Research report: Artificial intelligence literacy and innovative behavior among clinical nurses: the roles of AI self-efficacy and organizational innovation climate

A multicenter cross-sectional survey of 850 clinical nurses found that artificial intelligence literacy, confidence in applying AI, and a workplace climate that supports innovation were all positively associated with innovative behavior. The study, conducted in six tertiary medical institutions in Yueyang, Hunan, China, also found that AI self-efficacy statistically mediated part of the association between AI literacy and innovation, while organizational innovation climate strengthened the relationship between self-efficacy and innovative behavior. These were observational associations rather than evidence that training, confidence, or workplace support caused nurses to innovate.

Nurse innovative behavior was measured as a process in which nurses generate, promote, and implement new methods or solutions in response to clinical nursing problems and patient needs. The 10-item instrument used in the study assessed three components: generating ideas, obtaining support, and realizing ideas. Participants scored an average of 37.93 on a scale ranging from 10 to 50, with a standard deviation of 6.50.

Researchers used a stratified online survey conducted from June to July 2026. Eligible participants were registered nurses working in clinical roles, with at least one year of clinical experience, at two tertiary hospitals and four Grade-A tertiary hospitals in Yueyang. Four institutions were general hospitals and two were specialized hospitals.

Nursing units were stratified into internal medicine, surgery, and other categories, with recruitment targets of at least 40 nurses from each category per hospital. The team collected 892 questionnaires and excluded 42 that failed predetermined quality checks, including completion in less than five minutes, implausible demographic inconsistencies, and selection of the same answer for all 65 scale items. The resulting sample of 850 represented a valid-response rate of 95.3%.

A 12-item, seven-point Artificial Intelligence Literacy Scale assessed awareness, use, evaluation, and ethics, with higher scores indicating greater literacy. A 22-item scale measured AI self-efficacy through assistance, anthropomorphic interaction, comfort with AI, and technical skills. Organizational innovation climate was measured with 21 items covering organizational innovation incentives, resource provision, and management practices.

The Nurse Innovative Behavior Scale used 10 items to assess idea generation, support seeking, and implementation. Cronbach’s alpha values in the study were 0.90 for AI literacy, 0.96 for AI self-efficacy, 0.92 for organizational innovation climate, and 0.87 for innovative behavior. Confirmatory factor analysis indicated good model fit, with a comparative fit index of 0.978, Tucker–Lewis index of 0.971, root mean square error of approximation of 0.063, and standardized root mean square residual of 0.028.

All four principal variables were positively correlated, with coefficients ranging from 0.609 to 0.797 and P values below 0.01. AI literacy and AI self-efficacy had the strongest correlation, at 0.797, while organizational innovation climate and innovative behavior had a coefficient of 0.726. A multiple linear regression identified educational level, professional title, participation in research or innovation activities, AI literacy, AI self-efficacy, and organizational innovation climate as factors associated with innovative behavior.

Together, these variables explained 60.9% of the observed variance in innovative-behavior scores; the adjusted R-squared was 0.602. Organizational innovation climate had the largest standardized regression coefficient, 0.502, followed by AI self-efficacy at 0.211 and AI literacy at 0.124. These coefficients describe the study’s multivariable model and do not establish the independent causal influence of any one factor.

A statistical mediation analysis examined whether AI self-efficacy helped explain the association between AI literacy and innovative behavior. AI literacy was positively associated with self-efficacy, and self-efficacy was positively associated with innovation. After self-efficacy was included, the direct association between literacy and innovative behavior remained statistically significant, indicating partial rather than complete mediation.

The estimated indirect effect was 0.322, with a bootstrap 95% confidence interval of 0.243 to 0.403, while the direct effect was 0.287, with a 95% confidence interval of 0.203 to 0.372. The indirect path accounted for 52.90% of the model’s estimated total effect. Because the measures were collected at one point, this path describes the fitted statistical model rather than a demonstrated sequence in which additional AI knowledge changes confidence and subsequently produces innovation.

The researchers also tested whether organizational innovation climate altered the strength of the self-efficacy–innovation relationship. The interaction between AI self-efficacy and organizational innovation climate was positive, with a coefficient of 0.051 and P < 0.01. Simple-slope analysis showed that self-efficacy was positively associated with innovative behavior at both low and high levels of innovation climate, but the coefficient increased from 0.151 at low levels to 0.253 at high levels.

Conditional indirect effects through self-efficacy were also larger at higher climate levels: 0.120 at one standard deviation below the mean, 0.161 at the mean, and 0.202 at one standard deviation above the mean. None of the bootstrap 95% confidence intervals included zero. The index of moderated mediation was 0.041, with a bootstrap 95% confidence interval of 0.009 to 0.078, indicating that the estimated indirect relationship varied across levels of organizational innovation climate.

The analysis was organized around the Ability–Motivation–Opportunity framework, under which behavior is associated with individual capability, motivation-related beliefs, and external opportunity. Within the study model, AI literacy represented an ability foundation, AI self-efficacy reflected confidence in learning, using, and adapting to AI, and organizational innovation climate represented perceived support, resources, incentives, and practical opportunity. The pattern was consistent with nurses’ reported innovative behavior being related both to their own technology-related capabilities and confidence and to the environment in which they work.

The result does not show that individual confidence is sufficient on its own: the stronger association observed under more supportive innovation climates suggests that statistical links between confidence and behavior depended partly on organizational conditions. Several participant characteristics also differed in univariate comparisons of innovative behavior. Scores varied by age, educational level, years of work experience, professional title, marital status, work content, AI-related training, frequency of AI-tool use, and participation in nursing research or innovation activities.

In the multivariable model, nurses with bachelor’s or master’s degrees, those with higher professional titles, and those who had participated in or led innovation activities had higher innovative-behavior scores than the relevant comparison groups. The sample included 280 nurses who had attended AI-related training, 420 who reported using AI tools one to three times per week, and 202 who reported daily use. Whether AI systems had been deployed in the hospital was not significantly associated with innovative behavior in the univariate analysis, although that comparison did not establish the presence or absence of an effect in more complex models.

Several limitations constrain interpretation. The cross-sectional design, with all variables measured simultaneously, prevents determination of temporal order or causation. The central measures were self-reported, so reporting bias remains possible even though the preliminary common-method-bias test did not identify an obvious single-factor problem.

The mediation and moderated-mediation analyses were not adjusted for demographic covariates, and potential clustering of nurses within hospitals was not modeled. Although the sample included both general and specialized tertiary hospitals, it was restricted to one Chinese city and to tertiary institutions, limiting generalizability to other regions and hospital levels. Future research could combine nurse questionnaires with evaluations from colleagues and head nurses, as well as objective indicators such as completed innovation projects, patents, quality-improvement outcomes, and records of AI-tool use.

Longitudinal, multicenter, and intervention studies would be needed to test whether changes in AI competence, confidence, or organizational support lead to sustained improvements in nurse innovation.

Subject of Research: Associations among AI literacy, AI self-efficacy, organizational innovation climate, and innovative behavior among clinical nurses

Article Title: Artificial intelligence literacy and innovative behavior among clinical nurses: the roles of AI self-efficacy and organizational innovation climate

Article References: Li, J., Liu, X., Cao, Y., Tang, C., & Zhou, J. (2026). Artificial intelligence literacy and innovative behavior among clinical nurses: the roles of AI self-efficacy and organizational innovation climate. Frontiers in Public Health, 14, Article 1958854. https://doi.org/10.3389/fpubh.2026.1958854

Image Credits: AI Generated

DOI: 10.3389/fpubh.2026.1958854

Keywords: clinical nursing, artificial intelligence, AI literacy, AI self-efficacy, nurse innovation, organizational climate, cross-sectional survey, tertiary hospitals, China, Artificial, intelligence, literacy