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Beyond Algorithms

2025-12Artigo científico Acesso aberto
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dc.contributor.authorFrança, Tiago Jacob Fernandes
dc.contributor.authorMamede, José Henrique Pereira São
dc.contributor.authorBarroso, João Manuel Pereira
dc.contributor.authorSantos, Vítor Manuel Pereira Duarte dos
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.pblElsevier BV
dc.date.accessioned2025-11-19T21:10:45Z
dc.date.available2025-11-19T21:10:45Z
dc.date.issued2025-12
dc.descriptionFrança, T. J. F., Mamede, J. H. P. S., Barroso, J. M. P., & Santos, V. M. P. D. D. (2025). Beyond Algorithms: Artificial Intelligence Driven Talent Identification with Human Insight. Intelligent Systems with Applications, 28, Article 200604. https://doi.org/10.1016/j.iswa.2025.200604 --- This work is funded by national funds through FCT - Fundação para a Ciência e a Tecnologia, I.P., under the support UID/50014/2025 (https://doi.org/10.54499/UID/50014/2025).
dc.description.abstractThe rapid evolution of Artificial Intelligence (AI) is reshaping Human Resource Management (HRM), with growing interest in its role in talent identification. While AI has demonstrated effectiveness in analysing structured data, its limitations in assessing qualitative attributes such as creativity, adaptability, and emotional intelligence remain underexplored. This study addresses these gaps through an exploratory mixed-methods design, combining a global survey (n = 240) with semi-structured interviews of HR professionals. Quantitative analysis highlights patterns of association between key competencies, while qualitative findings provide contextual insights into perceptions of fairness, bias, and cultural resistance. The results suggest that AI can complement, but not replace, human judgement, supporting a Hybrid Evaluative Model that integrates algorithmic efficiency with human interpretation. The study contributes rare empirical evidence to a nascent field, highlights the ethical imperatives of bias mitigation and transparency, and underscores the importance of cultural context (collectivist versus individualist orientations) in shaping the acceptance and effectiveness of AI-enabled HR practices. These findings offer practical guidance for organisations and advance theory-building at the intersection of AI and HRM.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent29
dc.format.extent4642504
dc.identifier.doi10.1016/j.iswa.2025.200604
dc.identifier.issn2667-3053
dc.identifier.otherPURE: 135766292
dc.identifier.otherPURE UUID: afe7277e-baa2-4eea-801b-908d79699e7a
dc.identifier.othercrossref: 10.1016/j.iswa.2025.200604
dc.identifier.otherScopus: 105022514650
dc.identifier.otherWOS: 001623191100001
dc.identifier.otherORCID: /0000-0002-4223-7079/work/196993260
dc.identifier.urihttp://hdl.handle.net/10362/191087
dc.identifier.urlhttps://www.scopus.com/pages/publications/105022514650
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001623191100001
dc.language.isoeng
dc.peerreviewedyes
dc.relationhttps://doi.org/10.54499/UID/04152/2025
dc.relationhttps://doi.org/10.54499/UID/PRR/04152/2025
dc.subjectArtificial Intelligence
dc.subjectHuman Capital
dc.subjectPotential Assessment
dc.subjectTalent Management
dc.subjectNext-gen HR
dc.subjectComputer Science (miscellaneous)
dc.subjectSignal Processing
dc.subjectComputer Vision and Pattern Recognition
dc.subjectComputer Science Applications
dc.subjectArtificial Intelligence
dc.titleBeyond Algorithmsen
dc.title.subtitleArtificial Intelligence Driven Talent Identification with Human Insighten
dc.typejournal article
degois.publication.titleIntelligent Systems with Applications
degois.publication.volume28
dspace.entity.typePublication
rcaap.rightsopenAccess

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NIMS: MagIC - Artigos em revista internacional com arbitragem científica (Peer-Review articles in international journals)