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Record W4328024576 · doi:10.5267/j.uscm.2023.2.012

Green supply chain practices and their effects on operational performance: An experimental study in Jordanian private hospitals

2023· article· en· W4328024576 on OpenAlexvenueno aff
Nour Salem Ahmad AlBrakat, Sulieman Ibraheem Shelash Al‐Hawary, Suhaib Muflih

Bibliographic record

VenueUncertain Supply Chain Management · 2023
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsStructural equation modelingBusinessSample (material)Supply chainSustainabilityProduct (mathematics)Operations managementPopulationProcess (computing)Supply chain managementPrivate sectorMarketingEngineeringComputer scienceEconomicsMedicineStatisticsMathematicsEnvironmental health

Abstract

fetched live from OpenAlex

This study aims to identify the level of Green Supply Chain practices and their Impact on Operational Performance of the Jordanian Private Hospitals. The Jordanian private hospital sector consists of 71 private hospitals comprising the study population. The convenience sample was used by distributing the research tool to 280 of the subjects in the study in Jordanian private hospitals. The responses received were 257 responses. Moreover, the analyses related to the research were performed using version 24 of SPSS and AMOS software, and test hypotheses structural equation modeling (SEM) was used. The results indicated that green supply chain practices have an impact on operational performance. Based on the study results, the researchers recommend managers to increase operational efficiency by Integrate sustainability parameters into product design requirements and assess them throughout the design process.

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)0.004
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.265
Teacher spread0.250 · 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

Citations85
Published2023
Admission routes1
Has abstractyes

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