Moved to speak up: How prosocial emotions influence the employee voice process
Bibliographic record
Abstract
Employees often notice issues as they go about their work, but they are more likely to remain silent than to voice about those issues. This means that organizations miss out on critical opportunities for improvement. We deepen understanding of why and when employees do speak up by theorizing about voice episodes that arise when organizational issues (e.g. policies, actions) cause others to suffer. We suggest that when employees feel prosocial emotions—empathic concern, empathic anger, and/or guilt—in response to another’s suffering, they are more likely to voice about the issues creating that suffering. Specifically, we propose that these other-oriented emotions make it more likely that employees will see an opportunity for voice, feel sufficiently motivated to voice, and assess the potential benefits of speaking up as greater than the possible costs. We also posit that three contextual factors—relationship to sufferer, relational scripts, and emotional culture—influence whether (and how intensely) employees experience prosocial emotions in response to suffering triggered by an organizational issue, and thus affect the likelihood of voice. By theorizing the mechanisms through which prosocial emotions animate a specific episode of voice, we provide a foundation for understanding how employees can be moved to speak up.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".