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Record W3098167878 · doi:10.1108/ijoa-03-2020-2094

When workplace bullying spreads workplace deviance through anger and neuroticism

2020· article· en· W3098167878 on OpenAlexaff
Sadia Jahanzeb, Tasneem Fatima, Dirk De Clercq

Bibliographic record

VenueInternational journal of organizational analysis · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsBrock University
Fundersnot available
KeywordsAngerPsychologyNeuroticismDeviance (statistics)Social psychologyInterpersonal communicationWorkplace bullyingFeelingOriginalityPersonality

Abstract

fetched live from OpenAlex

Purpose With a basis in affective events theory, this study aims to investigate the mediating role of anger in the relationship between employees’ exposure to workplace bullying and their engagement in deviant behaviours, as well as the invigorating role of their neuroticism in this process. Design/methodology/approach Three-wave, time-lagged data were collected from employees and their peers in a sample of Pakistani organizations. Findings Workplace bullying spurs interpersonal and organizational deviance because it prompts feelings of anger in employees. This mechanism is more prominent among employees with high levels of neuroticism. Originality/value This study reveals that the experience of anger is a key feature by which bullying behaviours steer employees towards counterproductive work behaviours, and this harmful process is more likely to escalate when employees’ personality makes them more vulnerable to emotional distress.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.294
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations41
Published2020
Admission routes1
Has abstractyes

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