This research studies the effect of disconfirmation—the discrepancy between the expected and experienced assessment of the same product—on the behavior of consumers leaving online product reviews. We propose a modeling framework in which an individual’s prepurchase expectation is shaped by (1) the product ratings she observes and (2) the perception of the review system she has at the time of the purchase. Upon product consumption, the individual obtains the postpurchase evaluation and encounters a certain level of disconfirmation. Drawing on the Bayesian learning framework, we model individual perception of the review system as a subjective attitude underlying how well the aggregate ratings match one’s own usage experience. A hierarchical Bayesian model is developed and estimated using a rich data set comprising complete purchasing and rating activities on an e-commerce website. Our results suggest that an individual’s decisions of whether to post a rating and what rating to post are affected by disconfirmation in two distinct manners. Specifically, an individual is more likely to leave a review when the magnitude of disconfirmation she encounters is larger. In addition, when the individual decides to review a product, the rating she chooses may not neutrally reflect her postpurchase evaluation; the direction of such a bias is in accordance with the sign of disconfirmation. We also observe several moderating effects: the disconfirmation effect on posting is attenuated by the time gap between purchase and receipt of the same product but accentuated by the dissension in product evaluations among peer consumers. A more granular examination reveals that infrequent raters are systematically more susceptible to disconfirmation than frequent posters. The insights from this research lead to actionable strategies for marketers and designers of recommender systems.

No abstract

In online crowdfunding markets, backers face high uncertainty about the quality of a campaign. To mitigate such uncertainty, crowdfunding platforms often allow campaign creators to post communicative messages—that is, campaign updates and creator comments—to dynamically disclose further information about the campaigns. In addition, previous funding transactions of ongoing campaigns are made publicly available, giving rise to herding among backers. In this research, we aim to understand how communicative messages and herding interactively shape the behavior of backers contributing to crowdfunding campaigns. Our results show that the frequency of communicative messages has a positive effect on backer contributions; however, it attenuates successors' herding momentum toward predecessors, perhaps because the information disclosed in those messages lowers the informational value of previous funding transactions. To investigate the role of message contents, we extract topics addressed in update and comment messages using a Latent Dirichlet allocation model. The results reveal that distinct messages have different impacts on backers' contribution and herding behavior, and such discrepancies are found to be topic specific. This study not only contributes to operations management literature on crowdfunding but also offers implications for campaign creators and platform managers.

Through reimbursing a portion of the transactional amount to some consumers in a form of cash back, merchants are able to exercise third-degree price discrimination by offering two asymmetric prices via an online dual channel. To better understand such a novel pricing mechanism, we develop a game theoretical model and start our analyses with a market consisting of one merchant, one affiliate site, and consumers heterogeneous in their product valuation. From a price point of view, cash-back shopping appears to provide site users with a saving opportunity since the effective post-cash-back price they pay is perceived to be lower than the regular price targeted at nonusers. However, we find that under some conditions, this seemingly lower price could be actually higher, compared with the optimal uniform price when the merchant does not price discriminate. An important implication is that all consumers may end up suffering from higher prices in the presence of the cash-back mechanism. This surprising result, referred to as the cash-back paradox, defies a common intuition that a price-discriminating firm must raise the price for one segment of consumers but decrease it for the other. We also develop two extensions to seek explanations behind various industry practices. We find that it is in a merchant’s best interest to affiliate with multiple sites, and the resulting competition improves overall market efficiency. Moreover, merchants who are disadvantageous in brand valuation should target price-sensitive consumers by strategically offering cash-back deals. Our results, consistent with several real-world observations, have useful implications for marketers. The online appendix is available at https://doi.org/10.1287/isre.2017.0693

Lending-based real estate crowdfunding, which involves the use of real estate to secure loans, has emerged as a promising alternative with lower risk than peer-to-peer lending. This study provides insights into understanding how lenders’ investment behavior is shaped by various information in such an emerging market. Using a data set from a large platform over 17 months, the authors find that lenders as a whole prefer loans secured by a borrower’s house to those secured by a mortgage, as reflected in quicker and larger lending transactions. Experienced lenders tend to invest more aggressively, in both time and amount, but exhibit a weaker preference for loans secured by a borrower’s house. A rise in housing prices is associated with quicker lending decisions, and this association is found to be stronger for loans secured by a borrower’s house. When stock market volatility is large, lenders tend to slow down their investments; such a tendency is attenuated for loans secured by a mortgage. The authors suggest that lender heterogeneity in responding to different collateral types should be incorporated into the platform’s design of an automatic transaction or a recommender system. Moreover, platform managers should consider economic conditions at the macro level when deploying their marketing strategy.