Understanding Corporate Social Irresponsibility on Sports Organizations
Bibliographic record
Abstract
For this study, we sought to understand the conceptualization of sports organizations in the CSR context from the customer perspective. Previous studies have mainly focused on the positive effect of CSR and neglected the negative effect of irresponsible behaviors. The associations on these entities are often treated discretely, whereas the interrelatedness of the associations are overlooked. This research employs the brand concept map (BCM) method to elucidate the complex and interconnected associations of how a sports organization is perceived, and presents these findings in a graphical network structure. The aggregated network of associations in people’s memory, consisting of performance, setting, and auxiliary elements, constitutes the core (brand) images and relevant patronage values of an organization. This research contributes to the understanding of sports brand on the association and network levels. This study identifies useful individual associations such as baseball, players, mascots, related products and corporations, CSR-related behaviors, and the interconnection patterns between elements. These identified factors are useful in detecting and developing patronage and in identifying incongruence in the conceptualization of brand and organization image. The interconnectivity and network structure of people’s associations are considered by the employed in this methodology in this study. This study identifies the framework of relevant associations, elements, values, and interconnection patterns, which not only provide comprehensive information in understanding complex conceptual perceptions, but might also be leveraged and focused on by marketers for an enhanced creative marketing and communication strategy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".