IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v193y2023ics004016252300330x.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S004016252300330X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2023.122645?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Luo, Yadong & Zhang, Huan, 2016. "Emerging Market MNEs: Qualitative Review and Theoretical Directions," Journal of International Management, Elsevier, vol. 22(4), pages 333-350.
    15. 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.
    16. 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.
    17. 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).
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Miglena Stoyanova, 2022. "Impact Of Artificial Intelligence On Recruitment Process," INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE "HUMAN RESOURCE MANAGEMENT", University of Economics - Varna, issue 1, pages 184-191.
    2. Luther Yuong Qai Chong & Thien Sang Lim, 2022. "Pull and Push Factors of Data Analytics Adoption and Its Mediating Role on Operational Performance," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    3. Maude Lavanchy & Patrick Reichert & Jayanth Narayanan & Krishna Savani, 2023. "Applicants’ Fairness Perceptions of Algorithm-Driven Hiring Procedures," Journal of Business Ethics, Springer, vol. 188(1), pages 125-150, November.
    4. Hausladen, Iris & Schosser, Maximilian, 2020. "Towards a maturity model for big data analytics in airline network planning," Journal of Air Transport Management, Elsevier, vol. 82(C).
    5. Patrucco, Andrea S. & Marzi, Giacomo & Trabucchi, Daniel, 2023. "The role of absorptive capacity and big data analytics in strategic purchasing and supply chain management decisions," Technovation, Elsevier, vol. 126(C).
    6. Oesterreich, Thuy Duong & Teuteberg, Frank, 2019. "Behind the scenes: Understanding the socio-technical barriers to BIM adoption through the theoretical lens of information systems research," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 413-431.
    7. Abirami Raja Santhi & Padmakumar Muthuswamy, 2022. "Pandemic, War, Natural Calamities, and Sustainability: Industry 4.0 Technologies to Overcome Traditional and Contemporary Supply Chain Challenges," Logistics, MDPI, vol. 6(4), pages 1-32, November.
    8. Aristotelis Mavidis & Dimitris Folinas, 2022. "From Public E-Procurement 3.0 to E-Procurement 4.0; A Critical Literature Review," Sustainability, MDPI, vol. 14(18), pages 1-23, September.
    9. Vicky Ching Gu & Bin Zhou & Qing Cao & Jeffery Adams, 2021. "Exploring the relationship between supplier development, big data analytics capability, and firm performance," Annals of Operations Research, Springer, vol. 302(1), pages 151-172, July.
    10. Volkan Ezcan & Jack Steven Goulding, 2022. "Offsite Sustainability—Disentangling the Rhetoric through Informed Mindset Change," Sustainability, MDPI, vol. 14(8), pages 1-27, April.
    11. Erkip, Nesim Kohen, 2023. "Can accessing much data reshape the theory? Inventory theory under the challenge of data-driven systems," European Journal of Operational Research, Elsevier, vol. 308(3), pages 949-959.
    12. Marimuthu, Malliga & D'Souza, Clare & Shukla, Yupal, 2022. "Integrating community value into the adoption framework: A systematic review of conceptual research on participatory smart city applications," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    13. Amit Kumar Gupta & Harshit Goyal, 2021. "Framework for implementing big data analytics in Indian manufacturing: ISM-MICMAC and Fuzzy-AHP approach," Information Technology and Management, Springer, vol. 22(3), pages 207-229, September.
    14. Schoenherr, Tobias, 2023. "Supply chain management professionals’ proficiency in big data analytics: Antecedents and impact on performance," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).
    15. Lodemann, Sebastian & Kersten, Wolfgang, 2021. "Supply chain analytics implementation: A TOE perspective," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 411-434, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    16. Kristoffersen, Eivind & Mikalef, Patrick & Blomsma, Fenna & Li, Jingyue, 2021. "Towards a business analytics capability for the circular economy," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    17. De Obesso Arias, María de las Mercedes & Pérez Rivero, Carlos Alberto & Carrero Márquez, Oliver, 2023. "Artificial intelligence to manage workplace bullying," Journal of Business Research, Elsevier, vol. 160(C).
    18. Jozé Braz de Araújo & Silvia Novaes Zilber, 2016. "What Factors Lead Companies to Adopt Social Media in their processes: Proposal and Test of a Measurement Model," Brazilian Business Review, Fucape Business School, vol. 13(6), pages 260-290, November.
    19. Yousef A. M. Qasem & Rusli Abdullah & Yusmadi Yah Jusoh & Rodziah Atan & Shahla Asadi, 2021. "Analyzing Continuance of Cloud Computing in Higher Education Institutions: Should We Stay, or Should We Go?," Sustainability, MDPI, vol. 13(9), pages 1-37, April.
    20. Xu, Yingzi & Shieh, Chih-Hui & van Esch, Patrick & Ling, I-Ling, 2020. "AI customer service: Task complexity, problem-solving ability, and usage intention," Australasian marketing journal, Elsevier, vol. 28(4), pages 189-199.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:tefoso:v:193:y:2023:i:c:s004016252300330x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.