Mapping the Evolution and Paradoxes of Artificial Intelligence in Human Resource Management: A Bibliometric Synthesis (2002–2026)
Keywords:
Artificial Intelligence, Human Resource Management, Bibliometric Analysis, Systematic Literature Review, Paradoxical Tensions, Digital Transformation, Algorithmic Decision-MakingAbstract
Introduction: The rapid advancement of Artificial Intelligence (AI) is fundamentally transforming Human Resource Management (HRM) practices, shifting the paradigm from administrative automation to strategic, data-driven decision-making. However, the integration of intelligent systems into organizational workflows introduces complex ethical challenges and paradoxical tensions that remain conceptually fragmented in the existing literature. This study aims to systematically map the intellectual structure, evolutionary trends, and research hotspots of the AI-HRM domain over more than two decades. Method: Employing a hybrid approach, this research combines a Systematic Literature Review (SLR) following the PRISMA protocol with a comprehensive bibliometric analysis. A total of relevant peer-reviewed articles published between 2002 and 2026 were retrieved from the Scopus database. Data processing and visualization were performed using R-Biblioshiny and VOSviewer to conduct keyword co-occurrence, co-authorship, and thematic evolution analyses. Results: The findings reveal an exponential growth in scholarly output since 2016, with a significant surge following the COVID-19 pandemic. Four major thematic clusters were identified: (1) AI applications in recruitment and operational HR functions; (2) infrastructure and advanced technologies such as Machine Learning and Big Data; (3) ethical, legal, and privacy considerations; and (4) strategic HRM focusing on decision-making, leadership, and diversity. Furthermore, the analysis highlights a shift in research focus from purely technical applications toward a "human-centric" perspective and the management of paradoxical tensions, such as autonomy versus algorithmic control. Discussion: The study underscores the necessity of a balanced approach that integrates technological innovation with rigorous ethical governance to mitigate algorithmic bias and technostress. While AI offers transformative potential for organizational performance, the gap between technical promises and socio-cultural implementation remains a critical challenge. Future research should prioritize longitudinal studies on employee well-being, the impact of Generative AI, and context-sensitive investigations in emerging economies.
References
Aguilar-Poaquiza, J. B., Guadalupe, C. S. A., Cevallos-Silva, W. P., Lliquin, J. D. C., Hermida, C. E. C., Álvarez, O. D. G., & Cajamarca-Carrazco, D. (2025). Management conservation of ecosystem services based on artificial intelligence: A analysis of citations in Scopus. Data and Metadata. https://doi.org/10.56294/dm20251066
Al-Dekah, A. M., & Sweileh, W. (2025). Role of artificial intelligence in early identification and risk evaluation of non-communicable diseases: A bibliometric analysis of global research trends. BMJ Open. https://doi.org/10.1136/bmjopen-2025-101169
Al-Naemi, A.-A. S., & Botella-Carrubi, D. (2026). The challenges of applying predictive analytics and knowledge for decision-making in talent management. Journal of Innovation & Knowledge. https://doi.org/10.1016/j.jik.2026.101044
Alan, H. (2023). A systematic bibliometric analysis on the current digital human resources management studies and directions for future research. WSP Current Human Resource Management. https://doi.org/10.47297/wspchrmWSP2040-800502.20231401
Alesaily, Z., & Albialy, A. (2025). Future cities: A bibliometric review, 1875 to 2024. Sustainable Futures. https://doi.org/10.1016/j.sftr.2025.100801
Altinay, Z., Altinay, F., Tlili, A., & Vatankhah, S. (2025). “Keep your friends close, but your enemies closer:” ChatGPT in tourism and hospitality. Journal of Hospitality and Tourism Technology. https://doi.org/10.1108/JHTT-03-2024-0139
Autsadee, Y., Jeevan, J., Mohd Salleh, N. H. B., & Othman, M. R. B. (2023). Digital tools and challenges in human resource development and its potential within the maritime sector through bibliometric analysis. Maritime Business Review. https://doi.org/10.1080/25725084.2023.2286409
B, N., C, H., & P, P. (2025). Unveiling the future of artificial intelligence in talent acquisition: A bibliometric analysis of emerging trends and future directions. Sustainable Futures. https://doi.org/10.1016/j.sftr.2025.101410
Benabou, A., & Touhami, F. (2025a). Empowering human resource management through artificial intelligence: A systematic literature review and bibliometric analysis. International Journal of Production Management and Engineering. https://doi.org/10.4995/ijpme.2025.21900
Benabou, A., & Touhami, F. (2025b). Integration of artificial intelligence in human resource management: Analyzing opportunities, challenges, and human-AI collaboration. Management and Accounting Review, 24(1). https://doi.org/10.24191/MAR.V24i01-15
Bouhsaien, L., & Azmani, A. (2024). The potential of artificial intelligence in human resource management. Applied Computer Science. https://doi.org/10.35784/acs-2024-34
Chatti, H., & Argoubi, M. (2025). Artificial intelligence in knowledge management: Identifying intellectual milestones and emerging domains. Electronic Journal of Knowledge Management. https://doi.org/10.34190/ejkm.23.2.4260
Cruz-Martínez, G. A., Vega-Muñoz, A., Salazar-Sepúlveda, G., & Toledo-Aceituno, P. (2024). Analysis of studies on digital strategy: Bibliometric research of three decades. Sustainability, 16(20), 8789. https://doi.org/10.3390/su16208789
Deepa, R., Sekar, S., Malik, A., Kumar, J., & Attri, R. (2024). Impact of AI-focussed technologies on social and technical competencies for HR managers – A systematic review and research agenda. Technological Forecasting and Social Change. https://doi.org/10.1016/j.techfore.2024.123301
Farooq, M., Yen, Y. Y., Buzdar, H. Q., & Qureshi, Q. A. (2024). Exploring the research landscape of artificial intelligence integration with business management: A bibliometric analysis from 1994-2024. Journal of Library and Information Science Studies. https://doi.org/10.33168/JLISS.2024.0410
França, T. J. F., São Mamede, H., Barroso, J. M. P., & Santos, V. M. P. D. (2023). Artificial intelligence applied to potential assessment and talent identification in an organisational context. Heliyon. https://doi.org/10.1016/j.heliyon.2023.e14694
Gong, J., Sihag, V., Kong, Q., & Zhao, L. (2021). Visualizing knowledge evolution trends and research hotspots of personal health data research: Bibliometric analysis. JMIR Medical Informatics. https://doi.org/10.2196/31142
Hornungová, J., & Petrová, K. (2025). The role of digital transformation on sustainable human resource management. Studies in Business and Economics. https://doi.org/10.2478/sbe-2025-0004
Huang, Q., Xu, Y., Li, H., & Zhang, T. (2025). Global research trends and disparities in cesarean section-related placenta accreta spectrum disorders: A 93-year bibliometric analysis. Archives of Clinical Cases. https://doi.org/10.62713/aic.4093
Kozová, K., Húževka, M., Grenčíková, A., & Navickas, V. (2026). Global economic and technological challenges as determinants of human resource management transformation: A bibliometric analysis of research trends. Problems and Perspectives in Management, 24(2). https://doi.org/10.21511/ppm.24(2).2026.05
Li, Y., Han, F., & Zheng, Y. (2026). Artificial intelligence in surface water quality research and management: Recent progress and future directions. Environmental Health and Sustainability. https://doi.org/10.34133/ehs.0474
Ljungholm, D. P., & Popescu, V. (2023). Generative artificial intelligence algorithms in talent and performance management, job displacement and creation, and employee productivity and well-being. Contemporary Readings in Law and Social Justice, 15(2). https://doi.org/10.22381/CRLSJ15220231
Luo, F., Li, R. Y. M., Crabbe, M. J. C., & Pu, R. (2022). Economic development and construction safety research: A bibliometrics approach. Safety Science. https://doi.org/10.1016/j.ssci.2021.105519
Malik, A., Lirio, P., Budhwar, P., Nguyen, M., & Fauzi, M. A. (2025). Guest editorial: Artificial intelligence (AI) in the world of work: bibliometric insights and mapping opportunities and challenges. Personnel Review. https://doi.org/10.1108/PR-12-2024-1061
Maquen-Niño, G. L. E., Cárdenas, D. M. C., Roldán, C. M. G., Herrera, A. P., More, Y. d. M. C., & García, D. S. (2025). Impact of mathematical optimization on machine learning: A bibliometric review. Journal of Educational and Social Research. https://doi.org/10.36941/jesr-2025-0172
Obreja, D. M., Rughiniș, R., & Rosner, D. (2024). Mapping the conceptual structure of innovation in artificial intelligence research: A bibliometric analysis and systematic literature review. Journal of Innovation & Knowledge. https://doi.org/10.1016/j.jik.2024.100465
Polezi Munhoz, I., de Mesquita Spinola, M., Cardoso Durão, L. F., Rein, J., Schützer, K., Schleich, B., & Zancul, E. (2026). Integrating artificial intelligence and digital twins into a data architecture for small-batch manufacturing. The International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-026-18133-2
Said, O., & Nahid, A. (2026). Adoption of artificial intelligence in human resource management: Systematic review and bibliometric analysis. Multidisciplinary Reviews. https://doi.org/10.31893/multirev.2026510
Santiago, J. K. (2023). The progression in employer branding and employee-based brand equity: Scholar API-based systematic literature review. Journal of Economics and Management. https://doi.org/10.22367/jem.2023.45.11
Sarfo, I., Qiao, J., Effah, N. A. A., Yeboah, E., Alimo, P. K., Amara, D. B., Kwang, C., Qiankun, Z., Allotey, M. K., & Djan, M. A. (2024). Advances in global land use systems development and sustainability: A bibliometric analysis. Applied Sciences. https://doi.org/10.15576/ASP.FC/187717
Shabu, K., Priya, K. M., Mageswari, S. S., Prabhash, N., & Hari, A. (2025). Shaping the gig economy: Insights into management, technology, and workforce dynamics. International Journal of Engineering & Technology. https://doi.org/10.14419/sx68fc91
Sharma, C., Chanana, N., & Chen, H.-Y. (2025). Mapping the evolution: A bibliometric analysis of employee engagement and performance in the age of artificial intelligence-based solutions. Information, 16(7), 555. https://doi.org/10.3390/info16070555
Singh, S., Jaiswal, R., Gupta, S., & Kumar, C. (2024). What we know and what should we know about the future of blockchain in finance. F1000Research. https://doi.org/10.12688/f1000research.153215.1
Tran, B. X., Nghiem, S., Sahin, O., Vu, T. M., Ha, G. H., Vu, G. T., Pham, H. Q., Do, H. T., Latkin, C. A., Tam, W., Ho, C. S. H., & Ho, R. C. M. (2019). Modeling research topics for artificial intelligence applications in medicine: Latent dirichlet allocation application study. JMIR Medical Informatics. https://doi.org/10.2196/15511
Turcato, C. R. P., Pedroso, B., Arnold, M., & Picinin, C. T. (2024). Adapting to Industry 4.0 in France: Essential Competencies for a Future-Ready Workforce. Administrative Sciences, 14(12), 322. https://doi.org/10.3390/admsci14120322
Vasudevan, A., Mathushan, P., Kengatharan, N., Nanthagopan, Y., & Kumar, A. (2025). Uncovering the Paradox: Digital shifts in human resource management through mobile technology and wireless communication – A content co-occurrence analysis using Citespace. International Journal of Interactive Mobile Technologies, 19(14). https://doi.org/10.3991/ijim.v19i14.56871
Wijaya, E. F. A., & Qamari, I. N. (2024). Analysis of research on artificial intelligence in human resources management: A bibliometric analysis. International Research Journal of Modernization in Engineering Technology and Science, 5(2). https://doi.org/10.47857/irjms.2024.v05i02.0383
Zhou, L., Liu, C., & Song, X. (2025). Identification of core sub-team on scientific collaboration networks with Shapley method. PeerJ Computer Science. https://doi.org/10.7717/peerj-cs.3048
Zorpas, A. A., Inglezakis, V. J., Papamichael, I., Razis, P., Voukkali, I., Pérez-Gimeno, A., Naddeo, V., Rodríguez-Espinosa, T., Loizia, P., & Navarro-Pedreño, J. (2025). Planetary sustainability science and technology: Integrating Astro-soil, Astro-environmental engineering and Astro-habitat engineering for space exploration. Science of The Total Environment. https://doi.org/10.1016/j.scitotenv.2025.179959
