Keep it Simple: How Visual Complexity and Preferences Impact Search Efficiency on Websites
Amanda Baughan, Tal August, Naomi Yamashita, Katharina Reinecke
摘要
Past research has shown that people prefer different levels of visual complexity in websites: While some prefer simple websites with little text and few images, others prefer highly complex websites with many colors, images, and text. We investigated whether users' visual preferences reflect which website complexity they can work with most efficiently. We conducted an online study with 165 participants in which we tested their search efficiency and information recall. We confirm that the visual complexity of a website has a significant negative effect on search efficiency and information recall. However, the search efficiency of those who preferred simple websites was more negatively affected by highly complex websites than those who preferred high visual complexity. Our results suggest that diverse visual preferences need to be accounted for when assessing search response time and information recall in HCI experiments, testing software, or A/B tests.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper3
- From Paper to Card: Transforming Design Implications with Generative AIDonghoon Shin, Lucy Lu Wang, Gary HsiehCHI 2024 · 被引用 28 次
- Do Cross-Cultural Differences in Visual Attention Patterns Affect Search Efficiency on Websites?Amanda Baughan, Nigini Oliveira, Tal August, Naomi Yamashita 等CHI 2021 · 被引用 17 次
- Know Your Audience: The benefits and pitfalls of generating plain language summaries beyond the "general" audienceTal August, Kyle Lo, Noah A. Smith, Katharina ReineckeCHI 2024 · 被引用 11 次
相关 Paper
- Relationship Between Visual Complexity and Aesthetics of WebpagesAliaksei Miniukovich, Maurizio MarcheseCHI 2020 · 被引用 24 次
- How Relevant is Hick's Law for HCI?Wanyu Liu, Julien Gori, Olivier Rioul, Michel Beaudouin-Lafon 等CHI 2020 · 被引用 67 次
- Image or Information? Examining the Nature and Impact of Visualization Perceptual ClassificationAnjana Arunkumar, Lace M. K. Padilla, Gi-Yeul Bae, Chris BryanIEEE VIS 2023 · 被引用 15 次
- Quantifying Emotional Responses to Immutable Data Characteristics and Designer Choices in Data VisualizationsCarter Blair, Xiyao Wang, Charles PerinIEEE VIS 2024 · 被引用 5 次
- When Browsing Gets Cluttered: Exploring and Modeling Interactions of Browsing Clutter, Browsing Habits, and CopingRongjun Ma, Henrik Lassila, Leysan Nurgalieva, Janne LindqvistCHI 2023 · 被引用 16 次
