Artificial Intelligence in HRM: An Analysis of Its Effects on Recruitment Practices, Performance Management, and Employee Experience
Keywords:
INFORMATION TECHNOLOGY-BASEDAbstract
The integration of Artificial Intelligence (AI) into Human Resource Management (HRM) has initiated a significant transformation in key organizational functions, particularly recruitment, performance management, and employee experience. AI-enabled systems have enhanced recruitment processes through data-driven decision-making, enabling faster candidate screening while potentially minimizing human bias and promoting inclusivity. In parallel, AI-based performance management tools facilitate real-time monitoring and continuous feedback, supporting more adaptive and individualized employee development strategies.This study provides a comprehensive examination of the impact of AI on HRM practices, with a specific focus on recruitment efficiency, performance management effectiveness, and employee experience. A mixed-methods research design was employed to ensure a robust analysis. Qualitative data were collected through in-depth interviews with HR professionals across multiple industries to capture experiential insights regarding AI adoption and its organizational implications. Complementarily, quantitative data analysis was conducted using organizational metrics to evaluate measurable outcomes associated with AI implementation, including improvements in recruitment processes, employee performance, and satisfaction levels.The findings indicate that AI-driven recruitment technologies contribute to greater efficiency in candidate selection by automating resume screening and aligning applicant profiles with job requirements. While these tools show potential in reducing subjective bias, concerns remain regarding algorithmic transparency and fairness. In the area of performance management, AI facilitates continuous performance tracking, enabling timely feedback and more personalized development planning. However, the increased reliance on automated systems raises critical issues related to employee privacy, trust, and over-surveillance. The implications of this study provide strategic guidance for organizations aiming to integrate AI into HRM practices, emphasizing the importance of balancing technological innovation with ethical considerations. Moreover, the study highlights the need for more comprehensive and context-sensitive research to better understand the long-term impact of AI on employee well-being and organizational sustainability.
References
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Soh, C., & Connolly, D. (2020). New frontiers of profit and risk: The fourth industrial revolution ’ s impact on business and human rights. New Political Economy, 26(1), 168–185.
Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson.
Tong, S., Jia, N., Luo, X., & Fang, Z. (2021). The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance. Strategic Management Journal, 42(9), 1600 – 1631. https://doi.org/10.1002/smj.3322
Urba, O., Chervona, V., Panchenko, L., Artemenko, O., & Guk, O. (2015).
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Xu, M., David, J. M., & Kim, S. H. (2018). The fourth industrial revolution: Opportunities and challenges. International Journal of Financial Research, 9, 90– 95. https://doi.org/10.5430/ijfr.v9n2p90
Yawalkar, V. (2019). A study of artificial intelligence and its role in human resource management. International Journal of Research and Analytical Reviews
Aazam, M., Harras, K. A., & Zeadally, S. (2019). Fog computing for 5G tactile industrial internet of things: QoE-aware resource allocation model. IEEE Transactions on Industrial Informatics, 15(5), 3085–3092.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modelling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411.
Bader, V., & Kaiser, S. (2019). Algorithmic decision-making? The user interface and its role for human involvement in decisions supported by artificial intelligence. Organization, 26(5), 655 – 672. https://doi.org/10.1177/1350508419855714
Bag, S., Gupta, S., Kumar, A., & Sivarajah, U. (2021). An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance. Industrial Marketing Management, 92, 178–189. https://doi.org/10.1016/j.indmarman.2020.12.001
Bendick, M., Jr., Jackson, C. W., & Romero, J. H. (1997). Employment discrimination against older workers: An experimental study of hiring practices. Journal of Aging & Social Policy, 8, 25–46.
Bertrand, M., & Mullainathan, S. (2004). Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. American Economic Review, 94, 991–1013.
Black, J. S., & van Esch, P. (2020). AI-enabled recruiting: What is it and how should a manager use it? Business Horizons, 63, 215–226.
Buck, B., & Morrow, J. (2018). AI, performance management and engagement: Keeping your best their best. Strategic HR Review, 17(5), 261 – 262. https://doi.org/10.1108/SHR-10-2018-145
Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong, L. (2018). Unlocking the value of artificial intelligence in human resource management through AI capability framework. Human Resource Management Review.
Eberhard, B., Podio, M., Pérez Alonso, A., Radovica, E., Avotina, L., Peiseniece, L., Caamaño Sendon, M., Gonzales Lozano, A., & Solé-Pla, J. (2017). Smart work: The transformation of the labour market due to the fourth industrial revolution (I4.0). International Journal of Business and Economic Sciences Applied Research, 10(3), 47–66.
Eriksson, T., Bigi, A., & Bonera, M. (2020). Think with me, or think for me? On the future role of artificial intelligence in marketing strategy formulation. The TQM Journal, 32(4), 795–814. https://doi.org/10.1108/TQM-12-2019-0303
ET Bureau. (2022). Tool to offer companies a definitive edge for pinpointing and engaging top talent.
Euchner, J. (2019). Little AI, big AI — Good AI, bad AI. Research-Technology Management, 62(3), 10–12. https://doi.org/10.1080/08956308.2019.1587280
Grover, P., Kar, A. K., & Dwivedi, Y. K. (2020). Understanding artificial intelligence adoption in operations management: Insights from the review of academic literature and social media discussions. Annals of Operations Research, 289(1), 1–37. https://doi.org/10.1007/s10479-020-03683-9
Hinkin, T. R. (1995). A review of scale development practices in the study of organizations. Journal of Management, 21(5), 967–988.
Hsieh, Y. M., & Hsieh, A. T. (2003). Does job standardization increase job burnout?
International Journal of Manpower, 24(5), 590 – 614. https://doi.org/10.1108/01437720310491107
Ibarra, D., Ganzarain, J., & Igartua, J. I. (2018). Business model innovation through Industry 4.0: A review. Procedia Manufacturing, 22, 4–10.
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007
Kalischko, T., & Riedl, R. (2021). Electronic performance monitoring in the digital workplace: Conceptualization, review of effects and moderators, and future research opportunities. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.633031
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
Kline, R. B. (2016). Principles and practice of structural equation modeling. Guilford Press. Koslowsky, M., Aizer, A., & Krausz, M. (1996). Stressor and personal variables in the commuting experience. International Journal of Manpower, 17(3), 4 – 14. https://doi.org/10.1108/01437729610119478
Kovach, K. A., & Cathcart, C. E. (1999). Human resource information systems (HRIS): Providing business with rapid data access, information exchange and strategic advantage. Public Personnel Management, 28(2), 275–282.
Nazri, M. Z. A., Ghani, R. A., Abdullah, S., Ayu, M., & Nor Samsiah, R. (2019). Predicting academician publication performance using decision tree. International Journal of Recent Technology and Engineering, 8(2), 180–181.
Priyanka, R., Ravindran, K., Sankaranarayanan, B., & Ali, S. M. (2023). A fuzzy DEMATEL decision modeling framework for identifying key human resources challenges in start-up companies: Implications for sustainable development. Decision Analytics Journal, 6.
Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020). Mitigating bias in algorithmic hiring: Evaluating claims and practices. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 469–481).
Sarkar, S., Pramanik, A., Maiti, J., & Reniers, G. (2021). COVID-19 outbreak: A data-driven optimization model for allocation of patients. Computers & Industrial Engineering, 161.
Schumann, C., Foster, J. S., Mattei, N., & Dickerson, J. P. (2020). We need fairness and explainability in algorithmic hiring. In Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems (pp. 1716–1720).
Soh, C., & Connolly, D. (2020). New frontiers of profit and risk: The fourth industrial revolution ’ s impact on business and human rights. New Political Economy, 26(1), 168–185.
Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson.
Tong, S., Jia, N., Luo, X., & Fang, Z. (2021). The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance. Strategic Management Journal, 42(9), 1600 – 1631. https://doi.org/10.1002/smj.3322
Urba, O., Chervona, V., Panchenko, L., Artemenko, O., & Guk, O. (2015).
Features of the application of digital technologies for human resources management of an engineering enterprise. Ingénierie des Systèmes d’Information, 27(2).
Xu, M., David, J. M., & Kim, S. H. (2018). The fourth industrial revolution: Opportunities and challenges. International Journal of Financial Research, 9, 90– 95. https://doi.org/10.5430/ijfr.v9n2p90
Yawalkar, V. (2019). A study of artificial intelligence and its role in human resource management. International Journal of Research and Analytical Reviews
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