Student intention to use MUVEs: An integrated model of technology acceptance and learning engagement theories

With the development of virtual reality technology, 3D multi-user virtual environments (MUVEs) have attracted increasing research attention and are thought to bring many learning benefits in higher education. However, the widespread and sustained application of MUVEs in higher education lies in learners’ intention to use them, but the mechanism that affects this intention remains unclear. Based on this research gap, this study established and tested a structural equation model (SEM) to explore the sophisticated interwoven relationships between students’ technology acceptance, learning engagement, learning satisfaction, and their intention to use MUVEs. During COVID-19 pandemic, data collection was conducted at a university in central China and a sample of 237 participants were obtained. The core data of SEM includes path coefficients (e.g., standardized regression coefficients), which represent the direct or indirect effects between variables, and fit indices (e.g., CFI, TLI, RMSEA, and SRMR), which assess the degree of alignment between the model and the observed data. The results showed that: (1) Perceived usefulness has a stronger influence on predicting learning engagement than perceived ease of use. (2) Emotional engagement plays a key mediating role between technology use and satisfaction. (3) The direct predictive ability of satisfaction on intention to use is stronger than that of perceived usefulness. (4) The positive effect of the path cognitive engagement→emotional engagement→satisfaction→intention to use is the largest. Based on these findings, this study provides implications for optimizing the design and implementation of MUVEs in higher education from the perspectives of teachers, students, and platform developers.