Operations and Information Management
Ph.D. in Operations, Information and Decisions, University of Pennsylvania
M.E. in Computer Science, Chinese Academy of Sciences
B.S. in Applied Physics, Xi’an Jiaotong University
Areas of Expertise
Econometrics, E-Commerce, Social Media, Gig Economy, Online Healthcare
- Chen Liang, Jing Peng, YiliHong, and Bin Gu. (2022). The Hidden Costs and Benefits of Monitoring in the Gig Economy. Information Systems Research (forthcoming).
- Hongfei Li, Jing Peng, Xinxin Li, and Jan Stallaert. (2022). When More Can Be Less: The Effect of Add-on Insurance on the Consumption of Professional Services. Information Systems Research (forthcoming).
- Jing Peng. (2022). Identification of Causal Mechanisms from Randomized Experiments: A Framework for Endogenous Mediation Analysis. Information Systems Research (forthcoming).
- Jing Peng, Julie Zhang, and Ram Gopal. (2022). The Good, the Bad, and the Social Media: Financial Implications of Social Media Reactions to Firm-Related News. Journal of Management Information Systems (forthcoming).
- 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.
- 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.
- Jing Peng, Ashish Agarwal, Kartik Hosanagar, and Raghuram Iyengar. (2018). Network Overlap and Content Sharing on Social Media Platforms. Journal of Marketing Research, 55(4), p. 571-585.
I have a keen interest in developing novel econometric methods and have contributed three R packages to CRAN.
- CoxPlus: A fast and highly scalable R package (core code written in C++) estimating Cox model (proportional hazards model) when an event has more than one cause. It also supports random and fixed effects, tied events, and time-varying variables. This package can be used in multi-channel advertising, social influence identification, etc.
- PanelCount: A fast and scalable R package (speeded up by C++) implementing random effects and/or sample selection models for panel count data. This package can be applied to marketing campaigns with self-selection and repeated observations.
- endogeneity: A comprehensive package that implements various recursive two-stage models to address the endogeneity issue in the absence of instrumental variables. This package can be used to address the endogeneity of mediators in randomized experiments or treatment variables in observational studies.
OPIM 6201 – Research Methods for Operations and Information Management (focus on Econometrics)
OPIM 5671 – Data Mining and Business Intelligence
OPIM 5512 – Data Science using Python