Yikun Jiang
Hello! I am an Assistant Professor in Management Information Systems at the Mitch Daniels School of Business at Purdue University. I completed my Ph.D. in Economics in May 2025 at the University of California, Berkeley. Prior to my doctoral studies at Berkeley, I completed my bachelor's degree in Economics and Mathematics at McGill University in 2018 and spent one year as a Ph.D. student in Quantitative Marketing at the University of Toronto's Rotman School of Management.
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My research focuses on understanding individuals' decisions on digital platforms to inform platform strategies. I combine the design and implementation of large-scale field experiments, causal inference, structural modeling, machine learning, and the analysis of unstructured data to answer research questions.
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Selected Working Papers
Does Premium Version Adoption in mHealth Improve User Engagement and Health-Related Outcomes?, with Kosuke Uetake and Nathan Yang. (Accepted at Marketing Science)
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​Best Student Paper Award at the 15th Annual Conference on Health IT and Analytics (CHITA) 2025
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User-generated content is crucial to online content platforms, yet how platforms can design incentives to stimulate user content contributions remains unclear. This paper examines the roles of peer recognition (social recognition from other users) and platform rewards (platform functionalities unlocked at reputation thresholds) in motivating user content contributions. I design and implement a large-scale field experiment involving 12,182 users on a leading online question-and-answer platform, in which an experimental increase in reputation (via an anonymous upvote) simultaneously shifts both incentives. The treatment magnitude corresponds to a 19.6% increase in the median sampled user’s reputation. I find that the treatment significantly increases an individual’s probability of contributing additional answers by around 15% relative to baseline. The treatment effect emerges within the first three weeks, resulting in a persistent cumulative difference between the treatment and control groups. The effect is stronger for users close to earning new privileges and weaker for more experienced users, while the quality and effort of subsequent answers remain stable. To quantify these incentives, I structurally estimate a model of contribution decisions, finding that users value an additional privilege at approximately 55% of the value of an additional upvote. Finally, I leverage a large language model (LLM) to assess the quality of approximately 200,000 historical answers by sampled users. Structural simulation using LLM-based quality assessments shows that undervaluation of high-quality contributions by new users substantially discourages participation. Together, these findings provide the first experimental evidence examining the roles of peer recognition and platform rewards in content contribution, integrate structural and experimental evidence, and inform the scalable design of non-monetary incentives on digital platforms.
Victim of Your (Customer's) Own Success, with Nathan Yang. (Reject & Resubmit at Journal of Marketing Research)
Ongoing Projects
Several, Available Upon Request