The Development of Competency Model Perceived by Malaysian Human Resource Practitioners’ Perspectives
Bibliographic record
Abstract
The intent of this research was to identify Malaysian Human Resource Development (HRD) practitioners’ perceptions of competencies needed by HRD practitioners in organizations. The research was based on the American Society for Training and Development (ASTD) models for Workplace Learning and Performance (WLP). The purpose was to assess the perceptions of Malaysian HRD practitioners in organizations regarding the importance of competencies for human resource development in organizational contexts. This study employed quantitative, cross-sectional survey, and an existing ASTD competencies instrument. Organizations were chosen based on the Federation of Malaysian Manufacturer’s (FMM) database. Data for this study were collected from 144 HRD practitioners from various organizations in Malaysia who successfully completed the web-based survey. Data were analyzed using descriptive statistics and Exploratory Factor Analysis. The findings of the study indicated that the Malaysian HRD practitioners perceived certain competencies as currently important and others as important in the future for their organization. The results were supported by a number of statistical findings with medium to small effect sizes. By using exploratory factor analysis, this study revealed that the Malaysian HRD practitioners perceived only 25 of the 52 competency items to be important. The results from this study have implications for the ASTD competency model and provide evidence that the competencies needed by employees and in organizations are changing over time.
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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 | 0.004 | 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 | 0.001 | 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) | 0.001 | 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".