Making performance measurement systems more effective in public sector organizations
Bibliographic record
Abstract
Purpose Performance management in public sector organizations is a growing phenomenon worldwide. Increasingly, questions are being raised as to its effectiveness in achieving the objective of improving the performance of public sector organizations. Research has shown that there seems to be questionable benefits and many barriers, challenges and problems with implementing performance management and measurement in the public sector environment. The purpose of this paper is to argue that this is due to the lack of focus on the process of managing the implementation of performance measurement. The author aims to review the relevant extant literature to support these assertions and to provide a conceptual framework that integrates these ideas. Design/methodology/approach This paper reviews the extant literature on public sector performance management and measurement and develops a conceptual framework to explain how public sector performance measurement systems can be made more effective in light of the research evidence. Findings This paper suggests that three important factors need to be considered in the effective implementation of a performance measurement system in the public sector. They are managerial discretion, a learning and evaluative organizational culture and stakeholder involvement. These three factors are discussed and its impact on performance measurement is explored. Research limitations/implications A proposed integrative framework is presented that supports the assertion of the importance of these three factors in influencing how performance measurement can lead to improved performance in public sector organizations. Some potential environmental and institutional constraints are also discussed in implementing some of the suggestions proposed. Practical implications The paper provides a model that explains three important facors that need to be considered in implementing an effective performance measurement system in public sector organizations and suggestions for how it can be implemented effectively. Originality/value The paper integrates and synthesizes the literature on public sector performance measurement into a comprehensive conceptual framework that explains more explicitly the factors that can influence the effectiveness of a performance measurement system in the public sector.
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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 | 0.161 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.024 | 0.018 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| 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".