Moved to speak up: How prosocial emotions influence the employee voice process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 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 | 0.001 |
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 it