Analysis of policy regulations and market access as drivers of collaboration in enhancing sustainable competitiveness in the project creative industry
Bibliographic record
Abstract
This study uses a structural equation model to analyze the role of regulation and policy with market access through collaboration and networking to sustainably improve the creative industry's competitiveness. Questionnaires were distributed to respondents in the creative sector in four big cities in Indonesia, namely Surabaya, Bandung, Yogyakarta, and Solo. The results of distributing questionnaires to 280 respondents and 250 questionnaires can be further processed with a response rate of 89.29%. Based on the goodness of fit test results, the model tested fits the data very well. All evaluation criteria met or exceeded the recommended values. Market access is the most influential variable on collaboration, networking, and competitiveness in the creative industry, with a coefficient value of 0.60. Collaboration and networking are essential variables in improving creative sectors, with a coefficient of 0.59. Although influential on collaboration and networking, regulations and policies do not significantly affect the creative industry's competitiveness because the coefficient index value is -0.11. However, rules and guidelines can indirectly affect the competitiveness of the creative sector through collaboration and networking, with a coefficient value of 0.189. The practical contribution of research provides insight for the government to create policies and regulations that support the creative industry and protect imported products so they do not kill domestic creative products. Contribution for practitioners to build collaboration with various parties in the country and build market access so they can export products. Theoretical contributions can enrich studies on resources-based views and national competitiveness.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 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".