Predicting nurses' behavioral intention to adopt artificial intelligence in a resource-limited setting: an extended technology acceptance model study in Iran.
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.
Hospital-level resources, older age, and longer work experience were the factors most strongly associated with nurses’ AI training acceptance and AI tool usage, with work experience showing the strongest association with tool usage.
Linlin Guo, Qin Ding, M. Tang et al.· Frontiers in Public Health· 0 citations
Profile-tailored strategies may help nursing managers facilitate the effective and sustainable implementation of AI technologies in clinical practice and to explore the factors associated with profile membership with type affiliation.
The weak negative correlation between knowledge and attitudes suggests that greater awareness of AI may be accompanied by increased concerns regarding its use, and further educational initiatives are needed to enhance nurses’ preparedness for AI integration in clinical practice.
R. Elsayed, A. Nagy, Eman Sobhy El-Said Hussein et al.· BMC Nursing· 1 citation
Amid the global race toward intelligent healthcare systems, Saudi Arabia stands at a pivotal moment in its digital health transformation. Understanding how prepared healthcare professionals are to adopt artificial intelligence is essential for shaping successful national strategies.
This study aimed to assess healthcare professionals’ attitudes, perceptions, and intentions toward using AI in clinical practice; examine awareness and actual use; identify key predictors of AI adoption based on the Unified Theory of Acceptance and Use of Technology (UTAUT); and explore the mediating role of institutional support.
A cross-sectional survey was conducted among 521 healthcare professionals, including physicians, nurses, administrators, and allied health workers, across Saudi Arabia. The survey assessed awareness, usage, perceived usefulness, ease of use, social influence, facilitating conditions, perceived risks, and the intention to use AI. Data were analyzed using chi-squared tests, multiple regression, and mediation analyses.
Although AI awareness was remarkably high (89.1%) and optimism toward the future was strong (79.0%), only 51.2% of participants reported actual clinical use of AI, A 37.9 percentage-point awareness–use gap. Two factors consistently stood out as powerful drivers of intention: believing that AI is genuinely useful (
B
= 0.491,
p
< .001) and feeling confident in one's ability to use it (
B
= 0.224,
p
< .001). Institutional support played an important but mostly indirect role in shaping intentions by enhancing these two beliefs. Social influence had little effect and was negative for nurses, whereas perceived risk did not significantly deter adoption. Despite structural and ethical challenges, intention to use AI remained high (71.6%), A 20.4 percentage-point gap ahead of actual use, indicating that organizational barriers, rather than individual willingness, remain the primary obstacle to AI integration.
These dynamics provide unique opportunities. With 71.6% of professionals intending to adopt AI, targeted training initiatives, clearer governance frameworks, and organizational support may help facilitate the sustainable integration of AI into healthcare practice. This study offers empirical evidence and a roadmap for transforming enthusiasm into sustainable, safe, and meaningful AI integration, and may support healthcare leaders and policymakers in developing strategies for safe and sustainable AI integration within the Saudi healthcare system.
Advancing AI-enabled healthcare in Saudi Arabia requires investment not only in technology, but also in healthcare professionals’ preparedness and organizational support. Structured training programs, hands-on exposure to AI tools, supportive leadership, adequate infrastructure, and clear data governance frameworks may help healthcare professionals adopt AI more confidently and sustainably within clinical practice.
Yara Alrashed, N. Alsaheil· Frontiers in Health Services· 0 citations
Abstract Background The integration of mobile health apps (MHAs) into nursing practice is essential for improving efficiency, conserving resources, and enhancing patient care. To promote the acceptance of these apps in clinical care, it is important to assess current acceptance levels and identify influencing factors. Objective This study investigated the acceptance of MHAs and related factors among nurses in hospitals affiliated with Kashan University of Medical Sciences, Iran. Methods In this cross-sectional study, 250 nurses were selected via stratified random sampling in Kashan in 2022. Data were collected using the Nurses’ Mobile Health Device Acceptance Scale (NMHDA-S) and the Probable Predictors Questionnaire. The data were analyzed using SPSS (version 16; IBM Corp), 1-way ANOVA, independent t test, Pearson correlation coefficients, and multiple linear regression. Results The findings showed that nurses had a mean acceptance score of 4.207 (SD 0.740) on a Likert scale from 1 to 7 (95% CI 4.115‐4.299) regarding the use of MHAs. Multiple linear regression analysis indicated that 4 variables significantly predicted acceptance of MHAs (R²=0.230, F4,245=18.308, P<.001): interest in participating in relevant educational programs (β=0.392, P<.001), encouragement from health care professionals to use MHAs (β=0.133, P=.02), male participant (β=−0.116, P=.04), and duration of daily internet use (β=0.117, P=.04). Conclusions Although MHA acceptance among clinical nurses in Kashan is moderate, adoption can be enhanced through targeted training, facilitated internet access, and gender-specific incentive policies, particularly for female staff.
N. Mirabootalebi, Felix Holl, Walter Swoboda et al.· JMIR mHealth and uHealth· 0 citations
Background: In professional nursing practice, decision-making style and self-efficacy are important elements that directly impact patient safety, care equality, and healthcare outcomes. Objective: To examine the relationship between decision-making styles and self-efficacy among nurses in Jordanian hospitals. Methods: A cross-sectional design was used. A convenience sample of 150 nurses was recruited from Jordanian private and governmental hospitals in the central region of Jordan (Amman, Zarqa, and Balqa). The General Self-Efficacy Scale and the Nursing Decision-Making Instrument were used to gather data. Results: 88.7% were categorized as intuitively oriented decision makers, and the overall mean score of nurses’ decision-making styles was high (M=3.74/5). Nurses scored highest in the areas of documenting, anticipating the results of nursing interventions, and conducting structured patient evaluations. Additionally, there was a high level of self-efficacy (M=3.36/4), especially in problem-solving skills and handling clinical problems. Self-efficacy and decision-making styles showed a positive correlation (r=0.412, p<0.001). Significant differences in self-efficacy and decision-making styles were found based on age, years of experience, job title, and educational level. Significantly higher scores were reported in older nurses holding postgraduate degrees, more experienced, and having administrative positions. Nurses’ decision-making styles were significantly predicted by years of experience (β=0.24, p=0.021) and self-efficacy (β=0.34, p<0.001). Conclusions: To improve nurses' confidence, clinical reasoning, and decision-making skills, healthcare organizations should encourage continuous professional development, mentorship, and support structural training programs, which ultimately will improve patient care quality and healthcare outcomes.
Rabia S. Allari, Rahaf Abd Al-Salam Ahmad Allaham, Khulood Batarseh et al.· Al-Rafidain Journal of Medic...· 0 citations
