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Record W4391062649 · doi:10.5267/j.uscm.2023.12.004

An analysis of the relationship between servant leadership and organizational citizenship behavior: The effect of emotional intelligence as a mediating role

2024· article· en· W4391062649 on OpenAlexvenueno aff
Rokaya Albdareen

Bibliographic record

VenueUncertain Supply Chain Management · 2024
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsOrganizational citizenship behaviorServant leadershipEmotional intelligencePsychologySocial psychologyOrganizational commitmentCitizenshipTransactional leadershipPolitical science

Abstract

fetched live from OpenAlex

The current study's primary goal is to ascertain the impact of servant leadership on organizational citizenship behavior as being affected by emotional intelligence as the mediating variable. This descriptive study was applied to the academic staff of private and public universities. The questionnaire was used for data collection. The dimensions of servant leadership (SL) were nine, the dimensions of emotional intelligence were nine while five dimensions of organizational citizenship behavior were used and collected from literature review. The questionnaire was distributed to 304 of the academic staff. The findings indicate a moderate degree of the servant leadership practice. Also, the evaluation of organizational citizenship behavior practice and emotional intelligence was negative in academic institutions. The results showed that servant leadership affects positively organizational citizenship behavior using emotional intelligence as a mediator. Servant leadership is very important in higher educational organizations when followed properly and affects organizational citizenship behavior by considering the emotional aspects of the academic staff.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.053
GPT teacher head0.342
Teacher spread0.289 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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