Celebrity Endorsement for Nonprofit Organizations: The Role of Experience-based Fit between Celebrity and Cause
Bibliographic record
Abstract
Although using celebrities to raise awareness and funds for social causes is a popular technique these days, little research has offered a theoretical explanation for the effects of a celebrity’s personal values on socially oriented communications. This paper, therefore, aims to investigate the role of celebrity experience with a cause, as well as the celebrity endorser’s association with the not-for-profit organizations, in determining the effectiveness of the celebrity’s endorsement of the cause. Results reveal that a celebrity’s personal experience with the endorsed cause positively influences consumers’ perceived congruence between the celebrity and the cause, attributions of the celebrity altruistic motives for the endorsement, perceptions of the celebrity credibility, and attitudes toward the celebrity and the nonprofit organization. Additionally, a celebrity associated with an organization as a founder compared to a spokesperson appears to yield more favorable perceptions of celebrity credibility and attitudes toward the celebrity and the organization. Finally, interesting interaction effects between the celebrity-cause fit and the celebrity’s association with the nonprofit organization emerged. Findings of the present study provide insights into the potential benefits and liabilities of using a celebrity to promote a social cause in the nonprofit sector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".