Good and Guilt-Free: The Role of Self-Accountability in Influencing Preferences for Products with Ethical Attributes
Bibliographic record
Abstract
The market share of brands positioned using ethical attributes typically lags behind brands that promote attributes related to product performance. Across four studies, the authors show that situational factors that heighten consumers' self-accountability (i.e., activation of their desire to live up to their self-standards) lead to increased preferences for products promoted through their ethical attributes. They investigate their predictions regarding self-accountability in multiple ways, including examining the moderating roles of awareness of the discrepancy between a person's internal standards and actual behavior, self-accountability priming, and the presence of others in the decision context. Furthermore, they demonstrate that the subtle activation of self-accountability leads to more positive reactions to ethical appeals than explicit guilt appeals. Finally, they show that preference for a product promoted through ethical appeals is driven by the desire to avoid anticipated guilt, beyond the effects of impression management. Taken together, the results suggest that marketers positioning products through ethical attributes should subtly activate consumer self-accountability rather than using more explicit guilt appeals.
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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 | 0.018 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".