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Record W4414045803 · doi:10.5267/j.dsl.2025.7.002

Serial mediation of knowledge and commitment to strengthen leadership and green behavior

2025· article· en· W4414045803 on OpenAlexvenueno aff
Made Antara, I Gede Riana, I Made Artha Wibawa, Made Surya Putra

Bibliographic record

VenueDecision Science Letters · 2025
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsMediationSustainabilitySample (material)Organizational commitmentConstruct (python library)PopulationOrder (exchange)

Abstract

fetched live from OpenAlex

Hospitals are one of the health service facilities in Bali Province which contributes to maintaining the balance between social, economic, and environmental to achieve organizational sustainability. Improper and environmentally unfriendly medical waste management, in addition to having a negative impact on the health of living things, can also cause environmental pollution. Finding out how green inclusive leadership affects green behavior is the goal of this study, both directly and through the mediation of environmental knowledge and green commitment. This study targeted hospitals in Bali with a population of 311 and a sample of 175 ER nurses based on proportional random sampling. Data analysis using PLS-SEM method with SmartPLS tool. Green behavior and green inclusive leadership are not associated, according to the test results, but after being mediated by environmental knowledge and green commitment, its influence becomes significant. The results add insight to hospital management to follow up on indicators that are still lacking in improving the green behavior of all hospital personnel in order to achieve organizational sustainability goals.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.853
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.115
GPT teacher head0.401
Teacher spread0.286 · 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.

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
Published2025
Admission routes1
Has abstractyes

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