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Record W3198106737 · doi:10.5267/j.uscm.2021.8.008

Supply chain performance as a mediating variable effect of information technology on company competitiveness

2021· article· en· W3198106737 on OpenAlexvenueno aff
Slamet Riyadi, Musran Munizu, Donny Arif

Bibliographic record

VenueUncertain Supply Chain Management · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsnot available
Fundersnot available
KeywordsSupply chainBusinessStructural equation modelingIndustrial organizationInformation technologySupply chain managementMarketingDescriptive statisticsComputer scienceStatistics

Abstract

fetched live from OpenAlex

This study aims to analyze the effect of information technology on supply chain performance and information technology and supply chain performance on competitiveness. In addition, this study also investigates the role of supply chain performance in indirectly mediating the influence of information technology on company competitiveness. Primary data collection was carried out through questionnaires for managers of 112 manufacturing companies in East Java and South Sulawesi, Indonesia. This study uses descriptive analysis to describe the characteristics of respondents and variables. In addition, structural equation modelling (SEM) analysis is also carried out to test the research hypothesis. The results showed that the implementation of information technology has a positive and significant effect directly on supply chain performance and company competitiveness. Furthermore, supply chain performance has a positive and significant effect on company competitiveness. In addition, supply chain performance has a positive role in mediating the influence of information technology on company competitiveness.

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)0.002
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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.224
Teacher spread0.216 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

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