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Record W3205524257 · doi:10.18280/ijsdp.160520

Organizational Politics and Organizational Citizenship Behavior: Interaction and Analysis

2021· article· en· W3205524257 on OpenAlexvenueno aff
Ahmad Albloush, Hasan Ali Al-Zu’bi, Imad Almuala, Ghassan A. Al-Utaibi, Sadi Taha, Azlinzuraini Ahmad

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Leadership and Management Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsOrganizational citizenship behaviorOrganizational commitmentAffect (linguistics)CitizenshipPoliticsPublic sectorOrganizational performancePublic relationsOrganizational behavior and human resourcesOrganization developmentBusinessPsychologySocial psychologyPolitical scienceMarketing

Abstract

fetched live from OpenAlex

Perceptions of organizational politics are an essential aspect of organizational life for its members because they impact different practices, which eventually affect employee efficiency. This article is explored the relationship between Organizational Politics (OP) and Organizational Citizenship Behavior (organizational citizenship behavior for organization (OCB-O) and organizational citizenship behavior for individuals (OCB-I). Survey data is gathered from 200 employees work in Jordanian public sector. Partial least square (PLS-SEM) is employed to test the research hypotheses. Outcomes uncovered that OP has a negative relationship with OCB-O and OCB-I. Accordingly, the current study recommends that governments abolish or restrict OP activities in their organizations as much as possible. Besides, the findings show that OP activities harmed public-sector employee behavior. The study's limitations and recommendations for future studies are also considered.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.247
Teacher spread0.228 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

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