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Record W3123721630 · doi:10.1111/1468-2389.00201

Self‐Reported Counterproductive Behaviors and Organizational Citizenship Behaviors: Separate but Related Constructs

2002· article· en· W3123721630 on OpenAlexafffund
E. Kevin Kelloway, Catherine Loughlin, Julian Barling, Alison Nault

Bibliographic record

VenueInternational Journal of Selection and Assessment · 2002
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversity of TorontoSaint Mary's UniversityQueen's UniversitySt. Mary's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyOrganizational citizenship behaviorSocial psychologyConfirmatory factor analysisVariance (accounting)Scale (ratio)Construct (python library)Counterproductive work behaviorConstruct validityOrganizational behaviorOrganizational commitmentApplied psychologyPsychometricsDevelopmental psychologyStructural equation modelingStatistics

Abstract

fetched live from OpenAlex

The construct validity of self‐reported counterproductive work behaviors (CPBs) and organizational citizenship behaviors (OCBs) was explored by separating item content from item wording, through the confirmatory factor analysis of three scales: a CPB scale containing only negatively worded items, an OCB scale with only positively worded items, and the supervision subscale of the Job Descriptive Index (JDI) (Smith, Kendall and Hulin 1969) comprised of both negatively and positively worded items. Results (N = 475) suggest that self‐report measures of CPBs and OCBs are affected by method variance, but that the presence of such an influence does not compromise the substantive interpretation of these scales. Consequently, these scales do appear to be unique constructs.

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)0.004
metaresearch head score (Gemma)0.018
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 score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.270
Teacher spread0.257 · 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

Citations140
Published2002
Admission routes2
Has abstractyes

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Same venueInternational Journal of Selection and AssessmentSame topicJob Satisfaction and Organizational BehaviorFrench-language works237,207