Predicting nurses' behavioral intention to adopt artificial intelligence in a resource-limited setting: an extended technology acceptance model study in Iran.
TL;DR
This study demonstrates that extending the Technology Acceptance Model by incorporating nurses' knowledge and attitudes provides a meaningful framework for predicting AI adoption in resource-limited healthcare settings.
Abstract
Background
The integration of Artificial Intelligence (AI) into healthcare systems has the potential to substantially enhance clinical decision-making, workflow efficiency, and quality of patient care. However, the successful implementation of AI technologies largely depends on their acceptance by frontline healthcare providers, particularly nurses. While the Technology Acceptance Model (TAM) has been widely used to explain technology adoption, evidence remains limited regarding the role of nurses' cognitive and attitudinal factors in resource-limited settings.
Methods
A cross-sectional analytical study was conducted in 2025 among nurses working in hospitals affiliated with Ilam University of Medical Sciences, Iran. Using a census-based approach, 340 nurses were invited to participate, of whom 199 completed a validated online questionnaire. Data were collected using Davis's Technology Acceptance Model scales (Perceived Usefulness and Perceived Ease of Use) alongside an adapted instrument measuring knowledge, attitude, behavioral intention, and practical use of AI. Non-parametric tests and multiple linear regression analysis were performed using SPSS version 26.
Results
Most participants demonstrated low levels of AI-related knowledge (70.4%) and relatively unfavorable attitudes toward AI (66.8%). Behavioral intention to adopt AI was moderate (57.3%), while reported practical use was low (48.7%). Attitude emerged as the strongest predictor (β = 0.272, p < 0.001), followed by perceived ease of use (β = 0.198, p = 0.006), perceived usefulness (β = 0.174, p = 0.016), and knowledge (β = 0.127, p = 0.047).
Conclusion
This study demonstrates that extending the Technology Acceptance Model by incorporating nurses' knowledge and attitudes provides a meaningful framework for predicting AI adoption in resource-limited healthcare settings. Beyond technological considerations, fostering positive attitudes and foundational AI literacy among nurses is essential for successful implementation. Targeted educational and organizational interventions are urgently needed to prepare the nursing workforce for AI-enabled healthcare. CLINICAL TRIAL NUMBER Not applicable.