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Research Article · Open access · Published 21 September 2026

The Impact of Design and Security on Generation Z’s Continuance Intention in the AI-Enhanced Metaverse

Shailja Tripathi1

Journal of Computer Science, Volume 22, Issue 9, pp. 2834–2849 · doi.org/10.3844/jcssp.2026.2834.2849

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Abstract

Artificial Intelligence (AI) and the Metaverse are the source of quick and extensive changes in the virtual world. Unfortunately, such a point is rarely discussed in the academic community and the reasons why users may be able to accept an AI-enhanced Metaverse environment are not well explored. The purpose of this study is to further reconnoiter the relationship between AI and the Metaverse and how AI can help improve and spread the Metaverse. To develop the research model, the TAM was combined with the Expectancy Confirmation Model (ECM). Data were collected online through an online survey. For the Metaverse, 541 people participated, with an 89% response rate. Data was analyzed with Smart-PLS 4. The results showed that the level of security in the Metaverse is not directly related to perceived usefulness or ease of use but it affects the ability users have to use the Metaverse. The findings provide valuable insights for the metaverse designers to understand the metaverse design features and its security elements and how to promote its acceptance.

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Open access. This article is licensed under a Creative Commons Attribution 4.0 International Licence (CC BY 4.0), as printed on the article, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original authors and the source. No permission is needed.

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Tripathi, S. (2026). The Impact of Design and Security on Generation Z’s Continuance Intention in the AI-Enhanced Metaverse. Journal of Computer Science, 22(9), 2834–2849. https://doi.org/10.3844/jcssp.2026.2834.2849

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CC BY 4.0
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Volume 22, Issue 9
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The Impact of Design and Security on Generation Z’s Continuance Intention in the AI-Enhanced MetaverseJournal of Computer Science · Volume 22, Issue 9 · pp. 2834–2849 · CC BY 4.0Download