Digital risk intelligence and supplier failure in African public infrastructure delivery: evidence from Nigeria

Authors

  • Aondofa Tyozenda Joseph Sarwuan Tarkaa University Makurdi, NIGERIA
  • T. P. J. Jato Joseph Sarwuan Tarkaa University Makurdi, NIGERIA https://orcid.org/0000-0002-9489-0766
  • S. A. Tyoakosu Joseph Sarwuan Tarkaa University Makurdi, NIGERIA

DOI:

https://doi.org/10.58881/jcmts.v5i1.445

Keywords:

African public infrastructure, digital risk intelligence, e-procurement, predictive analytics, real-time monitoring systems, supplier failure

Abstract

Digital risk intelligence (DRI) has emerged as a strategic mechanism for mitigating supplier failures in public infrastructure delivery, particularly in African contexts where project delays, cost overruns, and non-compliance remain pervasive. This review critically examines empirical studies from the last nine years to explore how the dimensions of DRI—predictive analytics, real-time monitoring systems, and e-procurement with automated reporting—interact with supplier performance outcomes in Africa, with a particular focus on Nigeria. Findings indicate that predictive analytics enhances foresight in supplier risk assessment, enabling proactive interventions that reduce delays and cost escalations. Real-time monitoring systems facilitate continuous oversight, early detection of deviations, and improved contractual compliance, while e-procurement and automated reporting strengthen transparency, accountability, and governance mechanisms. Comparative analysis across Nigerian case studies, other African countries, and developed economies reveals both opportunities and constraints, highlighting institutional and infrastructural factors that mediate DRI effectiveness. The review further demonstrates theoretical implications for Agency Theory and Dynamic Capabilities Theory by illustrating how DRI reduces information asymmetry and operationalizes sensing, seizing, and transforming capabilities in procurement organizations. Despite growing evidence of DRI’s potential, gaps remain in longitudinal, comparative, and context-specific empirical research. The study concludes by recommending policy reforms, practical adoption strategies, and targeted empirical investigations to optimize DRI deployment and improve supplier performance in African public infrastructure delivery.

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Author Biographies

Aondofa Tyozenda, Joseph Sarwuan Tarkaa University Makurdi, NIGERIA

Digital risk intelligence (DRI) has emerged as a strategic mechanism for mitigating supplier failures in public infrastructure delivery, particularly in African contexts where project delays, cost overruns, and non-compliance remain pervasive. This review critically examines empirical studies from the last nine years to explore how the dimensions of DRI—predictive analytics, real-time monitoring systems, and e-procurement with automated reporting—interact with supplier performance outcomes in Africa, with a particular focus on Nigeria. Findings indicate that predictive analytics enhances foresight in supplier risk assessment, enabling proactive interventions that reduce delays and cost escalations. Real-time monitoring systems facilitate continuous oversight, early detection of deviations, and improved contractual compliance, while e-procurement and automated reporting strengthen transparency, accountability, and governance mechanisms. Comparative analysis across Nigerian case studies, other African countries, and developed economies reveals both opportunities and constraints, highlighting institutional and infrastructural factors that mediate DRI effectiveness. The review further demonstrates theoretical implications for Agency Theory and Dynamic Capabilities Theory by illustrating how DRI reduces information asymmetry and operationalizes sensing, seizing, and transforming capabilities in procurement organizations. Despite growing evidence of DRI’s potential, gaps remain in longitudinal, comparative, and context-specific empirical research. The study concludes by recommending policy reforms, practical adoption strategies, and targeted empirical investigations to optimize DRI deployment and improve supplier performance in African public infrastructure delivery.

T. P. J. Jato, Joseph Sarwuan Tarkaa University Makurdi, NIGERIA

Digital risk intelligence (DRI) has emerged as a strategic mechanism for mitigating supplier failures in public infrastructure delivery, particularly in African contexts where project delays, cost overruns, and non-compliance remain pervasive. This review critically examines empirical studies from the last nine years to explore how the dimensions of DRI—predictive analytics, real-time monitoring systems, and e-procurement with automated reporting—interact with supplier performance outcomes in Africa, with a particular focus on Nigeria. Findings indicate that predictive analytics enhances foresight in supplier risk assessment, enabling proactive interventions that reduce delays and cost escalations. Real-time monitoring systems facilitate continuous oversight, early detection of deviations, and improved contractual compliance, while e-procurement and automated reporting strengthen transparency, accountability, and governance mechanisms. Comparative analysis across Nigerian case studies, other African countries, and developed economies reveals both opportunities and constraints, highlighting institutional and infrastructural factors that mediate DRI effectiveness. The review further demonstrates theoretical implications for Agency Theory and Dynamic Capabilities Theory by illustrating how DRI reduces information asymmetry and operationalizes sensing, seizing, and transforming capabilities in procurement organizations. Despite growing evidence of DRI’s potential, gaps remain in longitudinal, comparative, and context-specific empirical research. The study concludes by recommending policy reforms, practical adoption strategies, and targeted empirical investigations to optimize DRI deployment and improve supplier performance in African public infrastructure delivery.

S. A. Tyoakosu, Joseph Sarwuan Tarkaa University Makurdi, NIGERIA

Digital risk intelligence (DRI) has emerged as a strategic mechanism for mitigating supplier failures in public infrastructure delivery, particularly in African contexts where project delays, cost overruns, and non-compliance remain pervasive. This review critically examines empirical studies from the last nine years to explore how the dimensions of DRI—predictive analytics, real-time monitoring systems, and e-procurement with automated reporting—interact with supplier performance outcomes in Africa, with a particular focus on Nigeria. Findings indicate that predictive analytics enhances foresight in supplier risk assessment, enabling proactive interventions that reduce delays and cost escalations. Real-time monitoring systems facilitate continuous oversight, early detection of deviations, and improved contractual compliance, while e-procurement and automated reporting strengthen transparency, accountability, and governance mechanisms. Comparative analysis across Nigerian case studies, other African countries, and developed economies reveals both opportunities and constraints, highlighting institutional and infrastructural factors that mediate DRI effectiveness. The review further demonstrates theoretical implications for Agency Theory and Dynamic Capabilities Theory by illustrating how DRI reduces information asymmetry and operationalizes sensing, seizing, and transforming capabilities in procurement organizations. Despite growing evidence of DRI’s potential, gaps remain in longitudinal, comparative, and context-specific empirical research. The study concludes by recommending policy reforms, practical adoption strategies, and targeted empirical investigations to optimize DRI deployment and improve supplier performance in African public infrastructure delivery.

References

Abdelalim, A. M., Salem, M., Salem, M., Al-Adwani, M., and Tantawy, M. (2025). An analysis of factors contributing to cost overruns in the global construction industry. Buildings, 15(1), 18.

Adebayo, V. (2016). Utilisation of electronic procurement solutions in curbing public sector corruption: Analysis from Nigeria. In European Conference on Digital Government (p. 1). Academic Conferences International Limited.

Afolabi, A., Ibem, E., Aduwo, E., and Tunji-Olayeni, P. (2022). Digitizing the grey areas in the Nigerian public procurement system using e-Procurement technologies. International Journal of Construction Management, 22(12), 2215-2224.

Ahmad, H., Abul Hassan, S. H., and Ismail, S. (2023). Transparency level of the electronic procurement system in Malaysia. Journal of Financial Reporting and Accounting, 21(3), 592-606.

Alfian, G., Syafrudin, M., and Rhee, J. (2017). Real-time monitoring system using smartphone-based sensors and nosql database for perishable supply chain. Sustainability, 9(11), 2073.

Alinaitwe, H., Apolot, R., and Tindiwensi, D. (2013). Investigation into the causes of delays and cost overruns in Uganda's public sector construction projects. Journal of Construction in Developing Countries, 18(2), 33.

Asiedu, R. O., and Adaku, E. (2020). Cost overruns of public sector construction projects: a developing country perspective. International Journal of Managing Projects in Business, 13(1), 66-84.

Asiedu, R. O., and Ameyaw, C. (2021). A system dynamics approach to conceptualise causes of cost overrun of construction projects in developing countries. International Journal of Building Pathology and Adaptation, 39(5), 831-851.

Aydin, B., and Seferoglu, S. S. (2025). Risks of the digital world: digital risk-taking scale (DRTS) validity and reliability study. Turkish Online Journal of Distance Education, 26(3), 85-96.

Ayebo, I. S., and Olajide, O. (2024). Predictive Analytics in Global Procurement: Risk Mitigation and Cost Efficiency.

Balogun, E. D., Ogunsola, K. O., and Samuel, A. D. E. B. A. N. J. I. (2021). A risk intelligence framework for detecting and preventing financial fraud in digital marketplaces. Iconic Research and Engineering Journals, 4(08), 134-149.

Costa, P. (2022). Harnessing Predictive Analytics in Procurement: Transforming Decision-Making through Data-Driven Insights. International Journal of AI, BigData, Computational and Management Studies, 3(3), 10-19.

Durdyev, S., and Hosseini, M. R. (2020). Causes of delays on construction projects: a comprehensive list. International journal of managing projects in business, 13(1), 20-46.

Enshassi, A., Kumaraswamy, M., and Al-Najjar, J. (2010). Significant factors causing time and cost overruns in construction projects in the Gaza strip: contractors’ perspective. International Journal of Construction Management, 10(1), 35-60.

Esan, O. J., Uzozie, O. T., Onaghinor, O., Etukudoh, E. A., and Omisola, J. O. (2023). Agile procurement management in the digital age: A framework for data-driven vendor risk and compliance assessment. Journal of Frontiers in Multidisciplinary Research, 4(1), 118-125.

Frimpong, Y., Oluwoye, J., and Crawford, L. (2003). Causes of delay and cost overruns in construction of groundwater projects in a developing countries; Ghana as a case study. International Journal of project management, 21(5), 321-326.

Handfield, R., Jeong, S., and Choi, T. (2019). Emerging procurement technology: data analytics and cognitive analytics. International journal of physical distribution and logistics management, 49(10), 972-1002.

Helo, P., and Shamsuzzoha, A. H. M. (2020). Real-time supply chain—A blockchain architecture for project deliveries. Robotics and Computer-Integrated Manufacturing, 63, 101909.

Klingler, D., and Telles, P. (2023). Non-compliance with government contract terms: a comparative view on procurement regulation and contractual remedies. In Contract Changes (pp. 48-67). Edward Elgar Publishing.

Kulworatit, C., Tuntiwongwanich, S., and Petsangsri, S. (2021). Development of an internet risks assessment using a digital intelligence quotient and a communication-based model. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(14), 1158-1164.

Kumar, P., Khan, A. M., and Aziz, S. (2023). Factors affecting the integration of e-procurement in the public sector of Pakistan with an emphasis on combating corruption. Dutch Journal of Finance and Management, 6(2), 1-16.

Mandl, C. (2023). Data-Driven Risk Management. In Procurement Analytics: Data-Driven Decision-Making in Procurement and Supply Management (pp. 217-266). Cham: Springer Nature Switzerland.

Mansfield, N. R., Ugwu, O. O., and Doran, T. (1994). Causes of delay and cost overruns in Nigerian construction projects. International journal of project Management, 12(4), 254-260.

Meckling, W. H., and Jensen, M. C. (1976). Theory of the Firm. Managerial behavior, agency costs and ownership structure, 3(4), 305-360.

Molepo, M., and Jahed, M. I. (2022). E-procurement as a monitoring tool to combat corruption in South Africa. Administratio Publica, 30(4), 232-254.

Mutangili, S. K. (2019). Role of e-procurement practices in fighting fraud and corruption in public procurement. Journal of Procurement and Supply Chain, 3(2), 36-47.

Omoregie, A., and Radford, D. (2006). Infrastructure delays and cost escalation: Causes and effects in Nigeria. In Proceedings of the 6th International Postgraduate Research Conference in the Built and Human Environment. 923xz.

Oyeyemi, B. B. (2023). Data-Driven Decisions: Leveraging Predictive Analytics in Procurement Software for Smarter Supply Chain Management in the United States.

Panova, Y., and Hilletofth, P. (2018). Managing supply chain risks and delays in construction project. Industrial Management and Data Systems, 118(7), 1413-1431.

Prałat, E. (2019). Public e-procurement tools in European Union. Nierówności społeczne a wzrost gospodarczy, (58), 188-197.

Rice, A. J. (2024). The rise of intelligent procurement: Embracing AI and data analytics for crossfunctional synergy. Journal of Supply Chain Management, Logistics and Procurement, 6(4), 319-329.

Rodriguez, E. (2024). Strategic risk, intelligence and digital transformation (Vol. 6). World scientific.

Ross, S. A. (1973). The economic theory of agency: The principal's problem. The American economic review, 63(2), 134-139.

Teece, D. J. (2007). Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strategic management journal, 28(13), 1319-1350.

Teece, D. J., Pisano, G., and Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic management journal, 18(7), 509-533.

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Published

2026-04-02

How to Cite

Tyozenda, A., Jato, T. P. J., & Tyoakosu, S. A. (2026). Digital risk intelligence and supplier failure in African public infrastructure delivery: evidence from Nigeria. Journal of Commerce, Management, and Tourism Studies, 5(1), 68–82. https://doi.org/10.58881/jcmts.v5i1.445

Issue

Vol. 5 No. 1 (2026): April 2026

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Copyright (c) 2026 Aondofa Tyozenda, T. P. J. Jato, S. A. Tyoakosu

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