The Modeling Mind: Behavior Patterns in Prozess Modeling

Considering the intense usage of business process modeling in all types of business contexts, the relevance of process models has become obvious. Yet, industrial process models display a wide range of quality problems. These problems have resulted in vivid research on the quality of process models with the goal of obtaining a better understanding of factors influencing the quality of process models. Thereby, existing research mostly focuses on the product of process modeling, i.e., the process model. Recently, a new stream of research emerged that aims at obtaining a general understanding of the process followed to create process models - the process of process modeling (PPM). Even though the PPM is a highly flexible process and PPM instances of modelers differ, existing research on the PPM suggests the existence of patterns of re-occurring behavior (PPM behavior patterns). However, a comprehensive understanding of PPM behavior patterns is missing. Moreover, it is unclear how these patterns relate to process model quality, how the different patterns are combined to modeling styles, and which factors determine the occurrence of PPM behavior patterns. The Modeling Mind project aims to close this research gap by identifying a comprehensive set of PPM behavior patterns considering the modeler`s interactions with the modeling environment, verbalizations of the modeler`s thoughts, and the modeler`s eye movements while creating a process model. Further, the relation of these patterns to process model quality is examined. In addition, the Modeling Mind project aims at deriving a set of modeling styles by investigating the co-occurrence of PPM behavior patterns. Moreover, the project aims to understand the factors determining the occurrence of PPM behavior patterns covering modeler-specific factors, e.g., working memory capacity and personality, and task-specific factors, e.g., specific model elements and task complexity. A better understanding of PPM behavior patterns and their influencing factors will allow giving advice on the design of better (personalized) modeling environments, but also facilitate the development of tailored training materials, leading to process models of higher quality.

No additional funding sources recorded.