Linking Dynamic Capabilities and Market Orientation to Sustainability Through Green Marketing in the Batik Industry
Bibliographic record
Abstract
The batik industry, which combines Indonesia's cultural heritage with its creative economy, faces sustainability pressures due to pollution from synthetic dyes, excessive water consumption, and market dynamics.Amid these challenges, an organization's ability to adapt and understand the market determines its long-term competitiveness.This study begins with the context of Java, a center of batik production, where environmentally friendly practices are still developing unevenly.The research objective is to examine how dynamic capabilities (DC) and market orientation (MO) drive sustainable industry performance (SIP) through the mediating role of the green marketing mix (GMM).The methods used were a quantitative survey of 320 batik entrepreneurs (January to March 2024) with measurement and structural model testing.The findings suggest that DC enhances social and environmental performance, while MO improves economic and social performance.The GMM acts as a potent mediator, enabling internal capabilities and MO to more effectively translate into sustainability outcomes.The model explains 63% of the variance in SIP with positive predictive relevance.However, the environmental dimension lags behind the economic and social dimensions.The integration of DC, MO, and GMM needs to be accelerated through green innovations (natural dyes, process efficiency) as well as incentive and training policy support, so that the batik industry becomes more competitive and environmentally responsible.
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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 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".