This is a joint seminar organized by HKU Business School’s Marketing Area and Institute of Digital Economy & Innovation (IDEI).

SPEAKER

Professor Praveen Kopalle
Signal Companies’ Professor of Management
Professor of Marketing
Tuck School of Business
Dartmouth College

ABSTRACT

Online product reviews are ubiquitous and helpful sources of information available to consumers for making purchase decisions. Consumers rely on both the quantitative aspects of reviews such as valence and volume as well as textual descriptions to learn about product quality and fit. In this paper we show how new achievements in natural language can provide an important assist for different kinds of review-related writing tasks. Working in the interesting context of wine reviews, we demonstrate that machines are capable of performing the critical marketing task of writing expert reviews directly from a fairly small amount of product attribute data (metadata). We conduct a kind of “Turing Test” to evaluate human response to our machine-written reviews and show strong support for the assertion that machines can write reviews that are indistinguishable from those written by experts. Rather than replacing the human review writer, we envision a workflow wherein machines take the metadata as inputs and generate a human readable review as a first draft of the review and thereby assist an expert reviewer in writing their review. We next modify and apply our machine-writing technology to show how machines can be used to write a synthesis of a set of product reviews. For this last application we work in the context of beer reviews (for which there is a large set of available reviews for each of a large number of products) and produce machine-written review syntheses that do a good job – measured again through human evaluation – of capturing the ideas expressed in the reviews of any given beer. For each of these applications, we adapt the Transformer neural net architecture. The work herein is broadly applicable in marketing, particularly in the context of online reviews. We close with suggestions for additional applications of our model and approach as well as other directions for future research.

SPEAKER

Dr. David Holtz
Assistant professor in the Management of Organizations (MORS) and
Entrepreneurship and Innovation groups
Haas School of Business
University of California, Berkeley

ABSTRACT

In this talk, I will present two different papers that both use a difference-in-differences empirical strategy to study a) the effects of shelter-in-place mandates on human mobility and b) the effects of firm-wide remote work on collaboration and communication. Both studies focus on quantifying not only direct effects, but also spillovers.

In the first paper, we study the effects of shelter-in-place mandates on human mobility. As local governments relaxed shelter-in-place orders worldwide during the summer of 2020, policy makers lacked evidence on how policies in one region affect mobility and social distancing in other regions and the consequences of uncoordinated regional policies adopted in the presence of such spillovers. Our analysis suggests the contact patterns of people in one region are significantly influenced by the policies and behaviors of people in other, sometimes distant, regions. When just one-third of a state’s social and geographic peer states adopt shelter-in-place policies, it creates a reduction in mobility equal to the state’s own policy decisions, highlighting the need for national coordination. The paper gives governors a roadmap for coordination in the absence of national leadership and applies globally to other regions lacking coordination.

In the second paper, we study the effects of firm-wide remote work on the collaboration and communication patterns of information workers. The COVID-19 pandemic caused a rapid shift to full-time remote work for many information workers. Viewing this shift as a natural experiment in which some workers were already working remotely before the pandemic enables us to separate the effects of firm-wide remote work from other pandemic-related confounding factors. Here, we use rich data on the emails, calendars, instant messages, video/audio calls and workweek hours of 61,182 US Microsoft employees over the first six months of 2020 to estimate the causal effects of firm-wide remote work on collaboration and communication. Our results show that firm-wide remote work caused the collaboration network of workers to become more static and siloed, with fewer bridges between disparate parts. Furthermore, there was a decrease in synchronous communication and an increase in asynchronous communication. Together, these effects may make it harder for employees to acquire and share new information across the network.

SPEAKER

Dr. Junhong Chu
Associate Professor
Department of Marketing, NUS Business School
National University of Singapore

ABSTRACT

We study how daily labor supply responds to unanticipated earnings shocks among Singapore’s taxi drivers using a novel identification strategy that uses idiosyncratic variation in booking cancellations and passenger no-shows (CNS) that drivers repeatedly receive. Our results provide new and more compelling evidence in support of the income-targeting model of labor supply. Not only are the average responses on the extensive margin consistent with the income-targeting model, but the responses on the intensive margin and the heterogeneous responses at different income levels and across driver characteristics are as well. We find that drivers work longer and earn more per hour following CNS, and the effects are robust after controlling for rich fixed effects, market supply and demand conditions, and drivers’ sunk cost of time. The CNS effects on ending a shift exhibit a U-shaped pattern, are strongest when cumulative income is close to the average shift income, and become insignificant when the income level is too low or too high. The effects are most pronounced in the first hour of CNS and fade away quickly afterwards. Drivers achieve higher productivity by reducing break time, taking more jobs, driving faster, driving to places with more earning opportunities, and having more time with passengers on board. Drivers choose the response strategies that are complementary to their abilities and circumstances such as schedule flexibility and potential for productivity improvement: Those with flexible working schedules tend to prolong their shifts, while those with flexible earnings rates tend to increase their subsequent productivity. Our novel identification strategy strengthens the empirical literature on daily labor supply, while our findings of heterogeneity effects offer new insights on income-targeting behaviors.

SPEAKER

Dr. Tianyi Li
Ph.D. Candidate in Business Administration (Marketing)
University of Illinois at Chicago

ABSTRACT

Consumers generally prefer natural to synthetic drugs; a tendency termed the “natural preference.” Across four experiments and one archival study, the current research shows the preference for natural drugs is stronger when the goal is to treat psychological rather than physical symptoms. Process evidence suggests this is because consumers are more concerned about preserving the true self when treating psychological symptoms, and natural drugs are perceived to affect the true self to a lesser degree than are synthetic drugs. The current research provides novel insight into consumers’ preferences for natural products. It also offers policy and managerial implications regarding the marketing of natural remedies and pharmacological treatments for mental health conditions.

SPEAKER

Mr. Tito Grillo
PhD Candidate in Marketing (Consumer Behavior)
University of Texas at Austin

ABSTRACT

Companies, policy-makers, and researchers typically try to understand consumers based on their current circumstances (e.g., where they live and their income). However, many behavioral and cognitive patterns are profoundly shaped during the early, formative periods of life. Thus, the social and economic conditions that mark these periods may have lasting consequences. The present research proposes that consumers with a resource-scarce upbringing develop an enduring “survival mindset” that, in adulthood, guides them to behave as if they had barely enough resources to handle pressing needs; in contrast, consumers from wealthier backgrounds develop a “surplus mindset” that guides them to behave as if they had extra resources available for non-pressing purposes. These two mindsets inform predictions involving consumers’ future-orientation, risk-taking tendencies, and resource management confidence in different domains of life. Nine studies explore these predictions in the contexts of financial decision-making and behaviors during COVID-19 using publicly available datasets and primary data from three countries. Controlling for current resources, consumers originally from wealthier backgrounds displayed greater financial future-orientation (e.g., longer planning horizons, stronger investing tendencies) and financial confidence (e.g., higher self-assessed financial skills, less financial anxiety), but also engaged in more behaviors involving coronavirus-related risks (e.g., eating in indoor areas of restaurants).

SPEAKER

Mr. Yu Ding
Ph.D. candidate in Marketing
Columbia Business School

ABSTRACT

The veracity of information is of critical importance given the explosion of news transmitted to, and shared by consumers across different media. However, the scale of existing fact-checking organizations is limited, resulting in a very small proportion of news articles being fact-checked. We address the challenge of scaling up fact-checking operations in the domain of science-related articles by proposing and testing a novel crowdsourcing solution. A big challenge with asking lay consumers to rate the credibility of scientific news articles is that they are likely to be biased by their prior beliefs. We overcome this bias by proposing the use of article similarity ratings rather than credibility ratings, using articles that have been rated for veracity by scientists as a starting point. We find that asking lay consumers to rate the similarity between scientist-rated and unrated articles can provide an unbiased, effective, and efficient way to scale up veracity ratings of scientific articles. Our proposed method (human similarity-judgments) outperforms algorithm-rated similarity (e.g., by TF-IDF and by Word Embedding) to more accurately predict an article’s scientific veracity. Our method also outperforms previous approaches to judging veracity such as using algorithms that detect semantic markers of false news. We compute a “transitivity index” to identify consumers who are likely to be more accurate at making similarity judgments and show how the veracity predictions can be improved by paying close attention to the consumer segments recruited for the similarity-judgment task. We demonstrate that our method can predict the scientific veracity of articles with over 95% accuracy and that both type-I and type-II errors are minimized. Involving consumers in fact-checking operations can not only help scale up these operations, but can also increase consumer trust in the media ecosystem driven by consumer involvement.

SPEAKER

Dr. Annie Haesung Jung
Postdoctoral Fellow in Annenberg Public Policy Center
University of Pennsylvania

ABSTRACT

Vaccination yields the direct individual benefit of protecting recipients from infectious diseases and also the indirect social benefit of reducing the transmission of infections to others, often referred to as herd immunity. This research examines how prosocial concern for vaccination, defined as people’s preoccupation with infecting others if they do not vaccinate themselves, motivates vaccination in more and less populated regions of the United States. A nationally representative, longitudinal survey of 2,490 Americans showed that prosocial concern had a larger positive influence on vaccination against influenza in sparser regions, as judged by a region’s nonmetropolitan status, lesser population density, and lower proportion of urban land area. Two experiments (total n = 800), one preregistered, provide causal evidence that drawing attention to prosocial (vs. individual) concerns interacted with social density to affect vaccination intentions. Specifically, prosocial concern led to stronger intentions to vaccinate against influenza and COVID-19 but only when social density was low (vs. high). Moderated mediation analyses show that, in low-density conditions, the benefits of inducing prosocial concern were due to greater perceived impact of one’s vaccination on others. In this light, public health communications may reap more benefits from emphasizing the prosocial aspects of vaccination in sparser environments.

SPEAKER

Mr. Arash Laghaie
Ph.D. Candidate in Quantitative Marketing
Goethe University Frankfurt

ABSTRACT

The hypothetical nature of choices collected in typical discrete choice experiments (DCEs) for market research has been a source of concern for both researchers in academia and industry. To the extent that processing the information in choice sets requires effort, classical economic theory questions the external validity of inferences from standard hypothetical (HYP) choices as elicited in market research. Recent studies in marketing indeed demonstrate increased external validity of inferences from choices that are properly incentivized (ICA). However, these studies model the difference between HYP and ICA data collected from the same population as if preferences change. Together, the classical economists’ critique of inference from HYP-DCEs and the notion of changing preferences has led to ignoring the information in HYP data when ICA data are available. In this paper we propose a model that links the information in HYP and ICA data collected in the same population. The model we propose is in the class of sequential-sampling models of choice and assumes that ICA leads respondents to increase their decision effort relative to the standard HYP market research setting, but subject to the same set of “deep” preference parameters. We show that increased amounts of cognitive processing under incentive-alignment materially and plausibly change choice probabilities and outcomes in our model, even if underlying, deep preference parameters are invariant. Our model yields a framework that parsimoniously bridges between data from HYP-DCEs and data from ICA-DCEs that potentially decreases data collection cost at the same level of external predictive validity.

SPEAKER

Ms. Ziwei Cong
Ph.D. Candidate in Quantitative Marketing
Hong Kong University of Science and Technology

ABSTRACT

The common belief about the growing medium of livestreaming is that its value lies in its “live” component. We study this belief by quantifying how the response of aggregate demand to price changes before, on the day of, and after the livestream. We leverage our unique access to rich datasets from the largest livestreaming platform for knowledge goods in China that allows consumers to purchase the recorded version of the livestream. We apply our data in a generalized Orthogonal Random Forest algorithm that can estimate heterogeneous treatment effects in the presence of high-dimensional confounders whose relationships with the treatment policy (i.e., price) and outcome of interests (i.e., demand) are complex but partially known. We find significant temporal dynamics in the price elasticity of demand over the entire product life-cycle. Specifically, demand gradually becomes less price sensitive over time to the livestreaming day and is inelastic on the livestreaming day. Over the post-livestream period, demand is still sensitive to price, but much less than the pre-livestream period. We further provide evidence for the mechanisms driving our main results. We find that consumers value the opportunity of real-time interaction with content creators, the quality level and the quality uncertainty level of the content, when making purchase decisions.

SPEAKER

Ms. Martina Pocchiari
Doctoral Candidate in Quantitative Marketing
Rotterdam School of Management
Erasmus University Rotterdam

ABSTRACT

Shared-interest communities generate tremendous functional, hedonic, and social-psychological benefits for their members, by offering community-organized activities. Increasingly, community organizers are offering digitized activities to their members. Digitized activities – which include webinars, webcasts, and live conferences – are often less expensive and more accessible than in-person activities. At the same time, these digitized activities may not always provide the same degree of social and psychological benefits to the participants as their in-person counterparts. The tension between convenience and meaningful social interactions may lead to higher or lower community participation. We investigate how increasing the extent of digitization of community activities impacts community participation, using data from the event-based community platform Meetup.com. Using both parametric and non-parametric models, we find that increasing the extent of activity digitization decreases members’ intentions to attend such events. A counterfactual analysis shows that completely digitizing in-person activities causes an average 1.3% decrease in positive RSVPs. Furthermore, we find that the effect is heterogeneous across communities and events. In particular, the heterogeneity in the effect of digitization can be explained by group- and event-level factors related to social presence, financial commitments, and the extent of information provided to members regarding the event. This research contributes to the growing literature on the effects of digitizing human interactions on people’s behavior in social groups. The study also informs community managers who need to evaluate the consequences of increasing the digitization of their communities.