An Enhanced Spherical Cubic fuzzy WASPAS Method and its Application for the Assessment of Service Quality of Crowdsourcing Logistics Platform

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Abstract
The service quality of crowdsourcing logistics platforms (CLP) represents the fulfillment of standardized requirements during service delivery. Evaluating this quality enables quantification of service performance, identification of deficiencies in key indicators and optimization of operational efficiency. Such assessment helps standardize courier behavior, enhance user satisfaction, and strengthen platform competitiveness. This study proposes an enhanced decision-making method for assessing CLP service quality under uncertainty using spherical cubic fuzzy (SCF) sets, which effectively capture fuzzy and uncertain information. We develop an SCF-based approach integrating Renyi entropy and the WASPAS method, featuring four key contributions: (1) definition of SCF Aczel-Alsina operations and four corresponding aggregation operators (weighted averaging, geometric, ordered weighted averaging, and ordered weighted geometric); (2) development of an SCF-Renyi entropy weight method using score functions to determine criteria weights; (3) improvement of the WASPAS method through SCF Aczel-Alsina operators; and (4) validation through a supplier selection case study demonstrating the method's applicability. Sensitivity analysis confirms the approach's stability and universality in handling multi-criteria decision analysis problems.

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