ArticleDigital health
Ability meets motivation: A TAM-Integrated approach using informatics competency and self-efficacy to explain variance in AI clinical system adoption: Cross-sectional study.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Background: Artificial intelligence (AI)-enabled clinical systems are integrated into nursing education and healthcare, yet nursing students remain inadequately prepared to use them. Although the Technology Acceptance Model (TAM) has been applied to explain technology adoption, limited evidence has examined informatics competency & digital self-efficacy as antecedents influencing AI adoption through separate acceptance pathways. Objective: To develop and validate an extended Technology Acceptance-Competency Structural Model (TAC-SM) by examining the direct & indirect effects of informatics competency & digital self-efficacy on undergraduate nursing students' behavioral intention to adopt AI-enabled clinical systems through perceived ease of use and perceived usefulness. Methods: A cross-sectional study included undergraduate nursing students at the College of Nursing, University of Ha'il, Saudi Arabia. Of 426 questionnaires received, 10 were excluded after screening, yielding a final sample of 416. Participants completed the Competency in Nursing Informatics & Computer Applications Scale, Digital Task Self-Efficacy Scale, and adapted TAM measures. Confirmatory factor analysis, structural equation modelling, and bootstrapped mediation analyses with 5,000 resamples were performed. Results: The TAC-SM demonstrated satisfactory model fit (χ Conclusion: The TAC-SM demonstrates that technical competence & motivational confidence influence AI adoption through distinct but complementary pathways. Integrating informatics competency & digital self-efficacy into nursing curricula may strengthen students' readiness to adopt AI-enabled clinical systems.
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