Data avatars: A theory-guided design and assessment for multidimensional data visualization

Abstract

We propose the design of a novel method for multidimensional data visualization, called data avatars. We rely on the design science approach and the dual-processing theory (System 1-System 2) to guide our design and test the data avatars method against two well-known data visualization methods. The test involves a lab experiment with 429 participants from the general public and uses industry data. Our results show that data avatars provide a viable method for visualizing multidimensional data in terms of user cognitive performance. Further, the results indicate the important role of perceived cognitive load and enjoyment in users’ preference for data visualization design.