Effects of Entrepreneurship Education on Start-up Propensity: Technical and Vocational College Lecturers' Perspectives
Bibliographic record
Abstract
This research aimed to engage the prevailing status that education in entrepreneurship is offered to college students in South Africa to enhance the learning and teaching efficiency of TVET college, which should lead to motivating and preparing graduates to start their own businesses. At the time of this study (2020), the total youth unemployment rate was 40.3%. In addition, the youth graduate unemployment rate was 55.2%. It is concerning that the Technical and Vocational Education and Training (TVET) college graduates face this challenge. It is significantly possible that South African TVET students will face unemployment after graduation due to the high unemployment rate and the competition in the job market from other universities. Accordingly, there is a need to transform TVET college students into entrepreneurs who can self-sustain upon completion of their studies.Ajzen's theory of planned behaviour was used to address the paucity of literature on entrepreneurial education and start-up propensity. Accordingly, a research questionnaire was structured to address the issue: "To what extent do the variables in the entrepreneurial environment (TVET colleges), in the form of knowledge of entrepreneurship, perceived self-efficacy and attitude towards entrepreneurship, affect the entrepreneurial intentions of students?" This study proved the direct relationship between entrepreneurship education, inclination, and intention to start a business.A few recommendations are made regarding investment in entrepreneurship education and infrastructure, partnering with private and public companies, and entrepreneurship as an integral part of development in the national policy framework that addresses unemployment.
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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".