Satisfaction with Behavior Trajectory Security Protection in Social Media Intelligent Recommendations

Abstract

Abstract Intelligent recommender systems provide personalized recommendation for users based on their behavior trajectories. Intelligent recommendation is a double-edged sword with increasing impacts. This study investigates the influencing mechanism of social media users’ satisfaction with behavior trajectory security protection in the context of intelligent recommendation, with the aim of promoting healthy development of mobile social media. This study applied the grounded theory method to identify relations among concepts and categories in terms of three-level coding. During open coding, 271 initial concepts and 26 subcategories were elicited; during axial coding, 10 categories were elicited; and during selective coding, relations among categories were identified and a theoretical model was developed. The results indicate that satisfaction with behavior trajectory security protection is directly influenced by security protection information quality, security protection system quality, security protection service quality, net benefits, external support, privacy concerns, and privacy cognitive biases. Privacy literacy has direct impacts on privacy concerns and privacy cognitive biases. Meanwhile, herd behavior directly impacts privacy concerns. Implications for theory and practice are discussed.