Alone, Together: A Model of Social (Mis)Learning from Reviews [Paper]
We develop a dynamic model of naïve social learning from consumer reviews. In our model, consumers decide which of two products to buy based on both expected quality and idiosyncratic taste. Products’ qualities are initially unknown, and are (mis)learned from reviews. At the heart of the model lies a dynamic feedback loop between reviews, beliefs, and choices: period t reviews influence t + 1 consumers’ beliefs, and thus choices; these determine the average of t + 1 reviews, which in turn influences t + 2 beliefs, choices and reviews. We show that in the long-run (t = ∞), reviews are systematically biased, leading some consumers astray. In particular, reviews relatively advantage lower quality and more polarizing products, since these products induce stronger taste-based consumer self-selection. Thus, in stark contrast with the winner-takes-all dynamics of classic observational learning models, in which consumers learn from the choices of their predecessors, social learning from opinions generates excessive choice fragmentation. Our findings have implications for interpreting the variance of reviews; pricing in presence of reviews; the design of crowd-sourced exploration; and the short and long term effectiveness of fake reviews.
Amazon and the Evolution of Retail (with Luís Cabral) [Paper] (Under Review)
The growth of Amazon and other online retailers questions the survival of bricks- and-mortar retail. We show that, in response to the online trend, offline retailers – especially smaller ones – optimally follow a specialization strategy, in particular specialization in nar- row niches. This may lead to an offline long tail that is thicker than the online long tail, contrary to existing research. Offline specialization benefits consumers; in fact, consumers would benefit from more specialization than it results in equilibrium. We discuss this and other relevant comparative statics based on a simple model of consumer demand and retail design. We complement our theoretical analysis with corroborative empirical evidence. To do so, we employ a large proprietary dataset obtained from a major US publisher detailing all sales to book retailers (both online and offline) over the 2016-2019 period.
When to Talk Politics in Business: Theory and Experimental Evidence of Stakeholder Responses to CEO Political Activism (with Vanessa Burbano and Fabrizio Dell'Acqua) [Paper] (Under Review)
CEO political activism, wherein firm leaders communicate public stances on overtly political issues unrelated to their core business, is a nascent and emerging, and thus understudied, phenomenon. We first propose a parsimonious model of firm political communication. In our model, stakeholders value both ideological proximity to firm’s political stances, and the perceived sincerity of such stances. The model generates a variety of predictions regarding when, and why, communication is beneficial to firms. We test these predictions by conducting two survey-based experiments to examine individuals’ responses to CEO political activism. Theoretically and empirically, we find that, on average, CEO political activism elicits a negative response amongst stakeholders, and that donation-backed communications can elicit stronger responses. However, because communication polarizes perceptions, the percentage of stakeholders holding extremely positive views increases as a result of political communication. We provide evidence of both a congruence benefit and an incongruence benefit (rather than the more commonly identified incongruence penalty) in the context of CEO political activism. Finally, we discuss some extensions to our model, as well as heterogeneous effects discovered in our experimental data, and conclude with additional strategic considerations. We thus shed light on circumstances under which it is more or less beneficial to talk politics in business.
The Good, the Bad and the Picky: Reference Dependence and the Reversal of Movie Ratings (with Michelangelo Rossi and Ryan Stevens) [Paper] (R&R, Management Science)
We explore the consequences of referent-dependent preferences on the nature of online reviews. Consumers differ in their experience, which has two effects. First, experience is instrumental to choice: experts purchase better products than non-experts. Second, because of their superior choices, experts endogenously form higher reference points, and post harsher ratings for given quality. Combined, these two facts imply a bias against higher quality products. When this bias gets large, ratings are non-monotonic in quality: higher-quality products can obtain lower ratings than their inferior alternatives. We test our theory using two large datasets obtained from well known movie rating websites and find strong support for it. We proxy users’ expertise with the total number of ratings posted on the platforms. Using external measures of quality, such as the Academy Awards, we show that experts rate movies of higher quality compared to non-experts. Moreover, experts post more stringent ratings for the same movies. Finally, we debias the ratings exploiting the full history of users’ ratings to level up their stringency levels. This approach leads to normalized aggregate ratings that reduce the bias against higher-quality products and are more in line with external measures of quality