Published March 13, 2025 | Version v1
Journal article Open

AI IN HUMAN RESOURCE MANAGEMENT: TRANSFORMING TALENT ACQUISITION AND EMPLOYEE ENGAGEMENT

Creators

  • 1. ROR icon American International University-Bangladesh
  • 2. ROR icon International University of Business Agriculture and Technology

Description

This study aims to explore the impact of Artificial Intelligence (AI) on Human Resource Management (HRM) practices in Bangladesh, focusing on talent acquisition and employee engagement. The research seeks to analyze the use of AI in recruitment processes, the extent to which AI enhances employee engagement, and the challenges organizations face in adopting AI technologies within HRM. A qualitative research design was employed, utilizing semi-structured interviews with HR professionals, employees, and industry experts across various sectors in Bangladesh. A total of 60 interviews were conducted to gather insights on AI adoption, its effects on HRM, and the challenges encountered during its implementation. The study reveals that AI has significantly improved recruitment efficiency, reduced bias in selection, and enhanced employee engagement through personalized experiences and real-time feedback systems. However, challenges such as high costs, resistance to change, and concerns about privacy and job security were also identified. This research provides valuable insights into the benefits and challenges of AI integration in HRM, contributing to the understanding of how AI can shape HR practices in developing economies like Bangladesh. It also opens avenues for further studies on AI adoption in HRM, particularly in emerging markets. Organizations in Bangladesh can leverage AI technologies to improve recruitment processes and enhance employee engagement. However, careful consideration of ethical issues, employee concerns, and infrastructural barriers is essential for successful adoption. AI in HRM can improve job matching, reduce bias, and enhance employee satisfaction. However, its adoption needs to be managed carefully to avoid potential negative impacts on job security and privacy. This study offers novel insights into AI adoption in HRM within the context of Bangladesh, providing a basis for future research and practical applications in HRM across developing countries. The study is limited to a small sample size and specific geographic context, which may limit the generalizability of the findings to other regions.

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Additional details

Dates

Available
2025-03-13

References

  • Agarwal, V., Mathiyazhagan, K., Malhotra, S., & Saikouk, T. (2022). Analysis of challenges in sustainable human resource management due to disruptions by Industry 4.0: an emerging economy perspective. International Journal of Manpower, 43(2), 513–541. https://doi.org/10.1108/IJM-03-2021-0192
  • Alam, M. J., Ogawa, K., Basharat, L., & Ahsan, A. H. M. (2025). Significance of quality higher education in the advancement of gender equality: the case of Bangladesh. Journal of Applied Research in Higher Education, 17(1), 23–35. https://doi.org/10.1108/JARHE-05-2023-0177
  • Aziz, F., Haque, A. T., Hossain, M. B., Rahman, A., & Siam, S. A. J. (2023). Customer Behavior Analysis Through Data Analytics in The Bangladeshi Retail Industry. Malaysian E Commerce Journal, 7(2), 78–84. https://doi.org/10.26480/mecj.02.2023.78.84
  • Bhuiyan, M. R. I., Husain, T., Islam, S., & Amin, A. (2025). Exploring the prospective influence of artificial intelligence on the health sector in Bangladesh: a study on awareness, perception and adoption. Health Education. https://doi.org/10.1108/HE-10-2024-0125
  • Burhan, Q.-A. (2025). Unraveling the AI enigma: how perceptions of artificial intelligence forge career adaptability through the crucible of career insecurity and skill development. Management Research Review, 48(3), 470–488. https://doi.org/10.1108/MRR-01-2024-0022
  • Emon, M. M. H., & Khan, T. (2025a). A Systematic Literature Review on Sustainability Integration and Marketing Intelligence in the Era of Artificial Intelligence. Review of Business and Economics Studies, 12(4), 6–28. https://doi.org/10.26794/2308-944X-2024-12-4-6-28
  • Chowdhury, N. T., Mahdzan, N. S., & Rahman, M. (2024). Investors in the Bangladeshi stock market: issues, behavioural biases and circumvention strategies. Qualitative Research in Financial Markets, 16(5), 860–879. https://doi.org/10.1108/QRFM-09-2022-0164
  • Budhwar, P., Malik, A., De Silva, M. T. T., & Thevisuthan, P. (2022). Artificial intelligence – challenges and opportunities for international HRM: a review and research agenda. The International Journal of Human Resource Management, 33(6), 1065–1097. https://doi.org/10.1080/09585192.2022.2035161
  • Esmaeilzadeh, P. (2024). Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations. Artificial Intelligence in Medicine, 151, 102861. https://doi.org/10.1016/j.artmed.2024.102861
  • Fettes, T., Evans, K., & Kashefpakdel, E. (2020). Putting skills to work: it's not so much the what, or even the why, but how…. Journal of Education and Work, 33(2), 184–196. https://doi.org/10.1080/13639080.2020.1737320
  • Emon, M. M. H., & Khan, T. (2025b). The mediating role of attitude towards the technology in shaping artificial intelligence usage among professionals. Telematics and Informatics Reports, 17, 100188. https://doi.org/10.1016/j.teler.2025.100188
  • Khan, A. N., Soomro, M. A., & Pitafi, A. H. (2024). AI in the Workplace: Driving Employee Performance Through Enhanced Knowledge Sharing and Work Engagement. International Journal of Human–Computer Interaction, 1–14. https://doi.org/10.1080/10447318.2024.2436611
  • Kambur, E., & Yildirim, T. (2023). From traditional to smart human resources management. International Journal of Manpower, 44(3), 422–452. https://doi.org/10.1108/IJM-10-2021-0622
  • Juhn, Y. J., Malik, M. M., Ryu, E., Wi, C.-I., & Halamka, J. D. (2024). Socioeconomic bias in applying artificial intelligence models to health care. In Artificial Intelligence in Clinical Practice (pp. 413–435). Elsevier. https://doi.org/10.1016/B978-0-443-15688-5.00044-9
  • Emon, M. M. H., Khan, T., Rahman, M. A., & Siam, S. A. J. (2024). Factors Influencing the Usage of Artificial Intelligence among Bangladeshi Professionals: Mediating role of Attitude Towards the Technology. 2024 IEEE International Conference on Computing, Applications and Systems (COMPAS), 1–7. https://doi.org/10.1109/COMPAS60761.2024.10796110
  • Johnson, B. A. M., Coggburn, J. D., & Llorens, J. J. (2022). Artificial Intelligence and Public Human Resource Management: Questions for Research and Practice. Public Personnel Management, 51(4), 538–562. https://doi.org/10.1177/00910260221126498
  • Ledro, C., Nosella, A., & Dalla Pozza, I. (2023). Integration of AI in CRM: Challenges and guidelines. Journal of Open Innovation: Technology, Market, and Complexity, 9(4), 100151. https://doi.org/10.1016/j.joitmc.2023.100151
  • Khan, T., Emon, M. M. H., Rahman, M. A., Hamid, A. B. A., & Yaakub, N. I. (2025). Bridging the Gap: Realizing GreenTech Potential. In AI and Green Technology Applications in Society (pp. 91–122). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-9879-1.ch004
  • Naim, M. F. (2023). Reinventing Workplace Learning and Development: Envisaging the Role of AI. In The Adoption and Effect of Artificial Intelligence on Human Resources Management, Part A (pp. 215–227). Emerald Publishing Limited. https://doi.org/10.1108/978-1-80382-027-920231011
  • Paramita, D., Okwir, S., & Nuur, C. (2024). Artificial intelligence in talent acquisition: exploring organisational and operational dimensions. International Journal of Organizational Analysis, 32(11), 108–131. https://doi.org/10.1108/IJOA-09-2023-3992
  • Teresa, B. M., Danilo, C., Berenice, F. N., Domenico, G., & Azzurra, R. (2024). Fostering Human Rights in Responsible AI: A Systematic Review for Best Practices in Industry. IEEE Transactions on Artificial Intelligence, 1–15. https://doi.org/10.1109/TAI.2024.3394389