The Pleasant Visual Path to Review Helpfulness: Picture-Evoked Emotional Valence and Picture-Text Alignment

Viewing pictures evokes pleasant or unpleasant feelings (valence) and influences perceptions. How valence evoked by pictures in online reviews impacts reader perceptions of review helpfulness remains understudied. Based on affect-as-information theory, we propose that both picture-evoked emotional valence (PEvoV) and its alignment with text-expressed emotional valence (TExpV) exhibit a positive effect on perceived review helpfulness. A large-scale field test and a series of laboratory experiments support our hypotheses. The positive effects are partially mediated by conceptual processing fluency. Additionally, PEvoV is associated with various interpretable picture features. Our empirical strategy involves techniques of computer vision, deep learning, and econometrics. From an emotion-focused perspective, our work deepens the understanding of helpful reviews, contributes to the literature on picture-text interaction in reviews, and derives theoretical insight into underlying mechanisms. It offers practical implications for online review platform design and online reputation management.