An Analysis of Factors Influencing the Behavioral Intention to Use AI-Based Social Media Among Computer Science Undergraduates in Sichuan China
Mai Jiang · AU-GSB e-JOURNAL · 2026
Purpose: This study investigates the influence mechanisms of artificial intelligence (AI)-based social media on the behavioral intention of Chinese college students. Research design, data and methodology: A total of 480 computer science undergraduates from three provincial universities in Sichuan, China, were selected as research participants. Data were collected through a structured questionnaire. Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were applied to examine the interrelationships among perceived ease of use, perceived usefulness, attitude, social influence, information quality, facilitating conditions, and behavioral intention. Results: Convenience exhibited the strongest direct impact on behavioral intention (β = 0.286), followed by social influence (β = 0.229) and information quality (β = 0.229). Perceived ease of use significantly influenced both perceived usefulness (β = 0.317) and attitude (β = 0.283), while perceived usefulness (β = 0.214) and attitude (β = 0.120) had relatively weaker direct effects on behavioral intention. The model’s overall explanatory power (R²) was 20.2%, revealing a complex mediation mechanism. Conclusions: Social media platforms should optimize algorithm design to improve information quality and ease of use. Meanwhile, universities should strengthen AI ethics education and guide students to rationally use AI social media through courses and algorithm audit practices.