Latest Seminars

When and Why Bundling Two Material Goods Makes an Experience
Prof. Sarah Moore, Professor of Marketing, Alberta School of Business, University of Alberta

Date 14.04.2023
Time 10:00 - 11:30am
Venue Online via Zoom

Market Shifts in the Sharing Economy: The Impact of Airbnb on Housing Rentals
Prof. Hui Li, Professor of Marketing, HKU Business School, The University of Hong Kong

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.

Date 31.03.2023
Time 10:30 - 12:00pm
Venue Room 4047, 4/F, LSK Business Building

Online Advertising as Passive Search
Prof. Raluca Ursu, Assistant Professor of Marketing, Leonard N. Stern School of Business, New York University

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 di erences 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 di erent forms of advertising.

Date 24.03.2023
Time 9:30 - 11:00am
Venue Online via Zoom

Is Relevancy Everything? A Deep Learning Approach to Understand The Coupling of Image and Text
Prof. Xiaolin Li, Assistant Professor, Department of Management, London School of Economics

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 a ects 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 e ect 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 e ect 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.

Date 24.02.2023
Time 10:30 - 12:00pm
Venue Room 4047, 4/F, LSK Business Building