- Ph.D MIT Sloan School of Management
- MSc. University of British Columbia
- BA. Tsinghua University
Shan Huang is an assistant professor at the Faculty of Business and Economics at the University of Hong Kong. From 2018 to 2020, she was an assistant professor at the Foster School of Business at the University of Washington, Seattle. She is a digital fellow at MIT Initiative on Digital Economy and Stanford Digital Economy Lab. Her research focuses on the digital economy, social networks, and business analytics (e.g., A/B testing). Shan’s current work aims to understand the business value and social implications of new social media. Specifically, her studies examine how social advertising and social referral affect product virality, how emotions shape online content diffusion, and how weak ties can or cannot break people out of the echo chamber, in massive social networks. She has a particular interest in understanding how certain phenomena vary across individuals, social ties, products, and markets, using population-scale datasets and large-scale field experiments, and uses various research methodologies (e.g., large-scale networked randomized field experiments, machine learning, network analysis, econometrics) to pursue her research agenda. Shan’s research has been published in prominent management journals, including Marketing Science and the Journal of Management Information Systems. She has been collaborating closely with the leading tech firms (e.g., Tencent) to understand the cutting-edge digital phenomena and the tools such as A/B testing.
- Digital Economy, Business Analytics and Computational Social Science
- Social Networks, Social Media and Digital Strategy
- Methodology: Large-scale Randomized Field Experiments, Econometrics, Network Analysis, Machine Learning
- Huang, S., Aral, S., Hu, Y. J., & Brynjolfsson, E. (2020). Social advertising effectiveness across products: A large-scale field experiment. Marketing Science, 39(6), 1142-1165.
- Chen, H., Hu, Y. J., & Huang, S. (2019). Monetary Incentive and Stock Opinions on Social Media. Journal of Management Information Systems, 36(2), 391-417.