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Record W2950682371 · doi:10.5430/ijfr.v10n5p327

A Conceptualization of the Effect of Organisational Justice on Turnover Intention: The Mediating Role of Organisational Citizenship Behaviour

2019· article· en· W2950682371 on OpenAlexvenueno aff
Oussama Saoula, Muhammad Fareed, Saiful Azizi Ismail, Nurul Sharniza Husin, Rawiyah Abd Hamid

Bibliographic record

VenueInternational Journal of Financial Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Leadership and Management Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsConceptualizationMediationCitizenshipBusinessTurnoverAffect (linguistics)Organizational citizenship behaviorEconomic JusticeTurnover intentionPsychologyPublic relationsSocial psychologyPolitical scienceOrganizational commitmentManagement

Abstract

fetched live from OpenAlex

Considering employees are the ultimate valuable assets, most companies nowadays give lots of effort and capitalise vital resources to preserve them. The turnover of those employees will affect the achievement of the organisations’ goals as well as the maintaining of the competitive advantage. Therefore, it is imperative to call for more studies to understand the factors affecting this phenomenon in different settings and contexts of research, particularly in the non-western perspectives such as Malaysia who is facing big challenges toward the employees’ turnover in many sectors. Therefore, the drive of this paper is to examine the relationship between organisational justice (OJ), organisational citizenship behaviour (OCB) (benefiting the individual OCB-I and benefiting the organisation OCB-O) and turnover intention (TI). Consequently, this study proposed framework to study the effect of organisational justice on turnover intention via the mediation role of organisational citizenship behaviour (OCB-I, OCB-O). Also, the direct impact between the variables has been discussed. Hence this paper is expected to fill the research gap and contribute to the body of knowledge in this area of research.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.604
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.030
GPT teacher head0.305
Teacher spread0.276 · 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
Published2019
Admission routes1
Has abstractyes

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