Competitiveness Model and Gap Analysis of Indonesian Palm Oil-Based Fatty Acid and Fatty Alcohol Industry
Bibliographic record
Abstract
Oleochemicals can be derived from natural oils or fats. Palm oil-based fatty acids and fatty alcohols are the most important oleochemicals. The aims of this study are to develop a competitiveness model of Indonesian palm oil-based fatty acid and fatty alcohol industry, and to analyze the gap between the current and ideal (future) conditions of the industry, using competitiveness framework being developed by the International Institute for Management Development (IMD). This study used literature review, in-depth interview, and questionnaire method to gather opinions from the experts and/or practitioners of the industry of the current and ideal (future) conditions. The non-parametric Mann-Whitney test is used to assess the differences between both conditions. The results of this study show that the biggest gap using IMD competitiveness framework is government efficiency while the smallest gap is business efficiency. The results of this study also show significant differences between the current and ideal (future) conditions of the industry at ?=5% for all factors and total IMD competitiveness. Only two sub-factors have no significant difference, namely employment and prices. The competitiveness improvement of the industry has to involve all stakeholders by resolving existing problems in systemic and systematic approaches.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".