Competitive vs coopetitive strategies: lessons from professional sport leagues
Bibliographic record
Abstract
Purpose Over the past decades, analyses of the functioning of professional sport leagues have been done from various angles: economic, financial and sociological; in some cases, comparisons were made between North-American and European leagues. The purpose of this paper is to look at this reality from a different angle, i.e. human capital management, by showing how different the models from both continents are. Design/methodology/approach Based on an identification of the major elements associated to human capital management in professional sport leagues in North America and Europe, this paper compares competitive and coopetitive strategies using an original framework based on consortium sourcing and pooling dimensions. Findings The paper underlines the benefits that North-American professional sport leagues get from acquiring players using a consortium sourcing perspective (coopetition). In Europe, the most powerful clubs use their financial resources to get the best players; as a result, it is always the same clubs with get the best results (competition). In the long run, the European approach might result in less attractiveness to TV viewers, and less revenues for TV networks. Originality/value This paper helps to understand the differences between professional sport leagues in North America and Europe; it also discusses the risk associated to the adoption, without any adjustment in the human capital management, in Europe of the North-American model based on a coopetitive perspective. This dimension is seldom mentioned in articles dealing with professional sport leagues.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".