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

Valizade, Danat ; Schulz, Felix LU orcid and Nicoara, Cezara (2024) In British Journal of Management 35. p.99-114
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... (More)
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. (Less)
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author
; and
publishing date
type
Contribution to journal
publication status
published
subject
in
British Journal of Management
volume
35
pages
99 - 114
publisher
Wiley-Blackwell
external identifiers
  • scopus:85142253414
ISSN
1467-8551
DOI
10.1111/1467-8551.12678
language
English
LU publication?
no
id
70f30f18-6007-4835-9025-4c534f1ac6c1
date added to LUP
2025-01-27 16:31:24
date last changed
2025-04-22 10:05:22
@article{70f30f18-6007-4835-9025-4c534f1ac6c1,
  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.}},
  author       = {{Valizade, Danat and Schulz, Felix and Nicoara, Cezara}},
  issn         = {{1467-8551}},
  language     = {{eng}},
  month        = {{01}},
  pages        = {{99--114}},
  publisher    = {{Wiley-Blackwell}},
  series       = {{British Journal of Management}},
  title        = {{Towardsa Paradigm Shift: How Can Machine Learning Extend the Boundaries of Quantitative Management Scholarship?}},
  url          = {{http://dx.doi.org/10.1111/1467-8551.12678}},
  doi          = {{10.1111/1467-8551.12678}},
  volume       = {{35}},
  year         = {{2024}},
}