Aligning governance, brand governance and social media strategies for improved organizational performance: a qualitative comparative analysis of national sport organizations
Bibliographic record
Abstract
Purpose This paper aims to explore the potential configurations of governance, brand governance and social media strategies leading to effective organizational performance. Design/methodology/approach A fuzzy-set Qualitative Comparative Analysis including 28 Canadian national sport organizations (NSOs) and six conditions highlighted two sufficient configurations for effective organizational performance, defined as either budget per capita or athlete numbers. Findings Although no single component of governance, brand governance, or social media strategy is necessary to succeed overall, brand reputation and the strategic use of social media to communicate NSO identity were common to both identified configurations. Accountability was important for effective organizational performance in terms of budget per capita, while transparency was more important for higher athlete numbers. Thus, condition specificity is paramount in non-profit organizations that often have multiple objectives. Originality/value This study provides substantial theoretical and managerial implications, including the need to integrate brand governance and social media in non-profit organizations' overall governance activities.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".