The effect of strategic planning on competitive advantages of small and medium enterprises
Bibliographic record
Abstract
This research starts from a phenomenon that indicates that the competitive advantage of Small and Medium Enterprises (SMEs) in increasingly fierce business competition has not yet achieved in Sukabumi, Indonesia. This is indicated by the inefficiency of production costs felt by SMEs which are not capable of creating competitive prices and the difficulty of making unique products. The purpose of this study is to determine the magnitude of the influence of dimensional strategic planning on the competitive advantage of SMEs. The results of the analysis and discussion are expected to find a concept regarding SME strategic planning. This study uses a quantitative approach, with an explanatory survey design that explains and describes the level of influence of strategic planning on the competitive advantage of SMEs in Sukabumi Regency, Indonesia. By using data analysis of Structural Equation Modeling (SEM), the results of the study indicate that there is a significant influence of strategic planning on the competitive advantage of SMEs in Sukabumi, Indonesia. Strategic planning which consists of three dimensions, namely: the desires of external stakeholder, the company's internal encouragement, and the company's database, significantly influences the competitive advantage of SMEs. Of the three dimensions of strategic planning, the dimensions of external stakeholder have the highest influence, while the company's database have the lowest effect. These results practically imply for SMEs to increase the consideration of company database in preparing the SME strategic planning.
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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 | 0.002 | 0.011 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".