Towards a Paradigm Shift: How Can Machine Learning Extend the Boundaries of Quantitative Management Scholarship?

Abstract

Abstract Management scholarship is beginning to grapple with the growing popularity of machine learning (ML) as an analytical tool. While quantitative research in our discipline remains heavily influenced by positivist thinking and statistical modelling underpinned by null hypothesis significance testing, ML is increasingly used to solve technical, computationally demanding problems. In this paper, we argue for a wider, more systematic adoption of the key tenets of ML in quantitative management scholarship, both in conjunction with and, where appropriate, as an alternative to canonical forms of statistical modelling. We discuss how ML can extend the boundaries of quantitative management scholarship, help management scholars to unpack complex phenomena, and improve the overall trustworthiness of quantitative research. The paper provides a representative review of the use of ML to date and uses a worked example to demonstrate the value of ML for management scholarship.

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