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Record W4404228741 · doi:10.3390/admsci14110297

Resilience for Sustainability: The Synergistic Role of Green Human Resources Management, Circular Economy, and Green Organizational Culture in the Hotel Industry

2024· article· en· W4404228741 on OpenAlexaff
Ibrahim A. Elshaer, Alaa M. S. Azazz, Chokri Kooli, Khaled Alqasa, Jehad Abdallah Atieh Afaneh, Eslam Ahmed Fathy, Amr Mohamed Fouad, Sameh Fayyad

Bibliographic record

VenueAdministrative Sciences · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsWilfrid Laurier UniversityRoyal Military College of CanadaUniversity of Ottawa
Fundersnot available
KeywordsResilience (materials science)SustainabilityBusinessCircular economyEnvironmental resource managementHuman resource managementOrganizational cultureManagementEconomicsEcology

Abstract

fetched live from OpenAlex

This research explores the extent to which Green Human Resource Management (GHRM) practices in the Egyptian hotel sector contribute to the adoption of Circular Economy (CE) practices and, eventually, organizational resilience. Using a sample of 402 employees from green-certified Egyptian hotels, the current study applied Partial Least Squares Structural Equation Modeling (PLS-SEM) on the data collected. The results show the positive effect of GHRM on the adoption of a circular economy that significantly enhances both internal and external organizational resilience. In addition, high Green Organizational Culture (GOC) strengthens the positive relationship of GHRM with the adoption of a circular economy. From this work, some empirical evidence is provided to show that circular economy practices can play a partial mediating role between GHRM and organizational resilience. These findings also present valuable insights for hotel managers and policymakers on how to achieve sustainability and resilience by means of integrated GHRM and circular economy strategies.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.005
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.003
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 head0.017
GPT teacher head0.275
Teacher spread0.258 · 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

Citations43
Published2024
Admission routes1
Has abstractyes

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