Debt capital access procedures for small and medium-sized enterprises in an emerging economy: does financial knowledge matter?
Bibliographic record
Abstract
Small and Medium-Sized Enterprises (SMEs) play a crucial role in the development of emerging economies. However, access to debt financing poses a major challenge to their sustenance and growth. This quantitative study explored the Perking order and trade-off theories to analyse data from 201 SME operators in Ghana on the effect of debt capital access procedures and financial knowledge. This study contributes to the ongoing discussion on SMEs’ sustainable financing, and may influence policy on financial support to SMEs operating in marginalised sectors of emerging economies. The findings indicate that bureaucratic debt approval processes significantly hinder SMEs’ access to debt finance. Again, we found that SME operators’ financial knowledge does not aid access to debt capital. It is therefore suggested that simplifying credit approval processes and improving SME operators’ financial literacy through training could enhance SMEs’ ability to secure debt finance. This will contribute to the financial inclusion of marginalised groups and bridge the economic inequality gap to promote inclusive and sustainable growth. Additionally, we recommend that government agencies responsible for SMEs provide special funds to augment those provided by the private sector.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".