Reinventing Human Resource Management Through Artificial Intelligence: A Systematic Review of Drivers, Barriers, and Outcomes

Wayan Arya Paramarta; Ni Ketut Laswitarni; Putu Mela Ratini

doi:10.62951/ijecm.v3i1.1089

Authors

Wayan Arya Paramarta Sekolah Tinggi Ilmu Manajemen Indonesia Handayani
Ni Ketut Laswitarni Sekolah Tinggi Ilmu Manajemen Indonesia Handayani
Putu Mela Ratini Sekolah Tinggi Ilmu Manajemen Indonesia Handayani

DOI:

https://doi.org/10.62951/ijecm.v3i1.1089

Keywords:

Artificial Intelligence, Barriers, Digital Transformation, Drivers, Human Resource Management, Outcomes, Systematic Literature Review

Abstract

The integration of Artificial Intelligence (AI) into Human Resource Management (HRM) is accelerating and reshaping how organizations attract, develop, manage, and retain talent. Despite abundant case examples and growing practitioner interest, academic findings remain fragmented regarding the antecedents (drivers), impediments (barriers), and organizational effects (outcomes) of AI-based HR transformation. This paper presents a PRISMA-guided systematic literature review of 112 peer-reviewed articles (2015–2025) to synthesize empirical and conceptual evidence on AI in HRM. Results identify three primary drivers: technological capability, strategic alignment, and a data-driven culture; three critical barriers: ethical concerns (bias, privacy, and transparency), skill and capability gaps, and resistance to change; and three outcome clusters: operational efficiency, enhanced employee experience, and elevated strategic HR contribution. We propose a socio-technical conceptual framework that models drivers moderated by barriers to outcomes, and we advance a research agenda focused on ethical governance, human–AI collaboration, capability measurement, and longitudinal evaluation. The review contributes to theory by integrating socio-technical and dynamic capability perspectives and provides actionable guidance for HR leaders on responsible AI adoption.

Downloads

Download data is not yet available.

References

Bauer, W., Heger, D., & Pullman, M. (2021). Algorithmic bias in human resources decision-making: A review and research agenda. Business Research, 14(3), 847–875.

https://doi.org/10.1007/s40685-021-00156-8

Benabou, A., & Touhami, F. (2025). Artificial Intelligence in Human Resource Management: A PRISMA based Systematic Review. Acta Informatica Pragensia, 14(3), Forthcoming article.

https://doi.org/10.18267/j.aip.264

Chen, Z. (2023). Ethics and discrimination in artificial intelligence-enabled recruitment practices. Humanit Soc Sci Commun, 10, 567.

https://doi.org/10.1057/s41599-023-02079-x

Chui, M., et al. (2023). The State of AI in 2023: Generative AI's Breakout Year. McKinsey & Company.

https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year

Deloitte. (2023). State of AI in the enterprise, 5th edition: Opening the aperture. Deloitte Insights.

https://www2.deloitte.com/us/en/insights/focus/cognitive-technologies/state-of-ai.html

Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50.

https://doi.org/10.1007/s11747-020-00749-9

Hyanghee Park, Daehwan Ahn, Kartik Hosanagar, and Joonhwan Lee. (2022). Designing Fair AI in Human Resource Management: Understanding Tensions Surrounding Algorithmic Evaluation and Envisioning Stakeholder-Centered Solutions. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI '22). Association for Computing Machinery, New York, NY, USA, Article 51, 1–22.

https://doi.org/10.1145/3491102.3517672

Langer, M., König, C. J., & Fitili, A. (2018). Information as a double-edged sword: Human reactions to explanations about algorithmic decision-making. Computers in Human Behavior, 81, 161–173.

https://doi.org/10.1016/j.chb.2017.12.009

McKinsey & Company. (2023). The economic potential of generative AI: The next productivity frontier. McKinsey Global Institute.

https://www.mckinsey.com/mgi/our-research/generative-ai-the-next-productivity-frontier

McKinsey & Company. (2024). State of AI in 2024: Adoption, impact, and the shifting talent landscape. McKinsey Global Institute.

https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2024

Meijerink, J. G., Bondarouk, T., & Lepak, D. P. (2016). Employees as active consumers of HRM: Linking employees' HRM competences with their perceptions of HRM service value. Human Resource Management, 55(2), 219–240.

https://doi.org/10.1002/hrm.21719

Meijerink, J., Bondarouk, T., & Lepak, D. P. (2021). New ways of working through artificial intelligence. Human Resource Management Review, 31(2), 100818.

https://doi.org/10.1016/j.hrmr.2020.100818

Meijerink, J., Boons, M., Keegan, A., & Marler, J. (2021). Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM. The International Journal of Human Resource Management, 32(12), 2545–2562.

https://doi.org/10.1080/09585192.2021.1925326

Mittelstadt, B. D. (2023). Principles alone cannot guarantee ethical AI. Nature Machine Intelligence, 5(1), 10–12.

https://doi.org/10.1038/s42256-022-00601-5

Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & Group, T. P. (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Medicine, 6(7), e1000097.

https://doi.org/10.1371/journal.pmed.1000097

Nature Editorial. (2022). The future of AI regulation must focus on accountability. Nature, 611(7934), 621.

https://doi.org/10.1038/d41586-022-03732-5

Nawaz, N., Sharma, R., & Kumar, S. (2023). Artificial intelligence for HR operations: A systematic evaluation. International Journal of Human Resource Studies, 13(1), 45–67.

Park, S., & Kang, D. (2023). Employee experience enhancement through AI-enabled HR support systems. Journal of Organizational Computing and Electronic Commerce, 33(2), 123–141.

Putri, A. R., & Santoso, H. B. (2021). Chatbot-based HR services and employee satisfaction: Evidence from digital HR adoption. Jurnal Sistem Informasi, 17(2), 89–102.

Strohmeier, S., & Piazza, F. (2015). Artificial Intelligence Techniques in Human Resource Management-A Conceptual Exploration. In C. Kahraman & S. Çevik Onar (Eds.), Intelligent Techniques in Engineering Management (pp. 149–172). Springer.

https://doi.org/10.1007/978-3-319-17906-3_7

Strohmeier, S., & Piazza, F. (2022). Handbook of Research on Artificial Intelligence in Human Resource Management. Edward Elgar Publishing.

https://doi.org/10.4337/9781839107535

Tambe, P., Cappelli, P., & Yakubovich, V. (2020). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15–42.

https://doi.org/10.1177/0008125619867910

Teece, D. J. (2007). Explicating Dynamic Capabilities: The nature and micro foundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–1350.

https://doi.org/10.1002/smj.640

Trist, E. L., & Bamforth, K. W. (1951). Some Social and Psychological Consequences of the Longwall Method of Coal-Getting. Human Relations, 4(1), 3–38.

https://doi.org/10.1177/001872675100400101

Vrontis, D., Christofi, M., & Pereira, V. (2022). HR analytics and AI in HRM: A systematic review and future research agenda. European Management Review, 19(4), 725–744.

Reinventing Human Resource Management Through Artificial Intelligence: A Systematic Review of Drivers, Barriers, and Outcomes

Authors

DOI:

Keywords:

Abstract

Downloads

References

Downloads

Published

How to Cite

Issue

Section

License

menu