Water tourism demand in the Mekong River basin
Bibliographic record
Abstract
This research aimed to explore the influences of water tourism demand on tourists' revisit intention. The data were collected by a questionnaire from a sample group of Thai and foreign tourists who used a water transportation service on the Mekong River in Luang Prabang, Lao PDR. The random sampling method was used as a convenience sampling technique by quota sampling and classified by ports that served tourists in the Luang Prabang area. Then, the data were analyzed by confirmatory factor analysis (CFA) using the ADANCO software program to verify the structural validity of the latent variables and analyze the model’s consistency. The study found that factors affecting the demand for water tourism consisted of two components: (1) Tourism factors and (2) water transportation factors on the Mekong River. Considering the factors that affected the demand for water tourism, it was found that the most influential factor was the water transportation of the Mekong River, especially the creation of a service product for tourists with the purpose of leisure and the type of water transportation service. Simultaneously, demand for water tourism was found to be one of the main factors reflecting the influence of tourists' revisit intention, especially water tourism on the Mekong River. According to the study, factors affecting the demand for water tourism in the Luang Prabang area greatly affected the demand for water tourism; in addition, the demand for water tourism affected the tourists' revisit intention as well. Therefore, entrepreneurs in water tourism should pay attention to water transportation, and whether it would be the type or design of the water transportation, standards of the water transportation, security measures, and setting a clear service schedule. etc., so that tourists could make their own travel plans to be more appropriate. Furthermore, the Lao government and the private sector should adopt common and unified policies for developing water tourism and promoting publicity and tourism marketing for the effectiveness of Luang Prabang’s water tourism activities.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".