Debt Financing and Growth Sustainability of Small and Medium-Sized Enterprises: The Moderating Effect of Debt Literacy
Bibliographic record
Abstract
This study investigates the moderating role of debt literacy in the relationship between debt financing (loan size, interest rate, and loan maturity) and the growth sustainability of Small and Medium-sized Enterprises (SMEs) in Lira City, Uganda. Guided by the Financial Capability Theory and employing Structural Equation Modeling (SEM) with bootstrapping, the study evaluates both direct and indirect relationships among financial structures, behavioral competencies, and business outcomes. Data were collected from 311 SMEs across diverse sectors and analyzed using exploratory and confirmatory factor analysis prior to SEM testing. The results reveal that loan size and interest rate have statistically significant negative effects on SME growth, while loan repayment shows no direct impact. Debt literacy was found to partially mediate the relationship between loan size and growth, and between loan repayment and growth. Moreover, debt literacy significantly moderated the relationship between loan size and growth sustainability, but did not moderate the link between interest rate and growth. The moderation effect on loan maturity, proxied by repayment behavior, remained inconclusive. These findings highlight the critical importance of integrating debt literacy into SME financing programs. The study contributes to theory by reinforcing the Financial Capability Theory and extends empirical literature by modeling debt literacy as both a mediating and moderating factor. Practical implications include policy recommendations for designing tailored financial education and supportive credit products that promote responsible borrowing and long-term business sustainability in developing economies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".