Timing online auctions to attract a large number of prospective buyers is important for sellers. This study examines whether online auction sellers need to account for exogenous effects like TV viewing when timing and predicting their auction results. An ongoing debate questions whether TV viewers can spread their attention across multiple devices while watching TV, for example, by concurrently shopping online or posting on social media. Recent research has focused on understanding cross-media effects; however, little attention has been given to TV viewership's relationship with a very important economic activity, namely participation in online auctions.
Conspicuous consumption affects anyone who cares about social status; it has intrigued sociologists and economists for more than 100 years. The idea that conspicuous consumption can increase social status, as a form of social capital, has been broadly accepted, yet researchers have not been able to test this effect empirically. In this work, we provide empirical evidence by analyzing the digital footprints of purchases and social interactions in different virtual worlds. We use a multimethod approach, such that we both analyze transactional data and conduct a randomized field experiment. Virtual worlds, as artificial laboratories, offer the opportunity to analyze the social capital of their inhabitants, subsequent to their purchase of virtual prestige goods, which provides a means to empirically test hypotheses that would be nearly impossible to test in real-world settings. Our results are consistent with the notion that conspicuous consumption represents an investment in social capital.
The Internet makes it easy to offer large assortments of products, tempting managers to chase the “long tail”—that is, the phenomenon in which niche products gain a significant share of demand among all products. Yet few studies empirically examine the existence and drivers of this long tail phenomenon. This study uses a unique data set with 843,922 purchases from 143,939 customers that a monopolistic video-on-demand operator observed over 111 weeks after its launch of the service. The current analysis centers on the effects of increasing assortment sizes and improved search technologies on measures of the long tail, such as per customer demand, the share of products purchased from the assortment, the distribution of demand across products, and the concentration of demand. Increases in assortment sizes and better assortment quality lead to increases in demand per customer and a longer tail. The length of the tail (i.e., share of purchased products) is also driven by new customers and seasonal effects, such as school vacations, whereas the presence of high-quality blockbuster products shortens the tail. Different search technologies can shift demand toward niche products as well as toward blockbuster products.
The enhanced abilities of online retailers to learn about their customers’ shopping behaviors have increased fears of dynamic pricing, a practice in which a seller sets prices based on the estimated buyer’s willingness-to-pay. However, among online retailers, a deviation from a one-price-for-all policy is the exception. When price discrimination is observed, it is often in the context of customer outrage about unfair pricing. One setting where pricing varies is the name-your-own-price (NYOP) mechanism. In contrast to a typical retail setting, in NYOP markets, it is the buyer who places an initial offer. This offer is accepted if it is above some threshold price set by the seller. If the initial offer is rejected, the buyer can update her offer in subsequent rounds. By design, the final purchase price is opaque to the public; the price paid depends on the individual buyer’s willingness-to-pay and offer strategy. Further, most forms of NYOP employ a fixed threshold price policy. In this paper, we compare a fixed threshold price setting with an adaptive threshold price setting. A seller who considers an adaptive threshold price has to weigh potentially greater profits against customer objections about the perceived fairness of such a policy. We first derive the optimal strategy for the seller. We analyze the effectiveness of an adaptive threshold price vis-à-vis a fixed threshold price on seller profit and customer satisfaction. Further, we evaluate the moderating effect of revealing the threshold price policy (adaptive versus fixed) to buyers. We test our model in a series of laboratory experiments and in a large field experiment at a prominent NYOP seller involving real purchases. Our results show that revealing the usage of an adaptive mechanism yields higher profits and more transactions than not revealing this information. In the field experiment, we find that applying a revealed adaptive threshold price can increase profits by over 20 percent without lowering customer satisfaction.
The interactive nature of the Internet promotes collaborative business models (e.g., auctions) and facilitates information-sharing via social networks. In Internet auctions, an important design option for sellers is the setting of a secret reserve price that has to be met by a buyer's bid for a successful purchase. Bidders have strong incentives to learn more about the secret reserve price in these auctions, thereby relying on their own network of friends or digital networks of users with similar interests and information needs. Information-sharing and flow in digital networks, both person-to-person and via communities, can change bidding behavior and thus can have important implications for buyers and sellers in secret reserve price auctions. This paper uses a multiparadigm approach to analyze the impact of information diffusion in social networks on bidding behavior in secret reserve price auctions. We first develop an analytical model for the effect of shared information on individual bidding behavior in a secret reserve price auction with a single buyer facing a single seller similar to eBay's Best Offer and some variants of NYOP. Next, we combine the implications from our analytical model with relational data that describe the individual's position in social networks. We empirically test the implications of our analytical model in a laboratory experiment, and examine the impact of information diffusion in social networks on bidding behavior in a field study with real purchases where we use a virtual world as proxy for the real world. We find that the amount and dispersion of information in the individualized context, and betweenness centrality in the social network context, have a significant impact on bidding behavior. Finally, we discuss the implications of our results for buyers and sellers.