A multi-criteria and empirical study for determining the influencing factors of Generative Artificial Intelligence adoption in companies
| dc.creator | Alcalá, Symone Gomes Soares | |
| dc.creator | De Nicolás, Victor Luis De Nicolás | |
| dc.creator | López-López, Álvaro Jesús | |
| dc.creator | Rodriguez, Mariano Jose Ventosa | |
| dc.date.accessioned | 2026-08-18T14:53:57Z | |
| dc.date.available | 2026-08-18T14:53:57Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | Generative artificial intelligence (GenAI) has emerged as a transformative force across business and society due to its ability to generate new content. This potential to reshape businesses introduces challenges and opportunities, necessitating a deeper understanding of GenAI's impact. Despite its promise, the factors that enable effective GenAI adoption within companies remain underexplored. Based on systems thinking principles, this study proposes a comprehensive approach to determine the most critical and influential factors for effective GenAI adoption in companies. Thirteen factors are identified and validated by experts and then aggregated within a technological, business, organizational and environmental framework. After that, a multicriteria approach is applied to identify critical and influential factors, considering their interrelationships and the judgements of chiefs on technology and information from Spanish companies representing several sectors and sizes. Findings indicate that organizational factors are critical in most cases. This study guides companies and individuals in navigating effective GenAI adoption and supports future research. | |
| dc.identifier.citation | ALCALÁ, Symone Gomes Soares et al. A multi-criteria and empirical study for determining the influencing factors of Generative Artificial Intelligence adoption in companies. Systems Research and Behavioral Science, [s. l.], v. 43, n. 2, p. 750-772, 2026. DOI: 10.1002/sres.3215. Disponível em: https://onlinelibrary.wiley.com/doi/full/10.1002/sres.3215. Acesso em: 29 jul. 2026. | |
| dc.identifier.doi | 10.1002/sres.3215 | |
| dc.identifier.issn | 1099-1743 | |
| dc.identifier.issn | e- 1099-1743 | |
| dc.identifier.uri | https://repositorio.bc.ufg.br//handle/ri/31388 | |
| dc.language.iso | eng | |
| dc.publisher.country | Estados unidos | |
| dc.publisher.department | Faculdade de Ciências e Tecnologia - FCT (RMG) | |
| dc.publisher.program | Programa de Pós-Graduação em Engenharia de Produção | |
| dc.rights | Acesso Aberto | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Adoption | |
| dc.subject | Analytic network process | |
| dc.subject | Companies | |
| dc.subject | GenAI | |
| dc.subject | Generative artificial intelligence | |
| dc.subject | Systems thinking | |
| dc.subject.ODS | 9 - Industria, inovação e infraestrutura | |
| dc.title | A multi-criteria and empirical study for determining the influencing factors of Generative Artificial Intelligence adoption in companies | |
| dc.type | Artigo |
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