One advantage of advertising on social media is leveraging users’ expression of “likes” to influence the perceptions and responses of others in their network. Through a largescale field experiment on WeChat, three online lab studies and a theoretical model, we explore whether and how displaying more “likes” in an ad can effectively lead to more ad “likes” and clicks. We find that displaying the first “like” can significantly increase users’ tendencies to both “like” and click on an ad. However, on average, showing additional “likes” does not further increase the clicking propensity, although it consistently attracts more “likes.” We further find that displaying more “likes” increases the clickthrough rate for lesser-known brands but not for well-known brands, and has a stronger impact on the “like” rate for more socially engaged users than for less socially engaged ones. These findings are consistent with the interplay between informational and normative social influences in social advertising. The public visibility of “likes” makes liking more susceptible to normative social influence than clicking. The coexistence of these two forces can lead to an enhanced conformity effect on liking and a crowding-out effect on clicking. Our findings offer novel implications for managing social advertising and designing social media platforms.
3917 1629
KK 1229
- Ph.D MIT Sloan School of Management
- MSc. University of British Columbia
- BA. Tsinghua University
Shan Huang is an Assistant Professor of Marketing at the Faculty of Business and Economics, The University of Hong Kong, a Digital Fellow at Stanford University’s Digital Economy Lab, and a consultant to Tencent. She earned her Ph.D. in Management Science from the MIT Sloan School of Management, an MSc from the University of British Columbia, and a B.BA from Tsinghua University.
Her research examines technology-enabled marketing strategies and decision-making. She integrates large-scale digital experimentation—such as A/B testing and causal inference—with artificial intelligence, including machine learning and large language models (LLMs), to generate insights into digital phenomena and to develop new methodological tools that advance data-driven decision-making. Empirically, her work investigates how social media platforms and algorithmic curation shape advertising effectiveness, social referrals, and content diffusion. Methodologically, she develops tools to systematically improve the external validity of A/B tests for both research and industry applications—for example, methods for estimating long-term and representative treatment effects from short-term experiments, and approaches that generalize historical experimental results into interpretable, LLM-assisted insights for decision-making. Her work has appeared in Management Science, Marketing Science, and other leading management journals, as well as premier computer science venues such as ACM EC, and has been supported by over HKD 3 million in competitive research grants.
Professor Huang views research, teaching, and practice as deeply interconnected. Since 2015, she has collaborated extensively with Tencent, launching WeChat’s first large-scale A/B test and co-developing its experimentation infrastructure. Her methodological innovations have been adopted by Tencent and ByteDance, directly influencing practice. At HKU, she designed and teaches Digital Experimentation Methods, a course that earned the Faculty Teaching Innovation Award in 2024. She is also co-authoring a forthcoming book with senior industry leaders, documenting China’s experience with experimentation and data-driven decision making for a global audience.
- Technology-Enabled Marketing Strategies and Decision-Making
- AI in Marketing Decisions, Causal and Experimentation Methods (A/B testing), New Social Media Platforms, Computational Social Science, Social Networks
- Shan Huang*†, Chen Wang*, Yuan Yuan*, Jinglong Zhao* & Jingjing Zhang (industry author) (2025), Estimating Effects of Long- Term Treatments, Management Science, forthcoming
- early version accepted by The Twenty-Fourth ACM Conference on Economics and Computation (EC’23)
- Yifan Yu*, Shan Huang*†, Yuchen Liu, & Yong Tan (2025), Emotions in Online Content Diffusion, Information Systems Research.
- Shan Huang† & Song Lin† (2024), Do More “Likes” Lead to More Clicks? Evidence from a Field Experiment on Social Advertising, Journal of Marketing.
- 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.
- Hailiang Chen†, Yu Hu & Shan Huang (2019). Monetary Incentive and Stock Opinions on Social Media. Journal of Management Information Systems, 36(2), 391-417.
- Chen Wang, Shan Huang†, & Shichao Han (industry author), Enhancing External Validity of Experiments with Ongoing Sampling, The Twenty-Fifth ACM Conference on Economics and Computation (EC’24)
- Shan Huang† & Yi Ji, 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)
† corresponding author;
* the authors are listed alphabetically or in reverse order of seniority
- Shan Huang†, Shipeng Yan, Zhenhui Jiang, & Minying Huang (2022), ESG at WeChat Pay to Support SMEs, Asia Case Research Centre
- Shan Huang†, Xiaoming Yuan, and Minying Huang (2025), Algorithm Innovation at Huawei Cloud, Asia Case Research Centre, forthcoming
- HK01 In-depth Feature: Academia–Industry Collaboration, Part I | University Courses in the Innovation Era: From “Teaching-Centered” to “Learning-Centered”
- HK01 In-depth Feature: Industry–Academia Collaboration II｜From Academia to Industry: How Innovation and Technology Faculty and Students Transform Their Roles
- FBE TPG Official WeChat Account: High-Impact MSBA Course | Teaching + Practice: Interviewers Asked About the Experiment Design from Class!
- MBAChina: WeChat Pay Empowers Small and Medium-Sized Enterprises