Every voice has its bright and dark sides: Understanding observers' reactions to coworkers' voice behaviours
Bibliographic record
Abstract
Abstract The majority of research on voice has focused on how employee voice influences voicers and targets of voice (e.g. supervisors and organizations). We advance theory on voice by examining how third‐party observers react to expressions of voice behavior by coworkers. Drawing from affective events theory (AET), we examine the potential benefits and detriments of coworker voice behaviours. Results from an experience sampling study and an experiment revealed that coworker voice was associated with an increase in third‐party observers' inspiration, prompting third‐party observers to engage in their own voice behaviours. Although coworker voice did not have a significant main effect on third‐party observers' distress, this relation was moderated by third‐party observers' zero‐sum beliefs. Specifically, daily coworker voice behaviour was more positively related to third‐party observers' distress when third‐party observers' zero‐sum beliefs were higher (vs. lower). Third‐party observers' distress, in turn, was associated with an increase in interpersonal deviance behaviours. Overall, our theorizing and model answer why, when and for whom the bright versus dark side of coworker voice is likely to occur for third‐party observers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".