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Critical exploration of AI‐driven HRM to build up organizational capabilities

  • HRM processes are increasingly AI-driven, and HRM supports the general digital transformation of companies’ viable competitiveness. This paper points out possible positive and negative effects on HRM, workplaces, and workersorganizations along the HR processes and its potential for competitive advantage in regard to managerial decisions on AI implementation regarding augmentation and automation of work. A systematic literature review that includes 62 international journals across different disciplines and contains top-tier academic and German practitioner journals was conducted. The literature analysis applies the resource-based view (RBV) as a lens through which to explore AI-driven HRM as a potential source of organizational capabilities. The analysis shows four ambiguities for AI-driven HRM that might support sustainable company development or might prevent AI application: job design, transparency, performance and data ambiguity. A limited scholarly discussion with very few empirical studies can be stated. To date, research has mainly focused on HRM in general, recruiting, and HR analytics in particular. The four ambiguities’ context-specific potential for capability building in firms is indicated, and research avenues are developed. This paper critically explores AI-driven HRM and structures context-specific potential for capability building along four ambiguities that must be addressed by HRM to strategically contribute to an organization’s competitive advantage.

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Metadaten
Author:Nicole Böhmer, Heike Schinnenburg
Title (English):Critical exploration of AI‐driven HRM to build up organizational capabilities
URN:urn:nbn:de:bsz:959-opus-40133
DOI:https://doi.org/10.1108/ER-04-2022-0202
ISSN:1758-7069
Parent Title (English):Employee Relations
Document Type:Article
Language:English
Year of Completion:2023
Release Date:2023/05/11
Tag:Ambiguity; Artificial Intelligence; Context; HRM; Literature Review; Resource-Based View
Page Number:41
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Veröffentlichter Artikel unter https://doi.org/10.1108/ER‐04‐2022‐0202
Faculties:Fakultät WiSo
DDC classes:300 Sozialwissenschaften / 330 Wirtschaft
Review Status:Akzeptierte Fassung
Licence (German):License LogoCreative Commons - CC BY-NC - Namensnennung - Nicht kommerziell 4.0 International