Financing Options and Sustainable Small Business Growth in Uganda: An Optimal Model
Bibliographic record
Abstract
Small businesses in Uganda continue to lag behind trends in terms of sales turnover profitability, employee growth, while others rarely live to celebrate their first birthday due to various constraints of which financing is at the forefront. This study set out to determine the relationship between various financing options and sustainable small business growth so as to suggest an optimal financing model to ensure sustainable small businesses. The study adopted a cross-sectional descriptive survey design to analyse a sample of 399 small businesses which were selected using stratified random sampling from Kampala Metropolitan Area. Data were collected using a researcher administered structured questionnaire and analysed using descriptive statistics. The relationship between the variables was determined using Spearman’s rank correlation coefficeint. The study established that there is a weak positive significant correlation between traditional debt finance and sustainable small business growth, a strong positive significant correlation between asset-based finance and sustainable small business growth, and a strong positive significant correlation between crowdfunding and sustainable small business growth. The study further established that there is a moderate positive significant relationship between equity finance and sustainable small business growth. The study concluded that improving on the available financing options would improve on the sustainable small business growth. It is recommended that the ideal model for financing small businesses should be the integration of the financing options, but giving priority to; asset based lending, crowdfunding, equity finance and lastly traditional debt finance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".