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I am an information system researcher who mainly focus on to understand the impacts of the behavior of artificial intelligence systems on social media platforms. Machines powered by artificial intelligence increasingly mediate our social, cultural, economic and political interactions. Understanding the behavior of artificial intelligence systems is essential to our ability to control their actions, reap their benefits and minimize their harms. My current research examines how the implementation of recommendation algorithms on social media platforms influence users’ content generation and patience (time preference). I am also interested in investigating the impacts of different digital platform policies on both consumer and producer sides.
I received my PhD degree from the School of Business and Management at the University of Hong Kong Science and Technology, and bachelor degree from Peking University.
Honours
2019 ICIS Doctoral Consortium Fellow
2019 PACIS Doctoral Consortium Fellow
2016 Dean's PhD Fellowship, HKUST
Recent Publications
Danuvasin Charoen, Guangrui Li, Warut Khern-am-nuai (2026), "Preparing Technology Managers for the Postconsumer Reviews Era", IEEE Engineering Management Review, 54(2), 14–20.
Abstract
Online consumer reviews have long been instrumental in shaping user behavior and guiding product development. However, their credibility, and thus their utility, is in steep decline due to threats, such as malicious reviews, incentivized reviews, and AI-generated reviews. As synthetic content becomes indistinguishable from genuine feedback and bad actors exploit platforms to manipulate perceptions, the foundational trust in user-generated reviews is rapidly eroding. This article explores the critical challenges facing review ecosystems and argues that technology managers must prepare for a transition beyond traditional reviews. It examines how alternative mechanisms, such as question-and-answer systems, expert editorial content, and synthetically generated summaries from aggregated sources, can provide more trustworthy, actionable insights. These alternatives emphasize verified engagement, structured expertise, and scalable synthesis, offering resilient feedback models. This article calls for a rethinking of how platforms collect, interpret, and present user reviews, outlining practical steps for managers to sustain trust and transparency in digital marketplaces.
Courses Taught
OMIS 1050 Managing Data for Business Decisions
Grants
Project Title Role Award Amount Year Awarded Granting Agency Project TitleHow does Popularity Information Affect Product Design? Roleco-PI Award Amount$5,000.00 Year Awarded2021 Granting AgencyTD Management Data and Analytics Lab Project TitleAlgorithmic Recommendations Change Guys’ Patience but Not Gals’: Evidence from a Field Experiment Roleco-PI Award Amount$5,000.00 Year Awarded2021 Granting AgencyBehavioural Economics in Action at Rotman Project TitleCurse or blessing: The welfare effects of algorithmic recommendations RolePI Award Amount$59,005.00 Year Awarded2023 Granting AgencySSHRC Insight Development Grants
