Exploring Configurational Factors Influencing Online Privacy Protection Behaviors of Internet Users

With the rapid development of the digital economy, internet users' privacy protection behaviors have become a focal point for both academia and industry. This study adopts a configurational perspective and employs fuzzy-set Qualitative Comparative Analysis (fsQCA) to investigate the synergistic mechanisms of multiple factors influencing users' privacy protection behaviors. Integrating social cognitive theory, the research constructs an analytical framework encompassing individual cognitive factors (e.g., data privacy sensitivity, self-efficacy, perceived risks/benefits) and social-environmental factors (e.g., descriptive norms, subjective norms, platform trust). Based on 357 valid questionnaires, the study identifies core condition configurations driving high-level privacy protection behaviors. Key findings include: Five distinct paths explain high-level privacy protection behaviors, with "risk-benefit trade-off" (high perceived risk + low perceived benefit) and "social norm-driven" (high descriptive norms + high subjective norms) as typical patterns; Substitution effects exist between individual cognitive factors (e.g., self-efficacy) and environmental factors (e.g., platform trust), with different user groups relying on distinct condition combinations; Configurational analysis reveals "multiple conjunctural causality" in privacy behaviors, suggesting traditional linear regression may underestimate synergistic effects among variables. The study provides differentiated strategy insights for platforms to optimize privacy design and extends the application of privacy calculus theory in configurational analysis.