Enhancing user information disclosure intention in dynamic conversations of intelligent recommendation systems based on large language models: A perspective of user gratification and privacy calculus

Intelligent recommender systems, as powerful tools for users to provide personalised recommended content, have penetrated into all aspects of life, influencing users' behaviour and experience. However, research on the impact of the interaction process between users and intelligent recommender systems on user use and disclosure is still limited. In order to fill this research gap, this study is based on the Use & Gratification Theory and the Privacy Disclosure Theory, conducting a research model under the Use-Gratification-Disclosure framework, exploring the use and satisfaction process of users when using intelligent recommender systems as well as the mechanism of the influence on the information disclosure intention, and analysing the variations between the elders and youths. The results indicate that diverse types of use processes lead to different sorts (utility, hedonic, and social) as well as various degrees of satisfaction for users. The relative satisfaction obtained by users had a significant positive effect on intention to disclose information. Moreover, this effect differed significantly between different age groups. The above results provide novel and valuable insights into understanding users' experiential needs, use satisfaction, and disclosure behaviour in the field of human-computer interaction.