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Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-44838

Reinecke, K. Culturally adaptive user interfaces. 2010, University of Zurich, Faculty of Economics.

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Abstract

One of the largest impediments for the efficient use of software in different cultural contexts is the gap between the software designs - typically following western cultural cues - and the users, who handle it within their cultural frame. The problem has become even more relevant, as today the majority of revenue in the software industry comes from outside market dominating countries such as the USA. While research has shown that adapting user interfaces to cultural preferences can be a decisive factor for marketplace success, the endeavor is oftentimes foregone because of its time-consuming and costly procedure. Moreover, it is usually limited to producing one uniform user interface for each nation, thereby disregarding the intangible nature of cultural backgrounds. To overcome these problems, this thesis introduces a new approach called 'cultural adaptivity'. The main idea behind it is to develop intelligent user interfaces, which can automatically adapt to the user's culture. Rather than only adapting to one country, cultural adaptivity is able to anticipate different influences on the user's cultural background, such as previous countries of residence, differing nationalities of the parents, religion, or the education level. We hypothesized that realizing these influences in adequate adaptations of the interface improves the overall usability, and specifically, increases work efficiency and user satisfaction. In support of this thesis, we developed a cultural user model ontology, which includes various facets of users' cultural backgrounds. The facets were aligned with information on cultural differences in perception and user interface preferences, resulting in a comprehensive set of adaptation rules. We evaluated our approach with our culturally adaptive system MOCCA, which can adapt to the users' cultural backgrounds with more than 115'000 possible combinations of its user interface. Initially, the system relies on the above-mentioned adaptation rules to compose a suitable user interface layout. In addition, MOCCA is able to learn new, and refine existing, adaptation rules from users' manual modifications of the user interface based on a collaborative filtering mechanism, and from observing the user's interaction with the interface. The results of our evaluations showed that MOCCA is able to anticipate the majority of user preferences in an initial adaptation, and that users' performance and satisfaction significantly improved when using the culturally adapted version of MOCCA, compared to its 'standard' US interface.

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Additional indexing

Item Type:Dissertation
Referees:Bernstein A, Jameson A
Communities & Collections:03 Faculty of Economics > Department of Informatics
DDC:000 Computer science, knowledge & systems
Language:English
Date:2010
Deposited On:07 Feb 2011 16:38
Last Modified:12 Sep 2012 14:53
Other Identification Number:1466

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