{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T14:16:42Z","timestamp":1788531402572,"version":"build-2803163510"},"reference-count":55,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T00:00:00Z","timestamp":1699488000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["anatomypubs.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Anatomical Sciences Ed"],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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\u20102 (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\u20132023 academic year. After 6\u2009weeks 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.<\/jats:p>","DOI":"10.1002\/ase.2353","type":"journal-article","created":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T07:40:39Z","timestamp":1699515639000},"page":"319-336","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Anatomy in the metaverse: Exploring student technology acceptance through the UTAUT2 model"],"prefix":"10.1002","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6077-9800","authenticated-orcid":false,"given":"Yusuf","family":"Kal\u0131nkara","sequence":"first","affiliation":[{"name":"Department of Information Technology Management Gaziantep Islam Science and Technology University  Gaziantep 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