Investigating the Role of AI in Transforming Recruitment: Insights from HR and Talent Acquisition
Authors
Department of HRM, Faculty of Management, University of Peradeniya (Sri Lanka)
Department of Interdisciplinary Studies, Faculty of Engineering, South Eastern University of Sri Lanka (Sri Lanka)
Department of Management Studies, Faculty of Communication and Business Studies, Trincomalee Campus, Eastern University (Sri Lanka)
Article Information
DOI: 10.47772/IJRISS.2025.910000569
Subject Category: Management
Volume/Issue: 9/10 | Page No: 6939-6954
Publication Timeline
Submitted: 2025-10-16
Accepted: 2025-10-21
Published: 2025-11-18
Abstract
The method of recruitment has changed dramatically due to the integration of Artificial Intelligence (AI) into Human Resource Management (HRM), which has increased both efficacy and efficiency. This study aims to examine how AI is transforming the recruitment process and its impact on candidate sourcing, screening, and selection. Chatbots, applicant tracking systems, and predictive analytics are examples of AI-based solutions which are increasingly used to automate repetitive tasks, reduce human bias, and raise the accuracy of decisions. This quantitative study investigates the impact of AI-driven recruitment systems in Sri Lanka, focusing on four organizations. Data was collected from 50 executive employees via a structured online survey using Google Forms, comprising Likert-scale items rated on a five-point scale. Statistical analysis was conducted using IBM SPSS Statistics version 30.0.0 and SmartPLS 4 for structural equation modeling (SEM). This study aims to provide empirical evidence on executives' perceptions of AI tools in recruitment, assess the reliability and validity of the measurement model, and examine the structural impact of variables. The findings offer practical implications for HR practitioners seeking to implement or optimize AI-based hiring processes in Sri Lankan organizational settings. AI technologies can enhance HRM innovation by aligning with organizational goals and ethical standards. However, successful implementation requires careful planning, transparency, and continuous evaluation to ensure fairness and accountability. HR practitioners must adapt to AI-driven recruitment solutions in Sri Lanka to improve performance and global competitiveness. Continued research in AI-driven recruitment systems is crucial to address new opportunities and challenges, ensuring businesses stay ahead of the curve in HRM innovation and best practices.
Keywords
AI in HRM, Future Recruitment, Recruitment effect on AI
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References
1. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. [Google Scholar] [Crossref]
2. Ajzen, I., & Fishbein, M. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Addison-Wesley. [Google Scholar] [Crossref]
3. Armstrong, M. (2006). A handbook of human resource management practice. Kogan Page. [Google Scholar] [Crossref]
4. Barber, A. E. (1998). Recruiting employees: Individual and organizational perspectives. Sage Publications. Bersin, J. (2018). AI in HR: A Real-World Guide to Success. Forbes. [Google Scholar] [Crossref]
5. Bersin, J. (2019). AI in HR: A Guide to Using Artificial Intelligence in Human Resources. Bersin by Deloitte. [Google Scholar] [Crossref]
6. Breaugh, J. A. (2008). Employee recruitment: Current knowledge and important areas for future research. Human Resource Management Review, 18(3), 103-118. [Google Scholar] [Crossref]
7. Breaugh, J. A., & Starke, M. (2000). Research on employee recruitment: So many studies, so many remaining questions. Journal of Management, 26(3), 405-434. [Google Scholar] [Crossref]
8. Cascio, W. F. (2018). Managing human resources: Productivity, quality of work life, profits. McGrawHill Education. [Google Scholar] [Crossref]
9. Chapman, D. S., & Mayers, D. (2015). Recruitment in the Internet age: A review and critique of current research and practice. International Journal of Selection and Assessment, 23(3), 247-264. [Google Scholar] [Crossref]
10. Cheng, M., & Jiang, H. (2020). AI-powered recruitment: A review and future directions. International Journal of Selection and Assessment, 28(2), 147-162. [Google Scholar] [Crossref]
11. Davenport, T. H., & Dyché, J. (2013). Big data in big companies. International Journal of Business Intelligence Research, 4(1), 1-12. [Google Scholar] [Crossref]
12. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. [Google Scholar] [Crossref]
13. Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Plenum Press. [Google Scholar] [Crossref]
14. Deci, E. L., & Ryan, R. M. (1992). Intrinsic motivation and self-determination in human behavior. Plenum Press. [Google Scholar] [Crossref]
15. Duggan, J., & Sherman, U. (2020). Automation, algorithmic decision-making, and the future of work: [Google Scholar] [Crossref]
16. Implications for HRM. Human Resource Management Review, 30(4), 100747. [Google Scholar] [Crossref]
17. Heneman, H. G., & Judge, T. A. (2009). Staffing organizations. McGraw-Hill. [Google Scholar] [Crossref]
18. Knockri. (n.d.). About Us. Retrieved from (link unavailable). [Google Scholar] [Crossref]
19. Levesque, L. L., & Whitaker, P. (2013). Effective recruitment and selection practices. In J. W. Hedge & W. C. Borman (Eds.), The Oxford handbook of work and organizational psychology (Vol. 2, pp. 381-404). [Google Scholar] [Crossref]
20. Oxford University Press. [Google Scholar] [Crossref]
21. Mya Systems. (n.d.). Conversational AI for Hiring. Retrieved from (link unavailable). [Google Scholar] [Crossref]
22. Parliament of Sri Lanka. (2022). Personal Data Protection Act, No. 9 of 2022. [Google Scholar] [Crossref]
23. Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the Technology Acceptance Model. International Journal of Electronic Commerce, 7(3), 101–134. [Google Scholar] [Crossref]
24. Rogers, E. M. (1995). Diffusion of innovations (4th ed.). Free Press. [Google Scholar] [Crossref]
25. Stone, D. L., Deadrick, D. L., Lukaszewski, K. M., & Johnson, R. D. (2015). The influence of technology on the future of human resource management. Human Resource Management Review, 25(2), 126-135. [Google Scholar] [Crossref]
26. Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and opportunities. California Management Review, 61(4), 15-42. [Google Scholar] [Crossref]
27. Taylor, M. S., & Collins, C. J. (2000). Organizational recruitment: Enhancing the intersection of research and practice. In C. L. Cooper & E. A. Locke (Eds.), Industrial and organizational psychology: Linking theory with practice (pp. 304-335). Blackwell. [Google Scholar] [Crossref]
28. Textio. (n.d.). How It Works. Retrieved from (link unavailable). [Google Scholar] [Crossref]
29. Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 125–143. [Google Scholar] [Crossref]
30. 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. [Google Scholar] [Crossref]
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