<![CDATA[Background: As precision push services (PPS) become increasingly embedded in mobile communication ecosystems, understanding how users perceive and respond to these services has become a pressing research concern. While prior studies have focused on technical accuracy and personalization algorithms, limited attention has been paid to how experiential and perceptual factors collectively influence user engagement and behavioral outcomes. Objective: This study aims to construct and empirically validate a comprehensive user-centered evaluation model for precision push services. It seeks to identify which experiential dimensions most significantly influence user perception, and how these perceptions translate into behavioral responses. Methods: A conceptual framework was developed integrating five experiential predictors—message validity and quality, non-interference with user experience, operability, user choice, and information transparency—alongside two perceptual mediators (effect and impact) and one behavioral outcome. A structured questionnaire using an eight-dimensional Likert scale was administered to 279 university students across multiple institutions. Data analysis involved reliability and validity testing, correlation analysis, ANOVA, and multiple regression to examine causal relationships and demographic influences. Results: The results indicate that user choice is the most influential factor affecting both perceived effect and impact of PPS. Information transparency and message quality also significantly predict perceptual outcomes, while non-interference showed strong correlations but no direct causal influence. The impact of push services emerged as a stronger determinant of user behavior than perceived effectiveness. Gender and geographic differences were statistically controlled and found to have minimal effect on the primary causal pathways. Conclusion: The study highlights the importance of user autonomy, transparency, and meaningful content delivery in designing effective PPS systems. By validating a full causal model and identifying critical user-centered variables, the research provides actionable insights for improving user engagement, trust, and behavioral response in personalized mobile push environments. Keywords: Precision Push Service, User Perception, Causal Modeling, Personalization, Personalization]]>
