How cheating undermines the perceived value of justice in the workplace: The mediating effect of shame.
Bibliographic record
Abstract
of cheating. Given that cheating violates moral norms that govern social relationships, it is critical to understand how cheating can influence social dynamics in the workplace. Drawing upon appraisal theories, we argue that cheating can have damaging consequences for individuals and their social relationships by eliciting shame. In turn, shame can reduce the extent to which individuals value receiving justice-a critical facilitator of social relationships in the workplace. We test our predictions across 6 studies using different samples and methodologies. In Study 1, we find that cheating is negatively associated with the importance people place on others upholding justice for them (i.e., overall justice values). In Studies 2-6, we demonstrate that shame plays a mediating role in this relationship, even in the presence of guilt and embarrassment. In Studies 3-5, we identify organizational identification as a moderator and show that the effect of cheating on shame is stronger for those with high (vs. low) identification. Theoretical implications include the importance of identifying the outcomes of cheating for individuals within organizational contexts, understanding the functional and dysfunctional consequences of shame, recognizing the differential effects of discrete emotions, and elucidating the role of identity within the context of cheating. We conclude with practical recommendations for managing cheating behaviors and their outcomes in the workplace. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".