Authors: Jagan Jacob
Addresses: Simon Business School, University of Rochester, Rochester NY 14627, USA
Abstract: In consumer-to-consumer online platforms that enable selling (e.g., eBay, Taobao) or sharing (e.g., Airbnb, Uber) of goods and services, information asymmetry between providers (e.g., sellers, hosts, drivers) and consumers (e.g., buyers, guests, passengers) pose challenges. Such platforms facilitate transactions between users (providers and consumers), who are often strangers. Stricter screening, background cheeks, and identity verification requirements may reduce the probability of bad users entering the platform. However, users are reluctant to share personal information on the internet. We design a matching mechanism to maximise platform profit when users are heterogeneous with some more likely to be good than others, but the platform does not know who. We argue that in some cases, the platform increases its profit by allowing users with a higher probability of being bad to join as well.
Keywords: game theory; information asymmetry; mechanism design; incentive compatibility; online platforms; trust; electronic commerce; sharing economy.
International Journal of Revenue Management, 2019 Vol.11 No.1/2, pp.89 - 125
Received: 07 Jan 2019
Accepted: 03 Jun 2019
Published online: 11 Oct 2019 *