Strategies to Recruit and Retain Technologically Competent Volunteers in Nonprofit Organizations
Bibliographic record
Abstract
Nonprofit organizations rely on volunteers as a part of their labor force. However, volunteer recruitment and retention are an ongoing challenge and concern and are potentially costly to a nonprofit organization. Grounded in the ability-motivation-framework and Herzberg’s two-factor theory, the purpose of this qualitative multiple case study was to explore the strategies that nonprofit leaders used to recruit and retain technologically competent volunteers. Three nonprofit leaders from different nonprofit organizations in Toronto, Ontario, Canada, participated in the study. Data were collected using virtual semistructured interviews and publicly accessible information. Data were analyzed using thematic analysis, and four themes emerged: (a) build volunteer relationships, (d) maintain motivated and engaged volunteers, (c) provide ongoing training to volunteers, (d) and understand an individual’s reasons for volunteering. A key recommendation for nonprofit leaders is to provide volunteer engagement opportunities to promote a supportive and positive nonprofit organizational culture. The implications for positive social change include the potential to improve the meaningfulness of volunteering and the well-being of volunteers, positively improve services to clients in crisis, and improve the overall health of the community they serve.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".