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A Cross-cultural Study on Information Architecture: Culture Differences on Attention Allocation to Web Components

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Abstract

A well-designed web information architecture (IA) supports the findability and usability of web content to ensure efficient and effective user experience. This study aims to investigate how web visitors from different cultures allocate their attention to the four main systems (labeling, organization, navigation, and searching) in IA. We conducted a user study comprised of observation tasks, sketch sessions, and questions regarding participants’ attention allocation, as well as background questionnaires regarding demographics, cultural dimensions, and personal traits. A total of 33 student participants from Taiwan (TW) and the US were recruited. Our preliminary results find that the less complicated content a website displays, the more participants are aware of IA and its components in general. We also found that US participants usually pay more attention to text labels on a webpage, whereas TW participants are more likely to evenly distribute their attention to both text and image objects. The ultimate goal of this study is to shed light on the topic of culture-specific IA in global web communities.

G.-M. Tang—This work was done while Gao-Ming Tang was at National Taiwan University.

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Acknowledgments

This work was financially supported by the Ministry of Science and Technology (MOST) in Taiwan, under #MOST 108‐2636‐H‐002‐002‐ and MOST 107-3017‐F‐002‐003‐, and the Center for Research in Econometric Theory and Applications (Grant no. 107L900204) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.

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Correspondence to Wei Jeng .

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Tang, GM., Hu, HY., Chen, SY., Jeng, W. (2020). A Cross-cultural Study on Information Architecture: Culture Differences on Attention Allocation to Web Components. In: Sundqvist, A., Berget, G., Nolin, J., Skjerdingstad, K. (eds) Sustainable Digital Communities. iConference 2020. Lecture Notes in Computer Science(), vol 12051. Springer, Cham. https://doi.org/10.1007/978-3-030-43687-2_31

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  • DOI: https://doi.org/10.1007/978-3-030-43687-2_31

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  • Publisher Name: Springer, Cham

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