My research leverages empirical data to study the societal and economic impact of emerging phenomena fueled by technology innovations in online platforms, including e-commerce, social media, gig economy, and digital health platforms.

Beyond empirical research, I have a deep interest in developing methodologies to address common statistical challenges facing empirical researchers, such as sample selection in panel count data and endogeneity of mediators in mediation analysis. Lately, my research on methodology has been dedicated to exploring the opportunities and challenges of applying machine learning in causal inference.


  1. Jing Peng and Chen Liang (2023) On the Differences Between View-Based and Purchase-Based Recommender Systems. MIS Quarterly, 47(2):875-900.
  2. Chen Liang, Jing Peng, YiliHong, and Bin Gu (2023) The Hidden Costs and Benefits of Monitoring in the Gig Economy. Information Systems Research, 34(1):297-318.
  3. Hongfei Li, Jing Peng, Xinxin Li, and Jan Stallaert (2023) When More Can Be Less: The Effect of Add-on Insurance on the Consumption of Professional Services. Information Systems Research, 34(1):363-382.
  4. Yili Hong, Jing Peng, Gordon Burtch, and Ni Huang (2021) Just DM Me (Politely): Direct Messaging, Politeness, and Hiring Outcomes in Online Labor Markets. Information Systems Research, 32(3): 675-1097.
  5. Shu He, Jing Peng, Jianbin Li, and Liping Xu (2020) Impact of Platform Owner’s Entry on Third-Party Stores. Information Systems Research, 31(4): 1467-1484.
  6. Zhu Zhang, Daniel Zeng, Ahmed Abbasi, Jing Peng, and Xiaolong Zheng (2013) A Random Walk Model for Item Recommendation in Social Tagging Systems. ACM Transactions on Management Information Systems, 4(2): 1-24.
  7. Jing Peng, Daniel Zeng, and Zan Huang (2011) Latent Subject-centered Modeling of Collaborative Tagging: An Application in Social Search. ACM Transactions on Management Information Systems, 2(3): 1-23.

Selected Working Papers