An Evaluation of Alternative Sources of Trade Finance For SMEs in Emerging Markets

Copyright © Author(s) 2025. All Rights Reserved. Published by GLOBAL PUBLICATION HOUSE. | Int. Journal of Business Management Page 92 of 107 An Evaluation of Alternative Sources of Trade Finance For SMEs in Emerging Markets By Author(s): Katherine Andy-Onugbu The Institute of International Trade and Development, Faculty of Social Sciences, University of Port Harcourt, Rivers State Nigeria. Abstract This study evaluated the efficacy of alternative sources of trade finance in enhancing the export performance of Small and Medium-sized Enterprises (SMEs) in Nigeria. The research examined which financial instruments and macroeconomic variables significantly influenced SME exports using time-series data from the Central Bank of Nigeria (1981–2023). A Generalized Linear Model (GLM) with a Gamma family and inverse link function was employed to account for the non-negative nature of the dependent variable. Two models were estimated: a baseline model (2000–2017) and an augmented model (2007–2017) that incorporated trade credit and overall trade performance. The findings revealed that Deposit Money Banks’ (DMBs) lending to SMEs showed a weak and inconsistent relationship with export performance—positive but insignificant in the baseline model and significantly negative in the augmented model. This suggests that general bank lending may not effectively support SME exports, particularly during periods of economic volatility. Conversely, Letters of Credit and the exchange rate were consistently positive and highly significant (p < 0.01), confirming their vital roles in mitigating payment risks and enhancing price competitiveness. Broader credit measures, such as total private-sector credit and direct export loans, were statistically insignificant, underscoring that financial depth alone does not address SMEs’ export constraints. The study concludes that specialized trade finance facilities, combined with stable and competitive exchange rate management, are essential for boosting SME participation in international trade, fostering inclusive growth, and promoting Nigeria’s economic diversification. Keywords Trade Finance, SMEs, Export Performance, Nigeria, Exchange Rate, Letters of Credit, Alternative Sources, Generalized Linear Model How to cite: Andy-Onugbu, K. (2025). An Evaluation of Alternative Sources of Trade Finance For SMEs in Emerging Markets. GPH-International Journal of Business Management, 8(10), 92-107. https://doi.org/10.5281/zenodo.17563395 ARTICLE ID: #02155 10.5281/ZENODO.17563395 VOLUME 08 ISSUE 10 OCT - 2025 e-ISSN 3027-0537 p-ISSN 3027-0375 Andy-Onugbu, K. (2025). An Evaluation of Alternative Sources of Trade Finance For SMEs in Emerging Markets. GPH-International Journal of Business Management, 8(10), 92-107. https://doi.org/10.5281/zenodo.17563395 © 2025 GLOBAL PUBLICATION HOUSE | International Journal of Business Management Introduction Small and medium-sized enterprises (SMEs) are the cornerstone of emerging economies, representing about 90% of global businesses and employing more than half of the global workforce. In many developing regions, formal SMEs contribute up to 40% of GDP, making them central to economic growth, employment creation, and poverty alleviation (World Bank, 2023). Yet, despite their importance, SMEs face persistent barriers to accessing trade finance and working-capital facilities. The International Finance Corporation (IFC, 2023) estimates that the global MSME financing gap stands at US$5.2 trillion annually, with the shortfall most pronounced in emerging and frontier markets where financial systems are underdeveloped and banks remain risk averse. The Asian Development Bank further notes that the global trade finance gap surged from US$1.7 trillion in 2020 to US$2.5 trillion by 2022 and has remained at this level into 2024 and 2025 (ADB, as cited in Global Trade Review, 2025). SMEs face the toughest barriers within this gap: although they constitute a large share of applicants, between 40% and 45% of SME applications are rejected, a pattern that has improved slightly but remains deeply exclusionary (ADB, as cited in Global Trade Review, 2025). The literature reveals debate over the structural and institutional factors driving this persistent exclusion. Some scholars argue that the problem is rooted in inefficiencies such as high transaction costs, weak credit registries, and risk aversion by banks that perceive SMEs as opaque borrowers. Others contend that the rise of digital finance and FinTech is reshaping access to trade credit, though the impact remains uneven across jurisdictions. For example, Sharma et al. (2023) find that FinTech tools such as crowdfunding, invoice trading, and platform-based lending significantly broaden access to financing for SMEs but warn that regulatory divergence often undermines scalability. Guan (2025), drawing on evidence from Chinese SMEs, similarly highlights the positive impact of supply chain finance (SCF) platforms on financing efficiency, though he stresses that the benefits are contingent on digital infrastructure and institutional maturity. Beyond private FinTech solutions, multilateral development banks (MDBs), export credit agencies (ECAs), and guarantee facilities are introducing risk mitigation mechanisms to derisk SME trade finance. The IFC, for instance, has launched targeted liquidity programs in partnership with global banks, including a US$1 billion IFC–HSBC trade finance facility covering 20 emerging market countries (Reuters, 2024). While such public–private interventions have proven effective in mobilizing capital, critics highlight concerns over their long-term sustainability and potential dependency effects when compared to market-driven innovations. Empirical data reinforces the urgency of addressing these challenges. The global trade finance market, which encompasses instruments such as letters of credit, SCF, and export factoring, was valued at approximately US$52.2 billion in 2024 and is projected to reach US$68.6 billion by 2030 (Grand View Research, 2025). In the same year, more than 91 million trade finance transactions were recorded worldwide, a 9% year-on-year increase, with emerging economies accounting for one third of this growth. Digital adoption has accelerated Page No. 93 An Evaluation of Alternative Sources of Trade Finance For SMEs in Emerging Markets Volume 8 Issue No 10 (2025) Access: https://gphjournal.org/index.php/bm sharply, with nearly 38,000 firms adopting digital trade finance systems in 2024 compared with 28,000 in 2022, thereby reducing average transaction times from 10 days in 2021 to about 6.9 days by 2024. However, the costs of compliance and regulatory bottlenecks remain high, with trade finance transaction costs rising by 13% in 2024, disproportionately affecting smaller banks and the SMEs they serve (Market Growth Reports, 2024). Against this backdrop, the objectives of this study are to empirically assess how alternative trade finance instruments such as letter of credit, bank credit to SME, banks loan to export, trade credits, credit to private sector, export intensity (proxy for SME performance) in Nigeria. The motivation stems from the trade finance gap’s direct implications for trade growth, employment generation, and the achievement of Sustainable Development Goals. This research therefore seeks to bridge that gap by generating country-specific data in Nigeria. Thus, this paper leverages the gap in the literature on the large unmet financing demand, and evolving public–private partnerships present both challenges and opportunities. A comprehensive and systematic evaluation of alternative trade finance instruments, considering empirical performance, contextual enablers, and sustainability implications, is essential for designing inclusive trade finance ecosystems that can unlock SME growth in emerging markets. This paper is decomposed into five stages namely; Introduction, Literature Review, Data methodology, discussion of findings, conclusion and recommendations. 2.0. Literature Review This study utilized multi-theoretical framework to examine the issues in this study. The Financing Gap Theory provides a foundational lens for understanding the challenges SMEs face in accessing trade finance. It posits that due to information asymmetries, high transaction costs, and banks’ risk-averse lending behavior, SMEs are systematically underserved by traditional credit markets (Beck & Demirgüç-Kunt, 2006; Berger & Udell, 1998). This is particularly evident in emerging economies where weak credit registries and limited collateral exacerbate exclusion, making alternative mechanisms such as supply chain finance, factoring, and digital trade-finance platforms critical corrective instruments. Complementing this view, Transaction Cost Economics underscores the inefficiencies inherent in conventional trade finance, where documentation, verification, and compliance requirements inflate costs and delay access to liquidity. Williamson (1985) explains that institutions or mechanisms that minimize transaction costs can improve efficiency, which in practice is evident in the role of fintech solutions and multilateral guarantee programs that streamline processes and lower risk premiums for SMEs. The role of institutions is further captured by Institutional Theory, which highlights how the quality of regulatory frameworks, enforcement capacity, and institutional maturity shape access to finance (North, 1990; Scott, 2008). Emerging markets often struggle with weak financial governance, underdeveloped legal systems, and inconsistent enforcement, all of which compound SME exclusion. This helps explain the cross-country variation in the Page No. 94 Andy-Onugbu, K. (2025). An Evaluation of Alternative Sources of Trade Finance For SMEs in Emerging Markets. GPH-International Journal of Business Management, 8(10), 92-107. https://doi.org/10.5281/zenodo.17563395 © 2025 GLOBAL PUBLICATION HOUSE | International Journal of Business Management success of alternative trade-finance tools. Building on this, the Financial Intermediation Theory highlights the importance of intermediaries such as banks, multilateral development banks (MDBs), and fintech platforms in reducing information asymmetries and allocating capital more efficiently (Diamond, 1984; Allen & Santomero, 1997). This framework is particularly useful in evaluating the comparative advantages of MDB-led interventions, such as risk-sharing facilities, against fintech-driven models like invoice trading or peer-to-peer lending. Since technological innovation is central to the emergence of alternative trade finance, Technology Adoption and Diffusion Theories—including the Technology Acceptance Model (Davis, 1989) and the Diffusion of Innovation model (Rogers, 2003)—offer insights into SME behavior toward adopting digital trade finance. These models explain why adoption varies based on factors such as perceived usefulness, ease of use, digital literacy, and the availability of enabling infrastructure. Finally, the broader developmental implications of trade-finance access are illuminated by the Inclusive Growth and Development Theory, which stresses the role of equitable financial systems in fostering employment, reducing poverty, and achieving the Sustainable Development Goals (Sen, 1999; UNDP, 2015). By linking SME trade finance to inclusive development, this framework emphasizes that financial inclusion through alternative instruments is not only an economic necessity but also a social imperative in emerging economies. 2.1 Empirical Review Empirical research examining alternative trade finance mechanisms for SMEs in emerging economies highlights both the promise and the complexity of these instruments. Nartey (2023) employed a cross-sectional survey of 257 SME managers in Ghana, analyzed via structural equation modeling, to assess determinants of supply chain finance (SCF) adoption. His findings reveal that innovative capability, information sharing, firm collaboration (both intraand inter-firm), access to external financing, and digitalization of the trade process all positively and significantly predict SCF uptake. He concludes that these factors offer SME managers a practical model to facilitate liquidity and working capital through SCF adoption. In another study, Ali, Ali, Gongbing, and Mehreen (2019) focused on textile SMEs in Asia, using structured questionnaires and covariance-based SEM analysis. Their investigation confirmed that SCF substantially enhances supply chain effectiveness by reducing transaction costs, optimizing working capital, lowering default risk, and fostering collaboration between SMEs and suppliers. The authors conclude that SCF is a secure financing solution that tangibly improves operational performance among textile SMEs. Turning to innovation outcomes, Wang et al. (2023) applied a DEA-SBM and two-way fixed-effects model using panel data from 267 Chinese manufacturing SMEs (2015–2019) to evaluate how SCF influences innovation efficiency. The results demonstrate that SCF notably increases comprehensive, technological, and organizational innovation efficiency particularly for private traditional firms, while it may inhibit the organizational innovation efficiency of state-owned high-tech Page No. 95 An Evaluation of Alternative Sources of Trade Finance For SMEs in Emerging Markets Volume 8 Issue No 10 (2025) Access: https://gphjournal.org/index.php/bm enterprises. The authors argue for policies that better support traditional manufacturing SMEs and foster broader SCF inclusion. Further extending the empirical lens, Wang et al. (2025) conducted a panel analysis of 757 ―Specialized, Refined, Niche, and Innovative‖ (SRNI) SMEs listed in Shanghai and Shenzhen (2013–2023), examining the interplay between SCF, fintech development, and financing efficiency. They found that SCF significantly improves financing efficiency, and that regions with more advanced fintech infrastructure amplify this effect. The analysis included endogeneity testing, reinforcing the robustness of the findings. They conclude that combining SCF with fintech leads to sustainable SME financing, especially for innovationdriven firms. Emerging advances in modeling techniques are also bringing fresh empirical insights. Wan and Cui (2024) developed an evolutionary game model involving banks, core enterprises, and SMEs to assess fintech’s role in agricultural SCF. Incorporating big data, blockchain, and AI-driven risk evaluation, their analysis showed that fintech applications reduce financing costs and mitigate financial risks by enhancing transaction reliability and risk identification. They argue that such digital solutions improve the stability of supply chain networks in emerging agricultural contexts. Wang, Shafie, and Kasim (2024) performed a systematic literature review (using PRISMA) of 81 articles on digital technologies’ impact on SCF performance among Chinese SMEs (2020–2024). The review concluded that blockchain enhances transparency and data security, AI improves credit decision-making precision, and big data analytics strengthens demand forecasting and risk management. Collectively, these technologies reduce information asymmetry, improve credit quality, and enhance coordination and cost-efficiency in SCF systems. 3.0. Data and Methodology To achieve the study’s objective, a quasi-experimental research design that uses secondary data and Generalized Linear Model and Generalized Least Squares were employed. The quantitative component allows for empirical evaluation of the efficiency, accessibility, and scalability of alternative trade finance instruments. The study will employ country-specific data between 1981 and 2023. Data were sourced from CBN statistical bulletin. To empirically assess how selected alternative trade finance instruments influence SMEs performance in Nigeria. The evaluation of trade finance for SMEs in emerging economy requires a Generalized Least Squares (GLS) and Generalized Linear Model result to ascertain robustness. The Generalized Linear model is suitable for analyzing non-normal data and offers predictability ability about the future outcomes based on predictors’ variables. Conversely, GLS is used to accommodate correlations and heteroskedasticity and non-normality in the variables. The baseline model for this study is specified as follows: SMEPerfit=α+β1TRDcreditit+β2CreditPrivatesectorit+β3Instit+β4LeCreditit+β5DMBExportit+ β6SMELoanDMBit + β7AverageFXit +μit (1) Page No. 96 Andy-Onugbu, K. (2025). An Evaluation of Alternative Sources of Trade Finance For SMEs in Emerging Markets. GPH-International Journal of Business Management, 8(10), 92-107. https://doi.org/10.5281/zenodo.17563395 © 2025 GLOBAL PUBLICATION HOUSE | International Journal of Business Management SMEPerfit=α+β1TRDcreditit+β2CreditPrivatesectorit+β3Instit+β4LeCreditit+β5DMBExportit+ β6SMELoanDMBit + β7AverageFXit + β8TradeCreditit + β9SMEPerfit(-1)it + μit (2) Where; SMEPerf = SME Performance (proxy by export intensity) INST = Institution (proxy by inflation) AverageFX = Average foreign exchange rate. Table 1: Dependent Variable: EXPORTINSTENSITYSME Method: Generalized Linear Model (Newton-Raphson / Marquardt steps) Date: 09/22/25 Time: 17:25 Sample (adjusted): 2000 2017 Included observations: 18 after adjustments Family: Gamma Link: Inverse Dispersion computed using Pearson Chi-Square Convergence achieved after 5 iterations Coefficient covariance computed using observed Hessian Variable Coefficient Std. Error z-Statistic Prob. CREDITTOPRIVATESECTOR -6.30E-14 1.62E-13 -0.388642 0.6975 DEPOSITMONEYBANKSLOANS TOSME 1.16E-11 2.60E-11 0.445088 0.6563 EXPORTLOANDEPOSITMONEY BANKS 2.14E-12 1.87E-12 1.143123 0.2530 INSTITUTION__INFLATION_ 1.26E-10 1.03E-10 1.213935 0.2248 LETTER_OF_CREDIT 7.31E-12 6.72E-12 1.088011 0.2766 AVERAGE_OFFICIAL_RATE_E XCHNAGE 7.75E-11 2.84E-11 2.724356 0.0064 C -6.79E-09 2.97E-09 -2.284383 0.0223 Mean dependent var 1.45E+08 S.D. dependent var 55180044 Sum squared resid 1.47E+16 Root MSE 28572296 Log likelihood -330.2715 Akaike info criterion 37.47461 Schwarz criterion 37.82086 Hannan-Quinn criter. 37.52235 Deviance 0.469455 Deviance statistic 0.042678 Restr. Deviance 3.463681 LR statistic 69.39880 Prob(LR statistic) 0.000000 Pearson SSR 0.474597 Pearson statistic 0.043145 Dispersion 0.043145 Source: E Views Output Table 1 employs a Generalized Linear Model (GLM) with a Gamma family and an inverse link function to investigate the impact of trade finance alternatives on SME performance (proxied by SME export intensity, a common proxy for SME performance) in Nigeria. This model specification is appropriate for handling continuous, positive-dependent variables like Page No. 97 An Evaluation of Alternative Sources of Trade Finance For SMEs in Emerging Markets Volume 8 Issue No 10 (2025) Access: https://gphjournal.org/index.php/bm trade financing for export, which are often right-skewed. The overall model is statistically significant, as indicated by the Prob(LR statistic) of 0.000000, meaning the set of independent variables jointly explains variations in SME export intensity better than a model with no predictors. However, an examination of the individual coefficients and their p-values at the 10% significance level reveals a more nuanced picture, with only one variable demonstrating statistical significance. The coefficient for average official rate exchange is positive (7.75E-11) and statistically significant at the 1% level (p-value = 0.0064). This finding suggests that a depreciation of the domestic currency (a higher exchange rate, meaning more domestic currency per unit of foreign currency) is associated with an increase in SME export intensity. This result is highly consistent with extant economic theory and empirical literature. A weaker domestic currency makes a country's exports cheaper and more competitive in the international market, thereby incentivizing higher export volumes. This aligns with the findings of studies across various countries that confirm the positive impact of real exchange rate depreciation on export performance (Kandilov & Leblebicioglu, 2021). Conversely, the other independent variables are statistically insignificant at the 10% level. The positive coefficient for deposit money banks loans to SME (p-value = 0.6563) and export loan from deposit money banks (p-value = 0.2530) suggests a positive relationship between bank lending targeted at SMEs and their export intensity, but the high pvalues indicate a lack of robust evidence for this relationship in this specific sample. This partial inconsistency with literature is noteworthy. While theory strongly posits that access to finance is a critical catalyst for SME internationalization by covering upfront costs like market research and product adaptation (Beck, 2013), the insignificance here could signal issues such as the loans not being utilized for export activities, the presence of binding constraints beyond finance, or potential multicollinearity with other financial variables in the model. Similarly, the positive but insignificant coefficient for letter of credit (p-value = 0.2766) indicates that this trade finance instrument, designed to reduce payment risk, does not show a statistically discernible effect on export intensity in this dataset, which contrasts with its theoretical importance in facilitating trade for smaller firms. The coefficient for credit to private sector is negative and insignificant (p-value = 0.6975). A negative sign could imply that broader credit growth in the economy might be flowing to larger, non-exporting firms (e.g., in services or construction), potentially crowding out credit for SMEs or driving up interest rates, but its insignificance makes this speculative. The positive coefficient for institution (inflation) (p-value = 0.2248) is counterintuitive and inconsistent with literature. High inflation typically erodes export competitiveness by increasing the cost of domestic inputs and creating macroeconomic instability (Campa & Goldberg, 2005). Its positive sign here captures correlation or be influenced by other unobserved factors. The primary consistent finding with literature is the significant positive role of exchange rate depreciation. The general insignificance of financial variables, however, points to a potential disconnect between the availability of credit and its effective deployment for enhancing SME Page No. 98 Andy-Onugbu, K. (2025). An Evaluation of Alternative Sources of Trade Finance For SMEs in Emerging Markets. GPH-International Journal of Business Management, 8(10), 92-107. https://doi.org/10.5281/zenodo.17563395 © 2025 GLOBAL PUBLICATION HOUSE | International Journal of Business Management exports in the context studied. This suggests that policy measures should look beyond merely increasing credit supply and focus on improving the targeting, terms, and complementary support services associated with SME financing to truly boost export performance. The small sample size (n=18) is a significant limitation, likely reducing the statistical power to detect significant relationships, and warrants caution in generalizing these findings. Table 2: Dependent Variable: EXPORTINSTENSITYSME Method: Generalized Linear Model (Newton-Raphson / Marquardt steps) Date: 09/22/25 Time: 17:30 Sample (adjusted): 2007 2017 Included observations: 11 after adjustments Family: Gamma Link: Inverse Dispersion computed using Pearson Chi-Square Convergence achieved after 6 iterations Coefficient covariance computed using observed Hessian Variable Coefficient Std. Error z-Statistic Prob. CREDITTOPRIVATESECTOR -1.12E-13 2.24E-13 -0.499239 0.6176 DEPOSITMONEYBANKSLOANSTOSME -2.04E-10 9.46E-11 -2.152076 0.0314 EXPORTLOANDEPOSITMONEYBANKS -3.08E-13 1.32E-12 -0.233468 0.8154 INSTITUTION__INFLATION_ -2.41E-10 1.83E-10 -1.316012 0.1882 LETTER_OF_CREDIT 4.76E-11 8.60E-12 5.538816 0.0000 AVERAGE_OFFICIAL_RATE_EXCHNAGE 7.41E-11 1.89E-11 3.917074 0.0001 TRADE_CREDIT 7.36E-12 1.19E-11 0.620677 0.5348 TRADE_GDP_PERFORMANCE -1.11E-08 4.21E-08 -0.264741 0.7912 C -5.04E-09 5.73E-09 -0.879847 0.3789 Mean dependent var 1.21E+08 S.D. dependent var 51813629 Sum squared resid 1.78E+14 Root MSE 4017369. Log likelihood -186.6945 Akaike info criterion 35.58081 Schwarz criterion 35.90636 Hannan-Quinn criter. 35.37560 Deviance 0.012729 Deviance statistic 0.006364 Restr. Deviance 2.487837 LR statistic 399.8292 Prob(LR statistic) 0.000000 Pearson SSR 0.012381 Pearson statistic 0.006190 Dispersion 0.006190 Source: E Views Output The augmentation of the baseline model in Table 1 with trade credit and trade performance variables in Table 2 reveals significant shifts in the determinants of SME export intensity, highlighting the critical role of trade facilitation mechanisms and the sensitivity of the model to both specification and sample period. Interpreting the coefficients requires caution due to the use of an inverse link function in the Gamma Generalized Linear Model (GLM); a positive coefficient indicates an inverse relationship with the dependent variable. Therefore, the analysis focuses primarily on the sign, significance, and directional changes between the models. At the 10% significance level, the results in Table 1 show that only the average official exchange rate is a statistically significant predictor of export intensity, with a positive Page No. 99 An Evaluation of Alternative Sources of Trade Finance For SMEs in Emerging Markets Volume 8 Issue No 10 (2025) Access: https://gphjournal.org/index.php/bm coefficient (p-value: 0.0064). This suggests that a depreciating local currency (a higher exchange rate) is associated with changes in export intensity, a finding consistent with traditional economic theory which posits that depreciation enhances export competitiveness by making goods cheaper for foreign buyers (Baum, Caglayan, & Ozkan, 2004). However, the most profound changes are observed in Table 2 after the inclusion of trade credit and trade performance, and with a adjusted sample period (2007-2017). The model fit improves markedly, as evidenced by the substantial reduction in the dispersion statistic from 0.043 to 0.006. Crucially, two additional variables become statistically significant at the 10% level. First, letter of credit emerges with a highly significant positive coefficient (p-value: 0.0000), underscoring its importance as a trade facilitation instrument. This finding is strongly consistent with extant literature, which identifies letters of credit as vital for mitigating payment risks and enabling SMEs to engage in international trade by reducing information asymmetries and counterparty risk (Niepmann & Schmidt-Eisenlohr, 2017). Second, and more strikingly, the coefficient for DBMs loan to SME turns from positive and insignificant in Table 1 to negative and significant in Table 2 (p-value: 0.0314). This negative relationship is counterintuitive and presents an inconsistency with much of the literature that generally finds a positive, albeit sometimes constrained, effect of bank financing on SME exports (Beck, Demirgüç-Kunt, & Maksimovic, 2005). This unexpected result could suggest that in the specific context of the study's sample period (which encompasses the global financial crisis), increased bank loans to SMEs were directed towards survival or domestic market stabilization rather than export expansion, or it may indicate potential multicollinearity issues with the newly added variables. The variables trade credit and trade performance themselves are not statistically significant, indicating that, in this model, they do not have a direct partial effect on SME export intensity beyond the other included financial and macroeconomic variables. The consistency of the averageFX coefficient across both tables, remaining positive and highly significant, reinforces the robustness of the exchange rate's role as a key macroeconomic determinant of export behavior. In conclusion, the augmentation of the model clarifies that while macroeconomic factors like the exchange rate are consistently important, the inclusion of trade-specific financial instruments reveals a more nuanced picture. The strong significance of letters of credit affirms their role in enabling SME exports, but the perplexing negative sign on direct bank loans to SMEs points to a complex and potentially context-dependent relationship that warrants further investigation, suggesting that the link between finance and export performance is not monolithic and may be influenced by mediating factors not captured in the model. 4.0. Discussion of Findings Based on results in Table 1 and 2, which examined the impact of alternative finance on SME performance (proxy by export intensity across two distinct temporal samples) in Nigeria. The findings reveal a complex interplay of factors, with the consistency of results with extant literature varying significantly across the different explanatory variables. Page No. 100 Andy-Onugbu, K. (2025). An Evaluation of Alternative Sources of Trade Finance For SMEs in Emerging Markets. GPH-International Journal of Business Management, 8(10), 92-107. https://doi.org/10.5281/zenodo.17563395 © 2025 GLOBAL PUBLICATION HOUSE | International Journal of Business Management Wang, Y., Shafie, S., & Kasim, M. (2024). Digital technologies and supply chain finance: A systematic review. International Journal of Supply Chain Management, 13(2), 45–67. Williamson, O. E. (1985). The economic institutions of capitalism: Firms, markets, relational contracting. Free Press. World Bank. (2020). World Development Report 2020: Trading for Development in the Age of Global Value Chains. The World Bank. World Bank. (2023). Small and medium enterprises (SMEs) finance. https://www.worldbank.org/en/topic/smefinance Page No. 107