Examining the Effect of Algorithmic Hiring, Perceived Fairness, and HR Tech Literacy on Recruitment Acceptance - Ilham, Rachmad (International Journal of Business, Law, and Education, 2025)
The increasing integration of algorithmic technologies in recruitment processes has raised questions about candidate acceptance and perceptions of fairness. This study examines the effects of algorithmic hiring, perceived fairness, and HR tech literacy on recruitment acceptance. Using a quantitative approach, data were collected from 350 job applicants through structured questionnaires and analyzed using multiple linear regression via SPSS. The results reveal that all three variables—algorithmic hiring, perceived fairness, and HR tech literacy—significantly and positively affect recruitment acceptance. Among them, perceived fairness emerged as the strongest predictor, suggesting that candidates’ perceptions of just and transparent processes play a critical role in shaping their acceptance of technology-driven recruitment methods. Additionally, HR tech literacy facilitates understanding and comfort with algorithmic systems, enhancing candidate receptivity. The findings contribute to theories of technology acceptance and organizational justice and provide practical implications for designing inclusive, ethical, and effective tech-based recruitment systems. This study underscores the importance of balancing innovation with fairness and transparency to build trust and improve outcomes in digital recruitment.