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

Online markets, like eBay, Amazon and others rely on electronic reputation or “feedback” systems to curtail moral hazard 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 that traders correctly interpret the data presented by these systems when forming their judgments. In this paper, we demonstrate that this assumption does not hold. Using controlled laboratory experiments simulating an online auction site with participants acting as buyers, we find that participants interpret seller feedback ratings in a biased (non-Bayesian) fashion. In addition, we find that the degree to which buyers misweight 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 interpret feedback data presented in an Amazon-like fashion in a more biased (less-Bayesian) manner than identical data presented in an eBay-like fashion. Further, we find that participants with online shopping experience interpreted feedback data in a more biased (less-Bayesian) manner than participants with online shopping experience. Finally, we find that participants with greater institution-based trust (i.e., structural assurance) interpreted feedback data in a more biased (less-Bayesian) manner.