Hailiang Chen
Prof. Hailiang CHEN
Innovation and Information Management
Assistant Dean (Taught Postgraduate)
Professor in Innovation and Information Management

3917 0016

KK 840

Academic & Professional Qualification
  • PhD, Purdue University
  • MS, Purdue University
  • BM, Tsinghua University

Hailiang Chen is interested in the research areas of social media, fintech, artificial intelligence, business analytics, venture capital, entrepreneurship, mobile and social commerce, economics of information systems, and design science. His research has been published in elite business journals in information systems, finance, and management, including Information Systems Research (ISR), Journal of Financial Economics (JFE), Journal of Management Information Systems (JMIS), Management Science (MS), Review of Financial Studies (RFS), and Strategic Management Journal (SMJ). His research received media coverage in outlets such as Wall Street Journal, Forbes, New York Times, Reuters, Seeking Alpha, TechSpot, and so on.

  • Business Analytics
  • Social Media
  • FinTech
  • Capstone Project
Selected Publications
  • Yu, Yinan, Liangfei Qiu, Hailiang Chen, Benjamin P. C. Yen. 2023. Movie Fit Uncertainty and Interplay between Traditional Advertising and Social Media Marketing. Marketing Letters 34(3) 429–448.
  • Xu, Ruiyun Rayna, Hailiang Chen, J. Leon Zhao. 2023. SocioLink: Leveraging Relational Information in Knowledge Graphs for Startup Recommendations. Journal of Management Information Systems 40(2) 655-682.
  • Chen, Hailiang, Yifan Dou, Yongbo Xiao. 2023. Understanding the Role of Live Streamers in Live-Streaming E-Commerce. Electronic Commerce Research and Applications 59, 101266.
  • Chen, Hailiang, Byoung-Hyoun Hwang. 2022. Listening in on Investors’ Thoughts and Conversations. Journal of Financial Economics 145(2) 426-444.
  • Yu, Yinan, Hailiang Chen, Chih-Hung Peng, Patrick Y.K. Chau. 2022. The Causal Effect of Subscription Video Streaming on DVD Sales: Evidence from a Natural Experiment. Decision Support Systems 157, 113767.
  • Clarke, Jonathan, Hailiang Chen, Ding Du, Yu Jeffrey Hu. 2021. Fake News, Investor Attention, and Market Reaction. Information Systems Research 32(1) 35-52.
  • Xie, Peng, Hailiang Chen, Yu Jeffrey Hu. 2020. Signal or Noise in Social Media Discussions: The Role of Network Cohesion in Predicting the Bitcoin Market. Journal of Management Information Systems 37(4) 933-956.
  • Chen, Hailiang, Yu Jeffrey Hu, Shan Huang. 2019. Monetary Incentive and Stock Opinions on Social Media. Journal of Management Information Systems 36(2) 391-417.
  • Chen, Hailiang, Yu Jeffrey Hu, Michael D. Smith. 2019. The Impact of E-book Distribution on Print Sales: Analysis of a Natural Experiment. Management Science 65(1) 19-31.
  • Akcura, Tolga, Kemal Altinkemer, Hailiang Chen. 2018. Noninfluentials and Information Dissemination in the Microblogging Community. Information Technology and Management 19(2) 89-106.
  • Lee, Joon Mahn, Byoung-Hyoun Hwang, Hailiang Chen. 2017. Are Founder CEOs more Overconfident than Professional CEOs? Evidence from S&P 1500 Companies. Strategic Management Journal 38(3) 751-769.
  • Chen, Hailiang, Prabuddha De, Yu Jeffrey Hu. 2015. IT-Enabled Broadcasting in Social Media: An Empirical Study of Artists’ Activities and Music Sales. Information Systems Research 26(3) 513-531.
  • Chen, Hailiang, Prabuddha De, Yu Jeffrey Hu, Byoung-Hyoun Hwang. 2014. Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media. Review of Financial Studies 27(5) 1367-1403.
  • Chen, Hailiang, Hongyan Liu, Jiawei Han, Xiaoxin Yin, Jun He. 2009. Exploring Optimization of Semantic Relationship Graph for Multi-relational Bayesian Classification. Decision Support Systems 48(1) 112-121.
Awards and Honours
  • Faculty Outstanding Researcher Award, Faculty of Business and Economics, The University of Hong Kong, 2022-23
  • INFORMS Information System Society (ISS) Sandra A. Slaughter Early Career Award, 2022
  • General Research Fund, Research Grants Council of Hong Kong, five consecutive years (2019, 2020, 2021, 2022, and 2023)
  • Essential Science Indicators’ (ESI) Highly Cited Paper (Top 1% in the field of Social Sciences, General), 2021
  • Association for Information Systems (AIS) Early Career Award, 2019
  • Essential Science Indicators’ (ESI) Highly Cited Paper (Top 1% in the field of Economics & Business), 2014
Service to the University / Community
  • Program Director, Master of Science in Business Analytics, HKU Business School, 2020-2023
  • Program Chair, International Conference on Smart Finance (ICSF), 2021 and 2022
  • Associate Editor, Journal of Management Information Systems (JMIS), Special Issue on Fake News, 2020
  • Associate Editor, MIS Quarterly, Special Issue on Managing AI, 2019
  • Associate Editor, Information Systems Research, Special Issue on FinTech, 2018
Recent Publications
SocioLink: Leveraging Relational Information in Knowledge Graphs for Startup Recommendations

While venture capital firms are increasingly relying on recommendation models in investment decisions, existing startup recommendation models fail to consider the uniqueness of venture capital context, including two-sided matching between investing and investee firms and a lack of information disclosure requirements on startups. Following the design science research paradigm and guided by the proximity principle from social psychology, we develop a novel framework called SocioLink by depicting and analyzing various relations in a knowledge graph via machine learning. Our experimental results show that SocioLink significantly outperforms state-of-the-art startup recommendation methods in both accuracy and quality. This improvement is driven by not only the inclusion of social relations but also the superiority of modelling relations via knowledge graph. We also develop a web-based prototype to demonstrate explainable artificial intelligence. This work contributes to the FinTech literature by adding an innovative design artifact—SocioLink—for decision support in the investment context.

Listening in on Investors’ Thoughts and Conversations

A large literature in neuroscience and social psychology shows that humans are wired to be meticulous about how they are perceived by others. In this paper, we propose that impression management considerations can also end up guiding the content that investors transmit via word of mouth and inadvertently lead to the propagation of noise. We analyze server log data from one of the largest investment-related websites in the United States. Consistent with our proposition, we find that investors more frequently share articles that are more suitable for impression management despite such articles less accurately predicting returns. Additional analyses suggest that high levels of sharing can lead to overpricing.


汇纳科技正在为整个实体商业构建一个新的基础坐标系。 对商业地产来说,数字化转型早已是大势所趋且探索已久,而今年的疫情黑天鹅更使这一进程加快了。 一直以来,数字化转型最大的难度就是思想意识上的改变,疫情改变了这一现状。数字化好处在于,企业在面临疫情影响的时候,业务波动性会比较低。 疫情期间对居民外出的限制,为实体渠道带来严重冲击。如果说此前的数字化转型对商业地产来说是锦上添花,那如今已是迫在眉睫。商业地产开始思索这场危机给行业带来的改变。

Signal or Noise in Social Media Discussions: The Role of Network Cohesion in Predicting the Bitcoin Market

Prior studies have shown that social media discussions can be helpful in predicting price movements in financial markets. With the increasingly large amount of social media data, how to effectively distinguish value-relevant information from noise remains an important question. We study this question by investigating the role of network cohesion in the relationship between social media sentiment and price changes in the Bitcoin market. As network cohesion is associated with information correlation within the discussion network, we hypothesize that less cohesive social media discussion networks are better at predicting the next-day returns than more cohesive networks. Both regression analyses and trading simulations based on data collected from Bitcointalk.org confirm our hypothesis. Our findings enrich the literature on the role of social media in financial markets and provide actionable insights for investors to trade based on social media signals.

The Impact of E-book Distribution on Print Sales: Analysis of a Natural Experiment

Digital distribution introduces many new strategic questions for the creative industries—notably, how the use of new digital channels will impact sales in established channels. We analyze this question in the context of e-book and hardcover sales by exploiting a natural experiment that exogenously delayed the release of a publisher’s new Kindle e-books in April and May 2010. Using new books released simultaneously in e-book and print formats in March and June 2010 as the control group, we find that delaying e-book availability results in a 43.8% decrease in e-book sales but no increase in print book sales on Amazon.com or among other online or offline retailers. We also find that the decrease in e-book sales is greater for books with less prerelease buzz. Together, we find no evidence of strong cannibalization between print books and e-books in the short term and no support for the sequential distribution of books in print versions followed by e-book versions.