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Record W4391230604 · doi:10.1037/apl0001180

My manager endorsed my coworkers’ voice: Understanding observers’ positive and negative reactions to managerial endorsement of coworker voice.

2024· article· en· W4391230604 on OpenAlexaff
E. C. Poulton, Szu‐Han Lin, Shereen Fatimah, Cony M. Ho, D. Lance Ferris, Russell E. Johnson

Bibliographic record

VenueJournal of Applied Psychology · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversity of Ottawa
FundersUniversity of Georgia
KeywordsPsychologyEmployee voiceSocial psychology

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.671
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.032
GPT teacher head0.291
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2024
Admission routes1
Has abstractyes

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