Can Induced Gratitude Improve Creative Performance on Repurposing Tasks?
Bibliographic record
Abstract
Abstract Urgent societal problems, including climate change, require innovation and can benefit from interdisciplinary solutions. A small body of research has demonstrated the potential of positive emotions (e.g., gratitude, awe) to promote creativity and prosocial behavior, which may help address these problems. This study integrates, for the first time, psychology research on a positive prosocial emotion (i.e., gratitude) with engineering-design creativity research. In a preregistered study design, engineering students and working engineers (pilot N = 49; full study N = 329) completed gratitude, positive-emotion-control, or neutral-control inductions. Design creativity was assessed through rated scores of responses to an Alternate Uses Task (AUT) and a Wind-Turbine-Blade Repurposing Task (WRT). No significant differences among AUT scores emerged across conditions in either sample. As only the pilot-study manipulation of gratitude was successful, recommendations are provided for further studies on the effect of gratitude on engineering-design creativity. The reported work may also inform other strategies to incorporate prosocial emotion to help engineers arrive at more original and effective concepts to tackle environmental sustainability, and in the future, other problems facing society.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".