With the popularity of social media platforms, user-generated content (UGC) has become one of the most important forms of advertising. As the competition for user flow becomes fierce, both UGC platforms and UGC creators want a deep understanding of the effectiveness of the textual and visual factors contained. We conduct the elaboration likelihood model to examine the moderating effect of user motivation on the relationship between factors and users' attitudes. Also, we look into the interaction between the central route and the peripheral route. This research helps the UGC platform in reducing the cost of filtering low-quality content and interpreting UGC characteristics. Additionally, this research provides the preliminary fact to support the gradual movement between the two routes. In future research, we will continue our test on the ELM continuum question.
Zhao, Ziqi; Chen, Xi; Yang, Jiang; and Zhou, Jun, "How Do the Visual and Textual Factors Influence User’s Attitudes? An Elaboration Likelihood Model Perspective" (2022). PACIS 2022 Proceedings. 130.
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