The Impact of Al Adoption and Transparency on Recruitment Efficiency: The Mediating Role of Satisfaction in the IT Sector

Authors

Dixitha A.

Research Scholar, Department of Management Studies, Marudhar Kesari Jain College For Women, Vaniyambadi, Tirupattur District, Tamil Nadu. (India)

Dr. G. Deepalakshmi

Research Supervisor & Head, Department of Management Studies, Marudhar Kesari Jain College For Women, Vaniyambadi, Tirupattur District, Tamil Nadu. (India)

Article Information

DOI: 10.51244/IJRSI.2026.1304000131

Subject Category: Artificial Intelligence

Volume/Issue: 13/4 | Page No: 1471-1476

Publication Timeline

Submitted: 2026-04-22

Accepted: 2026-04-28

Published: 2026-05-06

Abstract

Artificial Intelligence (Al) is transforming human resource management, particularly within the information technology (IT) sector. This study investigates the factors influencing recruitment efficiency by analyzing the effects of Al adoption and perceived transparency on recruitment outcomes. Using structural equation modeling, we examined data from IT professionals to determine how Al integration and organizational transparency influence satisfaction and, subsequently, recruitment efficiency. Our findings indicate that both Al adoption and transparency have significant positive impacts on satisfaction and recruitment efficiency, with satisfaction acting as a critical bridge. These results provide strategic implications for HR leaders aiming to optimize recruitment processes through technology.

Keywords

Perceived transparency, user satisfaction

Downloads

References

1. Madanchian, M., & Taherdoost, H. (2025). Barriers and enablers of Al adoption in human resource management: A critical analysis of organizational and technological factors. Information, 16(1), 51. https://doi.org/10.3390/nfo16010051 [Google Scholar] [Crossref]

2. Ncube, F. (2025). The impact of artificial intelligence on human resource management practices: An investigation. SA Journal of Human Resource Management, 23(1). https://se/brm.co.za/index.php/sajbrm/article/view/2960/4807 [Google Scholar] [Crossref]

3. Zhang, X., Antwi-Afari, M., Zhang, Y., & Xing, X. (2024). The impact of artificial intelligence on organizational justice and project performance: A systematic literature and science mapping review. Buildings, 14(1), 259. https://doi.org/10.3390/buildings14010259 [Google Scholar] [Crossref]

4. Alam, M., & Verma, S. (2023). Impact of AI-based HR systems on employee outcomes. International Journal of Human Resource Management. [Google Scholar] [Crossref]

5. Binns, R., Veale, M., Van Kleek, M., & Shadbolt, N. (2018). Like trainer, like bot? Fairness and accountability in algorithmic decision-making. [Google Scholar] [Crossref]

6. Black, J. S., & van Esch, P. (2021). AI-enabled recruiting: What is it and how should HR adapt? Business Horizons. [Google Scholar] [Crossref]

7. DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success. Journal of Management Information Systems. [Google Scholar] [Crossref]

8. Kaur, D., Uslu, S., Rittichier, K. J., & Durresi, A. (2023). Trustworthy AI: Explainability and transparency. ACM Computing Surveys. [Google Scholar] [Crossref]

9. Köchling, A., & Wehner, M. C. (2023). Discriminated by an algorithm: A systematic review of discrimination in AI recruitment. Business Research. [Google Scholar] [Crossref]

10. Nawaz, N., et al. (2023). AI-driven recruitment and organizational performance. Journal of Organizational Computing. [Google Scholar] [Crossref]

11. Rai, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science. [Google Scholar] [Crossref]

12. Raisch, S., & Krakowski, S. (2023). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review. [Google Scholar] [Crossref]

13. Rudin, C. (2023). Interpretable machine learning: Fundamental principles. Nature Machine Intelligence. [Google Scholar] [Crossref]

14. Shin, D. (2021). The effects of explainability and causability on trust in AI. Computers in Human Behavior. [Google Scholar] [Crossref]

15. Shin, D. (2023). User perceptions of algorithmic decisions: Fairness, transparency, and trust. Information Systems Frontiers. [Google Scholar] [Crossref]

16. Shin, D., & Park, Y. (2023). Role of explainability in AI-based decision-making. Telematics and Informatics. [Google Scholar] [Crossref]

17. Tambe, P., Cappelli, P., & Yakubovich, V. (2019). AI in HRM: A review. Journal of Management. [Google Scholar] [Crossref]

18. Upadhyay, A. K., & Khandelwal, K. (2018). Applying AI in recruitment. Strategic HR Review. [Google Scholar] [Crossref]

19. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance of IT. MIS Quarterly. [Google Scholar] [Crossref]

Metrics

Views & Downloads

Similar Articles