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Trepte, S., Teutsch, D., Masur, P.K., Eicher, C., Fischer, M., Hennhöfer, A., et al. (2015) Do People Know about Privacy and Data Protection Strategies? Towards the “online Privacy Literacy Scale” (OPLIS). In: Gutwirth, S., Leenes, R. and de Hert, P. Eds., Law, Governance and Technology Series, Springer, 333-365.
https://doi.org/10.1007/978-94-017-9385-8_14

has been cited by the following article:

• TITLE: Factors Influencing Online Privacy Literacy in Generative AI Contexts: A Multidimensional Development Theory Perspective Based on Undergraduate Students in Zhejiang, China

  AUTHORS: Yuhan Xue, Luhao Wang, Yanqing Wang

  KEYWORDS: Multidimensional Development Theory, Online Privacy Literacy, Generative Artificial Intelligence, PLS-SEM, Privacy Concerns

  JOURNAL NAME: Open Access Library Journal, Vol.13 No.5, May 26, 2026

  ABSTRACT: With the rapid diffusion of generative artificial intelligence (Gen AI), its widespread use among undergraduate students has introduced new privacy challenges. Drawing on multidimensional development theory (MDT), this study examines the determinants of online privacy literacy in Gen AI contexts, using Internet Users’ Information Privacy Concerns (IUIPC) as a proxy. Data from 224 undergraduate students in Zhejiang Province were analyzed using PLS-SEM. The results show that risk aversion, information sensitivity, perceived privacy protection measures, and social presence have significant positive effects on IUIPC, with risk aversion being the strongest predictor. In contrast, familiarity with government privacy regulations, prior privacy invasion experience, internet knowledge, and perceived benefits of information disclosure are not significant. These findings indicate that privacy concern formation in Gen AI environments is context-dependent and differs from traditional online settings. The study extends MDT to human–AI interaction contexts and provides implications for privacy education and platform design.Subject AreasArtificial Intelligence