Cross-Domain Effects of Guilt on Desire for Self-Improvement Products
Bibliographic record
Abstract
This research examines the notion that guilt, the negative emotion stemming from a failure to meet a self-held standard of behavior, leads to preferences for products enabling self-improvement, even in domains unrelated to the original source of the guilt. Examining consumer responses to real products, this research shows that such effects arise because guilt—by its focus on previous wrongdoings—activates a general desire to improve the self. This increase in desire for self-improvement products is only observed for choices involving the self (not others), is not observed in response to other negative emotions (e.g., shame, embarrassment, sadness, or envy), and is mitigated when people hold the belief that the self is nonmalleable. Building on past work that focuses on how guilt often leads to the motivation to alleviate feelings of guilt either directly or indirectly, the current research demonstrates an additional, novel downstream consequence of guilt, showing that only guilt has the unique motivational consequence of activating a general desire to improve the self, which subsequently spills into other domains and spurs self-improving product choices. These findings are discussed in light of their implications for research on the distinct motivational consequences of specific emotions and on consumer well-being.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".