Smart Contracts Emerge as the Key Driver of Blockchain Adoption in Food Supply Chains

Blockchain technology has long been touted as a fix for the opacity, fraud, and waste that plague the world’s food supply chains, but a new study suggests that companies hoping to reap its benefits may be deploying it in the wrong order. Research published in the journal Cognitive Computation applies a decision-modeling technique called Fuzzy-DEMATEL to map how eight blockchain adoption criteria influence one another, and the resulting causal map delivers a striking message: sustainability goals such as waste reduction and carbon tracking are downstream consequences, not starting points. The capabilities that should be built first are the transactional and security foundations—smart contracts, secure data storage, and payment systems—on which everything else depends.

The study, led by Ali Ala of TU Dublin and Saveetha Institute of Medical and Technical Sciences, together with Behnam Malmir, Hamed Baziyad, and Vladimir Simic, frames blockchain adoption not as a simple technology selection problem but as a cognitive reasoning challenge. Supply chain managers must judge interdependent criteria under uncertainty, weighing incomplete information and conflicting priorities. To capture that mental process, the researchers collected linguistic judgments from a panel of twenty experts—academics and industry practitioners, most with more than a decade of supply chain experience—and converted their assessments into triangular fuzzy numbers, a mathematical device that preserves the vagueness inherent in human language rather than forcing it into artificial precision.

The Fuzzy-DEMATEL method, an extension of the Decision-Making Trial and Evaluation Laboratory technique, then transforms those judgments into a total-relations matrix that captures both direct and indirect influences among criteria. From this matrix, the team computed two key statistics for each criterion: R, the total influence it exerts on the others, and C, the total influence it receives. The difference, R minus C, separates net causes from net effects, while the sum, R plus C, measures overall prominence within the system. The result is an impact relationship map—a visual, explainable picture of how experts believe the adoption ecosystem is wired.

The headline finding concerns smart contracts: self-executing, code-driven agreements that trigger transactions and certifications without intermediaries. Smart contracts recorded the highest outgoing influence of any criterion, at R = 3.46, and the strongest net causal effect, R minus C = +0.78. In plain terms, the experts judged that nearly every other capability in a blockchain deployment depends on contract logic. Smart contracts determine when a traceability record is written, under what conditions a payment settles, and what happens when a condition fails. Intervening on smart contracts changes what the system does; intervening on almost anything else changes only how much of it is visible.

Food traceability, by contrast, emerged as the most prominent criterion overall, with a combined influence score of R + C = 6.40, yet its net causality was nearly neutral at +0.06. The authors interpret this as the signature of a hub rather than a driver. Traceability simultaneously enables fraud detection and waste identification downstream while relying on smart contracts to generate records, secure storage to retain them, and payment events to populate them. It is the integration point of the system, not its origin. Alongside smart contracts, the analysis classified payment transactions, fraud prevention, and permanent secure storage as net causes, while food waste prevention, carbon emission detection, and power consumption fell firmly into the net-effect camp.

That last classification carries perhaps the most consequential practical implication. Food waste prevention and carbon emission detection sent no influence edges at or above the analytical threshold, marking them as pure receivers in the causal network. The authors argue this reflects a fundamental distinction between capabilities and measured outcomes: a blockchain deployment executes contracts, records provenance, and settles payments—things it does—whereas waste reduction and verified emissions are things it reveals or achieves. An outcome cannot be a precondition for the mechanism that produces it. For companies tempted to launch their first blockchain pilot around a sustainability story, the message is sobering: such pilots are likely to disappoint for reasons unrelated to the technology itself, because the capabilities they depend on have not yet been built.

The study’s robustness checks strengthen its claims considerably. Kendall’s coefficient of concordance showed strong agreement among the twenty experts (W = 0.72, p < 0.001), and a matrix-convergence test confirmed the stability of the aggregated judgments. Sensitivity analyses varied both the edge threshold and the prominence cutoff used to classify criteria; the cause-effect partition held across every setting for seven of the eight criteria. The lone exception was fraud prevention, which showed a slightly negative net causality at the lower bound of the fuzzy uncertainty range. Rather than dismissing this as noise, the authors read it as substantively meaningful: fraud prevention only operates where records are already trustworthy, so its causal power is conditional on prior infrastructure. They describe it as a conditional cause—high priority, but contingent on deployment maturity.

The findings also carry theoretical weight. The criteria were drawn from the Technology-Organization-Environment framework, transaction cost theory, and diffusion of innovation theory, yet the causal map revealed something the standard TOE formulation does not predict: technology-dimension criteria predominantly send influence while environment-dimension criteria predominantly receive it. If this pattern holds across contexts, the authors suggest, the three TOE dimensions may be organized sequentially rather than in parallel—a hypothesis they explicitly offer for future testing rather than a settled conclusion. Transaction cost theory, meanwhile, helps explain why smart contracts, traceability, and fraud prevention dominate: all three address the opportunistic behavior and information asymmetry that plague multi-party food chains.

For practitioners, the study proposes a three-stage implementation sequence. First comes the transactional foundation: define contract logic, data-writing rights, validation rules, and the consensus mechanism before any pilot begins—a stage at which energy concerns can also be largely resolved, since modern permissioned, Byzantine-fault-tolerant systems avoid the power-hungry proof-of-work designs of earlier blockchain generations. Second, once contract execution is reliable across at least two organizational boundaries, organizations should build operational capability in traceability, fraud prevention, and secure storage. Third, only after those layers exist should sustainability outcomes such as waste reduction and emissions verification be pursued. For policymakers, the analysis suggests that mandating sustainability disclosure before traceability infrastructure exists produces reports that cannot be independently verified; regulatory sequencing should mirror the implementation sequence, with data-format and interoperability standards first and verified sustainability reporting second.

The authors are careful about the limits of their evidence. The results derive from expert-elicited perceptions rather than longitudinal field performance data, from a single purposive panel, and from a criterion set deliberately restricted to eight items, excluding factors such as governance arrangements and interoperability with legacy systems. DEMATEL yields perceived directional relationships, not statistically tested causal effects. Still, the convergence between the expert-derived map and real-world deployment studies—including traceability platforms, halal-certification blockchains, and IoT-integrated smart-contract frameworks—lends external plausibility to the prioritization. Future work, the team notes, should test the proposed sequence with field data, larger and more diverse panels, and cross-country comparisons, and should extend the framework to the AI, IoT, and remote-sensing technologies increasingly layered atop distributed ledgers. For now, the study offers food supply chains something they have lacked: an explainable, evidence-based answer to the question of what to build first.

Subject of Research: Cognitive causal modeling of blockchain adoption criteria in agri-food supply chains using Fuzzy-DEMATEL

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

Article References: Ala, A., Malmir, B., Baziyad, H., & Simic, V. (2026). Cognitive Causal Modeling of Blockchain Adoption in Agri-Food Supply Chains: A Fuzzy-DEMATEL Approach. Cognitive Computation, 18(1), Article 114. https://doi.org/10.1007/s12559-026-10660-0

Image Credits: AI Generated

DOI: 10.1007/s12559-026-10660-0

Keywords: blockchain, agri-food supply chain, Fuzzy-DEMATEL, smart contracts, food traceability, multi-criteria decision-making, sustainability, fraud prevention, cognitive computation, expert judgment, impact relationship map, technology adoption