Exploring Generation Z Motivations to Use Metaverse for Travel Planning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Technology‐enabled travel planning has been adopted by businesses and consumers. Travel portals and aggregators increasingly offer technology tools, such as metaverse, AI applications, and chatbots to facilitate travel. The consumer motivations to use metaverse as a travel planning tool and its effect on purchase intention have been underexplored. To bridge this gap, this study explores how different dimensions of motivated consumer innovativeness (MCI) influence consumer attitudes toward metaverse and use intention. The study utilizes a sequential mixed‐method approach consisting of two phases. Phase 1 collected qualitative data through interviews with 30 Generation Z (Gen Z) adults with metaverse virtual travel and travel planning experience. Based on Phase 1 findings, Phase 2 surveyed 354 participants and applied quantitative analysis. The study revealed that Gen Z tourists were motivated by functional and cognitive factors when engaging with the metaverse. The study explains the role of metaverse in travel planning and offers practical implications for travel and tourism stakeholders. The findings highlight the need for engagement strategies that blend technological innovation with immersive experiences to align with Gen Z's views on innovation and interaction in order to enhance the metaverse experience.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it