Human-AI collaboration in recruitment: orchestrating person-job-fit (PJF) and person-organization-fit (POF) for quality of hire
DOI:
https://doi.org/10.17561/ree.n2.2026.9947Palabras clave:
ethical AI, hybrid recruitment model, bias mitigation, person–job fit, person–organization fit, neural-network-assisted analysisResumen
This research explores the transformative role of artificial intelligence (AI) in talent acquisition, focusing on its influence on screening efficiency, candidate–role alignment, hiring precision, and bias reduction. Adopting an organizational lens rooted in fit theory, the study applies a qualitative, manager-focused methodology. Semi-structured interviews were conducted and examined through a three-level thematic analysis, complemented by neural-network-assisted mapping to deepen pattern recognition. NVivo 14 was used to support systematic coding, cross-case comparison, and theme development. In parallel, Python 3.11, together with Keras (2.15.0) and TensorFlow (2.14.0), facilitated cluster analysis and visualization of relationships, particularly those associated with person–job fit (PJF) and person–organization fit (POF). The findings are interpreted through the dual mediating mechanisms of PJF and POF. Results indicate that, when guided by robust governance frameworks, AI improves resume screening, strengthens candidate–role matching, and enhances fairness in hiring decisions. Nonetheless, AI tools show limitations in evaluating interpersonal competencies and cultural compatibility, underscoring the continued importance of human judgment. Consequently, the study proposes a hybrid recruitment framework in which AI conducts initial triage to assess PJF, while structured human evaluations address POF. This balanced model improves efficiency, quality of hire, and overall candidate experience. The study further recommends algorithmic audits, explainable AI practices, strategic human intervention points, HR capability development, and strong privacy and accountability standards to ensure responsible AI integration in recruitment processes.
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Derechos de autor 2026 Dr. Musrrat Parveen, Dr. Abrar Rizq
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