A Comparison of the Ability Level of Human Resource Roles and Their Perceived Importance among HR Professionals in the Malaysian Government Linked Companies (GLCs)
Bibliographic record
Abstract
This paper compares the ability level against the perceived importance of the Human Resource roles in the Malaysian government linked companies. The companies comprise of fourteen government linked companies that make up the G20 group of GLCs. These companies were chosen because they contribute to more than 70% of capitalization of the listed GLCs and have a workforce of nearly 148,000 headcount. Sixty nine HR Managers who were involved in strategic decision making were represented in the study sample. The research design was a correlational study. A structured questionnaire was used to collect data. The questionnaire solicited the perception of the respondents on their ability level of each HR Roles. Four domains of HR Roles were studied: Strategic Partner Role, Change Champion Role, Admin Expert Role and Employee Advocate/Agent Role. The study found that there was a vast difference in their ability level against their perceived importance of the particular HR Role. The mean score for Admin Expert Role was the highest and the Strategic Partner Role was the lowest among the four roles. However, the total effect score showed that the Employee Advocate/Agent Role scored the highest and the Change Champion Role scored the lowest. This juxtaposition suggests that what is actually practiced (ability) is not the same as what is professed (importance). As such it would benefit the HR Managers and their superiors to know that there is a difference in role ability towards role expectations and hence, find ways to improve the performance of the HR Managers and minimize a disparity in role expectation. This will indivertibly increase job performance and satisfaction overall.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".