Acta Informatica Pragensia 2025, 14(3), 489-505 | DOI: 10.18267/j.aip.2645461

Artificial Intelligence in Human Resource Management: A PRISMA-based Systematic Review

Adil Benabou ORCID..., Fatima Touhami ORCID...
Faculty of Economics and Management, Sultan Moulay Slimane University, Beni Mellal, Morocco

Background: Artificial intelligence (AI) is rapidly transforming human resource management (HRM) by automating essential functions such as recruitment, employee performance evaluation and workforce planning. Despite the growing adoption of AI-driven tools, organizations face numerous challenges, including resistance from HR professionals, ethical concerns and data privacy issues. This transformation has sparked significant academic interest, yet several gaps remain in understanding how AI affects HRM practices and organizational outcomes.

Objective: This study aims to explore the integration of AI in HRM by analysing its potential opportunities and challenges. Additionally, it investigates how human-AI collaboration can enhance HR functions and drive organizational performance.

Methods: A systematic literature review approach was adopted, focusing on 141 recent peer-reviewed articles from Scopus-indexed journals. This review focused on studies published between 2019 and 2025. The analysis was structured around three key dimensions: AI-driven opportunities, challenges associated with AI adoption and its transformative impact on HRM practices.

Results: The findings reveal that AI offers significant benefits in HRM, such as improving efficiency, reducing bias and enhancing employee engagement. However, challenges remain regarding ethical decision-making, data security and maintaining human interaction in HR processes. The study also highlights the importance of augmenting, rather than replacing, human roles with AI tools to achieve optimal outcomes.

Conclusion: AI has the potential to reshape HRM by streamlining processes and enhancing decision-making. Nevertheless, its successful implementation requires addressing critical challenges such as resistance to change, ethical concerns and legal risks. Organizations should focus on fostering human-AI collaboration to unlock the full potential of AI-driven HRM.

Keywords: Human resource management; Artificial intelligence; Ethical AI in HR; AI-employee collaboration; Talent management; Systematic literature review.

Received: January 15, 2025; Revised: April 1, 2025; Accepted: April 2, 2025; Prepublished online: May 8, 2025; Published: August 19, 2025  Show citation

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Benabou, A., & Touhami, F. (2025). Artificial Intelligence in Human Resource Management: A PRISMA-based Systematic Review. Acta Informatica Pragensia, 14(3), 489-505. doi: 10.18267/j.aip.264
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    References

    1. Abid, U. b., Faisal, M. N., Al-Esmael, B., & Farooq, Z. H. (2024). Exploring the moderating role of technological competence and artificial intelligence in green HRM. Polish Journal of Management Studies, 29(2), 7-22. https://doi.org/10.17512/pjms.2024.29.2.01 Go to original source...
    2. Aggarwal, A., Sadhna, P., Gupta, S., Mittal, A., & Rastogi, S. (2022). Gen Z entering the workforce: Restructuring HR policies and practices for fostering the task performance and organizational commitment. Journal of Public Affairs, 22(3), e2535. https://doi.org/10.1002/pa.2535 Go to original source...
    3. Agrawal, P., Ghangale, S., Dhar, B. K., & Nirmal, N. (2024). Strategic management of employee churn: Leveraging machine learning for sustainable development and competitive advantage in emerging markets. Business Strategy and Development, 7(4), e70039. https://doi.org/10.1002/bsd2.70039 Go to original source...
    4. Alexander, L., Song, Q. C., Hickman, L., & Shin, H. J. (2025). Sourcing algorithms: Rethinking fairness in hiring in the era of algorithmic recruitment. International Journal of Selection and Assessment, 33(1), e12499. https://doi.org/10.1111/ijsa.12499 Go to original source...
    5. Allal-Cherif, O., Aranega, A. Y., & Sanchez, R. C. (2021). Intelligent recruitment: How to identify, select, and retain talents from around the world using artificial intelligence. Technological Forecasting & Social Change, 169, 120822. https://doi.org/10.1016/j.techfore.2021.120822 Go to original source...
    6. Alnsour, A. S., Kanaan, O. A., Salah, M., Alfayyad, L., Hijazi, Y., & Alsharif, D. (2024). The impact of implementing AI in recruitment on human resource management efficiency and organizational development effectiveness. Journal of Infrastructure Policy and Development, 8(8), 6186. https://doi.org/10.24294/jipd.v8i8.6186 Go to original source...
    7. Arslan, A., Cooper, C., Khan, Z., Golgeci, I., & Ali, I. (2022). Artificial intelligence and human workers interaction at team level: a conceptual assessment of the challenges and potential HRM strategies. International Journal of Manpower, 43(1), 75-88. https://doi.org/10.1108/IJM-01-2021-0052 Go to original source...
    8. Aycan, H. B., & Bal, M. (2024). Investigation of Turnover Tendency Predictions with Artificial Intelligence and Mathematical Models. Researches on Multidisiplinary Approaches, 4(2), 232-240.
    9. Aydin, E., & Turan, M. (2023). An AI-Based Shortlisting Model for Sustainability of Human Resource Management. Sustainability, 15, 2737. https://doi.org/10.3390/su15032737 Go to original source...
    10. Balcioğlu, Y. S., & Artar, M. (2024). Artificial intelligence in employee recruitment. Global Business and Organizational Excellence, 43(5), 56-66. https://doi.org/10.1002/joe.22248 Go to original source...
    11. Bankins, S. (2021). The ethical use of artifcial intelligence in human resource management: A decision making framework. Ethics and Information Technology, 23, 841-854. https://doi.org/s10676-021-09619-6 Go to original source...
    12. Basu, S., Majumdar, B., Mukherjee, K., Munjal, S., & Palaksha, C. (2023). Artificial Intelligence-HRM Interactions and Outcomes: A Systematic Review and Causal Configurational Explanation. Human Resource Management Review, 33(1), 1-16. https://doi.org/10.1016/j.hrmr.2022.100893 Go to original source...
    13. Bazrkar, A., Moradzad, M., & Shayegan, S. (2024). The use of artificial intelligence in employee recruitment in the furniture industry of Iran according to the role of contextual factors. Studia Universitatis "Vasile Goldis" Arad - Economics Series, 34(2), 86-109. https://doi.org/10.2478/sues-2024-0009 Go to original source...
    14. Becker, L., Ertz, E., & Büttgen, M. (2023). A relational perspective on supervisor-initiated turnover: Implications for human resource management based on a multi-method investigation of leader-member exchange relationships. Human Resource Management, 62, 547-564. https://doi.org/10.1002/hrm.22152 Go to original source...
    15. Bhatt, P., & Muduli, A. (2023). Artificial intelligence in learning and development: a systematic literature review. European Journal of Training and Development, 47(7), 677-694. https://doi.org/10.1108/EJTD-09-2021-0143 Go to original source...
    16. Boncella, R. (2024). AI and management: navigating the alignment problem for ethical and effective decision-making. Issues in Information Systems, 25(4), 194-204. https://doi.org/10.48009/4_iis_2024_116 Go to original source...
    17. Botha, A. P. (2019). A mind model for intelligent machine innovation using future thinking principles. Journal of Manufacturing Technology Management, 30(8), 1250-1264. https://doi.org/10.1108/JMTM-01-2018-0021 Go to original source...
    18. Bukartaite, R., & Hooper, D. (2023). Automation, artificial intelligence and future skills needs: an Irish perspective. European Journal of Training and Development, 47(10), 163-185. https://doi.org/10.1108/EJTD-03-2023-0045 Go to original source...
    19. Bursell, M., & Roumbanis, L. (2024). After the algorithms: A study of meta-algorithmic judgments and diversity in the hiring process at a large multisite company. Big Data & Society, 11(1). https://doi.org/10.1177/20539517231221758 Go to original source...
    20. Carter, C. (2024). Why the algorithmic recruiter discriminates: The causal challenges of data-driven discrimination. Maastricht Journal of European and Comparative Law, 31(3), 333-359. https://doi.org/10.1177/1023263X241248474 Go to original source...
    21. Chakraborty, D., & Biswas, W. (2019). Evaluating the impact of human resource planning programs in addressing the strategic goal of the firm: An organizational perspective. Journal of Advances in Management Research, 16(5), 659-682. https://doi.org/10.1108/JAMR-01-2019-0007 Go to original source...
    22. Chang, Y.-L., & Ke, J. (2023). Socially Responsible Artificial Intelligence Empowered People Analytics: A Novel Framework Towards Sustainability. Human Resource Development Review, 23(1), 88-120. https://doi.org/10.1177/15344843231200930 Go to original source...
    23. Chen, Z. (2023). Artificial Intelligence Virtual Trainer: Innovative Didactics Aimed at Personalized Training Needs. Journal of the Knowledge Economy, 14, 2007-2025. https://doi.org/10.1007/s13132-022-00985-0 Go to original source...
    24. Chen, Z. (2023). Collaboration among recruiters and artificial intelligence: removing human prejudices in employment. Cognition, Technology & Work, 25, 135-149. https://doi.org/10.1007/s10111-022-00716-0 Go to original source...
    25. Cheng, M. M., & Hackett, R. D. (2021). A critical review of algorithms in HRM: Definition, theory, and practice. Human Resource Management Review, 31(1), 100698. https://doi.org/10.1016/j.hrmr.2019.100698 Go to original source...
    26. Chilunjika, A., Intauno, K., & Chilunjika, S. R. (2022). Artificial intelligence and public sector human resource management in South Africa: Opportunities, challenges and prospects. SA Journal of Human Resource Management, 20, a1972. https://doi.org/10.4102/sajhrm.v20i0.1972 Go to original source...
    27. Chin, Y. S., Mohamad, A. A., & Lo, M. C. (2024). Harnessing the power of artificial intelligence (AI): a paradigm shift in HRM practices for employee sustainable performance. Global Knowledge, Memory and Communication, (in press). https://doi.org/10.1108/GKMC-06-2024-0355 Go to original source...
    28. Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Oscar Rodriguez-Espindola, A. A., & Truong, L. (2023). Unlocking the value of artificial intelligence in human resource management through AI capability framework. Human Resource Management Review, 33(1), 1-21. https://doi.org/10.1016/j.hrmr.2022.100899 Go to original source...
    29. Clavel, C., d'Armagnac, S., Hebrard, S., Hesters, T., & Potdevin, D. (2025). Humanized AI in hiring: an empirical study of a virtual AI job interviewer's social skills on applicants' reactions and experience. International Journal of Human Resource Management, 36(2), 206-234. https://doi.org/10.1080/09585192.2024.2440784 Go to original source...
    30. Cruz, I. F. (2024). Expert-AI pairings: Expert interventions in AI-powered decisions. Information and Organization, 34, 100527. https://doi.org/10.1016/j.infoandorg.2024.100527 Go to original source...
    31. DiClaudio, M. (2019). People analytics and the rise of HR: how data, analytics and emerging technology can transform human resources (HR) into a profit center. Strategic HR Review, 18(2), 42-46. https://doi.org/10.1108/SHR-11-2018-0096 Go to original source...
    32. Dutta, D., & Mishra, S. K. (2024). Artificial intelligence-based virtual assistant and employee engagement: an empirical investigation. Personnel Review, 54(3), 913-934. https://doi.org/10.1108/PR-03-2023-0263 Go to original source...
    33. Faqihi, A., & Miah, S. J. (2023). Artificial Intelligence-Driven Talent Management System: Exploring the Risks and Options for Constructing a Theoretical Foundation. Journal of Risk and Financial Management, 16(1), 31. https://doi.org/10.3390/jrfm16010031 Go to original source...
    34. Felderer, B., Kueck, J., & Spindler, M. (2023). Using Double Machine Learning to Understand Nonresponse in the Recruitment of a Mixed-Mode Online Panel. Social Science Computer Review, 41(2), 461-481. https://doi.org/10.1177/08944393221095194 Go to original source...
    35. Figueroa-Armijos, M., Clark, B. B., & Veiga, S. P. (2023). Ethical Perceptions of AI in Hiring and Organizational Trust: The Role of Performance Expectancy and Social Influence. Journal of Business Ethics, 186, 179-197. https://doi.org/10.1007/s10551-022-05166-2 Go to original source...
    36. Francisco, M., Benítez-Castro, M., Hidalgo-Tenorio, & Castro, J. L. (2022). A semi-supervised algorithm for detecting extremism propaganda diffusion on social media. Pragmatics and Society, 13(3), 532-554. https://doi.org/10.1075/ps.21009.fra Go to original source...
    37. Gao, Q., Xu, J., Wang, Q., & Wu, C. (2023). The Use of the Analytic Hierarchy Process in Improving Psychological Empowerment and Employee Performance. Journal of Organizational and End User Computing, 35(3), 1-22. https://doi.org/10.4018/JOEUC.321171 Go to original source...
    38. Gélinas, D., Sadreddin, A., & Vahidov, R. (2022). Artificial Intelligence in Human Resources Management: A Review and Research Agenda. Pacific Asia Journal of the Association for Information Systems, 14(6), Article 1. https://doi.org/10.17705/1pais.14601 Go to original source...
    39. Ghosh, D., Ghosh, R., Chowdhury, S. R., & Ganguly, B. (2024). AI-exposure and labour market: a systematic literature review on estimations, validations, and perceptions. Management Review Quarterly, 75, 677-704. https://doi.org/10.1007/s11301-023-00393-x Go to original source...
    40. Gibson, I., & Stubbs, G. (2024). A machine learning approach to police recruitment: Exploring the predictive value of social identity measurement instruments. The Police Journal: Theory, Practice and Principles, 97(4), 742-761. https://doi.org/10.1177/0032258X231221429 Go to original source...
    41. Gong, Q., Fan, D., & Bartram, T. (2025). Integrating Artificial Intelligence and Human Resource Management: A Review and Future Research Agenda" relevant to AI in recruitment. The International Journal of Human Resource Management, 36(1), 103-141. https://doi.org/10.1080/09585192.2024.2440065 Go to original source...
    42. Gupta, A., Chadha, A., Tiwari, V., Varma, A., & Pereira, V. (2023). Sustainable training practices: predicting job satisfaction and employee behavior using machine learning techniques. Asian Business & Management, 22, 1913-1936. https://doi.org/10.1057/s41291-023-00234-5 Go to original source...
    43. Gupta, P., Lakhera, G., & Sharma, M. (2024). Examining the impact of artificial intelligence on employee performance in the digital era: An analysis and future research direction. Journal of High Technology Management Research, 35, 100520. https://doi.org/10.1016/j.hitech.2024.100520 Go to original source...
    44. Haesevoets, T., Cremer, D. D., Dierckx, K., & Hiel, A. V. (2021). Human-machine collaboration in managerial decision making. Computers in Human Behavior, 119, 106730. https://doi.org/10.1016/j.chb.2021.106730 Go to original source...
    45. Hajal, G. E., & Yeoman, I. (2024). AI and the future of talent management in tourism and hospitality. Current Issues in Tourism, (in press). https://doi.org/10.1080/13683500.2024.2439395 Go to original source...
    46. Hao, W., Li, J., Shen, N., & Liu, G. (2024). Improvement of Optimal Human Resource Allocation Algorithm Based on Deep Latent Semantic Model. Journal of Organizational and End User Computing, 36(1), 1-23. https://doi.org/10.4018/JOEUC.358939 Go to original source...
    47. Hmoud, B. (2021). The adoption of artificial intelligence in Human Resource Management. Forum Scientiae Oeconomia, 9(1), 105-118. https://doi.org/10.23762/FSO_VOL9_NO1_7 Go to original source...
    48. Hu, Q. (2023). Unilever's Practice on AI-based Recruitment. Highlights in Business, Economics and Management, 16, 256-263. https://doi.org/10.54097/hbem.v16i.10565 Go to original source...
    49. Huang, X., Yang, F., Zheng, J., Feng, C., & Zhang, L. (2023). Personalized human resource management via HR analytics and artificial intelligence: Theory and implications. Asia Pacific Management Review, 28(4), 598-610. https://doi.org/10.1016/j.apmrv.2023.04.004 Go to original source...
    50. Indarapu, S. R., Vodithala, S., Kumar, N., Kiran, S., Reddy, S. N., & Dorthi, K. (2023). Exploring human resource management intelligence practices using machine learning models. Journal of High Technology Management Research, 34(2), 100466. https://doi.org/10.1016/j.hitech.2023.100466 Go to original source...
    51. Jia, X., & Hou, Y. (2024). Architecting the future: exploring the synergy of AI driven sustainable HRM, conscientiousness, and employee engagement. Discover Sustainability, 5, 30. https://doi.org/10.1007/s43621-024-00214-5 Go to original source...
    52. Johnson, R. D., Stone, D. L., & Lukaszewski, K. M. (2022). The benefits of eHRM and AI for talent acquisition. Journal of Tourism Futures, 7(1), 13. https://doi.org/10.1108/JTF-02-2020-0013 Go to original source...
    53. Joshi, A., & Masih, J. (2023). Enhancing Employee Efficiency and Performance in Industry 5.0 Organizations Through Artificial Intelligence Integration. European Economic Letters, 13(4), 300-315. https://doi.org/10.52783/eel.v13i4.589 Go to original source...
    54. Kaushal, N., Kaurav, R. P., Sivathanu, B., & Kaushik, N. (2021). Artifcial intelligence and HRM: identifying future research Agenda using systematic literature review and bibliometric analysis. Management Review Quarterly, 73, 455-493. https://doi.org/10.1007/s11301-021-00249-2 Go to original source...
    55. Kekez, I., Lauwaert, L., & Reðep, N. B. (2025). Is artificial intelligence (AI) research biased and conceptually vague? A systematic review of research on bias and discrimination in the context of using AI in human resource management. Technology in Society, 81, 102818. https://doi.org/10.1016/j.techsoc.2025.102818 Go to original source...
    56. Kelan, E. K. (2024). Algorithmic inclusion: Shaping the predictive algorithms of artificial intelligence in hiring. Human Resource Management Journal, 34(3), 694-707. https://doi.org/10.1111/1748-8583.12511 Go to original source...
    57. Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at Work: The New Contested Terrain of Control. Academy of Management Annals, 14(1), 366-410. https://doi.org/10.5465/annals.2018.0174 Go to original source...
    58. Khan, S., Faisal, S., & Thomas, G. (2024). Exploring the nexus of artificial intelligence in talent acquisition: Unravelling cost-benefit dynamics, seizing opportunities, and mitigating risks. Problems and Perspectives in Management, 22(1), 462-476. https://doi.org/10.21511/ppm.22(1).2024.37 Go to original source...
    59. Khera, S. N., & Divya. (2019). Predictive Modelling of Employee Turnover in Indian IT Industry Using Machine Learning Techniques. Vision: The Journal of Business Perspective, 23(1), 12-21. https://doi.org/10.1177/0972262918821221 Go to original source...
    60. Kim, J., Battaglia, S., & Kim, H. H. (2025). Collaborative intelligence achieved from AI-enabled recruitment: a case study of POSCO in South Korea. Asia Pacific Business Review, 31(1), 180-199. https://doi.org/10.1080/13602381.2024.2329099 Go to original source...
    61. Kock, F. S., Lievens, F., & Born, M. P. (2020). The profile of the 'Good Judge' in HRM: A systematic review and agenda for future research. Human Resource Management Review, 30(2), 100667. https://doi.org/10.1016/j.hrmr.2018.09.003 Go to original source...
    62. Koman, G., Bor¹o¹, P., & Kubina, M. (2024). The Possibilities of Using Artificial Intelligence as a Key Technology in the Current Employee Recruitment Process. Administrative Sciences, 14(7), 157. https://doi.org/10.3390/admsci14070157 Go to original source...
    63. Kulikowski, K. (2024). Defining analytical skills for human resources analytics: A call for standardization. Journal of Entrepreneurship, Management and Innovation, 20(4), 88-103. https://doi.org/10.7341/20242045 Go to original source...
    64. Langer, M., & König, C. J. (2023). Introducing a multi-stakeholder perspective on opacity, transparency and strategies to reduce opacity in algorithm-based human resource management. Human Resource Management Review, 33(1), 100881. https://doi.org/10.1016/j.hrmr.2021.100881 Go to original source...
    65. Lee, S. K., Kavya, P., & Lasser, S. C. (2021). Social interactions and relationships with an intelligent virtual agent. International Journal of Human-Computer Studies, 150, 102608. https://doi.org/10.1016/j.ijhcs.2021.102608 Go to original source...
    66. Li, P., Bastone, A., Mohamad, T. A., & Schiavone, F. (2023). How does artificial intelligence impact human resources performance. evidence from a healthcare institution in the United Arab Emirates. Journal of Innovation & Knowledge, 8(2), 100340. https://doi.org/10.1016/j.jik.2023.100340 Go to original source...
    67. Lukaszewski, K. M., & Stone, D. L. (2024). Will the use of AI in human resources create a digital Frankenstein? Organizational Dynamics, 53(1), 101033. https://doi.org/10.1016/j.orgdyn.2024.101033 Go to original source...
    68. Maity, S. (2019). Identifying opportunities for artificial intelligence in the evolution of training and development practices. Journal of Management Development, 38(8), 651-663. https://doi.org/10.1108/JMD-03-2019-0069 Go to original source...
    69. Malik, A. (2022). Work Design and HR Planning: A Strategic Perspective. In Strategic Human Resource Management and Employment Relations, (pp. 81-91). Springer. https://doi.org/10.1007/978-3-030-90955-0_8 Go to original source...
    70. Manroop, L., Malik, A., & Milner, M. (2024). The ethical implications of big data in human resource management. Human Resource Management Review, 34(2), 101012. https://doi.org/10.1016/j.hrmr.2024.101012 Go to original source...
    71. Mariani, M., & Dwivedi, Y. K. (2024). Generative artificial intelligence in innovation management: A preview of future research developments. Journal of Business Research, 175, 114542. https://doi.org/10.1016/j.jbusres.2024.114542 Go to original source...
    72. Marler, J. H. (2024). Artificial intelligence, algorithms, and compensation strategy: Challenges and opportunities. Organizational Dynamics, 53(1), 101039. https://doi.org/10.1016/j.orgdyn.2024.101039 Go to original source...
    73. Masud, A. A., Islam, M. T., Rahman, M. K., Rosid, M. H., Rahman, M. J., Akter, T., & Szabó, K. (2024). Fostering sustainability through technological brilliance: a study on the nexus of organizational STARA capability, GHRM, GSCM, and sustainable performance. Discover Sustainability, 5, 325. https://doi.org/10.1007/s43621-024-00495-w Go to original source...
    74. Mendy, J., Jain, A., & Thomas, A. (2024). Artificial intelligence in the workplace - challenges, opportunities and HRM framework: a critical review and research agenda for change. Journal of Managerial Psychology, (in press). https://doi.org/10.1108/JMP-05-2024-0388 Go to original source...
    75. Mer, A. (2023). Artificial Intelligence in Human Resource Management: Recent Trends and Research Agenda. In Contemporary Studies in Economic and Financial Analysis, (pp. 31-56). Emerald. https://doi.org/10.1108/S1569-37592023000111B003 Go to original source...
    76. Mikhlina, A., & Saukkonen, J. (2024). Using Personas in Human Capital Management: A Novel Concept Affected by Legal, Ethical and Technological Considerations. The Electronic Journal of Knowledge Management, 22(2), 50-62. https://doi.org/10.34190/ejkm.22.2.3594 Go to original source...
    77. Mishra, S., Kunte, M., Neelam, N., Bhattacharya, S., & mulay, p. (2021). HR Process Automation: A Bibliometric Analysis. Library Philosophy and Practice, 5577. Retrieved from https://digitalcommons.unl.edu/libphilprac/5577
    78. 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 Go to original source...
    79. Muridzi, G., Dhliwayo, S., & Isabelle, D. A. (2024). Artificial Intelligence In Transforming Hrm Processes Within Organizations. International Journal of Innovation Management, 28(9-10), 2440006. https://doi.org/10.1142/S1363919624400061 Go to original source...
    80. Nacheva, R. (2024). Hired by Artificial Intelligence: Digital Inclusion Practices for People With Disabilities. Journal of Underrepresented & Minority Progress, 8(2), 10-41. https://doi.org/10.32674/66c03968 Go to original source...
    81. Nankervis, A., Connell, J., Cameron, R., Montague, A., & Prikshat, V. (2021). 'Are we there yet?' Australian HR professionals and the Fourth Industrial Revolution. Asia Pacific Journal of Human Resources, 59(1), 3-19. https://doi.org/10.1111/1744-7941.12245 Go to original source...
    82. Nawaz, N., & Gomes, A. M. (2019). Artificial Intelligence Chatbots are New Recruiters. International Journal of Advanced Computer Science and Applications, 10(9), 1-5. https://doi.org/10.14569/IJACSA.2019.0100901 Go to original source...
    83. Ngayua, E. N., He, J., & Agyei-Boahene, K. (2021). Applying advanced technologies to improve clinical trials: a systematic mapping study. Scientometrics, 126, 1217-1238. https://doi.org/10.1007/s11192-020-03774-1 Go to original source...
    84. Niehueser, W., & Boak, G. (2020). Introducing artificial intelligence into a human resources function. Industrial and Commercial Training, 52(2), 121-130. https://doi.org/10.1108/ICT-10-2019-0097 Go to original source...
    85. Pan, Y., & Froese, F. J. (2023). An interdisciplinary review of AI and HRM: Challenges and future directions. Human Resource Management Review, 33(1), 100924. https://doi.org/10.1016/j.hrmr.2022.100924 Go to original source...
    86. Pandey, P., Sharma, A., Jeswani, S., Sharma, D., & Gupta, A. (2025). Spotlighting recruitments: is AI dominating human resource practices? Qualitative research using NVIVO. Quality & Quantity, 59, 1209-1228. https://doi.org/10.1007/s11135-025-02062-5 Go to original source...
    87. Parent-Rocheleau, X., & Parker, S. K. (2022). Algorithms as work designers: How algorithmic management influences the design of jobs. Human Resource Management Review, 32(3), 1-17. https://doi.org/10.1016/j.hrmr.2021.100838 Go to original source...
    88. Park, J. J. (2024). Unlocking training transfer in the age of artificial intelligence. Business Horizons, 67(3), 263-269. https://doi.org/10.1016/j.bushor.2024.02.002 Go to original source...
    89. Parviainen, H. (2022). Can algorithmic recruitment systems lawfully utilise automated decision-making in the EU? European Labour Law Journal, 13(2), 225-248. https://doi.org/10.1177/20319525221093815 Go to original source...
    90. Pereira, V., Hadjielias, E., Christofi, M., & Vrontis, D. (2023). A systematic literature review on the impact of artificial intelligence on workplace outcomes: A multi-process perspective. Human Resource Management Review, 33(1), 100857. https://doi.org/10.1016/j.hrmr.2021.100857 Go to original source...
    91. Pessach, D., Singer, G., Avrahami, D., Ben-Gal, H. C., Shmueli, E., & Ben-Gal, I. (2020). Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming. Decision Support Systems, 134, 113290. https://doi.org/10.1016/j.dss.2020.113290 Go to original source...
    92. Prentice, C., & Nguyen, M. (2020). Engaging and retaining customers with AI and employee service. Journal of Retailing and Consumer Services, 56, 102186. https://doi.org/10.1016/j.jretconser.2020.102186 Go to original source...
    93. Prikshat, V., Islam, M., Patel, P., Malik, A., Budhwar, P., & Gupta, S. (2023). AI-Augmented HRM: Literature review and a proposed multilevel framework for future research. Technological Forecasting and Social Change, 193, 122645. https://doi.org/10.1016/j.techfore.2023.122645 Go to original source...
    94. Prikshat, V., Patel, P., Varma, A., & Ishizaka, A. (2022). A multi-stakeholder ethical framework for AI-augmented HRM. International Journal of Manpower, 43(1), 226-250. https://doi.org/10.1108/IJM-03-2021-0118 Go to original source...
    95. Rabenu, E., & Baruch, Y. (2025). Cyborging HRM theory: from evolution to revolution - the challenges and trajectories of AI for the future role of HRM. Personnel Review, 54(1), 174-198. https://doi.org/10.1108/PR-02-2024-0111 Go to original source...
    96. R±b-Kettler, K., & Lehnervp, B. (2019). Recruitment in the Times of Machine Learning. Management Systems in Production Engineering, 27(2), 105-109. https://doi.org/10.1515/mspe-2019-0018 Go to original source...
    97. Radonjiæ, A., Duarte, H., & Pereira, N. (2024). Artificial intelligence and HRM: HR managers' perspective on decisiveness and challenges. European Management Journal, 42(1), 57-66. https://doi.org/10.1016/j.emj.2022.07.001 Go to original source...
    98. Raisch, S., & Krakowski, S. (2021). Artificial Intelligence and Management: The Automation-Augmentation Paradox. Academy of Management Review, 46(1), 192-210. https://doi.org/10.5465/amr.2018.0072 Go to original source...
    99. Rao, S., & Zhao, T. (2025). Ethical AI in HR: A Case Study of Tech Hiring. Journal of Computer Information Systems, (in press). https://doi.org/10.1080/08874417.2024.2446954 Go to original source...
    100. Revillod, G. (2024). Why Do Swiss HR Departments Dislike Algorithms in Their Recruitment Process? An Empirical Analysis. Administrative Sciences, 14(10), 253. https://doi.org/10.3390/admsci14100253 Go to original source...
    101. Rigotti, C., & Fosch-Villaronga, E. (2024). Fairness, AI & recruitment. Computer Law & Security Review, 53, 105966. https://doi.org/10.1016/j.clsr.2024.105966 Go to original source...
    102. Rodgers, W., Murray, J. M., Stefanidis, A., Degbey, W. Y., & Tarba, S. Y. (2023). An artificial intelligence algorithmic approach to ethical decision-making in human resource management processes. Human Resource Management Review, 33, 100925. https://doi.org/10.1016/j.hrmr.2022.100925 Go to original source...
    103. Roumbanis, L. (2025). On the present-future impact of AI technologies on personnel selection and the exponential increase in meta-algorithmic judgments. Futures, 166, 103538. https://doi.org/10.1016/j.futures.2025.103538 Go to original source...
    104. Sandeep, M., Lavanya, V., & Balakrishnan, J. (2025). Leveraging AI in recruitment: enhancing intellectual capital through resource-based view and dynamic capability framework. Journal of Intellectual Capital, (in press). https://doi.org/10.1108/JIC-05-2024-0155 Go to original source...
    105. Santhanam, N., Kumar, J. R., Kumar, V., & Saha, R. (2021). Employee turnover intention in the milieu of human resource management practices: moderating role of work-life balance. International Journal of Business Innovation and Research, 24(1), 57. https://doi.org/10.1504/ijbir.2021.111979 Go to original source...
    106. Seeber, I., Bittner, E., Briggs, R. O., De Vreede, T., De Vreede, G., Elkins, A., Maier, R., Merz, A. B., Oeste-Reiß, S., Randrup, N., Schwabe, G., & Söllner, M. (2019). Machines as teammates: A research agenda on AI in team collaboration. Information & Management, 57(2), 103174. https://doi.org/10.1016/j.im.2019.103174 Go to original source...
    107. Seppälä, P., & Ma³ecka, M. (2024). AI and discriminative decisions in recruitment: Challenging the core assumptions. Big Data & Society, 11(1). https://doi.org/10.1177/20539517241235872 Go to original source...
    108. Shet, S., & Nair, B. (2023). Quality of hire: expanding the multi-level fit employee selection using machine learning. International Journal of Organizational Analysis, 31(6), 2103-2117. https://doi.org/10.1108/IJOA-06-2021-2843 Go to original source...
    109. Simón, C., Revilla, E., & Sáenz, M. J. (2024). Integrating AI in organizations for value creation through Human-AI teaming: A dynamic-capabilities approach. Journal of Business Research, 182, 114783. https://doi.org/10.1016/j.jbusres.2024.114783 Go to original source...
    110. Sipior, J. C., Ward, B. T., Rusinko, C. A., & Lombardi, D. R. (2024). Bias in Using AI for Recruiting: Legal Considerations. Information Systems Management, 41(4), 399-412. https://doi.org/10.1080/10580530.2023.2294453 Go to original source...
    111. Sithambaram, R. A., & Tajudeen, F. P. (2023). Impact of artificial intelligence in human resource management: a qualitative study in the Malaysian context. Asia Pacific Journal of Human Resources, 61(4), 821-844. https://doi.org/10.1111/1744-7941.12356 Go to original source...
    112. Socoliuc, B. F., Nicolae, F., Pleșea, D. A., & Suciu, A. A. (2024). EU Maritime Industry Blue-Collar Recruitment: Sustainable Digitalization. Sustainability, 16(20), 8887. https://doi.org/10.3390/su16208887 Go to original source...
    113. Soleimani, M., Intezari, A., & Pauleen, D. J. (2022). Mitigating cognitive biases in developing ai-assisted recruitment systems: A knowledge-sharing approach. International Journal of Knowledge Management, 18(1), 1-18. https://doi.org/10.4018/IJKM.290022 Go to original source...
    114. Song, Y., & Wu, R. (2021). Analysing human-computer interaction behaviour in human resource management system based on artificial intelligence technology. Knowledge Management Research & Practice, (in press). https://doi.org/10.1080/14778238.2021.1955630 Go to original source...
    115. ©padina, H. (2023). Legal aspects of artificial intelligence in the employment process. Stanovni¹tvo , 61(2), 167-181. https://doi.org/10.59954/stnv.546 Go to original source...
    116. Sposato, M. (2024). Leadership training and development in the age of artificial intelligence. Development and Learning in Organizations, 38(4). https://doi.org/10.1108/DLO-12-2023-0256 Go to original source...
    117. Srivastava, P. R., & Eachempati, P. (2021). Intelligent Employee Retention System for Attrition Rate Analysis and Churn Prediction: An Ensemble Machine Learning and MultiCriteria Decision-Making Approach. Journal of Global Information Management, 29(6), 1-29. https://doi.org/10.4018/JGIM.20211101.oa23 Go to original source...
    118. Stone, D. L., Lukaszewski, K. M., & Johnson, R. D. (2024). Will artificial intelligence radically change human resource management processes? Organizational Dynamics, 53(1), 101034. https://doi.org/10.1016/j.orgdyn.2024.101034 Go to original source...
    119. Tay, C. E., Ying, C. Y., Yeo, S. F., & Cheah, C. S. (2024). Revolutionizing Recruitment: The Rise of Artificial Intelligence in Talent Acquisition. PaperASIA, 40(6). https://doi.org/10.59953/paperasia.v40i6b.270 Go to original source...
    120. Tian, X., Pavur, R., Han, H., & Zhang, L. (2023). A machine learning-based human resources recruitment system for business process management: using LSA, BERT and SVM. Business Process Management Journal, 29(1), 202-222. https://doi.org/10.1108/BPMJ-08-2022-0389 Go to original source...
    121. Tilmes, N. (2022). Disability, fairness, and algorithmic bias in AI recruitment. Ethics and Information Technology, 24, 21. https://doi.org/10.1007/s10676-022-09633-2 Go to original source...
    122. Tinguely, P. N., Lee, J., & He, V. F. (2023). Designing human resource management systems in the age of AI. Journal of Organization Design, 12, 263-269. https://doi.org/10.1007/s41469-023-00153-x Go to original source...
    123. Tolentino, R., Rodriguez, C., Hersson-Edery, F., Lane, J., & Rahimi, S. A. (2024). Perspectives on virtual interviews and emerging technologies integration in family medicine residency programs: a cross-sectional survey study. BMC Medical Education, 24, 975. https://doi.org/10.1186/s12909-024-05874-5 Go to original source...
    124. Tuffaha, M., & Perello-Marin, M. R. (2023). Artificial intelligence definition, applications and adoption in human resource management: a systematic literature review. International Journal of Business Innovation and Research, 32(3), 293-322. https://doi.org/10.1504/IJBIR.2023.134887 Go to original source...
    125. Tuffaha, M., Perello-Marin, Rosario, M., Suárez-Ruz, & Esperanza, M. (2022). Key elements in transferring knowledge of the AI implementation process for HRM in COVID-19 times: AI consultants' perspective. International Journal of Business Science and Applied Management, 17(1), 81-97. https://doi.org/10.69864/ijbsam.17-1.159 Go to original source...
    126. Tusquellas, N., Palau, R., & Santiago, R. (2024). Analysis of the potential of artificial intelligence for professional development and talent management: A systematic literature review. International Journal of Information Management Data Insights, 4(2), 100288. https://doi.org/10.1016/j.jjimei.2024.100288 Go to original source...
    127. Varma, A., Pereira, V., & Patel, P. (2024). Artificial intelligence and performance management. Organizational Dynamics, 53(1), 101037. https://doi.org/10.1016/j.orgdyn.2024.101037 Go to original source...
    128. Varma, S., Sanford, E. M., Marupudi, V., Shaffer, O., & Lea, R. B. (2024). Recruitment of magnitude representations to understand graded words. Cognitive Psychology, 153, 101673. https://doi.org/10.1016/j.cogpsych.2024.101673 Go to original source...
    129. Venugopal, M., Madhavan, V., Prasad, R., & Raman, R. (2024). Transformative AI in human resource management: enhancing workforce planning with topic modeling. Cogent Business & Management, 11(1), 2432550. https://doi.org/10.1080/23311975.2024.2432550 Go to original source...
    130. Verma, S., Rana, N., & Meher, J. R. (2024). Identifying the enablers of HR digitalization and HR analytics using ISM and MICMAC analysis. International Journal of Organizational Analysis, 32(3), 504-521. https://doi.org/10.1108/IJOA-01-2023-3611 Go to original source...
    131. Votto, A. M., Valecha, R., Najafirad, P., & Rao, H. R. (2021). Artificial Intelligence in Tactical Human Resource Management: A Systematic Literature Review. International Journal of Information Management Data Insights, 1(2), 1-15. https://doi.org/10.1016/j.jjimei.2021.100047 Go to original source...
    132. Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2023). Artificial intelligence, robotics, advanced technologies and human resource management: A systematic review. The International Journal of Human Resource Management, 33(6), 1237-1266. https://doi.org/10.1080/09585192.2020.1871398 Go to original source...
    133. Walker, D. O., & Larson, M. (2025). Leveraging Generative Artificial Intelligence (AI) for Human Resource Management: The AI Job Description Assignment. Journal of Management Education, 49(1), 113-141. https://doi.org/10.1177/10525629241294075 Go to original source...
    134. Wenting, L., Hussain, W. M., Xinlin, J., Na, M., & Alam, S. S. (2024). Analyzing the Impact on Talent Acquisition and Performance Management: HR and Data Analysis. Journal of Organizational and End User Computing, 36(1), 1-30. https://doi.org/10.4018/JOEUC.342603 Go to original source...
    135. Wuisan, D. S., Sunardjo, R. A., Aini, Q., Yusuf, N. A., & Rahardja, U. (2023). Integrating Artificial Intelligence in Human Resource Management: A SmartPLS Approach for Entrepreneurial Success. Aptisi Transactions on Technopreneurship, 5(3), 334-345. https://doi.org/10.34306/att.v5i3.355 Go to original source...
    136. Yi, P. K., Ray, N. D., & Segall, N. (2023). A novel use of an artificially intelligent Chatbot and a live, synchronous virtual question-and answer session for fellowship recruitment. BMC Medical Education, 152(23). https://doi.org/10.1186/s12909-022-03872-z Go to original source...
    137. Yuan, S., Kroon, B., & Kramer, A. (2024). Building prediction models with grouped data: A case study on the prediction of turnover intention. Human Resource Management Journal, 34(1), 20-38. https://doi.org/10.1111/1748-8583.12396 Go to original source...
    138. Yuan, S., Zhou, R., Li, M., & Lv, C. (2023). Investigating the influence of digital technology application on employee compensation. Technological Forecasting & Social Change, 195, 122787. https://doi.org/10.1016/j.techfore.2023.122787 Go to original source...
    139. Zhai, Y., Zhang, L., & Yu, M. (2024). AI in Human Resource Management: Literature Review and Research Implications. Journal of the Knowledge Economy, 15, 16227-16263. https://doi.org/10.1007/s13132-023-01631-z Go to original source...
    140. Zhang, D., & Pan, J. (2022). An Intelligent Scheduling Model of Computer Human Resources in Complex Scenarios Based on Artificial Intelligence. Wireless Communications and Mobile Computing, 2022, Article ID 8546634. https://doi.org/10.1155/2022/8546634 Go to original source...
    141. Zhang, H. (2024). Exploring the Impact of AI on Human Resource Management: A Case Study of Organizational Adaptation and Employee Dynamics. IEEE Transactions on Engineering Management, 71, 14991-15004. https://doi.org/10.1109/TEM.2024.3457520 Go to original source...
    142. Zheng, F., Zhao, C., Usman, M., & Poulova, P. (2024). From Bias to Brilliance: The Impact of Artificial Intelligence Usage on Recruitment Biases in China. IEEE Transactions on Engineering Management, 71, 14155-14167. https://doi.org/10.1109/TEM.2024.3442618 Go to original source...

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