Does Ai Recommendation System Quality Really Matter In Snss? A Moderating Role Of Ewom On Ai Recommendation System Quality And Attitude To Ho Chi Minh City’s Gen Z Intention To Watch Short Videos.

Abstract

Video platforms on social networks are increasingly exploding with the appearance of short videos, which enable users to access a large amount of information every day. In this context, the proposed system plays an important role in providing personalized experience for users, in a world with increasingly rich information. Accuracy, novelty, and diversity elements in the proposed system have also made important contributions to the quality of the system, so as to increase the user experience and provide each user with short films suitable for his characteristics and preferences. In order to clarify the influence of recommendation system quality on consumers' attitudes and intention to watch short videos, this paper establishes and verifies the research model by using the theory of the Human Information Process and the S-O-R model. In addition, the study also regards eWOM as a moderating factor that affects the relationship between the quality of the recommendation system and short video- watching intention, as well as the relationship between customers’ attitudes and intentions. To analyze and verify the validity of the model, we surveyed 391 students and employees aged 15-26 in Ho Chi Minh City by online questionnaire. The research model was re-verified by SPSS 20.0 tool and Smart PLS 4.1. Through investigation and experimental analysis, we proved the relationship between the above variables. Consequently, this paper will provide a theoretical basis for expanding the application scope of the S-O-R model, and at the same time provide a new perspective on the relationship between the suggestion system and the customer's intention under the adjustment of eWOM. Therefore, the problem of effectively applying an electronic suggestion and transmission system at the same time in short videos is put forward to marketers, which is attributed to providing novel, diverse, and accurate inclusive quality content.