Web-based systems that establish reputation are central to the viability of many electronic markets. We present theory that identifies the different dimensions of online reputation and characterizes their influence on the pricing power of sellers. We provide evidence that existing, numeric reputation scores conceal important seller-specific dimensions of reputation and we validate our theory further by proposing a new text mining technique that identifies and quantitatively evaluates further dimensions of importance in reputation profiles. We also suggest that the buyer seller network contains critical reputation information that we can further exploit to improve the design of a reputation mechanism. Our experimental evaluation validates the predictions of our model using a new data set containing over 12,000 transactions for consumer software on Amazon.com’s online secondary marketplace. This paper is the first attempt to integrate econometric methods and text and link mining techniques towards 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.
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