The effect of the marketing mix on the demand of Thai and foreign to
Bibliographic record
Abstract
This research aimed to analyze the influence of the marketing mix on the demand of Thai and foreign tourists along the Mekong Riverside in Nong Khai and Bueng Kan provinces, Thailand that linked to the Lao People's Democratic Republic (Lao PDR), in particular the Vientiane Capital and Bolikhamxay province.Questionnaires were used to collect the data from 410 samples selected by convenience sampling.The data were analyzed via structural equation modeling (SEM) with the WarpPLS 6.0 program.The results showed that the marketing mix comprising product, price, promotion, and process affected the demand of tourists.In consequence, the tourism-related units of Thailand and Lao PDR should focus on a touristic marketing strategy by conserving the standard of the tourist attractions, touristic marketing promotion, and an appropriate price.Moreover, the related stakeholders should become aware of providing services to tourists as means to respond to the demand of Thai and foreign tourists.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".