The role of supply chain on the competitiveness and the performance of restaurants
Bibliographic record
Abstract
The purpose of this study is to analyze the effect of supply chain management on competitiveness, the positive effect of supply chain management on performance and the positive influence of competitiveness on performance in restaurants in the province of Banten Indonesia. The research method uses a hypothesis testing model, the respondents of this research are tourists who want to travel to a tourist attraction in Banten Province. Data collection is done online, using the Google Form website. Researchers distributed questionnaires to tourists using social media who had visited Banten province. This study uses a convenience sampling technique, where the sample members are respondents who are easy to find, and the convenience makes data collection more effective and efficient since it saves time and costs. The sample in this study was 290 tourists who had visited Banten Province. The technique of collecting data in this study used an online questionnaire, data analysis using structural equation modeling (SEM) by applying SmartPLS 3.0 software. Based on the results of data analysis, the results show there is a significant relationship between supply chain management and competitiveness, a significant relationship exists between supply chain management and restaurant performance and finally, competitiveness influences positively the restaurant performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".