Alone, Together: A Model of Social (Mis)Learning from Reviews [Paper]
We develop a model of social learning from consumer ratings in a horizontally differentiated market. At the core of our model lies the dynamic feedback loop between choices, ratings and beliefs, of which we characterize the long-run properties. We show that long-run, equilibrium ratings are biased in systematic and sizable ways. They relatively advantage lower quality and more polarizing products. Thus, in stark contrast with the winner-takes-all dynamics of classic observational learning models, learning from consumer opinions (as opposed to actions) generates excessive choice fragmentation compared to the normative optimum. Our results are robust to different assumptions about consumer learning and rating behavior. We provide corroborative evidence for our results using data obtained from Goodreads, a popular consumer book ratings platform, and Book Marks, a professional book critics ratings aggregator. Our findings have implications for the optimal design of crowdsourced exploration, cast apparent cognitive biases such as the “love for large numbers” in a new light, and inform the debate on the impact of fake reviews.
Amazon and the Evolution of Retail (with Luís Cabral) [Paper]
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 Responses to CEO Political Activism (with Vanessa Burbano and Fabrizio Dell'Acqua) [Draft available upon request]
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 con- siderations. 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: Consumer Heterogeneity and the Reversal of Movie Ratings (with Ryan Stevens) [Paper]
We explore the consequences of consumer heterogeneity on product ratings. Consumers differ in their experience, which has two effects. First, experience is instrumental to choice: experts purchase (and thus review) better products than non-experts. Second, because of their superior choices, experts endogenously form higher expectations, and thus post more stringent ratings, for any given quality. Combined, these two forces imply that the better the product, the higher the standard it is held to, the more stringent its rating. Thus, relative ratings are biased: low quality products enjoy unfairly high ratings compared to their superior alternatives. When this bias gets large, reputation needs not be increasing in quality. The bias needs not disappear, and can worsen, over time: because it is mostly non-experts who rely on product reviews, products which received unfairly high ones will attract more of them, reinforcing their advantage. We test our theory by scraping data from a well known movie ratings website. We find strong evidence for both of our hypotheses, and that this bias is quantitatively important. We then debias the ratings, and find that the new ones better correlate with the opinions of external critics.