Bibliographic record
Abstract
The efficient use of public money is a concern of all society. The more efficient the government machinery, the smaller the portion of funds raised assigned to operating costs and more invested in health, education, security, and transport, for instance. Shared service centres (SSCs) have contributed to this, allowing billions of dollars cost cut in the public service in several countries. However, cases of SSCs failures are causing billion-dollar losses, and it is necessary to understand and overcome the causes of failures. This scenery has motivated me to study the factors that contribute to the performance of SSCs and to explore whether there are SSCs models in the public sector that are simply copies of SSCs models of the private sector (without the necessary adaptations). Three objectives were established for the research: to analyse the factors that could influence the performance of SSC; to investigate to which extent there are significant differences between private and public SSCs; and to analyse if there are evidence of copy problems. Also, a principal research question: to what extent does factors such as culture, leadership, resources and readiness for change, influence on service excellence, market orientation and performance of Shared Services Centres? An action research design was defined with a mixed, quantitative and qualitative approach. The quantitative approach refers to a conceptual model with seven constructs (culture, leadership, resources, readiness for change, service excellence, market orientation, and performance), individually validated by previous studies. This proposed model was validated empirically through a survey with 146 SSCs respondents from countries like the USA, the UK, Canada, and Brazil, and the research hypotheses were confirmed. On the qualitative approach, were applied open-ended questions submitted later to content analysis, and the quantitative and qualitative results were discussed with an Action Learning Set ii composed of SSC managers and public-sector experts. The main findings were the confirmation of the proposed model variables' relationship, influencing the SSC performance. This allows managers to establish actions to improve the similar dimensions of their SSC, improving the overall performance. It was also confirmed the existence of significant differences in the context of public SSCs operation regarding the private. These findings were also discussed in the Action Learning Set and resulted in eight measures proposed for the best adaptation of public SSC models to the reality of the public sector. For further research, I suggest investigating whether the SSCs of the public sector have in fact the minimum requirements to be classified as SSCs or are just departments that centralised services from other areas and were named SSC for convenience. Another opportunity for research is to verify to what extent the New Public Management has been successful in encouraging the adoption of SSCs, e.g. in countries like the UK and the US, so that they were more oriented to their clients, as this research found evidence there are public SSCs not oriented to their customers.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".