An enhanced T-spherical fuzzy generalized multi-criteria decisioning model for evaluating the effectiveness of digital transformation in the sustainability of agri-food systems

The rapid advancement and widespread integration of artificial intelligence (AI) have provided substantial technical support for digital transformation (DT), positioning it as a key enabler of sustainable development across various industries. However, research on the role of DT in achieving sustainability within the agri-food sector remains limited. To address this gap, this study proposes a hybrid decision-making approach to assess the impact of DT on sustainability in the agri-food sector, particularly under conditions of uncertainty. The proposed framework integrates a modified generalized TODIM (an acronym in Portuguese for Interactive and Multicriteria Decision Making), a T-spherical fuzzy weighted Heronian mean operator, and Cronbach's coefficient to enhance decision-making reliability. The model evaluates DT's effectiveness in fostering sustainable agri-food systems by aggregating expert judgments through the Relative Closeness Coefficient (RCC) method, ensuring comprehensive factor interaction analysis. Additionally, a weighted Minkowski distance Heronian aggregation operator is introduced to prioritize organizational performance in sustainability efforts. To validate the proposed approach, an empirical case study illustrates its application in evaluating DT-driven sustainability within the agri-food sector. The findings highlight traceability and visibility as critical factors enhancing production efficiency. Sensitivity and comparative analyses further confirm the robustness and reliability of the proposed decision-support framework. This study contributes to the literature by offering a novel methodological approach for assessing DT's role in sustainable agri-food systems, providing valuable insights for both academia and industry stakeholders.