Employability skills, job performance and promotability of employees working in SMEs Malaysia
Bibliographic record
Abstract
The study aimed to examine the relationships between employability skills of university graduates and three variables namely self-directed learning readiness (SDLR), job performance and promotability. All the respondents who participated in the study were employers in small and medium enterprises in Malaysia. They must qualify these criteria - human resource managers (or any managers who are in the capacity to recruit and hire people) of a company in small and medium enterprises (SMEs) and they have been hiring university graduates. In total, there were 104 respondents. The respondents were required to rate the university graduates they have hired for the past 2 years. The study results managed to indicate that all the employability skills (except for ICT skills) were significantly related to each other. This implies that all these skills are complementing each other and university graduates have to have all these skills in them especially if they wish to work in SMEs. It was also revealed that among all the five SDLR dimensions, only joy for learning that was unrelated to any of the employability skills. The case was different from the other dimensions especially independent learner and initiative to learn that were significantly related to many employability skills. Unlike SDLR, it was found that most employability skills were significantly related to job performance and promotability (except for ICT and oral/written communication skills) It is also important to note that the most preferred CGPA level of university graduates hired by SMEs are between 3.0 to 3.4.
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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".