There have been many claims that the Internet represents a new nearly “frictionless market. ” Our research empirically analyzes the characteristics of the Internet as a channel for two categories of homogeneous products—books and CDs. Using a data set of over 8,500 price observations collected over a period of 15 months, we compare pricing behavior at 41 Internet and conventional retail outlets. We find that prices on the Internet are 9–16 % lower than prices in conventional outlets, depending on whether taxes, shipping, and shopping costs are included in the price. Additionally, we find that Internet retailers ’ price adjustments over time are up to 100 times smaller than conventional retailers ’ price adjustments—presumably reflecting lower menu costs in Internet channels. We also find that levels of price dispersion depend importantly on the measures employed. When we compare the prices posted by different Internet retailers we find substantial dispersion. Internet retailer prices differ by an average of 33 % for books and 25 % for CDs. However, when we weight these prices by proxies for market share, we find dispersion is lower in Internet channels than in conventional channels, reflecting the dominance of certain heavily branded retailers. We conclude that while there is lower friction in many dimensions of Internet competition, branding, awareness, and trust remain important sources of heterogeneity among Internet retailers.

This paper presents a signalling model in which high-quality firms underprice at the initial public offering (IPO) in order to obtain a higher price at a seasoned offering. The main assumptions are that low-quality firms must invest in imitation expenses to appear to be high-quality firms, and that with some probability this imitation is discovered between offerings. Underpricing by high-quality firms at the IPO can then add sufficient signalling costs to these imitation expenses to induce low-quality firms to reveal their quality voluntarily. The model is consistent with several documented empirical regnlarities and offers new testable implications. In addition, the paper provides empirical evidence that many firms raise substantial amounts of additional equity capital in the years after their IPO.

Reputation mechanisms harness the bi-directional communication capabilities of the Internet in order to engineer large-scale word-of-mouth networks. Best known so far as a technology for building trust and fostering cooperation in online marketplaces, such as eBay, these mechanisms are poised to have a much wider impact on organizations. This paper surveys our progress in understanding the new possibilities and challenges that these mechanisms represent. It discusses some important dimensions in which Internet-based reputation mechanisms differ from traditional word-of-mouth networks and surveys the most important issues related to their design, evaluation, and use. It provides an overview of relevant work in game theory and economics on the topic of reputation. It discusses how this body of work is being extended and combined with insights from computer science, marketing, and psychology in order to take into consid-eration the special properties of online environments. Finally, it identifies opportunities that this new area presents for information systems research. 1

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Our research empirically analyzes consumer behavior at Internet shopbots — sites that allow consumers to make “one-click ” price comparisons for product offerings from multiple retailers. By allowing researchers to observe exactly what information the consumer is shown and their search behavior in response to this information, shopbot data has unique strengths for analyzing consumer behavior. Furthermore, the method in which the data is displayed to consumers lends itself to a utility-based evaluation process, consistent with econometric analysis techniques. While price is an important determinant of customer choice, we find that, even among shopbot consumers, branded retailers and retailers a consumer visited previously hold significant price advantages in head-to-head price comparisons. Further, customers are very sensitive to how the total price is allocated among the item price, the shipping cost, and tax, and are also quite sensitive to the ordinal ranking of retailer offerings with respect to price. We also find that consumers use brand as a proxy for a retailer’s credibility with regard to non-contractible aspects of the product bundle such as shipping time. In each case our models accurately predict consumer behavior out of sample, suggesting

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participants at the Marketing Sciences Institute’s “Marketing to and Serving Customers Through the Internet ” Conference (December 2001) for valuable comments on this paper. Christopher Muenchhoff generously provided data regarding customer behavior at Dealtime.com and providing invaluable insight into the shopbot market. The Carnegie Bosch Institute and the Berkman Foundation provided generous financial support. The Impact of Shopbots on Electronic Markets Internet shopbots are automated tools that allow customers to easily search for prices and product characteristics from online retailers. Some market observers have predicted that shopbots will benefit consumers at the expense of retailers. In this view, shopbots will radically reduce consumer search costs, reduce retailer opportunities to differentiate their products, and as a result will drive retailer margins toward zero. However, a review of the literature suggests that, while shopbots may place pressure on retailer margins in some circumstances, retailers retain numerous opportunities to differentiate their products, leverage brand names, set strategic prices, and reduce the effectiveness of consumer

There is growing evidence that consumers are influenced by Internet-based opinion forums before making a variety of purchase decisions. Firms whose products are being discussed in such forums are therefore tempted to manipulate consumer perceptions by posting costly anonymous messages that praise their products. This paper offers a theoretical analysis of the impact of such behavior on firm profits and consumer surplus. There are three main results. First, if every firm’s manipulation strategy is a monotonically increasing (decreasing) function of that firm’s true quality, strategic manipulation of online forums increases (decreases) the information value of a forum to consumers. This result implies the existence of settings where online forum manipulation benefits consumers. Second, equilibria where strategies are monotonically increasing (decreasing) functions of a firm’s true quality exist in settings where the firm’s net payoff function, inclusive of the cost of manipulation, is supermodular (submodular) in the firm’s quality and manipulation action. Third, in a broad class of settings, if the precision of honest consumer opinions that firms manipulate is sufficiently high, firms of all types, as well as society, would be strictly better off if manipulation of online forums was not possible. Nonetheless, firms are locked into a “rat race” and forced to spend resources on such profit-reducing activities; if they don’t, consumer perceptions will be biased against them. The social cost of online manipulation can be reduced by developing “filtering” technologies that make it costlier for firms to manipulate. Interestingly, as the amount of user-contributed online content increases, it is firms, and not consumers, that have most to gain from the development of such technologies.

Di erences in the e ectiveness and side e ects of di erent drugs introduces uncertainty into pharmaceutical demand. This in turn introduces dynamic aspects to drug choice: patients have an incentive to experiment to gain information useful for future choices. Exploiting a rich panel dataset on anti-ulcer drug prescriptions, the purpose of this paper is to measure the importance of uncertainty and learning for pharmaceutical drugs. To do so, we specify and estimate a dynamic structural model of demand for anti-ulcer drugs which embeds drug choice in a patient's multi-period expected utility maximization problem. Patients learn in Bayesian fashion about the e ectiveness of alternative drugs. Our results suggest that patients have considerable uncertainty as they begin treatment, but that it decreases quickly with drug use. Simulations imply increases in treatment costs due to uncertainty are modest, ranging from 1.5-4.2 % depending on illness severity.

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