OM Forum—The Best of Both Worlds: Machine Learning and Behavioral Science in Operations Management
- 1 September 2024
- journal article
- research article
- Published by Institute for Operations Research and the Management Sciences (INFORMS) in Manufacturing & Service Operations Management
- Vol. 26 (5) , 1605-1621
Abstract
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.Keywords
This publication has 65 references indexed in Scilit:
- Artificial intelligence, ethics, and intergenerational responsibilityJournal of Economic Behavior & Organization, 2022
- Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysisJournal of Behavioral and Experimental Finance, 2021
- Sentiment Analysis2020
- A systematic review of algorithm aversion in augmented decision makingJournal of Behavioral Decision Making, 2019
- Machine Learning in MedicineNew England Journal of Medicine, 2019
- Deep learning for sentiment analysis: A surveyWIREs Data Mining and Knowledge Discovery, 2018
- A User's Guide to Debiasing2015
- Deep learningNature, 2015
- Algorithm aversion: People erroneously avoid algorithms after seeing them err.Journal of Experimental Psychology: General, 2015
- Experimental research in financial accountingAccounting, Organizations and Society, 2002