ArticleScientific reports2026
A decision making algorithm for enhancing consumer value in purchasing behavior based on circular spherical fuzzy CRITIC WASPAS method.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The problems of consumer value in purchasing behavior are usually associated with their high degree of uncertainty, hesitation, and contradicting opinions among the decision-makers. Conventional fuzzy and intuitionistic fuzzy models cannot adequately represent such an achievement and directional uncertainty. Given this limitation, the study offers a novel hybrid multi-attribute group decision-making (MAGDM) model that integrates weighted aggregated sum product assessment (WASPAS) and criteria importance through intercriteria correlation (CRITIC) models into the circular spherical fuzzy set (CSFS) framework. The objective aspect of the proposed method is that it employs the CRITIC method to calculate the weight of the attributes based on the intensity of contrast, and the weighted product model is used to view the summation of the weighted attributes and product models. By doing so, the WASPAS method produces a strong alternative ranking. Representation of membership, abstinence, non-membership, and radius-based hesitation can be more expressive in the CSFS environment, which reflects the uncertainty of decision-makers in a more realistic way. The relevance and suitability of the proposed model are applied to a hypothetical case study of consumer value in buying behavior. The results indicate that "Simplifying the Buying Process" is the optimal strategy across all evaluation scenarios. Sensitivity and comparative analysis also prove the stability, robustness, and high-quality of the proposed CRITIC-WASPAS model in the framework of CSFS as compared to the current fuzzy decision-making methods. The proposed work offers feasible decision support to businesses and offers novel directions to advanced MAGDM applications in the face of complex uncertainty.
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