A novel complex intuitionistic fuzzy AHP method and its application to electric vehicle selection

Multi-criteria decision-making (MCDM) methods often involve uncertainty and subjective judgment. The Analytic Hierarchy Process (AHP) has been widely used to structure and solve such problems, and its extension with intuitionistic fuzzy sets (IF-AHP) allows for modeling both membership and non-membership degrees. However, ordinary intuitionistic fuzzy sets may not fully capture complex uncertainties that include phase or directional information. This paper proposes a Complex Intuitionistic Fuzzy AHP (CIF-AHP) approach, integrating complex intuitionistic fuzzy sets into the traditional AHP framework. The methodology is applied to capture consumers’ purchasing preferences regarding electric vehicles (EVs) under uncertainty, and a comparative analysis is conducted between CIF-AHP and ordinary IF-AHP. The results demonstrate that CIF-AHP provides richer information and enhanced differentiation among alternatives, offering a more nuanced decision-making tool in uncertain environments. The contribution of this study to the literature lies in identifying and weighting the key factors influencing EV adoption through a method that explicitly accounts for vagueness and hesitation in decision-making.