In 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.

在国际商业的现实世界中，机器学习 (ML) 已成为从金融和物流到营销和战略等许多运营中的基本要素。然而，机器学习作为一种分析工具在国际商务 (IB) 作为一门科学中还远未得到普及。在本文中，我们通过提供模拟数据和真实数据的说明性分析来论证为什么这种情况应该改变。我们认为，如果采用算法机器学习技术作为标准分析工具包的一部分（除了传统的概率统计），IB 作为一个研究社区可以取得实质性进展。这不仅是因为机器学习提高了预测准确性，还因为这样做可以凭经验解决复杂性并促进 IB 的理论发展，从而公正地对待复杂的国际企业世界。在此过程中，我们通过实用教程提供提示和技巧，所有这些都与典型的 ML 流程管道相关。