Junhong CHU
Prof. Junhong CHU
Associate Director, Centre for Innovation and Entrepreneurship

3910 3087

KK 720

Academic & Professional Qualification
  • 2001-2006 Ph.D. in Business Administration (Marketing), University of Chicago
  • 2001-2006 MBA, University of Chicago
  • 1991-1996 Ph.D. in Law (Demography), Peking University
  • 1987-1991 BA in Economics, Peking University

Junhong Chu is a professor of marketing at the University of Hong Kong (HKU). Before joining HKU, she worked at the NUS Business School as a dean’s chair and a tenured associate professor of marketing and earlier at Peking University as an associate professor of economics. Professor Chu has also visited Harvard University as a research fellow and the University of Michigan as an associate professor.

Professor Chu is an empirical modeler, works on big data, and does quantitative research in marketing and industrial organization. Her research interests include platform markets and the sharing economy, e-commerce, social media, P2P markets, and distribution channels. She applies both the classical and Bayesian approach to study firm competition and consumer behavior.

Professor Chu’s research has been published in leading academic journals such as Marketing ScienceJournal of Marketing ResearchManagement ScienceJournal of MarketingProceedings of the National Academy of Sciences (PNAS), Nature Human BehaviourPopulation and Development Review, and Demography. She was an MSI (Marketing Science Institute) 2011 Young Scholar and has also won several research awards.

Professor Chu earned a BA in economics and a PhD in Law (Demography) from Peking University, and an MBA and PhD in Business Administration (Marketing) from the University of Chicago.



  • MSMK7032: Foundational Quantitative Skills in Marketing
  • MKTG3522: Platform Business Models and the Sharing Economy
  • MSMK7029: Platform Business Models and the Sharing Economy
  • MKTG6006: Empirical Marketing Models


  • Customer Relationship Management
  • Marketing Research
  • Marketing Models
  • Marketing Strategy (in Chinese)
Research Interest

I am an empirical modeler and work on big data, doing empirical industrial organization and structural modeling research. I am interested in platforms and the sharing economy, e-Commerce, and social media. I employ both the classical approach and Bayesian approach to study consumer and firm behavior and interactions between agents in these businesses.

Selected Publications
Awards and Honours
  • The 2024 Best Marketing Paper in Management Science
  • Best Paper Award, the Spanish Marketing Academy and ASEDAS, 2013
  • Best Paper Award, Public University of Navarra, Spain, 2013
  • Runner up, the MSI and IJRM Award for Best Paper in IJRM Special Issue on Marketing in Emerging Markets, 2013
  • MSI (Marketing Science Institute) Young Scholar, 2011
Service to the University/ Community
  • Editorial Review Board, Marketing Science, from January 2019
  • Editorial Review Board, Journal of Interactive Marketing, from April 2016
Recent Publications
A Dynamic Model of Owner Acceptance in Peer-to-Peer Sharing Markets

Peer-to-peer (P2P) sharing marketplaces enable sharing of idle resources. When a renter requests an owner’s resource, the owner needs to decide whether to accept the request: accepting it helps the owner fill up the idle periods of the resource and generate a payoff but reduces the flexibility to serve a future request for a longer duration. This paper develops a framework to uncover the tradeoffs faced by owners on these platforms when making acceptance decisions, which can be used by owners to optimize their decisions and by platforms to improve their operations. The model explicitly accommodates two types of owners: some are attentive to the availability states of their cars and forward-looking, whereas others myopically make the acceptance decisions. Applying the model to unique data from a leading peer-to-peer car sharing platform in China, we obtain similar sizes of both types of owners and find that female, experienced, and younger owners are more likely to be strategic. The results also reveal the differentiated preferences of the two types of owners toward their renters. Building on model estimates, we calibrate the option value of each day in the future (i.e., the value of having the day available) for strategic owners and find it to first increase, then decrease. Two counterfactual analyses are conducted. The first analysis shows that if the platform imposes a minimum rental duration, strategic owners may become more reluctant to accept requests, even if the current availability state entails a higher expected payoff. The second analysis shows that with better understanding of its owners, the platform can greatly improve the matching efficiency by optimal (re)allocation of rental requests, a move that benefits almost all participants in the business.