Strategic use of fare-reward schemes in a ride-sourcing market: An equilibrium analysis

Reward schemes are common in ride-sourcing markets. Each reward scheme incentivizes users with economic benefits as rewards for rides from the ride-sourcing platform. In practice, reward schemes differ in terms of their operational features. Operational features are the ways in which a ride-sourcing platform manages a reward scheme and users perceive and redeem their rewards. Therefore, different reward schemes involve different decision-making contexts for the platforms. As a result, it poses a challenge to understand the behavior of platforms with different types of reward schemes in the market. To address this challenge, this study analyzes the impacts of point-based and crypto-based fare-reward schemes on a ride-sourcing market. The market is defined as a mathematical model in which a ride-sourcing platform determines trip fares, commission fees, and rewards for passengers and drivers to maximize matching profits. A points-based reward scheme offers loyalty points with fixed redemption values. Issuance of point rewards incurs a liability for the reward scheme to accept users' point redemptions in the future. The crypto-based approach involves the cost of securing cryptocurrencies to reward users. Crypto rewards have various redemption values. Therefore, users perceive the value of crypto rewards based on their beliefs regarding their future value. This study performed an equilibrium analysis to elucidate the relationships between platform decisions and endogenous variables in the market using two fare-reward schemes. Subsequently, the properties of the optimal market solutions are further explored. These properties provide insights into the platform's reward and pricing decisions as follows: First, it is shown that a ride-sourcing platform is likely to adopt a single-sided reward strategy; that is, the platform's reward decisions with reward schemes are made exclusively between passengers and drivers. Second, the heterogeneity of user redemption behavior polarizes the platform's pricing decision on trip fares; that is, a ride-sourcing platform has incentives to set lower and higher trip fares when deciding to reward drivers and passengers, respectively. Third, such distinctive pricing decisions balance passenger demand with driver supply. Therefore, the ride-sourcing market can remain stable and efficient, even in the presence of the platform's reward decisions. A series of numerical experiments were performed to verify the findings of the current study.