Rethinking Technological Innovations Strategies: Challenges and Insights in the Performance of Micro, Small and Medium Enterprises in Kenya
Bibliographic record
Abstract
Micro, Small, and Medium Enterprises (MSMEs) in Kenya, totaling 7.4 million, are crucial for socio-economic development and job creation. However, they face significant challenges due to inadequate access to digital services, especially fintech platforms. This study explored how the ease of accessing digital credit, its associated costs, information availability, and the regulatory landscape influence MSME growth in Uasin Gishu County, Kenya. Using an explanatory research design with simple and stratified random sampling, 121 top-level managers or owners were selected. Primary data was collected via semi-structured questionnaires. Data analysis involved descriptive statistics (percentages, frequencies, means, and standard deviations) and inferential statistics (correlation and multiple regression). Findings indicate that the ease of access to digital credit (r=0.673, p<0.001), information availability (r=0.701, p<0.001), and digital credit regulation (r=0.669, p<0.001) all positively and significantly influence MSME growth. Conversely, the cost of digital credit showed a significant negative correlation with MSME growth (r=−0.610, p<0.001). Collectively, these factors explained 60.0% of the variance in MSME growth (Adjusted R2=0.584), with the overall model being statistically significant (F (4,104) =38.921, p<0.001). The study recommends lowering interest rates, government regulation of digital lending practices to protect MSMEs, and policy frameworks that encourage easy information sharing on digital lending to foster MSME growth.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".