The influence of organizational social responsibility on involvement behavior in community sport membership associations
Bibliographic record
Abstract
Abstract Membership‐based associations are critical to their local communities and the overall social impact of the nonprofit sector. This study examines how organizational social responsibility within nonprofit membership associations influences positive member involvement behaviors, including volunteering, speaking positively about the club, and member loyalty. Self‐administered online questionnaires were completed by 735 members within seven grassroots membership associations in Ontario, Canada offering community‐based sport programs. Results show that members are somewhat aware of and felt positively about their organization's socially responsible efforts. Awareness of these efforts had a positive direct effect on the involvement behaviors of members, including intention to stay involved with their club and speaking positively about their club to others (i.e., word of mouth). Members' level of social consciousness was found to have a positive direct effect on word of mouth. Furthermore, members' positive evaluation of sport clubs' socially responsible initiatives was found to partially mediate the positive relationship between social consciousness and involvement behavior, as well as partially mediate the positive relationship between awareness of those efforts and involvement behavior. Results of this research provide grassroots membership associations with an in‐depth understanding of how their organization's efforts toward social responsibility influence member perceptions and behaviors, which may help them focus their efforts and more effectively manage their social change agenda moving forward.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".