Visual representation of information as an antecedent of perceptive efficiency: The effect of experience

Lisa Falschlunger, Othmar Lehner, Horst Treiblmaier, Christoph Eisl

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

7 Citations (Scopus)

Abstract

Visual representation of information is ubiquitous, from traditional management reports via dynamic management cockpits to modern 3D and 4D visualizations of 'big data'. Little is known about how the perception in terms of efficiency of such a visual representation can be predicted ex-ante. The visualization/task complexity nexus is widely accepted to be a solid foundation of such a predictive model, although evidence points to a variety of additional factors, including data complexity, visual complexity, spatial ability, and experience. While most of these factors can be readily operationalized, the construct of experience is vastly under-researched in this context. In this paper we test the influence of the aforementioned factors on the perceptive efficiency of visualization - given a certain task complexity - by using experiments and structural equation modelling. In addition we look at four different conceptualizations of experience. The adjusted R2 of the model is 39% and all four proxies of experience are significant at the p.

Original languageEnglish
Title of host publicationProceedings of the 49th Annual Hawaii International Conference on System Sciences, HICSS 2016
EditorsRalph H. Sprague, Tung X. Bui
Number of pages9
PublisherIEEE computer society press
Publication date07.03.2016
Pages668-676
Article number7427264
ISBN (Electronic)9780769556703
DOIs
Publication statusPublished - 07.03.2016
MoE publication typeA4 Article in conference proceedings
Event49th Annual Hawaii International Conference on System Sciences, HICSS 2016 - Koloa, United States
Duration: 05.01.201608.01.2016

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
Volume2016-March
ISSN (Print)1530-1605

Keywords

  • 113 Computer and information sciences

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