Prices and Price Dispersion on the Web: Evidence from the Online Book Industry

Using data collected between August 1999 and January 2000 covering 399 books, including New York Times bestsellers, computer bestsellers, and random books, we examine pricing by thirty-two online bookstores. One common prediction is that the reduction in search costs on the Internet relative to the physical channel would cause both price and price dispersion to fall. Over the sample period, we find no change in either price or price dispersion. Another prediction of the search literature is that the prices and price dispersion of advertised items or items that are purchased repeatedly will be lower than for unadvertised or infrequently purchased items. Prices across categories of books appear to conform to this prediction, with New York Times bestsellers having the lowest prices as a fraction of the publisher's suggested price and random books having the highest prices. Interestingly, price dispersion does not conform with this prediction, apparently for reasons related to stores' decisions to carry particular books. One reason why we may not observe convergence in prices is because stores have succeeded in differentiating themselves even though they are selling a commodity product. We observe differentiation (or attempted differentiation) by a significant number of firms.

Effect of Electronic Secondary Markets on the Supply Chain

We present a model to investigate the competitive implications of electronic secondary markets that promote concurrent selling of new and used goods on a supply chain. In secondary markets where suppliers cannot directly utilize used goods for practicing intertemporal price discrimination and where transaction costs of resales is negligible, the threat of cannibalization of new goods by used goods become significant. We examine conditions under which it is optimal for suppliers to operate in such markets, explaining why these markets may not always be detrimental for them. Intuitively, secondary markets provide an active outlet for some highvaluation consumers to sell their used goods. The potential for such resales lead to an 05 ghose.pmd 91 8/26/2005, 1:10 PM 92 GHOSE, TELANG, AND KRISHNAN increase in consumersâ valuation for a new good, leading them to buy an additional new good. Given sufficient heterogeneity in consumerâ s affinity across multiple suppliersâ products, the â market expansion effectâ accruing from consumersâ cross-product purchase affinity can mitigate the losses incurred by suppliers from the direct â cannibalization effect.â We also highlight the strategic role that used goods commission set by the retailer plays in determining profits for suppliers. We conclude the paper by empirically testing some implications of our model using a unique data set from the online book industry, which has a flourishing secondary market.NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc

A Computational Approach to Compare Information Revelation Policies

Revelation policies in an electronic marketplace differ in terms of the level of competitive information disseminated to participating sellers. Since sellers who repeatedly compete against one another learn based on the information revealed and alter their future bidding behavior, revelation policies affect welfare parameters—consumer surplus, producer surplus, and social welfare—of the market. Although different revelation policies are adopted in several traditional and Web-based marketplaces, prior work has not studied the implications of these policies on the performance of a market. In this paper, we study and compare a set of revelation policies using a computational marketplace. Specifically, we study this in the context of a reverse-market where each seller’s decision problem of choosing an optimal bid is modeled as an MDP (Markov decision process). Results and analysis presented in this paper are based on market sessions executed using the computational marketplace. The computational model, which employs a machine-learning technique proposed in this paper, ties the simulation results to the model developed using the game-theoretic models. In addition to this, the computational model allows us to relax assumptions of the game-theoretic models and study the problem under a more realistic scenario. Insights gained from this paper will be useful in guiding the buyer in choosing the appropriate policy

Contrasting Multiple Social Network Autocorrelations for Binary Outcomes, With Applications To Technology Adoption

The rise of socially targeted marketing suggests that decisions made by consumers can be predicted not only from their personal tastes and characteristics, but also from the decisions of people who are close to them in their networks. One obstacle to consider is that there may be several different measures for "closeness" that are appropriate, either through different types of friendships, or different functions of distance on one kind of friendship, where only a subset of these networks may actually be relevant. Another is that these decisions are often binary and more difficult to model with conventional approaches, both conceptually and computationally. To address these issues, we present a hierarchical model for individual binary outcomes that uses and extends the machinery of the auto-probit method for binary data. We demonstrate the behavior of the parameters estimated by the multiple network-regime auto-probit model (m-NAP) under various sensitivity conditions, such as the impact of the prior distribution and the nature of the structure of the network, and demonstrate on several examples of correlated binary data in networks of interest to Information Systems, including the adoption of Caller Ring-Back Tones, whose use is governed by direct connection but explained by additional network topologies

Durable Goods Cpmpetition in Secondary Electronic Markets

We develop a game-theoretic framework to investigate the competitive implications of consumer-to-consumer electronic marketplaces, which promote concurrent selling of new and used goods. In many e-marketplaces, where suppliers cannot directly use second-hand goods for practicing inter-temporal price discrimination, the threat of cannibalization of new goods by used goods become significant. We examine conditions under which it is optimal for suppliers to operate in such markets, explaining why used-goods markets may not be predatory for them. While a monopolist supplier is worse off in the presence of a secondary market, competition can in fact make it better off. The presence of used-goods markets provides an active outlet for some consumers to sell their second-hand goods. Such sales lead to an increase in their disposable income. This increased income can then be used to buy an additional new good. Contrary to conventional wisdom, our model predicts the reduction in the price of new goods when there are used-goods markets. We highlight the strategic role that used goods commission plays in determining optimal profits. Overall, for a wide range of parameters, there is an increase in social welfare from establishing such secondary markets

Recomended for You: The Impact of Profit Incentives on the Relevance of Online Recommendations

Recommender systems are commonly used by Internet firms to improve consumers’ shopping experience and increase firm sales and profits. A large stream of work on recommender design has studied the problem of identifying the most relevant items to recommend to users. In parallel, recent empirical work has started to provide evidence that real-world recommenders contribute to increased sales and profitability for the firms. However, maximizing consumer welfare and firm profit are not the same. Given that recommenders impact sales and profits, a natural question is what is the impact of firm’s profit incentives on recommender design? This paper studies optimal recommender design in a profit-maximizing framework to answer the question and identifies the conditions under which a profit-maximizing recommender recommends the item with highest margins and those under which it recommends the most relevant item. We further elaborate on the social cost of the mismatch between consumer and firm incentives

On the integration of data and mathematical modeling languages

This paper examines ways in which the addition of data modeling features can enhance the capabilities of mathematical modeling languages, and demonstrates how such integration might be achieved as an application of the embedded languages technique proposed by Bhargava and Kimbrough. Decision making, and decision support systems, require the representation and manipulation of both data and mathematical models. Several data modeling languages as well as several mathematical modeling languages exist, but they have differences sets of capabilities. We motivate with a detailed example the need for the integration of these languages. We describe the benefits that might result, and claim that this could lead to a significant improvement in the functionality of model management systems. Then we present our approach for the integration of these languages, and specify how the claimed benefits are realized.This paper examines ways in which the addition of data modeling features can enhance the capabilities of mathematical modeling languages, and demonstrates how such integration might be achieved as an application of the embedded languages technique proposed by Bhargava and Kimbrough, [4]Decision-making, and decision support systems, require the representation and manipulation of both data and mathematical models. Several data modeling languages as well as several mathematical mod- eling languages exist, but they have differences sets of capabilities. We motivate with a detailed example the need for the integration of these languages. We describe the benefits that might result, and claim that this could lead to a significant improvement in the functionality of model management systems. Then we present our approach for the integration of these languages, and specify how the claimed benefits are realized.Naval Postgraduate School, Monterey, California.Approved for public release; distribution is unlimited

Forgotten Third Parties: Analyzing the Contingent Association Between Unshared Third Parties, Knowledge Overlap, and Knowledge Transfer Relationships with Outsiders

Third parties play a prominent role in network-based explanations for successful knowledge transfer. Third parties can be either shared or unshared. Shared third parties signal insider status and have a predictable positive effect on knowledge transfer. Unshared third parties, however, signal outsider status and are believed to undermine knowledge transfer. Surprisingly, unshared third parties have been ignored in empirical analysis, and so we do not know if or how much unshared third parties contribute to the process. Using knowledge transfer data from an online technical forum, we illustrate how unshared third parties affect the rate at which individuals initiate and sustain knowledge transfer relationships. Empirical results indicate that unshared third parties undermine knowledge sharing, and they also indicate that the magnitude of the negative unshared-third-party effect declines the more unshared third parties overlap in what they know. Our results provide a more complete view of how third parties contribute to knowledge sharing. The results also advance our understanding of network-based dynamics defined more broadly. By documenting how knowledge overlap among unshared third parties moderates their negative influence, our results show when the benefits provided by third parties and by bridges (i.e., relationships with outsiders) will be opposed versus when both can be enjoyed