The Best of Both Worlds: Machine Learning and Behavioral Science in Operations Management

• 成果类型: Article
• 署名作者: Davis, Andrew M.; Mankad, Shawn; Corbett, Charles J.; Katok, Elena
• 署名单位: Cornell University; North Carolina State University; University of California System; University of California Los Angeles; University of Texas System; University of Texas Dallas
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.2022.0553
• 发表日期: 2024
• 页码: 1605-1621
• 关键词: Operations Management; Machine Learning; behavioral science

摘要

Problem definition: : Two disciplines increasingly applied in operations management (OM) are machine learning (ML) and behavioral science (BSci). Rather than treating these as mutually exclusive fields, we discuss how they can work as complements to solve important OM problems. Methodology/results: : We illustrate how ML and BSci enhance one another in non-OM domains before detailing how each step of their respective research processes can benefit the other in OM settings. We then conclude by proposing a framework to help identify how ML and BSci can jointly contribute to OM problems. Managerial implications: : Overall, we aim to explore how the integration of ML and BSci can enable researchers to solve a wide range of problems within OM, allowing future research to generate valuable insights for managers, companies, and society.