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AI-Augmented HRM_ Literature review and a proposed multilevel framework for future research_CQU.pdf (3.73 MB)

AI-Augmented HRM: Literature review and a proposed multilevel framework for future research

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journal contribution
posted on 2024-05-26, 20:39 authored by Verma Prikshat, M Islam, P Patel, A Malik, P Budhwar, S Gupta
The research using artificial intelligence (AI) applications in HRM functional areas has gained much traction and a steep surge over the last three years. The extant literature observes that contemporary AI applications have augmented HR functionalities. AI-Augmented HRM HRM(AI) has assumed strategic importance for achieving HRM domain-level outcomes and organisational outcomes for a sustainable competitive advantage. Moreover, there is increasing evidence of literature reviews pertaining to the use of AI applications in different management disciplines (i.e., marketing, supply chain, accounting, hospitality, and education). There is a considerable gap in existing studies regarding a focused, systematic literature review on HRM(AI), specifically for a multilevel framework that can offer research scholars a platform to conduct potential future research. To address this gap, the authors present a systematic literature review (SLR) of 56 articles published in 35 peer-reviewed academic journals from October 1990 to December 2021. The purpose is to analyse the context (i.e., chronological distribution, geographic spread, sector-wise distribution, theories, and methods used) and the theoretical content (key themes) of HRM(AI) research and identify gaps to present a robust multilevel framework for future research. Based upon this SLR, the authors identify noticeable research gaps, mainly stemming from - unequal distribution of previous HRM(AI) research in terms of the smaller number of sector/country-specific studies, absence of sound theoretical base/frameworks, more research on routine HR functions(i.e. recruitment and selection) and significantly less empirical research. We also found minimal research evidence that links HRM(AI) and organisational-level outcomes. To overcome this gap, we propose a multilevel framework that offers a platform for future researchers to draw linkage among diverse variables starting from the contextual level to HRM and organisational level outcomes that eventually enhance operational and financial organisational performance.

History

Volume

193

Start Page

1

End Page

19

Number of Pages

19

eISSN

1873-5509

ISSN

0040-1625

Publisher

Elsevier BV

Additional Rights

CC BY 4.0 DEED

Language

en

Peer Reviewed

  • Yes

Open Access

  • Yes

Acceptance Date

2023-05-15

Era Eligible

  • Yes

Journal

Technological Forecasting and Social Change

Article Number

122645

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