Past Good Deeds Can Make You Bad: Moral Credential Effect of Organisational Identification on Careerism
Bibliographic record
Abstract
ABSTRACT Drawing on moral licencing theory, this study explores how organisational identification may paradoxically lead to unethical outcomes in the workplace. We hypothesised that organisational identification is linked to negative workplace gossip, which in turn fosters careerism. Additionally, we proposed that workplace toxicity moderates both the direct relationship between organisational identification and negative workplace gossip and the indirect relationship between organisational identification and careerism via gossip. Using a multiwave single‐source research design, we collected data from 209 employees working in the service sector. Employing PROCESS macro analysis, our findings support a mediated relationship between organisational identification and careerism through negative workplace gossip. Furthermore, the effect of organisational identification on gossip was stronger in toxic work environments, and a toxic environment also amplified the indirect effect on careerism. This topic is significant because it challenges the conventional positive framing of organisational identification by highlighting its potential to contribute to self‐serving behaviours in adverse workplace conditions. Our study extends research on the dark side of organisational identification by revealing its effects on gossip and careerism and underscores the role of workplace toxicity in exacerbating these outcomes.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".