AI-based Recruitment and Candidate satisfaction: The Mediating Roles of Trust and Perceived Fairness in the Indian IT Sector

Abstract

Artificial intelligence has transformed recruitment and employee selection by introducing automated screening, algorithmic matching, and data-driven decision-making. Although these technologies offer operational benefits, their implications for candidate satisfaction remain insufficiently understood, particularly in emerging economies. This study examines the relationships between AI-assisted recruitment systems and candidate satisfaction in the Indian IT sector, focusing on the mediating roles of trust and perceived fairness. The research employs a quantitative, cross-sectional design based on survey responses from 300 final-year students and graduates in Maharashtra who had experience with AI-assisted recruitment. Structural equation modelling is used to assess construct reliability and validity, estimate direct relationships, and examine indirect effects through trust and perceived fairness. The results indicate positive relationships between AI-assisted recruitment systems, candidate experience, trust, perceived fairness, and satisfaction. Perceived fairness demonstrates the largest direct standardised coefficient among the predictors of candidate satisfaction (β = 0.285), followed by trust (β = 0.256). The reported indirect effects of AI-assisted recruitment systems on satisfaction through trust and perceived fairness are 0.060 and 0.062, respectively, with confidence intervals excluding zero. The proposed model explains 31.2% of the variance in candidate satisfaction. These findings suggest that candidate satisfaction is associated not only with perceptions of recruitment technology but also with confidence in automated decisions and evaluations of procedural fairness. The study contributes to digital human resource management by integrating technological and candidate-related factors within a common explanatory framework. Its practical implications concern the development of transparent, reliable, and candidate-oriented AI-assisted recruitment procedures. Future research should employ longitudinal and comparative designs to examine these relationships across different industries, applicant populations, and recruitment technologies.