AI-Based Hiring Vs Traditional Hiring

Abstract

Artificial intelligence (AI) is increasingly embedded in recruitment activities such as candidate sourcing, resume screening, shortlisting, communication and initial assessment. This study examines whether AI-supported recruitment practices are associated with perceived recruitment effectiveness, whether transparency and human oversight are associated with procedural fairness, and whether AI-supported practices and human interaction affect candidate experience. A quantitative, descriptive, explanatory and cross-sectional survey design was used. Primary data were collected through a structured closed-ended questionnaire from 101 respondents with awareness, experience or exposure to recruitment and selection. A five-point Likert scale was used. Data were coded in Microsoft Excel and analysed using Jamovi through descriptive statistics, Cronbach's alpha, Pearson correlation, simple linear regression and multiple linear regression. The results did not support any of the three alternative hypotheses. AI-practices/integration showed an almost zero association with perceived recruitment effectiveness (r = .011, p = .910; R² = .000129). Transparency/human oversight showed no significant relationship with procedural fairness (r = −.084, p = .406; R² = .00699). AI-supported practices and human interaction together explained only 1.05% of the variance in candidate experience (R² = .0105), and neither predictor was significant. The findings should be interpreted cautiously because several composite measures showed very low internal consistency. The study therefore suggests that AI should not be evaluated simply as a replacement for traditional recruitment; instead, individual AI practices, human safeguards, transparency and interaction should be assessed separately using stronger measurement scales. Index Terms - Artificial Intelligence Recruitment, Traditional Hiring, Recruitment Effectiveness, Procedural Fairness, Human Oversight, Candidate Experience, Human–AI Recruitment