Abstract
As the theory of cognitive fit does not give a valid explanation of which factors are of importance, no clear guidelines for choosing and designing the right visualization for a specific task can be deduced so far. This paper sets out to further our understanding of how individual factors influence information processing and as a consequence decision making while viewing visualizations. Using a controlled longitudinal experimental setting (leading to 4,460 individual observations), this study first provides evidence for the need to include visual complexity, task complexity, data complexity as well as individual complexity into a predictive model for the perceptual efficiency of information visualizations, and second suggests an enhanced structural model, showing a significant increase in predictive value over the previous simple task complexity/visualization model introduced by Vessey and later Speier. The predictive power of this model is enhanced by including data density, knowledge, experience, time of day, as well as spatial ability, with R² 0.344 rising to 0.439 and Q2 0.357 rising to 0.437.
Original language | English |
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Title of host publication | Proceedings in Finance and Risk Series'15 |
Pages | 1-25 |
Publication status | Accepted/In press - 2015 |
Event | 15th Finance, Risk and Accounting Perspectives (FRAP15) - Steyr, Austria Duration: 19 Oct 2015 → 21 Oct 2015 http://www.acrn.eu/finance/ |
Conference
Conference | 15th Finance, Risk and Accounting Perspectives (FRAP15) |
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Country/Territory | Austria |
City | Steyr |
Period | 19.10.2015 → 21.10.2015 |
Internet address |
Keywords
- cognitive fit
- information visualization
- task complexity
- PLS
- structural equation modelling