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Record 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 on OpenAlexaboutno aff

Bibliographic record

VenueuO Research (University of Ottawa) · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceSport managementDescriptive statisticsOn boardProfessional sportDescriptive researchDecision-making models

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.261

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0060.008
Scholarly communication0.0100.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.136
GPT teacher head0.339
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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