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

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  • Prikshat, Verma
  • Islam, Mohammad
  • Patel, Parth
  • Malik, Ashish
  • Budhwar, Pawan
  • Gupta, Suraksha

Abstract

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.

Suggested Citation

  • Prikshat, Verma & Islam, Mohammad & Patel, Parth & Malik, Ashish & Budhwar, Pawan & Gupta, Suraksha, 2023. "AI-Augmented HRM: Literature review and a proposed multilevel framework for future research," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    • Handle: RePEc:eee:tefoso:v:193:y:2023:i:c:s004016252300330x
    DOI: 10.1016/j.techfore.2023.122645
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    as
    1. NoorUl Ain & Giovanni Vaia & William Delone & Mehwish Waheed, 2019. "Two decades of research on business intelligence system adoption, utilization and success – A systematic literature review," Post-Print hal-03882087, HAL.
    2. Wang, Gang & Gunasekaran, Angappa & Ngai, Eric W.T. & Papadopoulos, Thanos, 2016. "Big data analytics in logistics and supply chain management: Certain investigations for research and applications," International Journal of Production Economics, Elsevier, vol. 176(C), pages 98-110.
    3. Verma Prikshat & Parth Patel & Arup Varma & Alessio Ishizaka, 2022. "A multi-stakeholder ethical framework for AI-augmented HRM," International Journal of Manpower, Emerald Group Publishing Limited, vol. 43(1), pages 226-250, January.
    4. Peter Buxmann & Thomas Hess & Jason Thatcher, 2019. "Call for Papers, Issue 1/2021," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(4), pages 545-547, August.
    5. Xin Wang & Li Wang & Li Zhang & Xiaobo Xu & Weiyong Zhang & Yingcheng Xu, 2017. "Developing an employee turnover risk evaluation model using case-based reasoning," Information Systems Frontiers, Springer, vol. 19(3), pages 569-576, June.
    6. Luo, Yadong & Zhang, Huan, 2016. "Emerging Market MNEs: Qualitative Review and Theoretical Directions," Journal of International Management, Elsevier, vol. 22(4), pages 333-350.
    7. Ahmad Arslan & Cary Cooper & Zaheer Khan & Ismail Golgeci & Imran Ali, 2021. "Artificial intelligence and human workers interaction at team level: a conceptual assessment of the challenges and potential HRM strategies," International Journal of Manpower, Emerald Group Publishing Limited, vol. 43(1), pages 75-88, July.
    8. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    9. Du, Shuili & Xie, Chunyan, 2021. "Paradoxes of artificial intelligence in consumer markets: Ethical challenges and opportunities," Journal of Business Research, Elsevier, vol. 129(C), pages 961-974.
    10. Ulrich Leicht-Deobald & Thorsten Busch & Christoph Schank & Antoinette Weibel & Simon Schafheitle & Isabelle Wildhaber & Gabriel Kasper, 2019. "The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity," Journal of Business Ethics, Springer, vol. 160(2), pages 377-392, December.
    11. Malik, Ashish & De Silva, M.T. Thedushika & Budhwar, Pawan & Srikanth, N.R., 2021. "Elevating talents' experience through innovative artificial intelligence-mediated knowledge sharing: Evidence from an IT-multinational enterprise," Journal of International Management, Elsevier, vol. 27(4).
    12. Kaplan, Andreas & Haenlein, Michael, 2020. "Rulers of the world, unite! The challenges and opportunities of artificial intelligence," Business Horizons, Elsevier, vol. 63(1), pages 37-50.
    13. Lucila P. Cascante & Michel Plaisent & Lassana Maguiraga & Prosper Bernard, 2002. "The Impact of Expert Decision Support Systems on the Performance of New Employees," Information Resources Management Journal (IRMJ), IGI Global, vol. 15(4), pages 64-78, October.
    14. Sinclear R. Ndemewah & Martin R. W. Hiebl, 2022. "Management Accounting Research on Africa," European Accounting Review, Taylor & Francis Journals, vol. 31(4), pages 1029-1057, August.
    15. Arunachalam, Deepak & Kumar, Niraj & Kawalek, John Paul, 2018. "Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 416-436.
    16. Guest Editors: Hemant Jain & Balaji Padmanabhan & Paul A. Pavlou & Raghu T. Santanam, 2018. "all for Papers—Special Issue of Information Systems Research —Humans, Algorithms, and Augmented Intelligence: The Future of Work, Organizations, and Society," Information Systems Research, INFORMS, vol. 29(1), pages 250-251, March.
    17. Abubakar, A. Mohammed & Behravesh, Elaheh & Rezapouraghdam, Hamed & Yildiz, Selim Baha, 2019. "Applying artificial intelligence technique to predict knowledge hiding behavior," International Journal of Information Management, Elsevier, vol. 49(C), pages 45-57.
    18. van Esch, Patrick & Black, J. Stewart, 2019. "Factors that influence new generation candidates to engage with and complete digital, AI-enabled recruiting," Business Horizons, Elsevier, vol. 62(6), pages 729-739.
    19. Guinan, Patricia J. & Parise, Salvatore & Langowitz, Nan, 2019. "Creating an innovative digital project team: Levers to enable digital transformation," Business Horizons, Elsevier, vol. 62(6), pages 717-727.
    20. Kevin Zhu & Kenneth L. Kraemer & Sean Xu, 2006. "The Process of Innovation Assimilation by Firms in Different Countries: A Technology Diffusion Perspective on E-Business," Management Science, INFORMS, vol. 52(10), pages 1557-1576, October.
    21. Black, J. Stewart & van Esch, Patrick, 2020. "AI-enabled recruiting: What is it and how should a manager use it?," Business Horizons, Elsevier, vol. 63(2), pages 215-226.
    22. Parbudyal Singh & Dale Finn, 2003. "The Effects of Information Technology on Recruitment," Journal of Labor Research, Transaction Publishers, vol. 24(3), pages 395-408, July.
    23. Jennifer R. Burnett & Timothy C. Lisk, 2019. "The Future of Employee Engagement: Real-Time Monitoring and Digital Tools for Engaging a Workforce," International Studies of Management & Organization, Taylor & Francis Journals, vol. 49(1), pages 108-119, January.
    24. Cubric, Marija, 2020. "Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study," Technology in Society, Elsevier, vol. 62(C).
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