Novel Q-Rung orthopair fuzzy correlation measure based on spearman’s correlation scheme with application in vehicle selection problem

Background: Q-rung orthopair fuzzy sets (Q-ROFS) have been widely employed in decision-making problems due to their strong ability to handle uncertainty, indecision, and imprecision. Consequently, several q-rung orthopair fuzzy correlation measures (Q-ROFCM) have been developed and applied in various decision-making contexts. However, many existing correlation measures exhibit inherent limitations, which reduce their effectiveness in addressing practical, real-world problems. Methods: In this study, a novel q-rung orthopair fuzzy correlation coefficient (Q-ROFCC) based on Spearman’s correlation scheme is proposed to overcome the shortcomings of existing approaches. The fundamental mathematical properties of the proposed correlation measure are rigorously analyzed to ensure compliance with the standard axioms of correlation coefficients. Furthermore, the proposed method is incorporated into a multi-attribute decision-making (MADM) framework. Results: The results demonstrate that the proposed Spearman-based Q-ROFCM technique is reliable, effective, and accurate when compared with existing methods. Its applicability is illustrated through a vehicle selection problem, where the most suitable alternative is identified based on optimal performance and user satisfaction. Comparative analysis confirms the superiority of the proposed approach over Pearson-based Q-ROFCM approaches. Conclusions: The proposed Q-ROFCM technique provides a robust and efficient alternative for solving MADM problems under uncertainty. Owing to its improved performance and practical applicability, the method is well suited for real-life decision-making scenarios.