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How to cite this paper
APA: Al-Maaitah, T. (2026). Integration of generative artificial intelligence and human resource practices. International Journal of Data and Network Science, 10(2), 867-876.
Chicago/Turabian: Al-Maaitah, T. 2026. "Integration of generative artificial intelligence and human resource practices." International Journal of Data and Network Science 10, no. 2 (2026): 867-876.
AMA: Al-Maaitah, T. Integration of generative artificial intelligence and human resource practices. International Journal of Data and Network Science. 2026;10(2):867-876.
References
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Meijerink, J., Bondarouk, T., & Lepak, D. P. (2020). When HRM meets the digital age: A review of the role of HRM technologies. Human Resource Management Review, 30(1), 100688.
Mikalef, P., Krogstie, J., Pappas, I. O., & Giannakos, M. (2018). Big data analytics capabilities: A systematic literature review and research agenda. Information Systems and e-Business Management, 16(3), 547–578.
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 1–21.
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63, 539–569.
Stone, D. L., Deadrick, D. L., Lukaszewski, K. M., & Johnson, R. (2015). The influence of technology on the future of human resource management. Human Resource Management Review, 25(2), 216–231.
Strohmeier, S., & Piazza, F. (2015). Artificial intelligence in human resource management: A call for research. German Journal of Human Resource Management, 29(3–4), 235–237.
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.
Ulrich, D., Brockbank, W., Johnson, D., Sandholtz, K., & Younger, J. (2008). HR competencies: Mastery at the intersection of people and business. Society for Human Resource Management.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
Binns, R., Veale, M., Van Kleek, M., & Shadbolt, N. (2018). ‘It’s reducing a human being to a percentage’: Perceptions of justice in algorithmic decisions. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems.
Bondarouk, T., & Ruël, H. (2009). Electronic human resource management: Challenges in the digital era. The International Journal of Human Resource Management, 20(3), 505–514.
Brougham, D., & Haar, J. (2018). Smart technology, artificial intelligence, and the future of work. Journal of Business Research, 103, 269–274.
Bučková, J., & Przyluska-Schmitt, J. (2025). The use of generative artificial intelligence in human resource management: Transformation of HR processes and challenges for the future. In Lecture Notes on Data Engineering and Communications Technologies (Vol. 253, pp. 361–372). Springer.
George, G., Haas, M. R., & Pentland, A. (2014). Big data and management. Academy of Management Journal, 57(2), 321–326.
Garcia, R. L. F. (2025). Generative artificial intelligence in human resource management: A critical reflection on impacts, resilience and roles. International Journal of Contemporary Hospitality Management.
Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.
Huang, M.-H., & Rust, R. T. (2021). Artificial intelligence in service. Journal of Service Research, 24(1), 3–20.
Jarrah, M. H. (2018). Artificial intelligence and the future of work: Human–AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586.
Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Scherer, A., & Wildhaber, I. (2019). The challenges of algorithm-based HR decision-making for personal integrity. Journal of Business Ethics, 160(2), 377–392.
Margherita, A., & Braccini, A. M. (2022). Business process management for industry 4.0: Towards digital transformation. Business Process Management Journal, 28(1), 89–107.
Meijerink, J., Bondarouk, T., & Lepak, D. P. (2020). When HRM meets the digital age: A review of the role of HRM technologies. Human Resource Management Review, 30(1), 100688.
Mikalef, P., Krogstie, J., Pappas, I. O., & Giannakos, M. (2018). Big data analytics capabilities: A systematic literature review and research agenda. Information Systems and e-Business Management, 16(3), 547–578.
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 1–21.
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63, 539–569.
Stone, D. L., Deadrick, D. L., Lukaszewski, K. M., & Johnson, R. (2015). The influence of technology on the future of human resource management. Human Resource Management Review, 25(2), 216–231.
Strohmeier, S., & Piazza, F. (2015). Artificial intelligence in human resource management: A call for research. German Journal of Human Resource Management, 29(3–4), 235–237.
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.
Ulrich, D., Brockbank, W., Johnson, D., Sandholtz, K., & Younger, J. (2008). HR competencies: Mastery at the intersection of people and business. Society for Human Resource Management.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.