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Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

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

Verma, Prikshat, Islam, Mohammad, Patel, Parth, Malik, Ashish, Budwar, Professor Pawan and Gupta, Suraksha (2023) AI-Augmented HRM: Literature review and a proposed multilevel framework for future research. Technological Forecasting & Social Change, 193. p. 122645. ISSN 0040-1625

Item Type: Article


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.

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Depositing User: Mohammad Islam


Item ID: 17269
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ISSN: 0040-1625
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ORCID for Mohammad Islam: ORCID iD

Catalogue record

Date Deposited: 30 Jan 2024 12:04
Last Modified: 30 Jan 2024 12:04


Author: Mohammad Islam ORCID iD
Author: Prikshat Verma
Author: Parth Patel
Author: Ashish Malik
Author: Professor Pawan Budwar
Author: Suraksha Gupta

University Divisions

Faculty of Business, Law and Tourism > Sunderland Law School


Business and Management > Human Resource Management

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