Unveiling opportunities and challenges of the metaverse in the tourism and the hospitality sector: A qualitative investigation
Bibliographic record
Abstract
Purpose – Owing to recent technological advancements and the growing use of virtual communication tools, particularly during and after the COVID-19 period, this research has investigated the usefulness of immersive technologies in the hospitality industry. Moreover, this research has obtained a deep and comprehensive understanding of how the metaverse can pose challenges and opportunities for employers and users in the tourism sector. Design/methodology/approach – We utilized an exploratory qualitative approach and conducted interviews with fifteen industry experts who were actively involved in Morocco’s tourism and hospitality sector. Based on the use of a thematic analysis approach, the findings have been presented. Findings – The findings suggest that the metaverse has provided numerous benefits for promoting tourist destinations through immersive and personalized virtual experiences. However, it has also presented challenges related to cost, security, data protection, and accessibility. Overall, this research contributes to the understanding of the possibilities offered by immersive technologies in the field of hospitality and will serve as a foundation for further research in this ever-evolving domain. Originality/value – Extensive research has examined the metaverse’s theoretical applications in tourism and hospitality. However, empirical validation remains scarce. This study addresses this gap, being one of the first in North Africa to leverage qualitative methods for in-depth exploration. We validate theoretical propositions and provide unique insights into the metaverse’s impact on both tourism employers and users. Our analysis informs recommendations for successful hotel adoption, particularly in emerging economies facing a spectrum of opportunities and challenges.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".