Measuring usability: are effectiveness, efficiency, and satisfaction really correlated?
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
"Trust me, I'm an online vendor": towards a model of trust for e-commerce system design
CHI '00 Extended Abstracts on Human Factors in Computing Systems
Analysis of web sites with the repertory grid technique
CHI '01 Extended Abstracts on Human Factors in Computing Systems
From mental effort to perceived usability: transforming experiences into summary assessments
CHI '04 Extended Abstracts on Human Factors in Computing Systems
Do knobs have character?: exploring diversity in users' inferences
CHI '08 Extended Abstracts on Human Factors in Computing Systems
CHI '08 Extended Abstracts on Human Factors in Computing Systems
The interplay of beauty, goodness, and usability in interactive products
Human-Computer Interaction
User experience over time: an initial framework
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Characterizing the diversity in users' perceptions
INTERACT'07 Proceedings of the 11th IFIP TC 13 international conference on Human-computer interaction
'Dealing with My Emails': Latent user needs in email management.
Computers in Human Behavior
Introducing VERO: visual experiential requirements organizer
CHI '11 Extended Abstracts on Human Factors in Computing Systems
An initial model for designing socially translucent systems for behavior change
Proceedings of the Biannual Conference of the Italian Chapter of SIGCHI
Blending the repertory grid technique with focus groups to reveal rich design relevant insight
Proceedings of the 6th International Conference on Designing Pleasurable Products and Interfaces
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In this paper we argue against averaging as a common practice in the analysis of subjective attribute judgments, both across and within subjects. Previous work has raised awareness of the diversity between individuals' perceptions. In this paper it will furthermore become apparent that such diversity can also exist within a single individual, in the sense that different attribute judgments from a subject may reveal different, complementary, views. A Multi-Dimensional Scaling approach that accounts for the diverse views on a set of stimuli is proposed and its added value is illustrated using published data. We will illustrate that the averaging analysis provides insight to only 1/6th of the total number of attributes in the example dataset. The proposed approach accounts for more than double the information obtained from the average model, and provides richer and semantically diverse views on the set of stimuli.