Return on Reputation in Online Auction Markets

Abstract

This research develops an assurances framework to assess the effect of a seller’s feedback “reputation” on the closing price of an online auction. The framework treats bidders as if they were ‘intuitive statisticians’ in their use of reputation cues. The framework leads to hypotheses about how information-based assurances (particularly feedback scores and the seller’s provision of information about the product) may be used by buyers to reduce their trading risks. The hypotheses are tested using data collected from the eBay web site on auctions of Palm Pilot personal digital assistants. It is found that the higher the seller’s reputation the higher is the average closing price of the auction, which constitutes a return on reputation (ROR). Thus the reputation infrastructure rewards good behavior with positive economic outcomes and helps to sustain a virtuous cycle in the online ecosystem. More specifically, it is found that: (a) negative comments are weighted more heavily than positive ones (b) negative feedback is subject to diminishing returns (c) the assurance offered by reputation is more salient among risk-averse buyers and (d) a high level of information about the merchandise also provides another form of assurance. However, when faced with a high level of information, risk-averse buyers seem to rely less on the sellers reputation score for assurance (a three way negative interaction between risk aversion, reputation and high information). The economic impact of these findings is that the seller of a Palm Pilot who has a high reputation can expect to receive as much as 12.6% more for the item than if he had a low reputation score. Of course, eBay also participates in this upside through its commission structure as well as the wider impact that the feedback seems to have. It is argued that the market for assurances is a contributor to eBay’s success and is also essential to the optimal functioning of any online market.

Keywords: Reputation, Assurances, Risk, Trust, Online Auctions, Ratings and Reviews, User Generated Content