IEEE VIS 2024 Content: Building and Eroding: Exogenous and Endogenous Factors that Influence Subjective Trust in Visualization

Building and Eroding: Exogenous and Endogenous Factors that Influence Subjective Trust in Visualization

R. Jordan Crouser - Smith College, Northampton, United States

Syrine Matoussi - Smith College, Northampton, United States

Lan Kung - Smith College, Northampton, United States

Saugat Pandey - Washington University in St. Louis, St. Louis, United States

Oen G McKinley - Washington University in St. Louis, St. Louis, United States

Alvitta Ottley - Washington University in St. Louis, St. Louis, United States

Screen-reader Accessible PDF

Room: Palma Ceia I

2024-10-16T13:21:00ZGMT-0600Change your timezone on the schedule page
2024-10-16T13:21:00Z
Exemplar figure, described by caption below
A recursive partitioning approach to identifying exogenous and endogenous predictors of trust behavior.
Fast forward
Full Video
Keywords

Trust, data visualization, individual differences, personality

Abstract

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.