The purpose of this study is to investigate the potential of integrating metaverse technologies into sharing economy systems to enhance sustainability, optimize resource utilization and improve operational efficiency, particularly in the transportation sector. In this context, the study specifically addresses the challenges of modeling complex and interdependent decision information under uncertainty. To achieve this, an advanced multi-criteria decision-making framework under a complex intuitionistic fuzzy (CIF) environment is developed for systematic evaluation.
This study proposes two novel aggregation operators, namely the complex intuitionistic fuzzy reducible weighted Maclaurin symmetric mean (CIFRWMCSM) and the complex intuitionistic fuzzy reducible weighted dual Maclaurin symmetric mean (CIFRWDMCSM), to address the shortcomings of existing CIF-weighted Maclaurin symmetric mean (MCSM) operators, such as the lack of idempotency and reducibility. The mathematical formulations, fundamental properties and special cases of the proposed operators are rigorously investigated. Furthermore, the weighted aggregated sum product assessment (WASPAS) method is extended by incorporating the proposed operators to handle CIF-based decision-making problems. The applicability and effectiveness of the developed framework are demonstrated through a real-world case study on metaverse-enabled sharing economy solutions in transportation.
The results reveal that the proposed CIFRWMCSM- and CIFRWDMCSM-based WASPAS framework provides stable, consistent and reliable ranking outcomes while effectively capturing interrelationships among decision criteria. The proposed approach also ensures improved aggregation consistency due to the satisfaction of reducibility and idempotency properties. Sensitivity analysis and comparative studies confirm that the proposed approach exhibits superior robustness and performance compared with the existing method.
The proposed framework offers decision-makers a practical and reliable tool for evaluating metaverse-driven sharing economy initiatives, supporting informed decision-making aimed at sustainability improvement, efficiency enhancement and safer transportation systems. It is particularly useful in scenarios involving complex, uncertain and multi-dimensional decision environments.
This study introduces reducible and idempotent CIF-based MCSM aggregation operators for the first time and integrates them into an extended WASPAS framework. By resolving key limitations of existing MCSM operators and enabling the modeling of higher-order attribute interactions, the proposed approach advances the theoretical development of CIF aggregation operators and provides a novel, robust decision-support methodology for assessing metaverse-enabled sharing economy applications.
