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Record W2023495041 · doi:10.1002/job.738

Understanding daily citizenship behaviors: A social comparison perspective

2011· article· en· W2023495041 on OpenAlexaff
Jeffrey S. Spence, D. Lance Ferris, Douglas J. Brown, Daniel Heller

Bibliographic record

VenueJournal of Organizational Behavior · 2011
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsUniversity of WaterlooUniversity of Guelph
FundersSingapore Management University
KeywordsPsychologySocial psychologyOrganizational citizenship behaviorPerspective (graphical)Affect (linguistics)Multilevel modelSocial comparison theoryCitizenshipTest (biology)Organizational commitmentStatistics

Abstract

fetched live from OpenAlex

Abstract Research that has sought to understand why employees engage in organizational citizenship behaviors (OCB) has concentrated on between‐person variables, typically ignoring intraindividual influences. Accordingly, we know much about who engages in OCB, in general, but know relatively little regarding under what circumstances people engage in OCB. By integrating social comparison with affective events and just‐world theories, we propose and test a dynamic model wherein directional social comparisons are expected to have direct (automatic‐motivational) and indirect (affective) intraindividual effects on OCB. The hypotheses were tested using multilevel modeling on 1076 observations from 99 participants that were collected via an interval‐contingent experience sampling methodology. The results provide support for the hypotheses that social comparisons are related to OCB through positive affect and the direct effects of social comparisons on OCB are moderated by beliefs in a just world. Theoretical and practical implications are discussed. Copyright © 2011 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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.989

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.000

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 head0.449
GPT teacher head0.465
Teacher spread0.016 · 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.

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

Citations102
Published2011
Admission routes1
Has abstractyes

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