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Record W2146985462 · doi:10.1002/job.494

Meta‐analytic tests of relationships between organizational justice and citizenship behavior: testing agent‐system and shared‐variance models

2007· article· en· W2146985462 on OpenAlexafffund
Neil E. Fassina, David A. Jones, Krista L. Uggerslev

Bibliographic record

VenueJournal of Organizational Behavior · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsOrganizational citizenship behaviorPsychologyVariance (accounting)Social psychologyOrganizational justiceCitizenshipEconomic JusticeOrganizational behaviorOrganizational commitmentLawPolitical science

Abstract

fetched live from OpenAlex

Abstract Research on the unique effects of different types of perceived fairness on citizenship behavior that benefits individuals (organizational citizenship behavior (OCB‐I) and organizations (OCB‐O) has produced mixed results. We assert that how OCB‐O and OCB‐I are conceptualized affects the patterns of results, and we hypothesize that, when OCB is conceptualized appropriately, an agent‐system model is supported (interactional and procedural justice are the strongest unique predictors of OCB‐I and OCB‐O, respectively). We also hypothesize that shared variance among the justice types explains additional variance in OCB. Analyses of semi‐ partial correlations conducted on meta‐analytic coefficients supported our hypotheses. Theoretical and practical implications are discussed. Copyright © 2007 John Wiley & Sons, Ltd.

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)0.136
metaresearch head score (Gemma)0.265
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.721

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1360.265
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0070.032
Bibliometrics0.0110.011
Science and technology studies0.0010.003
Scholarly communication0.0060.004
Open science0.0050.004
Research integrity0.0020.003
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 head0.109
GPT teacher head0.282
Teacher spread0.173 · 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 designMeta-analysis
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

Citations157
Published2007
Admission routes2
Has abstractyes

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