Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Healthcare and Information Technology
Junhao, Fan
Unknown Affiliation
Computer Science & IT Public Health

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Understanding the Chinese Medical Students’ AI Acceptance Intention in Healthcare: from a Facilitator and Barrier Perspective Junhao, Fan
International Journal of Healthcare and Information Technology Vol. 3 No. 2 (2026): January (In Progress)
Publisher : P3M Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/ijhitech.v3i2.6428

Abstract

Current studies have documented that medical experts’ AI acceptance is influenced by the benefit-and-cost evaluation of its applications in healthcare systems. Guided by the Technology Acceptance Model, the research aim of this study is to explore how positive factors and risk factors might influence Chinese medical students’ AI acceptance intention. This study used a quantitative approach, and the data were collected by a convenience sample from an online survey (N = 419). The statistics software tools, including SPSS 28 and SmartPLS4, were used to perform hypothesis testing. The results revealed that Chinese medical students’ AI acceptance intention was positively related to perceived usefulness and perceived ease of use, but risk factors, which were supposed to inhibit their acceptance intention, showed a positive relationship with AI acceptance intention as well. The perceived usefulness is positively correlated with the risk factors. This means that participants in this study hold a positive attitude towards AI, even though they sensed a slight risk of its application. The theoretical contributions are two-fold. Firstly, this study discussed the impacts of two risk factors and added them to the research model simultaneously. Secondly, this study explored how the perceived usefulness might serve as an antecedent of risk evaluations of AI applications. As for practical implications, this study recommends that in-hand experience with AI practice is crucial for medical students.
Co-Authors