Internet retailers have been making significant investments in Web technologies, such as zoom, alternative photos, and color swatch, that are capable of providing detailed product-oriented information and, thereby, mitigating the lack of “touch and feel,” which, in turn, is expected to lower product returns. However, a clear understanding of the relationship between these technologies and product returns is still lacking. Our study attempts to fill this gap by using several econometric models to explore the said relationship. Our unique and rich data set from a women's clothing company allows us to measure technology usage at the product level for each consumer. The results show that, in this context, zoom usage has a negative coefficient, suggesting that a higher use of the zoom technology is associated with fewer returns. Interestingly, we find that a higher use of alternative photos is associated with more returns and, perhaps more importantly, with lower net sales. Color swatch, on the other hand, does not seem to have any effect on returns. Thus, our findings show that different technologies have different effects on product returns. We provide explanations for these findings based on the extant literature. We also conduct a number of tests to ensure the robustness of the results.

We investigate the impact of delegating decision making to information technology (IT) on an important human decision bias—the sunk cost effect. To address our research question, we use a unique data set containing actual market transaction data for approximately 7,000 pay-per-bid auctions. In contrast with the laboratory experiments of previous related studies, our research presents the unique advantage of investigating the effects of IT-enabled automated bidding agents on the occurrence of a decision bias in real market transactions. We identify normatively irrational decision scenarios and analyze consumer behavior in these situations. Our findings show that participants with a higher behavioral investment are more likely to violate the assumption of normative economic rationality because of the sunk cost effect. More importantly, we observe that the delegation of auction participation, i.e., actual bidding, to IT significantly reduces the occurrence of the sunk cost effect in subsequent decisions made by the same individual. We can attribute this reduction to the comparably lower behavioral investments incurred by auction participants who delegate their bidding to IT. In particular, by mitigating different contributors of behavioral investments, delegating to IT reduces the likelihood of the occurrence of the sunk cost effect by more than 50%. This paper was accepted by Sandra Slaughter, information systems.

In this paper, we study how restricting the availability of patches to legal users impacts the vendor’s profits, market share, software maintenance decisions, and welfare outcomes. Prior work on this topic assumes that the hacker’s effort is independent of the vendor’s decision to release the patch freely or not. Clearly, if the patch is not available to everyone, the hacker finds it easier to exploit the vulnerability in the product and, as a result, is likely to alter his effort. To understand the role of a strategic hacker, we build a game-theoretic model, where the hacker’s decision is endogenous. With this model, we find that the hacker’s effort may, on the one hand, decrease the utility that the vendor can extract from the consumers but, on the other hand, may help differentiate the legal version of the product from the pirated version. A vendor can strategically exploit the hacker’s behavior in its pricing and software maintenance decisions. The endogeneity of the hacker’s actions drives several of our findings that have interesting policy implications. For example, the vendor may increase the price and reduce market share to exploit the differentiation. In such a case, there may be more pirates in the restricted-patch case than when the patch is freely available, a result that runs counter to typical arguments provided for restricting patches. This paper was accepted by Chris Forman, information systems.

Despite the widespread adoption of search and recommendation technologies on the Internet, empirical research that examines the effect of these technologies is scarce. How do online consumers use these technologies? Does consumers' technology usage have an effect on the sales to them or their purchasing patterns? This paper empirically measures consumers' usage of website technologies by analyzing server log data. We match technology usage data to sales data, controlling for consumers' historical purchasing behavior. Our unique data set allows us to reveal the relationship between technology usage and online sales. Our analyses show that consumers' information technology usage has a significant effect on the sales to them, but this effect varies for different technologies and across different products. In particular, the use of directed search has a positive effect on the sales of promoted products, whereas it has a negative effect on the sales of nonpromoted products. In contrast, the use of a recommendation system has a positive effect on the sales of both promoted and nonpromoted products. Surprisingly, the use of nondirected search has an insignificant effect on online sales.

A key question for Internet commerce is the nature of competition with traditional brick-and-mortar retailers. Although traditional retailers vastly outsell Internet retailers in most product categories, research on Internet retailing has largely neglected this fundamental dimension of competition. Is cross-channel competition significant, and if so, how and where can Internet retailers win this battle? This paper attempts to answer these questions using a unique combination of data sets. We collect data on local market structures for traditional retailers, and then match these data to a data set on consumer demand via two direct channels: Internet and catalog. Our analyses show that Internet retailers face significant competition from brick-and-mortar retailers when selling mainstream products, but are virtually immune from competition when selling niche products. Furthermore, because the Internet channel sells proportionately more niche products than the catalog channel, the competition between the Internet channel and local stores is less intense than the competition between the catalog channel and local stores. The methods we introduce can be used to analyze cross-channel competition in other product categories, and suggest that managers need to take into account the types of products they sell when assessing competitive strategies.