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

Langner, Moritz; Toreini, Peyman; Maedche, Alexander

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 eyetracking 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.

Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2020
Sprache Englisch
Identifikator KITopen-ID: 1000120930
Erschienen in Proceedings. NeuroIS Retreat 2020. Virtual Conference, June 2-4, 2020, Ed.: Fred Davis
Seiten 11 S.
Bemerkung zur Veröffentlichung [Preprint]. Proceedings of the NeuroIS Retreat 2020: Abstracts. The final proceedings will be published by Springer
Externe Relationen Abstract/Volltext
Schlagwörter Attention, Eye-Tracking, Quantified-Self, Self-Tracking, Design Science Research, NeuroIS
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