Dr. Chao DING
Principal Lecturer
BBA (IS)/BBA (BA) Programme Director

3917 1684

KK 807

Academic & Professional Qualification
  • PhD: University of Florida
  • MSc: The Hong Kong University of Science and Technology
  • BBA: Sun Yat-sen University

Chao Ding obtained his Ph.D. degree in the Department of Information Systems and Operations Management in Warrington College of Business and Administration at University of Florida in 2014. Dr. Ding holds a B.S. in optical engineering from Sun Yat-sen University and an M.S. in Economics from Hong Kong University of Science and Technology.

  • BUSI0036 – Quantitative analysis for business decisions
  • IIMT3636 – Decision and risk analysis
  • IIMT2601 – Management information systems
  • IIMT2602 – Business programming
  • MSBA7001 – Business intelligence and analytics
Research Interest
  • E-Commerce
  • Social Media
  • Internet Financing
  • Information Goods
Selected Publications
  • “Electoral Competition in the Age of Social Media” with W. Jabr and H. Guo, Management Information Systems Quarterly, accepted.
  • “Impact of Credit Default Swaps on Firms’ Operational Efficiency” with L. Qiu, R. Liu, Y. Jin, Y. Fan, and A.C.L. Yeung, (2022), Production and Operations Management, 31(9), 3611-3631.
  • “Friends or Foes? Strategic Technology Opening and Adopting under Competition between Technological Firms” with Y. Wei, G. Nan, and S. Li, (2022), Information and Management, 59(3), 103624.
  • “Strategic Waiting for Disruption Forecasts in Cross‐Border E‐Commerce Operations” with B. Niu, K. Chen, L. Chen, and X. Yue, (2021), Production and Operations Management, 30(9), 2840-2857.
  • “Click to Success? The Temporal Effects of Facebook Likes on Crowdfunding” with Y. Jin, Y. Duan, and H.K. Cheng, (2020), Journal of the Association for Information Systems, 21(5), 1191-1213.
  • “Retail Clusters in Developing Economies” with X. Zhao, A. Lim, H. Guo, and J.S. Song, (2019), Manufacturing & Service Operations Management, 21(2), 452-467.
  • “To Join or Not to Join Group Purchasing Organization: A Vendor’s Decision” with Y.C. Yang, H.K. Cheng, and S. Li, (2017), European Journal of Operational Research, 258(2), 581-589.
  • “The Power of the ‘Like’ Button: The Impact of Social Media on Box Office” with H.K. Cheng, Y. Duan, and Y. Jin, (2017), Decision Support Systems, 94, 77-84.
Awards and Honours
  • Faculty Teaching Innovation Award 2022-23
  • Faculty Outstanding Teacher Award (Undergraduate Teaching) 2021-22
  • Faculty Outstanding Teacher Award (Postgraduate Teaching) 2020-21
  • Faculty Undergraduate Teaching Reward 2017-18
Recent Publications
學者:網上眾籌平台 社媒難取代


Retail Clusters in Developing Economies

We develop a game-theoretic model to explore why retail clusters are so popular in developing economies and when governments should facilitate the formation of retail clusters to improve social welfare. First, we find two determinants of retailer clusters: the valuation-cost ratio (consumers’ maximum valuation over retailers’ production cost) and retailer density (the number of retailers over unit transportation cost). The valuation-cost ratio and retailer density indicate retailers’ profit potential and competition intensity, respectively. Second, the equilibrium cluster size increases in the valuation-cost ratio. This finding explains the phenomenon that clusters are usually larger in developing economies (where numerous retailers sell unrecognized brands with low profit potential) than in developed economies. Third, when the retailer density of a product market exceeds a certain threshold, the market coverages of clusters overlap with each other (i.e., the overlapping case). Furthermore, when compared with the nonoverlapping case, the equilibrium cluster size in the overlapping case is larger for low-profit-potential products but smaller for high-profit-potential products. Together, valuation-cost ratio and retailer density define four types of clusters: overlapping massive clusters, nonoverlapping large clusters, nonoverlapping small clusters, and overlapping mini-clusters. Finally, the socially optimal cluster size is larger than the equilibrium cluster size, and the gap between these two cluster sizes decreases in the valuation-cost ratio.