Influence of technology and government regulations on the competitiveness of the textile industry: Case study of Indonesia
Bibliographic record
Abstract
This paper promotes supply chain performance, sustainability, and Industry 4.0 integration to boost competitiveness. Indonesia's textile industry's competitiveness depends on many factors. Indigenous factors, technology, vertical integration, and global supply networks drive competitiveness. This study uses descriptive data to examine 143 textile workers in Indonesia and provides important context for the sector. It demonstrates that various factors, such as native elements, technical readiness, vertical integration, and global supply chain participation, impact the competitiveness of Indonesia's textile sector. The findings suggest that policymakers and industry leaders should take strategic actions to enhance competitiveness, including encouraging collaboration, technical advancement, and local enterprise, as well as investing in technology, vertical integration, and global networking. Despite its cross-sectional approach and contextual complexity, the study is believed to enhance the competitiveness of the global textile industry. Our guidelines help stakeholders make strategic decisions that utilize regional strengths, adopt cutting-edge technologies, and integrate into global supply networks. Subsequent studies can examine industry differences and how these links change. This research helps Indonesian textile industry stakeholders make competitive decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".