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Ability meets motivation: A TAM-Integrated approach using informatics competency and self-efficacy to explain
Nader Alnomasy1, Sudharani Banappagoudar2, Habib Alrashedi1
1Medical Surgical Nursing Department, College of Nursing, University of Hail, Ha'il, Saudi Arabia.
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 (χ2/df = 2.11, CFI = 0.94, TLI = 0.93, RMSEA = 0.062, SRMR = 0.041). Informatics competency was positively associated with perceived ease of use (β = 0.42, p < .001), whereas digital self-efficacy was positively associated with perceived usefulness (β = 0.47, p < .001). Perceived usefulness (β = 0.55, p < .001) and perceived ease of use (β = 0.21, p < .01) were directly associated with behavioral intention. Informatics competency and digital self-efficacy demonstrated significant indirect associations with behavioral intention through the proposed TAM pathways, with the strongest indirect pathway observed for digital self-efficacy → perceived usefulness → behavioral intention (β = 0.259, p < .001). Students with prior informatics training reported higher scores across the study constructs.
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.
