The effect of supply chain innovation and e-procurement implementation on supply chain performance of manufacturing organization
Bibliographic record
Abstract
The purpose of this study is to analyze the effects of the e-procurement on supply chain performance and supply chain innovation. The study also investigates the effect of supply chain innovation on supply chain performance. The research method is a quantitative survey, and the research data is obtained by distributing online questionnaires on a scale from 1 to 7 distributed via social media. Respondents in this study are 250 managers of manufacturing organizations in Indonesia determined by simple random sampling method. The model used in this study is the causality model and to test the hypotheses proposed in this study, the analytical technique used is Structural Equation Modeling (SEM) with SmartPLS software as a data analysis tool. The independent variable of this research is e-procurement implementation, supply chain innovation and the dependent variable is supply chain performance. The stages of data analysis are validity test, reliability test and hypothesis testing. The results of this study indicate that the application of e-procurement had a positive and significant effect on supply chain performance, the application of e-procurement had a positive and significant effect on supply chain innovation, supply chain innovation had a positive and significant effect on supply chain performance and supply chain innovation was able to mediate the effect of e-procurement on supply chain performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".