Influence of Cognitive Factors on Self-Employment Intention Among Students in Technical, Vocational Education and Training in Kenya
Bibliographic record
Abstract
Entrepreneurship has been identified as a crucial activity for economic growth and employment generation worldwide. However, this has not been effective in most developing countries, Kenya as an example, has a high rate of unemployment among the young graduates emerging from universities and tertiary institutions. One of the government challenges is transforming the mindset of students to venture into business rather than seeking employments. This study thus examined the relationship between cognitive factors, entrepreneurship education and how these variables influence self-employment intentions among Technical and Vocational Education and Training in Kenya. The study objectives were to determine the influence of cognitive factors and the moderating effect of entrepreneurship education on students’ self-employment intentions. The study adopted a survey research design. Self-administered questionnaire was developed and administered to 400 diploma engineering finalist sampled from 41 public Institutions spread in five geographical regions in the country using multistage and simple random sampling approach. The data were analyzed using descriptive statistics and inferential statistics with the help of the Statistical Package for Social Sciences version 20. Pearson’s Coefficient Correlation was used to examine reliability of data. Factor analysis was conducted to investigate the internal structure among the set of variables. Multiple linear regressions analysis was used to examine the effect of independent variables on the dependent variable. The results of findings showed that there was a positive and significant relationship between cognitive factors and self-employment intention. The results also showed that entrepreneurship education enhances cognitive factors and thus strongly influence self-employment intentions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".