Assessing adoption barriers to digital technology in the natural gas supply chain using an spherical fuzzy RAFSI model

Digital transformation plays a crucial role in attaining intelligent objectives for the governance of natural resources, characterized by the ability to perceive, acquire knowledge, govern effectively, and adapt accordingly. As a significant natural resource, natural gas greatly contributes to economic growth. Nevertheless, the adoption barriers to digital technology in the natural gas sector are seldom explored, especially from a supply chain perspective. Consequently, this work presents a synthetical spherical fuzzy (SF) decision framework to assess the barriers to digital technology adoption in the natural gas supply chain (NGSC). First, the SF-weighted Heronian mean aggregation operator (WHMA) is employed to form a group rating matrix, which can reflect the interaction between input preference data. Then, the SF criteria importance through entropy measure and rank sum methods are combined to derive the integrated significance of barriers. Next, a developed SF-RAFSI (Ranking of Alternatives through Functional Mapping of Criterion Subintervals into a Single Interval) model incorporating the matrix-generating method and integrated weighting method is proposed to rank the barrier level of NGSC. Finally, a case study of NGSC is described to demonstrate the application of the developed model. The result shows that the option a2 ''manufacturers'' (0.5416) has the highest barrier level. Also, the merits of the developed model are tested through sensitivity and comparison analyses. The model that has been developed has the potential to assist managers and policymakers in identifying the primary barriers to digital technology adoption in NGSC, enabling them to allocate their efforts and resources accordingly. Additionally, our findings suggest that policymakers should focus on increasing investments in manufacturers to enhance the efficacy of digital technology adoption in NGSC. •Digital technology adoption barriers are evaluated using an integrated MADM method.•The barriers ranking issue is solved by extended RAFSI with the CE-RS method.•The interactions between preference data and barriers are considered in the barriers ranking process.•A case study, including sensitivity and comparison analyses, is presented to test the proposed method.