EXPLORING FAT-CAT MODEL OF AI ADOPTION IN AI-ENABLED RECRUITMENT: UNDERSTANDING THE MODERATING ROLE OF AUSTRALIAN RECRUITERS’ PERCEPTION

Abstract

Recruitment is being transformed by incorporation of AI, which brings efficiency however also concerns the algorithms’ transparency. The effects of AI's FAT-CAT model (explain-ability and augmentation capabilities) on teamwork, hiring, and morality in the Australian hiring process are the focus of this study. Quantitative data was researched and analyzed. To gather data, questionnaire was sent out to various human resources managers and recruiters in various industries in Australia. With a 5% margin of error, the sample size was 178 out of a population of 300. This study used snowball sampling. SPSS was used to analyze data to determine how explain-ability, augmentation, and human-AI adoption affect Australian recruitment. Moreover, moderation of individual perception was also investigated. Findings suggests that explain-ability characteristics marginally affect AI adoption, whereas augmentation features greatly do. AI's promise and problems in recruitment need user-friendly, bias-aware tools and continual recruiter coaching, according to the report. Ensure explain-ability, openness, and empower recruiters with continuing training. Individual perception moderates the association of explainability and augmentation features of AI adoption among Australian recruiters. AI should boost recruiters' skills without replacing human judgement, preserving their crucial position in talent acquisition.

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