The Dimensions of Reputation in Electronic Markets

Abstract

We present a framework for identifying the different dimensions of online reputation and characterizing their influence on the pricing power of sellers. Our theory predicts that sellers with better recorded online reputation can successfully charge higher prices than competing sellers of identical products, and that their pricing power increases with their recorded level of experience. We develop and implement a new text mining technique that identities and quantitatively assesses dimensions of importance in reputation profiles, and use this technique to create a new data set containing detailed reputation profiles and prices for sellers in over 9,500 transactions for consumer software on Amazon.com's online secondary marketplace. The estimation of a set of econometric models on this data set validates the predictions of our theory, and further, ranks these dimensions of reputation based on their effect on measured seller value, identifying those that have the most significant impact on reputation. This paper is the first study that integrates econometric and text mining techniques toward a more complete analysis of the information captured by reputation systems, and it presents new evidence of the importance of their effective and judicious design.