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Record W4405258753 · doi:10.5267/j.dsl.2024.10.007

Agriculture supply chain management and environmental sustainability in Alkharj: Moderating role of economic and social sustainability

2024· article· en· W4405258753 on OpenAlexvenueno aff
Haider Mahmood

Bibliographic record

VenueDecision Science Letters · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Waste Reduction and Sustainability
Canadian institutionsnot available
FundersPrince Sattam bin Abdulaziz University
KeywordsSustainabilitySocial sustainabilitySustainability organizationsBusinessAgribusinessAgricultureNexus (standard)Environmental economicsIncentiveEconomicsEngineeringGeography

Abstract

fetched live from OpenAlex

The agriculture sector and other agribusinessescan have a long-lasting effect on the environment. The present studyinvestigates the effect of Agricultural Supply Chain Management (ASCM), fromproducer to consumer, on Environmental Sustainability (ES) in the Alkharj governorateby collecting primary data from 312 respondents in the ASCM in Alkharj and byapplying Structural Equation Modelling (SEM). Moreover, the moderating roles ofeconomic and social sustainability in the nexus between ASCM and the ES arealso tested. The results of the analyses show that ASCM directly improves theES in the agriculture sector. Moreover, ASCM also improves both economic andsocial sustainability. Consequently, economic and social sustainability improvesthe ES. Thus, economic and social sustainability have positively moderated therelationship between ASCM and the ES. The results suggest that the governmentof Alkharj governorate should further improve the economic sustainability ofagribusinesses in Alkharj by providing incentives. Moreover, education andtraining programs should be initiated to improve social sustainability. Thus,both improved social and economic sustainability of agribusinesses could encouragesustainable practices to promote the ES in the whole ASCM in Alkharj.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.868
Threshold uncertainty score0.283

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 studies0.0000.001
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 head0.005
GPT teacher head0.227
Teacher spread0.221 · 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 designOther design
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

Citations4
Published2024
Admission routes1
Has abstractyes

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