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Record W4294636315 · doi:10.5267/j.uscm.2022.7.004

The effects of sustainable supply chain management and organizational learning abilities on the performance of the manufacturing companies

2022· article· en· W4294636315 on OpenAlexvenueno aff
Mohammad Hafi, Ujianto Ujianto, Tri Andjarwati

Bibliographic record

VenueUncertain Supply Chain Management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Optimization Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessProduct (mathematics)Sample (material)ManufacturingQuality (philosophy)MarketingPopulationSupply chainSustainabilitySupply chain managementProduct innovationCompetitive advantageIndustrial organizationKnowledge managementComputer science

Abstract

fetched live from OpenAlex

This study aims to analyze the effect of supply chain management sustainability and organizational learning ability on company performance which is also measured by intervening variables of product design innovation and competitive advantage and moderated by environmental uncertainty. This study used a sample of 383 companies from a population of 5,495 in East Java Province, Indonesia. This study also uses a quantitative approach with more emphasis on social aspects with a deductive model. Some of the findings in this study are that there is no significant effect on company performance and organizational learning ability if mediated by product design innovation, even though this is very important for the company's progress in the future and proves that many companies in this province. have not fully implemented design innovation as a measure of the quality of the goods produced. And the second is that all the components of the variables analyzed produce significant values for all dependent variables so that the adjustment to the impact of the pandemic gets a good response from the industry that is working.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.004
GPT teacher head0.179
Teacher spread0.175 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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