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Record W3083693504 · doi:10.5430/rwe.v11n5p266

The Effect of Emotional Intelligence of Operational Team Leaders on the Performance of Team Members

2020· article· en· W3083693504 on OpenAlexvenueno aff
Moayyad Al-Fawaeer, Ayman Wael Al‐Khatib

Bibliographic record

VenueResearch in World Economy · 2020
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsEmotional intelligencePsychologyApplied psychologyEmpathyTask (project management)Sample (material)Social intelligenceSimple random samplePopulationData collectionSocial psychologyKnowledge managementManagementComputer scienceStatistics

Abstract

fetched live from OpenAlex

This study is aimed at identifying the effect of emotional intelligence with its dimensions (self-awareness, self-regulation, empathy, motivation, and social skills) on the performance of working teams with its dimensions (task performance, contextual performance, and counterproductive performance) among employees on the operational lines of industrial companies operating in the Jordanian city of Sahab. The analysis is limited to employees in those companies, and the questionnaire is used as a data collection tool, taking a simple random sample to represent the study population. In addition to the analysis of 216 questionnaires, the SPSS program is used as a data analysis tool in the study. The study emphasizes the importance of emotional intelligence dimensions for operational team leaders, especially motivation and social skills dimensions because they have a higher effect on the task performance and contextual performance levels, while all dimensions of emotional intelligence have a negative effect on counterproductive performance for operational team members.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.148
GPT teacher head0.415
Teacher spread0.267 · 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.

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

Citations5
Published2020
Admission routes1
Has abstractyes

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