Sustainability of Business Strategy Based on Indigenous Product Creativity in the Weaving Industry of Palm Oil Waste in Riau, Indonesia
Bibliographic record
Abstract
This study analyzes knowledge management and innovation based on indigenous product creativity to achieve a sustainable competitive advantage.The unit of analysis in this study was the artisans of the woven handicraft industry from palm oil stem waste, totaling 50 respondents using a census sampling of the entire population.The analysis tool of this research uses SEM PLS.The results of this study indicate that knowledge management and innovation affect sustainable competitive advantage with indigenous product creativity as mediation in the woven handicraft industry from waste palm oil stems.The findings succeeded in filling the gap in previous research that the heterogeneity of company resources is getting higher to achieve sustainable competitive advantage due to the indigenous factor of creativity as a mediation that can provide added value different from competitors.This research implication contributes to developing strategic management science with a resource approach with empirical evidence that original product creativity is a substantial intermediary variable to achieve sustainable competitive advantage.The practical implication of this research is that business actors in the palm oil stem waste industry must pay more attention to their knowledge resources and product innovation on indigenous product creativity.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".