Determining the Importance of Competency and Person-Job Fit for the Job Performance of Service SMEs Employees in Malaysia
Bibliographic record
Abstract
The small and medium enterprises (SMEs) have contributed to the economic growth and competitiveness of many countries. However, as the SMEs continue to grow as an important entity in many economies including Malaysia, many factors have dampened such progress. While previous studies had focused on its macro perspective in terms of firm level and industry level performance, this study attempted to address the basic issue of SMEs employees in terms of their job performance. This study is underpinned by the theory of job performance and further supported by the theory of congruence. The main objective of this research is to investigate on the relationship that may exist between three variables comprised of competency, person-job fit and the employees’ job performance in the context of service SMEs. Using a quantitative method, a sample of 324 responses was collected using a mail survey from 1500 distributed questionnaires. Results show significant relationships between competency, person-job fit and the job performance of employees. Conclusions and implications of the study were discussed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".