Risk factors in the adoption of artificial intelligence by SMES: a comprehensive study
The main objective of this study is to analyse the barriers that limit the adoption of artificial intelligence (AI) in SMEs. To do so, seven risk categories will be examined: functional (FUR), hedonic (HR), security (SR), social (SOR), financial (FR), performance (PR) and psychological (PYR). The ch...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:dnet:idus________::9877a69b36037fa9a3e3b8e06d505f6a |
| Acceso en línea: | https://hdl.handle.net/11441/183876 https://doi.org/10.1108/EJIM-06-2025-0719 |
| Access Level: | acceso abierto |
| Palabra clave: | Artificial intelligence SMEs Barriers to adoption Distrust Risks Technostress Hesitation |
| Sumario: | The main objective of this study is to analyse the barriers that limit the adoption of artificial intelligence (AI) in SMEs. To do so, seven risk categories will be examined: functional (FUR), hedonic (HR), security (SR), social (SOR), financial (FR), performance (PR) and psychological (PYR). The choice of these seven categories is based on a comprehensive review of the literature on technology adoption and perceived risks, and these categories are considered to comprehensively capture the main risks associated with AI as perceived by SMEs. Additionally, the influence of factors such as technostress (TE), distrust (DT) and hesitancy (HES) on the adoption process will be investigated.This study represents one of the first investigations to comprehensively address the risks associated with the use of AI by firms, and to address the level of direct risk that this represents with respect to their behaviour. The treatment of non-adoption issues is an innovation in the academic approach, allowing for more comprehensive conceptual modelling of findings. This study provides a theoretical basis for a comprehensive analysis of the risks affecting the adoption of AI in firms, marking an academic innovation by focussing the analysis on non-adoption behaviour. |
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