Evaluation of Virtual Commerce Applications for the Metaverse Using Spherical Linear Diophantine‐Based Modeling Approach

Abstract

The rise of the metaverse has ignited a surge of interest among researchers and decision‐makers, seeking to develop effective virtual commerce (v‐commerce) applications that cater to business demands and customer preferences. v‐commerce, an emerging concept, redefines the future of shopping experiences and customer‐product interactions. While businesses are actively exploring the potential of immersive technologies to deliver captivating and engaging shopping experiences, there remains a lack of consensus on what constitutes an ideal v‐commerce experience and how to identify optimal v‐commerce stores effectively. Considering this, benchmarking v‐commerce applications for the metaverse is crucial for its development. This endeavor falls within the realm of multiple‐criteria decision‐making, given various critical issues such as the multitude of design attributes, uncertainty regarding their relative importance, and data variability. This study proposes an innovative approach that extends the fuzzy‐weighted zero‐inconsistency (FWZIC) method with spherical linear Diophantine fuzzy sets (FSs) (SLDFSs) to determine the weights of v‐commerce attributes. The obtained weights are integrated with the ranking alternatives by trace median index (RATMI) method to select the optimal v‐commerce application for the metaverse. Criterion weighting results reveal that “ease of navigation” and “recommendation agents” are the most significant criteria in assessing v‐commerce solutions. Based on these results, 24 v‐commerce solutions were evaluated. Additionally, sensitivity analysis and comparative evaluation were used to assess the robustness and validity of the proposed framework. This research provides essential insights for decision‐makers and practitioners to facilitate business growth, consumer satisfaction, and further research in this domain.