Exploring the role of digital marketing and brand image on the decisions to visit tourists to improve the community's economy in Indonesi
Bibliographic record
Abstract
This research aims to analyze the relationship between brand image and visiting decisions, digital marketing and visiting decisions, and brand image with digital marketing. This research method is quantitative through surveys, research data was obtained by distributing online questionnaires to 489 tourism industry managers who were selected using a simple random sampling method, and the online questionnaire was designed using statement items with a Likert scale of 1 to 7. Data analysis used a structural equation model based on covariance (CB-SEM) with SmartPLS 4.0 software to analyze research data. The independent variables are brand image and digital marketing, and the dependent variable is the decision to visit. The stages of data analysis are validity testing, reliability testing, model suitability testing and significance testing of hypothesis testing. The results of this research are that brand image has a positive and significant relationship with the decision to visit, digital marketing has a positive and significant relationship with the decision to visit, and brand image has a positive and significant relationship with digital marketing. Based on the results of this research, several suggestions can be made that tourism must explore the potential of various digital marketing channels. From the results of this research, the channel with the lowest contribution is the presence on social media which is still very minimal and the quality of the newsletter is less attractive. The researcher's next suggestion is to take advantage of the marketing potential of social media. Tourism does not only rely on guest recommendations to get the next guests but must be more active in various marketing efforts, especially digital marketing because the enormous potential in disseminating information about tourism's unique selling points through digital media is unlimited.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".