Nonprofit Sport Organization Board Members’ Engagement in Decision Making
Bibliographic record
Abstract
This study explored the engagement of nonprofit sport organization (NPSO) board members in decision making. Featuring six NPSO boards, data were collected from 36 board meeting observations, 18 interviews, and over 900 documents. Data analysis was completed quantitatively (i.e., descriptive statistics, hierarchical cluster analysis, independent sample t test) and qualitatively (i.e., codebook thematic analysis). Board members demonstrated instances of high and low engagement in decision making, with Chief Executive Officers and Chairs being the most engaged. Engagement is impacted by board members’ ability to have an alignment of priorities with their NPSO, an awareness of their responsibilities, the necessary competencies, and a feeling of trust and safety. Theoretical contributions include the operationalization of constructs and consideration for boardroom processes to empirically understand NPSO board member engagement. Practically, to promote higher levels of engagement, Chairs should embody a collective leadership style and avoid situations of groupthink or subgroups on the board.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".