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Artificial intelligence in recruitment and selection: a conceptual model and research agenda

Penny Williams & Paula McDonald

Journal of Information Communication and Ethics in Society2026https://doi.org/10.1108/jices-10-2025-0269article
ABDC B
Weight
0.50

Abstract

Purpose This paper aims to review how artificial intelligence (AI) shapes the evolution of recruitment and selection practice in organisations. The paper presents a new research agenda, supported by a conceptual model, to advance understanding of how technical aspects of AI-enabled recruitment are configured by relationships between various actors in the recruitment process and in different institutional and cultural contexts. Design/methodology/approach This paper applies a social informatics (SI) lens to review and identify gaps in the literature on AI and recruitment and selection and propose a new conceptual model to guide future research. Findings The paper explains how complex interactions between technical and non-technical resources influence the reliability and validity of AI-enabled recruitment processes and fairness for job candidates. It also identifies two critical actors largely missing from existing research – the outsourced recruitment firm and the recruitment technology developer. Originality/value In a novel contribution, this paper adopts an SI lens to research on AI in recruitment and selection, presenting a comprehensive framework for future research that addresses areas where research lags behind industry practice.

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https://doi.org/https://doi.org/10.1108/jices-10-2025-0269

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@article{penny2026,
  title        = {{Artificial intelligence in recruitment and selection: a conceptual model and research agenda}},
  author       = {Penny Williams & Paula McDonald},
  journal      = {Journal of Information Communication and Ethics in Society},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/jices-10-2025-0269},
}

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Artificial intelligence in recruitment and selection: a conceptual model and research agenda

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.