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Feedback Mechanisms, Judgment Bias, and Trust Formation in Online Auctions


  • This research was partially funded by a grant from Ohio State University's Jensen-Wallin-Young Fund and by an Illinois State University Caterpillar Scholars Award. We also thank the reviewers and editors for constructive comments on an earlier version of this manuscript.

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Online markets, like eBay, Amazon, and others rely on electronic reputation or feedback systems to curtail adverse selection and moral hazard risks and promote trust among participants in the marketplace. These systems are based on the idea that providing information about a trader's past behavior (performance on previous market transactions) allows market participants to form judgments regarding the trustworthiness of potential interlocutors in the marketplace. It is often assumed, however, that traders correctly process the data presented by these systems when updating their initial beliefs. In this article, we demonstrate that this assumption does not hold. Using a controlled laboratory experiment simulating an online auction site with 127 participants acting as buyers, we find that participants interpret seller feedback information in a biased (non-Bayesian) fashion, overemphasizing the compositional strength (i.e., the proportion of positive ratings) of the reputational information and underemphasizing the weight (predictive validity) of the evidence as represented by the total number of transactions rated. Significantly, we also find that the degree to which buyers misweigh seller feedback information is moderated by the presentation format of the feedback system as well as attitudinal and psychological attributes of the buyer. Specifically, we find that buyers process feedback data presented in an Amazon-like format—a format that more prominently emphasizes the strength dimension of feedback information—in a more biased (less-Bayesian) manner than identical ratings data presented using an eBay-like format. We further find that participants with greater institution-based trust (i.e., structural assurance) and prior online shopping experience interpreted feedback data in a more biased (less-Bayesian) manner. The implications of these findings for both research and practice are discussed.