Brand Strength’s Influence on Volunteers’ Retention and Support Intentions
Bibliographic record
Abstract
We developed and tested a conceptual model’s influence on a sample of active volunteers’ retention intentions and on their intentions to support their organizations in other ways (e.g. donations). Approximately 200 nonprofit organizations were contacted to participate in this study by asking their volunteers to complete our online survey. This resulted in over 600 completed questionnaires. Data were analyzed using PLS-SEM techniques. We examined the influence of brand strength on six outcome variables (1-year retention intentions, 5-year retention intentions, donation intentions, bequest intentions, volunteer recruitment intentions, and word-of-mouth intentions). Brand strength’s effects on all the outcome variables were significant. The influence of brand strength on bequest and donation intentions was partially mediated through its influence on organizational transparency. Additionally, the influence of seven proposed moderators (organizational transparency, volunteer morale, organizational socialization, training program quality, organizational trust, confidence in leadership, organizational pride, and value congruence) was also tested, with mixed results.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".