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

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

Pricing Digital Goods: Discontinuous Costs and Shared Infrastructure

We develop and analyze a model of pricing for digital products with discontinuous supply functions. This characterizes a number of information technology-based products and services for which variable increases in demand are fulfilled by the addition of 'blocks' of computing or network infrastructure. Examples include internet service, telephony, online trading, on-demand software, digital music, streamed video-on-demand and grid computing. These goods are often modeled as information goods with variable costs of zero, although their actual cost structure features a mixture of positive periodic fixed costs, and zero marginal costs. The pricing of such goods is further complicated by the fact that rapid advances in semiconductor and networking technology lead to sustained rapid declines in the cost of new infrastructure over time. Furthermore, this infrastructure is often shared across multiple goods and services in distinct markets. The main contribution of this paper is a general solution for the optimal nonlinear pricing of such digital goods and services. We show that this can be formulated as a finite series of more conventional constrained pricing problems. We then establish that the optimal nonlinear pricing schedule with discontinuous supply functions coincides with the solution to one specific constrained problem, reduce the former to a problem of identifying the optimal number of 'blocks' of demand that the seller will fulfil under their optimal pricing schedule, and show how to identify this optimal number using a simple and intuitive rule (which is analogous to 'balancing' the marginal revenue with average 'marginal cost'). We discuss the extent to which using 'information-goods' pricing schedules rather than those that are optimal reduce profits for sellers of digital goods. A first extension includes the rapidly declining infrastructure costs associated with Moore's Law to provide insight into the relationship between the magnitude of cost declines, infrastructure planning and pricing strategy. A second extension examines multi-market pricing of a set of digital goods and services whose supply is fulfilled by a shared infrastructure. Our paper provides a new pricing model which is widely applicable to IT, network and electronic commerce products. It also makes an independent contribution to the theory of second-degree price discrimination, by providing the first solution of monopoly screening when costs are discontinuous, and when costs incurred can only be associated with the total demand fulfilled, rather than demand from individual customers.We develop and analyze a model of pricing for digital products with discontinuous supply functions. This characterizes a number of information technology-based products and services for which variable increases in demand are fulfilled by the addition of 'blocks' of computing or network infrastructure. Examples include internet service, telephony, online trading, on-demand software, digital music, streamed video-on-demand and grid computing. These goods are often modeled as information goods with variable costs of zero, although their actual cost structure features a mixture of positive periodic fixed costs, and zero marginal costs. The pricing of such goods is further complicated by the fact that rapid advances in semiconductor and networking technology lead to sustained rapid declines in the cost of new infrastructure over time. Furthermore, this infrastructure is often shared across multiple goods and services in distinct markets. The main contribution of this paper is a general solution for the optimal nonlinear pricing of such digital goods and services. We show that this can be formulated as a finite series of more conventional constrained pricing problems. We then establish that the optimal nonlinear pricing schedule with discontinuous supply functions coincides with the solution to one specific constrained problem, reduce the former to a problem of identifying the optimal number of 'locks' of demand that the seller will fulfil under their optimal pricing schedule, and show how to identify this optimal number using a simple and intuitive rule (which is analogous to 'balancing' the marginal revenue with average 'marginal cost'). We discuss the extent to which using 'information-goods' pricing schedules rather than those that are optimal reduce profits for sellers of digital goods. A first extension includes the rapidly declining infrastructure costs associated with Moore's Law to provide insight into the relationship between the magnitude of cost declines, infrastructure planning and pricing strategy. A second extension examines multi-market pricing of a set of digital goods and services whose supply is fulfilled by a shared infrastructure. Our paper provides a new pricing model which is widely applicable to IT, network and electronic commerce products. It also makes an independent contribution to the theory of second-degree price discrimination, by providing the first solution of monopoly screening when costs are discontinuous, and when costs incurred can only be associated with the total demand fulfilled, rather than demand from individual customers

Is Oprah Contagious? Identifying Demand Spillovers in Product Networks

We study the online contagion of exogenous demand shocks generated by book reviews featured on the Oprah Winfrey TV show and published in the New York Times, through the co-purchase recommendation network on Amazon.com. These exogenous events may ripple through and affect the demand for a 'network' of related books that were not explicitly mentioned in a review but were located 'close' to reviewed books in this network. Using a difference-in-differences matched-sample approach, we identify the extent of the variations caused by the visibility of the online network and distinguish this effect from variation caused by hidden product complementarities. Our results show that the demand shock diffuses to books that are upto five links away from the reviewed book, and that this diffused shock persists for a substantial number of days, although the depth and the magnitude of diffusion varies widely across books at the same network distance from the focal product. We then analyze how product characteristics, assortative mixing and local network structure, play a role in explaining this variation in the depth and persistence of the contagion. Specifically, more clustered local networks 'trap' the diffused demand shocks and cause it to be more intense and of a greater duration but restrict the distance of its spread, while less clustered networks lead to wider contagion of a lower magnitude and duration. Our results provide new evidence of the interplay between a firm's online and offline media strategies and we contribute methods for modeling and analyzing contagion in networks