How Do Video Features Matter in Visual Advertising? An Elaboration Likelihood Model Perspective

Danyang Song, Shichao Wang, Carol Ou, Xi Chen, Ruitao Liu, Haihong Tang

Abstract: Description Video content is gaining more popularity than traditional text- and image-based content on e-commerce platforms. Drawing on the elaboration likelihood model (ELM), we empirically investigate how dynamic video-related features (e.g., content complexity, design complexity, and motion of the video) and static frame-related features (e.g., color and feature complexity of the video) influence the effectiveness of video advertising by using a large and unique choice-level dataset from a leading e-commerce platform in the world. The results show that in the context of exploratory search, the central route factors of the ELM that require more cognitive effort negatively affect consumer behavior, while the effects of the peripheral route factors of the ELM are pronounced. We also find that the matching level between consumers and videos has a positive moderating effect on the relationship between the peripheral route factors and consumer behavior. This study provides novel insights for both research and practice.