O nline peer-to-peer lending (P2P lending) has emerged as an appealing new channel of financing in recent years. A fundamental but largely unanswered question in this nascent industry is the choice of market mechanisms, i.e., how the supply and demand of funds are matched, and the terms (price) at which transactions will occur. Two of the most popular mechanisms are auctions (where the "crowd" determines the price of the transaction through an auction process) and posted prices (where the platform determines the price). While P2P lending platforms typically use one or the other, there is little systematic research on the implications of such choices for market participants, transaction outcomes, and social welfare. We address this question both theoretically and empirically. We first develop a game-theoretic model that yields empirically testable hypotheses, taking into account the incentive of the platform. We then test these hypotheses by exploiting a regime change from auctions to posted prices on one of the largest P2P lending platforms. Consistent with our hypotheses, we find that under platform-mandated posted prices, loans are funded with higher probability, but the preset interest rates are higher than borrowers' starting interest rates and contract interest rates in auctions. More important, all else equal, loans funded under posted prices are more likely to default, thereby undermining lenders' returns on investment and their surplus. Although platform-mandated posted prices may be faster in originating loans, auctions that rely on the crowd to discover prices are not necessarily inferior in terms of overall social welfare.

We study peer effects on individuals’ contributions to a major form of word of mouth—online reviews. Provided by either consumers or third‐party professionals, online reviews influence consumer purchasing decisions and hence sales. Individuals have conflicting incentives of free riding and contributing to social capital when writing reviews. We leverage a “natural experiment,” which led to an exogenous expansion in the user population of a major online review platform, to better understand the trade‐off. Our empirical findings are mainly twofold. First, we find that a larger population of audience and peer review writers, an immediate consequence of the exogenous shock, cause individuals to write more reviews with higher quality and assign higher but also more diverse ratings. In addition, we find heterogeneity in peer effects by user activeness, expertise, and popularity. Our findings have implications for companies in managing online feedbacks and for platforms that rely on user contributions.

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As cities debate how to regulate Airbnb and other home-sharing services, we study the impacts of home sharing on local residential real estate markets. By accommodating transient travelers with short-term rental properties, home-sharing platforms have evolved as a major alternative channel that attracts the growing supply of residential properties. However, on the demand side of local residential markets, home-sharing platforms are not a viable option for residents. To demonstrate this dynamic between home sharing and local residential markets, we leverage a unique quasi-experiment on Airbnb—a platform policy that caps the number of properties a host can manage in a city—and find that the policy reduced rents (in the long-term rental markets) and home values (in the for-sale housing markets) by about 3% and did not affect the price-to-rent ratio. Consistent with the conjecture, we find that the policy impacts can be attributed to increased supply in local residential markets because of the policy. Quantitatively, our estimates suggest that, if the density of affected Airbnb properties is 1% higher in a market, the policy may further decrease rents and home values by about 0.03%–0.06%, which is similar across each policy-affected city. Our empirical findings add to the debate about the impacts of home sharing on local residential markets with a novel data set and a unique identification strategy. Practically speaking, our research is a timely response to the debate on regulating home sharing and has implications for various stakeholders of the residential real estate markets. This paper was accepted by Chris Forman, information systems.

Social media has been increasingly integrated into firm operations. Past literature documented the operational value of the content generated by social media users but paid little attention to the users’ incentives to generate and share content. We fill in the gap by linking a user’s social network to her content contribution. Specifically, we distinguish the role of the followee network (the group of people being followed by the user) from the role of the follower network. When a user follows more people, she may spend more time in consuming content than generating content (the substitution effect); she may gain more conflicting information from her followees, obfuscating her incentives to generate content (the information overload effect). Conversely, gathering more information from her followees may facilitate her own content generation (the information sharing effect). Through different identification strategies using multiple datasets from two influential social media platforms, we find that the effects of followees and followers are asymmetric in signs and different in magnitudes. Most notably, a user generates less content with a larger followee network, especially when she faces more time constraints. Our findings suggest social media platforms and companies leveraging social media in their operations incorporate network analytics to promote their user engagement.