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.