Nursing students' intent to use AI-based healthcare technology: Path analysis using the unified theory of acceptance and use of technology
- Authors
- Kwak, Yeunhee; Seo, Y.H.; Ahn, J.-W.
- Issue Date
- Dec-2022
- Publisher
- Churchill Livingstone
- Keywords
- Anxiety; Artificial intelligence; Attitude; Health technology; Healthcare; Nursing; Self-efficacy; UTAUT model
- Citation
- Nurse Education Today, v.119
- Journal Title
- Nurse Education Today
- Volume
- 119
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/59096
- DOI
- 10.1016/j.nedt.2022.105541
- ISSN
- 0260-6917
1532-2793
- Abstract
- Background: Marked advances in artificial intelligence (AI)-based technologies throughout industries, including healthcare, necessitate a broader understanding their use. Particularly, intent to use AI-based healthcare technologies and its predictors among nursing students, who are prospective healthcare professionals, is required to promote the utilization of AI. Objective: This study conducted a path analysis to predict nursing students' intent to use AI-based healthcare technologies based on the unified theory of acceptance and use of technology. Design: A cross-sectional survey was performed. Participants: The participants were 210 nursing students from two nursing schools in Korea. Methods: This study established hypothetical paths for the influence of performance expectancy, effort expectancy, social influence, facilitating conditions, self-efficacy, and anxiety on intent to use AI-based technologies. Mediation of positive and negative attitudes and facilitating conditions' direct effects on intent to use were examined. Results: Positive attitude toward AI (β = 0.485, p = .009) and facilitating conditions (β = 0.117, p = .045) predicted intent to use, whereas the path from negative attitude to intent to use was not significant. Performance expectancy, self-efficacy, and effort expectancy predicted positive attitude. Performance expectancy and self-efficacy had a negative effect on the path to negative attitude, whereas anxiety had a positive effect. Facilitating conditions did not significantly predict positive or negative attitude and only directly predicted intent to use. Social influence did not have a significant effect on intent to use. Conclusions: Intervention programs and other measures should be developed to provide education and information to boost performance expectancy, effort expectancy, facilitating conditions, and self-efficacy regarding the use of AI to lower anxiety and foster positive attitude toward AI-based health technologies. © 2022 Elsevier Ltd
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Collections - Red Cross College of Nursing > Department of Nursing > 1. Journal Articles
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