Acceptance of Artificial Intelligence (AI) Among Patients Receiving University Nursing Services: A Predictive Model
DOI:
https://doi.org/10.22458/urj.v18i1.6507Keywords:
attitude toward technology, perceived benefit, technological innovation, user profiles, technology adoption, healthcare services, psychometricsAbstract
ABSTRACT. Predictive model for the acceptance of artificial intelligence in university nursing services. Introduction: The integration of artificial intelligence (AI) in nursing services has increased in recent years, creating opportunities to improve the quality and efficiency of care; however, its implementation largely depends on user acceptance, and there is limited evidence in Latin American contexts to understand this phenomenon. Objective: We analyzed perceptions, attitudes, benefits, expectations, and level of support toward AI in nursing services in order to develop a predictive model to classify users according to their technological acceptance profile. Methods: We conducted a quantitative, descriptive, cross-sectional, and analytical study with 704 employees from a public university, to whom we applied a validated 21-item Likert-type psychometric scale grouped into five dimensions; we then performed exploratory and confirmatory factor analyses, cluster analysis, and discriminant analysis. Results: We identified three user profiles: sceptics (26%), neutrals (41%), and enthusiasts (33%), with significant differences across all dimensions; the predictive model achieved high classification accuracy in both the training sample (98%) and the validation sample (97%). We observed a strong correlation between AI perception and perceived benefits, as well as the influence of variables such as age and job tenure on technological acceptance. Conclusion: AI acceptance in nursing is a heterogeneous and multidimensional phenomenon structured into distinct user profiles associated with their perceptions, attitudes, and expectations toward technology.
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