My manager endorsed my coworkers’ voice: Understanding observers’ positive and negative reactions to managerial endorsement of coworker voice.
Bibliographic record
Abstract
Research on managerial voice endorsement has primarily focused on the processes and conditions through which voicers receive their managers' endorsement. We shift this focus away from the voicers, focusing instead on the dual reactions that endorsement generates for observing employees. Drawing from an approach-avoidance framework, we propose that managerial endorsement of coworker voice could be perceived as a positive and negative stimulus for observers, prompting them to approach opportunities and avoid threats, respectively. Results from a preregistered experiment and a multiwave, multisource field study revealed that managerial endorsement of coworker voice was positively related to observers' voice instrumentality, thus prompting them to engage in approach behaviors (i.e., voice). We also found that managerial endorsement of coworker voice was positively related to observers' voice threat, triggering avoidant behaviors (i.e., avoidance-oriented counterproductive work behaviors). Further, we found that the avoidant reactions more pronounced for observers with higher (vs. lower) neuroticism. Overall, our research extends theory by demonstrating the rippling effects that voice endorsement can ignite throughout the workgroup. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".