Optimizing Scatterplot-Matrices for Decision-Support: An Experimental Eye-Tracking Study Assessing Situational Cognitive Load

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

Abstract

The scatterplot matrix is definedto be a standard method for multi-variate data visualization; nonetheless, their use for decision-support in a corpo-rate environment is scarce. Amongst others, longstanding criticism lies in the lack of empirical testing to investigate optimal design specifications as well as areas of application from a business related perspective. Thus, onthe basis ofan innovative approach to assess a visualization’s fitness for efficient and effective decision-making given a user’s situational cognitiveload, this study investi-gates the usability of a scatterplot matrix while performing typical tasks associ-ated with multidimensional datasets (correlation and distribution assessment). Alaboratory experiment recording eye-tracking data investigates the design of the matrix and its influence on the decision-maker’s ability to process the presented information. Especially, the information content presented in the diagonal as well as the size of the matrix are tested and linked to the user’s individual pro-cessing capabilities. Results showthat the design of the scatterplot as well as the size of the matrix influenced the decision-making greatly
OriginalspracheEnglisch
TitelProceedings NeuroIS Retreat 2021
Seiten73-85
Seitenumfang13
PublikationsstatusVeröffentlicht - Jun 2021
VeranstaltungNeuroIS Retreat 2021 : Virtual Conference - Online, Online
Dauer: 1 Jun 20213 Jun 2021
http://www.neurois.org

Konferenz

KonferenzNeuroIS Retreat 2021
KurztitelNeuroIS
OrtOnline
Zeitraum01.06.202103.06.2021
Internetadresse

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