Integrating AI into Human Resource Management: Implications for Recruitment and Retention
DOI:
https://doi.org/10.53555/jaes.v22i1.135Keywords:
Artificial Intelligence, Human Resource Management, Recruitment, Employee Retention, Algorithmic Decision-MakingAbstract
Artificial Intelligence (AI) is increasingly transforming Human Resource Management (HRM) by enabling data-driven, automated, and predictive decision-making across the employee lifecycle. This review article examines how the integration of AI into HRM reshapes recruitment and employee retention, two critical functions for organizational sustainability and competitive advantage. Drawing on an extensive body of interdisciplinary literature, the review synthesizes conceptual, empirical, and ethical perspectives on AI-enabled HR practices. The analysis highlights how AI applications such as algorithmic screening, automated interviews, people analytics, and predictive retention models enhance efficiency, consistency, and strategic alignment in HR decision-making. At the same time, the review identifies significant challenges related to fairness, transparency, algorithmic bias, employee perceptions, and human autonomy. The findings emphasize that the effectiveness of AI in HRM depends not only on technological sophistication but also on human-centered implementation, ethical governance, and contextual sensitivity. By integrating insights across recruitment, retention, and algorithmic management, this review addresses fragmentation in existing research and identifies key theoretical, methodological, and contextual gaps. The article concludes by outlining future research directions aimed at advancing responsible, sustainable, and human-centric AI adoption in HRM.
References
1. Acikgoz, Y., Davison, K. H., Compagnone, M., & Laske, M. (2020). Justice perceptions of artificial intelligence in selection. International Journal of Selection and Assessment, 28(4), 399-416.
2. Albrecht, S. L., Bakker, A. B., Gruman, J. A., Macey, W. H., & Saks, A. M. (2015). Employee engagement, human resource management practices and competitive advantage: An integrated approach. Journal of organizational effectiveness: People and performance, 2(1), 7-35.
3. Andrieux, P., Johnson, R. D., Sarabadani, J., & Van Slyke, C. (2024). Ethical considerations of generative AI-enabled human resource management. Organizational Dynamics, 53(1), 101032.
4. 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.
5. Black, J. S., & van Esch, P. (2020). AI-enabled recruiting: What is it and how should a manager use it?. Business horizons, 63(2), 215-226.
6. Bogen, M., & Rieke, A. (2018). Help wanted: An examination of hiring algorithms, equity, and bias. Upturn, December, 7.
7. Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G. J., Beltran, J. R., ... & Varma, A. (2023). Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT. Human Resource Management Journal, 33(3), 606-659.
8. Bujold, A., Roberge-Maltais, I., Parent-Rocheleau, X., Boasen, J., Sénécal, S., & Léger, P. M. (2024). Responsible artificial intelligence in human resources management: a review of the empirical literature. AI and Ethics, 4(4), 1185-1200.
9. Chamorro-Premuzic, T., Winsborough, D., Sherman, R. A., & Hogan, R. (2016). New talent signals: Shiny new objects or a brave new world?. Industrial and Organizational Psychology, 9(3), 621-640.
10. 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.
11. Collins, C., & Kehoe, R. (2017). Examining strategic fit and misfit in the management of knowledge workers. ILR Review, 70(2), 308-335.
12. Connelly, C. E., Fieseler, C., Černe, M., Giessner, S. R., & Wong, S. I. (2021). Working in the digitized economy: HRM theory & practice. Human Resource Management Review, 31(1), 100762.
13. Davenport, T. H., Harris, J., & Shapiro, J. (2010). Competing on talent analytics. Harvard business review, 88(10), 52-58.
14. De Cremer, D., & Kasparov, G. (2021). AI should augment human intelligence, not replace it. Harvard Business Review, 18(1), 1-8.
15. Dietvorst, B. J., Simmons, J. P., & Massey, C. (2018). Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Management science, 64(3), 1155-1170.
16. Dineen, B. R., & Allen, D. G. (2016). Third party employment branding: Human capital inflows and outflows following “best places to work” certifications. Academy of Management Journal, 59(1), 90-112.
17. Dong, J. Q., & Yang, C. H. (2020). Business value of big data analytics: A systems-theoretic approach and empirical test. Information & Management, 57(1), 103124.
18. Folger, N., Brosi, P., Stumpf-Wollersheim, J., & Welpe, I. M. (2022). Applicant reactions to digital selection methods: A signaling perspective on innovativeness and procedural justice. Journal of Business and Psychology, 37(4), 735-757.
19. França, T. J. F., São Mamede, H., Barroso, J. M. P., & Dos Santos, V. M. P. D. (2023). Artificial intelligence applied to potential assessment and talent identification in an organisational context. Heliyon, 9(4).
20. Fritts, M., & Cabrera, F. (2021). AI recruitment algorithms and the dehumanization problem. Ethics and Information Technology, 23(4), 791-801.
21. Garg, S., Sinha, S., Kar, A. K., & Mani, M. (2022). A review of machine learning applications in human resource management. International Journal of Productivity and Performance Management, 71(5), 1590-1610.
22. Halim, Z., Maria, Waqas, M., Edwin, C. A., & Shah, A. (2020). Identifying factors for employee retention using computational techniques: an approach to assist the decision-making process. SN Applied Sciences, 2(9), 1612.
23. Hausknecht, J. P., & Trevor, C. O. (2011). Collective turnover at the group, unit, and organizational levels: Evidence, issues, and implications. Journal of management, 37(1), 352-388.
24. Hickman, L., Bosch, N., Ng, V., Saef, R., Tay, L., & Woo, S. E. (2022). Automated video interview personality assessments: Reliability, validity, and generalizability investigations. Journal of Applied Psychology, 107(8), 1323.
25. Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business horizons, 61(4), 577-586.
26. 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.
27. Köchling, A., & Wehner, M. C. (2020). Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development. Business Research, 13(3), 795-848.
28. Küpper, D. M., Klein, K., & Völckner, F. (2021). Gamifying employer branding: An integrating framework and research propositions for a new HRM approach in the digitized economy. Human Resource Management Review, 31(1), 100686.
29. Langer, M., Baum, K., König, C. J., Hähne, V., Oster, D., & Speith, T. (2021). Spare me the details: How the type of information about automated interviews influences applicant reactions. International Journal of Selection and Assessment, 29(2), 154-169.
30. Langer, M., König, C. J., & Papathanasiou, M. (2019). Highly automated job interviews: Acceptance under the influence of stakes. International Journal of Selection and Assessment, 27(3), 217-234.
31. Lee, M. K. (2018). Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management. Big data & society, 5(1), 2053951718756684.
32. Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G. (2022). The challenges of algorithm-based HR decision-making for personal integrity. In Business and the Ethical Implications of Technology (pp. 71-86). Cham: Springer Nature Switzerland.
33. Malin, C. D., Fleiß, J., Seeber, I., Kubicek, B., Kupfer, C., & Thalmann, S. (2024). The application of AI in digital HRM–an experiment on human decision-making in personnel selection. Business Process Management Journal, 30(8), 284-312.
34. Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR Analytics. The International Journal of Human Resource Management, 28(1), 3-26.
35. McCarthy, J. M., Bauer, T. N., Truxillo, D. M., Anderson, N. R., Costa, A. C., & Ahmed, S. M. (2017). Applicant perspectives during selection: A review addressing “So what?,”“What’s new?,” and “Where to next?”. Journal of Management, 43(6), 1693-1725.
36. Meijerink, J., & Bondarouk, T. (2023). The duality of algorithmic management: Toward a research agenda on HRM algorithms, autonomy and value creation. Human resource management review, 33(1), 100876.
37. 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.
38. Newlands, G. (2021). Algorithmic surveillance in the gig economy: The organization of work through Lefebvrian conceived space. Organization studies, 42(5), 719-737.
39. 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.
40. Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020, January). Mitigating bias in algorithmic hiring: Evaluating claims and practices. In Proceedings of the 2020 conference on fairness, accountability, and transparency (pp. 469-481).
41. Sharma, R., Shishodia, A., Gunasekaran, A., Min, H., & Munim, Z. H. (2022). The role of artificial intelligence in supply chain management: mapping the territory. International Journal of Production Research, 60(24), 7527-7550.
42. Strohmeier, S. (2020). Digital human resource management: A conceptual clarification. German Journal of Human Resource Management, 34(3), 345-365.
43. Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California management review, 61(4), 15-42.
44. Tursunbayeva, A., Di Lauro, S., & Pagliari, C. (2018). People analytics—A scoping review of conceptual boundaries and value propositions. International journal of information management, 43, 224-247.
45. Upadhyay, A. K., & Khandelwal, K. (2018). Applying artificial intelligence: implications for recruitment. Strategic HR Review, 17(5), 255-258.
46. Van Esch, P., Black, J. S., & Ferolie, J. (2019). Marketing AI recruitment: The next phase in job application and selection. Computers in Human Behavior, 90, 215-222.
47. Veen, A., Barratt, T., & Goods, C. (2020). Platform-capital’s ‘app-etite’for control: A labour process analysis of food-delivery work in Australia. Work, employment and society, 34(3), 388-406.
48. Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of marketing, 80(6), 97-121.
49. Wesche, J. S., & Sonderegger, A. (2021). Repelled at first sight? Expectations and intentions of job-seekers reading about AI selection in job advertisements. Com
50. puters in Human Behavior, 125, 106931.
