Entrepreneurs Characteristics of Thematic Small and Medium Industries in Innovation Sub District of Palu City
Bibliographic record
Abstract
Nowadays, the development and growth of Small and Medium Industries (SMEs) have become the government’s centre of attention which leads the government's commitment, policies and programs are always continuously improved, with the aim that SMEs in Indonesia can keep developing and being competitive. SMEs play an important role in being the backbone of the national economy, and they are even able to stand up straight during unstable global economic conditions. Therefore, this research aimed to produce a Model of the Success of Thematic SMEs in the Innovation Sub district of Palu City, which was predicted to be influenced by the variables of characteristics of group ethnic entrepreneurs, marketing innovation, and the competence of counterparts. The research sample consisted of 150 business group members from 30 Thematic SMEs, analysed using structural equation modelling (PLS-SEM). The results showed that of the seven research hypothesis models, the results were acceptable with the structural model constructed, namely the characteristics of group ethnic entrepreneurs, marketing innovation and the competence of counterparts influenced significantly on the success of the Thematic SMEs businesses in Innovation Sub-districts in Palu City. The biggest influence contribution was the competence of business counterparts by 0.526, and the group ethnic entrepreneurship on the marketing innovation by 0.443.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".