How visual cognition influences process model comprehension

Razvan Petrusel*, Jan Mendling, Hajo A. Reijers

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Process analysts and other professionals extensively use process models to analyze business processes and identify performance improvement opportunities. Therefore, it is important that such models can be easily and properly understood. Previous research has mainly focused on two types of factors that are important in this context: (i) properties of the model itself, and (ii) properties of the model reader. The work in this paper aims at determining how the performance of subjects varies across different types of comprehension tasks, which is a new angle. To reason about the complexity of comprehension tasks we take a theoretical perspective that is grounded in visual cognition. We test our hypotheses using a free-simulation experiment that incorporates eye-tracking technology. We find that model-related and person-related factors are fully mediated by variables of visual cognition. Moreover, in comparison, visual cognition variables provide a significantly higher explanatory power for the duration and efficiency of comprehension tasks. These insights shed a new perspective on what influences sense-making of process models, shifting the attention from model and reader characteristics to the complexity of the problem-solving task at hand. Our work opens the way to investigate and develop effective strategies to support readers of process models, for example through the context-sensitive use of visual cues.

Original languageEnglish
Pages (from-to)1-16
Number of pages17
JournalDecision Support Systems
Volume96
Issue number-
DOIs
Publication statusPublished - 1 Apr 2017

Keywords

  • Business process model comprehension
  • Complexity of process model comprehension task
  • Eye-tracking experiment business process model
  • Theoretical perspective model comprehension
  • Visual cognition factors of model comprehension

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