Impact of AI-focussed technologies on social and technical competencies for HR managers – A systematic review and research agenda

R. Deepa, Srinivasan Sekar, Ashish Malik, Jitender Kumar and Rekha Attri

Technological Forecasting and Social Change, 2024, vol. 202, issue C

Abstract: Research on the application of Artificial Intelligence (AI)-based technologies in the HRM domain has attracted significant scholarly attention. Yet, few studies have consolidated key trends in adopting AI for HRM, especially on managerial competencies required for adopting AI-based technologies and identifying the key research directions for HR managers, including the development of an AI-focused competency framework for HR managers. A systematic literature review (SLR) and bibliometrics analysis were conducted to identify the current research direction for managers adopting AI in HRM. Several themes of managerial capabilities required for adopting AI in HRM were identified, utilizing the Dynamic Capabilities View (DCV). The SLR identified applications of various AI tools and techniques in HR functions, recruitment and selection was one with the broadest use of AI applications. Managerial cognitive capability, managerial human capital, and managerial social capital of DCV were considered the initial coding categories under which various managerial competencies are required for the adoption of AI in HRM. This study utilized SLR, Bibliometric, and directed content analysis as three distinct but interrelated sets of methodologies for extracting novel insights into the adoption of AI for HRM. It highlights the associated managerial capabilities that need mapping for its adoption.

Keywords: Artificial Intelligence (AI); Human Resource Management (HRM); Dynamic Capabilities View (DCV); Managerial capabilities; Systematic literature review (SLR); Bibliometric analysis; Content analysis (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:202:y:2024:i:c:s0040162524000970

DOI: 10.1016/j.techfore.2024.123301

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