Your browser doesn't support javascript.
loading
Nursing students' intent to use AI-based healthcare technology: Path analysis using the unified theory of acceptance and use of technology.
Kwak, Yeunhee; Seo, Yon Hee; Ahn, Jung-Won.
Afiliação
  • Kwak Y; Red Cross College of Nursing, Chung-Ang University, 84 Heukseok-ro Dongjak-gu, Seoul 06974, Republic of Korea. Electronic address: kwak0613@cau.ac.kr.
  • Seo YH; Department of Nursing, Yeoju Institute of Technology, 338, Sejong-ro, Yeoju-si, Gyeonggi-do 12652, Republic of Korea.
  • Ahn JW; Department of Nursing, Gangneung-Wonju National University, 150 Namwon-ro, Heungeop-myeon, Wonju-si, Gangwon-do 26403, Republic of Korea. Electronic address: jwahn@gwnu.ac.kr.
Nurse Educ Today ; 119: 105541, 2022 Dec.
Article em En | MEDLINE | ID: mdl-36116387
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 (ß = 0.485, p = .009) and facilitating conditions (ß = 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.
Assuntos
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Estudantes de Enfermagem Tipo de estudo: Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Estudantes de Enfermagem Tipo de estudo: Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article