Entrepreneurs Psychological Perspective and Enterprise Graduation in Eldoret Town, Kenya
Bibliographic record
Abstract
Micro and Small Enterprises (MSEs) enormously contribute to the Kenyan economy through employment and income generation. In spite of this contribution, their performance in terms of graduation is little understood. The evidence supporting the view of micro and small enterprises as the engine of growth is limited. This study was based on Schein’s model by Edgar Schein career Anchors was anticipated to shade light on the motivation on the graduation of MSEs. The main objective of the study was to determine the effect of psychological perspective of an entrepreneur on enterprise graduation in Eldoret town Kenya. The study was conducted in Eldoret Municipality Central Business District (CBD) in Uasin Gishu County targeting micro and small enterprises licensed and registered by Eldoret Municipal Council. A sample size of 189 was selected in this study. Stratified-simple random sampling technique was used. Primary and secondary data was used to achieve the objective of the study. Primary data was collected using questionnaires and secondary data was collected from Eldoret Municipal achieve. Data was analyzed using descriptive statistics and inferential statistics. The study findings indicated that; the psychological perspective of an entrepreneur had a statistical significant effect with graduation. The study concludes that entrepreneurers’ should enhance their entrepreneurial competence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".