Designing Attentive Information Dashboards

Abstract

Information dashboards are a critical capability in contemporary business intelligence and analytics systems. Despite their strong potential to support better decision-making, the massive amount of information they provide challenges users performing data exploration tasks. Accordingly, dashboard users face difficulties in managing their limited attentional resources when processing the presented information on dashboards. Also, studies have shown that the amount of concentrated time humans can spend on a task has dramatically decreased in recent years; thus, there is a need for designing user interfaces that support users attention management. In this design science research project, we propose attentive information dashboards that provide individualized visual attention feedback (VAF) as an innovative artifact to solve this problem. We articulate theoretically grounded design principles and instantiate a software artifact leveraging users eye movement data in real time to provide individualized VAF. We evaluated the instantiated artifact in a controlled lab experiment with 92 participants. The results from analyzing users eye movement after receiving individualized VAF reveal that our proposed design has a positive effect on users attentional resource allocation, attention shift rate, and attentional resource management. We contribute a system architecture for attentive information dashboards that support data exploration and two theoretically grounded design principles that provide prescriptive knowledge on how to provide individualized VAF. Practitioners can leverage the prescriptive knowledge derived from our research to design innovative systems that support users information processing by managing their limited attentional resources.