Abstract
The Decision-Making Trial and Laboratory (DEMATEL) methodology excels in the analysis of interdependent factors within complex systems, with correlation data typically presented in crisp values. Nevertheless, the judgments made by decision-makers often possess a degree of fuzziness and uncertainty, rendering the sole reliance on precise values inadequate for representing real-world scenarios. To address this issue, our study extends the DEMATEL approach to more effectively and efficiently handle intuitionistic fuzzy information, which denotes the factor correlation information from decision-makers in the form of intuitionistic fuzzy terms. The paper aggregates the intuitionistic fuzzy correlation information from each decision-maker, employing operators designed for managing intuitionistic fuzzy numbers. The significance and categorization of factors are determined through intuitionistic fuzzy matrix operations. Additionally, a causal and effect diagram is constructed to elucidate the distinct roles of these factors. Finally, this study illustrates the applicability of our proposed method with a real-world case in the context of electric vehicles (EVs). The study's results identify four cause factors and six effect factors within EV battery technology. The identification and categorization of these factors will assist EV companies in implementing targeted measures to foster the advancement of the battery technology.