TY - GEN
T1 - Building and Eroding
T2 - 2024 IEEE Visualization and Visual Analytics Conference, VIS 2024
AU - Crouser, R. Jordan
AU - Matoussi, Syrine
AU - Kung, Lan
AU - Pandey, Saugat
AU - McKinley, Oen G.
AU - Ottley, Alvitta
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Trust is a subjective yet fundamental component of human-computer interaction, and is a determining factor in shaping the efficacy of data visualizations. Prior research has identified five dimensions of trust assessment in visualizations (credibility, clarity, reliability, familiarity, and confidence), and observed that these dimensions tend to vary predictably along with certain features of the visualization being evaluated. This raises a further question: how do the design features driving viewers' trust assessment vary with the characteristics of the viewers themselves? By reanalyzing data from these studies through the lens of individual differences, we build a more detailed map of the relationships between design features, individual characteristics, and trust behaviors. In particular, we model the distinct contributions of endogenous design features (such as visualization type, or the use of color) and exogenous user characteristics (such as visualization literacy), as well as the interactions between them. We then use these findings to make recommendations for individualized and adaptive visualization design.
AB - Trust is a subjective yet fundamental component of human-computer interaction, and is a determining factor in shaping the efficacy of data visualizations. Prior research has identified five dimensions of trust assessment in visualizations (credibility, clarity, reliability, familiarity, and confidence), and observed that these dimensions tend to vary predictably along with certain features of the visualization being evaluated. This raises a further question: how do the design features driving viewers' trust assessment vary with the characteristics of the viewers themselves? By reanalyzing data from these studies through the lens of individual differences, we build a more detailed map of the relationships between design features, individual characteristics, and trust behaviors. In particular, we model the distinct contributions of endogenous design features (such as visualization type, or the use of color) and exogenous user characteristics (such as visualization literacy), as well as the interactions between them. We then use these findings to make recommendations for individualized and adaptive visualization design.
KW - data visualization
KW - individual differences
KW - personality
KW - Trust
UR - http://www.scopus.com/inward/record.url?scp=85215323871&partnerID=8YFLogxK
U2 - 10.1109/VIS55277.2024.00069
DO - 10.1109/VIS55277.2024.00069
M3 - Conference contribution
AN - SCOPUS:85215323871
T3 - Proceedings - 2024 IEEE Visualization Conference - Short Papers, VIS 2024
SP - 306
EP - 310
BT - Proceedings - 2024 IEEE Visualization Conference - Short Papers, VIS 2024
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 13 October 2024 through 18 October 2024
ER -