Multi-Criteria Decision-Making Methodology for Ranking and Selecting Optimal Metaverse Platform

Abstract

This study adopted a decision-making model for ranking and sectioning the best Metaverse platforms. This section has various criteria such as effectiveness, safety, user experience, etc. So, the Multi-Criteria Decision-Making (MCDM) methodology is used to deal with multiple criteria. The MABAC method is an MCDM methodology used to rank the alternatives. The proposed methodology is applied to nine criteria and fifteen alternatives. The criteria weights are computed. The sensitivity analysis is conducted to show the stability of the final rank. The ten cases in criteria weights are proposed, and ten ranks are shown. The results show the rank of alternatives under different cases is stable. The comparative analysis is conducted with other MCDM methods such as TOPSIS, VIKOR, EDAS, and COPRAS. The results show the proposed methodology is effective with other MCDM methods.