers engage in income-targeting behavior, and furthermore, we estimate the income targets for heterogeneous drivers. Through counterfactual analysis, we illustrate how the optimal bonus scheme varies when the platform faces different driver compositions and market conditions. We also find that, compared with the platform’s previous bonus setting, the optimal bonus strategy improves the capacity level during peak hours by as much as 26%, boosting the total profit by $4.3 million per month. 

Managerial implications: It is challenging to develop a flexible self-scheduled supply of drivers that can match the ever-changing demand and maintain the market share of the ridesharing platform. When offering monetary bonuses to incentivize drivers to work longer, the drivers’ income-targeting behavior can undermine the effectiveness of such bonus schemes. The platform needs to understand the heterogeneity of drivers’ behavioral preferences regarding monetary rewards to design an effective bonus strategy.

共享经济；拼车平台；主从博弈；最优奖金策略

Keywords: sharing-economy, ridesharing platform, Stackelberg game, optimal bonus-setting decisions