To speak up effectively or often? The effects of voice quality and voice frequency on peers' and managers' evaluations
Bibliographic record
Abstract
Summary Prior research connecting employee voice with better career outcomes has almost exclusively focused on how frequently employees speak up. In the current research, we shift the focus to voice quality —recipients' perceptions of the value of an employee's voice communications, as inferred by message characteristics (i.e., rationale, feasibility, organizational‐focus, and novelty). Grounded within social exchange theory, we argue that peers and managers develop more positive evaluations (i.e., higher performance and promotion ratings) of employees who express higher‐quality voice, above and beyond how frequently they speak up, because voice quality better demonstrates employees' capability, commitment, and helpful intentions, which obligates the reciprocation of rewards. We further assert that voice frequency moderates these effects, such that high‐quality voicers are evaluated more positively, and low‐quality voicers are evaluated more negatively, as voice frequency increases. After conducting four studies through which we developed and validated a superordinate measure of voice quality, we conducted time‐lagged surveys with peers and managers to assess these hypotheses. Results fully supported our predictions for the direct benefits of voice quality on voicers' outcomes, above and beyond voice frequency; yet, the hypothesized interaction only emerged for peer‐rated 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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".