Yu Ding

Assistant Professor, Marketing

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Yu Ding

Assistant Professor of Marketing

Academic Area:

Marketing

Research Statement

Ding's research sheds light on the general topic of trust and its implications for how firms and brands can address trust deficits. His research topics cover misinformation, consumer trust, and debiasing. Ding employs different methods to study these problems, including lab experiments, field studies, archival data analyses, and statistical and machine learning techniques. His recent research addresses the problem of information overload that results in a lack of ability to discern true from false information, and hence general distrust, ambivalence, and skepticism towards information.

Research Interests

Consumer Trust
Misinformation
Debiasing and Persuasion
Cultural and Social Influence

Teaching Statement

Ding believes that an educator should develop deep expertise in the subject matter of courses they teach, and at the same time should engage with students so that they are active participants in their own education. Students learn best when they are involved and proactively engaged through activities such as company projects, case discussions, and simulations. Ding aims to bring theory and practice closer together in his teaching, and hope to discuss and teach the latest research findings and methodological tools in classes, as appropriate.

Bio

Ding is an assistant professor of marketing at Stanford Graduate School of Business. He received his PhD in marketing from Columbia University. He also holds a master’s degree in statistics from National University of Singapore and a bachelor’s degree in marketing from Fudan University. His research has been published in top marketing, management, and psychology journals, such as the Journal of Marketing Research, Organizational Behavior and Human Decision Processes, and Journal of Personality and Social Psychology. He has been awarded prestigious fellowships and awards, including the American Marketing Association’s Rising Star Award (2022), John A. Howard/AMA Doctoral Dissertation Award (2022), ACR-Sheth Foundation Dissertation Award (2020), and Computational Social Science Grant from Russell Sage Foundation (2020).