Anatomy in the metaverse: Exploring student technology acceptance through the UTAUT2 model
Open accessAbstract
The impact of technology on educational domains has been a subject of research for many years. Therefore, understanding how students perceive and utilize technologies for educational purposes is crucial. Especially in a critical subject like anatomy education, it is essential to employ various models to determine students' technology acceptance and usage levels. One of these models is the unified theory of acceptance and use of technology-2 (UTAUT2). In this study, the acceptance and usage levels of metaverse technology by students in the context of anatomy education in the metaverse environment have been investigated within the framework of the UTAUT2 model. The study was conducted with students from the Department of Midwifery at the Faculty of Health Sciences during the fall semester of the 2022-2023 academic year. After 6 weeks of anatomy education in the metaverse environment, the student's acceptance and usage levels of metaverse technology were assessed using the UTAUT2 scale. The collected data were analyzed using the partial least squares structural equation modeling method. The research has revealed the technology acceptance and usage levels of students who utilized metaverse technology in anatomy education. According to the obtained results, it has been observed that social influence affects behavioral intention, facilitating conditions influence behavioral intention, and habit variable affects behavioral intention, while habit, in turn, influences usage. Other variables explain behavioral intention with a proportion of 75.30%, whereas other variables explain usage behavior with 54.90%. Upon analyzing the responses to the UTAUT2 scale, it has been concluded that there is the highest level of participation in hedonic motivation. In contrast, the habit variable has the lowest participation level. The findings of this study have enabled the identification of the impacts of metaverse technologies on anatomy and health education. These results will likely contribute to improving the utilization of metaverse technologies in anatomy and health education.
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FC serves as a measure of students' perceptions regarding the robustness of institutional resources, ranging from hardware availability to the immediacy of technical assistance.
“FC serves as a measure of students’ perceptions regarding the robustness of institutional resources, ranging from hardware availability to the immediacy of technical assistance [62].”
The positive effects of FC are consistent with previous findings showing that technical and environmental support can improve users' perceptions of usefulness and ease of use in psychological intervention and AR-related contexts.
“The positive effects of FC are consistent with previous findings showing that technical and environmental support can improve users’ perceptions of usefulness and ease of use in psychological intervention and AR-related contexts [62,100].”
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- Authors
- Kalınkara, Y., & Özdemir, O.
- Affiliations
- Gaziantep İslam Bilim ve Teknoloji Üniversitesi (TR)
- Fırat University (TR)
- Corresponding author
- Kalınkara, Yusuf
- Venue
- Anatomical Sciences Education
- Publisher
- Wiley
- Published
- 2023-11-09
- Volume
- 17
- Issue
- 2
- Pages
- 319-336
- ISSN
- 1935-9772 (ISSN-L)
- 1935-9780
- OpenAlex
- W4388527632
- Cited by
- 46
- References
- 43
- Citations by year
- 2026: 12 · 2025: 20 · 2024: 14
- FWCI
- 11.83
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Anatomical Sciences Education (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DSpace - FIRAT (Fırat University) (submittedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Physical Sciences › Computer Science › Human-Computer Interaction › Virtual Reality Applications and Impacts
- Physical Sciences › Engineering › Biomedical Engineering › Anatomy and Medical Technology
- Keywords
- metaverse, anatomy education, technology acceptance, UTAUT2, midwifery students, behavioral intention, social influence, facilitating conditions, habit, hedonic motivation, virtual learning environments
- MeSH terms
- Anatomy, Humans, Motivation, Surveys and Questionnaires, Students, Technology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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