Online retailers are increasingly using information technologies to provide value-added services to customers. Prominent examples of these services are online recommender systems and consumer feedback mechanisms, both of which serve to reduce consumer search costs and uncertainty associated with the purchase of unfamiliar products. The central question we address is how recommender systems affect sales. We take into consideration the interaction among recommendations, sales, and price. We then develop a robust empirical model that incorporates the indirect effect of recommendations on sales through retailer pricing, potential simultaneity between sales and recommendations, and a comprehensive measure of the strength of recommendations. Applying the model to a panel data set collected from two online retailers, we found that the strength of recommendations has a positive effect on sales. Moreover, this effect is moderated by the recency effect, where more recently released recommended items positively affect the cross-selling efforts of sellers. We also show that recommender systems help to reinforce the long-tail phenomenon of electronic commerce, and obscure recommendations positively affect cross-selling. We also found a positive effect of recommendations on prices. These results suggest that recommendations not only improve sales but they also provide added flexibility to retailers to adjust their prices. A comparative analysis reveals that recommendations have a higher effect on sales than does consumer feedback. Our empirical results show that providing value-added services, such as digital word of mouth and recommendations, allows retailers to charge higher prices while at the same time increasing demand by providing more information regarding the quality and match of products.
The ability to collect and disseminate individually identifiable microdata is becoming increasingly important in a number of arenas. This is especially true in health care and national security, where this data is considered vital for a number of public health and safety initiatives. In some cases legislation has been used to establish some standards for limiting the collection of and access to such data. However, all such legislative efforts contain many provisions that allow for access to individually identifiable microdata without the consent of the data subject. Furthermore, although legislation is useful in that penalties are levied for violating the law, these penalties occur after an individual's privacy has been compromised. Such deterrent measures can only serve as disincentives and offer no true protection. This paper considers security issues involved in releasing microdata, including individual identifiers. The threats to the confidentiality of the data subjects come from the users possessing statistical information that relates the revealed microdata to suppressed confidential information. The general strategy is to recode the initial data, in which some subjects are "safe" and some are at risk, into a data set in which no subjects are at risk. We develop a technique that enables the release of individually identifiable microdata in a manner that maximizes the utility of the released data while providing preventive protection of confidential data. Extensive computational results show that the proposed method is practical and viable and that useful data can be released even when the level of risk in the data is high.