Recently cognitive principles have been discussed for Conceptual Modeling with the aim to increase domain understanding, model comprehension and modeling efficiency. In particular, the principle of Perceptual Discriminability, which discusses the visual differences of modeling constructs, reveals potential for model comprehension if human attention is influenced in a way that important modeling constructs are more easily detected, and can hence faster be processed. Yet, so far no conditions how the human gaze can be influenced have been defined and evaluated for Conceptual Modeling. This dissertation extends Perceptual Discriminability for conditions to attract human attention for those constructs that are important for model comprehension. Furthermore, these conditions are applied to constructs of two different modeling grammars in general as well as to elements of the process flow of Business Process Models. To evaluate the results a laboratory experiment of extended Perceptual Discriminability is described in which significant differences have been identified for process flow comprehension. For the demonstration of the potential of extended Perceptual Discriminability BPMN secondary notation is improved by emphasizing those constructs that are most important for model comprehension. Therefore, those constructs that are important for model comprehension have been identified within a content analysis and have been worked on according to the conditions of extended Perceptual Discriminability for those visual variables that are free for an application in secondary notation.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa.de:bsz:14-qucosa-226605 |
| Date | 08 March 2018 |
| Creators | Stark, Jeannette |
| Contributors | Technische Universität Dresden, Fakultät Wirtschaftswissenschaften, Prof. Dr. Werner Esswein, Prof. Dr. Susanne Strahringer |
| Publisher | Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | doc-type:doctoralThesis |
| Format | application/pdf |
Page generated in 0.0029 seconds