3.1.1 Field studies

A substantial amount of recent research has focused on empirical evaluations of existing reputation mechanisms [2, 3, 4, 5, 6]. The majority of these works focus on eBay’s feedback mechanism and concentrates on studying the relationship between a seller’s reputation score, auction closing prices, and the probability that an auction will receive at least one bid. Researchers have conducted observational studies of listings in particular categories, with increasing methodological sophistication. One controlled experiment sold matched items under different seller identities with different feedback profiles. In general, researchers have found that buyers do reward sellers with better reputations, but only by a small amount. Results have not been entirely consistent, however, in particular about whether positive feedback and/or negative feedback makes a difference. A panel compared some of the methods and results, and a summary table was distributed.

Other topics addressed by field studies include understanding the drivers and evolution of buyer participation on eBay’s feedback mechanism. Preliminary results indicate that that a buyer’s propensity to leave feedback for a seller has a positive correlation with the amount of positive seller reciprocation that this buyer has experienced in the past. Furthermore, experienced buyers (buyers who have completed large numbers of transactions), as well as buyers who transact with high frequency tend to leave feedback less often.

The industry representatives from eBay and epinions have conducted customer focus groups and observed their users’ reactions to various proposed and actual changes in their systems [1]. They emphasized two critical factors. The first is that people care a lot about their own reputation scores, even beyond any commercial impact those scores might carry. Second, their users strongly prefer metrics that are easily understood. For example, if there are several separate scores that are then aggregated into a composite score, they want to understand how the components are weighted.

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About Paul Resnick

Professor, University of Michigan School of Information Personal home page
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