Crafting Design Strategy on Seaweed Industry in Indonesia
Bibliographic record
Abstract
The global demand for seaweed is expected to increase in the coming years due to new product development using seaweed. There are many benefits of seaweed such as pharmaceuticals, cosmetics, food industries, textiles, paper, and bioenergy production. On the other hand, Indonesia, as the archipelago country with the second-longest coastline after Canada, is expected to achieve competitiveness so Indonesia benefit more from the seaweed industry. This study aims to get the landscape of the seaweed industry and to select strategies. The strategies are designed using the Structural Equation Modelling Method – Partial Least Square (SEM – PLS). The findings of this study are the competitive advantage of Indonesia's carrageenan-producing seaweed industry using descriptive statistical methods through the perception model show considered weak. The three main parameters of concern are assessed as low-cost leadership, ability to increase export value and self-sufficiency in meeting the needs of domestic seaweed. The analysis also shows that the influence of innovation and stakeholder support on increasing the competitive advantage of Indonesia's carrageenan-producing seaweed industry is considered significant. Innovation is represented by the latent variable attributes of innovation, communication channels and the role of change agents, which are concluded to have a significant effect both directly and indirectly on the industry's competitive advantage. Keywords: seaweed industry, competitiveness, carrageenan, innovation, SEM-PLS, stakeholder support
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".