Abstract

User data has been identified as one of the important knowledge bases for inclusive design. In order to explore the influential factors that may affect the reliability of data and then build up a more effective and efficient data-collection framework, we carried out an experimental study to collect data from older people (aged 50-70) in China, which included users’ capability, psychological and social- cultural attributes. Users’ actual product interaction performance was also investigated. Three issues were discussed based on the outcome of data analyses: a) mood states have significant effects on respondent’s self-reporting results; b) compared with maximum settings, people may have a wider range of perceptions of “comfortable” settings and it is possible to predict the performance in a “comfortable ” setting based on “maximum” data; c) social-cultural variables, vision, hearing, dexterity, cognition and psychological characteristics can predict successful product interaction tasks at different levels by using multiple logistic regression analysis.

Keywords:

inclusive design; user data; capability

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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Jun 17th, 12:00 AM

Towards designing inclusion: insights from a user data collection study in China

User data has been identified as one of the important knowledge bases for inclusive design. In order to explore the influential factors that may affect the reliability of data and then build up a more effective and efficient data-collection framework, we carried out an experimental study to collect data from older people (aged 50-70) in China, which included users’ capability, psychological and social- cultural attributes. Users’ actual product interaction performance was also investigated. Three issues were discussed based on the outcome of data analyses: a) mood states have significant effects on respondent’s self-reporting results; b) compared with maximum settings, people may have a wider range of perceptions of “comfortable” settings and it is possible to predict the performance in a “comfortable ” setting based on “maximum” data; c) social-cultural variables, vision, hearing, dexterity, cognition and psychological characteristics can predict successful product interaction tasks at different levels by using multiple logistic regression analysis.

 

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