The effects of entrepreneurial skills, benchmarking, and innovation performance on culinary micro-small-medium enterprises
Bibliographic record
Abstract
This study was to analyze the effect of entrepreneurial skills and benchmarking on the performance of culinary innovations (restaurants, restaurants, and cafes) of micro-small-medium enterprises (MSMEs) in Indonesia sub-urban areas. Online questionnaires were used as the instrument to collect data, and the data were analyzed by deception analysis to illustrate various features of the variables studied. Hypothesis test was conducted by Partial Least Square Path Modeling (SEM-PM). The MSME population was 231 and the representative sample was 144 culinary companies. It was found that entrepreneurial skills, benchmarking, and performance of culinary MSME innovations tended to be lower than expected. The results of this study revealed that entrepreneurship and benchmarking skills had significant effects on innovation performance. To improve innovation performance, companies must pay more attention to practical knowledge, especially knowledge of bookkeeping and digital marketing, and make more comparisons on financial aspects.
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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 | 0.010 |
| 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 | 0.001 |
| 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 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".