Intention to apply e-commerce in marketing communication activities in the supply chain of community-based tourism in Vietnam
Bibliographic record
Abstract
This study represents a survey data of 466 community-based tourism establishments in the northern provinces of Vietnam such as, Yen Bai, Ha Giang, Tuyen Quang etc., with strongly developed community-based tourism activities based on the technology acceptance model (TAM). The results show that perceived ease of use of e-commerce has a positive and strong impact compared to perceived effectiveness of e-commerce on the intention to apply e-commerce to marketing communication activities in the supply chain of community-based tourism in Vietnam. In addition, if community-based tourism businesses perceive themselves as being modern, it will positively affect the perception of the ease of use and effectiveness of e-commerce, thereby indirectly bringing about a positive impact on the intention to apply e-commerce. Conversely, if they perceive themselves being traditional, it will negatively affect this relationship. Accordingly, this study helps provide practical evidence for promoting the application of e-commerce in tourism in remote, economically difficult areas in Vietnam and elsewhere. Nonetheless, the study remains limited when it has not been done a multi-group analysis to consider different influences of the factors of region, destination characteristics, type of tourism on intention to apply e-commerce for marketing communication activities in community-based tourism establishments.
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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 | 0.000 | 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".