How sport management can address sustainability: Creating and testing a scale
Bibliographic record
Abstract
Sports and sustainability are two important aspects of modern society that are sometimes seen as conflicting (e.g., the large amounts of resource use and wastes for mega events like the Olympics can be problematic even though the event is popular with many). The relationship between the two is complex and multifaceted. In this study, best practices that sport management can apply to address sustainability are identified and assessed. In particular, we seek practical solutions for sustainability in the sports sector and present them to sports managers. The study involves three main steps: 1) a comprehensive review of previous studies and opinions of experts to identify initial relevant variables (25 variables); 2) application of factor analysis (FA) by the exploratory factor analysis test in SPSS 22.0 to create a new scale, after which five factors with 20 items remained; and 3) performance of confirmatory factor analysis using smart-PLS on the data. Eventually, in the revised CFA test and after elimination of one variable, the model was approved. The results reveal five key sport management actions and practices to address sustainability: implementing sustainable practices, educating stakeholders, developing policies, monitoring and reporting, and research and innovation. By addressing the environmental impact of sports events and promoting sustainable practices within the sports industry, a more sustainable future for both sports and the planet can be attained.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".