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Enregistrement W7024573695

Strategic Risk Management in the Municipal and Public Sector: An Exploration of Critical Success Factors and Barriers to Strategic Risk Management within the Province of Newfoundland and Labrador
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2010· report· en· W7024573695 sur OpenAlexaboutno aff

Notice bibliographique

RevueMemorial University Research Repository (Memorial University) · 2010
Typereport
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueMachine Learning in Bioinformatics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRisk managementPublic sectorEnterprise risk managementGovernment (linguistics)Critical success factorStrategic planningPublic policyLocal government
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

As more organizations are focusing on systemic and controllable business risks, as well as Enterprise Risk Management (ERM), a question emerges as to how this fits within a public sector organization managing its strategic objectives. Moreover, how does a public sector organization manage ‘strategic risks’? It is unclear from the literature exactly what constitutes strategic risk or how it is managed by organizations. This prompts the need for a common understanding of strategic risk within the domain of the public sector. 
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\nFrom a regional policy development perspective, public sector organizations in the province of Newfoundland and Labrador will be facing substantial risks in the coming years in how to best allocate resources emerging from natural resources royalties as well as understanding the twin factors of a declining birth rate and out-migration. Should resources be invested in municipal governments that cannot be economically and administratively sustainable? What are the risks in not managing the potential ‘windfall’ from oil and gas revenue? Moreover, what should the role of public sector managers at both the municipal and provincial levels be in addressing these risk questions? 
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\nUsing content analysis and semi-structured interviews, we use a mixed methodology approach to explore the question of barriers and key success factors of strategic risk management within municipal governments and the public sector. Specifically our research collates information on public sector risk management while addressing the practice of public sector strategic risk management through a series of semi-structured interviews and content analysis of municipal plans. Finally, we explored the potential barriers and key success factors to addressing strategic risk management in the province of Newfoundland and Labrador. 
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\nFindings included a wide range of risks being identified in the municipal plans within the province`s municipal plans. It is noted that there were some specific barriers to municipal strategic risk management including the state of municipal planning documents, lack of clear implementation plans and an apparent non-identification of flood risk. 
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\nTwo major themes emerged, the importance of risk culture and methodologies as well as risk management processes and models. We believe these two major themes have significant implications on public sector risk management as well as for researchers in this area. We conclude that this is an area that needs further research including understanding how risk management varies across different public sector activities. We found that risk decisions and organizational cultures are complex processes and systems. The need for a common methodology and approach to address risk management at all levels of the public sector is noted. Other recommendations emerging from the research include better risk identification, the use of third parties in assisting the development of municipal plans and dealing with specific risks that are relevant to the province of Newfoundland and Labrador including flood and economic risks. 
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\nExamining public sector risk management is an opportunity for researchers as well as the university. Better understanding and management of strategic risk can lead to more effective strategy development and planning ultimately leading to the betterment of communities and the province.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,705
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,044
Tête enseignante GPT0,301
Écart entre enseignants0,257 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2010
Routes d'admission1
Résumé présentoui

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