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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.880
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.002
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 head0.004
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