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Record W3043581168 · doi:10.1037/apl0000810

The whiplash effect: The (moderating) role of attributed motives in emotional and behavioral reactions to abusive supervision.

2020· article· en· W3043581168 on OpenAlexfundno aff
Lingtao Yu, Michelle K. Duffy

Bibliographic record

VenueJournal of Applied Psychology · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsnot available
FundersUniversity of British ColumbiaUniversity of Minnesota
KeywordsAbusive supervisionPsychologySocial psychologyOrganizational citizenship behaviorAttributionHarmAngerPsycINFOModerated mediationAggressionOrganizational commitment

Abstract

fetched live from OpenAlex

-the notion that subordinates may display different emotional and behavioral reactions to supervisory abuse depending on their attributions for abuse. We conduct 3 studies to examine this effect at both the between- and within person level. Results from a multisource, time-lagged field study (between-person) and a laboratory-based experiment (between-person) indicate that when subordinates believe that the abusive supervisor is motivated by desires to cause harm (i.e., injury initiation attribution is higher), abusive supervision is more likely to engender anger, which, in turn, elicits more deviant behaviors and fewer organizational citizenship behaviors; however, when subordinates believe the abusive supervisor is motivated by desires to improve performance (i.e., performance promotion attribution is higher), abusive supervision is more likely to evoke guilt, which, in turn, elicits fewer deviant behaviors and more organizational citizenship behaviors. These results were then expanded in an experience sampling study (within-person), which allowed us to further examine how general interpretations of supervisors' motives behind abusive supervision shape employees' momentary emotional and behavioral responses toward daily abusive supervisor behavior. Theoretical and practical implications are discussed. (PsycInfo Database Record (c) 2021 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score0.234

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.018
GPT teacher head0.285
Teacher spread0.267 · 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 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

Citations114
Published2020
Admission routes1
Has abstractyes

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