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Unraveling the dynamics of digital equality and trust in AI-empowered metaverses and AI-VR-convergence

Author

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  • Jin, Seunga Venus
  • Ryu, Ehri

Abstract

Metaverses may present innovative channels for business and finance, education, and workplaces. Drawing from the Diffusion of Innovation Theory and the Unified Theory of Acceptance and Use of Technology (UTAUT) as well as the literature on digital equality, this research attempts to unravel the dynamics of digital equality and trust in AI-empowered metaverses for various industry sectors. Three cross-sectional surveys (NTotal = 1086) examined US Internet users' intention to adopt metaverses for business, education, and workplaces. Structural equation models were estimated using Mplus 8.8. Study 1 indicates dynamic relationships among digital equality (commerce dimension), blockchain transparency, privacy concerns, and adoption intention. Study 2 shows dynamic associations among digital equality (educational, social, and political dimensions), digital adaptability, loneliness, and adoption intention. Study 3 demonstrates dynamic interconnections among digital equality (labor, government, and health dimensions), digital adaptability, identity threat, and adoption intention. Across three datasets, trust mediates the relationship between digital equality and adoption intention. Theoretical contributions to the emerging literature on decentralized finance (DeFi), AI-driven digital transformation, and AI-VR-convergence are discussed. DeFi businesses, enterprises, policy makers, educators, professional training providers, and workforce developers need to consider third-level digital (in)equality and digital adaptability in developing equitable, sustainable, and inclusive user experience (UX) in AI-empowered metaverses.

Suggested Citation

  • Jin, Seunga Venus & Ryu, Ehri, 2025. "Unraveling the dynamics of digital equality and trust in AI-empowered metaverses and AI-VR-convergence," Technological Forecasting and Social Change, Elsevier, vol. 210(C).
    • Handle: RePEc:eee:tefoso:v:210:y:2025:i:c:s0040162524006759
    DOI: 10.1016/j.techfore.2024.123877
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