Jingcun CAO
Prof. Jingcun CAO
Marketing
Assistant Professor

3917 1121

KK 718

Academic & Professional Qualification
  • Ph.D., Indiana University
  • M.A., Indiana University
  • B.S., Xiamen University
  • Visiting PhD, The University of Chicago, Booth School of Business
  • Visiting Student, National Tsing Hua University
Biography

Jingcun Cao joined the University of Hong Kong in 2020. He gained his bachelor degree in Computational Mathematics, master degrees in Economics and Business, and PhD degree in Marketing.

His research mainly focuses on substantively important and managerially relevant problems, and tries to solve the problems with the most adequate methods, including econometrics, field experiment, and machine learning and statistics. His expertise lies in mobile marketing, online education, healthcare, applied machine learning, new media platform, entertainment industry, and policy intervention.

Dr. Cao has a series of research in mobile app ecosystem, including users’ mobile apps usage behavior, mobile app developers’ monetization strategies, in-app targeted promotion, mobile app stores’ regulation policy on fake apps. In the meantime, his research utilizes multidisciplinary methods, including deep learning, machine learning, econometrics, field experiment and lab experiment, to better understand consumers’ behavior and firms’ strategies. Dr. Cao holds several machine-learning and deep-learning algorithm related patents (pending stage).

Dr. Cao also works closely with hi-tech and internet firms to gain deep understandings in the most innovated business models and practice in the industry, and also helps firms to get empowered with cutting-edged marketing research, especially on Business Intelligence and Big Data Analytics.

Teaching
  • Big Data Marketing (MKTG 3530)
  • Introduction to Marketing (MKTG 2501)
  • Executive Education (EE) Courses in MarTech, Business Analytics, Brand Management
Research Interest
  • Substantive: Mobile App Ecosystem, Online Education, Health Care, Digital Marketing, Environmental Policy;
  • Methodology: Causal Inference, Applied Machine Learning, Randomized Field Experiment, Econometrics;

I recruit Research Assistant (RA). If you are interested in the RA position, please send your CV and transcript to jcao@hku.hk

Selected Publications

Selected Publications

  • Leo Bao, Jingcun Cao, Lata Gangadharan, Difang Huang, Chen Lin, “Effects of Lockdowns in Shaping Socioeconomic Behaviours.” Forthcoming at PNAS 

(All authors with equal contributions)

  • Jingcun Cao, Xiaolin Li, and Lingling Zhang, “Is Relevancy Everything? A Deep Learning Approach to Understand the Effect of Image-Text Congruence.” Forthcoming at Management Science.
  • Jingcun Cao, Pradeep Chintagunta, and Shibo Li, “From free to paid: Monetizing a non-advertising-based app.” Journal of Marketing Research (2023).

Selected Working Papers

  • “The Effect of Subsidizing Digital Educational Content: Evidence from a Field Experiment” with Catherine Tucker, Yifei Wang, and Xiru Pan
  • “Driving towards Purchase: Investigating the Impact of Product Scarcity on Consumers’ Search Behavior” with Pradeep Chintagunta, and Shibo Li
Recent Publications
From Free to Paid: Monetizing a Non-Advertising-Based App

Non-advertising-based mobile apps face several critical challenges when trying to monetize their free services—among them, the choice of pricing strategies (hard landing vs. soft landing; i.e., a “pay or churn” paywall vs. continuing to offer limited free services to existing users after monetization) and aspects of product design (whether to provide exclusive secondary offerings to paying users). The authors implemented a large-scale randomized field experiment with an app firm to test the causal effects of such pricing and product design strategies. Results show that both soft landing and exclusive secondary offerings decrease existing app users’ willingness to subscribe, but there is a positive interaction between these two strategies on subscriptions. The authors propose a theoretical framework, discuss potential mechanisms that might be at play, and conduct robustness checks to rule out several alternative explanations. A customer survey by the firm and an experiment on Prolific provide further support for the theoretical mechanism. To assess generalizability, the authors conducted a second field experiment and obtained consistent results. They also report the results from the actual implementation of the best-performing strategy by the firm. This research provides guidance on possible theoretical underpinnings of users’ responses and important managerial implications for app monetization.