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Record W4391072430 · doi:10.5267/j.uscm.2024.1.013

Green HRM practices, green commitment, and green innovative work behavior in UAE higher education institutes

2024· article· en· W4391072430 on OpenAlexvenueno aff
Eman Juma Ali AlShayeb AlNaqbi, Faridahwati Mohd Shamsudin, Muhammad Turki Alshurideh

Bibliographic record

VenueUncertain Supply Chain Management · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsnot available
Fundersnot available
KeywordsStructural equation modelingBusinessHuman resource managementEnvironmentally friendlySustainabilityWork (physics)Organizational commitmentMarketingManagementEngineeringEconomicsMechanical engineering

Abstract

fetched live from OpenAlex

This study examines the relationships among green human resource management (GHRM), green commitment, green innovative work behavior (GIWB), and the moderating effect of environmentally specific servant leadership (ESSL) in UAE higher education institutes of the United Arab Emirates (UAE). Using a sample of employees, data were collected through a survey from 243 employees working in different universities across the UAE and analyzed using Structural Equation Modeling (SEM). The SEM analysis confirms robust relationships between GHRM, environmentally specific servant leadership, green commitment, and green innovative workplace behavior. GHRM has a positive impact on GHRM. ESSL fosters the relationship between GHRM and green commitment, while green commitment positively impacts green innovative workplace behavior. Females were found to be more environmentally aware of the needed adjustments compared to male workers at the UAE campuses. The study suggests that higher education institutes in the UAE should adopt ESSL to support eco-conscious behaviors and green practices on their campuses and contribute to the achievement of national 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.275
Teacher spread0.251 · 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

Citations17
Published2024
Admission routes1
Has abstractyes

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