The Role of ESG Performance in Enhancing Intellectual Capital and Sustainability in European Football Clubs: A First Empirical Application
Bibliographic record
Abstract
This study investigates the relationship between Environmental, Social, and Governance (ESG) performance and intellectual capital (IC) in professional football clubs. Using a quali-quantitative approach, the research analyzes secondary data from ESG scores and IC metrics of 17 European football clubs. Pearson correlation coefficients are employed to assess the links between ESG performance and IC dimensions, including player market value (human capital), social media engagement (relational capital), and stadium ownership (structural capital). The findings indicate that ESG practices positively influence intellectual capital, especially when IC components are considered collectively, demonstrating a stronger positive association than individual components. This underscores the synergistic effect of integrating intangible assets into a sustainability framework. The study contributes to theoretical understanding by linking ESG practices to value-creation strategies in professional sports. Practical implications highlight how football clubs can align ESG initiatives with recruitment, fan engagement, and infrastructure investments to enhance financial and social outcomes. Policymakers can use the results to promote ESG adoption in sports, while investors may view ESG performance as a marker of long-term stability and growth. This research empirically explores ESG and IC interplay in football, offering actionable insights and a framework for future studies.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".