| Abstrakt: |
The increasing popularity of sharing platforms is raising concerns about a lack of information on the demand side: the cost of serving specific buyers is typically unknown to sellers. To mitigate this concern, some platforms, such as Airbnb, have adopted the bilateral review system (BRS), allowing buyers and sellers to rate each other. This differs from the traditional unilateral review system (URS), which allows only buyers to rate sellers. In this study, we examine how these two review systems impact the operation of a peer-to-peer (P2P) sharing platform, where sellers with different qualities are matched with buyers with different serving costs. Our analysis reveals that under URS, even with perfect seller information, high-quality sellers can still be driven out of the market due to unknown information about the buyer and the "coproduction" nature of the serving cost. This differs from the adverse selection problem often observed in the used car market, for example, where sellers of high-quality products withdraw due to generally unknown product quality. Additionally, we highlight the critical role of the expected buyer cost: When this cost is high, BRS can benefit the platform by alleviating the adverse selection problem; however, when this cost is low, BRS becomes detrimental to the platform. Our analysis also shows that BRS helps buyers but can hurt sellers. However, BRS has the potential to generate a favorable outcome for all parties involved, resulting in a win-win-win situation benefiting the platform, buyers, and sellers. Our results not only shed light on review system design but also provide a plausible explanation for the widespread use of BRS by sharing platforms such as Airbnb, Fiverr, and Turo. [ABSTRACT FROM AUTHOR] |
