Investigating continuous intention to use metaverse in higher education institutions: a dual-staged structural equation modeling-artificial neural network approach

Abstract

Abstract The current study explores metaverse adoption among higher education institutions (HEIs) in the light of a theoretical framework to empower future perspectives of the metaverse as a learning platform. Even though this technology was just recently introduced to the higher education sector, very few attempts have been made to evaluate its impact. The purpose of this research is to analyze the elements that influence the continuous intention (CI) to utilize the metaverse technology in learning. The technology acceptance model (TAM) and the self-determination theory (SDT) are both included in this study. A questionnaire was developed and distributed to students attending private universities in order to obtain the data that was needed for the proposed model. Using a hybrid approach that consists of partial least squares structural equation modeling (PLS-SEM) and an artificial neural network (ANN) model, which combines a linear PLS model with compensation and a nonlinear ANN model without compensation, the effect of CI on using the metaverse as a learning platform is investigated. This approach was chosen because it contains both of these types of models. When it comes to explaining the use of metaverse technology among students attending higher education institutions in Egypt, the research findings suggested that autonomy and perceived usefulness (PU) are major determinants. Nevertheless, the continuing intention was unaffected by the perceived ease of use (PEOU) of the product. Furthermore, according to the data provided by the ANN model, the most significant predictors are relatedness, PEOU, autonomy, and PU. It has been determined that the results obtained from the PLS-SEM and ANN modes are identical. Additionally, both theoretical and practical implications are discussed in this article.

Keywords

• Continuous intention
• Metaverse
• TAM
• SDT
• Neural network
• PLS-SEM