Publication
Title
Machine learning in international business
Author
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.
Language
English
Source (journal)
Journal of international business studies / Academy of International Business [East Lansing, Mich.] - Newark, N.J., 1970, currens
Publication
Basingstoke : Palgrave macmillan ltd , 2024
ISSN
0047-2506 [print]
1478-6990 [online]
DOI
10.1057/S41267-024-00687-6
Volume/pages
55 :6 (2024) , p. 676-702
ISI
001187469600001
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 02.05.2024
Last edited 21.08.2024
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