The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.

The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.

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a b s t r a c tBusiness process modeling is heavily applied in practice, but important quality issues have not been addressed thoroughly by research. A notorious problem is the low level of modeling competence that many casual modelers in process documentation projects have. Existing approaches towards model quality might be of benefit, but they suffer from at least one of the following problems. On the one hand, frameworks like SEQUAL and the Guidelines of Modeling are too abstract to be applicable for novices and non-experts in practice. On the other hand, there are collections of pragmatic hints that lack a sound research foundation. In this paper, we analyze existing research on relationships between model structure on the one hand and error probability and understanding on the other hand. As a synthesis we propose a set of seven process modeling guidelines (7PMG). Each of these guidelines builds on strong empirical insights, yet they are formulated to be intuitive to practitioners. Furthermore, we analyze how the guidelines are prioritized by industry experts. In this regard, the seven guidelines have the potential to serve as an important tool of knowledge transfer from academia into modeling practice.

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