Increasing the competitive advantage and the performance of SMEs using entrepreneurial marketing architectural innovation capability in North Sumatera, Indonesia
Bibliographic record
Abstract
The aim of this study is to analyze how to improve the competitive advantage and performance of small and medium-sized enterprises (SMEs) in North Sumatra through marketing entrepreneurship and architectural innovation capabilities. The type of research is quantitative research. The study focused on UMKM in North Sumatra who offered the following categories of products: handicrafts, food and beverages, coffee shops, bakeries, fashion, clothing, and services. The research population consists of all 84,758 UMKMs in North Sumatra. As for the sample number ranging between 100 and 200, or at least 5 times the number of variable indicators, when using Structural Equation Modeling (SEM). The entire sample size for stage 1 was 102 SME samples from Medan city. To analyze the research data, the study used SmartPLS (Partial Least Squares). The findings of this study show that market orientation (MO) has a negative and insignificant impact on SME performance from data processing and hypothesis testing results. Entrepreneurship Marketing Architecture Innovation Capacity (EMAIC) has a beneficial and significant impact on Competitive Advantage (CA). The ability of Enterprise Marketing Architectures (EMAC) to innovate has a significant and beneficial impact on their success. Entrepreneurship orientation (EO) provides tangible proof of SME success through competitive advantage (CA). Corporate orientation has no direct impact on SME performance through competitiveness (CA).
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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 | 0.000 | 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 | 0.000 | 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) | 0.002 | 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".