An empirical study of data security risks in China’s intelligent connected vehicles: Public information processing and behavioral intentions

Abstract

With the rapid development of the global digital economy and the continuous expansion of the Internet of Vehicles (IoV) industry ecosystem, the risks associated with data security, software vulnerabilities, communication hijacking, and privacy breaches in connected vehicles have become increasingly pronounced. Automotive data security now presents unprecedented complexities and challenges. Balancing data security and the development of intelligent connected vehicles has become a critical issue for the global automotive industry. Understanding how the public processes risk information and the key factors influencing their behavioral intentions in response to data security concerns in intelligent connected vehicles is essential for promoting the sustainable development of the IoV industry. In this study, we examined a typical data breach incident involving Chinese intelligent connected vehicles that occurred in December 2022 and conducted a survey of 468 vehicle owners. Using a two-stage hybrid Partial Least Squares Structural Equation Model (PLS-SEM) and Artificial Neural Network (ANN) technique, we analyzed how the public processes information and forms behavioral intentions in response to this risk, as well as the relevant influencing factors. The results indicate that product knowledge, risk perception, and systematic information processing significantly affect citizens' coping behavioral intentions. Among them, product knowledge emerges not only as the strongest predictor of behavioral intentions but also as a key antecedent of risk perception, information need, and information seeking. Risk perception also facilitates information need and systematic processing to varying degrees. Furthermore, information need plays a significant mediating role between both product knowledge and information processing, as well as between risk perception and information processing.