Cognitive Causal Modeling of Blockchain Adoption in Agri-Food Supply Chains: A Fuzzy-DEMATEL Approach

Background / Introduction: Blockchain can improve agri-food supply-chain transparency, but adoption decisions are cognitively complex because managers judge interdependent criteria under uncertainty. This study frames adoption as cognitive causal decision modeling, aligning with cognitive computation through expert judgment, linguistic uncertainty, causal reasoning, and explainable decision support. Methods: We applied Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy-DEMATEL) to judgments from 20 experts. Eight technology-adoption criteria were modeled using triangular fuzzy numbers, and an impact relationship map was used to classify criteria by outgoing influence (R), incoming influence (C), prominence (R + C), and net causality (R − C). Results: Smart Contracts showed the strongest net causal influence (R = 3.46; R − C = + 0.78). Food Traceability was the most prominent criterion (R + C = 6.40) and the most influenced by other criteria (C = 3.17). Smart Contracts, Payment Transactions, Food Traceability, Fraud Prevention, and Secure Storage were net causes, whereas Food Waste Prevention, Carbon Emission Detection, and Power Consumption were net effects. Sensitivity analysis confirmed a stable cause/effect partition across threshold and prominence-cutoff variation, with Fraud Prevention the only criterion whose sign reverses at the lower fuzzy bound. Payment Transactions and Secure Storage sit close to the core-driving boundary. Conclusions: The findings support an explainable prioritization logic: transactional and information-security capabilities should be addressed before downstream sustainability outcomes. Because the evidence is expert-elicited rather than longitudinal field performance data, the results should be interpreted as structured decision-support guidance.