Aesthetic quality matters: The visual effect of review helpfulness evaluation

Abstract

Both photos and text are essential components of online reviews that jointly influence subsequent consumers’ decision-making. While the cognitive appeals of review text are widely investigated, the visual appeals of review photos are often overlooked. Based on the elaboration likelihood model, this study explores the interactive impact of photos and text, as peripheral and central cues respectively, on the perceived review helpfulness. A Deep Convolutional Neural Network model is applied to assess the aesthetic quality of over 126 K review photos, which is further revealed to significantly drive review helpfulness via the peripheral route. Such an effect is particularly strong when the review text is subjective and has a neutral sentiment. In addition, while it is the aesthetic quality of review photos that mainly drives the helpfulness of positive reviews, helpful negative reviews are expected to present objective arguments and distinct sentiment positions. The highlighted effects of photo aesthetics conditioning on textual features and review types largely inform the strategic management of visual content within online reviews.