Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».