Giving sense and changing perceptions in the implementation of the performance management system in public sector organisations in developing countries
Bibliographic record
Abstract
Purpose Change in public organisations has become inevitable in modern times. Yet, implementing change continues to be problematic, especially the attempt to introduce performance management (PM) in the sector. The purpose of this paper is to examine how HR managers are using sensegiving processes to attempt to institutionalise PM in public organisations in Ghana PM in public organisations in Ghana. Design/methodology/approach The paper utilises the mixed methods approach to examine the process of sensegiving. In using this method, the authors used focus group, as well as individual interview techniques and a quantitative survey of some selected organisations in the public sector. Findings The results of the study show that, four main activities, i.e. workshops, seminars and training, one-on-one communication, and unit meetings are employed in the process. The analysis indicates that these activities have become quite effective in the quest to change perceptions about PM in the sector. Research limitations/implications The research was limited to a few organisations. Hence, it will be necessary to expand it, if possible to the entire public sector to see if the same results will be obtained. Practical implications It shows that reformers must be cognisant of the views of employees in developing and implementing reforms that focus on changing both individual orientations and organisational and culture. Originality/value This is the first time such a study has been done in Ghana. Furthermore, studies on PM institutionalisation and implementation have either been qualitative or quantitative in nature. Studies using the mixed methods approach are rare, with those we know coming mostly from the Western World. Thus, this paper is one of the few to examine this issue using the mixed methods approach and more so from a developing country’s perspective.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".