Digital Connectivity and Agricultural Supply Chain Resilience: Empirical Evidence from Smallholder Farmers in Post-Conflict Liberia

Authors

Monica D. Jones

College of Economics, Sichuan Agriculture University (SAU) (Liberia)

Melissa Hawa Isabel Sackie

College of Economics, Sichuan Agriculture University (SAU) (Liberia)

Christophe A. Lavall, Jr

School of Mechanical and Electrical Engineering (SMEE), University of Electronic Science and Technology of China (UESTC) (Liberia)

Ezekie Z. Ziah

School of Public Administration (SPA) , University of Electronic Science and Technology of China (UESTC) (Liberia)

Article Information

DOI: 10.47772/IJRISS.2026.100300512

Subject Category: Agriculture

Volume/Issue: 10/3 | Page No: 7014-7033

Publication Timeline

Submitted: 2026-03-25

Accepted: 2026-04-01

Published: 2026-04-15

Abstract

This study examines the relationship between digital connectivity and agricultural supply chain resilience among smallholder farmers across all 15 counties in post-conflict Liberia. Adopting a cross-sectional quantitative design, the research uses seven validated secondary datasets from 2016 to 2022 to analyze the impacts of ICT access, digital literacy, and gender equity on core supply chain outcomes, including post-harvest losses, farm-gate prices, transport costs, and formal market participation. Four novel composite key performance indicators, the ICT-SCM Performance Index, Digital Supply Chain Readiness Score, Supply Chain Vulnerability Index, and Market Integration Score, are developed to enable standardized and replicable resilience measurement in data-scarce post-conflict contexts. Empirical results demonstrate strong positive associations between digital connectivity and supply chain resilience: a one-unit increase in the ICT Development Index reduces post-harvest losses by 6.2 percentage points, while digital literacy significantly improves farm-gate prices and market integration. Gender gaps in digital access moderate the ICT–resilience relationship, weakening the conversion of digital resources into supply chain benefits. Spatial analysis reveals significant clustering of vulnerability and digital readiness, with Montserrado County outperforming all regions and southeastern counties trapped in overlapping deficits of digital access, infrastructure, and income. Findings confirm that ICT effectiveness depends on complementary road infrastructure and household resources. This study addresses critical empirical gaps in fragile-state agricultural digitalization research and provides evidence to support gender-responsive, spatially targeted policy interventions for Liberia’s National Digital Strategy and agricultural resilience programs, offering a transferable framework for other post-conflict economies in Sub-Saharan Africa.

Keywords

Digital literacy, Supply chain resilience, Post-conflict agriculture, ICT access, Spatial clustering

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References

1. Afonso, A., & Blanco-Arana, M. C. (2024). Does financial inclusion enhance per capita income in the least developed countries? International Economics, 177. https://doi.org/10.1016/j.inteco.2024.100479 [Google Scholar] [Crossref]

2. Aker, J. C., & Mbiti, I. M. (2010a). Mobile Phones and Economic Development in Africa. Journal of Economic Perspectives, 24(3), 207–232. https://doi.org/10.1257/jep.24.3.207 [Google Scholar] [Crossref]

3. Allen, A. D., & Diallo, K. (n.d.). Agricultural Dynamics in Liberia: Current Issues and Solutions. https://doi.org/10.47772/IJRISS [Google Scholar] [Crossref]

4. Association of Social Anthropologists. (2021). Ethical guidelines for good research practice. [Google Scholar] [Crossref]

5. Balié, J., Del Prete, D., Magrini, E., Montalbano, P., & Nenci, S. (2019). Does Trade Policy Impact Food and Agriculture Global Value Chain Participation of Sub-Saharan African Countries? American Journal of Agricultural Economics, 101(3), 773–789. https://doi.org/10.1093/ajae/aay091 [Google Scholar] [Crossref]

6. Barney, J. (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108 [Google Scholar] [Crossref]

7. Chiappetta Jabbour, C. J., Fiorini, P. D. C., Ndubisi, N. O., Queiroz, M. M., & Piato, É. L. (2020). Digitally-enabled sustainable supply chains in the 21st century: A review and a research agenda. Science of The Total Environment, 725, 138177. https://doi.org/10.1016/j.scitotenv.2020.138177 [Google Scholar] [Crossref]

8. Creswell, J. W., & David Creswell, J. (n.d.). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. [Google Scholar] [Crossref]

9. Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. Management Information Systems Quarterly, 13(3), 319–340. [Google Scholar] [Crossref]

10. https://doi.org/10.2307/249008 [Google Scholar] [Crossref]

11. Duff Rutherford, D., Burke, H. M., Cheung, K. K., Field, S., & Rutherford, D. D. (2016). Title: Impact of an Agricultural Value Chain Project on Smallholder Farmers, Households, and Children in Liberia Order of Authors. https://www.elsevier.com/open-access/userlicense/1.0/ [Google Scholar] [Crossref]

12. Fabregas, R., Kremer, M., Lowes, M., On, R., & Zane, G. (2025). Digital Information Provision and Behavior Change: Lessons from Six Experiments in East Africa. American Economic Journal: Applied Economics, 17(1), 527–566. https://doi.org/10.1257/app.20220072 [Google Scholar] [Crossref]

13. Fackler, P., Goodwin, B., Fackler, P., & Goodwin, B. (2001). Spatial price analysis. 1, Part 2, 971–1024. https://EconPapers.repec.org/RePEc:eee:hagchp:2-17 [Google Scholar] [Crossref]

14. FAO. (2023). FAOSTAT: Food and agriculture data. [Google Scholar] [Crossref]

15. Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39. [Google Scholar] [Crossref]

16. https://doi.org/10.2307/3151312 [Google Scholar] [Crossref]

17. GSMA (GSM Association). (2023). Mobile gender gap report 2023. [Google Scholar] [Crossref]

18. Han, J. ; K. M. ; P. J. (2022). Data mining: Concepts and techniques (4th ed.). Morgan Kaufmann. [Google Scholar] [Crossref]

19. Hwang, S., Wang, W., Li, Z., & Meng, Q. (2025). Digital Transformation Drivers, Technologies, and Pathways in Agricultural Product Supply Chains: A Comprehensive Literature Review. Applied Sciences 2025, Vol. 15, Page 10487, 15(19), 10487. https://doi.org/10.3390/app151910487 [Google Scholar] [Crossref]

20. Ivanov, D. (2020). Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic. Annals of Operations Research 2020 319:1, 319(1), 1411–1431. https://doi.org/10.1007/s10479-020-03640-6 [Google Scholar] [Crossref]

21. Kamalahmadi, M., & Parast, M. M. (2016). A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research. International Journal of Production Economics, 171, 116–133. https://doi.org/10.1016/j.ijpe.2015.10.023 [Google Scholar] [Crossref]

22. LISGIS. (2016). Household income and expenditure survey 2016. [Google Scholar] [Crossref]

23. LISGIS (Liberia Institute of Statistics and Geo-Information Services). (2021). Liberia demographic and health survey 2019–2020. [Google Scholar] [Crossref]

24. Lwoga, E. T., & Lwoga, N. B. (2017). User acceptance of mobile payment: The effects of user-centric security, system characteristics and gender. Electronic Journal of Information Systems in Developing Countries, 81(1), 1–24. https://doi.org/10.1002/j.1681-4835.2017.tb00595.x [Google Scholar] [Crossref]

25. Minten, B., & Barrett, C. B. (2008). Agricultural Technology, Productivity, and Poverty in Madagascar. World Development, 36(5), 797–822. https://doi.org/10.1016/j.worlddev.2007.05.004 [Google Scholar] [Crossref]

26. Mohammad Ali, I. (2024). A Guide for Positivist Research Paradigm: From Philosophy to Methodology. Idealogy Journal, 9(2). https://doi.org/10.24191/idealogy.v9i2.596 [Google Scholar] [Crossref]

27. Nakasone, E., Torero, M., & Minten, B. (2014). The power of information: The ICT revolution in agricultural development. Annual Review of Resource Economics, 6(1), 533–550. https://doi.org/10.1146/annurev-resource-100913-012714 [Google Scholar] [Crossref]

28. Osei-Kyei, R., Chan, A. P. C., & Ameyaw, E. E. (2017). A fuzzy synthetic evaluation analysis of operational management critical success factors for public-private partnership infrastructure projects. Benchmarking, 24(7), 2092–2112. https://doi.org/10.1108/BIJ-07-2016-0111 [Google Scholar] [Crossref]

29. Patrick, K., Jeffrey, V., & Pilja, P. V. (2022). Agriculture in sub-Sahara Africa developing countries and the role of government: Economic perspectives. African Journal of Agricultural Research, 18(7), 493–509. https://doi.org/10.5897/ajar2022.15990 [Google Scholar] [Crossref]

30. Ponomarov, S. Y., & Holcomb, M. C. (2009b). Understanding the concept of supply chain resilience. The International Journal of Logistics Management, 20(1), 124–143. [Google Scholar] [Crossref]

31. https://doi.org/10.1108/09574090910954873 [Google Scholar] [Crossref]

32. Teece, D. J. (2022). Fundamental issues in strategy: Time to reassess? Strategic Management Review, 3(2), 103–144. [Google Scholar] [Crossref]

33. Tukamuhabwa, B. R., Stevenson, M., Busby, J., & Zorzini, M. (2015). Supply chain resilience: Definition, review and theoretical foundations for further study. International Journal of Production Research, 53(18), 5592–5623. https://doi.org/10.1080/00207543.2015.1037934 [Google Scholar] [Crossref]

34. USAID. (2022). GROW Liberia agricultural market assessment report. [Google Scholar] [Crossref]

35. WFP. (2022). Liberia comprehensive food security and vulnerability analysis (CFSVA). [Google Scholar] [Crossref]

36. World Bank. (2022). Gender and digital finance in Africa: Closing the gap. [Google Scholar] [Crossref]

37. World Bank. (2023). World development indicators. [Google Scholar] [Crossref]

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