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Record W2342464343 · doi:10.5539/res.v8n2p183

Simple and Multiple Relationships between Ethical Leadership, Transformational Leadership and Ethical Climate and Organizational Spirituality among the Employees of the Iran National Steel Industrial Group

2016· article· en· W2342464343 on OpenAlexvenueno aff
Morteza Golestanipour

Bibliographic record

VenueReview of European Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Spirituality and Leadership
Canadian institutionsnot available
Fundersnot available
KeywordsTransformational leadershipWorkplace spiritualityEthical leadershipPsychologySpiritualitySimple random sampleStratified samplingPopulationSocial psychologySociologyStatisticsMathematicsDemography

Abstract

fetched live from OpenAlex

<p>This study aimed to investigate the simple and multiple relationships of ethical leadership, transformational leadership, and ethical climate with organizational spirituality among the employees of the Iran National Steel Industrial Group in Ahvaz. This is a correlational study with a statistical population of all employees of an industrial organization in Ahvaz, of whom, 400 subjects were selected using the stratified random sampling method. The research instruments included ethical leadership questionnaire of Brown et al., General Transformational Leadership (GTL) questionnaire, ethical climate questionnaire of Hunt et al., and organizational spirituality questionnaire of Milliman et al., with acceptable validity and reliability. For testing the research hypotheses, the Spearman correlation coefficient and multivariate regression analysis were used. The results showed a significant relationship between ethical leadership, transformational leadership and ethical climate and organizational spirituality. The results of multiple regression analysis showed that ethical climate is the most important factor in explaining and predicting the organizational spirituality.</p>

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)0.011
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.519
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.375
GPT teacher head0.375
Teacher spread0.000 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

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