Exploiting a non-mainstream financial scheme to innovate: SMEs in the developing world
Bibliographic record
Abstract
Purpose The study aims to explore the role of non-mainstream financial schemes in supporting innovation within SMEs in developing countries, particularly in sub-Saharan Africa. It investigates how informal credit, business group affiliation and foreign and state ownership arrangements influence SMEs’ innovative activities in environments with limited access to formal financial resources. Design/methodology/approach The research utilizes data from the World Bank’s Enterprise Surveys, focusing on 8,466 firms across 11 sub-Saharan African countries from 2011 to 2020. A logistic regression analysis was conducted to assess the impact of various financial sources on SMEs’ innovation outputs, particularly incremental innovations, due to data constraints on radical innovations. Findings The findings reveal that informal credit significantly supports SME innovation, while business group resources can hinder innovative activities by restricting firms to routine tasks. State ownership positively influences innovation, whereas the impact of foreign ownership is inconclusive. These results highlight the critical role of alternative financial mechanisms in the innovation activities of SMEs in resource-limited settings. Originality/value This study contributes to the literature by providing empirical evidence on the effects of non-mainstream financial schemes on SME innovation in developing countries. It offers new theoretical insights into how SMEs navigate financial constraints to foster innovation and suggests policy implications for improving financial support systems for SMEs in such contexts. The research underscores the importance of contextualizing entrepreneurship studies to better understand the unique challenges and opportunities faced by SMEs in developing regions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".