Sports Ecosystem of the “Triad of São Paulo”: Sports Marketing Management According to Fans
Bibliographic record
Abstract
A sports ecosystem aims to guide marketers to propose, design and operate a marketing plan with the purpose of obtaining several sources of financial funding for undertaking new business strategies for the sports club. Thus, this paper aims to understand and analyse the sports ecosystem of Corinthians, Palmeiras and São Paulo sports clubs according to their fans opinion, and checking the similarities and differences among the clubs. Concerning the methodology, an exploratory study was designed comprising 79 topics using the Likert scale to be administered to 704 sports fans in 9 matches between February and March 2017. In general, the analysis procedure followed four stages: (i) calculating the parametric analysis (ANOVA) and post-hoc schefee tests; (ii) selecting process of the issues which achieved the significance <= 0,05 (5%); (iii) observing and settling on which group of fans that agreed or disagreed with other fans; (iv) standing out that topics (and axis) that are most similar and most divergent. Regarding the findings and results, Corinthians is different from Palmeiras and São Paulo in six out of seven axes; club management, stadium, and partnerships and sponsorships are the most critical dimensions; and São Paulo has the best club management axis. Therefore, just one hypothesis and a half were confirmed. Knowing the sports ecosystem axes increases the chances of designing the sport business and marketing plan suitable according to customer-fan orientation principle.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".