Research Interests:
Applied AI, Supply Chain Network, Innovation, Risk Management, E-commerce
Panel Models, Social Network Analysis, Network Optimization, Machine Learning
Book Chapters (* correspondence):
Li, Y.*, Chen, K., Zhang, J. (2025). AI-Powered Supply Chain and Operations Management (SCOM): Capabilities and Challenges. In: Ivanov, D., Dolgui, A., Sokolov, B. (eds) Handbook of Ripple Effects in the Supply Chain. International Series in Operations Research & Management Science, vol 276. Springer, Cham. https://doi.org/10.1007/978-3-031-85508-5_7 [read]
Journal Articles (* correspondence):
Chen, K., Mani, A.*, Linderman, K., Wang, B. (2025). Where to Invest in Resilience in a Facility Network? Production and Operations Management. https://doi.org/10.1177/10591478251363447 [read]
Chen, K., Su, H. C., Linderman, K.*, Li, W. (2025). Last-Minute Coordination: Adapting to Demand to Support Last-Mile Operations. Journal of Operations Management, 71(2), 176-194. https://doi.org/10.1002/joom.1297 [read]
Winner, 2023 AOM Operations and Supply Chain Management Division Chan Hahn Best Paper Award
Li, Y., Chai, X., Su, R.*, Chen, K. (2025). Impact of Accelerated Depreciation Policies for Fixed Assets on Firms' Green Innovation Performance. Finance Research Letters. 74, 106816. https://doi.org/10.1016/j.frl.2025.106816 [read]
Yang, H., Chen, Y., Chen, K., Wang, H.* (2024). Temporal-spatial dependencies enhanced deep learning model for time series forecast. International Review of Financial Analysis. 94, 103261. https://doi.org/10.1016/j.irfa.2024.103261 [read]
Yang, H., Zhang, Y., Chen, K.*, Li, J. (2023). The double-edged sword of delivery guarantee in E-commerce. Decision Support Systems, 175, 114042. https://doi.org/10.1016/j.dss.2023.114042 [read]
Chen, K., Liu, X., Li, Y., Linderman, K.* (2023). Government support and cross-border innovation: The effect of China's innovative city policy on Chinese firms' patenting in the United States. Production and Operations Management, 32(6), 1793-1811. https://doi.org/10.1111/poms.13941 [read]
Runner-Up, 2022 INFORMS Technology, Innovation Management, and Entrepreneurship Section (TIMES) Best Working Paper Award
Chen, K., Li, Y., Linderman, K.* (2022). Supply Network Resilience Learning: An Exploratory Data Analytics Study. Decision Sciences, 53(1), 8-27. https://doi.org/10.1111/deci.12513 [read]
Li, Y., Chen, K., Collignon, S., Ivanov, D.* (2021). Ripple Effect in the Supply Chain Network: Forward and Backward Disruption Propagation, Network Health and Firm Vulnerability. European Journal of Operational Research, 291(3), 1117-1131. https://doi.org/10.1016/j.ejor.2020.09.053 [read]
Chen, K., Li, W.*, Wang, S. (2020). An Easy-to-Implement Hierarchical Standardization for Variable Selection Under Strong Heredity Constraint. Journal of Statistical Theory and Practice, 14(38). https://doi.org/10.1007/s42519-020-00102-x [read]
Working Papers:
Marrying Theory and Practice: Product Network-Inspired Deep Learning for Sales Forecasting. With Hu Yang and Teng Huang. https://papers.ssrn.com/abstract=5118746
Abstract: Problem definition: Accurate sales forecasting is increasingly critical in operations management (OM), particularly in e-commerce operations. While deep learning (DL) techniques improve predictive accuracy, they are often criticized for their "black-box" nature and lack of connection to OM theoretical foundations. This study explores how integrating OM theories and domain knowledge into DL can improve forecast accuracy. Methodology/results: We propose a novel DL approach, Product Network-enhanced Sales Forecasting (PNet-SF), inspired by substitutable and complementary product relationships modeled as product networks. PNet-SF integrates a long short-term memory layer, a graph convolution layer, and an attention layer, allowing it to capture both historical time series patterns and interdependencies among products. Empirical analysis using data from JD.com demonstrates its effectiveness and performance superiority over conventional DL models that do not incorporate product networks. We further show that PNet-SF is robust and generalizable. Managerial implications: This study contributes to the OM literature by demonstrating the value of integrating OM theories and domain knowledge into DL for sales forecasting. The proposed PNet-SF approach is easy to implement with enhanced interpretability and provides implications for e-commerce platforms regarding what information to share with merchants to improve operational effectiveness.
Efficiency-Innovation Trade-off in Creative Industries: Team-Level Empirically Grounding Analytics. With Yi-Su Chen and Yuhong Li. https://papers.ssrn.com/abstract=4616121
Finalist, 2024 POMS College of Operational Excellence Junior Scholar Best Paper Award
Abstract: In creative industries, new products often originate from creators who coalesce temporarily to work in teams. These teams must efficiently deliver innovative offerings to remain competitive. This challenge of balancing efficiency and innovation has been well-documented at the organizational level, but it has yet to be examined at the team level, especially in creative industries where creative teams form organically without organizational intervention. This study focuses on board game design teams and investigates the relationship between team characteristics and team performance. By applying empirically grounded analytical methods to data retrieved from BoardGameGeek.com, we evaluate the efficiency of these design teams and reveal a trade-off between efficiency and innovation. Drawing on coordination theory and the knowledge sourcing perspective, we explore two pivotal team characteristics at the heart of this trade-off: staff and structure, operationalized as team size and average centrality, respectively. Both of these aspects have a dampening effect on efficiency but foster innovation. This research is among the first to empirically investigate project-based board game design teams without organizational intervention, validating the well-documented efficiency-innovation trade-off at the team level and offering insights into the underlying mechanisms. The findings provide managerial implications for team design and team operations within creative industries.