AOM IM Division Research Methods Workshop
Machine Learning in IB Research
8 am (EST) on July 24, 2024
· 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
· Noman Shaheer, University of Sydney
· Liang Chen, Singapore Management University
· Min Jung Kim, University of Illinois at Urbana-Champaign
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 would algorithmic ML techniques be 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 tutorial, 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.
Bas Bosma is a Professor of Complex Adaptive Systems at the School of Business and Economics of the Vrije Universiteit Amsterdam. His research interests include the complexity of institutions and the application of machine learning and artificial intelligence to uncover non-classical patterns in economics, business, and politics.
Arjen van Witteloostuijn is a Professor of Business and Economics at the Vrije Universiteit (VU) Amsterdam and Dean of the VU School of Business and Economics in the Netherlands, as well as a Research Professor in Business, Economics and Governance at the Antwerp Management School in Belgium. He is a member of the editorial team of the British Journal of Management, Industrial and Corporate Change, and the Journal of International Business Studies. He has published widely in such international journals as the Academy of Management Journal, Academy of Management Review, Accounting, Organizations & Society, American Journal of Political Science, American Journal of Sociology, American Sociological Review, Economica, Industrial Relations, International Journal of Industrial Organization, Journal of International Business Studies, Journal of Management, Journal of Management Studies, Journal of Public Administration Research and Theory, Management Science, Organization Science, Public Administration Review, and Strategic Management Journal. He is AIB Fellow.
Once you've signed up, please be sure to keep an eye on your email inbox. We look forward to seeing you online soon!
------------------------------
Min Jung Kim
University of Illinois at Urbana-Champaign
------------------------------