Aim/Purpose: The successful integration of immersive virtual reality (imVR) in education depends on various factors, including educators’ views and intentions. Pre-service teachers represent an important demographic as they can play a pivotal role in shaping future educational practices. Therefore, understanding the factors that shape their intentions to use this technology is important. Background: The Unified Theory of Acceptance and Use of Technology 2 (UTAUT-2) model is widely used in educational technology research to examine behavioral intentions across various groups regarding technology use. However, the model is not without its limitations. One of the most significant shortcomings is that it considers its constructs as exogenous factors influencing behavioral intention, without delving into their interrelationships. To address this limitation, the present study proposed and examined an extensively modified version of the UTAUT-2 model. Additionally, self-efficacy is incorporated as an important factor in this framework. Methodology: A total of 202 senior students studying at a Department of Education participated in the study, enrolled in a course designed to familiarize them with the design and use of educational imVR applications. Data were collected at the end of the semester, using a questionnaire designed to examine the factors included in the modified UTAUT-2 model. Contribution: By demonstrating strong in-sample and out-of-sample predictive power, the proposed model offers a strong theoretical and practical framework for understanding pre-service teachers’ intentions to use imVR and for promoting imVR adoption in teacher preparation contexts. Findings: Hedonic motivation, habit, and performance expectancy impacted behavioral intention. Self-efficacy emerged as a central determinant, shaping participants’ perceptions of effort expectancy, performance expectancy, and hedonic motivation. Facilitating conditions significantly enhanced self-efficacy, effort expectancy, and habit. Age, sex, and prior experience showed limited or no impact. The structural model demonstrated strong in- and out-of-sample predictive/explanatory power, while the Importance-Performance Map Analysis identified habit and hedonic motivation as key areas requiring improvement. Recommendations for Practitioners: Education stakeholders should focus on building pre-service teachers’ confidence and competence in using imVR through structured, hands-on training and consistent access to well-supported technological environments. Efforts should prioritize cultivating habitual use of imVR by embedding it into regular teaching activities, lesson planning, and semester-long coursework, supported by readily available technical assistance. Finally, enhancing the enjoyment and engagement of imVR experiences can boost teachers’ intrinsic motivation, further strengthening their intention to integrate this technology into their future instructional practices. Recommendation for Researchers: The study illustrates the need to integrate constructs such as self-efficacy, which are not fully addressed in traditional behavioral intention frameworks. Experimental designs that pay attention to sample selection are strongly advised to avoid random or invalid responses when evaluating users’ perceptions and intentions related to advanced or emerging technologies. Finally, the study demonstrates the necessity of suggesting and examining models that capture the complex and multifaceted dynamics of imVR adoption. Future Research: Researchers should further validate the modified UTAUT‑2 across diverse educational contexts and participant demographics. Longitudinal and mixed‑methods designs are also advised. Expanding the model to include additional contextual variables can provide a more comprehensive understanding of barriers and facilitators influencing teachers’ intention to adopt imVR.
Fokides et al. (Thu,) studied this question.