Bibliographic record
Abstract
This research aims to explore the benefits of budget coffee shop chains pursued by the consumers, conduct cost effectiveness analysis on product purchases, compare the differences between the different types of consumer characteristics, and attempt to conduct an in-depth analysis on the budget coffee shop chains’ current situations and their relation with consumer behavior. The source of the research sample is 480 randomly selected consumers in Taichung, including 168 males and 312 females. The data was collected via a closed questionnaire, and the linear relationships for basic individual information and consumer behavior in regards to consumer consideration and characteristics were tested via the SPSS version 12.0 of hierarchical regression. The results showed that, amongst elements of consumer considerations, ‘server friendliness’ seems to be most important, and ‘media advertising’ seems to be the least important. As for consumer characteristics, ‘personal preference’ is most important, and ‘coffee has already become a part of life’ is the least important. As indicated from the regression analysis, consumers with monthly incomes between NT$20,000 and NT$30,000 have significantly higher consumer characteristics than consumers with monthly incomes of lower than NT$20,000. Moreover, consumers with steady jobs possess significantly higher consumer characteristics than consumers without jobs. Consumers purchasing more than once a week have higher consumer characteristics than those who make purchases less than once a week, and weekday consumers have significantly higher consumer characteristics than weekend consumers.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".