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The article argues that international business research can make substantial progress by adopting algorithmic ML techniques alongside traditional probabilistic statistics. Benefits include improved predictive accuracy, better empirical handling of complexity, and strengthened theory development. 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(2024) . Machine learning in international business. Journal of International Business Studies, 55(6), 676-702 . [https://doi.org/10.1057/s41267-024-00687-6](https://doi.org/10.1057/s41267-024-00687-6)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal  \nTake down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nE-mail address:  \n[vuresearchportal.ub@vu.nl](vuresearchportal.ub@vu.nl)  \n[Download date: 01](Download date: 01) . Oct. 2025  \nJournal of International Business Studies (2024) 55:676–702  \n[https://doi.org/10.1057/s41267-024-00687-6](https://doi.org/10.1057/s41267-024-00687-6)  \nMachine learning in international business  \nBas Bosma1 · Arjen van Witteloostuijn1,2  \nReceived: 2 December 2022 / Revised: 23 October 2023 / Accepted: 29 November 2023 / Published online: 19 March 2024 © Academy of International Business 2024  \nAbstract  \nIn the real world of international business, machine learning (ML) is well established as an essential element in many operations, from finance and logistics to marketing and strategy. However, ML as an analytical tool is still far from widespread in international business (IB) as a science. In this article, we offer arguments as to why this should change by providing illustrative analyses with simulated and real data. We argue that IB as a research community could produce substantial progress if algorithmic ML techniques were adopted as part of the standard analytical toolkit, next to traditional probabilistic statistics. This is not only so because ML improves predictive accuracy but also because doing so would permit empirically addressing complexity and facilitate theory development in IB that does justice to the complex world of international businesses. Along the way, we provide tips and tricks by way of practical tutorial, all relating to a typical ML process pipeline.  \nKeywords Machine learning · Complexity · Inductive quantitative methodology · Hypothesis testing · Prediction · Internationalization  \nIndustrial Revolution 4.0  \nArtificial intelligence, big data, digitalization, and ML are all manifestations of the technological innovation that many believe pushed the world toward an Industrial Revolution 4.0 (see, e.g. , Mayer-Schönberger & Cukier, 2013) . ML isan Industrial Revolution 4.0 innovation that is potentially highly relevant from the perspective of IB as an empirical science, too. In IB’s quantitative research tradition, we routinely apply traditional probabilistic statistics, which is our dominant analytical toolkit. Although traditional statistics can be non-parametric, our main workhorse is the parametric estimation of multivariate models aimed at deductively testing hypotheses regarding main (linear or non-linear),  \nAccepted by Herman Aguinis, Area Editor, 29 November 2023. This article has been with the authors for t","cbCaigWIWF8C0pZA","https://ap.wps.com/l/cbCaigWIWF8C0pZA","pdf",5743117,28,"English","# Abstract\n# Keywords\n# Industrial Revolution 4.0\n# Machine learning in international business","[{\"question\":\"Why should international business (IB) adopt machine learning techniques?\",\"answer\":\"The article argues adoption would improve predictive accuracy and enable empirically addressing complexity, supporting more adequate theory development in IB. It also positions ML as part of the standard analytical toolkit alongside traditional probabilistic statistics.\"},{\"question\":\"How does the article define machine learning in this context?\",\"answer\":\"ML is described as a field that enables computers to learn patterns in data without explicitly programming the data generation process. It relies on flexible algorithms that gradually improve predictive accuracy.\"},{\"question\":\"What are the main tensions between ML and the deductive NHST tradition?\",\"answer\":\"The article highlights that many ML algorithms are relatively opaque, making the mapping from inputs to outputs hard to identify in the familiar detail used in NHST. It also stresses that prediction differs from explanation.\"}]","Machine learning in international business - Research article - tips and tricks for an ML process pipeline | PDF",71]