How Affective Displays and Self-Construal Impact Consumers’ Generosity
Bibliographic record
Abstract
Nonprofit brands vary widely in their positioning to consumers, ranging from crisis and desperation to joy and optimism. The literature, however, provides limited direction for the many nonprofit organizations that seek to align their brand with positive emotions. Herein, we examine the relationship between affective displays (sad vs. happy) portrayed in charitable advertisements and consumer self-construal in shaping consumer generosity. We employ one field study (study 1) and one lab experiment (study 2), using different charitable causes (i.e., Kiva.org [study 1] and a fictitious children’s cancer charity [study 2]) and currencies (i.e., lending money [study 1] and volunteering time [study 2]). Taken together, we find that happy (sad) affective displays are most effective for consumers who hold an independent (interdependent) self-construal, and that this alignment heightens empathy and in turn increases perceptions of efficacy, which increases generosity. Implications for future research and nonprofit practice are discussed.
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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 | 0.001 | 0.005 |
| 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 | 0.000 | 0.001 |
| 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".