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Affective Experiences Linking Abusive Supervision to Voluntary Work Behavior

2014· article· en· W2322202971 on OpenAlexaff
Ann C. Peng, John Schaubroeck

Bibliographic record

VenueAcademy of Management Proceedings · 2014
Typearticle
Languageen
FieldNursing
TopicHealthcare Education and Workforce Issues
Canadian institutionsWestern University
Fundersnot available
KeywordsAbusive supervisionShamePsychologyAngerSocial psychologyAttributionAbusive relationshipTurnoverPoison controlHuman factors and ergonomicsDomestic violenceManagement

Abstract

fetched live from OpenAlex

This study examines the interactive effect of abusive supervision directed toward oneself (own abusive supervision) and toward work unit peers (coworker abusive supervision) on employees’ voice and counterproductive behavior through discrete affective states. Based on attribution theory, we propose that own and coworker abusive supervision interact in distinct ways in influencing employees’ shame, anger, and fear at work. We tested the hypotheses using a sample of 216 full-time workers from 49 work units. We found that own abusive supervision was more strongly associated with experience of shame when coworker abusive supervision was low (vs. high). In contrast, personal abuse by the leader had a stronger positive association with fear when coworker abusive supervision was high. In addition, whereas shame was negatively related to voice behavior, anger was positively related to counterproductive work behavior (CWB). We discuss the study’s implications for theory development concerning abusive supervision and for future research opportunities.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.701
Threshold uncertainty score0.781

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.329
Teacher spread0.307 · 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 designOther design
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

Citations0
Published2014
Admission routes1
Has abstractyes

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