Explorative Study of Tourist Behavior in Seeking Information to Travel Planning
Bibliographic record
Abstract
This study aims to explore the behavior of domestic tourists in seeking information to travel during the COVID-19 pandemic and whether there are significant differences with tourist behavior before the pandemic and what are the best strategies so that they can be helpful in tourism actors in creating and developing digital marketing strategies based on the latest information technology phenomena. This study uses a qualitative paradigm. Data collection techniques used in the study were observation, in-depth interviews, literature study, and documentation. The results of this exploratory research can then be used as a basis for the following research stage, namely descriptive analysis. Online focus group discussions and surveys were conducted to achieve the objectives of this research. Tourist behavior during the COVID-19 pandemic has changed, that tourists will always look for travel information using window shopping or online search through social media and ask personal questions (individuals) in travelling to a tourist destination. The right strategy for tourism actors in developing sustainable digital marketing includes building personal trust to tourists, implementing innovation strategies that involve guests, and mutual integration and collaboration, which are the main keys to the success of tourism actors in creating or developing sustainable digital marketing. The COVID-19 pandemic is an unprecedented and ongoing crisis for the global tourism industry. The key to the recovery of the worldwide tourism industry will be to encourage tourism activities both domestically and internationally.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".