The influence of 360-degree VR videos on tourism web usage behaviour: the role of web navigability and visual interface design quality
Bibliographic record
Abstract
Purpose Using the stimulus–organism–response (SOR) theoretical model, this empirical research aims to examine and validate how tourists’ interactions with 360-degree virtual reality (VR) videos incorporating Web navigability and visual interface design quality (stimulus) on travel websites can make tourists more engaged and then satisfied (organism), which could make them more likely to visit destinations and spread electronic word of mouth (e-WOM) (response). Design/methodology/approach Using the convenience sampling method, 975 responses were collected through a questionnaire. The data were analysed using Smart-PLS 4 software to investigate the hypothesised relationships. Findings The findings demonstrate that 360-degree VR videos significantly create Web user engagement, and Web user engagement is significantly associated with Web user satisfaction. The result shows that Web user engagement and satisfaction impact the intention to visit the tourist destination. Additionally, Web user satisfaction impacts e-WOM. Lastly, navigability and visual interface design quality are significantly moderated between 360-degree VR videos and Web user engagement. Research limitations/implications This research only examined the tourists’ behavioural intentions with 360-degree VR videos during the pre-travel phase. In contrast, future research may investigate how tourists behave with VR during the on-site and post-travel phases. Practical implications Tourism marketers should not only adopt 360-degree VR but also integrate Web navigability and visual interface design to boost Web users’ engagement on tourism websites. Originality/value This study advances the study of VR from the tourists’ psychological perspective. Moreover, the SOR theory has been re-examined from the VR and technology tourism perspectives. It provides a general understanding of the behavioural aspects of tourists in developing countries.
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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 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".