The Dimensions of Reputation in Electronic Markets

Abstract

In this paper, we analyze how different dimensions of a seller's reputation affect pricing power in electronic markets. Given the interplay between buyers' trust and sellers' pricing power, we use text mining techniques to identify and structure dimensions of importance from feedback posted on reputation systems. By aggregating and scoring these dimensions based on the sentiment they contain, we use them to estimate a series of econometric models associating reputation with price premiums. We find that different dimensions do indeed affect pricing power differentially, and that a negative reputation hurts more than a positive one helps on some dimensions but not on others. We provide evidence that sellers of identical products in electronic markets differentiate themselves based on a distinguishing dimension of strength, and that buyers vary in the relative importance they place on different fulfillment characteristics. We highlight the importance of textual reputation feedback further by demonstrating that it substantially improves the performance of a classifier we have trained to predict future sales. Our results also suggest that online sellers distinguish themselves on specific and varying fulfillment characteristics that resemble the unique selling points highlighted by successful brands. We conclude by providing explicit examples of IT artifacts (buyer and seller tools) that use our interdisciplinary approach to enhance buyer trust and seller efficiency in online environments. This paper is the first study that integrates econometric, text mining and predictive modeling 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 in online markets.