Digital Trade Financing and SMEs Export Intensity in Economic Community of West African States (ECOWAS)

Publication Details

Publication Date

10/11/2026

Volume / Issue

Vol 1, Issue 6 (2026)

Article No.

025

Journal

British Journal of Contemporary Research

DOI

https://doi.org/10.67693/BJCR-Z9XQ6B88

Received

15 Oct 2026

Views

23

Downloads

17

Abstract

This study examined the impact of digital trade financing on the performance of Small and Medium Enterprises export intensity in the Economic Community of West African States region (proxy for Developing Economies) over the period 2019 to 2024. The study employed panel data from fifteen ECOWAS member and utilized a robust methodological framework comprising Hausman specification tests, random effects estimations, Difference Generalized Method of Moments, and System Generalized Method of Moments estimators to address issues of endogeneity, unobserved heterogeneity, and dynamic panel bias. The Hausman test results found that, random effect is appropriate hence the application of GMM instrument. The Difference GMM results confirmed strong persistence in export intensity with lagged dependent variable coefficient 0.190627 and probability value 0.0000. The J-statistics for GMM estimations were effectively zero, confirming instrument validity. The study concludes that, digital trade financing exerts significant and heterogeneous effects on SME export intensity performance in the ECOWAS region. Digital standby letters of credit showed negative effects on export intensity. Dynamic discounting strongly supports digital transactions and exports. Internet penetration consistently enables export intensity. The study recommends that, policymakers prioritize digital standby letter of credit development, accelerate digital infrastructure investments and support continued research on digital trade finance and SME export intensity in the ECOWAS region.

License

This article is published under the Creative Commons Attribution 4.0 International License . Free to read, share, and adapt with attribution.