The growth of Internet price search tools, notably shopbots, has reduced consumers' search costs, allowing consumers to easily become informed of price and product characteristics among competing sellers online. While a variety of analytic models predict that increased consumer search through shopbots will lower price levels among competing retailers, there is no consensus in the empirical literature as to whether price dispersion will increase or decrease in response to increased consumer search through shopbots. Moreover, there are no papers that have analyzed this question using direct observation of variation in shopbot use over time.This paper seeks to examine the impact of changes in shopbot use over time on pricing behavior in the Internet book market. Using price data obtained from a leading shopbot, combined with clickstream data on shopbot usage from August 1999 to July 2001, we show that an increase of 1% in shopbot use is correlated with a $0.41 decrease in price levels, after controlling for book characteristics and the market competition condition. We also show that price dispersion decreases with shopbot use nonlinearly. This relationship is robust when we control for potential simultaneity bias and the possible influence of prominent retailers, bestsellers, holiday effect, and online book industry structural changes.

Peer-to-peer networks have emerged as a popular alternative to traditional client-server architectures for the distribution of information goods. Recent academic studies have observed high levels of free-riding in various peer-to-peer networks, leading some to suggest the imminent collapse of these communities as a viable information sharing mechanism. Our research develops an analytic model to analyze the behavior of P2P networks in the presence of free-riding. In contrast to previous predictions we find that P2P networks can operate effectively in the presence of significant free-riding. In future work we plan to explore how much peerto-peer network performance could be improved if free-riding were eliminated and discuss both the costs and benefits of managerial mechanisms to limit free-riding.

Abstract

T he multibillion-dollar online advertising industry continues to debate whether to use the cost per click (CPC) or cost per action (CPA) pricing model as an industry standard. This paper applies the economic framework of incentive contracts to study how these pricing models can lead to risk sharing between the publisher and the advertiser and incentivize them to make efforts that improve the performance of online ads. We find that, compared with the CPC model, the CPA model can better incentivize the publisher to make efforts that can improve the purchase rate. However, the CPA model can cause an adverse selection problem: the winning advertiser tends to have a lower profit margin under the CPA model than under the CPC model. We identify the conditions under which the CPA model leads to higher publisher (or advertiser) payoffs than the CPC model. Whether publishers (or advertisers) prefer the CPA model over the CPC model depends on the advertisers' risk aversion, uncertainty in the product market, and the presence of advertisers with low immediate sales ratios. Our findings indicate a conflict of interest between publishers and advertisers in their preferences for these two pricing models. We further consider which pricing model offers greater social welfare.

Trust is particularly important in online markets to facilitate the transfer of sensitive consumer information to online retailers. In electronic markets, various proposals have been made to facilitate these information transfers. We develop analytic models of hidden information to analyze the effectiveness of these regimes to build trust and their efficiency in terms of social welfare.We find that firms' ability to influence consumer beliefs about trust depends on whether firms can send unambiguous signals to consumers regarding their intention of protecting privacy. Ambiguous signals can lead to a breakdown of consumer trust, while the clarity and credibility of the signal under industry self-regulation can lead to enhanced trust and improved social welfare. Our results also indicate that although overarching government regulations can enhance consumer trust, regulation may not be socially optimal in all environments because of lower profit margins for firms and higher prices for consumers.