Determinants of revisit intention on rafting tourism in Bali mediated by trust and brand love
Bibliographic record
Abstract
This research used a quantitative method designed based on positivism to examine the determinants of revisit intention on rafting tourism MSMEs in Bali Mediated by trust and brand love. Data were collected from 328 Indonesian Tourists with rafting history using questionnaires. The data collected were descriptively and inferentially analyzed using SPSS_29 and SEM with SmartPLS_3 software, respectively. The results showed that 1) The effect of Attitude, perceived risk, trust, and brand love, on revisit intention had a positive significant, while the effect of service quality had a positive insignificant; 2) Trust is positively and significantly influenced by attitudes, service quality, and perceived risk; 3) brand love is positively and significantly influenced by attitude; 4) Trust is able to partially mediate the influence of attitude, perceived risks, on revisit intention, while on the effect of service quality on the revisit intention is full mediation; 5) Brand love plays a role in mediating influence of attitude towards revisit intention. Theoretically, contributed to the enrichment of the Theory of Planned Behavior, Experiential Marketing, and Risks. In conclusion, practical implications are needed to educate managers on how to make rafting very memorable, hire guides who master the terrain, determine affordable prices, manage brand, and make rafting tours in Bali more attractive to increase intention to revisit. Practical implications for local governments for the accuracy of data on the number of visits require an integrated and connected system, as well as the need for guaranteed protection and security for tourists.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".