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Record W4364357724 · doi:10.18280/ijsdp.180312

The Impact of Humanitarian Supply Chain on Non-Government Organizations Performance Moderated by Organisation Culture

2023· article· en· W4364357724 on OpenAlexvenueno aff
Ahmed Ali Atieh Ali, Zulkifli Mohamed Udin, Hussein Mohammed Abualrejal

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain Resilience and Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessSupply chainGovernment (linguistics)Organizational cultureIndustrial organizationSupply chain managementProcess managementMarketingPublic relationsPolitical science

Abstract

fetched live from OpenAlex

The current research seeks to understand how humanitarian supply chains affect the performance of non-governmental organizations in Jordan.This humanitarian sector is the central pillar to alleviate the burden of asylum and the suffering of displaced people from neighbouring countries and survivors of civil wars like the Syrian civil war.The descriptive statistical analysis approach was used and a program was adopted SMART PLS This research is considered quantitative research, where the questionnaire tool was built, where the study population consisted of non-governmental organizations operating in Jordan, numbering 1640.The study population consisted of 311, and the questionnaire was distributed to executive and logistical support managers working in these organizations.After the response of the study sample, the search results in research results showed a statistically significant relationship between the research variables and the Performance of the organizations with the enhanced organizational culture.One of the most important recommendations recommended by the current study is to conduct more research related to humanitarian supply chains, and the current research recommends the need to test leadership as a mediating variable in the relationship between supply chains and the performance of organizations.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.234
Teacher spread0.227 · 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

Citations16
Published2023
Admission routes1
Has abstractyes

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