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Record W4210544757 · doi:10.5267/j.uscm.2021.12.011

The role of supply chain management and competitive advantage on the performance of Indonesian SMEs

2022· article· en· W4210544757 on OpenAlexvenueno aff
Andala Rama Putra Barusman, Habiburrahman Habiburrahman

Bibliographic record

VenueUncertain Supply Chain Management · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsnot available
Fundersnot available
KeywordsCompetitive advantageBusinessSmall and medium-sized enterprisesSupply chain managementSupply chainSimple random sampleData collectionStructural equation modelingIndonesianSample (material)Industrial organizationKnowledge managementPopulationMarketingComputer scienceStatistics

Abstract

fetched live from OpenAlex

The purpose of this study is to analyze the effect of supply chain management on competitive advantage, the effect of supply chain management on performance and the effect of competitive advantage on performance of small and medium enterprises (SMEs). The object of this research includes all SMEs in Tangerang, the population in this study also covers all 680 Small and Medium Enterprises in Tangerang, Indonesia. The research method uses quantitative methods and the technique used for data collection is by using an online questionnaire. Questionnaires are given to 210 owners or managers of Small and Medium Enterprises who were selected using the simple random sampling method. Data analysis used Structural Equation Modeling (SEM) with data processing tools using SmartPLS software. The results of data analysis show that supply chain management has a positive and significant effect on the performance of SMEs. Supply chain management has a positive and significant effect on competitive advantage. Finally, competitive advantage has a positive and significant effect on performance.

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)0.003
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.008
GPT teacher head0.230
Teacher spread0.222 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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