Understanding the impact of innovation and other business support interventions on SMEs’ development – Lessons from sub-Saharan Africa from an evidence-based review
Bibliographic record
Abstract
Small, and medium-sized enterprises (SMEs) play significant roles in economic growth, development, and job creation in sub-Saharan Africa (SSA). This study employed a Systematic Literature Review (SLR) approach to identify empirical evidence on the impact of innovation and business support activities on SMEs’ performance across countries in SSA. Based on geographic concentration and approaches, the study focused on 48 articles that evaluated programmes aimed at supporting SMEs in SSA. The results show that, on average, innovation and support programmes had positive implications on firm performance, employment generation, export performance and labour productivity. Furthermore, socioeconomic factors and inclusivity, technical expertise of employees, access to finance and credit, firm’s social capital and enabling government policies among others were found to drive successful integration of innovation and business support programmes and its subsequent impacts among SMEs. Few of the studies identified focused on equity, diversity, and inclusivity issues. The study concludes that unlocking the region’s growth potential will require bridging the credit gap, strengthening SME value chains and boosting productivity through digitalization, technology adoption, and adaptation. Further studies should pay attention to equity, diversity, and inclusivity issues, as well as embed both qualitative and quantitative approaches in the enquiry of programme impact.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".