Working Papers

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Alone, Together: A Model of Social (Mis)Learning from Consumer Reviews [Paper] (Revised & Resubmitted at Marketing Science; Extended Abstract in EC '23)

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.

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Amazon and the Evolution of Retail (with Luís Cabral) [Paper] (Reject & Resubmit, Management Science)

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 narrow niches. The intuition for this result is that the growth of online platforms like Amazon hurts all bricks-and-mortar stores, but it especially hurts large stores selling popular-appeal items. Specialization may lead to offline markets being more niche-concentrated than online ones, contrary to the conventional wisdom of the “embarrassment of niches” induced by online sales. We discuss this and other relevant comparative statics based on a simple model of consumer demand and retail design. We develop various extensions, including pricing, consumer eclecticism, offline amenities, and the role of offline-to-offline competition. We also show theoretically that offline-store specialization benefits consumers, and that in equilibrium bricks-and-mortar stores fall short of what consumers would prefer in terms of specialization.

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The Good, the Bad and the Picky: Consumer Heterogeneity and the Reversal of Product Ratings (with Michelangelo Rossi and Ryan Stevens) [Paper] (Accepted at Management Science; Extended Abstract in EC '23)

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.

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When (Not) To Talk Politics in Business: Experimental Evidence (with Vanessa Burbano and Fabrizio Dell'Acqua) [Paper] (Revised & Resubmitted at Strategic Management Journal) 

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.

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Privacy and Polarization: An Inference-Based Framework [Paper] (with Omid Rafieian and Yunfei (Jesse) Yao)

Advances in behavioral targeting allow news publishers to monetize based on advertising. However, behavioral targeting requires consumer tracking, which has heightened privacy concerns among consumers and regulators. In this paper, we examine how stricter privacy regulations that ban consumer tracking affect news publishers’ content strategies. We develop a theoretical framework that captures a change in privacy policies as a shift in publishers’ inference about consumer types. We consider a model where news publishers choose the content and advertising, and ideologically heterogeneous consumers select their preferred content based on their ideology and idiosyncratic shocks. We compare two salient informational environments: (1) behavioral targeting, where perfect inference about consumers is allowed, and (2) contextual targeting, where consumer tracking is banned due to privacy regulations, and publishers can only infer consumer types based on their content choice. We show that privacy regulations that ban behavioral targeting incentivize publishers to shift towards more extreme and polarizing content in both monopoly and duopoly settings, even though the shift to more extreme content can hurt both demand and consumer welfare. In summary, our research uncovers a previously unexplored relationship between privacy and polarization, shedding light on the potential unintended consequences of privacy regulations in media markets.

 

 

Work in Progress

 

Range Effects in Multi-Attribute Choice: An Experiment (with Daniel Csaba, Evan Friedman and Salvatore Nunnari)
 

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