Identification, Priorization and Management of Professional Football Clubs’ Stakeholders
Bibliographic record
Abstract
The aim of this paper is to analyze how professional football clubs manage relationships with their most important stakeholders and their multiple expectations in an efficient framework. Our analysis is based on a case study about ‘Olympique de Marseille’ (OM) – a recognized French club in Ligue 1 – from a participant observation. The period covers by the study mainly runs from 2004 to 2015. We adopt a two-step approach consisting first in identifying and prioritizing club stakeholders and then in analyzing the management set up for those who matter most. Stakeholders play an important part in the way club is managed, especially those defined as definitive given their power, legitimacy and urgency attributes. Relationships with the latter can take different forms from involvement to control. Moreover they do not always exist through pre-defined procedures insofar as a significant part of them remains informal. This study enriches the knowledge of the environment of professional football clubs by taking their stakeholders into account. Recommendations are made to improve their stakeholder management practices by considering the specific nature of each relationship, the importance of dialogue in the relationship, the articulation of the formal and informal dimensions of the relationship, the regularity of the relationship and the flexibility of the relationship.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".