Junhong CHU
Prof. Junhong CHU
市场学
Professor
EMBA International Programme Director
Associate Director, Centre for Innovation and Entrepreneurship
Associate Director, HKU Jockey Club Enterprise Sustainability Global Research Institute

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
Biography

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.

Teaching

At HKU:

  • 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

At NUS:

  • 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
  • HONG KONG openBlM/openGIS AWARDS2024
  • 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
  • Associate Editor, Marketing Science, from January 2025
  • Senior Editor, Journal of Business Research, from January 2025
  • Editorial Review Board, Marketing Science, from January 2019 to December 2024
  • Editorial Review Board, Journal of Interactive Marketing, from April 2016
Recent Publications
Heterogeneous Complementarity and Team Design: The Case of Real Estate Agents

Workers often possess characteristics such as soft skills that are important for teamwork but unobserved by managers. In this paper, we develop a teamwork model based on the econometric teamwork framework in Bonhomme [Bonhomme S (2021) Teams: Heterogeneity, sorting, and complementarity. Becker Friedman Institute for Economics Working Paper No. 2021-15, University of Chicago, Chicago] and stochastic blockmodels for binary outcomes (e.g., Bickel et al. [Bickel P, Choi D, Chang X, Zhang H (2013) Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels. Ann. Statist. 41(4):1922–1943]) when only team-level outputs are observed. Our model does not impose any functional form restrictions on the complementarity between workers with unobserved characteristics, which are modeled as latent types. We apply our model to a data set from a leading Chinese real estate company; the data contain the complete history of team assignments, team performances, and property details. We find that complementarities between different agent types are heterogeneous and cannot be captured by commonly used production functions. More specifically, workers with intermediate solo performance complement all other workers the most, whereas those with the best solo performance are not the best team players. Our results suggest that firms can boost productivity by redesigning teams without incurring additional hiring costs. Leveraging our complementarity estimates, our counterfactual experiments demonstrate that reorganizing teams could enhance overall team output by up to 26.6%.