Latest Seminars

Regional Poverty Alleviation Partnership and E-Commerce Trade
Prof. Zachary Zhong, Assistant Professor of Marketing, Rotman School of Management, University of Toronto

Date 08.05.2023
Time 10:30 - 12:00pm
Venue Room 4047, 4/F, LSK Business Building

Zero to One: Sales Prospecting with Augmented Recommendation
Prof. Yuting Zhu, Assistant Professor of Marketing, NUS Business School, National University of Singapore

Date 05.05.2023
Time 10:30 - 12:00pm
Venue Room 6045, 6/F, LSK Business Building

Boosting Strengths or Fixing Weaknesses? A Preference Mismatch Between Product Providers and Choosers in Product Improvement Decisions
Prof. Yanping Tu, Associate Professor of Marketing, Guanghua School of Management, Peking University

Date 28.04.2023
Time 10:30 - 12:00pm
Venue Room G003, G/F, LSK Business Building

Causal Inference in Unstructured Data: The Case of Impossible Meat Launch
Prof. Tong Guo, Assistant Professor, Fuqua School of Business, Duke University

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.

Date 21.04.2023
Time 10:30 - 12:00pm
Venue Online via Zoom

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