Self-Benefit versus Other-Benefit Marketing Appeals: Their Effectiveness in Generating Charitable Support
Bibliographic record
Abstract
Despite the growing need, nonprofit organization marketers have not yet fully delineated the most effective ways to position charitable appeals. Across five experiments, the authors test the prediction that other-benefit (self-benefit) appeals generate more favorable donation support than self-benefit (other-benefit) appeals in situations that heighten (versus minimize) public self-image concerns. Public accountability, a manipulation of public self-awareness, and individual differences in public self-consciousness all moderate the effect of appeal type on donor support. In particular, self-benefit appeals are more effective when consumers’ responses are private in nature; in contrast, other-benefit appeals are more effective when consumers are publicly accountable for their responses. This effect is moderated by norm salience and is related to a desire to manage impressions by behaving in a manner consistent with normative expectations. The results have important managerial implications, suggesting that rather than simply relying on one type of marketing appeal across situations, marketers should tailor their marketing message to the situation or differentially activate public self-image concerns to match the appeal type.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".