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Record W2025046593 · doi:10.1108/01437730210429061

Emotional intelligence, moral reasoning and transformational leadership

2002· article· en· W2025046593 on OpenAlexaff
Niroshaan Sivanathan, G. Cynthia Fekken

Bibliographic record

VenueLeadership & Organization Development Journal · 2002
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsQueen's University
Fundersnot available
KeywordsPsychologyTransformational leadershipEmotional intelligenceResidenceSocial psychologySupervisorTransactional leadershipMoral reasoningManagementSociology

Abstract

fetched live from OpenAlex

Using university residence staff as our leaders of interest, we explored the association of emotional intelligence and moral reasoning to leadership style and effectiveness. A total of 58 residence staff completed questionnaires assessing their emotional intelligence and moral reasoning. Subordinates (n=232) rated the residence staff’s leadership behaviours and effectiveness. Residence staff’s supervisors (n=12) also provided similar effectiveness ratings. Analysis showed that leaders who reported higher levels of emotional intelligence were perceived by their followers as higher in transformational leadership and more effective. Interestingly, having high emotional intelligence was not related to supervisor’s ratings of effectiveness. Supervisors associated greater job effectiveness with higher moral reasoning. Theoretical implications and practical applications of these findings are discussed.

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)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.225
GPT teacher head0.297
Teacher spread0.073 · 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

Citations230
Published2002
Admission routes1
Has abstractyes

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