The Effects of Up-regulated Happiness on Othersâ Prosocial Behavior: The Role of Cultural Thinking Style
Bibliographic record
Abstract
Past research has suggested that up-regulating displays of happiness leads to positive social outcomes. In my dissertation research, I suggest that whether the up-regulation of displays of happiness leads to positive outcomes depends on observers’ culture. I developed and tested a model of how one aspect of observers’ culture, analytic-holistic thinking, influences the relation between expressers’ displays of happiness and observers’ prosocial behavior. In two studies, I examined how observers’ analytical-holistic thinking moderates the effect of expressers’ inauthentic displays of happiness (regulated through surface acting) and expressers’ authentic displays of happiness (regulated through deep acting) on observers’ prosocial behavior and trust inferences. In a laboratory experiment (Study 1), observers’ analytical-holistic thinking moderated the effect of project leaders’ inauthentic displays of happiness (vs. neutral emotional displays) on observers’ trust in the expresser, but not on observers’ prosocial behavior (time taken to edit a cover letter). In a field fundraising experiment (Study 2), observers’ analytical-holistic thinking moderated the effect of fundraisers’ inauthentic displays of happiness (vs. authentic displays of happiness) on observers’ trust in fundraisers, intentions to volunteer for the charity organization, and monetary donations to the charity organization. Further, observers’ trust in fundraisers mediated the moderating effect of observers’ analytical-holistic thinking on the relation between fundraisers’ inauthentic displays of happiness and observers’ intention to volunteer. Contributions to the literature on the social effects of emotions and to the literature on fundraising and helping as well as practical implications 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".