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Record W1991805777 · doi:10.5539/ass.v8n13p220

Individual Differences as Antecedents of Counterproductive Work Behaviour

2012· article· en· W1991805777 on OpenAlexvenueno aff
Sarah Waheeda Muhammad Hafidz

Bibliographic record

VenueAsian Social Science · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsCounterproductive work behaviorPsychologyLocus of controlAgreeablenessSocial psychologyPersonalityBig Five personality traitsHierarchical structure of the Big FiveConscientiousnessHedonismConformityBig Five personality traits and cultureOrganizational commitmentOrganizational citizenship behaviorExtraversion and introversion

Abstract

fetched live from OpenAlex

Counterproductive work behaviour (CWB) has recently gained more interest in industrial and organizational psychology, as the impact of engagement of CWB is big; influencing not just the organization but also other stakeholders. The objective of this study was to look at individual factors as antecedents of CWB, focusing on personality, locus of control, and values. Data were collected from 267 students studying psychology by means of a questionnaire measuring CWB, the Big-Five factor personality, work locus of control, and values. Only agreeableness and conscientiousness (out of the five personality factor) was found to be negatively correlated to CWB. Work locus of control showed a positive correlation with CWB. Hedonism and power was found to be positively related to CWB, whereas benevolence and conformity was found to be negatively related to CWB. The findings on personality and locus of control as antecedents of CWB are consistent with past research, meaning that employers can use this finding in their selection process. The findings on values have given a new insight to an area that can be researched further in the process of understanding why individuals engage in CWB.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.019
GPT teacher head0.271
Teacher spread0.252 · 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

Citations18
Published2012
Admission routes1
Has abstractyes

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