Debt capital access procedures for small and medium-sized enterprises in an emerging economy: does financial knowledge matter?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it