Are certain types of causes more feared than others? Exploring the role of feared causes, the feared self, and fear appeals in charitable giving.
Bibliographic record
Abstract
Individuals engage in charitable giving for various reasons, such as due to the personal relevance of the cause (e.g., donating to the shelter one’s pet was adopted from), empathy toward the beneficiaries (e.g., victims of a natural disaster), and/or emotional (e.g., warm glow) or material (e.g., tax rebates) benefits, among many others. Although prior research has identified several determinants of charitable giving, gaps remain regarding why donors may contribute more money to certain charitable causes than others. The current research aims to address these gaps by examining the impact of fear as a determinant of charitable giving, and more specifically investigating whether 1) certain types of charitable causes are more (vs. less) feared than others due to 2) being more (vs. less) likely to evoke donors’ feared self, and 3) whether a cause’s inherent level of fear interacts with the use of fear (vs. neutral or hope) appeals in its marketing communications. Two pre-tests and four online experiments were conducted to test these hypotheses. The findings revealed that a more feared cause produced more favorable attitude and donation intentions compared to a less feared cause, by prompting higher levels of feared self (study 1). Further, fear (or neutral/hope) appeals did not reliably impact how donors responded to more (vs. less) feared causes (studies 2 and 3). Finally, theoretical, and managerial implications of the findings are discussed, as well as directions for future research.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".