Trading in the online consumer-to-consumer (C2C) auction market necessitates buyers and sellers to engage in transactions with anonymous counterparts. The sequence of paying first and then taking delivery introduces a great amount of risk for potential buyers. In order to assist buyers in dealing with this risk, online auction markets (OAMs) are employing reputation-scoring systems and traders can be classified in terms of their online reputation scores. A review of the literature suggests a conspicuous absence of the study on any standard classification of sellers in OAMs. Lack of such a classification hinders systematic research and theory development. Therefore, a classification of sellers, based on the total number of unique feedbacks (a surrogate measure for certainty regarding repetition of past behaviors), negative feedback rate (a surrogate measure for risk based on prior poor performance), and nature of negative feedbacks (a surrogate measure for the degree of risk), is proposed to advance our understanding of the online C2C auction markets. Toward demonstrating the classification’s systemic power, we present a propositional inventory developed from the classification and discuss how the classification accommodates current research and furthers theory building.
Appan, Radha and Lin, Zhangxi
"Sellers in Online Auction Markets: Introducing a Feedback-Based Classification,"
Journal of International Technology and Information Management: Vol. 15:
1, Article 3.
Available at: https://scholarworks.lib.csusb.edu/jitim/vol15/iss1/3