Tesary Lin
Dean’s Research Scholar
Assistant Professor, Marketing
Tesary Lin is the Isabel Anderson Career Development Assistant Professor of Marketing at Boston University Questrom School of Business, a Junior Faculty Fellow at the Hariri Institute for Computing, and a Faculty Affiliate of the Technology & Policy Research Initiative.
Her research focuses on how information and data moderate the relationship between firms and consumers. Specific topics include consumer privacy preferences and their impacts on firm analytics, analytics tools that adapt to the privacy-first data landscape, and how companies use choice architecture to moderate consumers’ data sharing decisions. Professor Lin has received recognition for this line of work, including the John D.C. Little Best Paper Award, the Alessandro di Fiore Best Paper Award, the Sheth Foundation ISMS Doctoral Dissertation Award, and the MSI Alden G. Clayton Doctoral Dissertation Award.
For a complete list of her research, including ongoing projects, visit https://tesarylin.github.io/.
Education
PhD, University of Chicago, 2020
Selected Research Presentations
Farronato, C. , Lin, T. Designing Consent: Choice Architecture and Consumer Welfare in Data Sharing, FTC Conference on Microeconomics, 2026
Lin, T. Designing Consent: Choice Architecture and Consumer Welfare in Data Sharing, Google Economics Seminar, 2025
Lin, T. Designing Consent: Choice Architecture and Consumer Welfare in Data Sharing, SITE-Market Failures and Public Policy Workshop, 2025
Fradkin, a. Designing Consent: Choice Architecture and Consumer Welfare in Data Sharing, ACM Conference on Economics and Computation (EC), 2025
Lin, T. Designing Consent: Choice Architecture and Consumer Welfare in Data Sharing, Summer Institute in Competitive Strategy, 2025
Lin, T. Designing Consent: Choice Architecture and Consumer Welfare in Data Sharing, New Data for Consumer Insights Conference, 2025
Lin, T. Designing Consent: Choice Architecture and Consumer Welfare in Data Sharing, Behavioral IO and Marketing Symposium, 2025
Lin, T. Designing Consent: Choice Architecture and Consumer Welfare in Data Sharing, Digital Competition and Tech Regulation Conference, 2025
Farronato, C. Designing Consent: Choice Architecture and Consumer Welfare in Data Sharing, 2025
Lin, T. Designing Consent: Choice Architecture and Consumer Welfare in Data Sharing, NBER Digital Economics and AI Spring Meeting, 2025
Lin, T. COPPAcalypse? The Impact of YouTube-FTC Settlement on Kids Content, UK Office of Communication Seminar, Online, 2024
Lin, T. COPPAcalypse? The Impact of YouTube-FTC Settlement on Kids Content, Marketing Science Institute Webinar, Online, 2024
Lin, T. Revealing Privacy Preferences: Experimental Insights on Consumer Data Sharing Decisions (keynote speech), Conference on Digital Experimentation, Cambridge, MA, 2024
Lin, T. Data Sharing and Website Competition: The Role of Dark Patterns, The FTC Bureau of Economics Seminar, Washington, DC, 2024
Lin, X. Understanding Data Selection Bias in Consent-Based Privacy Regulatory Regimes, MSI-Brookings Institution Workshop: Intended and Unintended Effects of Privacy Regulations on Marketing, The Brookings Institution, 2023
Farronato, C. , Fradkin, A. , Lin, X. Data Sharing and Website Competition: The Role of Dark Patterns, INFORMS Annual Meeting, 2023
Publications
Dubé, J., Lynch, J., Bergemann, D., Demirer, M., Goldfarb, A., Johnson, G., Lambrecht, A., Lin, T., Tuchman, A., Tucker, C. (2025). “Frontiers: The Intended and Unintended Consequences of Privacy Regulation for Consumer Marketing”, Marketing Science, 44 (5), 975-984
Farronato, C., Fradkin, A., Lin, T. (2025). “Designing Consent: Choice Architecture and Consumer Welfare in Data Sharing”, Proceedings of the 26th ACM Conference on Economics and Computation 788-788
Lin, T. (2022). “Valuing Intrinsic and Instrumental Preferences for Privacy”, Marketing Science, 41 (4), 663-869
Lin, T., Misra, S. (2022). “Frontiers: The Identity Fragmentation Bias”, Marketing Science, 41 (3), 433-440
Lin, X. (2021). “Shiny App: Demo for Identity Fragmentation Bias”,