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Record W2103028417 · doi:10.7469/jksqm.2014.42.3.407

A study on the influence of supply chain management efficiency on the training supporting project in small and medium-sized enterprises

2014· article· en· W2103028417 on OpenAlexaff
Yohan Seo, Kwang‐Yong Kim, Jong Su Sung

Bibliographic record

VenueJournal of the Korean society for quality management · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOutsourcing and Supply Chain Management
Canadian institutionsCanadian Standards Association
Fundersnot available
KeywordsSupply chainBusinessSupply chain managementModerationKnowledge managementTraining (meteorology)Regression analysisOperations managementProcess managementMarketingComputer scienceEngineering

Abstract

fetched live from OpenAlex

Purpose: The purpose of this study was to propose supply chain management efficiency by analyzing moderating effect on the training support project in small and midium-sized enterprise. Methods: The collected data through the survey were analyzed using multi?regression analysis. The measurement tools used for this study were divided into three dimensions such as personal characteristics, supportive environment and training effect. Results: The results of this study are as follows; first, it was found that the effects of personal characteristics and supportive environment were significant on training effect. Second, significant differences were found between supply chain management active group and inactive group. Third, moderating effect of supply chain manage efficiency was found between variables. Conclusion: SME supporting project in training program needs supply chain management efficiency for training effect. Supply chain management needs to be promoted in SMEs not only for performance but also for their workplace learning and learning culture.

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)0.016
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.047
GPT teacher head0.291
Teacher spread0.244 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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