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

• Autorzy: Gupta Sumit , Ansari Hammad , Joshi Deepika , Gupta Rahul , Jagtap Sandeep , Prakash Surya , Mor Rahul

Identyfikatory

DOI

10.17270/J.LOG.001243

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

supply chain management; agri-food industry; blockchain technology; IMF; SWARA

PL

zarządzanie łańcuchem dostaw; przemysł rolno-spożywczy; technologia blockchain

• Wydawca: Wyższa Szkoła Logistyki
• Czasopismo: LogForum
• Rocznik: 2025
• Tom: Vol. 21, no 3
• Strony: 405--418
• Opis fizyczny: Bibliogr. 39 poz., tab.

Twórcy

autor

Gupta Sumit

• Department of Mechanical Engineering, Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India

autor

Ansari Hammad

• Amity Business School, Amity University Uttar Pradesh, Noida, India

autor

Joshi Deepika

• Operations and Supply Chain Management Department, St. Joseph’s Institute of Management, Bangalore, India

autor

Gupta Rahul

• Amity Business School, Amity University Uttar Pradesh, Noida, India

autor

Jagtap Sandeep

• Division of Engineering Logistics, Lund University, Lund, Sweden
• Sustainable Manufacturing Systems Centre, Cranfield University, Cranfield, United Kingdom

autor

Prakash Surya

• Great Lakes Institute of Management, Gurgaon, Haryana, India

autor

Mor Rahul

• Faculty of Business and Law, University of Northampton, United Kingdom

Bibliografia

• 1. Alshamsi, A., & Andras, P. (2019). User perception of Bitcoin usability and security across novice users. International Journal of Human-Computer Studies, 126, 94–110. https://doi.org/10.1016/j.ijhcs.2019.02.004
• 2. Azzi, R., Chamoun, R. K., & Sokhn, M. (2019). The power of a blockchain-based supply chain. Computers & Industrial Engineering, 135, 582–592. https://doi.org/10.1016/j.cie.2019.06.042
• 3. Babich, V., Hilary, G. (2019). OM Forum—Distributed ledgers and operations: What operations management researchers should know about blockchain technology. Manufacturing & Service Operations Management, 22, 223–240. https://doi.org/10.1287/msom.2018.0752
• 4. Bryceson, K. P., & Ross, A. (2020). Agrifood chains as complex systems and the role of informality in their sustainability in small scale societies. Sustainability, 12(16), 6535. https://doi.org/10.3390/su12166535
• 5. Camilleri, M. A., Troise, C., Strazzullo, S., & Bresciani, S. (2023). Creating shared value through open innovation approaches: Opportunities and challenges for corporate sustainability. Business Strategy and the Environment, 32(7), 4485–4502. https://doi.org/10.1002/bse.3377
• 6. Choi, D.; Chung, C.Y.; Lee, K.Y. (2018). Sustainable diffusion of inter-organizational technology in supply chains: An approach to heterogeneous levels of risk aversion. Sustainability, 10, 2108. https://doi.org/10.3390/su10062108
• 7. Clohessy, T., & Acton, T. (2019). Investigating the influence of organizational factors on blockchain adoption: An innovation theory perspective. Industrial Management & Data Systems, 119(7), 1457–1491. https://doi.org/10.1108/IMDS-08-2018-0365
• 8. de Vries, J. R., Turner, J. A., Finlay-Smits, S., Ryan, A., & Klerkx, L. (2023). Trust in agri-food value chains: A systematic review. International Food and Agribusiness Management Review, 26(2), 175–197. https://doi.org/10.22434/IFAMR2022.0032
• 9. Durrant, A., Markovic, M., Matthews, D., May, D., Leontidis, G., & Enright, J. (2021). How might technology rise to the challenge of data sharing in agri-food? Global Food Security, 28, 100493. https://doi.org/10.1016/j.gfs.2021.100493
• 10. Fawcett, S.E., Wallin, C., Allred, C., Magnan, G. (2009). Supply chain information-sharing: Benchmarking a proven path. Benchmarking, 16, 222–246. https://doi.org/10.1108/14635770910948231
• 11. Ge, L., Brewster, C., Spek, J., Smeenk, A., Top, J., van Diepen, F., ... & de Wildt, M. D. R. (2017). Blockchain for agriculture and food: Findings from the pilot study (No. 2017-112). Wageningen Economic Research. https://doi.org/10.18174/426747
• 12. Hackius, N., & Petersen, M. (2017). Blockchain in logistics and supply chain: trick or treat?. In Digitalization in Supply Chain Management and Logistics: Smart and Digital Solutions for an Industry 4.0 Environment. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 23 (pp. 3-18). Berlin: epubli GmbH. https://doi.org/10.15480/882.1444
• 13. Hasanova, H., Baek, U. J., Shin, M. G., Cho, K., & Kim, M. S. (2019). A survey on blockchain cybersecurity vulnerabilities and possible countermeasures. International Journal of Network Management, 29(2), e2060. https://doi.org/10.1002/nem.2060
• 14. Johnson, J. (2020). CHAOS 2020: Beyond Infinity. Standish Group. https://www.standishgroup.com/news/49
• 15. Joshi, D., Gupta, S., Vishwakarma, A., & Jagtap, S. (2024). Why IoT enablement of agrifood transportation disappoints its stakeholders: Unravelling barriers for enhanced logistics. Journal of Food Quality, 2024(1), 9289098. https://doi.org/10.1155/2024/9289098
• 16. Keršuliene, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (SWARA). Journal of Business economics and management, 11(2), 243–258. https://doi.org/10.3846/jbem.2010.12
• 17. Khan, D., Jung, L. T., & Hashmani, M. A. (2021). Systematic literature review of challenges in blockchain scalability. Applied Sciences, 11(20), 9372. https://doi.org/10.3390/app11209372
• 18. Kouhizadeh, M., Saberi, S., & Sarkis, J. (2021). Blockchain technology and the sustainable supply chain: Theoretically exploring adoption barriers. International Journal of Production Economics, 231, 107831. https://doi.org/10.1016/j.ijpe.2020.107831
• 19. Krzyzanowski Guerra, K., & Boys, K. A. (2022). A new food chain: Adoption and policy implications to blockchain use in agri‐food industries. Applied Economic Perspectives and Policy, 44(1), 324–349. https://doi.org/10.1002/aepp.13163
• 20. Kshetri, N. (2017). Blockchain's roles in strengthening cybersecurity and protecting privacy. Telecommunications policy, 41(10), 1027–1038. https://doi.org/10.1016/j.telpol.2017.09.003
• 21. Kumar, A., Liu, R., & Shan, Z. (2020). Is blockchain a silver bullet for supply chain management? Technical challenges and research opportunities. Decision Sciences, 51(1), 8–37. https://doi.org/10.1111/deci.12396
• 22. Mahmud, H., Islam, A. N., & Mitra, R. K. (2023). What drives managers towards algorithm aversion and how to overcome it? Mitigating the impact of innovation resistance through technology readiness. Technological Forecasting and Social Change, 193, 122641. https://doi.org/10.1016/j.techfore.2023.122641
• 23. Mangla, S. K., Govindan, K., & Luthra, S. (2017). Prioritizing the barriers to achieve sustainable consumption and production trends in supply chains using fuzzy analytical hierarchy process. Journal of Cleaner Production, 151, 509–525. https://doi.org/10.1016/j.jclepro.2017.02.099
• 24. Mendling, J., Weber, I., Aalst, W. V. D., Brocke, J. V., Cabanillas, C., Daniel, F., ... & Zhu, L. (2018). Blockchains for business process management-challenges and opportunities. ACM Transactions on Management Information Systems (TMIS), 9(1), 1-16.https://doi.org/10.1145/3183367
• 25. Mohanta, B. K., Jena, D., Ramasubbareddy, S., Daneshmand, M., & Gandomi, A. H. (2020). Addressing security and privacy issues of IoT using blockchain technology. IEEE Internet of Things Journal, 8(2), 881-888. https://doi.org/10.1109/JIOT.2020.3008906
• 26. Moslem, S., Deveci, M., & Pilla, F. (2024). A novel best-worst method and Kendall model integration for optimal selection of digital voting tools to enhance citizen engagement in public decision making. Decision Analytics Journal, 10, 100378. https://doi.org/10.1016/j.dajour.2023.100378
• 27. Moslem, S., Stević, Ž., Tanackov, I., & Pilla, F. (2023). Sustainable development solutions of public transportation: An integrated IMF SWARA and Fuzzy Bonferroni operator. Sustainable Cities and Society, 93, 104530. https://doi.org/10.1016/j.scs.2023.104530
• 28. Nazifi, A., Murdy, S., Marder, B., Gäthke, J., & Shabani, B. (2021). A bit (coin) of happiness after a failure: An empirical examination of the effectiveness of cryptocurrencies as an innovative recovery tool. Journal of Business Research, 124, 494–505. https://doi.org/10.1016/j.jbusres.2020.11.012
• 29. Perçin, S. (2018). Evaluating airline service quality using a combined fuzzy decision-making approach. Journal of Air Transport Management, 68, 48–60. https://doi.org/10.1016/j.jairtraman.2017.07.004
• 30. Puška, A., Štilić, A., & Stević, Ž. (2023). A comprehensive decision framework for selecting distribution center locations: a hybrid improved fuzzy SWARA and fuzzy CRADIS approach. Computation, 11(4), 73. https://doi.org/10.3390/computation11040073
• 31. Ruan, Z. (2023, November). Blockchain technology for security issues and challenges in IoT. In 2023 International Conference on Computer Simulation and Modeling, Information Security (CSMIS) (pp. 572–580). IEEE. https://doi.org/10.1109/CSMIS60634.2023.00108
• 32. Sajjad, A.; Eweje, G.; Tappin, D. (2015). Sustainable supply chain management: Motivators and barriers. Business Strategy and the Environment, 24, 643–655. https://doi.org/10.1002/bse.1898
• 33. Salim, T. A., El Barachi, M., Mohamed, A. A. D., Halstead, S., & Babreak, N. (2022). The mediator and moderator roles of perceived cost on the relationship between organizational readiness and the intention to adopt blockchain technology. Technology in Society, 71, 102108. https://doi.org/10.1016/j.techsoc.2022.102108
• 34. Stanković, M., Stević, Ž., Das, D. K., Subotić, M., & Pamučar, D. (2020). A new fuzzy MARCOS method for road traffic risk analysis. Mathematics, 8(3), 457. https://doi.org/10.3390/math8030457
• 35. Verma, R., Merigó, J. M., & Mittal, N. (2018, November). Triangular fuzzy partitioned Bonferroni mean operators and their application to multiple attribute decision making. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 941–949). IEEE. https://doi.org/10.1109/SSCI.2018.8628728
• 36. Vrtagić, S., Softić, E., Subotić, M., Stević, Ž., Dordevic, M., & Ponjavic, M. (2021). Ranking road sections based on MCDM model: New improved fuzzy SWARA (IMF SWARA). Axioms, 10(2), 92. https://doi.org/10.3390/axioms10020092
• 37. Wamba, S. F., Queiroz, M. M., & Trinchera, L. (2020). Dynamics between blockchain adoption determinants and supply chain performance: An empirical investigation. International Journal of Production Economics, 229, 107791. https://doi.org/10.1016/j.ijpe.2020.107791
• 38. Zhou, Q., Huang, H., Zheng, Z., & Bian, J. (2020). Solutions to scalability of blockchain: A survey. IEEE Access, 8, 16440-16455. https://doi.org/10.1109/ACCESS.2020.2967218
• 39. Zhou, Y., Wang, H., & Lan, H. (2024). Why and how executive equity incentive influences digital transformation: the role of internal and external governance. Technology Analysis & Strategic Management, 36(12), 4217–4231. https://doi.org/10.1080/09537325.2023.2250012

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2026).