Towards an Advanced Economy Through Batik SMEs: The Strategic Role of Green Innovation and Knowledge Management in Local Competitiveness
Bibliographic record
Abstract
This study explores the potential of the East Java Bakorwil II batik industry, a creative cultural sector hindered by weak branding, low design innovation, and minimal adoption of environmentally friendly practices.The research aims to design a model highlighting the role of green innovation as a mediating factor in strengthening the competitive advantage of local batik products.Using quantitative Partial Least Squares Structural Equation Modeling (PLS-SEM), 154 batik industry players participated in the study, with ethical clearance obtained.The results indicate that green innovation significantly mediates the relationship between knowledge management and competitive advantage (O = 0.282; T = 4.482; p = 0.000), and brand image and competitive advantage (O = 0.239; T = 4.648; p = 0.000).The study also finds significant direct effects of knowledge management on green innovation (O = 0.507; T = 8.338; p = 0.000), brand image on green innovation (O = 0.431; T = 7.078; p = 0.000), and green innovation on competitive advantage (O = 0.555; T = 5.723; p = 0.000).However, the direct effect of brand image on competitive advantage was not significant (p = 0.088).This study contributes to the understanding of how integrating green innovation and knowledge management can enhance the competitiveness of batik MSMEs, offering practical implications for strengthening their market position through sustainable strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".