Behavioral Intelligence Lab
行为智能实验室
The Behavioral Intelligence Lab works toward a human-centered future where AI truly serves human wellbeing. Many of the hardest problems in today's wellbeing crisis are behavioral: people who need services and care don't seek it, don't stay with it, and are rarely reached by the providers. These are problems of matching, engagement, personalization, and measurement — and they are the problems we work on.
We develop the science and methods for understanding how humans and AI agents behave and make decisions. We apply that understanding to match people with the care, services, and products they need and to help them stay engaged. We also turn behavioral data into instruments that can make providers more productive and improve the wellbeing of the people they serve.
Our members are from multidisciplinary backgrounds, including quantitative marketing, computer science, economics, cognitive science, and psychology. We work to be a place where these different ways of thinking sharpen one another.
Research Theme
1. Computational Behavioral Science of AI and Human
We model, analyze, and explain the behavior of human and AI agents, as service providers or consumers, from individuals to markets. For example:
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Human-AI collaboration in professional service
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Design of AI memory and behavior for customer service
2. AI for the Wellbeing Economy
We hope our research can contribute to a human-centered future in which AI technology serves human wellbeing. For example:
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AI-enabled systems for enhancing efficiency in healthcare
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Human–AI affective relationships for social and emotional wellbeing
Join Us
Postdoc, PhD, and RA positions are available. We look for candidates who share our research interests and have relevant research experience. If interested, please email me a copy of your resume, transcripts, papers, and other relevant materials.
Selected Working Papers and Publication
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Bonding with AI Companions: Love Expression and User Engagement.
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Working paper with Hang Xu, Mengze Shi (2026)
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Love, Actually? A Randomized Experiment on AI Companion Use and Real-life Romantic Relationships.
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Working paper with Jiaming Jiang, Zoey Jiang, Kannan Srinivasan, George Loewenstein (2026)
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Welfare Implications of Democratization in Content Creation: Generative AI and Beyond.
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Tianxin Zou, Zijun (June) Shi, Yue Wu (2026)
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Lead article
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Information Silos on Social Media: Experimental Evidence from TikTok
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Working paper with Tianyu Han, Wenbo Wang (2026)
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The Effect of Voice AI on Digital Commerce
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Chenshuo Sun, Zijun (June) Shi, Xiao Liu, Anindya Ghose (2025)
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Information Systems Research, 36(2):1147-1166.
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On the Role and Design of Resale Royalties
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Working paper with Wenxiao Yang, Song Lin (2025)
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Gender-Neutral Marketing: Evidence from a Leading E-Commerce Platform
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Working paper with Jiaqi Chen, Tong Guo, Shuo Zhang (2025)
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How Do Fast Fashion Copycats Affect the Popularity of Premium Brands? Evidence from Social Media
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Zijun (June) Shi, Xiao Liu, Dokyun Lee, Kannan Srinivasan (2023).
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Journal of Marketing Research, 60(6), 1027-1051.
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Hype News Diffusion and Risk of Misinformation: The Oz Effect in Healthcare
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Zijun (June) Shi, Xiao Liu, Kannan Srinivasan (2022).
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Journal of Marketing Research, 59(2), 327-352.
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Winner, Paul E. Green Award, 2023
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Winner, AMA MR-SIG Don Lehmann Award, 2023
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Design of Platform Reputation System: Optimal Information Disclosure
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Zijun (June) Shi, Kannan Srinivasan, Kaifu Zhang (2022).
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Marketing Science, 42(3), 500-520.
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Freemium as an Optimal Strategy for Market Dominant Firms
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Zijun (June) Shi, Kaifu Zhang, Kannan Srinivasan (2019).
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Marketing Science, 38(1), 150-169.
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Achim Ingo Czerny, Zijun (June) Shi, Anming Zhang (2016).
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Transportation Research Part A, Vol. 91 (September), 260-272.
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Funding
We would like to thank our corporate sponsors and the funding agencies (the HK Research Grants Council, HKUST, MSI) for supporting our research.