Supply chain performance and visit interest of restaurants: The role of buzz and viral marketing strategic
Bibliographic record
Abstract
The purpose of this study was to analyze the relationship between buzz and viral marketing strategy on supply chain performance and visit interest of restaurants in Banten Indonesia. This type of research used explanatory with a quantitative approach using SEM-based variance analysis, this is because the dependent and independent variables in this study amounted to more than one so that they could use variance-based SEM to summarize the formulation of the analysis. The study was conducted on 120 restaurant owner respondents in the province. Banten Indonesia. The distribution of the questionnaire in this study was carried out in two stages, namely the distribution of online questionnaires via google form to restaurant owner consumers. The sampling technique used in this study is accidental sampling, namely the determination of the sample based on accidental samples. The results of this study are buzz marketing has a significant effect on supply chain performance, buzz marketing has a significant effect on visit interest, viral marketing has a significant effect on supply chain performance, viral marketing has a significant effect on visit interest, visit interest has a significant effect on supply chain 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 | 0.001 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".