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Record W2411535302 · doi:10.1037/apl0000034

Extending the multifoci perspective: The role of supervisor justice and moral identity in the relationship between customer justice and customer-directed sabotage.

2015· article· en· W2411535302 on OpenAlexaff
Daniel P. Skarlicki, Danielle D. van Jaarsveld, Ruodan Shao, Young Ho Song, Mo Wang

Bibliographic record

VenueJournal of Applied Psychology · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicEthics in Business and Education
Canadian institutionsUniversity of ManitobaMcGill UniversityUniversity of British Columbia
Fundersnot available
KeywordsEconomic JusticePsychologyPerspective (graphical)Identity (music)Procedural justiceSocial psychologySample (material)Organizational justiceEmpirical researchService (business)Interactional justiceMarketingOrganizational commitmentBusinessPolitical scienceLaw

Abstract

fetched live from OpenAlex

The multifoci perspective of justice proposes that individuals tend to target their (in)justice reactions toward the perceived source of the mistreatment. Empirical support for target-specific reactions, however, has been mixed. To explore theoretically relevant reasons for these discrepant results and address unanswered questions in the multifoci justice literature, the present research examines how different justice sources might interactively predict target-specific reactions, and whether these effects occur as a function of moral identity. Results from a sample of North American frontline service employees (N = 314, Study 1) showed that among employees with lower levels of moral identity, low supervisor justice exacerbated the association between low customer justice and customer-directed sabotage, whereas this exacerbation effect was not observed among employees with higher levels of moral identity. This 3-way interaction effect was replicated in a sample of South Korean employees (N = 265, Study 2).

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)0.011
metaresearch head score (Gemma)0.006
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 score0.225
Threshold uncertainty score0.661

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.294
GPT teacher head0.492
Teacher spread0.198 · 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

Citations115
Published2015
Admission routes1
Has abstractyes

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