Supply chain management for water tourism in northeast Thailand
Bibliographic record
Abstract
This research aimed to study the factors affecting the supply chain efficiency of water tourism entrepreneurs in the Northeast provinces of Thailand around the Mekong River Basin. A questionnaire was distributed to and subsequently collected from 246 samples in 7 provinces including Loei, Nong Khai, Bueng Kan, Nakhon Phanom, Mukdahan, Ubon Ratchathani, and Amnat Charoen by proportional allocation and convenience sampling using Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) by ADANGO. According to the results, it was found that customer orientation directly affected the supply chain efficiency of water tourism. Knowledge management directly affected the supply chain efficiency of water tourism and supply chain marketing implementation capability as a transmission factor for the supply chain efficiency of water tourism. For these reasons, water tourism entrepreneurs should focus mainly on customer orientation with full-service efficiency. Internal management should be planned as well, e.g., communication training, service training and cultural revitalization of communities around tourist attractions through cooperation with involved agencies, which will enhance the supply chain efficiency of water tourism.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".