Primer on using Monte Carlo simulations to evaluate research methodology in strategic management

Strategic management scholars increasingly rely on diverse research methodologies to investigate the field’s central questions. In this paper, the authors advance knowledge by demonstrating how Monte Carlo simulations (MCSs) can serve as an essential tool for evaluating and enhancing these techniques. To do so, the authors first overview strategy research that has used MCSs to adjudicate statistical or econometric techniques. The authors then describe the intuition and main steps for specifying and using MCSs, which involve defining the research question, designing a data generation process, analyzing data and summarizing/communicating results. To facilitate the implementation of MCSs, the authors developed a custom generative pretrained transformer that enables researchers to produce the code (R, Stata and/or Excel) necessary to design and execute MCSs. The authors close with a research agenda for strategy scholars interested in using MCSs to advance their own methodological pursuits.

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Publication Details

Journal
Strategic Management Review
Published
2026-06-30
DOI
https://doi.org/10.1108/smr-05-2025-1018
Primary Topic
Qualitative Comparative Analysis Research
Type
article
Field-Weighted Citation Impact
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article

Primer on using Monte Carlo simulations to evaluate research methodology in strategic management

John R. Busenbark, Chi Hon Li, Michael Withers, S. Trevis Certo
Strategic Management Review
Qualitative Comparative Analysis Research
article

Primer on using Monte Carlo simulations to evaluate research methodology in strategic management

John R. Busenbark, Chi Hon Li, Michael Withers, S. Trevis Certo
article en

Abstract

Strategic management scholars increasingly rely on diverse research methodologies to investigate the field’s central questions. In this paper, the authors advance knowledge by demonstrating how Monte Carlo simulations (MCSs) can serve as an essential tool for evaluating and enhancing these techniques. To do so, the authors first overview strategy research that has used MCSs to adjudicate statistical or econometric techniques. The authors then describe the intuition and main steps for specifying and using MCSs, which involve defining the research question, designing a data generation process, analyzing data and summarizing/communicating results. To facilitate the implementation of MCSs, the authors developed a custom generative pretrained transformer that enables researchers to produce the code (R, Stata and/or Excel) necessary to design and execute MCSs. The authors close with a research agenda for strategy scholars interested in using MCSs to advance their own methodological pursuits.

Strategic Management Review
University of Notre Dame (US), Tilburg University (NL), Arizona State University (US)
Openalex Percentile: Top 4%
Qualitative Comparative Analysis Research
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