Basu, S, Majumdar, B, Mukherjee, K et al. (2 more authors) (2023) Artificial Intelligence–HRM Interactions and Outcomes: A Systematic Review and Causal Configurational Explanation. Human Resource Management Review, 33 (1). 100893. ISSN 1053-4822
Abstract
Artificial intelligence (AI) systems and applications based on them are fast pervading the various functions of an organization. While AI systems enhance organizational performance, thereby catching the attention of the decision makers, they nonetheless pose threats of job losses for human resources. This in turn pose challenges to human resource managers, tasked with governing the AI adoption processes. However, these challenges afford opportunities to critically examine the various facets of AI systems as they interface with human resources. To that end, we systematically review the literature at the intersection of AI and human resource management (HRM). Using the configurational approach, we identify the evolution of different theme based causal configurations in conceptual and empirical research and the outcomes of AI-HRM interaction. We observe incremental mutations in thematic causal configurations as the literature evolves and also provide thematic configuration based explanations to beneficial and reactionary outcomes in the AI-HRM interaction process.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Published by Elsevier Inc. All rights reserved. This is an author produced version of an article published in Human Resource Management Review. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Artificial intelligence; HRM; Systematic review; Thematic causal configurations; Fuzzy set qualitative comparative analysis |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > International Business Division (LUBS) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 26 Jan 2022 09:49 |
Last Modified: | 14 Mar 2024 01:13 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.hrmr.2022.100893 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:182852 |