Giving against the Odds: When Tempting Alternatives Increase Willingness to Donate
Bibliographic record
Abstract
The authors examine how a reference to an unrelated product in the choice context affects consumers’ likelihood of donating to charity. Building on research on self-signaling, the authors predict that consumers are more likely to give when the donation appeal references a hedonic product than when a utilitarian product is referenced or when no comparison is provided. They posit that this phenomenon occurs because referencing a hedonic product during a charitable appeal changes the self-attributions, or self-signaling utility, associated with the choice to donate. A series of hypothetical and actual choice experiments demonstrate the predicted effect and show that the increase in donation rates occurs because the self-attributions signaled by a choice not to donate are more negative in the context of a hedonic reference product. Finally, consistent with these experimental findings, a field experiment shows that referencing a hedonic product during a charitable appeal increases real donation rates in a nonlaboratory setting. The authors discuss the theoretical implications for both consumer decision making and the self-signaling motives behind prosocial choice.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.008 | 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".