A Confidence-Interval Circular Intuitionistic Fuzzy Method for Optimal Master and Sub-Franchise Selection: A Case Study of Pizza Hut in Europe

Effective franchise selection is crucial for global brands like Pizza Hut to maintain consistent quality and operational excellence amidst a competitive landscape. This paper introduces a novel confidence-interval circular intuitionistic fuzzy set (CIC-IFS) framework, designed to address the intricate challenges of master and sub-franchise selection in the European market. By integrating competence coefficients of decision-makers into the final evaluations, the model allows for a more accurate representation of expert judgments. Decision-makers can choose from various scenarios, ranging from super pessimistic to super optimistic, using ten forms of aggregation operations over index matrices. The proposed approach leverages confidence intervals within the circular intuitionistic fuzzy set paradigm to capture the uncertainty, vagueness, and hesitancy inherent in the decision-making process. A case study involving Pizza Hut’s European operations demonstrates the model’s efficacy in differentiating potential franchisees and identifying those best aligned with the brand’s values. The results indicate a significant improvement in selection accuracy compared to traditional methods and other fuzzy approaches, thereby enabling Pizza Hut to make more informed decisions and solidify its market position.