We study how multiattribute product choices are affected by peer influence. We propose a two-stage conjoint-based approach to examine three behavioral mechanisms of peer influence. We find that when faced with information on peer choices, consumers update their attribute preferences in a Bayesian manner. This suggests that greater uncertainty in the attribute preferences of a focal consumer and lesser uncertainty in preferences of peers both lead to greater preference revision. Greater number of peers is associated with greater preference revision, although the extent of preference revision diminishes with increasing number of peers. Furthermore, to address the significant time and costs associated with collecting sociometric data, we estimate the accuracy of predicted consumer choices when peer influence data are unavailable. Online social network membership and frequency of peer interactions provide better proxies than more common demographic similarity measures. These findings have key implications, especially for word-of-mouth marketing.preference revision, Bayesian updating, attribute preference uncertainty, social networks, social influence, conjoint analysis, Bayesian estimation
W e propose a structural model to study the effect of online product reviews on consumer purchases of experiential products. Such purchases are characterized by limited repeat purchase behavior of the same product item (such as a book title) but significant past usage experience with other products of the same type (such as books of the same genre). To cope with the uncertainty in quality of the product item, we posit that consumers may learn from their experience with the same type of product and others' experiences with the product item. We model the review credibility as the precision with which product reviews reflect the consumer's own product evaluation. The higher the precision, the more credible the information obtained from product reviews for the consumer, and the larger the effect of reviews on the consumer's choice probabilities. We extend the Bayesian learning framework to model consumer learning on both product quality and review credibility. We apply the model to a panel data set of 1,919 book purchases by 243 consumers. We find that consumers learn more from online reviews of book titles than from their own experience with other books of the same genre. In the counterfactual analysis, we illustrate the profit impact of product reviews and how it varies with the number of reviews. We also study the phenomenon of fake reviews. We find that fake reviews increase consumer uncertainty. The effects of more positive reviews and more numerous reviews on consumer choice are smaller on online retailing platforms that have fake product reviews.
T his paper investigates early stage "modern" grocery retail adoption in an emerging market using primary household-level panel data on grocery purchases in India's largest city, Mumbai. Specifically, we seek insight on which socioeconomic class is more likely to adopt, and why. We model adoption as a two-stage process of modern retail choice followed by category expenditures within a shopping trip. We find a nonmonotonic (V-shaped) relationship between socioeconomic class and preferences for modern retail; specifically, modern retail spending and relative preference are greater among the upper and lower middle classes, relative to the middle middle class. Upper middle class preference of modern retail is driven by credit card acceptance, shorter store distance (relative to other segments), and higher vehicle ownership; whereas lower prices and low travel costs drive the preferences of the lower middle class. Modern retail is preferred more for branded and less for perishable categories. Interestingly, the lower middle class share of modern grocery retail's revenues is largest, and this share is projected to grow as prices fall and store density increases. To address concerns of endogeneity and generalizability, we replicate the key results with a "conjoint" type study with exogenous variation in price and distance in two cities-Mumbai and Bangalore. We discuss implications for targeting and public policy in emerging markets.
In 2008, New York City mandated that all chain restaurants post calorie information on their menus. For managers of chain and standalone restaurants, as well as for policy makers, a pertinent goal might be to monitor the impact of this regulation on consumer conversations. We propose a scalable Bayesian topic model to measure and understand changes in consumer opinion about health (and other topics). We calibrate the model on 761,962 online reviews of restaurants posted over eight years. Our model allows managers to specify prior topics of interest such as “health” for a calorie posting regulation. It also allows the distribution of topic proportions within a review to be affected by its length, valence, and the experience level of its author. Using a difference-in-differences estimation approach, we isolate the potentially causal effect of the regulation on consumer opinion. Following the regulation, there was a statistically small but significant increase in the proportion of discussion of the health topic. This increase can be attributed largely to authors who did not post reviews before the regulation, suggesting that the regulation prompted several consumers to discuss health in online restaurant reviews. Data and the online appendix are available at https://doi.org/10.1287/mksc.2017.1048 .
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