We analyze how di erent dimensions of a seller's reputation a ect pricing power in electronic markets. We do
so by using 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,
and using them to estimate a series of econometric models associating reputation with price premiums. We
nd that di erent dimensions do indeed a ect pricing power di erentially, and that a negative reputation
hurts more than a positive one helps on some dimensions but not on others. We provide the rst evidence
that sellers of identical products in electronic markets di erentiate themselves based on a distinguishing
dimension of strength, and that buyers vary in the relative importance they place on di erent ful lment
characteristics. We highlight the importance of textual reputation feedback further by demonstrating it
substantially improves the performance of a classi er we have trained to predict future sales. This paper is
the rst 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 e ective and judicious design.NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc
