Extending Technology Acceptance Model in AI-Enabled Recruitment: Perceived Ease of Use and Trust as Mediators of Applicant Attitudes

Ulul Azmi Abid Anantri (Management, Economic and Business, Universitas Negeri Semarang)
Nury Ariani Wulansari (Management, Economic and Business, Universitas Negeri Semarang)

Jurnal Bisnis dan Manajemen
Vol. 13 No. 1 (2026): Jurnal Bisnis dan Manajemen Volume 13 Nomor 1 Tahun 2026

The rapid advancement of Artificial Intelligence (AI) has transformed contemporary recruitment practices through the automation of selection processes, including resume screening, recruitment chatbots, and AI-based interviews. Although AI is widely credited with improving recruitment efficiency, applicant acceptance remains a critical concern, primarily because of unresolved issues of trust, transparency, and system usability. Prior studies have largely examined these factors in isolation: some focus on Perceived Ease of Use (PEU) as a usability-driven determinant of technology adoption. While others emphasize Perceived Trust (PT) as dominant predictor of applicant attitudes toward AI-based selection. What remains absent, however, is an integrative model that simultaneously positions PEU and PT as parallel mediators linking applicant’s perception of AI use their attitudes toward it. This omission is non-trivial, because PEU and PT capture conceptually distinct mechanisms cognitive effort versus relational confidence that may operate jointly rather than independently in shaping acceptance. The present study directly addresses this gap by specifying and testing such an integrative mediation model. This study examines the effect of Perceived Use of AI in the Hiring Process (PUAHP) on Attitude Toward AI-Enabled Recruitment (ATT), both directly and through the mediating roles of Perceived Ease of Use (PEU) and Perceived Trust (PT). A quantitative explanatory study was conducted using a cross-sectional survey of 184 job applicants in Semarang City who had participated in AI-based recruitment processes within the preceding twelve months. The data were analysed using Structural Equation Modelling–Partial Least Squares (SEM-PLS). The findings shows that PUAHP has a positive and significant influence on ATT, PEU, and PT. Mediation analyses further demonstrate that PEU and PT significantly mediate the relationship between PUAHP and ATT, with PT emerging as the dominant mediator. These results indicate that applicants' trust in AI systems is the principal mechanism shaping favourable attitudes toward AI-enabled recruitment. Theoretically, this study extends the Technology Acceptance Model (TAM) by integrating trust into the analysis of AI-enabled selection. Practically, it suggests that organisations should strengthen the transparency, fairness, and usability of AI systems to strengthen applicant acceptance of AI-enabled recruitment technologies.