The effect of digital procurement and supply chain innovation on SMEs performance
Bibliographic record
Abstract
Technological changes accompanied by rapid market changes have made it increasingly difficult for SMEs to develop their business in the future. Today, many organizations are shifting to e-procurement as an integrated supply chain support function to achieve strategic business goals. E-procurement or electronic procurement and supply chain Innovation have allowed for more flexibility in responding to market changes and improving the performance of the company's supply chain. The purpose of this study was to determine the impact of the implementation of e-procurement and supply chain innovation on the supply chain performance of SMEs in Indonesia. The method used in this study is a quantitative survey method using structural equation modeling (SEM) and partial least squares (PLS) with data processing tools, namely SmartPLS 3.0 software. Respondents in this study were 390 employees of SMEs in Indonesia determined by the simple random sampling method. The research data was obtained through an online questionnaire distributed through social media. From the results of the analysis, it can be concluded that the implementation of e-procurement has a significant effect on supply chain performance. Supply Chain Innovation has also a significant influence on SMEs 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 | 0.003 | 0.011 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".