Areas of Expertise

Impact of IT on Organizations, Platforms, Social Networks, Machine Learning, Social Media

Academic Degrees

• Ph.D. in Operations, Information and Decisions, The Wharton School, University of Pennsylvania, 2016
• B.A. in Finance, Peking University, 2011
• B.S. in Statistics, Peking University, 2011

Awards, Honors & Certificates

• ODT Research Scholar, 2022
• Winner of FACET Mumford Excellence in Extraordinary Teaching, 2021
• 3M Non-tenured Faculty Fellowship, 2020, 2019, 2018, 2017
• Indiana University Trustees Teaching Award Finalist, 2023, 2020
• CIBER Grant, 2020
• Kelley School of Business, Innovative Teaching Award Finalist, 2020
• Wharton Risk Center Russell Ackoff Doctoral Student Fellowship, 2016, 2015
• William and Phyllis Mack Institute for Innovation Management Research Grant, 2015
• Baker Retail Center PhD Research Grant, 2011

Selected Publications

• Jin, F., Cheng, Y., Li, X., and Hu, Y. J. (2025). Connecting the Last Mile: Empirical Study on the Impact of Dockless Bike-sharing on Public Transportation. Production and Operations Management, 34(12), 3904-3919.
• Wang, T., Jin, F., Hu, Y. J., Feng, L., and Cheng, Y. (2023). Making Early and Accurate Deep Learning Predictions to Help Disadvantaged Individuals in Medical Crowdfunding, Production and Operations Management, in press.
• Hur, Y., Jin, F., Li, X., Cheng, Y., and Hu, Y. J. (2023). Does Social Influence Change with Other Information Sources? A Large-Scale Randomized Experiment in Medical Crowdfunding. Information Systems Research, 34(4), 1476-1492.
• Chen, W., Jin, F., and Ling, X. (2022). Flourish or Perish? The Impact of Mergers and Acquisitions on Contributions to Open-Source Software Projects. Information Systems Research, 33(3), 867-886.
• Tan, X., Jin, F., and Dennis, A. R. (2022). How Appreciation and Attention Affect Contributions to Electronic Networks of Practice? Journal of Management Information Systems, 39(4), 1037-1063.
• Wang, T., He, C., Jin, F., and Hu, Y.J. (2022). Evaluating the Effectiveness of Marketing Campaigns for Malls Using A Novel Interpretable Machine Learning Model. Information Systems Research, 33(2), 659–677.
• Wu, L., Jin, F., and Hitt, L. M. (2018). Are all spillovers created equal? A network perspective on information technology labor movements. Management Science, 64(7), 3168-3186 

  Abstract

  Abstract

  This study examines how characteristics of a firm’s labor-flow network affect firm productivity. Using employee job histories, Fujie and her colleagues construct inter-firm labor-flow networks for both IT-labor and non-IT labor and analyze how a firm’s network structure for the two types of labor affects firm performance. They find that hiring IT workers from a structurally-diverse network of firms can substantially improve firm productivity, but the same is not true for hiring non-IT labor. These results demonstrate the importance of incorporating a network perspective in understanding the full impact of spillover effects from organizational hiring activities.

• Clemons, E. K., Wilson, J., Matt, C., Hess, T., Ren, F., Jin, F., and Koh, N. S. (2016). Global Differences in Online Shopping Behavior: Understanding the Factors that Lead to Trust. Journal of Management Information Systems, 33(4), 1117-1148.

Edited on December 5, 2025