Expert Systems with Applications, cilt.296, 2026 (SCI-Expanded, Scopus)
The integration of metaverse technology into higher education institutions (HEIs) offers transformative potential for enhancing pedagogical innovation and addressing evolving educational demands. However, its effective implementation requires a systematic assessment of enablers and implementation feasibility across diverse HEI contexts. In this study, a novel T-spherical fuzzy (T-SF) hybrid model is developed to address this issue. The proposed model first collects expert evaluation information using T-spherical fuzzy sets (T-SFSs) and utilizes a similarity measure to determine expert weights. The weighted Heronian mean aggregation (WHMA) operator is then integrated to derive conservative aggregated values. Furthermore, the T-SF-WHMA-MULTIMOOSRAL hybrid model evaluates and ranks the metaverse integration across different types of HEIs. Finally, a case study is presented to illustrate the application of the T-SF-WHMA-MULTIMOOSRAL hybrid model. The results indicate that the continual feedback for teachers and students (Es7) and the sustainability and environmental impact (Es6) as the most influential enablers of metaverse integration. This study evaluates and ranks the potential for metaverse integration across four distinct types of HEIs, with the results showing that higher vocational colleges (O1) ranked highest. The findings of this study provide practical insights for guiding the adoption of metaverse technology in diverse and complex educational environments.