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 Systems (URS), in which customers rate sellers. However, sharing economy platforms (e.g., Uber, Lyft, Airbnb) have adopted Bilateral Rating Systems (BRS) that also allows service providers to rate customers, and even select customers based on their ratings. Customers’ ratings may reflect the externalities that will be imposed on the service providers during the service, which can induce extra utility or disutility to the service providers in addition to their revenue share per transaction. BRS unlocks the hidden value in the customer ratings by revealing the ratings to the service providers before they make accept/reject decisions, and is often purported to be better than URS. We compare URS and BRS in the context of a ride-sharing service to study their impact on the decisions as well as revenue/welfare of all stakeholders. We find that BRS may change driver’s effort structure significantly, and will influence the pricing policy of the platform, as well as the welfare of drivers and customers. Bilateral ratings, despite containing more information and allowing drivers to select customers based on ratings, are not necessarily better for the drivers and the platform and can affect welfare and revenue in unexpected ways.