From gender policies and practices to organisational performance of sport governing bodies
Bibliographic record
Abstract
Research question Gender policies reflect identity-conscious human resource management structures that aim to reduce unfair treatment based on gender. Drawing on signalling theory and the categorisation-elaboration model, the literature suggests that the relationship between gender policies and organisational performance is moderated by several factors (gender practices, organisational culture, decision-making quality), which have not yet been empirically studied. The purpose of this study is to investigate the presence of gender policies in sport governing bodies (SGBs) and examine their relationship with organisational performance.Research methods An online questionnaire was sent to representatives of German SGBs (n = 202). Structural equation modelling was used to examine the relationships between gender policies, gender practices (i.e. board gender diversity), organisational culture, decision-making quality, and organisational performance.Results and findings Gender policies are rarely present in German SGBs, and can only shape the organisational culture when they are connected to daily routines (in contrast to the simple presence of a written statement). Gender policies and practices are positively associated with decision-making quality and organisational performance.Implications The findings contribute to the literature by shedding light on the theoretical mechanisms (i.e. organisational culture, decision-making quality) through which group diversity affects organisational performance. Sport managers should connect gender policies to the daily work instead of simply including them in good governance guidelines, especially because they also benefit the organisation in terms of better performance. Politicians should consider mandatory regulations to make gender policies more effective.
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 | 0.007 | 0.021 |
| 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 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".