Research On The Users' Continuous Use Intention Of News Algorithm Recommendation APP And Its Influencing Factors

News algorithm recommendation APP has now become one of the main channels to obtain news.Although it provides convenience for users,it also generates problems such as low quality content and privacy infringement,which leads to the loss of users.In the fierce market competition,how to enhance users' continuous use intention is the focus of news algorithm recommendation APP to enhance competitiveness.This study takes Toutiao as the research object,and developed a model based on the expectation confirmation model and the cognition-affect-conation pattern to explain users' continuous use intention.Questionnaires and depth interview were employed in the survey.The research results are showed as follow.Firstly,users' expectation confirmation,perceived usefulness,perceived ease of use and perceived content quality all positively affect their satisfaction,while privacy concerns have positive impact on concern.Secondly,perceived usefulness and perceived content quality both positively affect the continuous use intention,while perceived ease of use and privacy concern have no significant influence on it.Thirdly,users'satisfaction has positive impact on the continued usage intention,and plays a mediating role in the relationship between perceived usefulness,perceived ease of use,perceived content quality and the continuous use intention.Concern has no significant effect on the continuous use intention,and doesn't play a mediating role in the relationship between privacy concerns and the continuous use intention.Some optimization suggestions are put forward for algorithm news recommendation APP according to the above conclusion.According to the results of quantitative analysis and depth interview,this paper puts forward corresponding optimization suggestions for news algorithm recommendation APP.The suggestions contain three aspects,they are enhancing users'satisfaction,improving the content quality of the platform and strengthening privacy protection.