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Conflicting impacts of shadow AI usage on knowledge leakage in metaverse-based business models: A Yin-Yang paradox framing

Author

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  • Chin, Tachia
  • Li, Qi
  • Mirone, Francesco
  • Papa, Armando

Abstract

The accelerated evolution of artificial intelligence (AI) technology enables firms to create metaverse-based business models (MBMs) where the use of shadow AI is prevalent. However, employees' use of shadow AI not only provides benefits but also poses risks concerning organizational knowledge leakage (OKL). How to properly manage the usage of shadow AI has become a critically important yet under-researched issue for firms within MBMs. To fill this gap, this paper considers the paradoxical nature of shadow AI usage and thereby adopts a Yin-Yang dialectical frame to investigate the conflicting mechanisms between shadow AI usage and OKL and their impacts on the sustainability of MBMs. Using a questionnaire survey method, we gathered from enterprises within metaverse-related businesses in China and employed moderated hierarchical regression and bootstrap analysis to test the hypotheses. Results show that there existed an inverted U-shaped relationship between shadow AI usage and OKL and a negative link between shadow AI usage and the sustainability of MBMs. We also examined the mediating role of OKL and the moderating role of AI knowledge-oriented human resource management practices. Theoretically, this article conducts an unorthodox Yin-Yang paradox frame to address the dynamic tensions among shadow AI usage, OKL, and the sustainability of MBMs, enriching the theoretical underpinning of cross-disciplinary research at the intersection of technological change, knowledge management, and business sustainability. Practically, our findings allow practitioners and policymakers to gain a better understanding of the paradoxical interplay between employees' unethical behavior toward AI usage and its consequences. This indicates the unconventional yet crucial challenges facing firms within MBMs in the process of fulfilling the United Nation's Sustainable Development Goals.

Suggested Citation

  • Chin, Tachia & Li, Qi & Mirone, Francesco & Papa, Armando, 2025. "Conflicting impacts of shadow AI usage on knowledge leakage in metaverse-based business models: A Yin-Yang paradox framing," Technology in Society, Elsevier, vol. 81(C).
    • Handle: RePEc:eee:teinso:v:81:y:2025:i:c:s0160791x24003415
    DOI: 10.1016/j.techsoc.2024.102793
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    3. Bai, Kaige & Liu, Yuliang, 2025. "Scarcity, symbolism, and social identity (3S): Unpacking drivers of conspicuous consumption of virtual gear in the metaverse," Journal of Retailing and Consumer Services, Elsevier, vol. 87(C).
    4. Abbas, Jawad & Dabić, Marina & Stojčić, Nebojša, 2026. "Digital divide in industry 5.0: Role of generative AI knowledge bases and intellectual capital in organizational resilience performance under territorial proximity," Technovation, Elsevier, vol. 149(C).
    5. Bao, Haixu & Liu, Wenfei & Dai, Zheng, 2025. "Artificial intelligence vs. public administrators: Public trust, efficiency, and tolerance for errors," Technological Forecasting and Social Change, Elsevier, vol. 215(C).
    6. Li, Jian & Zhao, Jingdi & Huang, Jinsong, 2025. "Social avoidance needs boost AI's nonsocial attribute valuation in secret consumption," Technology in Society, Elsevier, vol. 81(C).
    7. Xi, Yujuan & Wang, Xiangyang & Zhu, Yunxia, 2025. "How does knowledge creation really promote dynamic capabilities? The lens of routine renewal and technological turbulence," Technology in Society, Elsevier, vol. 82(C).
    8. Hajdas, Monika & Radomska, Joanna & Szpulak, Aleksandra & Kawa, Arkadiusz, 2025. "The trade-offs of metaverse value creation. Real-Time-Delphi-based scenario analysis by 2040," Technology in Society, Elsevier, vol. 83(C).

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