Individual Innovativeness, General Attitudes Toward Artificial Intelligence, and Perceptions of an AI-Equipped Robotic Nursing Support System Among Intensive Care Nurses: An Analytical Cross-Sectional Study

Objective: To examine individual innovativeness and general attitudes toward artificial intelligence (AI), together with perceptions of a specific AI-equipped robotic nursing support system, among intensive care nurses with direct workplace exposure to the system. Methods: This single-center analytical cross-sectional study was conducted in Istanbul, Türkiye, using a census-oriented recruitment approach. Of the 120 intensive care nurses employed in participating intensive care units where the robotic nursing support system was available, 100 participated in the study (participation rate: 83.3%). Individual innovativeness was assessed with the Individual Innovativeness Scale (IIS), and general AI attitudes with the Positive and reverse-scored Negative subscales of the General Attitudes towards Artificial Intelligence Scale (GAAIS). Primary associations were examined using Pearson correlations with 95% confidence intervals (CIs). Robust multivariable models, rank-based sensitivity analyses, and false-discovery-rate-adjusted subgroup comparisons were added during peer review as supplementary, non-prespecified analyses; subgroup findings are exploratory. The study had no pre-exposure baseline or unexposed comparison group. Results: Mean IIS, Positive GAAIS, and reverse-scored Negative GAAIS scores were 71.59 ± 8.96, 48.56 ± 6.53, and 31.82 ± 4.57, respectively. Internal consistency was high for IIS (α = 0.905), Positive GAAIS (α = 0.886), and reverse-scored Negative GAAIS (α = 0.842). IIS was moderately associated with Positive GAAIS (r = 0.441, 95% CI 0.267–0.586; p < 0.001) and reverse-scored Negative GAAIS (r = 0.426, 95% CI 0.251–0.574; p < 0.001). In supplementary HC3-adjusted models, IIS remained associated with Positive GAAIS (B = 0.188, 95% CI 0.045–0.332; standardized β = 0.258; p = 0.011) and reverse-scored Negative GAAIS (B = 0.151, 95% CI 0.054–0.248; β = 0.297; p = 0.003). Previous AI/robot nursing education was also independently associated with both attitude dimensions. Perceived professional benefit (86%) and perceived workload reduction (86%) reflected nurses’ perceptions of AI/robots in nursing rather than objectively measured clinical outcomes. Additionally, 70% of participants reported satisfaction with the specific robotic nursing support system. Conclusions: Among robot-exposed intensive care nurses at one center, greater individual innovativeness was associated with more favorable general AI attitudes in the prespecified primary analysis, and previous AI/robot nursing education showed a similar association in supplementary, exploratory analyses. General AI attitudes were measured separately from, and should not be equated with, acceptance or actual use of the specific robotic system. These cross-sectional associations should not be interpreted as evidence of causal effects, objective workload improvement, or readiness for sustained clinical implementation.