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.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".