Machine learning in action

Panelists:
· Arjen van Witteloostuijn, Dean and Professor of Business and Economics, Vrije Universiteit (VU) Amsterdam
· Bas Bosma, Professor of Complex Adaptive Systems, School of Business and Economics, Vrije Universiteit Amsterdam
Organizers:
· Noman Shaheer, University of Sydney
· Liang Chen, Singapore Management University
· Min Jung Kim, University of Illinois at Urbana-Champaign
Workshop Description
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 workshop, we introduce ML in two steps. First, 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 are 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 to empirically address 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 tutorials, all relating to a typical ML process pipeline. Second, because many believe that ML's strengths come at the cost of explanatory insights, which form the basis for theorization, we explain how ML can have a place in a full empirical research cycle. When used as a part of a full research process, including inductive, deductive, and abductive steps, machine learning can offer explanatory insights and provide a solid basis for theorization. We present a systematic five-step theory-building and theory-testing cycle that consists of 1. Element identification (reduction); 2. Exploratory analysis (induction); 3. Hypothesis development (retroduction); 4. Hypothesis testing (deduction); and 5. Theorization (abduction). We demonstrate the usefulness of this approach, which we refer to as co-duction, in a vignette where we study firm growth with real-world observational data.