Unveiling the implementation barriers to the digital transformation in the energy sector using the Fermatean cubic fuzzy method

Digital transformation has been regarded as a primary styrategy to promote transitions in diverse fields, but industry pioneers believe that the existing barriers may hamper the speed of digital transformation. Hence, this paper presents a synthetical decision model integrating the weighted Heronian mean aggregation (WHMA) operator, the Level-Based Weight Assessment (LBWA) model, the CRITIC (criteria importance through intercriteria correlation) method, and the Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval (RAFSI) model with Fermatean cubic fuzzy sets to evaluate the barriers to digital transformation implementation in energy transitions with unknown weights of experts and criteria. In this framework, an extended WHMA operator with the deviation-based method is established to fuse experts' preference information. The LBWA model and CRITIC method with FCF setting are combined to derive the weights of barriers. Next, these methods are incorporated into the RAFSI model to analyze these barriers. A numerical example of evaluating barriers to digital transformation implementation in the power sector displays the application of the RAFSI model-based decision method. The result reveals that a3 “Equipment manufacturer” (0.7063) has the highest barrier level, and a4 “Consumers of smart power electronic” (0.4391) have the lowest barrier level. After that, sensitivity and comparative explorations are applied to examine the feasibility and reliability of the synthetical model. The results show that the proposed model can provide a more practical and stable evaluation result for supporting the decision of stakeholders associated with ET.