Changing Entrepreneurial Leadership Knowledge Competency in Higher Education: A Way to Move Forward
Bibliographic record
Abstract
University Leaders should have the entrepreneurial capacity to strengthen the Malaysia higher education sector. The role of higher education in economically improving Malaysia is undeniable. One of the functions of higher education is to build graduate entrepreneurs capable of creating jobs. Therefore, those talents should be led by leaders who have an entrepreneurial mindset. Unfortunately, less effective informal entrepreneurship education that relates to entrepreneurial leadership has been executed to inculcate the knowledge and skills of entrepreneurial leadership. Hence previous research that indicated the effective entrepreneurial leadership training which regards to entrepreneurship body of knowledge is scarce. Therefore, this study will also disclose the findings that relate to effective entrepreneurial leadership training that can change the knowledge of university leaders and fill in the gaps in the entrepreneurship body of knowledge. This study employs a Quantitative method that utilizes the Kirkpatrick Training Effectiveness Analysis Model. The descriptive and mean score analysis is used to indicate the changes in entrepreneurial leadership knowledge. It is found that the awareness of entrepreneurial leadership has increased, and they believe that entrepreneurship skills and behaviors can be carried out accordingly. Future research should enhance this study by utilizing Kirkpatrick Training Effectiveness Analysis Level Three.
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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 | 0.005 | 0.007 |
| 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 | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".