How Generative AI Operated by Large Language Models Fosters Sustainable Green Practices in Green Metaverse: Mediating Role of Green Cybersecurity

Pham Quang Huy () and Vu Kien Phuc
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Pham Quang Huy: University of Economics Ho Chi Minh City (UEH)
Vu Kien Phuc: University of Economics Ho Chi Minh City (UEH)

SN Operations Research Forum, 2025, vol. 6, issue 3, 1-36

Abstract: Abstract This research aims to enhance understanding of how generative artificial intelligence based on large language models (GAL) promotes sustainable green practices (SGP) within the framework of the green metaverse. This research targets at exploring the mediation mechanism of green cybersecurity (GC) in the connection between GAL and SGP. Importantly, it also explores the moderating function of metaverse leadership (ML) in the interactions among GAL, GC, and SGP by conducting small and medium enterprise survey. The data analysis was conducted using partial least squares structural equation modelling (PLS-SEM) technique with the assistance of SmartPLS 4.1.0.2. The outcomes confirm the significantly positive relationship between GAL and SGP. Alternatively, GC acts as a mediator in this connection. Remarkably, ML is confirmed to moderate the interconnections between GAL, GC, and SGP. This study takes a unique approach by conducting an exploratory effort to experimentally address a recognized need to improve “lessons learned” for responding to the urgent claims of sustainable development. The results can be useful for policymakers and practitioners looking to strategically implement GAL to reach SGP. These findings deepen the comprehension of how GC can serve as a thorough steppingstone for governments to get ready for SGP through the adoption of GAL. Businesses can lessen the impact of these threats by taking application use into account when designing security policies.

Keywords: Artificial intelligence; Green cybersecurity; Metaverse; Sustainable green management practices (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s43069-025-00485-2

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