An Evaluation of the Entrepreneurs’ Perception of Business-Incubation Services in Kenya
Bibliographic record
Abstract
Business incubators provide an important service network for new and fledgling Small and Medium Enterprises (SMEs) in Kenya. To ascertain the perception of the importance of business incubation process and how recipients perceive the service to be rendered, perceptions of 124 entrepreneurs are compared. The survey endeavored to cover all types of business-incubation programme in Kenya that target SMEs, but it was found that although close to twenty-five institutions operated some form of business incubation services or another, only twelve were confirmed as business incubators per se. The findings on the entrepreneurs’ respondent, first from the descriptive statistics shows that the mean scores for the importance of services of business-incubation processes is higher than the rating of how actually the services were received. The hypothesis empirically tested using paired t-test indicates that a gap exist between how entrepreneurs’ perceive business-incubation (services) process and what they actually receive. Based on the means of the two, they actually received less than anticipated. However, being a quantitative study the exact details of the real nature of business-incubation- services attached to the importance/ rendered services are not documented in the study. While the research provided new insights into business-incubation services in Kenya, numerous questions ring out in mind.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".