Published Papers

Brands increasingly face pressure from consumers to take a stance on political issues, but there is limited empirical evidence on the effect of political consumerism on sales. In this paper, we quantify the consequences of a brand taking a political stance. In July 2020, the chief executive officer of Goya, a large Latin food brand, praised then president Donald Trump, triggering a boycott and a counter "buycott" movement supporting the brand. Using consumer-level purchase data, we measure the net effect of the boycott/buycott movements on sales. Boycott-related social media posts and media coverage dominated buycott ones, but the sales impact was the opposite: Goya sales temporarily increased by 22%. However, this net sales boost fully dissipated within three weeks. We then explore heterogeneity in the sales response with the goal of understanding which households are most likely to engage in political consumerism and what factors serve as frictions to participation. We document large sales increases (56.4%) in heavily Republican counties but do not find a strong countervailing boycott effect in heavily Democratic counties or among Goya's core customer base-Latino consumers. Finally, we show that brand loyalty and switching costs are potential explanations for the limited evidence of boycotting among experienced Goya customers.

We study how an online marketplace's personalized product recommendations and its consumer profiling accuracy affect third-party sellers' competition and the market outcomes. Sellers strategically adjust prices to compete for the marketplace's recommendations. As the marketplace more accurately predicts consumers' preferences, the equilibrium price first decreases and then increases, and both the marketplace's and the sellers' profits may decrease despite the improved recommendation accuracy. Moreover, recommending the most profitable product for each recommendation may reduce profits of the marketplace and the sellers, and the marketplace can benefit from excluding pricing information in its recommendation decisions to prevent sellers' recommendation competition. Counterintuitively, regulations that bar recommendations from considering profit margin information can lead to higher prices and thus harm consumers. These results are driven by competing sellers' three distinctive incentives: competing for recommendations, exploiting targeted consumers, and undercutting rivals' prices. We also find that our key insights remain qualitatively unchanged if the marketplace recommends products based on consumer surplus, and the equilibrium price will be lower in comparison. Finally, various extensions demonstrate the robustness of these results.

We use posts on the investor-focused StockTwits social media network to generate new insights regarding investor disagreement, disclosure processing costs, and trading volume around earnings announcements. Using social media-based measures of disagreement, we find that both preannouncement disagreement and increases in disagreement around an earnings announcement are positively associated with trading volume. Drawing upon the disclosure processing costs literature, we provide evidence that the effects of disagreement increase when disclosure processing costs are lower. Our social media measures of disagreement remain significant after including traditional analyst earnings estimate measures of disagreement in the model. Our study provides new evidence on the importance of disclosure processing costs and is consistent with lower disclosure processing costs amplifying both the resolution of preannouncement disagreement and new disagreement about earnings information.

This study examines the informational value of local news outlets and how they affect insider trading. We hypothesize that local news coverage is a critical channel through which outsiders acquire local information, which restricts insiders' ability to profit from their information advantage. We argue that a loss of local news coverage increases information opacity faced by outsiders, while making it easier for insiders to seize profitable trading opportunities. Exploring the staggered shutdown of local newspapers, our difference-in-differences estimation presents novel evidence that insiders from closure counties trade more profitably after local newspaper closures, particularly in small firms that lack alternative news sources. Further analyses reaffirm that the post-closure increase in trading profits is unlikely to be wholly driven by regional economic conditions and is likely driven by increased information costs. Our results highlight that local newspapers play a meaningful role in mitigating information asymmetry between insiders and outsiders.

We find that mutual funds whose managers are socially connected with firm auditors hold more shares of these firms and generate superior portfolio returns. Cross-sectional results reveal that the relation between social connections and mutual fund stockholdings is more pronounced: when the social connections are stronger, when the auditor is in a better position or has stronger incentives to acquire private information, when the fund manager exercises more power, for small audit firms, for auditors in areas with poor investor protection, and for public firms with greater business opacity or private information. Other results are consistent with fund managers electing to schedule their corporate site visits to coincide with the fieldwork of their connected auditors, as would be expected if fund managers time their visits to meet with these auditors to facilitate information transfer. Additionally, we observe associations between fund trading prior to earnings surprises and audit opinions, and the presence of social connections between fund managers and firm auditors. Finally, we show that mutual funds and firms in which they invest tend to appoint connected auditors and pay them higher fees. Collectively, we document empirical patterns that would arise if socially connected auditors and mutual fund managers share information.

We identify a specific organizational resource in brokerage houses--information sharing among analyst colleagues who cover economically related industries along a supply chain. After controlling for brokerage selection effects, we show evidence consistent with the benefit of this resource to analyst research performance. Specifically, we find that analysts whose colleagues cover more economically connected industries have better research performance, especially when their colleagues produce higher-quality research. We further show that colleagues' coverage of downstream (upstream) industries is positively related to the accuracy of only analysts' revenue (expense) forecasts and that analysts and their highly connected colleagues tend to issue earnings forecast revisions contemporaneously. Last, we find that analysts with economically connected colleagues tend to have a higher level of industry specialization. Overall, our findings suggest that analysts rely on organizational resources to produce high-quality research. Hence, a portion of their performance and reputation is not transferable across employers.

Between 2010 and 2017, Chinese investors used an investor interactive platform (IIP) to ask public companies around 2.5 million questions, the vast majority of which received a reply within two weeks. We analyze these IIP dialogues using a BERT-based algorithm and provide preliminary evidence on their causes and consequences. Our analyses show most questions reflect investors' difficulties in processing information already in the public domain. Controlling for other news, higher IIP activity is associated with increases in trading volume, return volatility, market liquidity, and price informativeness as well as decreases in bid-ask spread. Financial statement-related postings increase around the adoption of new accounting standards. Collectively, our results show that investors face significant information processing costs but that IIP activities help reduce these costs, leading to improvements in stock price formation.

Investors are central to the incorporation of firm information in capital markets, yet it is challenging to observe the particular information they use and struggle with. Lee and Zhong (2022) use online investor interactions with Chinese public firms to document evidence that investors face significant processing costs. They find that when investor interactions occur, capital markets behave as if the information environment has improved, with increased trading activity, liquidity, and timely pricing of the quarter's earnings in returns. My discussion highlights the contributions of Lee and Zhong's findings to the processing cost, retail investor, and investor interactions literatures. I also describe empirical challenges faced by this and similar studies. I encourage using the details of interactions to disentangle the nature of processing costs and to increase support for causal conclusions more generally. Finally, I note several topics related to investor inter- action that would benefit from further research.

Consumers often become "stuck in a rabbit hole" when consuming media. They may watch several YouTube videos in the same category or view several thematically similar artistic images on Instagram in a row, finding it difficult to stop. What causes individuals to choose to consume additional media on a topic that is similar to (vs. different from) what they just experienced? The authors examine a novel antecedent: the consecutive consumption of multiple similar media. After viewing multiple similar media consecutively, more consumers choose to (1) view additional similar media over dissimilar media or (2) complete a dissimilar activity entirely, even when the prior consumption pattern is externally induced. The rabbit hole effect occurs because of increased accessibility of the shared category: when a category is more accessible, people feel immersed in it and anticipate that future options within that category will be more enjoyable. The authors identify three characteristics of media consumption that contribute to the rabbit hole effect by increasing category accessibility: similarity, repetition, and consecutiveness of prior media consumption. This research contributes to literature on technology, choice, and variety seeking, and it offers implications for increasing (vs. slowing) similar consumption.

Swearing can violate norms and thereby offend consumers. Yet the prevalence of swear word use suggests that an offensiveness perspective may not fully capture their impact in marketing. This article adopts a linguistic perspective to develop and test a model of how, why, and when swear word use affects consumers in online word of mouth. In two field data sets and four experiments, the authors show that relative to reviews with no swear words, or with non-swear-word synonyms (e.g., super), reviews with swear words (e.g., damn) impact review readers. First, reviews with swear words are rated as more helpful. Second, when a swear word qualifies a desirable [undesirable] product attribute, readers' attitudes toward the product increase [decrease] (e.g., "This dishwasher is damn quiet [loud]!"). Swear words impact readers because they convey meaning about (1) the reviewer and (2) the topic (product) under discussion. These two meanings function as independent, parallel mediators that drive the observed effects. Further, these effects are moderated by swear word number and style: they do not emerge when a review contains many swear words and are stronger for uncensored and euphemistic swear words (e.g., darn) than censored swear words (e.g., d*mn). Overall, swear words in reviews provide value to readers-and review platforms-because they efficiently and effectively convey two meanings.

This research examines the interaction effect of two dimensions of preference on social contagion: preference similarity between a consumer (i.e., who seeks recommendation) and a peer (i.e., who potentially provides recommendation) and the fit of an experience good with the consumer's preference. For empirical analyses, the authors collected rich information from Last.fm, a music social networking website, including individual users' music play histories, friendship information, social tags (i.e., user-generated keywords associated with artists and songs), and new song profiles. The results show that consumers' trial of a song that fits less with their preference is influenced more by peers with similar preferences. By contrast, consumers' trial of a song that fits more with their preference is influenced more by peers with dissimilar preferences. This research enriches the understanding of the nuanced role of preference in social contagion and offers managerial implications to better leverage social dynamics.

The authors study how strangers become friends within an evolving online social network. By modeling the coevolution of individual users' friendship tie formations (when and with whom) and their concurrent online activities, the authors uncover important drivers underlying individuals' friendship decisions and, at the same time, quantify the resulting peer effects on individuals' actions. They estimate their model using a novel data set capturing the continuous development of a network and users' entire action histories within the network. The results reveal that similarity (homophily) with a potential friend, the properties of a potential friend's network, and the potential friend's domain expertise all play a role in friendship formation. Via prediction exercises, the authors find that stimulating anime watching is the most effective sitewide intervention, which leads to the highest overall site traffic and the largest number of active users, and that recommending a friend of a friend as a potential friend is the most effective strategy in stimulating friendship tie formation. In contrast to the common finding for static networks, the results indicate that seeding to users with the most friends is not the most effective strategy to increase users' activity levels in an evolving network.