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Record W2107079116 · doi:10.1177/0018726713482992

To act out, to withdraw, or to constructively resist? Employee reactions to supervisor abuse of customers and the moderating role of employee moral identity

2013· article· en· W2107079116 on OpenAlexaff
Rebecca L. Greenbaum, Mary B. Mawritz, David M. Mayer, Manuela Priesemuth

Bibliographic record

VenueHuman Relations · 2013
Typearticle
Languageen
FieldDecision Sciences
TopicEthics in Business and Education
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsSocial psychologyDeviance (statistics)PsychologyDeontic logicConstructiveOrganizational justiceResistance (ecology)Organizational commitmentEpistemology

Abstract

fetched live from OpenAlex

We extend the deontic model of justice (Folger, 1998, 2001) by arguing that not all employees respond to third-party injustices by experiencing an eye-for-an-eye retributive response; rather, some employees respond in ways that are higher in moral acceptance (e.g. increasing turnover intentions, engaging in constructive resistance). We predict that the positive relationship between supervisor abuse of customers and organizational deviance is weaker when employees are high in moral identity. In contrast, we hypothesize that the relationships between supervisor abuse of customers and turnover intentions and constructive resistance are more strongly positive when employees are high in moral identity. Regression results from two field studies ( N = 222 and N = 199, respectively) provide general support for our theoretical model.

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)0.005
metaresearch head score (Gemma)0.020
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 score0.005
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.154
GPT teacher head0.399
Teacher spread0.245 · 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

Citations137
Published2013
Admission routes1
Has abstractyes

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