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This paper examines the informational role of product ratings. We build a theoretical model in which ratings can help consumers figure out how much they would enjoy the product. In our model, a high average rating indicates a high product quality, whereas a high variance of ratings is associated with a niche product, one that some consumers love and others hate. Based on its informational role, a higher variance would correspond to a higher subsequent demand if and only if the average rating is low. We find empirical evidence that is consistent with the theoretical predictions with book data from Amazon.com and BN.com. A higher standard deviation of ratings on Amazon improves a book's relative sales rank when the average rating is lower than 4.1 stars, which is true for 35% of all the books in our sample. This paper was accepted by Pradeep Chintagunta, marketing.

This article proposes a utilitarian model in which recycling could reduce consumers' negative emotions from wasting resources (i.e., taking more resources than what is being consumed) and increase consumers' positive emotions from disposing of consumed resources. The authors provide evidence for each component of the utility function using a series of choice problems and formulate hypotheses on the basis of a parsimonious utilitarian model. Experiments with real disposal behavior support the model hypotheses. The findings suggest that the positive emotions associated with recycling can overpower the negative emotions associated with wasting. As a result, consumers could use a larger amount of resources when recycling is an option, and more strikingly, this amount could go beyond the point at which their marginal consumption utility becomes zero. The authors extend the theoretical model and introduce acquisition utility and the moderating effect of the costs of recycling (financial, physical, and mental). From a policy perspective, this research argues for a better understanding of consumers' disposal behavior to increase the effectiveness of environmental policies and campaigns.

Many scholars argue that when incentivized by ad revenue, content providers are more likely to tailor their content to attract \"eyeballs,\" and as a result, popular content may be excessively supplied. We empirically test this prediction by taking advantage of the launch of an ad-revenue-sharing program initiated by a major Chinese portal site in September 2007. Participating bloggers allow the site to run ads on their blogs and receive 50% of the revenue generated by these ads. After analyzing 4.4 million blog posts, we find that, relative to nonparticipants, popular content increases by about 13 percentage points on participants' blogs after the program takes effect. About 50% of this increase can be attributed to topics shifting toward three domains: the stock market, salacious content, and celebrities. Meanwhile, relative to nonparticipants, participants' content quality increases after the program takes effect. We also find that the program effects are more pronounced for participants with moderately popular blogs, and seem to persist after participants enroll in the program. This paper was accepted by Pradeep Chintagunta, marketing.

We investigate in a competitive setting the consequences of mobile geo targeting, the practice of firms targeting consumers based on their real-time locations. A distinct market feature of mobile geo targeting is that a consumer could travel across different locations for an offer that maximizes his total utility. This mobile-deal seeking opportunity motivates firms to carefully balance prices across locations to avoid intrafirm cannibalization, which in turn mitigates interfirm price competition and prevents firms from going into a prisoner’s dilemma. As a result, a firm’s profit can be higher under mobile geo targeting than under uniform or traditional targeted pricing. We extend our model in three different directions: (a) a fraction of consumers are not aware of mobile offers outside of their permanent locations, (b) mobile offers can be collected when consumers travel for other reasons, and (c) firms use both permanent and real-time locations when setting prices. Our findings have important managerial implications for marketers who are interested in optimizing their mobile geo-targeting strategies. The online appendix is available at https://doi.org/10.1287/mksc.2017.1030 .

Consumers often need to search for product information before making purchase decisions. We consider a tractable (continuous-time) model of gradual learning, in which consumers incur search costs to learn further product information, and update their expected utility of the product at each search occasion. We characterize the optimal stopping rules for either purchase, or no purchase, as a function of search costs and of the importance/informativeness of each attribute. This paper also characterizes how the likelihood of purchase changes with the ex ante expected utility, search costs, and the importance/informativeness of each attribute. We discuss optimal pricing, the impact of consumer search on profits and social welfare, and how the seller chooses its price to strategically affect the extent of the consumers' search behavior. We show that lower search costs can hurt the consumer because the seller may then choose to charge higher prices. Discounting creates asymmetry in the purchase and no-purchase search thresholds, and may lead to lower prices if search occurs in equilibrium, or higher prices if there is no search in equilibrium. This paper also considers searching for signals of the value of the product, heterogeneous importance of attributes, endogenous intensity of search, and social learning. This paper was accepted by Pradeep Chintagunta, marketing.

This paper examines the informational role of product ratings. We build a theoretical model in which ratings can help consumers figure out how much they would enjoy the product. In our model, a high average rating indicates a high product quality, whereas a high variance of ratings is associated with a niche product, one that some consumers love and others hate. Based on its informational role, a higher variance would correspond to a higher subsequent demand if and only if the average rating is low. We find empirical evidence that is consistent with the theoretical predictions with book data from Amazon.com and BN.com. A higher standard deviation of ratings on Amazon improves a book's relative sales rank when the average rating is lower than 4.1 stars, which is true for 35% of all the books in our sample.

A seller often needs to determine the amount of product information to provide to consumers. We model costly consumer information search in the presence of limited information. We derive the consumer’s optimal stopping rule for the search process. We find that, in general, there is an intermediate amount of information that maximizes the likelihood of purchase. If too much information is provided, some of it is not as useful for the purchase decision, the average informativeness per search occasion is too low, and consumers end up choosing not to purchase the product. If too little information is provided, consumers may end up not having sufficient information to decide to purchase the product. The optimal amount of information increases with the consumer’s ex ante valuation of the product, because with greater ex ante valuation by the consumer, the firm wants to offer sufficient information for the consumer to be less likely to run out of information to check. One can also show that there is an intermediate amount of information that maximizes the consumer’s expected utility from the search problem (social welfare under some assumptions). Furthermore, this amount may be smaller than that which maximizes the probability of purchase; that is, the market outcome may lead to information overload with respect to the social welfare optimum. This paper can be seen as providing conditions under which too much information may hurt consumer decision making. Numerical analysis shows also that if consumers can choose to some extent which attributes to search through (but not perfectly), or if the firm can structure the information searched by consumers, the amount of information that maximizes the probability of purchase increases, but is close to the amount of information that maximizes the probability of purchase when the consumer cannot costlessly choose which attributes to search through.

Many scholars argue that when incentivized by ad revenue, content providers are more likely to tailor their content to attract \"eyeballs,\" and as a result, popular content may be excessively supplied. We empirically test this prediction by taking advantage of the launch of an ad-revenue-sharing program initiated by a major Chinese portal site in September 2007. Participating bloggers allow the site to run ads on their blogs and receive 50% of the revenue generated by these ads. After analyzing 4.4 million blog posts, we find that, relative to nonparticipants, popular content increases by about 13 percentage points on participants' blogs after the program takes effect. About 50% of this increase can be attributed to topics shifting toward three domains: the stock market, salacious content, and celebrities. Meanwhile, relative to nonparticipants, participants' content quality increases after the program takes effect. We also find that the program effects are more pronounced for participants with moderately popular blogs, and seem to persist after participants enroll in the program.

Conformity toward the majority choice among a user’s friends on a social-networking site first decreases and then increases with the adoption rate of that choice. We explore how people balance their needs to belong and to be different from their friends by studying their choices of wall color in a virtual house on a leading Chinese social-networking site. The setting enables us to randomize both the popular color and the adoption rate at the individual level so that our experimental design minimizes informational social influence, homophily, and group-identity signaling to the general public. We find that there exists significant social influence within a user’s friend circle. While learning about the most popular color among a user’s friends generally increases the likelihood for the user to adopt that color, conformity first decreases and then increases with the adoption rate of that choice, which ranges from 50% to 100%. In addition, users who are of a minority or lower socioeconomic status or are newer are more likely to conform upon learning about the popular choice. Our findings are consistent with optimal distinctiveness and middle-status conformity theories and have implications for designing normative marketing campaigns. Simulated data and the online appendix are available at https://doi.org/10.1287/mksc.2018.1133 .

Consumers often need to search for product information before making purchase decisions. We consider a tractable (continuous-time) model of gradual learning, in which consumers incur search costs to learn further product information, and update their expected utility of the product at each search occasion. We characterize the optimal stopping rules for either purchase, or no purchase, as a function of search costs and of the importance/informativeness of each attribute. This paper also characterizes how the likelihood of purchase changes with the ex ante expected utility, search costs, and the importance/informativeness of each attribute. We discuss optimal pricing, the impact of consumer search on profits and social welfare, and how the seller chooses its price to strategically affect the extent of the consumers' search behavior. We show that lower search costs can hurt the consumer because the seller may then choose to charge higher prices. Discounting creates asymmetry in the purchase and no-purchase search thresholds, and may lead to lower prices if search occurs in equilibrium, or higher prices if there is no search in equilibrium. This paper also considers searching for signals of the value of the product, heterogeneous importance of attributes, endogenous intensity of search, and social learning.