Regulatory Compliance Management in the Professional Sport Industry: Evidence from the Italian Serie A
Bibliographic record
Abstract
The football industry has grown consistently in the last three decades and now is capable to generate revenues for approximately 18.5 billions euros per year. Despite this growth, football teams failed to translate this opportunity into profits and financial sustainability, thus incurring in substantial losses. For this reason the Union of European Football Associations (UEFA) has issued a regulation to induce a change this behavior, reducing debt, decreasing employees costs and reaching the break event point. However, if we use the regulatory compliance management theory to analyse and predict the extent to which sports teams will comply with UEFA's financial regulations, we find that there are several reasons to believe that such compliance will not be achieved. Gathering data from Aida - Bureau van Dijk – we have investigated Italian teams compliance, comparing the economic results achieved before and after the introduction of the Financial Fair Play regulation in a nine-year period of observation. Result show that there are no significant differences in firms’ performance, thus our hypothesis has been confirmed. Furthermore, we have investigated if any remarkable change has been produced in terms of competition in the Italian major football league. Consistently with our hypothesis, results confirm that an unwilling process of concentration, in terms of on the field results, is taking place.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".