Conceptual Integration of Blockchain for SME Transaction Transparency and Vendor Trust Assurance

Authors

  • Ebehiremen Faith Iziduh  Shell Nigeria (SCiN), Lagos, Nigeria
  • Olawale Olasoji  TotalEnergies E&P Nigeria, Ltd, Nigeria
  • Oluwatobi Opeyemi Adeyelu  Independent Researcher, Lagos, Nigeria

Keywords:

Small and Medium Enterprises (SMEs), Blockchain Technology, Transaction Transparency, Vendor Trust, Smart Contracts, Supply Chain Management

Abstract

Small and medium enterprises (SMEs), representing 90% of global businesses, face significant challenges in ensuring transaction transparency and vendor trust, critical for supply chain efficiency and competitiveness. Opaque transactions and lack of trust result in 40% of SMEs experiencing vendor disputes, costing $50,000 annually, while 60% report delayed payments due to verification issues. Blockchain technology, with its decentralized, immutable ledger, offers a transformative solution by enabling transparent, tamper-proof records and smart contract-based trust mechanisms. This paper proposes a conceptual framework for integrating blockchain to enhance SME transaction transparency and vendor trust assurance, incorporating smart contracts, digital identity verification, and regulatory compliance. Using a mixed-method approach, the study combines a systematic literature review of 150 peer-reviewed articles and industry reports (2017–2023), framework development, and pilot testing with 25 SMEs and 10 vendors across retail, manufacturing, and agriculture in North America, Europe, Asia, and Africa. The framework achieves a 45% increase in transaction transparency, reduces dispute resolution time by 50%, and enhances vendor trust by 40%. Key findings highlight scalability across SMEs with 10–250 employees, affordability ($5,000–$20,000 implementation), and alignment with regulations like GDPR and ISO 9001. Challenges include technological literacy, blockchain scalability, and regulatory fragmentation, while opportunities involve AI-enhanced analytics, stablecoin payments, and public-private partnerships. The study contributes to blockchain and SME literature by offering a practical, scalable framework bridging technological, operational, and regulatory needs. For SMEs, it provides tools to streamline transactions and build trust; for policymakers, it offers strategies to promote digital adoption; and for researchers, it lays a foundation for exploring blockchain scalability and SME trust models. Future directions include AI-driven trust analytics, cross-border blockchain networks, and frameworks for developing regions. By addressing these issues, this paper underscores the transformative potential of blockchain in fostering transparent, trustworthy SME ecosystems.

References

  1. E. O. Alonge, N. L. Eyo-Udo, B. C. Ubanadu, A. I. Daraojimba, E. D. Balogun, and K. O. Ogunsola, “The Role of Predictive Analytics in Enhancing Customer Experience and Retention,” vol. 7, no. 1, 2023.
  2. M. M. Pellegrini, F. Ciampi, G. Marzi, and B. Orlando, “The relationship between knowledge management and leadership: mapping the field and providing future research avenues,” J. Knowl. Manag., vol. 24, no. 6, pp. 1445–1492, Jun. 2020, doi: 10.1108/JKM-01-2020-0034.
  3. D. Lessard, D. J. Teece, and S. Leih, “The Dynamic Capabilities of Meta‐Multinationals,” Glob. Strategy J., vol. 6, no. 3, pp. 211–224, Aug. 2016, doi: 10.1002/gsj.1126.
  4. F. C. Okolo, E. A. Etukudoh, O. Ogunwole, G. O. Osho, and J. O. Basiru, “Systematic Review of Business Analytics Platforms in Enhancing Operational Efficiency in Transportation and Supply Chain Sectors,” Int. J. Multidiscip. Res. Growth Eval., vol. 4, no. 1, pp. 1199–1208, 2023, doi: 10.54660/.IJMRGE.2023.4.1.1199-1208.
  5. D. I. Ajiga, “Strategic Framework for Leveraging Artificial Intelligence to Improve Financial Reporting Accuracy and Restore Public Trust,” Int. J. Multidiscip. Res. Growth Eval., vol. 2, no. 1, pp. 882–892, 2021, doi: 10.54660/.IJMRGE.2021.2.1.882-892.
  6. F. C. Okolo, E. A. Etukudoh, O. Ogunwole, G. O. Osho, and J. O. Basiru, “Strategic Approaches to Building Digital Workforce Capacity for Cybersecure Transportation Operations and Policy Compliance,” Int. J. Multidiscip. Res. Growth Eval., pp. 1209–1218, 2023, doi: 10.54660/.IJMRGE.2023.4.1.1209-1218.
  7. T. Bridgman and E. Bell, “Seeing and Being Seen as a Management Learning and Education Scholar: Rejoinder to ‘Identifying Research Topic Development in Business and Management Education Research Using Legitimation Code Theory,’” J. Manag. Educ., vol. 40, no. 6, pp. 692–699, Dec. 2016, doi: 10.1177/1052562916662103.
  8. A. Poth, M. Kottke, and A. Riel, “Scaling agile on large enterprise level -- systematic bundling and application of state of the art approaches for lasting agile transitions,” Sep. 04, 2019, arXiv: arXiv:1909.01624. doi: 10.48550/arXiv.1909.01624.
  9. T. J. Chermack, Scenario Planning in Organizations: How to Create, Use, and Assess Scenarios. Berrett-Koehler Publishers, 2011.
  10. “systems-12-00220.”
  11. A. J. Vaid and R. Chaudhary, “Review paper on impact of behavioral biases in financial decision- making,” World J. Adv. Res. Rev., vol. 16, no. 2, pp. 989–997, 2022, doi: 10.30574/wjarr.2022.16.2.1236.
  12. Christian Chukwuemeka Ike, Adebimpe Bolatito Ige, Sunday Adeola Oladosu, Peter Adeyemo Adepoju, Olukunle Oladipupo Amoo, and Adeoye Idowu Afolabi, “Redefining zero trust architecture in cloud networks: A conceptual shift towards granular, dynamic access control and policy enforcement,” Magna Sci. Adv. Res. Rev., vol. 2, no. 1, pp. 074–086, Jun. 2021, doi: 10.30574/msarr.2021.2.1.0032.
  13. O. O. Ogbuagu, A. O. Mbata, O. D. Balogun, O. Oladapo, O. O. Ojo, and M. Muonde, “Quality assurance in pharmaceutical manufacturing: bridging the gap between regulations, supply chain, and innovations,” Int. J. Multidiscip. Res. Growth Eval., vol. 4, no. 1, pp. 823–831, 2023, doi: 10.54660/.IJMRGE.2023.4.1-823-831.
  14. O. J. Esan, O. T. Uzozie, O. Onaghinor, G. O. Osho, and E. A. Etukudoh, “Procurement 4.0: Revolutionizing Supplier Relationships through Blockchain, AI, and Automation: A Comprehensive Framework,” J. Front. Multidiscip. Res., vol. 3, no. 1, pp. 117–123, 2022, doi: 10.54660/.IJFMR.2022.3.1.117-123.
  15. O. J. Esan, O. T. Uzozie, O. Onaghinor, G. O. Osho, and J. O. Omisola, “Policy and Operational Synergies: Strategic Supply Chain Optimization for National Economic Growth,” Int. J. Soc. Sci. Except. Res., vol. 1, no. 1, pp. 239–245, 2022, doi: 10.54660/IJSSER.2022.1.1.239-245.
  16. “(PDF) Large-Scale Agile Frameworks: A Comparative Review,” ResearchGate, Apr. 2025, doi: 10.31284/j.jasmet.2021.v2i1.1832.
  17. O. O. Ogbuagu, A. O. Mbata, O. D. Balogun, O. Oladapo, O. O. Ojo, and M. Muonde, “Optimizing supply chain logistics for personalized medicine: Strengthening drug discovery, production, and distribution,” Int. J. Multidiscip. Res. Growth Eval., vol. 4, no. 1, pp. 832–841, 2023, doi: 10.54660/.IJMRGE.2023.4.1-832-841.
  18. E. D. Balogun, K. O. Ogunsola, and A. Samuel, “A Cloud-Based Data Warehousing Framework for Real- Time Business Intelligence and Decision-Making Optimization,” vol. 5, no. 2, 2021.
  19. B. S. Adelusi, D. Osamika, M. C. Kelvin-Agwu, A. Y. Mustapha, and N. Ikhalea, “A Deep Learning Approach to Predicting Diabetes Mellitus Using Electronic Health Records,” J. Front. Multidiscip. Res., vol. 3, no. 1, pp. 47–56, 2022, doi: 10.54660/.IJFMR.2022.3.1.47-56.
  20. A. S. Ogunmokun, E. D. Balogun, and K. O. Ogunsola, “A Conceptual Framework for AI-Driven Financial Risk Management and Corporate Governance Optimization,” Int. J. Multidiscip. Res. Growth Eval., vol. 2, no. 1, pp. 772–780, 2021, doi: 10.54660/.IJMRGE.2021.2.1.772-780.
  21. A. Abisoye and J. I. Akerele, “A High-Impact Data-Driven Decision-Making Model for Integrating Cutting-Edge Cybersecurity Strategies into Public Policy, Governance, and Organizational Frameworks,” Int. J. Multidiscip. Res. Growth Eval., vol. 2, no. 1, pp. 623–637, 2021, doi: 10.54660/.IJMRGE.2021.2.1.623-637.
  22. B. I. Adekunle, E. C. Chukwuma-Eke, E. D. Balogun, and K. O. Ogunsola, “A Predictive Modeling Approach to Optimizing Business Operations: A Case Study on Reducing Operational Inefficiencies through Machine Learning,” Int. J. Multidiscip. Res. Growth Eval., vol. 2, no. 1, pp. 791–799, 2021, doi: 10.54660/.IJMRGE.2021.2.1.791-799.
  23. E. D. Balogun, K. O. Ogunsola, and A. S. Ogunmokun, “A Risk Intelligence Framework for Detecting and Preventing Financial Fraud in Digital Marketplaces,” vol. 4, no. 8, 2021.
  24. A. H. Adepoju, B. Austin-Gabriel, O. Hamza, and A. Collins, “Advancing Monitoring and Alert Systems: A Proactive Approach to Improving Reliability in Complex Data Ecosystems,” vol. 5, no. 11, 2022.
  25. O. Hamza, A. Collins, A. Eweje, and G. O. Babatunde, “Agile-DevOps Synergy for Salesforce CRM Deployment: Bridging Customer Relationship Management with Network Automation,” Int. J. Multidiscip. Res. Growth Eval., vol. 4, no. 1, pp. 668–681, 2023, doi: 10.54660/.IJMRGE.2023.4.1.668-681.
  26. F. C. Okolo, E. A. Etukudoh, O. Ogunwole, G. O. Osho, and J. O. Basiru, “Advances in Cyber-Physical Resilience of Transportation Infrastructure in Emerging Economies and Coastal Regions,” Int. J. Multidiscip. Res. Growth Eval., vol. 4, no. 1, pp. 1188–1198, 2023, doi: 10.54660/.IJMRGE.2023.4.1.1188-1198.
  27. F. E. Adikwu, C. O. Ozobu, O. Odujobi, F. O. Onyekwe, and E. O. Nwulu, “Advances in EHS Compliance: A Conceptual Model for Standardizing Health, Safety, and Hygiene Programs Across Multinational Corporations,” vol. 7, no. 5, 2023.
  28. Afees Olanrewaju Akinade, Peter Adeyemo Adepoju, Adebimpe Bolatito Ige, Adeoye Idowu Afolabi, and Olukunle Oladipupo Amoo, “Advancing segment routing technology: A new model for scalable and low-latency IP/MPLS backbone optimization,” Open Access Res. J. Sci. Technol., vol. 5, no. 2, pp. 077–095, Aug. 2022, doi: 10.53022/oarjst.2022.5.2.0056.
  29. M. Ruotsala, “Agile and Lean processes on an IoT development”.
  30. M. Pop, “Agile Virtualization – The importance of Scrum frame- work in creating synergies in global organizations”.
  31. O. T. Uzozie, O. Onaghinor, O. J. Esan, G. O. Osho, and J. O. Omisola, “AI-Driven Supply Chain Resilience: A Framework for Predictive Analytics and Risk Mitigation in Emerging Markets,” Int. J. Multidiscip. Res. Growth Eval., vol. 4, no. 1, pp. 1141–1150, 2023, doi: 10.54660/.IJMRGE.2023.4.1.1141-1150.
  32. A. Abisoye, “AI Literacy in STEM Education: Policy Strategies for Preparing the Future Workforce,” J. Front. Multidiscip. Res., vol. 4, no. 1, pp. 17–24, 2023, doi: 10.54660/JFMR.2023.4.1.17-24.
  33. D. C. Ayodeji, I. Oyeyipo, V. Attipoe, N. J. Isibor, and B. A. Mayienga, “Analyzing the Challenges and Opportunities of Integrating Cryptocurrencies into Regulated Financial Markets,” Int. J. Multidiscip. Res. Growth Eval., vol. 4, no. 6, pp. 1190–1196, 2023, doi: 10.54660/.IJMRGE.2023.4.6.1190-1196.
  34. Y. G. Hassan, A. Collins, G. O. Babatunde, A. A. Alabi, and S. D. Mustapha, “Blockchain and zero-trust identity management system for smart cities and IoT networks,” Int. J. Multidiscip. Res. Growth Eval., vol. 4, no. 1, pp. 704–709, 2023, doi: 10.54660/.IJMRGE.2023.4.1.704-709.
  35. productioneditor, “Blockchain-enabled asset management: Opportunities, risks and global implications,” Comprehensive Research and Reviews in Multidisciplinary Studies. [Online]. Available: https://crrjournals.com/crrms/content/blockchain-enabled-asset-management-opportunities-risks-and-global-implications
  36. F. Reginaldo and G. Santos, “Challenges in Agile Transformation Journey: A Qualitative Study,” in Proceedings of the XXXIV Brazilian Symposium on Software Engineering, Natal Brazil: ACM, Oct. 2020, pp. 11–20. doi: 10.1145/3422392.3422436.
  37. O. Ilori, C. I. Lawal, S. C. Friday, N. J. Isibor, and E. C. C.- Eke, “Cybersecurity Auditing in the Digital Age: A Review of Methodologies and Regulatory Implications,” J. Front. Multidiscip. Res., vol. 3, no. 1, pp. 174–187, 2022, doi: 10.54660/.IJFMR.2022.3.1.174-187.
  38. E. O. Alonge, N. L. Eyo-Udo, B. C. Ubanadu, A. I. Daraojimba, E. D. Balogun, and K. O. Ogunsola, “Data-Driven Risk Management in U.S. Financial Institutions: A Theoretical Perspective on Process Optimization,” vol. 6, no. 7, 2023.
  39. “[No title found],” Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol..
  40. L. G. A. Beesley and C. Cooper, “Defining knowledge management (KM) activities: towards consensus,” J. Knowl. Manag., vol. 12, no. 3, pp. 48–62, Jan. 2008, doi: 10.1108/13673270810875859.
  41. K. O. Ogunsola and E. D. Balogun, “Enhancing Financial Integrity Through an Advanced Internal Audit Risk Assessment and Governance Model,” Int. J. Multidiscip. Res. Growth Eval., vol. 2, no. 1, pp. 781–790, 2021, doi: 10.54660/.IJMRGE.2021.2.1.781-790.
  42. E. O. Alonge, N. L. Eyo-Udo, B. C. Ubanadu, A. I. Daraojimba, E. D. Balogun, and K. O. Ogunsola, “Enhancing Data Security with Machine Learning: A Study on Fraud Detection Algorithms,” J. Front. Multidiscip. Res., vol. 2, no. 1, pp. 19–31, 2021, doi: 10.54660/.IJFMR.2021.2.1.19-31.
  43. G. O. Babatunde, O. O. Amoo, C. Ike, and A. B. Ige, “A Penetration Testing and Security Controls Framework to Mitigate Cybersecurity Gaps in North American Enterprises,” vol. 5, no. 12, 2022.
  44. Chisom Elizabeth Alozie, J. I. Akerele, E. Kamau, and T. Myllynen, “Fault Tolerance in Cloud Environments: Techniques and Best Practices from Site Reliability Engineering,” 2025, Unpublished. doi: 10.13140/RG.2.2.25813.54242.
  45. M. Janssen and H. Van Der Voort, “Adaptive governance: Towards a stable, accountable and responsive government,” Gov. Inf. Q., vol. 33, no. 1, pp. 1–5, Jan. 2016, doi: 10.1016/j.giq.2016.02.003.
  46. B. Fitzgerald and K.-J. Stol, “Continuous software engineering and beyond: trends and challenges,” in Proceedings of the 1st International Workshop on Rapid Continuous Software Engineering, in RCoSE 2014. New York, NY, USA: Association for Computing Machinery, Jun. 2014, pp. 1–9. doi: 10.1145/2593812.2593813.
  47. O. T. Uzozie, O. Onaghinor, O. J. Esan, G. O. Osho, and J. O. Omisola, “Global Supply Chain Strategy: Framework for Managing Cross-Continental Efficiency and Performance in Multinational Operations,” Int. J. Multidiscip. Res. Growth Eval., vol. 3, no. 1, pp. 938–943, 2022, doi: 10.54660/.IJMRGE.2022.3.1.938-943.
  48. “DesignThinkingforSaaSProductDevelopment_1704092_250101_022804.”
  49. O. Hamza, A. Collins, A. Eweje, and G. O. Babatunde, “A Unified Framework for Business System Analysis and Data Governance: Integrating Salesforce CRM and Oracle BI for Cross-Industry Applications,” Int. J. Multidiscip. Res. Growth Eval., vol. 4, no. 1, pp. 653–667, 2023, doi: 10.54660/.IJMRGE.2023.4.1.653-667.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-06-25

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Volume 9, Issue 3, May-June-2023

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Research Articles

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[1]
Ebehiremen Faith Iziduh, Olawale Olasoji, Oluwatobi Opeyemi Adeyelu, " Conceptual Integration of Blockchain for SME Transaction Transparency and Vendor Trust Assurance " International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 3, pp.864-876, May-June-2023.