ArticleJournal of advanced nursing2026
Effects of Performance and Effort Expectancy on AI-Generated Information Adoption Among Chinese Nursing Professionals: Survey-Based SEM Analysis.
Article in Journal of advanced nursing, 2026. 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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Abstract
aimTo examine determinants of nurses' adoption of generative artificial intelligence outputs in clinical practice using a technology acceptance model and an integrated structural equation modelling framework.
designCross-sectional online survey.
methodsRegistered nurses in mainland China completed an anonymous questionnaire assessing perceived performance benefits, perceived ease of use, perceived information quality, perceived source credibility, social influence, facilitating conditions, adoption intention and adoption behaviour. Structural equation modelling was used to evaluate the measurement model and estimate a primary mediation model in which perceived performance benefits and perceived ease of use predicted adoption intention, and adoption intention predicted adoption behaviour. An integrated model added information quality, source credibility, social influence and facilitating conditions as additional determinants. Sensitivity analyses were conducted using an ordinal estimator to assess robustness.
resultsThe analytic sample comprised 330 nurses. In the primary model, higher perceived performance benefits and greater perceived ease of use were associated with stronger adoption intention, and stronger adoption intention was associated with higher self-reported adoption behaviour. The integrated model showed that perceived information quality contributed to adoption intention beyond core expectancy beliefs, while perceived source credibility showed a small direct association with adoption behaviour. Social influence demonstrated a modest association with adoption intention, whereas facilitating conditions showed weaker associations after accounting for other determinants. Model conclusions were consistent across estimation approaches.
conclusionNurses' adoption of generative artificial intelligence outputs is shaped by perceived performance benefits, ease of use and perceived information quality, with adoption intention functioning as the proximal determinant of self-reported use. Implementation strategies should focus on demonstrable workflow gains, reducing interaction burden and strengthening governance and verification to support safe adoption.
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