Image Risk and the Decision to Remedial Voice: The Moderating Role of Moral Identity
Bibliographic record
Abstract
In this study, we examine perceptions of image risk as a determinant of targets’ remedial voicing in response to experienced interpersonal mistreatment. Drawing from the Morrison (2014) motivators-inhibitors model of antecedents and outcomes of employee voice and silence, we argue that when a voice opportunity arises, targets’ perceptions of image risk act as an inhibitor of the decision to remedial voice. We further conceptualize three individual differences factors – targets’ political skill, moral identity, and core self-evaluations – as motivators of remedial voice that serve as boundary conditions of the perceived image risk-remedial voice relationship. Specifically, we argue that high political skill, moral identity, and core self-evaluations will attenuate the negative perceived image risk-remedial voice relationship. Using time-lagged data collected over two time intervals, one month apart, from 177 employees, we demonstrate that perceived image risk is negatively related to targets’ remedial voice. We also found that high moral identity moderated the perceived image risk-remedial voice relationship such that targets with high moral identity were more likely to remedial voice under conditions of high perceived image risk. We did not find support for the moderating roles of high political skill and core self-evaluation. The study’s implications for research and practice are discussed in this paper.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".