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Versioning and Quality Distortion in Software? Evidence from E-Commerce Panel Data


Anindya Ghose


New York University - Leonard N. Stern School of Business

Arun Sundararajan


New York University (NYU) - Leonard N. Stern School of Business

October 2005

NET Institute Working Paper No. 05-14

Abstract:     
We present a framework for measuring software quality using pricing and demand data, and empirical estimates that quantify the extent of quality degradation associated with software versioning. Using a 7-month, 108-product panel of software sales from Amazon.com, we document the extent to which quality varies across different software versions, estimating quality degradation that ranges from as little as 8% to as much as 56% below that of the corresponding flagship version. Consistent with prescriptions from the theory of vertical differentiation, we also find that an increase in the total number of versions is associated with an increase in the difference in quality between the highest and lowest quality versions, and a decrease in the quality difference between neighboring versions. We compare our estimates with those derived from two sets of subjective measures of quality, based on CNET editorial ratings and Amazon.com user reviews, and discuss competing interpretations of the significant differences that emerge from this comparison. As the first empirical study of software versioning that is based on both subjective and econometrically estimated measures of quality, this paper provides a framework for testing a wide variety of results in IS that are based on related models of vertical di¤erentiation, and its findings have important implications for studies that treat web-based user ratings as cardinal data.

Number of Pages in PDF File: 31

Keywords: software quality, vertical differentiation, price discrimination, quality distortion, information goods, Internet, electronic commerce, economics of IS

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Date posted: November 21, 2005  

Suggested Citation

Ghose, Anindya and Sundararajan, Arun, Versioning and Quality Distortion in Software? Evidence from E-Commerce Panel Data (October 2005). NET Institute Working Paper No. 05-14. Available at SSRN: http://ssrn.com/abstract=850466 or http://dx.doi.org/10.2139/ssrn.850466

Contact Information

Anindya Ghose (Contact Author)
New York University - Leonard N. Stern School of Business ( email )
44 West 4rth Street
New York, NY 10012
United States
Arun Sundararajan
New York University (NYU) - Leonard N. Stern School of Business ( email )
44 West 4th Street, KMC 8-93
New York, NY 10012
United States
212-998-0833 (Phone)
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