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

Langner, Moritz ORCID iD icon 1; Toreini, Peyman 1; Maedche, Alexander 1
1 Karlsruher Institut für Technologie (KIT)


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.

DOI: 10.1007/978-3-030-60073-0_31
Zitationen: 4
Zitationen: 4
Zugehörige Institution(en) am KIT Fakultät für Wirtschaftswissenschaften – Institut für Informationswirtschaft und Marketing (IISM)
Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2020
Sprache Englisch
Identifikator ISBN: 978-3-030-60072-3
ISSN: 2195-4968
KITopen-ID: 1000120930
Erschienen in Information Systems and Neuroscience : NeuroIS Retreat 2020, Virtual Conference, June 2-4, 2020, www.NeuroIS. Ed.: Fred D. Davis
Veranstaltung Information Systems and Neuroscience (2020), Online, 02.06.2020 – 04.06.2020
Verlag Springer International Publishing
Seiten 266-275
Serie Lecture Notes in Information Systems and Organisation ; 43
Externe Relationen Abstract/Volltext
Schlagwörter Attention, Eye-Tracking, Quantified-Self, Self-Tracking, Design Science Research, NeuroIS
Nachgewiesen in Dimensions
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