How Virtual Tourism Environment Influences Purchase Intention? The Role of Mental Imagery and Affective Forecasting
Bibliographic record
Abstract
Given the continuous advancement of information technologies in the presentation of online products, tourism practitioners have already increased the use of technology-mediated preview modes: web-based tours (2D) and virtual reality (VR). These digital technologies have influenced the way through which tourists search, book, plan, and experience travel (Beck & Egger, 2018). The existing body of tourism research has started to recognize the process of predicting future travel (Karl et al., 2021); however, far too little attention has been paid to affective forecasting in the tourism literature Specifically, drawing on the PAD model, this research proposes a model that examines the effect of mental imagery (in VR vs. 2D) on purchase intention through and affective forecasting, and the moderating role of temporal distance on the association betwwen mental imagery and purchase intention through affective forecasting. An experimental study was conducted and the research model was tested using PROCESS macro with a sample of 526 participants. The finding demonstrated that VR surpasses 2D as a destination marketing tool due to its ability to break the temporal distance, resulting in greater predicted pleasure, predicted arousal and predicted dominance, and purchase intention. This study offers valuable insights for tourism and hotel managers who are considering the use of VR technology compared to 2D.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.008 | 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".