Networking capability, entrepreneurial marketing, competitive advantage, and marketing performance
Bibliographic record
Abstract
Business success is closely related to the marketing process. A good defined marketing strategy conducted to increase a sales business and marketing. The elements that must be developed are networking capability, entrepreneurial marketing, competitive advantage, and marketing performance. The sustainability of Indonesian businesses, especially small and medium enterprises (SMEs), is a challenge for entrepreneurs. SMEs are an important factor that affects the increase of national economic growth. This research investigated variables such as networking capability, entrepreneurial marketing, competitive advantage, marketing performance of embroidery SMEs in Tasikmalaya City West Java Indonesia. The technique sampling used in this research was purposive sampling. Samples gained were 120 SMEs. In analyzing the data, researchers employed Structural Equation Model (SEM) method from software AMOS. Entrepreneurial marketing activities are closely related to creating competitive advantage through innovation in the creation of better products, processes, and strategies to satisfy customer needs and desires. Hypothetical testing results showed that network capability and entrepreneurial marketing are important factors that significantly influence competitive advantage of SMEs. Networking capability, entrepreneurial marketing, and competitive advantage are important factors that significantly influence marketing performance of SMEs. Therefore, networking capability, entrepreneurial marketing, competitive advantage, and marketing performance must be developed for sustainable the successfulness of SMEs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 teacher head, 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".