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Record W2943875627 · doi:10.1017/jmo.2019.31

Political skill and organizational identification: Preventing role ambiguity from hindering organizational citizenship behaviour

2019· article· en· W2943875627 on OpenAlexaff
Dirk De Clercq, Imanol Belausteguigoitia

Bibliographic record

VenueJournal of Management & Organization · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsBrock University
Fundersnot available
KeywordsOrganizational citizenship behaviorOrganizational identificationAmbiguityPoliticsPublic relationsOrganizational commitmentPerceptionIdentification (biology)PsychologySocial psychologyNature versus nurtureBusinessPolitical scienceSociology

Abstract

fetched live from OpenAlex

Abstract This research investigates how employees' perceptions of role ambiguity might inhibit their propensity to engage in organizational citizenship behaviour (OCB), with a particular focus on the potential buffering roles of two personal resources in this process: political skill and organizational identification. Survey data collected from a manufacturing organization indicate that role ambiguity diminishes OCB, but this effect is attenuated when employees are equipped with political skill and have a strong sense of belonging to their organization. The buffering role of organizational identification also is particularly strong when employees have adequate political skills, suggesting the reinforcing, buffering roles of these two personal resources. Organizations that want to foster voluntary work behaviours, even if they cannot provide clear role descriptions for their employees, should nurture adequate personal resources within their employee ranks.

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)0.019
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 score0.004
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.007
GPT teacher head0.210
Teacher spread0.204 · 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

Citations15
Published2019
Admission routes1
Has abstractyes

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