In the paper “A Dynamic Model of Owner Acceptance in Peer-to-Peer Sharing Markets” forthcoming at Marketing Science, PHBS Assistant Professor Tang Chuang and his coauthors, Yao Dai at The Hong Kong Polytechnic University, Chu Junhong at The University of Hong Kong, develop a framework to uncover the tradeoffs faced by owners on peer-to-peer sharing platforms when making acceptance decisions, which can be used by owners to optimize their decisions and by platforms to improve their operations.


Peer-to-peer sharing platforms have emerged as important marketplaces to facilitate resource sharing. In contrast to the conventional rental business, these platforms match individual customers with their peers instead of firms and may draw renters who demand a great variety of products and accommodate owners who possess products of great heterogeneity. Such a platform-based business model has been experimented in a wide range of product categories including accommodation, car, apparel, accessories, and so on, with Airbnb, Getaround, Turo, and StyleLend emerging as leading platforms.


In a typical P2P sharing platform, owners list their resources on the platform and exchange the idle usage time with usage fee paid by prospective renters, who do not own resources but are in need of them. Renters visit the platform and seek to satisfy their needs with its resource listings. A match between an owner and a renter starts with the renter searching for an ideal rental option. Once an option is identified, the renter then sends a rental request to the owner, stating when the resource is needed and for how long, together with a deposit larger than the total payment that will be held by the platform. The request exclusively books the usage of the resource during the requested time period temporarily. The owner, upon receiving the request, decides whether to accept it at own discretion. If the owner accepts the request, the total payment will be made by the renter and dispensed to the owner, and the balance will be released to the renter after the transaction is complete. Alternatively, the owner may reject the request and release the usage period to other renters. In this case, the renter needs to find an alternative for the needs, either from within the platform or elsewhere.

When a renter requests an owner’s resource, the owner needs to decide whether to accept the request: accepting it helps the owner fill up the idle periods of the resource and generate a payoff but reduces the flexibility to serve a future request for a longer duration.

Tang and his coauthors develop a new framework to uncover the tradeoffs faced by owners when making acceptance decisions, which can subsequently be used by owners to optimize their decisions and by platforms to improve their operations. The authors explicitly model the preferences and decisions of two distinct types of owners using a latent class approach. Myopic owners make their acceptance decisions for each incoming request in isolation, whereas forward-looking owners make their acceptance decisions for the current request after taking into account how the decisions affect the availability state of the car and the arrivals of future requests.

They apply the model to unique data from a leading peer-to-peer car sharing platform in China, and obtain similar sizes of both types of owners and find that female, experienced, and younger owners are more likely to be strategic. The results also reveal the differentiated preferences of the two types of owners toward their renters. Building on model estimates, they find that the option value of an available day for forward-looking owners is found to first increase then decrease, and the most important day for a five-day window is the third day. In addition, the increase in this value prior to this most important day is slow, whereas the decrease afterward is rapid. The results allow forward-looking owners to determine, within their ranges of maneuver, which of the days available in the future for prospective renters could be better forgone, and assess the associated loss in revenue, should own usage of the car or other conflicts in usage arise. The platform could ideally also make use of this information in its practice of optimal (re)allocation of rental requests.

They conduct two counterfactual analyses. The first analysis shows that if the platform imposes minimum rental duration, strategic owners may become more reluctant to accept requests, even if the current availability state entails a higher expected payoff. The second analysis shows that with better understanding of its owners, the platform can greatly improve the matching efficiency by optimal (re)allocation of rental requests, a move that benefits almost all participants in the business.