Initial evidence on whether use of professional fundraising services increases fundraising effectiveness
Bibliographic record
Abstract
Abstract We provide initial evidence on whether use of professional fundraising services by US nonprofit organizations (NPOs) increases the effectiveness of NPOs' fundraising efforts. To a well‐tested model of organization‐level donations, we add an interaction term that captures the impact that professional fundraising fees an NPO incurs has on the effectiveness of an NPOs' spending on fundraising in raising donations. We find that professional fundraising fees has a significant positive impact on the effectiveness of fundraising efforts in raising donations for NPOs in the full, education, and health samples, but no impact for NPOs in the arts and human services samples. For NPOs in the full sample and NPOs in the education sample, one quarter of the effect of fundraising on donations stems from the positive impact of professional fundraising services on the effectiveness of fundraising in raising donations. For NPOs in the health sample, one half of the effect of fundraising on donations stems from the positive impact of professional fundraising services on the effectiveness of fundraising in raising donations. These results suggest that professional fundraising services significantly enhance the effectiveness of fundraising for these types of NPOs. While the results of this study seem to confirm the decisions of managers of education and health NPOs to utilize professional fundraising services, the results also suggest that managers of arts and human services NPOs may want to reconsider using professional fundraising services, at least the types of services they currently purchase and the way they currently utilize such services. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.020 | 0.129 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".