Latent privacy management profiles on algorithmic social media: cross-cultural insights into privacy protection motivations and management behaviors

Abstract Personalized marketing driven by AI-powered algorithms on social media has introduced significant privacy challenges, prompting users to adopt various privacy management strategies. This study employs latent profile analysis (LPA) to identify distinct user profiles based on privacy management patterns and explore the protection motivation factors that predict them. Using a cross-national survey in the United States and Singapore (N = 2,078), we identified four latent user profiles in both countries. “Privacy-benefit Maximizers” actively engaged in all privacy management strategies, including information withdrawal, disclosure management, and avoidance. The “Privacy Unnerved” group relied predominantly on avoidance strategies while showing low engagement in other privacy practices. “Balanced Guardians” adopted a moderate approach across all strategies. The fourth group, unique to each country, exhibited distinct patterns in their reliance on privacy management strategies. Among privacy motivation factors, privacy self-efficacy emerged as a key predictor of profile membership across different user groups in both countries.