Business performance concept development apparel industry MSMEs in Bali
Bibliographic record
Abstract
The apparel industry is one of the business sectors included in Micro, Small and Medium Enterprises (MSMEs) in Bali Province. These apparel industry MSMEs contribute to employment and economic growth in Bali, so their business sustainability must be maintained. Measuring the business performance of MSMEs in the apparel industry needs to be carried out on an ongoing basis to ensure that these MSMEs can survive in increasingly fierce competition. The objectives of this study are: first, to explain the role of innovation strategy in mediating the effect of entrepreneurial orientation on MSME business performance, and second, to explain the role of technological resources in moderating the effect of entrepreneurial orientation on MSME business performance. The research was conducted on apparel MSMEs in Bali with 220 respondents taken randomly. Data were analyzed using the Structural Equation Modeling-Partial Least Square (SEM-PLS) technique. The analysis results show that entrepreneurial orientation has a significant positive effect on the performance of MSMEs. Entrepreneurial orientation has a significant positive effect on innovation strategy, and then innovation strategy has a significant positive effect on MSME business performance. Technological resources strengthen the influence of entrepreneurial orientation on MSME business performance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.004 | 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".