The more aesthetic, the better? The impact of photo aesthetics on perceived review helpfulness

IF 6.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems Pub Date : 2025-06-22 DOI:10.1016/j.dss.2025.114496

Yu Han , Ziqiong Zhang , Carol X.J. Ou , Zili Zhang

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引用次数: 0

Abstract

Review helpfulness is crucial for assessing the quality of online reviews and mitigating information overload. Although numerous studies have explored the impact of textual and reviewer characteristics on review helpfulness, the role of photo aesthetics remains important but underexplored. This study addresses this gap by investigating the impact of photo aesthetics on perceived review helpfulness and its underlying mediating effects. The hotel review data from TripAdvisor.com exhibit an inverted U-shaped effect of photo aesthetics on perceived review helpfulness, in which review text length moderates this relationship. To further validate this causal relationship and explore the underlying mediating effects, an experimental study is conducted. The experimental results confirm the causal impact of photo aesthetics on perceived review helpfulness and reveal that perceived pleasure, reviewer effort and review authenticity mediate the relationship. These novel insights challenge the notion that “the more aesthetic, the better” for review photos, offering new theoretical and practical implications.

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越美观越好？照片美学对感知评论有用性的影响

评论的帮助性对于评估在线评论的质量和减轻信息过载至关重要。虽然许多研究已经探讨了文本和审稿人特征对审稿有用性的影响，但照片美学的作用仍然很重要，但尚未得到充分的探索。本研究通过调查照片美学对感知评论帮助性的影响及其潜在的中介作用来解决这一差距。TripAdvisor.com的酒店评论数据显示，照片美学对感知评论有用性的影响呈倒u型，其中评论文字长度调节了这一关系。为了进一步验证这一因果关系并探索潜在的中介效应，我们进行了一项实验研究。实验结果证实了照片美学对评论帮助感的因果影响，并揭示了感知愉悦、评论者努力和评论真实性在这一关系中起中介作用。这些新颖的见解挑战了评论照片“越美越好”的观念，提供了新的理论和实践意义。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Decision Support Systems 工程技术-计算机：人工智能

CiteScore

14.70

自引率

6.70%

发文量

119

审稿时长

13 months

期刊介绍： The common thread of articles published in Decision Support Systems is their relevance to theoretical and technical issues in the support of enhanced decision making. The areas addressed may include foundations, functionality, interfaces, implementation, impacts, and evaluation of decision support systems (DSSs).