A study on the Eating Out Behaviors of a Cold Noodle Restaurant Customer
Bibliographic record
Abstract
This study was to analyse the eating out behaviors of customers who visit a Koran restaurant especially focused on CNR(cold noodle: naengmyun restaurant) and to find out the marketing promotion points. Through the snowball sampling, 423 customers data were surveyed in summer and winter as respects of seasonal variation. The collecting data were analysed descriptive data and statistical different using the Statistical Package for the Social Science(SPSS version 10.0). The results were as follows; The participants of the study were composed of 209 man(49.4%) and 204 woman(50.6%). Most customers were 30's(36.2%), office worker(27.5%) and spend 5,00010,000 won(46.3%) for eating out. The consumer more preferred a specialty restaurant, the reason was to expect better taste(37.1%). In visiting CNR, the customer frequently ordered complement menu(90.1%) with cold noodle, complement menu should be developed periodically. The important factor to visiting CNR was the accessing convenience for the shop and desirable taking time was within 15 minutes. The buckwheat noodle in broth(mulnaengmyun) was the most favorite selecting menu. And the noodle texture was key evaluation factor in all types of cold noodle and the other factor was different according to the types of cold noodle. The visiting frequencies of CNR were not significantly different according to seasonal variation and sociodemographic variable. Above the half of customers visited at CNR with his/her family. This study find out the suggestion that consumer eating concepts about CNR was family eating therefore the cold noodle. specialty restaurant should be create more delight atmosphere and developed menu for families' eating out place.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".