Evaluating the barriers affecting cybersecurity behavior in the Metaverse using PLS-SEM and fuzzy sets (fsQCA)

While offering novel user experiences, the Metaverse introduces complex cybersecurity challenges due to the sophisticated interaction of augmented reality (AR), virtual reality (VR), and web technologies. Addressing the barriers to cybersecurity behavior is essential to protect users against risks such as identity theft and loss of digital assets. Therefore, this research aims to investigate these barriers by developing a theoretical model that draws factors from the Technology Threat Avoidance Theory (TTAT) and considers variables such as privacy concerns, perceived risks, and response costs. The data were collected from 395 Metaverse users and were analyzed using the Partial Least Squares-Structural Equation Modeling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA). The PLS-SEM findings showed that perceived threats, privacy concerns, and response costs have a significant negative impact on cybersecurity behavior, while perceived risks have an insignificant negative influence. The fsQCA results revealed that there is not a single pathway leading to robust cybersecurity behavior. Instead, eight configurations that include the presence and absence of certain conditions can lead to this desirable outcome. The findings not only advance the academic conversation on Metaverse security but also offer actionable strategies for stakeholders to reinforce user protection in this dynamic virtual environment.