How to mitigate privacy cynicism? The role of dual-list design in activating users' privacy-protective behaviors

Privacy cynicism is a pervasive and complex phenomenon that often leads users to ignore privacy concerns and inhibits privacy-protective behavior. However, few studies have proposed or empirically tested design mechanisms aimed at mitigating privacy cynicism. To address this gap, we integrate design science with experimental and survey research to examine the influence of the design of dual lists, an emerging privacy‑notice mechanism mandated in China on privacy cynicism reduction during app use. Drawing on Construal Level Theory (CLT), we identify and implement three dual-list design features, namely data sharing occurrence, data type label, and dual-list salience, which are theoretically linked to three distinct dimensions of psychological distance. Using the Elaboration Likelihood Model (ELM), we further propose distinct routes through which these features influence users' privacy cynicism. Results show that data sharing occurrence and data type label mitigate users' privacy cynicism by reducing privacy uncertainty via the central route of information processing, both independently and through their interaction. Dual-list salience mitigates privacy cynicism through the peripheral route, ultimately shaping users' privacy-protective behavior. Interestingly, dual-list salience is effective in mitigating privacy cynicism only when the occurrence of data sharing is disclosed. We also find that privacy uncertainty exerts a double-edged effect on users' privacy-protective behavior through a competitive mediation mechanism. Theoretically, this study advances privacy cynicism research by shifting the focus from explanation and prediction to theory‑driven, design-based interventions. Practically, this study offers actionable insights for users, service providers, and policymakers seeking to foster a more trustworthy and healthier digital ecosystem.