Digital marketing based on social media marketing in marine tourism destinations
Bibliographic record
Abstract
The aim of this research is to empirically test a new marketing model from a digital marketing perspective based on social media marketing, which is a crucial factor for consumers in the digital era when making decisions (especially tourists) to visit the Togean Islands marine tourism destination in Tojo Una-Una Regency. Social media marketing comprises three dimensions known as 4C: context, communication, collaboration, and connection. This type of research employs exploratory or confirmatory research methods. The data analysis method utilizes the PLS-SEM version 4 approach, and the research sample consists of 160 respondents, including both foreign tourists and Indonesian tourists. The research results indicate that the communication dimension has the highest loading factor value of 0.813, followed by collaboration with 0.770, connection with 0.745, and context with 0.703. Furthermore, the path coefficient value for the collaboration dimension is the highest compared to the other three dimensions, at 0.813. These findings imply that decision-makers can derive meaningful insights for redesigning new marketing models in the tourism sector amidst the digitalization era.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".