Credit counselling: a contemporary strategy for survival of micro small and medium-sized enterprises in under-developed financial markets post COVID-19 pandemic
Bibliographic record
Abstract
Purpose The main purpose of this paper is to establish the mediating effect of credit counselling in the relationship between access to microcredit and survival of micro small and medium-sized enterprises (MSMEs) in developing countries in sub-Saharan Africa post COVID-19 pandemic with data collected from rural Uganda. Design/methodology/approach Structural equation modelling (SEM) through SmartPLS 4.0 was used to generate the standardized parameters to test whether credit counselling mediates the relationship between access to microcredit and survival of MSMEs in developing countries in sub-Saharan Africa post COVID-19 pandemic with data collected from rural Uganda. Findings The SEM bootstrap results revealed that credit counselling enhances access to microcredit by 27% to promote survival of MSMEs in developing countries in sub-Saharan Africa post COVID-19 pandemic with data collected from rural Uganda. Research limitations The current study focused only on women MSMEs. Future studies may possibly collect data from all the MSMEs to draw better generalization of the findings within the sector. Practical implications The findings can help public finance policy to ensure provision of credit counselling to microentrepreneurs who borrow from different financial institutions to reduce the problem of loan defaults and delinquency rampant in lending. This could be done through conducting routine business education and counselling sessions for microentrepreneurs who often need credit to grow their businesses. Originality/value This study is amongst the first few studies to establish the mediating effect of credit counselling in the relationship between access to microcredit and survival of MSMEs in developing countries in sub-Saharan Africa in the aftermath of COVID-19 pandemic with data collected from rural Uganda. There is a dearth in literature and theory on the rehabilitative and preventive role of credit counselling in reducing repayment defaults amongst borrowers within the credit market to spur survival of MSMEs seen as the main enabler of economic growth, especially in developing countries. In fact, credit counselling acts as a safety net by substituting financial literacy and education to solve the rampant problem of overindebtedness amongst borrowers who are debt illiterate within the credit market.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".