The Impact of Entrepreneurial Marketing and Business Development on Business Sustainability: Small and Household Footwear Industries in Indonesia
Bibliographic record
Abstract
Entrepreneurial marketing is a marketing approach that is more appropriate in terms of resource constraints andproblems that exist in small and medium enterprises (SMEs). Although the footwear industry is one of thegovernment-supported SMEs sector in Indonesia, the development of the industry is still relatively slow. Variousgovernment policies have been implemented related to export and import settings, but it does not seem toprovide significant benefits to the national development of the footwear industry. The main purpose of this studyis to formulate a model of entrepreneurial marketing for business development and sustainability of small andhousehold footwear industries based on the analysis of structural equation modeling (SEM) with partial leastsquares (PLS), entrepreneurial marketing shows positive influence on the development of the value of pathcoefficient of 0.511 (alpha = 5%). In addition, the ability of entrepreneurial marketing also has a positiveinfluence on the value of business sustainability path coefficient of 0.430 (alpha = 5%). This positive influencemeans that businesses with a higher ability of entrepreneurial marketing will have higher levels of businessdevelopment and sustainability. It can be concluded that entrepreneurial marketing plays an important role inshaping business development and sustainability of small and household footwear industries.
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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 | 0.000 |
| 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 | 0.001 |
| 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".