Peran Keputusan Berkunjung Dalam Meningkatkan Pengaruh Pemasaran Media Sosial Dan Citra Destinasi Terhadap Kepuasan Wisatawan Di Taman Wisata Alam (TWA) Angke Kapuk
Bibliographic record
Abstract
Tourist satisfaction has been extensively discussed in various academic studies owing to its potential to yield managerial benefits while simultaneously upholding the sustainability of tourist destinations. Prior research has yielded varied findings concerning the impact of social media marketing and destination image on tourist satisfaction. Discrepancies in these findings have prompted inquiries into potential mediating variables. This study aims to investigate the direct influence of social media marketing and destination image on tourist satisfaction, as well as the potential mediation by visiting decisions. Data was collected via questionnaires administered to 154 tourists at TWA Angke Kapuk and analyzed using path analysis techniques. The findings revealed that both social media marketing and destination image significantly and positively influenced visiting decisions and tourist satisfaction. Moreover, the impact of social media marketing and destination image on tourist satisfaction was found to be greater when mediated by visiting decisions. Consequently, managers at TWA Angke Kapuk must enhance the utilization of various social media platforms and enrich content to bolster the positive perception of the destination.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".