Analysis of Usage Factors of Restaurant Customers in Traditional Markets
Bibliographic record
Abstract
Background/Objectives: The traditional market, which was at the center of the domestic distribution industry, is undermined by the trend of large size, specialization, and service. Successful cases are often seen through the coexistence of traditional markets and dining services. In the traditional market, eating out is the main product, and the customer's preference is very high.Methods/Statistical analysis: The purpose of this study is to summarize the usage factors that are important to customers of traditional market restaurants, and to analyze the priorities of the usage factors through the Analytic Hierarchy Process (AHP). In this study, we analyzed the level1 factors of traditional market restaurant customers into traditional market environment, restaurant environment, and food feature, and proposed an analysis model that classifies detailed factors of each factor.Findings: The analysis results are as follows. First, Level 1 showed relatively high importance in order of food feature (0.41), restaurant environment (0.33), and traditional market environment (0.26). Second, the usage factor of food feature had high priority of flavor and price. In the restaurant environment, cleanliness and kindness were high priority. In the traditional market environment, cleanliness and accessibility were important. Third, overall priority was high in order of flavor, price, and restaurant cleanliness.Improvements/Applications: These results will help to improve the management of vulnerable and small traditional market restaurants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".