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Record W2012923515 · doi:10.5539/ass.v9n4p113

Modeling the Influence of Group Norms and Self-regulatory Efficacy on Workplace Deviant Behaviour

2013· article· en· W2012923515 on OpenAlexvenueno aff
Kabiru Maitama Kura, Faridahwati Mohd Shamsudin, Ajay Chauhan

Bibliographic record

VenueAsian Social Science · 2013
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsDeviance (statistics)PsychologySocial psychologyStructural equation modelingPath analysis (statistics)Interpersonal communicationNorm (philosophy)Multilevel modelDescriptive statisticsSelf-efficacyConformityPolitical scienceStatistics

Abstract

fetched live from OpenAlex

This study investigated the influence of group norms and self-regulatory efficacy on workplace deviant behaviour. A web-based survey was used to collect data from 217 teaching staff from various higher education institutions in Nigeria. The data collected was analysed using Partial Least Squares (PLS) path modeling. As predicted, the path coefficient results supported the direct influence of perceived injunctive norms and self-regulatory efficacy on organisational deviance. Similarly, perceived injunctive norm and self-regulatory efficacy were found to be significant predictors of interpersonal deviance. On the contrary, perceived descriptive norms were not significant predictors of both organisational deviance and interpersonal deviance. In addition, self-regulatory efficacy does not moderate the relationship between perceived descriptive norms and organisational deviance. We also found support for the moderating role of self-regulatory efficacy on the relationship between perceived injunctive norms and dimensions of workplace deviance. The moderating role of self-regulatory efficacy on the relationship between perceived descriptive norms and interpersonal deviance was also supported. Finally, the policy implications of the study are discussed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.915
Threshold uncertainty score0.430

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.028
GPT teacher head0.353
Teacher spread0.325 · 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

Citations14
Published2013
Admission routes1
Has abstractyes

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