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 ver- sioning. 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 ver- sion.
Consistent with prescriptions from the theory of vertical
di¤erentiation, 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 differentiation, and its findings have important
implications for studies that treat web-based user ratings as cardinal data
