PhD thesis, University of Innsbruck, Department of Computer Science, 2014.
Business process models have gained significant importance due to their critical role for managing business processes. In particular, process models support the common understanding of a company’s business processes, enable the discovery of improvement opportunities, and serve as drivers for the implementation of business processes. Still, a wide range of quality problems have been observed. For example, literature reports on error rates between 10% and 20% in industrial process model collections. Most research in the context of quality issues of process models puts a strong emphasis on the outcome of the process modeling act by analyzing the resulting model. However, it is rarely considered that process model quality is presumably dependent on the process followed to create the process model.
This thesis strives for addressing this gap by specifically investigating the process of creating process models. In this context, different actions on several levels of abstraction might be considered, including elicitation and formalization of process models. During elicitation information is gathered, which is used in formalization phases for actually creating the formal process model. This thesis focuses on the formalization of process models, which can be considered a process by itself—the Process of Process Modeling (PPM).
Due to the lack of an established theory, we follow a mixed method approach to exploratively investigate the PPM. This way, different perspectives are combined to develop a comprehensive understanding. In this context, we attempt to address the following research objectives. First, means for recording and performing a detailed analysis of the PPM are required. For this, a specialized modeling environment—Cheetah Experimental Platform (CEP)—is developed, allowing a systematic investigation of the PPM. Further, a visualization for the PPM, i.e., Modeling Phase Diagrams (MPDs), is presented to support data exploration and hypotheses generation. Second, we attempt to observe and categorize reoccurring behavior of modelers to develop an understanding on how process models are created. Finally, we investigate factors that influence the PPM to understand why certain behavior can be observed. The findings are condensed to form a model on the factors that influence the PPM.
Summarized, this thesis proposes means for analyzing the PPM and presents initial findings to form an understanding on how the formalization of process models is conducted and why certain behavior can be observed. While the results cannot be considered an established theory, this work constitutes a first building block toward a comprehensive understanding of the PPM. This will ultimately improve process model quality by facilitating the development of specialized modeling environments, which address potential pitfalls during the creation of process models.
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