A Decision-Making Framework for Benchmarking Virtual Commerce Applications for the Metaverse using Spherical Linear Diophantine Fuzzy Sets - BILQUISE, GAZALA (2024)
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 engaging shopping experiences, there remains a lack of consensus on what constitutes an ideal v-commerce experience and how to identify optimal virtual commerce stores effectively. Moreover, despite numerous efforts to design v-commerce applications, none of the current applications exhibit all the desired developmental attributes. Considering this, benchmarking v-commerce applications for the metaverse is crucial for its development. This endeavor falls within the realm of Multi-Criteria Decision-Making (MCDM), considering various critical issues such as the numerous design attributes, uncertainty regarding their relative importance, and data variability. This study defines thirteen essential design attributes of v-commerce applications through an investigative approach and proposes a novel decision-making framework to benchmark the v-commerce applications based on the identified attributes. The novel method extends the Fuzzy-Weighted Zero-InConsistency (FWZIC) method with Spherical Linear Diophantine Fuzzy Sets (SLDFS), integrated with the Ranking Alternatives by Trace Median Index (RATMI) method, to formulate a strategy for selecting an optimal v-commerce application for the metaverse. Three decision matrices are constructed by intersecting 24 v-commerce application alternatives, labelled A01 to A24, with thirteen application attributes. Subsequently, the proposed method is utilized to determine the significance of these attributes and rank the applications. Criteria weighting results reveal that “Ease of Navigation” and “Recommendation Agents” are the most significant criteria in assessing v-commerce solutions. Based on ranking results, applications “A04”, “A15” and “A19” were ranked the most optimal solutions in the augmented reality (AR), virtual reality (VR) and mixed reality (MR) categories respectively. Finally, sensitivity analysis, systematic ranking, and comparative analysis procedures are used to assess the robustness and validity of the proposed decision-making framework. This research provides essential insights for decision-makers and practitioners to facilitate business growth, consumer satisfaction, and further research in this domain.