Implications of Generic Skills on Innovative Behavior Towards Opportunity Recognition in Youth
Bibliographic record
Abstract
The high competition in the employment market and high unemployment rate prompted the government to encourage entrepreneurship as a career option for students further. Many entrepreneurship programs and courses have been developed and offered in higher learning institutions to encourage innovative behavior and the ability to recognize opportunities, especially in the emerging digitization world and the high unemployment rate in Malaysia. Generic skills such as creativity, proactiveness, risk-propensity, leadership, motivation, and self-efficacy are said to be essential determinants for innovative behavior. This paper aims to investigate the impact of generic skills on innovative behavior and opportunity recognition empirically. The online survey was conducted on 225 students who took a technology entrepreneurship course at a Malaysian university. Data were then analyzed using Partial Least Square software. Only creativity and proactive have a strong influence on innovative behavior and opportunity recognition. The mixed results implied that more efforts to carry out to enhance further the innovative behavior of students in preparing them to real-world challenges. It is timely to readdress how to improve further and strengthen the generic skills of students. Recommendation and suggestions are presented.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".