Chen Jin, Postdoctoral Researcher, The Wharton School; Kartik Hosanagar, Operations, Information and Decisions, The Wharton School; and Senthil Veeraraghavan, Operations, Information and Decisions, The Wharton School
Abstract: Traditional online platforms (e.g., Amazon Marketplace) use Unilateral Rating System (URS), in which customers rate sellers. However, sharing economy platform (e.g., Uber, Airbnb) have adopted Bilateral Rating Systems (BRS) that also allows service providers to rate customers, and even selects customers based on their ratings. BRS is often purported to be better than URS, as BRS unlocks more hidden information than URS for one-time interactions, by revealing ratings of customers to service providers before they make acceptance/rejection decisions. We compare URS and BRS in the context of a ride-sharing service to study their impact on the platform’s pricing policy as well as revenue/welfare implications of all stakeholders. We find that BRS facilitates supply-demand balancing and can improve the average quality of riders that drivers encounter. For the average quality of drivers that riders may face, depending on the riders’ valuation of the service, BRS can improve or reduce the drivers’ average quality. Comparing to URS, BRS always improves drivers’ welfare but may reduce the platform’s revenue when the riders’ valuation of the service is high. Surprisingly, riders’ welfare may be improved in BRS, despite the fact that riders could be rejected by drivers. In BRS, drivers’ ability to reject low rating riders can remove excess demand and hence can alleviate the demand-supply imbalance issue, which helps to reduce inconvenience cost that all riders may suffer from. Finally, it is possible for BRS to improve all stakeholders’ welfare when riders’ valuation of the service is low.