Marketing & Behavioural Science Division

Research seminars

Date: Friday, October 16, 2020
Speaker: Hui Li, Carnegie Mellon University
Topic: Market Shifts in the Sharing Economy: The Impact of Airbnb on Housing Rentals
Time: 10:00AM - 11:05AM
Abstract: 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 causing some landlords to switch from long-term rentals, thereby affecting rental housing supply and affordability. Despite recent government regulations to address this concern, it remains unclear whether 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 long-term rental supply. Cities where Airbnb is more popular experience a larger reduction in rental supply; however, these cities do not necessarily have a larger percentage of switchers. Interestingly, we find that affordable units are the major sources of both the negative and positive impacts of Airbnb, as they see a larger rental supply reduction and a larger market expansion effect. Although Airbnb harms local renters by reducing affordable rental supply, it also serves as a valuable income source for local hosts with affordable units. Policy makers need to trade off between local renters' affordable housing concerns and local economically disadvantaged hosts' income source needs. The counterfactual results suggest 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 and show that it outperforms existing policies in terms of reducing cannibalization and alleviating social inequality. Finally, Airbnb and rent control can exacerbate each other's negative impacts.

Date: Friday, October 23, 2020
Speaker: Sara Dommer, Pennsylvania State University 
Topic: TMI: How and Why Intimate Self-Disclosure Affects the Persuasiveness of Consumer Online Word of Mouth
Time: 10:00AM - 11:05AM
Abstract: Consumers frequently disclose intimate personal information (e.g., intimate experiences related to health or family) along with their product experiences when writing online reviews. Interpersonal relationship research suggests that disclosing intimate information might improve review helpfulness as people tend to like others who share (Collins and Miller 1994), and likeability can enhance message persuasiveness (Cialdini 2007). Counter to this premise, the present research finds a negative effect on persuasion when reviewers disclose intimate information about themselves on online review platforms. Unlike prior findings on intimate self-disclosure and liking, which is rooted in the friend context, online review platforms are filled with strangers. The authors argue that sharing intimate personal information is seen as socially inappropriate among strangers. Consequently, reviewers who share such information are seen as socially inappropriate, which in turn lowers their likeability, and reduces persuasion. These ideas are tested in two field studies using real reviews (N = 36,138) and three highly-controlled laboratory studies. These studies support the proposed framework and its underlying mechanism, shed light on boundaries, and demonstrate external validity.

Date: Friday, October 30, 2020
Speaker: Yang Li, Cheung Kong Graduate School of Business 
Topic: Conversational Dynamics: When Does Employee Language Impact the Customer?
Time: 3:00PM - 4:05PM
Abstract: Firms increasingly use text analysis for marketing insight. While this has begun to shed light on what firms should say to customers, when to say those things is less clear. Take customer service: agents could adopt a certain speaking style early in a conversation, at the end, or throughout. How can firms identify when specific language will be beneficial? To examine this question, we introduce a semiparametric Sparse Functional Regression with Group-Lasso approach and apply it to the “warmth/competence trade-off.” Prior work suggests an affective (i.e., warm) speaking approach will lead employees to be seen as less competent, so a more cognitive approach should be prioritized. In contrast, our analysis of nearly 20 hours of recorded service conversations (over 12,000 conversational turns) suggests conversational outcomes are better when both approaches are used, but each is deployed at specific times. Satisfaction and purchases are higher when agents speak affectively at a conversation’s beginning and end but lower when the agent uses such language during the middle “business” portion. The opposite pattern holds for cognitive language associated with competence. Our approach demonstrates the importance of considering language’s temporal flow, deepens understanding of person perception, and provides insight into improving conversational outcomes.

Date: Friday, November 13, 2020
Speaker: Alixandra Barasch, New York University 
Topic: Fairness and the Psychology of Technological Disruption
Time: 10:00AM - 11:05AM
Abstract: Society has benefited greatly over time from the displacement of farm labor by farm machinery, of video rental clerks by video streaming services, and of hand calculations by computer programs. Nonetheless, consumers often bristle at the use of new technologies (e.g., ticket scalping bots, algorithmic trading, exploitation of big data) in the present. In this research, we examine one reason why. Across five studies, we find that the exact same marketplace outcomes are judged as less fair when sellers obtain them through technology versus human effort, even when total effort and resource investment are held constant. We demonstrate that consumers neglect the initial fixed cost investments required to develop and deploy new technologies, and thus perceive the reductions in variable costs those technologies enable as unfair. These findings help explain why cycles of resistance to technological market disruptions recur so regularly and seem not to depend on specific technological features. Our work yields important implications for firms and policymakers alike, and suggests that generating public support for forward-looking investments may require joint consideration of the psychology of fairness and intertemporal choice.  

Date: Friday, November 20, 2020
Speaker: Cristina Conati, Department of Computer Science at UBC
Topic: The Eyes Are the Windows to the Mind: Implications for AI-Driven Personalized Interaction
Time: 10:00AM - 11:05AM
Abstract: Eye-tracking has been extensively used both in psychology for understanding various aspects of human cognition, as well as in human computer interaction (HCI) for evaluation of interface design or as a form of direct input. In recent years, eye-tracking has also been investigated as a source of information for machine learning models that predict relevant user states and traits (e.g., attention, confusion, learning, perceptual abilities). These predictions can then be leveraged by AI agents to model their users and personalize the interaction accordingly. In this talk, Dr. Conati will provide an overview of the research her lab has done in this area, including detecting and modeling user cognitive skills, and affective states, with applications to user-adaptive visualizations, intelligent tutoring systems and health.

Date: Friday, March 12, 2021
Speaker: Quentin André, Erasmus University 
Topic: TBA
Time: 10:00AM - 11:05AM
Abstract: TBA

Date: Friday, March 26, 2021
Speaker: Brad Shapiro, University of Chicago Booth School of Business 
Topic: TBA
Time: 10:00AM - 11:05AM
Abstract: TBA

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