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Shan Huang is currently an Assistant Professor at the Faculty of Business and Economics at the University of Hong Kong. Previously, she served as an Assistant Professor at the Foster School of Business at the University of Washington, Seattle, from 2018 to 2020. Additionally, she holds the position of consultant for Tencent, and she is a Digital Fellow at the Stanford Digital Economy Lab.

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Shan's research focuses on the digital economy, social networks, and business analytics with a focus on A/B testing (online controlled experiments). She combines large-scale field experiments (A/B tests, causal inference) with machine learning to gain insights into contemporary digital phenomena and to develop novel experimentation methods that contribute to more effective data-driven decision-making processes. 

 

She investigates how emerging social media platforms influence economic activities, such as social advertising, social referrals, and content diffusion. She examines how social cues, such as friends' likes and comments appearing in ads, can impact the effectiveness of social advertisements, how customers actively match products with their contacts in social referrals, and how emotional expression and weak ties impact the content spread online through different channels, such as direct messaging and broadcasting. Further, she compares social networks versus algorithms in shaping the diffusion of content on social media platforms. 

 

She also explores the effective use of A/B tests to improve managerial decision-making. Her recent research focuses on enhancing the external validity of experiments, including estimating treatment effects in a long-term and for a targeted population using data from short-term experiments. 

 

Her research findings have been published in prestigious journals and presented at renowned conferences. Throughout her career, Shan has actively collaborated with industry partners to gain insights and analyze various aspects of the digital landscape. 

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She earned her bachelor's degree from Tsinghua University and completed her Ph.D. at the MIT Sloan School of Management.

Research Interests:

  • ​Digital Economy and AI in Marketing

  • Social Networks, Social Media and Computational Social Science

  • Causal Methods and Experimentation Methods (A/B testing)

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Publications:

  1. Shan Huang, Sinan Aral, Yu Hu & Erik Brynjolfsson (2020). Social Advertising Effectiveness Across Products: A Large-Scale Field Experiment, Marketing Science, 39(6), 1142-1165.

  2. Hailiang Chen, Yu Hu & Shan Huang (2019). Monetary Incentive and Stock Opinions on Social Media. Journal of Management Information Systems, 36(2), 391-417.

  3. Shan Huang & Song Lin (2024), Do More "Likes" Lead to More Clicks? Evidence from a Field Experiment on Social Advertising, Journal of Marketing, forthcoming

  4. Chen Wang, Shichao Han, & Shan Huang (2024), Enhancing External Validity of Experiments with Ongoing Sampling Process, The Twenty-Fifth ACM Conference on Economics and Computation (EC'24)

  5. Shan Huang & Yi Ji (2024), Algorithmic vs. Friend-based Recommendations in Shaping Novel Content Engagement: A Large-scale Field Experiment, The Twenty-Fifth ACM Conference on Economics and Computation (EC'24)

  6. Shan Huang, Chen Wang, Yuan Yuan, Jinglong Zhao & Jingjing Zhang (2023), Estimating Effects of Long- Term Treatments, The Twenty-Fifth ACM Conference on Economics and Computation (EC'23)

  7. Shan Huang, Shipeng Yan, Zhenhui Jiang, & Minying Huang (2022), ESG at WeChat Pay to Support SMEs, Asia Case Research Centre

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