Key Performance Indicators (KPIs) in the Public Sector: A Study in Malaysia
Bibliographic record
Abstract
Several years ago Key Performance Indicators (KPIs) becoming a best measurement practiced by the government sectors. By practicing this approach in government agencies, the failure of delivery system which always voice out the public will address accordingly. Thus, this paper aims to look at the implementation of KPIs) mainly in Malaysian public sectors in response with Government Circular 20. With some adjustment of reformation in the public sector, the government has developed their own set of KPIs to measure the performance among agencies. In design of study, qualitative method through semi-structured interview was conducted at National Registration Department, Pulau Pinang to explore the use of KPIs in this frontline agency and how they approach this performance measurement tools as one way to measure their performance. The findings reveal that the use of KPIs has been successful in measuring their organizational and individual performance as well. Though it is hard at the initial stage to adopt this approach, but after several years of implementation, the use of KPIs contributed to several improvements in their administration and services. Nevertheless, since this study is explanatory study based on archival data, it provides limited insights into how organizational managers perceived the relevance and usefulness of key performance indicators in measuring the organizational performance. This paper suggests that the implementation of KPIs should be implemented not only in the frontline agency but to all public sectors as well and they should come out with greater standard of its KPIs. In future research, the government should provide the best indicators to measure the performance of government agencies by putting some value added in implementation of KPIs. Furthermore, third parties such as government servant and stakeholders should togetherness in performing their jobs to make sure everybody is comply with KPIs sets by its agencies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".