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AttentionBoard: A Quantified-Self Dashboard for Enhancing Attention Management with Eye-Tracking

In: Information Systems and Neuroscience

Author

Listed:
  • Moritz Langner

    (Karlsruhe Institute of Technology (KIT), Institute of Information Systems and Marketing (IISM))

  • Peyman Toreini

    (Karlsruhe Institute of Technology (KIT), Institute of Information Systems and Marketing (IISM))

  • Alexander Maedche

    (Karlsruhe Institute of Technology (KIT), Institute of Information Systems and Marketing (IISM))

Abstract

In the age of information, office workers process huge amounts of information and distribute their attention to several tasks in parallel. However, attention is a scarce resource and attentional breakdowns, such as missing important information, may occur while using information systems (IS). Currently, there is a lack of support to understand and improve attention management to avoid such breakdowns. In the meantime, self-tracking applications are becoming popular due to the increasing sensory capabilities of smart devices. These systems support their users in understanding and reflecting their behavior. In this research-in-progress paper, we suggest leveraging self-tracking concepts for attention management while working with ISs and describe the design of the NeuroIS-based system called “AttentionBoard”. The goal of AttentionBoard is to help office workers in improving their attention management competencies. The system records attention allocation in real-time using eye-tracking and presents the aggregated data as metrics and visualizations on a dashboard. This paper presents the first step by motivating and introducing an initial design following the design science research (DSR) methodology.

Suggested Citation

  • Moritz Langner & Peyman Toreini & Alexander Maedche, 2020. "AttentionBoard: A Quantified-Self Dashboard for Enhancing Attention Management with Eye-Tracking," Lecture Notes in Information Systems and Organization, in: Fred D. Davis & René Riedl & Jan vom Brocke & Pierre-Majorique Léger & Adriane B. Randolph & Thomas (ed.), Information Systems and Neuroscience, pages 266-275, Springer.
    • Handle: RePEc:spr:lnichp:978-3-030-60073-0_31
    DOI: 10.1007/978-3-030-60073-0_31
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