Machine learning in international business

Abstract

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.