Integration and collaboration of determinants of entrepreneurial competitiveness
Bibliographic record
Abstract
The advancement of sustainable competitiveness and its contribution to the sustainability of sustainable development are becoming increasingly important research topics in sustainability science. The main purpose of this study is to disseminate knowledge in the practice of entrepreneurship and to strengthen the theoretical foundation to support sound and appropriate decision making as well as to improve the performance of the business competitiveness. The main source of data material in this paper is the primary micro fundamental survey with path analysis model. The result of this study shows that technological innovation not only produces new products but can also be the best way to achieve advancement. New Product Development (NPD) is the output of technological innovation, and the best way that must be developed as a strong foundation for decision makers to achieve sustainable competitiveness progress is the new product development and institutional integration. If productivity and economic growth are the main keys to success in achieving sustainable competitiveness, the NPD and institutional integration is the main determinant of the progress of sustainable competitiveness. The conclusion of this study is that productivity and economic growth is not enough to be the main key of the strength of competitiveness, but it also requires collaboration from various fundamental factors and the NPD and institutional integration as a driving force, the best way, and the main determinant of the achievement of the performance progress of sustainable development competitiveness.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".