The Retaliatory Feedback Problem: Evidence from eBay and a Proposed Solution

Introduction

The past few years have seen the explosive growth of online auction transactions. In 2005, E-Bay listed 1.9 billion items for auction, representing a 33% increase over the previous year. Those listing were responsible for $44 billion in transactions (a 29.6%) increase over 2004 (E-Bay, 2006). While E-Bay is the major player in this area, it is not the only one. Many other companies, such as Amazon, Yahoo, and Overstock, offer consumer-to-consumer (C2C) online auctions.

While online auction sites are an increasingly popular avenue for completing electronic transactions, they are characterized by a high degree of uncertainty. They face what Akerlof (1970) calls a “Lemons” market; that is, they have a high amount of uncertainty about the quality of the information and/or goods. Uncertainty primarily derives from the fact that buyers and sellers typically know little about each other, are involved in one-time transactions, and pictures and descriptions of goods provide the only means for assessing the quality of goods available for bidding (Montano, Porter, Malaga, & Ord, 2005). This lack of information available to auction bidders, termed information asymmetry, leads to a higher level of uncertainty about potential outcomes from an auction transaction than if a bidder were able to learn more about the auction seller and his product prior to bidding (Liang & Huang, 1998).

In order to reduce information asymmetry and increase the level of trust between auction participants, reputation systems have been developed. Wilson (1985, pp. 27-28) states, “in common usage, reputation is a characteristic or attribute ascribed to one person, industry, and so forth, by another (e.g., A has a reputation for courtesy).” This is typically represented as a prediction about likely future behavior (e.g., “A is likely to be courteous”). It is, however, primarily an empirical statement (e.g., “A has been observed in the past to be courteous”). The predictive power of reputation depends on the supposition that past behavior is indicative of future behavior. Reputation systems (sometimes called feedback systems) allow the participants in a transaction to rate each other. Individuals’ ratings are aggregated and are available for everyone to see. These systems promote trust between buyers and sellers because they serve as a benchmark for seller reliability. Trust has been shown to serve as a key factor in the success of online transactions, including electronic auctions (Brynjolfsson & Smith, 2000; Resnick, Zeckhauser, Friedman, & Kuwabara, 2000; Hoffman & Novak, 1999).

This chapter proceeds as follows. The next section discusses the existing literature on trust and reputations systems. Following that, the retaliatory feedback problem is further defined. The research methodology is then discussed. Finally, future trends and conclusions are detailed.