The Case for Quality: Development and Validation of the Voice Quality Construct
Bibliographic record
Abstract
Research on employee voice - discretionary and challenging communication intended to improve organizational conditions - suggests that voice may result in either positive or negative employee outcomes for the voicer, even though it is constructively intended. As voice is commonly measured based on how often employees speak out, we propose that research will benefit from a deeper understanding of voice by accounting for the quality of the message being expressed. As such, we propose the construct of voice quality, defined as perceptions of the expected utility that voice provides based on message content. First, we followed a multi-study construct development procedure through which we validated a 20-item voice quality scale. Results from these studies provide support that voice quality is a valid and reliable higher-order construct comprised of five dimensions: rationale, feasibility, organizational-focus, ownership, and novelty. Second, we assessed the predictive validity of voice quality using separate archival and survey-based studies. Results from our predictive validity studies suggest that voice quality is significantly associated with valued employee and organizational level outcomes, including coworker evaluations of voice, managerial consideration of voice, and managerial ratings of employee promotability and performance.
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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 | 0.135 | 0.235 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".