Do Ratings Cut Both Ways? Impact of Bilateral Ratings on Platforms

86 Pages Posted: 9 Nov 2017 Last revised: 7 Jan 2020

See all articles by Chen Jin

Chen Jin

National University of Singapore

Kartik Hosanagar

University of Pennsylvania - Operations & Information Management Department

Senthil K. Veeraraghavan

University of Pennsylvania - The Wharton School - Operations, Information and Decisions

Date Written: March 14, 2018

Abstract

Traditional online platforms (e.g., Amazon Marketplace) use Unilateral Rating System (URS), where customers rate sellers. However, sharing economy platforms (e.g., Uber, Airbnb) have adopted Bilateral Rating System (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 by revealing ratings of customers to service providers before they make acceptance/rejection decisions. We compare URS and BRS in the context of the ride-sharing service to study its impact on all stakeholders in the system. Conventional wisdom suggests that BRS would harm riders, as riders can be rejected by drivers while platforms should benefit from BRS, since it unlocks the value in the riders’ rating that can be potentially extracted. We find that contrary to this intuition, BRS may increase riders’ welfare under some conditions, as drivers rejecting low-rating riders can reduce the excess demand and the associated inconvenience cost born by all riders in the system. BRS can both improve or reduce the platform’s revenue, as there is a tug-of-war between the platform and drivers on the revenue share. Under the right conditions, BRS can result in a win-win-win situation for the drivers, riders and the platform.

Keywords: sharing economy, rating systems, online platforms

Suggested Citation

Jin, Chen and Hosanagar, Kartik and Veeraraghavan, Senthil K., Do Ratings Cut Both Ways? Impact of Bilateral Ratings on Platforms (March 14, 2018). Available at SSRN: https://ssrn.com/abstract=3066988 or http://dx.doi.org/10.2139/ssrn.3066988

Chen Jin (Contact Author)

National University of Singapore ( email )

13 Computing Drive
Singapore, 117417
Singapore

Kartik Hosanagar

University of Pennsylvania - Operations & Information Management Department ( email )

Philadelphia, PA 19104
United States

Senthil K. Veeraraghavan

University of Pennsylvania - The Wharton School - Operations, Information and Decisions ( email )

Philadelphia, PA 19104
United States

HOME PAGE: http://https://oid.wharton.upenn.edu/profile/senthilv/

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