Artificial Intelligence–Driven Employee–Job Profile Matching and Employee Performance: The Mediating Role of Person–Job Fit

Abstract

Purpose: Artificial intelligence (AI) has transformed recruitment and selection by enabling faster, more objective, and data-driven hiring decisions. This study examines the relationship between AI-driven employee–job profile matching and employee performance while investigating the mediating role of person–job fit. Although AI adoption in human resource management (HRM) has increased, limited empirical evidence explains how AI-driven matching enhances employee performance through person–job fit.

Design/Methodology/Approach: A quantitative cross-sectional research design was employed using data collected from 321 employees working in organizations that had implemented AI-enabled recruitment systems. Data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) with SmartPLS 4. The measurement model was assessed for reliability and validity, while the structural model was evaluated using bootstrapping with 5,000 resamples.

Findings: The results indicate that AI-driven employee–job profile matching positively influences person–job fit and employee performance. Person–job fit also has a significant positive effect on employee performance and partially mediates the relationship between AI-driven employee–job profile matching and employee performance. The structural model explained 42.6% of the variance in person–job fit and 63.8% of the variance in employee performance, demonstrating good predictive capability.

Originality/Value: This study extends Person–Environment Fit Theory to AI-enabled recruitment by demonstrating that person–job fit is the key psychological mechanism through which AI-driven employee–job profile matching enhances employee performance. The findings show that the value of AI recruitment extends beyond improving hiring efficiency to strengthening employee–job alignment and organizational performance.

Practical Implications: The findings encourage organizations to implement transparent, ethical, and data-driven AI recruitment systems alongside effective talent management practices. Future research should employ longitudinal and cross-cultural designs and examine additional mediating and moderating variables, such as AI trust, organizational support, and digital competency.

References

Alwali, J., & Alwali, W. (2026). Linking AI-driven HRM and emotional intelligence to leadership effectiveness and employee performance. Leadership & Organization Development Journal, 47(3), 477–497. https://doi.org/10.1108/LODJ-05-2025-0358

Cable, D. M., & DeRue, D. S. (2002). The convergent and discriminant validity of subjective fit perceptions. Journal of Applied Psychology, 87(5), 875–884. https://doi.org/10.1037/0021-9010.87.5.875

Channar, H. B., Abbasi, D. M., Chandio, S., & Rani, D. P. (2026). The Strategic Value of ESG HR Practices: Exploring the Impact on Performance via Employee Engagement and ethical leadership. Social Science Review Archives, 4(2), 2124–2141. https://doi.org/10.70670/sra.v4i2.2307

Chen, A., Han, F., Zhang, X., & Lu, Y. (2025). Cracking the AI recruitment code: Striving for transparency in finding the right person–job fit. Information & Management, 62(5), 104156. https://doi.org/10.1016/j.im.2025.104156

Chen, C.-Y., Yen, C.-H., & Tsai, F. C. (2014). Job crafting and job engagement: The mediating role of person-job fit. International Journal of Hospitality Management, 37, 21–28. https://doi.org/10.1016/j.ijhm.2013.10.006

Dishankan, V, & Shafana, A. R. F. (2023). AI-Driven Candidate Profiling: A Comprehensive Review of Methodologies, Technologies, and Future Directions. Journal of Information and Communication Technology, 9-12. Journal of Information and Communication Technology, 1(2), 9–12.

Faqihi, A., & Miah, S. J. (2023). Artificial Intelligence-Driven Talent Management System: Exploring the Risks and Options for Constructing a Theoretical Foundation. Journal of Risk and Financial Management, 16(1), 31. https://doi.org/10.3390/jrfm16010031

Gemmano, C. G., Molinaro, D., Bellini, D., De Simone, S., Giancaspro, M. L., Mondo, M., Buono, C., Barbieri, B., Spagnoli, P., & Manuti, A. (2026). Making Artificial Intelligence Work at Work: The Role of Human Resource Practices and Personal Attitudes in Fostering Meaningful Work with Artificial Intelligence. Behavioral Sciences, 16(2), 238. https://doi.org/10.3390/bs16020238

Gupta, P., Lakhera, G., & Sharma, M. (2024). Examining the impact of artificial intelligence on employee performance in the digital era: An analysis and future research direction. The Journal of High Technology Management Research, 35(2), 100520. https://doi.org/10.1016/j.hitech.2024.100520

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2019). Multivariate data analysis.

Hussain, S., Channar, P., Abbasi, M., Rani, P., & Soomro, A. (2026). ESG-ORIENTED HUMAN RESOURCE MANAGEMENT PRACTICES AND ORGANIZATIONAL OUTCOMES: THE MEDIATING ROLE OF EMPLOYEE ENGAGEMENT AND ETHICAL LEADERSHIP. Journal of Business and Management Research, 5(2), 804–825. https://doi.org/10.5281/ZENODO.20696815

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

Jatoi, G. M, Soomro, A, & Rani, P. (2026). Green Project Management and Sustainable Procurement Performance: Evidence from the Finance Sector. Bulletin of Management Review, 3(1), 998–1021.

Jia, X., & Hou, Y. (2024). Architecting the future: Exploring the synergy of AI-driven sustainable HRM, conscientiousness, and employee engagement. Discover Sustainability, 5(1), 30. https://doi.org/10.1007/s43621-024-00214-5

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

Kim, T., Schuh, S. C., & Cai, Y. (2020). Person or Job? Change in Person‐Job Fit and Its Impact on Employee Work Attitudes over Time. Journal of Management Studies, 57(2), 287–313. https://doi.org/10.1111/joms.12433

Kvale, H. E. H. (2026). When AI becomes a colleague: Persona–organization fit and relational sensemaking in public organizations. International Journal of Organizational Analysis, 1–25. https://doi.org/10.1108/IJOA-09-2025-5953

Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G. (2022). The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity. In K. Martin, K. Shilton, & J. Smith (Eds.), Business and the Ethical Implications of Technology (pp. 71–86). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-18794-0_5

Lu, C., Wang, H., Lu, J., Du, D., & Bakker, A. B. (2014). Does work engagement increase person–job fit? The role of job crafting and job insecurity. Journal of Vocational Behavior, 84(2), 142–152. https://doi.org/10.1016/j.jvb.2013.12.004

Qi, F., & Liu, M. (2026). An AI-driven reinforcement learning framework for graduate employability prediction and career decision support. Discover Artificial Intelligence. https://doi.org/10.1007/s44163-026-01612-6

Qin, C., Zhu, H., Xu, T., Zhu, C., Ma, C., Chen, E., & Xiong, H. (2020). An Enhanced Neural Network Approach to Person-Job Fit in Talent Recruitment. ACM Transactions on Information Systems, 38(2), 1–33. https://doi.org/10.1145/3376927

Rani, P., Chandio, S., Hussain, S., & Soomro, A. (2026). Driving Financial Success with AI Supply Chains: The Crucial Role of Sustainable Development in Emerging Economies. Inverge Journal of Social Sciences, 5(3), 341–357. https://doi.org/10.63544/ijss.v5i3.305

Rani, P, Jatoi, Z, Bajwa, A. A, & Hassan, M. U. (2025). The Transformative Impact of Artificial Intelligence on Customer Relationship Management (CRM) Strategies. Qualitative Research Review Letter, 3(4), 187–209.

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. https://doi.org/10.1177/0008125619867910

Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2022). Artificial intelligence, robotics, advanced technologies and human resource management: A systematic review. The International Journal of Human Resource Management, 33(6), 1237–1266. https://doi.org/10.1080/09585192.2020.1871398

Williams, R., Cloete, R., Cobbe, J., Cottrill, C., Edwards, P., Markovic, M., Naja, I., Ryan, F., Singh, J., & Pang, W. (2022). From transparency to accountability of intelligent systems: Moving beyond aspirations. Data & Policy, 4, e7. https://doi.org/10.1017/dap.2021.37

Zahrani, A. A. (2025). AI in HRM: A Unified Model of AI Capabilities and Process Efficiency for Enhanced HRM Effectiveness. In Review. https://doi.org/10.21203/rs.3.rs-7685386/v1