How Artificial Intelligence can be used in International Human Resources Management: A Case Study

Lutz Sommer ()
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Lutz Sommer: Albstadt-Sigmaringen University, Jakobstr. 1-6, 72458, Albstadt, Germany Author-2-Name: Author-2-Workplace-Name: Author-3-Name: Author-3-Workplace-Name: Author-4-Name: Author-4-Workplace-Name: Author-5-Name: Author-5-Workplace-Name: Author-6-Name: Author-6-Workplace-Name: Author-7-Name: Author-7-Workplace-Name: Author-8-Name: Author-8-Workplace-Name:

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Abstract: " Objective - Artificial Intelligence (AI) tools are becoming more accessible and more manageable in terms of practical implementation, enabling them to be used in many new areas, including the selection of international managers based on their international experience. The choice of personnel in a global environment is a challenge that has been the subject of heated debate for decades, both in practice and theory. Wrong decisions are cost-intensive and possibly contribute to economic failure. The present study aimed to test machine learning algorithms - as sub-disciplines of Artificial Intelligence (AI) - on a low-coding basis. Methodology/Technique - A fictitious use case with a corresponding data set of 75 managers was generated for this purpose. Its applicability in relation to personnel selection for an international task was tested. In the next step, selected AI algorithms were used to test which of these algorithms led to high prediction accuracy. Finding - The results show that with minimal programming effort, the ML algorithm achieved an accuracy of over 80% when selecting suitable managers for international assignments - based on the international experience of this group of people. The linear discriminant analysis has proven particularly relevant, and both the training and validation data provided values above 80%. In summary, ML algorithms' usefulness and feasibility in personnel selection in an international environment could be confirmed. Novelty - It could be confirmed that for implementing the manager selection, freely available algorithms in Python achieve sufficiently good results with an accuracy of 80%. Type of Paper - Empirical"

Keywords: Artificial Intelligence; International Experience; Manager; Machine Learning; Decision Making; Human Resources Management. (search for similar items in EconPapers)
JEL-codes: C89 M16 (search for similar items in EconPapers)
Pages: 9
Date: 2023-03-31
New Economics Papers: this item is included in nep-cmp and nep-sea
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Published in Global Journal of Business and Social Science Review, Volume 11, Issue 1.

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Persistent link: https://EconPapers.repec.org/RePEc:gtr:gatrjs:gjbssr632

DOI: 10.35609/gjbssr.2023.11.1(2)

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