The effect of supply chain innovation and e-procurement implementation on supply chain performance of manufacturing organization
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.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it