Publication
Understanding recruiters’ acceptance of artificial intelligence: Insights from the technology acceptance model
| datacite.subject.fos | Ciências Sociais::Psicologia | |
| datacite.subject.sdg | 08:Trabalho Digno e Crescimento Económico | |
| dc.contributor.author | Filomena Almeida | |
| dc.contributor.author | Junça-Silva, Ana | |
| dc.contributor.author | Lopes, Sara L. | |
| dc.contributor.author | Braz, Isabel | |
| dc.date.accessioned | 2025-06-02T19:10:42Z | |
| dc.date.available | 2025-06-02T19:10:42Z | |
| dc.date.issued | 2025-01-14 | |
| dc.description.abstract | The integration of new technologies in professional contexts has emerged as a critical determinant of organizational efficiency and competitiveness. In this regard, the application of Artificial Intelligence (AI) in recruitment processes facilitates faster and more accurate decision-making by processing large volumes of data, minimizing human bias, and offering personalized recommendations to enhance talent development and candidate selection. The Technology Acceptance Model (TAM) provides a valuable framework for understanding recruiters’ perceptions of innovative technologies, such as AI tools and GenAI. Drawing on the TAM, a model was developed to explain the intention to use AI tools, proposing that perceived ease of use and perceived usefulness influence attitudes toward AI, which subsequently affect the intention to use AI tools in recruitment and selection processes. Two studies were conducted in Portugal to address this research objective. The first was a qualitative exploratory study involving 100 interviews with recruiters who regularly utilize AI tools in their professional activities. The second study employed a quantitative confirmatory approach, utilizing an online questionnaire completed by 355 recruiters. The qualitative findings underscored the transformative role of AI in recruitment, emphasizing its potential to enhance efficiency and optimize resource management. However, recruiters also highlighted concerns regarding the potential loss of personal interaction and the need to adapt roles within this domain. The results also supported the indirect effect of perceived ease of use and perceived usefulness on the use of AI tools in recruitment and selection processes via positive attitudes toward the use of these tools. This suggests that AI is best positioned as a complementary tool rather than a replacement for human decision-making. The insights gathered from recruiters’ perspectives provide actionable recommendations for organizations seeking to leverage AI in recruitment processes. Specifically, the findings show the importance of ethical considerations and maintaining human involvement to ensure a balanced and effective integration of AI tools. | por |
| dc.description.sponsorship | Fundação para a Ciência e Tecnologia - FCT | |
| dc.identifier.citation | Almeida, F.; Junça Silva, A.; Lopes, S.L.; Braz, I. Understanding recruiters’ acceptance of artificial intelligence: Insights from the technology acceptance model. Appl. Sci. 2025, 15, 746. https://doi.org/ 10.3390/app15020746 | |
| dc.identifier.doi | 10.3390/app15020746 | |
| dc.identifier.issn | 2076-3417 | |
| dc.identifier.uri | http://hdl.handle.net/10400.12/13531 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | MDPI AG | |
| dc.relation | UIDB/00315/2020 | |
| dc.relation.ispartof | Applied Sciences | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.title | Understanding recruiters’ acceptance of artificial intelligence: Insights from the technology acceptance model | eng |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 26 | |
| oaire.citation.issue | 2 | |
| oaire.citation.startPage | 1 | |
| oaire.citation.title | Applied Sciences | |
| oaire.citation.volume | 15 | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Junça-Silva | |
| person.givenName | Ana | |
| person.identifier | E-6831-2016 | |
| person.identifier.ciencia-id | 9119-E45C-1D23 | |
| person.identifier.orcid | 0000-0001-6341-9771 | |
| person.identifier.scopus-author-id | 57188922847 | |
| relation.isAuthorOfPublication | 68a06aa7-ab64-444c-873b-cd75071a32a0 | |
| relation.isAuthorOfPublication.latestForDiscovery | 68a06aa7-ab64-444c-873b-cd75071a32a0 |