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 (nonBayesian) 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.

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The goal of this study was to survey farmers and agribusiness owners about their perceptions of cyber security, and how age, gender, and education might affect those perceptions. Using the Health Belief Model as a framework, the survey measured the constructs of perceived susceptibility, severity, benefits, barriers, self-efficacy and cues to action. In addition to the framework, levels of previous cyber-crime victimization and technology implementation were measured. The results of this survey demonstrated that perceived susceptibility to cyber-attacks and the perceived benefits of protective technology are related to an individual’s choice to implement cyber security technology. Over half of the respondents had been victims of a computer security incident, demonstrating that even individuals working in agriculture can be impacted by computer crime incidents. This project deepens the understanding of how individuals react to known threats, and what motivates them to adopt protection technologies.

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