Performance Management of a Government Organization: Abu Dhabi’s Experience
Bibliographic record
Abstract
The purpose of this investigation is to understand how the performance management system (PMS) was implemented and used in a government organization in Abu Dhabi and how the PMS was nestled with specific organizational rationalities and context. This case study used semi-structured interviews and documents and drew upon Ferreira and Otley’s (2009) PMS framework and the Broadbent and Laughlin’s (2009) conceptual model. The former framework was used to understand the functional characteristics and use of PMS in a specific organization and the later model facilitated the understanding of the organizational context and the underlying rationality with respect to PMS. The findings exhibit that the institutionalization of PMS is a slow learning process and needs support of the top management. Specifically, the PMS was implemented in phases spanning over a period of more than five years. There was no unified reward system specifically linked to performance in the PMS and incentives/rewards varied in different divisions. Once implemented at corporate and divisional levels, the PMS was being cascaded down to individual level, to align individual goals/objectives with organizational goals. The paper contributes to the understanding of implementation and operation of PMS within a specific context in Abu Dhabi. The PMS was guided by the context where communicative rationality was dominant and that resulted in the acceptance of PMS by most of the employees. However, the field insights suggest that the case organization needs to invest in the development of human resources to support the operation of PMS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".