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  • January 2021
  • Article
  • Strategic Management Journal

Machine Learning for Pattern Discovery in Management Research

By: Prithwiraj Choudhury, Ryan Allen and Michael G. Endres
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Abstract

Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used for exploratory inductive or abductive research, or for post-hoc analysis of regression results to detect patterns that may have gone unnoticed. However, ML models should not be treated as the result of a deductive causal test. To demonstrate the application of ML for pattern discovery, we implement ML algorithms to study employee turnover at a large technology company. We interpret the relationships between variables using partial dependence plots, which uncover surprising nonlinear and interdependent patterns between variables that may have gone unnoticed using traditional methods. To guide readers evaluating ML for pattern discovery, we provide guidance for evaluating model performance, highlight human decisions in the process, and warn of common misinterpretation pitfalls. An online appendix provides code and data to implement the algorithms demonstrated in the paper.

Keywords

Machine Learning; Supervised Machine Learning; Induction; Abduction; Exploratory Data Analysis; Pattern Discovery; Decision Trees; Random Forests; Neural Networks; ROC Curve; Confusion Matrix; Partial Dependence Plots; AI and Machine Learning

Citation

Choudhury, Prithwiraj, Ryan Allen, and Michael G. Endres. "Machine Learning for Pattern Discovery in Management Research." Strategic Management Journal 42, no. 1 (January 2021): 30–57.
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About The Author

Prithwiraj Choudhury

Technology and Operations Management
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  • Location-Specificity and Relocation Incentive Programs for Remote Workers By: Thomaz Teodorovicz, Prithwiraj Choudhury and Evan Starr
  • Enerjisa Üretim: The Digital Era of Electricity Generation By: Prithwiraj Choudhury and Sadika El Hariri
  • Loss of Peers and Individual Worker Performance: Evidence From H-1B Visa Denials By: Prithwiraj Choudhury, Kirk Doran, Astrid Marinoni and Chungeun Yoon
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