A study of incentives in charitable fundraising
Bibliographic record
Abstract
Purpose The purpose of this study is to determine how self-driven (intrinsic motivators) and monetary incentives (extrinsic motivators) are mediated by an effort to affect fundraising outcomes. This integration sheds light on crowding out between the two types of incentives as well the drivers of fundraising outcomes, specifically effort and donations. Design/methodology/approach A field experiment is conducted over a two-month period, involving an online fundraising campaign with over 300 volunteers assigned to one of five different incentive conditions. A special website was created to monitor fundraiser efforts. Fundraisers filled out pre- and post-study surveys. Findings While high monetary incentives result in the greatest immediate increase in funds raised, they crowd out future intentions to volunteer once incentives are withdrawn. Mediation analyzes show that fundraiser effort fully mediates the effect of intrinsic motivators and partially mediates the direct effect of extrinsic motivators on funds raised. Research limitations/implications A major limitation of field experiments is the lack of control, resulting in higher variation. However, while a more controlled experiment will reduce this variation, this goes at the expense of lower external validity. Practical implications Results indicate that – at least in the short run – monetary incentives can result in higher fundraising outcomes. However, this goes at the expense of a reduction in future volunteering once the incentives are withdrawn. Originality/value This study examines whether extrinsic or intrinsic motivators have a greater impact on funds raised and whether extrinsic motivators crowd out future intentions to volunteer. Different from previous research in which effort is a latent variable, the effort is directly observed over time.
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 | 0.016 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".