Hui LI
Prof. Hui LI
Marketing
Professor
BSc (MAT) Programme Director

3910 3081

KK 721

Academic & Professional Qualification
  • Ph.D. in Economics, University of Pennsylvania 2015
  • B.A. in Economics, Peking University, China 2010
Biography

Professor Hui Li’s research interest is in the area of quantitative marketing. Her current works investigate two-sided platforms, sharing economy, online-to-offline commerce, new technology and digital products, and their impact on traditional industries. Trained as an economist, she is also interested in their social impact and policy implications. Methodologically, she believes research question determines the most appropriate methodology. She has used various methodologies, including structural models, econometric models, causal inference, game-theoretical models, and machine learning.

Hui’s research has been published in Marketing Science, Journal of Marketing Research, Management Science, Quantitative Marketing and Economics, Information Systems Research and was named a finalist for the John D.C. Little Award (2019). She has been invited to present her research at leading academic institutions and government agencies including the Antitrust Division of the U.S. Department of Justice (DOJ) and the Federal Trade Commission (FTC). She has been recognized as the MSI Young Scholar in 2021.

Hui received her Ph.D. in Economics from University of Pennsylvania in 2015 and B.A. in Economics from Peking University in 2010. She joined HKU Business School in 2022. Prior to joining HKU Business School, Hui was an Associate Professor of Marketing at Tepper School of Business, Carnegie Mellon University, U.S.A.

Research Interest
  • Two-sided platform
  • Sharing economy
  • Online-to-offline commerce
  • New technology and digital product
Selected Publications
  • Arslan Aziz, Hui Li, Rahul Telang. (2022) “The Consequences of Rating Inflation on Platforms: Evidence from a Quasi-Experiment.” Information Systems Research, 34(2):590-608.
  • Hui Li, Yijin Kim, Kannan Srinivasan. (2022) “Market Shifts in a Sharing Economy: Impact of Airbnb on Housing Rentals.” Management Science, 68(11):8015-8044.
  • Hui Li. (2021) “Are E-Books a Different Channel? Multichannel Management of Digital Products.” Quantitative Marketing and Economics, 19: 179–225.
  • Hui Li, Feng Zhu. (2021) “Information Transparency, Multi-Homing and Platform Competition: A Natural Experiment in the Daily Deals Market.” Management Science, 67(7): 4384-4407.
  • Hui Li, Qiaowei Shen, Yakov Bart, (2021) “Dynamic Resource Allocation on Multi-Category Two-Sided Platforms.” Management Science, 67(2): 661-1328.
  • Hui Li, Kannan Srinivasan, (2019) “Competitive Dynamics in the Sharing Economy: An Analysis in the Context of Airbnb and Hotels.” Marketing Science (Lead Article), 38(3): 365-391.
    • John D.C. Little Award (Finalist), awarded to the best marketing paper published in Marketing Science, Management Science, or another INFORMS journal, 2019.
  • Hui Li, (2019) “Intertemporal Price Discrimination with Complementary Products: E-Books and E-Readers.” Management Science, 65(6): 2665-2694.
  • Hui Li, Qiaowei Shen, Yakov Bart, (2018) “Local Market Characteristics and Online-to-Offline Commerce: An Empirical Analysis of Groupon.” Management Science, 64(4): 1860-1878.
  • Timothy P. Derdenger, Hui Li, Kannan Srinivasan, (2018) “Firms’ Strategic Leverage of Unplanned Exposure and Planned Advertising: An Analysis in the Context of Celebrity Endorsements.” Journal of Marketing Research, 55(1): 14-34.
Awards and Honours
  • 2021 MSI Young Scholar, Marketing Science Institute
  • 2019 John D.C. Little Award (Finalist)
  • 2017 Lave-Weil Faculty Research Prize, Tepper School of Business, Carnegie Mellon University
  • 2015 William Polk Carey Prize for the Outstanding Economics Dissertation, University of Pennsylvania
  • 2015 Paul Taubman Memorial Prize for Empirical Economics Research, University of Pennsylvania
Recent Publications
The Consequences of Rating Inflation on Platforms: Evidence from a Quasi-Experiment

Informative online ratings enable digital platforms to reduce the search cost for buyers to find good sellers. However, rating inflation, a phenomenon in which average rating increases and rating variance across listings decreases, threatens the informativeness of ratings. We empirically identify the consequences of rating inflation by conducting a quasi-experiment with a digital platform that exogenously changed its rating display rule in a treated neighborhood, which resulted in rating inflation. Using a differences-in-differences approach, we find that platforms benefit from one aspect of rating inflation: user purchases and seller sales increase because of the increased average rating. However, they also face negative consequences: rating inflation causes a decrease in user trial and a greater concentration of sales among popular restaurants. Overall, our results illustrate the potential consequences of rating inflation that platforms need to consider when designing and managing their rating system.

Get to know Professor Hui Li