How Much Are Fans Willing to Pay to Help “Their” Soccer Clubs to Overcome a Crisis? An Analysis of Central European Fans during the COVID-19 Pandemic
Bibliographic record
Abstract
Through restrictions and people’s behavioral changes with regard to public events, the COVID-19 pandemic has had a massive financial impact on professional team sports clubs. Particularly, many smaller clubs that are more dependent on match-day revenues were facing an existential struggle. In this study, we examined the willingness of fans to contribute financially to help their favorite teams to overcome financial difficulties caused by this unforeseen operational risk. Moreover, we investigated the significance of the level of team identification among fans as an antecedent for willingness to pay. Analyzing the data from an online survey with 178 respondents, we found that fans would be willing to participate in fundraising campaigns to support their favorite teams. Among the fans of small clubs, the level of identification drives the willingness to support. On the one hand, the findings are encouraging for clubs as they underscore the potential role fans could play in overcoming the current crisis while showing that including fans in future risk management strategies is a promising approach. On the other hand, for this to be successful, clubs need to unravel and invest in measures for nurturing the fans’ identification with the team.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".