Development of a Blockchain-Based Traceability System for Food Quality Assurance

Thesis Abstract

The pervasive issue of food safety and traceability in contemporary supply chains underscores the need for innovative technological solutions to enhance transparency, accountability, and consumer confidence. Despite advancements in food processing and logistics, existing traceability systems often suffer from fragmentation, lack of transparency, and susceptibility to fraud, which undermine efforts to ensure food quality and safety. This study aims to develop a robust blockchain-based traceability system tailored for food quality assurance, enabling immutable recording of product provenance, processing history, and distribution channels. The specific objectives include designing a blockchain architecture suitable for the food supply chain, implementing a prototype platform, evaluating its functional efficiency, and assessing stakeholders’ perceptions of its usability and trustworthiness. To achieve these objectives, the research employs a mixed-methods approach grounded in a pragmatic research design. The quantitative component comprises a survey administered to 150 key stakeholders in the food industry, including producers, distributors, retailers, and regulatory officials, selected through stratified random sampling. The qualitative component involves semi-structured interviews with 20 industry experts to gain in-depth insights into adoption barriers and perceived benefits. Primary data is collected using structured questionnaires validated through content and construct validity assessments, and interview protocols refined for thematic analysis. The system prototype is developed utilizing Hyperledger Fabric as the blockchain framework, integrated with Internet of Things (IoT) devices for real-time data input. Data analysis involves descriptive statistics and inferential techniques such as multiple regression analysis to determine factors influencing stakeholder adoption and trust in the system. Thematic analysis is employed for qualitative interview data, following Braun and Clarke’s six-phase approach, to identify prevailing themes regarding system usability, transparency, and security concerns. The system’s functional performance is evaluated through transaction throughput testing, security vulnerability assessments, and user acceptance testing using the Technology Acceptance Model (TAM) as a theoretical lens. The anticipated findings indicate that the blockchain-based system significantly improves traceability accuracy, reduces data tampering, and enhances stakeholder confidence in food safety verification. Quantitative results are expected to reveal that perceived usefulness, ease of use, and trust significantly influence users’ willingness to adopt the platform, with regression analysis showing a model fit with an R-squared of 0.65. Qualitative insights are projected to uncover critical challenges such as integration with existing supply chain infrastructure, costs of implementation, and data privacy concerns. The research contributes novel empirical evidence to the field of food blockchain applications by demonstrating how blockchain technology can be embedded within existing supply chain processes to enhance traceability and compliance with safety standards. This study’s contribution to knowledge lies in providing a comprehensive framework for implementing blockchain-based traceability systems in the food industry, supported by empirical validation and stakeholder perceptions. It offers a scalable model adaptable to different food commodities and supply chain environments, alongside guidelines for policy formulation and technological integration. The main conclusion emphasizes that blockchain technology, when coupled with IoT and stakeholder engagement, holds substantial potential to revolutionize food quality assurance mechanisms. Recommendations include fostering collaboration among industry stakeholders to facilitate technology adoption, investing in capacity building, and developing regulations to standardize digital traceability practices. Future research suggestions highlight exploring machine learning integration for predictive quality analysis and expanding the system to accommodate emerging food safety challenges.