Visual information and appearance: The impact of visual attributes of user-generated photos on review helpfulness

Abstract

Images have become integral to consumers’ sharing of consumption experiences due to their abilities of carrying rich and vivid information. Drawing from the perspective of information theory and the elaboration likelihood model (ELM), this study investigates impacts of visual information and visual appearance of user-generated photos (UGPs) on customers’ perceived review helpfulness. We utilize deep learning techniques to calculate the breadth and depth of photos (visual information), and evaluate the aesthetic value of the photos (visual appearance). By collecting a substantial amount of review, reviewer and restaurant information from the Yelp platform, our results demonstrate that the visual breadth and visual depth of review photos have a significant positive impact on review helpfulness. Notably, reviewer status and review length moderate these effects. These insights offer valuable strategies for both restaurant managers and online restaurant platforms regarding UGP management.