Consider a variation on the classic Weitzman search problem, in which competing firms can choose how much information about their product to reveal to a consumer. If there are no search frictions, there is a unique symmetric equilibrium in pure strategies; and for any market of finite size, the firms are not fully informative. With search frictions, if the expected value of the prize is sufficiently high, there is a unique symmetric equilibrium in which firms are fully informative. Remarkably, a small search cost leads to the perfect competition level of information provision: consumers gain when firms are forced to compete on information.
There are a number of papers in the search literature that bear mentioning as well. In particular, the vein of research that focuses on is relevant. Two such papers are Ellison and Wolitzky (2009) @cite_12 and Ellison and Ellison (2009) @cite_25 . In the first, the authors extend the model of Stahl (1989) @cite_14 by allowing firms to choose the length of time it takes for consumers to learn its price. Allowing for such delays hurts consumers, since obfuscation leads to longer search times and higher prices. Ellison and Ellison, in turn, provide empirical evidence suggesting that as technology has made price search easier for consumers, firms have responded by taking actions that make price search more difficult.
@cite_14 @cite_25 @cite_12
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This paper develops search-theoretic models in which it is individually rational for firms to engage in obfuscation. It considers oligopoly competition between firms selling a homogeneous good to a population of rational consumers who incur search costs to learn each firm's price. Search costs are endogenized: obfuscation is equated with unobservable actions that make it more time-consuming to inspect a product and learn its price. We note two mechanisms by which obfuscation can affect consumer beliefs about future search costs: a direct effect that applies when search costs are convex in time spent searching and a signal-jamming effect that applies when an informational link is present. As long as obfuscation is costless for firms, the presence of either of these mechanisms guarantees that obfuscation must occur in equilibrium, unless consumer search costs are already so high that consumers are willing to purchase at the highest equilibrium price in the absence of obfuscation. Changes in consumer search costs are at least partially offset by changes in the equilibrium level of obfuscation, raising doubts about whether reductions in consumer search costs must make markets more competitive. We also examine patterns of obfuscation and show that higher markups are usually associated with more obfuscation. (This abstract was borrowed from another version of this item.) We study the interplay between deception and consumer search in a search market where firms may deceive some naive consumers with inferior products that display hidden (bad) attributes. We derive an equilibrium in which both superior and inferior quality is offered and show that as search frictions vanish, superior goods are entirely driven out of the market. Deception harms sophisticated consumers, as it forces them to search longer to find a superior product. We argue that policy interventions that reduce search frictions such as the standardization of price and package formats may harm welfare. In contrast, reducing the number of naive consumers through transparency policies and education campaigns as well as a minimum quality standard can improve welfare. We consider an oligopoly model in which consumers engage in sequential search based on partial product information and advertised prices. By applying Weitzman's (1979) optimal sequential search solution, we derive a simple static condition that fully summarizes consumers' shopping outcomes and translates the pricing game among the sellers into a familiar discrete‐choice problem. Exploiting the discrete‐choice reformulation, we provide sufficient conditions that guarantee the existence and uniqueness of market equilibrium and analyze the effects of preference diversity and search frictions on market prices. Among other things, we show that a reduction in search costs raises market prices. The frequently changing user preferences and or item profiles have put essential importance on the dynamic modeling of users and items in personalized recommender systems. However, due to the insufficiency of per user item records when splitting the already sparse data across time dimension, previous methods have to restrict the drifting purchasing patterns to pre-assumed distributions, and were hardly able to model them rather directly with, for example, time series analysis. Integrating content information helps to alleviate the problem in practical systems, but the domain-dependent content knowledge is expensive to obtain due to the large amount of manual efforts. In this paper, we make use of the large volume of textual reviews for the automatic extraction of domain knowledge, namely, the explicit features aspects in a specific product domain. We thus degrade the product-level modeling of user preferences, which suffers from the lack of data, to the feature-level modeling, which not only grants us the ability to predict user preferences through direct time series analysis, but also allows us to know the essence under the surface of product-level changes in purchasing patterns. Besides, the expanded feature space also helps to make cold-start recommendations for users with few purchasing records. Technically, we develop the Fourier-assisted Auto-Regressive Integrated Moving Average (FARIMA) process to tackle with the year-long seasonal period of purchasing data to achieve daily-aware preference predictions, and we leverage the conditional opportunity models for daily-aware personalized recommendation. Extensive experimental results on real-world cosmetic purchasing data from a major e-commerce website (JD.com) in China verified both the effectiveness and efficiency of our approach.