The Pleasant Visual Path to Review Helpfulness: Picture-Evoked Emotional Valence and Picture-Text Alignment
基于情绪即信息理论，研究了在线评论中图片引发的情绪效价及其与文字情绪效价的一致性如何正向影响读者对评论有用性的感知，并通过大规模实地测试和实验验证了概念加工流畅性的中介作用。
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.