Tourist visiting interests: The role of social media marketing and perceived value
Bibliographic record
Abstract
This research is intended to measure the influence of Social Media Marketing which uploads tourist destinations in Banten Province with interest in visiting mediated by perceived value. The research method uses a hypothesis testing model and uses a cross sectional model, where data is collected completely within a certain time. The study uses a convenience sampling technique, where the sample members are respondents who are easy to find, and this convenience makes data collection more effective and efficient since it saves time and costs. The sample in this study were 290 tourists who had visited Banten Province. The technique of collecting data in this study used an online questionnaire, data analysis using structural equation modeling (SEM) using SmartPLS 3.0 software. The study concluded that there was a significant relationship between Social Media Marketing and Perceived Value. There was a significant relationship between Social Media Marketing and Visiting Interests. There was a significant relationship between Perceived Value and Visiting Interests.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".