Emotional influences on user continuous use intention in recommended news apps: A study of algorithm appreciation and fatigue within the cognition-affect-conation framework

Based on the Cognition-Affect-Conation Model, this study explores the role of emotions in the relationship between users' perceptions and intention to continue using recommended news apps. TouTiao (recommended news application with the largest number of registered users in China) was chosen as the research object and a total of 1014 valid questionnaires were collected. The results based on mediation and moderation tests show that algorithm appreciation plays a mediating role among the perceived news value, the perceived recommendation personalization, the perceived recommendation explainability and the continuance usage intention. Algorithm fatigue plays a mediating role among users' perceived information narrowing, perceived content low quality, perceived information overload and continuance usage intention. Privacy concerns negatively moderate the positive effect of algorithm appreciation on continuance usage intention, while positively moderating the negative effect of algorithm fatigue on continuance usage intention. To enhance users' continuance usage intention recommended news apps, it is necessary to pay attention to the two emotional elements of algorithm appreciation and algorithm fatigue, thereby improving user experience, and achieving a win-win situation between the platform and users.