The role of spiritual destination image in mediating attachment to virtual tours and social media promotion on return visit intentions
Bibliographic record
Abstract
This study intends to evaluate the role that spiritual destination image plays in mediating the effects of attachment to virtual tours and the promotion of spiritual destinations through social media with the intention to revisit spiritual places in Bali. The research primarily examines individuals who have participated in tourism-related endeavors and visited spiritual destinations in Bali. A purposive selection strategy was used to choose a sample size of 160 tourists for the study. The analytical methodology employed in this study involves the utilization of Path Analysis, specifically implementing Structural Equation Modeling with Partial Least Squares (SEM-PLS). The study found that emotional attachment with virtual tours improves the spiritual destination's image and intention to revisit. The impact of social media promotion on the perception and intention to revisit a spiritual place has been found to be substantial and beneficial. Furthermore, the portrayal of the spiritual destination has a notable and favorable impact on an individual's propensity to partake in subsequent visits. Furthermore, the image of a spiritual destination is able to mediate attachment to a virtual tour and social media promotion with the intention to revisit partially. Hence, it is imperative for managers overseeing spiritual destinations in Bali to consistently devise virtual tour models and employ social media promotions to enhance the image of these spiritual destinations. This strategic approach aims to augment tourists' inclination to visit such destinations, thereby fostering sustainable tourism practices in Bali.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".