Latest Seminars

To make reviews more helpful, marketers and online review platforms often encourage consumers to write highly personalized reviews. As a result, reviewers frequently disclose intimate personal information; however, little is yet known about how intimate self-disclosure affects review persuasiveness. Using both laboratory studies and a secondary data set (examining 34,141 Amazon reviews), we show that, counter to expectations, reviewer intimate self-disclosure hurts review persuasion. Unlike sharing intimate information with familiar others (e.g., friends, colleagues, family), online reviews largely occur between strangers. In this context, sharing intimate information is seen as socially inappropriate, which lowers a reviewer’s likeability and reduces their persuasiveness. In other words, results suggest that marketers’ attempts to increase review helpfulness by encouraging personalization may have the unintended consequence of reducing review helpfulness in many circumstances. We address alternative explanations for this effect (e.g., information relevance) while also identifying theoretically motivated variables (e.g., reviewer likeability) and managerially actionable ways to mitigate this negative effect (e.g., by altering how intimate information is displayed).

We propose a novel strategy to causally identify the impact of news coverage on product entries in local markets via intermediaries. Our identification relies on interacting the common time-series of the global social media discussion obtained from a semi-supervised topic model with the local shares of media consumption irrespective of the products being studied. We demonstrate our identification strategy in the case of the early-stage launching of impossible meat, a novel food technology that synthesizes meat substitutes by closely simulating the texture, flavor, and appearance of real meat. To study the impact of social media news on restaurant adoption of impossible meat products, we construct a novel location-specific adoption metric that accurately measures the decisions of local standalone restaurants and stores using their social media announcements. We further construct the exogenous measure of county-quarter-level intensity of topic-specific news coverage as the interactions between the global time series of social media discussion about various aspects of impossible meat products (e.g., financials of the key manufacturer, Beyond Meat) during 2015-2019 and local share of genre-specific media consumption in 2014 (e.g., percentage of financial content in social media news among food industry). Arguably, the constructed measures are exogenous to local demand shocks given the local share of media consumption is pre-determined thus irrespective of the new product being studied. We further control for county and quarter fixed effects, local-dynamic confounders, and cross-regional information spillovers. Our results suggest that local news coverage on financing of the new technology is the most impactful topic among all news topics in increasing the regional launching of impossible meat products.

This paper examines the impact of Airbnb on the local rental housing market. Airbnb provides landlords an alternative opportunity to rent to short-term tourists, potentially leading some landlords to switch from long-term rentals and thereby, affecting rental housing supply and affordability. Despite recent government regulations to address this concern, it remains unclear how many and what types of properties are switching. Combining Airbnb and American Housing Survey data, we estimate a structural model of property owners’ decisions and conduct counterfactual analyses to evaluate various regulations. We find that Airbnb mildly cannibalizes the long-term rental supply. Cities where Airbnb is more popular experience a larger rental supply reduction, but they do not necessarily have a larger percentage of switchers. Affordable units are the major sources of both the negative and positive impacts of Airbnb. They cause a larger rental supply reduction, which harms local renters; they also create a larger market expansion effect, which benefits local hosts who own affordable units and may be less economically advantaged. Policy makers need to strike a balance between local renters’ affordable housing concerns and local hosts’ income source needs. We also find that imposing a linear tax is more desirable than limiting the number of days a property can be listed. We propose a new convex tax that imposes a higher tax on expensive units and show that it can outperform existing policies in terms of reducing cannibalization and alleviating social inequality. Finally, Airbnb and rent control can exacerbate each other’s negative impacts.

Standard search models assume that consumers actively decide on the order, identity, and number of products they search. We document that online, a large fraction of searches happen in a more passive manner, with consumers merely reacting to online advertisements that do not allow them to choose the timing or the identity of products to which they will be exposed. Using a clickstream panel data set capturing full URL addresses of websites consumers visit, we show how to detect whether a click is ad-initiated. We then report that in the apparel category ad-initiated clicks account for more than half of all website arrivals, are more concentrated early on in the consumer search process, and lead to less in-depth searches and fewer transactions, consistent with the passive nature of these searches. To account for these systematic dierences between active and passive searches, we propose and estimate a simple model that accommodates both types of searches. Our results show that incorrectly treating all searches as active inflates the estimated value of brands that advertise frequently. Finally, we show that our model can more accurately recover data patterns, especially for advertising brands, and we explore two extensions of it, accounting for ad targeting and dierent forms of advertising.

Firms increasingly use a combination of image and text description when displaying products or engaging consumers.  Existing research examined consumers' response to text and image separately, but has yet to systematically consider the semantic relationship between them. In this research, we examine how the congruence between image- and text-based product representation aects consumer preference by adopting a multi-method approach.  First, to measure the image-text congruence, we propose a state-of-the-art Two-Branch Neural Networks model based on Wide-Residual-Networks (WRN) and BERT.  We apply this deep-learning method to individual-level consumption data from an online reading platform and discover a U-shape eect for image-text congruence: consumers prefer a product when the image-text congruence is either high or low, but not in the medium level.  We further conduct lab experiments to validate the causal eect of this nding and explore underlying mechanisms with an online study.  Our study contributes to the literature of consumer information processing both methodologically and substantively, and it also provides crucial and actionable managerial implications to marketing practitioners and online content creators.