Services Marketing Mix of Starbucks Coffee in Bangkok, Thailand
Bibliographic record
Abstract
The purposes of this study were to identify the importance of services marketing mix influencing the Starbucks consumers in Bangkok, Thailand, to examine the relationship between personal factors and consumer behaviors and to examine the relationship between services marketing mix and consumers behaviors.A questionnaire survey was used. The sample was 400 Starbucks coffee consumers. It was found that the respondents were female and male of 63.90 percent and 36.10 percent respectively. The majority respondents were in age group 21-30 years old, the majority occupation were office workers with level of monthly income more than 30,000 baht. The majority of respondents went to Starbucks coffee 1-2 times a week. The average expenditure of each purchase was 101-200 baht with the beverage size of Grande (16 oz.).The majority favorite beverage was Iced Cappuccino. The reason for buying Starbucks beverages was the flavor of coffee.The results indicated that the most importance of services marketing mix was product, process, people, place, physical environment, promotion and price, respectively. Both personal factors and components of services marketing mix affected consumer behaviors. The results showed that gender affected three consumer behaviors; the frequency of visit at Starbucks shop, cup sizes and favorite beverage. Career affected all five consumer behaviors. Age affected the frequency of visit the Starbucks shop and favorite beverage. While education and income level affected two consumer behaviors; the frequency of visit the Starbucks coffee shop and expenditure of each purchase.
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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 | 0.000 |
| 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) | 0.004 | 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".