Artificial intelligence and HRM: identifying future research Agenda using systematic literature review and bibliometric analysis

Neelam Kaushal (), Rahul Pratap Singh Kaurav (), Brijesh Sivathanu () and Neeraj Kaushik ()
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Neelam Kaushal: National Institute of Technology
Rahul Pratap Singh Kaurav: Fortune Institute of International Business (FIIB)
Brijesh Sivathanu: College of Engineering Pune (COEP)
Neeraj Kaushik: National Institute of Technology

Management Review Quarterly, 2023, vol. 73, issue 2, No 1, 455-493

Abstract: Abstract The present research aims to identify significant contributors, recent dynamics, domains and advocates for future study directions in the arena of integration of Artificial Intelligence (AI) with Human Resource Management (HRM), in the context of various functions and practices in organizations. The paper adopted a methodology comprising of bibliometrics, network and content analysis (CA), on a sample of 344 documents extracted from the Scopus database, to identify extant research on this theme. Along with the bibliometric analysis, systematic literature review was done to propose an Artificial Intelligence and Human Resource Management Integration (AIHRMI) framework. Five clusters were recognized, and CA was conducted on the documents placed in the group of articles. It was found that vital research concentration in this arena is primarily about AI embeddedness in various HRM functions such as recruitment, selection, onboarding, training and learning, performance analysis, talent acquisition, as well as management and retention. The study proposes an AIHRMI framework developed from various studies considered in the current research. This model can provide guidance and future directions for several organizations in expansion of use of AI in HRM.

Keywords: Artificial intelligence; Human resource management; Talent management; Bibliometric analysis; Systematic review (search for similar items in EconPapers)
JEL-codes: O15 O33 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11301-021-00249-2

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