Exploring Generation Z Motivations to Use Metaverse for Travel Planning
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".