Guilt by Design: Structuring Organizations to Elicit Guilt as an Affective Reaction to Failure
Bibliographic record
Abstract
In this article, we outline a model of how organizations can effectively shape employees’ affective reactions to failure. We do not suggest that organizations eliminate the experience of negative affect following performance failures—instead, we propose that they encourage a more constructive form of negative affect (guilt) instead of a destructive one (shame). We argue that guilt responses prompt employees to take corrective action in response to mistakes, whereas shame responses are likely to elicit more detrimental effects of negative affect. Furthermore, we suggest that organizations can play a role in influencing employees’ discrete emotional reactions to the benefit of both employees and the organization. We describe the necessary antecedents for encouraging guilt responses without simultaneously eliciting shame. In essence, employees are more likely to experience guilt (but not shame) if they feel they had control over a specific negative event and the event resulted in a negative outcome for others. Given these necessary preconditions, we identify a set of organizational characteristics—autonomy, specificity of performance feedback, and outcome interdependence—that can be modified to make the experience of guilt more likely than that of shame in the workplace. The ethical and practical limits of shaping employees’ emotional experiences within a negative affective domain are also addressed.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".