Meng Zhu received her Ph.D. from Carnegie Mellon University (Major: Marketing; Minor: Social and Decision Sciences) in 2011. Before joining Virginia Tech, she was Professor of Marketing and Health at Johns Hopkins University where she was the recipient of Faculty Service and Mentorship Award, Alliance for a Healthier World’s Impact Grant, President’s Frontier Award Semifinalist, Early Career Catalyst Award, and Dean’s Award for Faculty Excellence. She has recently served as Co-chair for American Marketing Association Doctoral Dissertation Award, European Association for Consumer Research Conference (Judgment and Decision-Making Track), American Marketing Association Winter Conference (Consumer Behavioral Track), and Co-editor for Journal of the Association for Consumer Research (Healthcare and Medical Decision-making). Her research interests include causes and consequences of and solutions to inefficiency and welfare loss, health and medical decision-making, human-AI collaboration, and contextual influences on preference construction. Her research has been published in premier business journals as well as specialized medical journals; funded by National Institute of Health, National Institute of Aging, and National Science Foundation; and featured in the media across the globe, such as Forbes, NPR, New York Times, The Guardian, The Independent, Wall Street Journal, and Washington Post. She teaches Strategic Marketing (a capstone undergraduate course) and Judgment and Decision Marking (a doctoral seminar) at Pamplin College of Business.

EDUCATION

Ph.D. in Marketing & Social and Decision Sciences, Tepper School of Business, Carnegie Mellon University, 2011 

M.S. in Industrial Administration, Tepper School of Business, Carnegie Mellon University, 2008  

B.A. in Comparative Literature, School of Liberal Arts, Nanjing University, 2001

ACADEMIC APPOINTMENTS

Virginia Tech (Professor, Pamplin College of Business), Aug 2023-present

Johns Hopkins University, Aug 2011-2024 (Professor /Associate Professor /Assistant Professor, since Aug 2021/2016/2011) 

Bocconi University & Stanford University (Visiting Scholar),  Fall 2019   

RESEARCH INTERESTS

Causes and Consequences of and Solutions to Inefficiency and Welfare Loss

Contextual Influences on Preference Construction  

Health and Medical Decision Making

Human and AI Collaboration

HONORS AND GRANTS

National Science Foundation Grant, 2025-2028 (Institutional Transformation: Transforming Culture of Responsible Research through the Development of Ethics Expertise and Self-Efficacy among Faculty through Social Networks, with L. Lee et al.)

Pamplin Duo+ Seed Grant, 2025-2026 (At the Intersection of AI, Marketing, and Tourism: Enhancing Visitor Experience through Adaptive Spatial Intelligence, with Lamoureux et al.)

Institute for Creativity, Arts, and Technology Major SEAD Grant, 2024-2025 (AI-Accessibility: Reconceiving The Co-Design of Building Elements and Navigation Software for Individuals with Vision Impairment, with A. Gipe-Lazarou et al.)

4-VA Collaborative Research Grant, 2024-2025 (Artificial Intelligence, Job Replacement, and Mental Health, with J. Ni et al.)

Faculty Service and Mentorship Award, Johns Hopkins Carey Business School, 2023 

Alliance for a Healthier World's Impact Grant, 2022-2024 (Reducing Health Gap for Slum Communities in Rural India by Leveraging Key Digital Health Interventions, with L. Paina et al.)

National Institute on Aging Grant, 2022-2024 (Towards Reduction of Harmful Overuse of Healthcare in Older Adults, with J. Segal et al.) 

National Institutes of Health Grant, 2015-2020 (Contingency Management to Reduce Secondhand Smoke Exposure in Asthmatic Children, with M. Jassal et al.) 

President’s Frontier Award Semifinalist, Johns Hopkins University, 2019    

Early Career Catalyst Award, Johns Hopkins University, 2018   

Dean's Award for Faculty Excellence, Johns Hopkins University, 2019, 2018, 2017, 2016

Herbert A. Simon Doctoral Dissertation Award, Carnegie Mellon University, 2011    

Berkeley Behavioral Camp Fellow, 2010    

AMA Sheth Foundation Doctoral Consortium Fellow, 2009   

Center for Behavioral Decision Research Grant, 2008   

William Larimer Mellon Fellowship, 2006-2010  

GRADUATE STUDENT ADVISORY

Iqbal Ahmed, PhD Candidate, Executive Business Research, Virginia Tech (Dissertation Committee)

Jennifer Brown, PhD, Johns Hopkins Bloomberg School of Public Health; Placement: Assistant Scientist, Institute for Global Tobacco Control, 2020 (Preliminary Oral Exam Committee Chair & Graduate Board Oral Committee Chair)

Sophia Fan, PhD, Marketing Department, Hong Kong Polytechnic University; Placement: Assistant Professor, Hong Kong University of Science and Technology, 2019 (Dissertation Committee)

Jianhui Li, PhD, Department of Economics, Johns Hopkins University; Placement: Capital One, 2019 (Graduate Board Oral Committee Chair)   

Chuhan Liu, PhD, Department of Economics, Johns Hopkins University; Placement: Analyst Group, 2023 (Graduate Board Oral Committee Chair)

Aaron Lyvers, PhD Candidate, Executive Business Research, Virginia Tech (Dissertation Committee)

Dwayne McGraw, PhD Candidate, Executive Business Research, Virginia Tech (Dissertation Committee)

Gayoung Park, PhD Candidate, Marketing Department, Virginia Tech (Dissertation Committee)

Jiang Qian, PhD (MSF & Research Assistant, Johns Hopkins University); Placement: Assistant Professor, University of Sydney, 2020 (MS Research Supervisor)

Yuechen Wu, PhD, Marketing Department, University of Maryland; Placement: Assistant Professor, Oklahoma State University, 2022 (Postdoc Mentor)   

Yi Xin, PhD, Department of Economics, Johns Hopkins University; Placement: Assistant Professor, California Institute of Technology, 2018 (Graduate Board Oral Committee)

RECENT COURSES TAUGHT

Strategic Marketing: This capstone undergraduate course provides an in-depth understanding of marketing strategy through three essential components, underlying theories and methodologies, comprehensive case problems, and real-world applications. First, we will explore classic as well as emerging scientific research in marketing, psychology, economics, and other related disciplines to build the tools students will need to interpret scientific findings and base decisions on them. Second, students will experience the role of senior business executives in formulating, implementing, and evaluating marketing strategies through analysis of the most recent business cases. Finally, students will work in teams to assume the role of a consultant team hired by a consumer product or service company to analyze and solve a current marketing problem faced by the company. [Recent sections offered: MKTG 4754 - Sections #18193, Spring 2025 & Sections #18102, #18103, #21250, Spring 2024] 

Judgment and Decision Making: This doctoral seminar examines selected research pertinent to consumer decision making via a framework grounded in behavioral decision research. The topics covered in this seminar should be of interest to doctoral students studying related topics in Business, Psychology, and Economics. The primary course objectives are to (1) provide a selective but intensive exposure to research in key substantive and methodological issues in the area of judgment and decision making; (2) synthesize a framework for understanding both the normative and descriptive principles that may govern consumer decisions in marketing contexts; (3) develop a critical perspective that enables students to identify opportunities for theoretical advances, methodological innovations and relevant applications in marketing; and (4) quip students to conceptualize, design and implement original research on consumer decision making issues in marketing. The course will primarily be driven by research paper presentations and discussions. [Recent section(s) offered: MKTG 6204 - Section #21232, Spring 2025] 

Innovation Field Project: In this foundational experiential course in the full-time MBA program, students work in teams to define and scope problems posed by partner organizations, and deliver evidence-based solutions. Students use the concepts and tools studied in their first year – such as research methods, data analytics, creative problem-solving techniques, and discipline-specific knowledge – to analyze the business issue at hand and provide innovative, actionable recommendations. The business problem might relate to a wide range of issues, including process or service design, strategy development, financial risk management, or marketing. Projects might also encompass large scale thematic issues facing contemporary organizations, such as the ethical dilemmas that leaders face. [Recent projects advised: Alliance of Community Health Plans, Applied Physics Laboratory, Merck, Philips Healthcare, San Francisco VA Health Care System, Sibley Memorial Hospital & Skypoint; Spring 2021, 2022 & 2023]

SELECTED PROJECTS AND PUBLICATIONS

Exploring the Barries and Incentive Architecture for Modifying Smoke Exposures among Asthmatics (with M. S. Jassal et al.)

The socio-structural barriers for reducing secondhand smoke exposure among children with asthma may be insurmountable for low-income caregivers. Health promoting financial incentives are increasingly being used in the adult population to motivate and sustain tobacco-reduction behaviors. We assessed barriers to secondhand smoke exposure reduction and means to overcome them through the design of an incentives-based, caregiver-targeted secondhand smoke exposure reduction study. Using a mixed-methods design, we conducted semi-structured in-depth interviews among low-income primary caregivers of children with asthma residing in Baltimore City. Quantitative data were collected to augment interview findings. Home smoking restrictions were a frequently referenced strategy for decreasing secondhand smoke exposure  but participants described the complex social pressures that undermine reduction efforts. Caregivers redirected conversations from broadly implemented smoking bans towards targeted reduction strategies among mothers and members of their social network who are active smokers. Participants converged on the notion that sustainable secondhand smoke exposure reduction strategies are realizable only for mothers who are active smokers, possess high self-efficacy and social structures that promote cessation. Additional empirical data clarified the multiple contexts that underlie pediatric secondhand smoke exposure and preferred incentive architecture that included fixed, recurrent payments contingent on reduced nicotine biomarkers and completion of basic asthma education classes.  

AI Adoption and Access to Healthcare Resources: An Empirical Analysis of Mental Health Therapies (with J. Ni et al.) 

Telemedicine through virtual visits has become prominent since the pandemic. As an alternative to in-person office visits, virtual visits allow patients from regions with scarce health resources to access high-quality care. The convenience of teletherapy compared to office visits certainly attracts more people to choose online services. This is especially true for non-emergency care like mental health therapies, where the difference in service quality between the online and offline offerings is arguably not substantial. However, patients might face considerable uncertainty when selecting among therapists through traditional webpages that typically contain only texts and profile pictures. Many service platforms have started to offer AI tools to providers for free. While these AI tools can help promote therapists to patients, they have to be enabled by providers manually. The adoption of AI on one hand could facilitate preference matching, further enabling patients to choose the proper therapists. Yet, on the other hand, the more efficient matching might generate increased demand, which could potentially raise the price of therapy, subsequently exacerbating inequalities in access to care. In our study, using a dataset from one of the largest mental health teletherapy platforms in the U.S., we examined over 140,000 therapists and their therapy prices. To address the endogeneity problem, we employed the border strategy using the discrete nature of state AI legislations. After constructing the border experiment on a county-by-county basis at state level , we examined the impact of AI adoption on therapy pricing within border counties using a difference-in-different approach. We find that therapists' decision to adopt AI leads to a premium for the price of offline, in-person therapy. To further examine whether this effect varies across different population groups, we employed the casual forest approach to estimate heterogeneity, which revealed that the lower the average county-level household income, the bigger the price increase in in-person therapy. Our findings carry policy implications for the nuanced interplay between technology adoption and healthcare delievery.

Complementarity Neglect: When People Fail to Select AI Collaborators with Non-Overlapping Mistakes (with M. Jorling & Y. Li)

Cooperation is particularly helpful when the partners involved complement each other. In the current research, we find that individuals have difficulty recognizing complementarity and exhibit a tendency to choose cooperation partners who have the same strengths and weaknesses as themselves (even when such a choice leads to objectively worse outcomes). We refer to this phenomenon as “complementarity neglect”. In a series of pre-registered studies (N > 3,500), we document complementarity neglect using an image classification paradigm with stimuli selected from the 4,800 noise-distorted images developed by Steyvers et al. 2022. More specifically, individuals first performed 10 trial classifications where they observed performance of a complementary supporter with non-overlapping mistakes and a non-complementary supporter with overlapping mistakes, and then chose a supporter for the official round in which they made 10 incentivized classifications with the supporter of their choice. Individuals are less likely to choose the complementary cooperation partner because of their focus on partner’s failures in their own strengths instead of partner’s strengths in their own failures, which leads to a stronger downgrading of partner’s perceived competence. Explicitly asking about potential complementarity with possible cooperation partners, as well as contrasting the respective strengths and weaknesses, mitigates complementary neglect. The research is of particular importance in light of the fact that human-machine collaborations are promising especially in domains where mutual complementarity exists; yet complementarity neglect potentially thwarts such collaborations.

Beauty Penalty and Beauty Premium (with J. Vosgerau et al.)

Discrimination in favor of attractive workers, the so-called beauty premium, is a robust phenomenon. We conjecture that, because people believe the attractive to have better social skills, they discriminate in favor of the attractive in activities/professions that require social skills. Furthermore, we hypothesize that people discriminate against the attractive (a beauty penalty occurs) when analytical skills are important, because the plain-looking are believed to incur a cost in social interactions and thus specialize in activities/professions in which social skills are less important. Finally, we argue discrimination based on beauty to be dynamic, such that people change their evaluations when confronted with objective information that runs counter to their beliefs. Consequently, both beauty premium and beauty penalty can be observed for the same skill set. In a preregistered experiment (N = 5,704), the ratings of 240 photos sampled from the Chicago Face Database provided by participants show that good looks are believed to be associated with superior social but inferior analytical skills. This pattern of results was replicated using 40 professional headshots and shown to consequently give rise to a beauty premium/penalty in teaching/research evaluations of computer science professors. Further, across a random sample of users (N =126,573) from Stack Overflow, a prominent Q&A site for programmers where analytical skills are valued, employing Coarsened Exact Matching and Propensity Score Matching models, we observe a beauty penalty in the evaluation of posts from programmers with low reputation scores. This pattern reverses to a beauty premium for programmers with high reputation scores, because users believe more attractive programmers must produce much higher quality posts to overcome the predominant beauty penalty in this domain.

Providing Assets in the Sharing Economy: Low Socioeconomic Status as a Barrier (with Y. Wu et al.)

In the past few decades, the modern marketplace has provided consumers with a proliferation of models for consumption based on sharing and access across a wide range of domains, from car sharing (e.g., Turo), to renting rooms (e.g., Airbnb), clothing (e.g., StyleLend), books (e.g., Barnes & Noble), and tools (e.g., Quupe). While extant literature has examined consumers’ motivation of consuming products via sharing on the demand side, it remains understudied what factors might impact consumers’ asset-providing behavior on the supply side. The current paper investigates how a fundamental factor, socioeconomic status, impacts consumers’ willingness to provide their unused assets for sharing. Results from the analysis of a national-level field dataset and four preregistered studies (combined N = 45,289) reveal that lower socioeconomic status experienced in one’s early life exerts an independent impact on the asset providing behavior beyond the influences of current socioeconomic status and asset availability, leading to decreased willingness to provide one's unused assets for sharing. We find converging evidence for the effects across different domains, including lodging, transportation, and tool sharing, across different cultural backgrounds, and across both the laboratory and field settings. Further, we identify greater territorial feelings toward one’s own possessions as a central mechanism driving the decreased asset providing behavior of consumers with lower childhood socioeconomic background. While consumers who grew up in a low versus high socioeconomic environment are similarly motivated by the financial incentives provided by renting out their unused assets such as a spare room, individuals with lower childhood socioeconomic status tend to develop stronger territorial feelings toward their own possessions, discouraging them from providing their own assets for sharing.

The Mere Urgency Effect (with Y. Yang & C. K. Hsee)

In everyday life, people are often faced with choices between tasks of varying levels of urgency and importance. How do people choose? Normatively speaking, people may choose to perform urgent tasks with short completion windows, instead of important tasks with larger outcomes, because important tasks are more difficult and further away from goal completion, urgent tasks involve more immediate and certain payoffs, or people want to finish the urgent tasks first and then work on important tasks later. The current research identifies a mere urgency effect, a tendency to pursue urgency over importance even when these normative reasons are controlled for. People are more likely to perform unimportant tasks (i.e., tasks with objectively lower payoffs) over important tasks (i.e., tasks with objectively better payoffs), when the unimportant tasks are characterized merely by spurious urgency (e.g., an illusion of expiration). The mere urgency effect documented in this research violates the basic normative principle of dominance—choosing objectively worse options over objectively better options. People behave as if pursuing an urgent task has its own appeal, independent of its objective consequence. Further, the mere urgency effect is more pronounced among people who perceive themselves as busy in general. This occurs because busy people tend to be more tuned into time dimension, chronically paying more attention to task expiration time, which diverts their focus away from the magnitudes of task outcomes. Thus, interventions that broaden people's attention to the final outcomes of everyday tasks should be particularly effective at attenuating the mere urgency effect, leading us to allocate more resources to activities that matter most to our wellbeing and the long-run welfare our society as a whole. 

Prediction Aversion: The Unbearable Heft of Knowing the Future (with E. Helzer & Y. Wu)

Although the future is inherently uncertain, it is becoming more and more subjected to predictions with the recent development of technology such as machine learning and artificial intelligence. While people themselves may wonder what their future will be, such as whether their relationship will last and whether they will have enough money after retirement, we find that people also resist predictions about their future made by others. Across a series of preregistered studies using various life domains, we document a general prediction aversion effect: individuals are aversive to predictions about their future relative to similar information not being framed as predictions, an effect that emerges regardless of the source of the information (e.g., artificial vs. human intelligence; experts vs. non-experts). We offer explanations for the prediction aversion effect based on loss aversion, and provide support for this account by showing that people who are more satisfied with their current situation and/or perceive a lower likelihood of their situation changing are particularly aversive to predictions about their own future. Several other factors may also have contributed to the prediction aversion effect: life might be more enjoyable when the future stays uncertain; unveiling the future could be considered as a form of tempting fate; predictions may decrease perceived control over one's own future. We observe an attenuation of the prediction aversion effect for respondents perceiving a high likelihood of their current situation changing. This result suggests that the anticipated losses of switching from the status quo serve as an independent driver of prediction aversion.

Emerging Marketing Research on Healthcare and Medical Decision Making: Toward a Consumer-Centric and Pluralistic Methodological Perspective (with D. Chakravarti et al.) 

The healthcare system has shifted focus away from the traditional disease-oriented model to a patient-centered approach toward care and support. At the same time, technological innovation and transdisciplinary advances have helped double the volume of available healthcare, biomedical, and social research data every 12 months or so. The pandemic has further accelerated the evolution of a healthcare ecosystem that is both patient-centered and data-driven. On one hand, patients have access to traditional care modalities, support systems, as well as digital tracking and virtual care. On the other hand, advanced analytics uses provider-and patient-generated clinical as well as health and wellness data, along with financial and social structure data. We propose a substantive consumer-centric perspective in marketing research on healthcare topics aimed at building a corpus of work that embraces a pluralistic methodological stance. Specifically, we suggest four broad categories of focal dependent variables, including health awareness and perception outcomes, preventive care, diagnostic care, and wellness promotion, and four categories of independent variables, including contextual influences, structural determinants, antecedent individual differences, and consumer segments or clusters. In terms of methodology, healthcare research in marketing can benefit from a pluralistic methodological perspective that combines experimental, econometric and newer AI-based approaches to both identify and isolate factors that may serve as barriers or facilitators of ameliorating interventions, and to extract insights from large aggregates of secondary and survey data that can inform the practice of personalized medicine and precision public health. Importantly, the data collection, analytics, and reporting of such research must reflect due regard for individual privacy and data security. They must be transparent, shared, and developed through collaborative networks, and disseminated so as to be accessible to all entities in the health care ecosystem.