Assessing the influence of supply chain collaboration value innovation, market demand, and competitive advantage on improving the performance of ceramic SMEs
Bibliographic record
Abstract
MSMEs in the ceramics industry are still experiencing a difficult period even though they have entered the post-pandemic period. Will competitive advantage be created from supply chain collaboration in terms of obtaining resource information which will ultimately create innovation and increase market demand? This research involved 200 ceramic MSMEs in Bali with the condition that the perpetrators had been running their business for more than one year, analyzed with SEM-PLS with the SmartPls processing tool. The results show that the relationship between market demands has a positive effect on competitive advantage and will give a boost to the performance of ceramics SMEs. While the collaborative relationship of innovation value does not have a positive effect on performance and competitive advantage because so far ceramic SMEs, especially photocatalysts, are still relatively new and are still running their business in one small group and no organization has yet been formed to support the acquisition of information related to the innovations to be carried out. This research also shows that the mediation of competitive advantage is the perfect mediation in creating market demand in improving the performance of MSMEs.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".