Modelling and analysis of blockchain implementation challenges in Indian agri-food supply chain management using the IMF-SWARA technique

Background: India's agriculture and food sector is the backbone of the nation, sustaining a large portion of the population and contributing to global exports. Small and medium-sized enterprises (SMEs) generate the bulk of the world's food despite lacking adequate technological infrastructure and operational standards. This study identifies and evaluates the main blockchain challenges affecting food SMEs. The adoption of blockchain technology (BCT) in the agri-food supply chain offers numerous benefits, including improved supply chain performance, transparent information exchange, and reduced data tampering. Methods: This study examines the challenges encountered during the adoption of BCT and aims to highlight the factors that inhibit its implementation in the Indian agri-food supply chain (AFSC). Challenges were first identified through a literature review and then validated by a panel of five experts via a questionnaire survey. To prioritise these challenges, the Improved Fuzzy Stepwise Weight Assessment Ratio Analysis (IMF-SWARA) integrated with the Triangular Fuzzy Bonferroni Mean (TFBM) method was applied. Results: The identified challenges were evaluated using the integrated IMF-SWARA and TFBM approach. Lack of management commitment, negative perception of BCT, and high implementation costs emerged as the primary obstacles to BCT adoption in the Indian AFSC. Conclusion: Agriculture remains the foundation of livelihoods in India, with the nation still highly dependent on the sector, unlike Western countries. The research identified and prioritised the challenges of BCT implementation in the Indian agri-food supply chain using the integrated IMF-SWARA and TFBM approach. The findings are valuable for supply chain professionals and policymakers seeking to adopt blockchain technology. Furthermore, this research can be extended to explore blockchain challenges in specific functions such as procurement, warehousing, and distribution within the Indian agri-food industry. Future studies could employ more advanced multi-criteria decision-making (MCDM) fuzzy integrated approaches to analyse the data and enable more robust comparisons, thereby validating and complementing the results obtained through IMF SWARA and TFBN.