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Modelling the effects of perceived system quality and personal innovativeness on the intention to use metaverse: a structural equation modelling approach.
- Source :
- PeerJ Computer Science; Oct2024, p1-22, 22p
- Publication Year :
- 2024
-
Abstract
- The metaverse, an interactive and immersive 3D virtual environment, has recently become popular and is widely used in several fields, including education. However, the successful use of metaverse relies on the extent to which users intend to adopt and use it. Close examination of this critical issue reveals a lack of research that examines the effects of certain factors on users' intentions toward using metaverses. Thus, this study extends the technology acceptance model by integrating two constructs—perceived system quality and students' personal innovativeness. Using a survey to collect data, 164 responses were received from students at the University of Ha'il in Saudi Arabia. Two steps in structural equation modelling (SEM) using the AMOS software were applied to analyse the data and test the research hypotheses. The results revealed that perceived system quality had a significant effect on students' intentions to use metaverses through perceived ease of use. Furthermore, personal innovativeness had a significant effect on students' intentions through the perceived usefulness of the metaverse. In addition, perceived usefulness affected students' intentions to use a metaverse. Surprisingly, perceived ease of use had an insignificant effect on students' intentions to use the metaverse. Although the proposed model and its findings contribute to the technology acceptance model (TAM) literature, the study's practical value is significant because it can help educational policymakers and authorities to understand the effect of each factor and plan future strategies. Additionally, the findings of this study can assist practitioners, designers, and developers in designing and promoting the utilisation of metaverses. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 23765992
- Database :
- Complementary Index
- Journal :
- PeerJ Computer Science
- Publication Type :
- Academic Journal
- Accession number :
- 180806807
- Full Text :
- https://doi.org/10.7717/peerj-cs.2331