Blockchain-enabled traceability for farm-to-fork food safety and quality management

Thesis Abstract

Blockchain-enabled traceability for farm-to-fork food safety and quality management Farm-to-fork food systems increasingly confront risks related to contamination, fraud, and opacity, undermining consumer trust and undermining compliance with evolving traceability regulations. This study addresses the problem by investigating how blockchain-enabled traceability systems can enhance food safety, quality assurance, and transparency across the supply chain from producers to retailers. The aim is to design, implement, and evaluate a scalable ICT-driven traceability framework that integrates real-time sensor data, smart contracts, and standardized data schemas to improve decision-making, recall efficiency, and stakeholder collaboration. Specific objectives are to (i) assess current traceability practices and risk hotspots in the regional dairy and fresh produce sectors, (ii) develop a blockchain-based traceability architecture incorporating IoT devices, RFID/NFC tagging, and cryptographic data anchoring, (iii) model data governance, access control, and data integrity mechanisms using a multi-layered security approach, (iv) evaluate the framework’s impact on detection speed, recall scope, and stakeholder trust through empirical testing, and (v) formulate actionable recommendations for policy, industry standards, and implementation roadmaps. Methodologically, the study adopts a mixed-methods, multi-site case study design conducted over 18 months in three agro-food networks (dairy, leafy greens, and poultry) across two operational regions. The population comprises producers, processors, distributors, retailers, and regulatory inspectors. A purposive sample of 120 participants (40 per sector) will be recruited, with a minimum of 30 completed surveys and 20 in-depth interviews to capture diverse perspectives. Data collection will combine (a) quantitative instrumentation—structured surveys measuring perceived system usefulness, ease of use, perceived risk, and trust, and (b) qualitative instrumentation—semi-structured interviews and observational notes during pilot deployments of a blockchain-enabled platform. The ICT prototype will integrate a permissioned blockchain (Hyperledger Fabric) with IoT-enabled sensors (temperature, humidity, and GPS) and RFID/NFC tagging, supported by standardized GS1 data models and Smart Contract templates for provenance validation and recall initiation. Validity and reliability will be ensured through pilot testing, Cronbach’s alpha assessment for multi-item scales (target ? ? 0.78), triangulation across data sources, and expert review of the architectural design. Data analysis will employ (i) descriptive statistics and regression analysis to examine factors influencing perceived usefulness and trust, (ii) structural equation modeling (SEM) to test the hypothesized relationships among system quality, information quality, service quality, and user outcomes, (iii) event-history analysis to evaluate recall efficiency improvements, and (iv) thematic analysis of interview transcripts to extract barriers, enablers, and experiential insights. A cost-benefit analysis will be conducted to compare the total cost of ownership against estimated reductions in recall scope and time. The analytical framework draws on Technology Acceptance Model (TAM) extended with Trust and Knowledge Sharing constructs and the Information Security Theory to address data integrity and privacy concerns. Expected findings indicate that the blockchain-enabled framework improves traceability accuracy from farm to fork, reduces recall initiation time by up to 40%, and expands recall precision by limiting actions to affected lots. The integration of IoT data with immutable ledger entries is anticipated to enhance transparency, strengthen regulatory compliance, and bolster consumer confidence. The study also anticipates identifying socio-technical barriers—data governance ambiguities, interoperability challenges, and cost considerations—and delineating governance mechanisms, stakeholder roles, and standardized data schemas to facilitate wider adoption. The study contributes to knowledge by operationalizing a scalable, interoperable blockchain-enabled traceability model for complex food supply networks and providing empirical evidence on performance gains, user acceptance, and governance requirements. The theoretical contribution lies in extending TAM with trust and privacy-first design principles in the context of food supply chains and validating a practical mapping from provenance data to actionable recall decision-support. Policy and practice implications include a blueprint for regulatory alignment, standardization of data exchange protocols, and a stepwise implementation roadmap that accommodates smallholders and large-scale enterprises. The main conclusion is that blockchain-enabled traceability, when integrated with robust IoT sensing, standardized data models, and governance-adjusted smart contracts, can meaningfully enhance farm-to-fork safety and quality management, with measurable improvements in recall efficacy and stakeholder trust; recommendations emphasize phased deployment, open interoperability standards, and capacity-building programs for actors across the supply chain.