Algorithmic versus human screener: An experimental investigation of applicants’ perception on organizational justice,trust,and intentions in AI-supported selection process

Abstract

Background

Organizations are leveraging AI technologies to streamline processes, including recruitment and selection.

Purpose

This study uses an experimental approach to examine potential job applicants’ perceptions of organizational justice, trust in AI, and intentions to pursue job offers across screening configurations. Specifically, it explores how variations in AI involvement and fairness-related features, such as transparency, reliability, and bias mitigation, affect applicants’ perceptions of distributive, procedural, and interpersonal justice, as well as their trust in the recruitment process. The study also investigates whether perceptions of organizational justice and trust in AI are associated with applicants’ intentions to pursue job opportunities.

Research Design and Method

This study adopts a quantitative experimental approach. Purposive sampling technique was applied, whereby final-year students, who represent the prospect of AI-driven recruitment at the entry level were recruited. 74 participants were randomly assigned to five scenarios describing different recruitment processes, varying in AI autonomy, human involvement, procedural design and outcome fairness. Participants’ perceptions were measured using validated scales for distributive, procedural, and interpersonal justice; trust in AI (transparency, reliability and bias mitigation); and behavioural intentions. Data were analysed using univariate and multivariate analysis of variance.

Results

Results showed that recruitment scenarios with low fairness and unjust outcomes led to lower perceptions of organizational justice and trust in AI. In contrast, hybrid human–AI systems that emphasized transparency, bias mitigation, fair outcomes, and human oversight were associated with higher trust, more positive justice perceptions, and stronger job pursuit intentions compared to AI-only systems. The findings highlight the importance of transparent and human-centered AI practices in recruitment and selection.

Conclusion

The study demonstrates that AI-supported recruitment systems are perceived more positively by job applicants when they incorporate transparency, bias mitigation, fair outcomes, and meaningful human oversight, highlighting the importance of ethical and human-centred AI implementation in organizational hiring practices.