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Enregistrement W6888596618 · doi:10.20381/ruor-28251

Opening the "Black Box": Exploring Board Decision Making in Non-Profit Sport Organizations Operating in a Multi-Level Governance System

2022· other· en· W6888596618 sur OpenAlexaboutno aff

Notice bibliographique

RevueuO Research (University of Ottawa) · 2022
Typeother
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCorporate governanceSport managementDescriptive statisticsOn boardProfessional sportDescriptive researchDecision-making models

Résumé

récupéré en direct d'OpenAlex

The purpose of this dissertation was to explore Board decision making in non-profit sport organizations operating in a multi-level governance system. Four major research objectives were addressed: (1) the way non-profit sport organization Boards make decisions, (2) the types and impacts of non-profit sport organization Boards' internal factors on their decision making, (3) the types and impacts of non-profit sport organization Boards' external factors on their decision making, and (4) the similarities and differences in non-profit sport organization Boards' decision making within and between levels of a federated sport model. Strategic decision-making theory is applied alongside internal (i.e., organization size; organization age; Board structure; Board size; leader-member exchanges; professionalization; socio-demographic indicators; motivation; and skills, expertise, and experience) and external factors (i.e., legal requirements, institutional pressures, inter-organizational relationships, market conditions, collaboration, stakeholders, and federated sport model) - originating from the Integrated Board Performance Model and relevant sport governance literature - to comprise the dissertation's theoretical framework. A multiple case study methodology was used featuring six non-profit sport organizations Boards (two national and four provincial/territorial) operating in the Canadian sport system. Data were collected longitudinally through three methods: non-participant overt observations, semi-structured interviews, and documents. Data were thematically analyzed via NVivo12, and SPSS was used for descriptive statistics and comparisons of the observed Board decisions (i.e., t-tests, ANOVA). Board decision making in non-profit sport organizations was identified as information and engagement based, which incorporated multiple sources of internal and external information, involved five members, and occurred over two meetings with some informal interactions (e.g., email discussions between Board members). Five internal factors impacted Board decision making: Board composition, Board size, Chair-Chief Executive Officer relationship, Board meeting practices and environment, and technology. The first four had a positive impact, while the latter resulted in both a positive and negative impact on Board decision making. Two external factors had a negative impact on Board decision making: the sport system structure and market conditions. Seven statistically significant differences were identified in Board decision making at the provincial/territorial level (none for national non-profit sport organizations) and 21 between levels (i.e., national versus provincial/territorial) of the federated sport model. More similarities than differences were found when comparing Board decision making within (i.e., two non-profit sport organizations at the national level, four non-profit sport organizations at the provincial/territorial level) and between (i.e., national versus provincial/territorial non-profit sport organizations) levels of a federated sport model, notably related to duration and interactions. However, differences were attributed to sources of delays, the process to acquire information, and the types of information sources used. Overall, non-profit sport organizations Boards' decision making in a federated sport model is characterized with complexities arising from internal and external factors, thereby having a positive or negative impact on duration, delays, interactions, process to acquire information, and types of information sources used to make decisions. These notions are illustrated in the developed Non-Profit Sport Organization Board Decision Making Model, which address the dissertation's overall purpose. Altogether, this dissertation offers theoretical and practical contributions. Notably, it demonstrated strategic decision-making theory's temporal and contextual boundary to investigate the chosen phenomenon at the group level (i.e., Boards) of non-profit sport organizations in a federated sport model. Further, the conceptual rigour of the applied theory is developed as novel variables (e.g., technology) to measure sub-constructs (e.g., impediments) identified in this dissertation should be incorporated to better understand decision making. Results also contribute to the broader sport governance literature as the approach undertaken in this dissertation supports the value and need for multi-method, in situ, and longitudinal research designs to better understand process-based phenomena (e.g., Board decision making). Practically, this dissertation's results develop strategies and recommendations for Boards of non-profit sport organizations. Specifically, Boards should understand virtual meetings are convenient, cost-friendly, and allow decisions to be made even when restrictions are imposed during a health crisis (e.g., travel, social). However, delays and challenges in engagement are found during virtual meetings. To engage members during decision making, Chairs have an important role to ensure a diverse set of perspectives are gathered from individual members, thereby making a better informed decision. Formalizing decision making with purposefully developed documents (e.g., Board papers) and an action registry is also vital for Boards to be transparent and accountable in their decisions made.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,007
score de la tête « metaresearch » (Gemma)0,010
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,131
Score d'incertitude au seuil0,261

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,010
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,002
Études des sciences et des technologies0,0060,008
Communication savante0,0100,004
Science ouverte0,0010,003
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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 Opus0,136
Tête enseignante GPT0,339
Écart entre enseignants0,203 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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
Publié2022
Routes d'admission1
Résumé présentoui

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