Demonstrates how Monte Carlo simulations improve statistical methods in strategic management research, suggesting new evaluation techniques.
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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Certo et al. (2026) studied this question.
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