Investigating the trade-off between the effectiveness and efficiency of process modeling

Despite recent efforts to improve the quality of process models, we still observe a significant dissimilarity in quality between models. This paper focuses on the syntactic condition of process models, and how it is achieved. To this end, a dataset of 121 modeling sessions was investigated. By going through each of these sessions step by step, a separate ‘revision’ phase was identified for 81 of them. Next, by cutting the modeling process off at the start of the revision phase, a partial process model was exported for these modeling sessions. Finally, each partial model was compared with its corresponding final model, in terms of time, effort, and the number of syntactic errors made or solved, in search for a possible trade-off between the effectiveness and efficiency of process modeling. Based on the findings, we give a provisional explanation for the difference in syntactic quality of process models

The origin and evolution of syntax errors in simple sequence flow models in BPMN

How do syntax errors emerge? What is the earliest moment that potential syntax errors can be detected? Which evolution do syntax errors go through during modeling? A provisional answer to these questions is formulated in this paper based on an investigation of a dataset containing the operational details of 126 modeling sessions. First, a list is composed of the different potential syntax errors. Second, a classification framework is built to categorize the errors according to their certainty and severity during modeling (i.e., in partial or complete models). Third, the origin and evolution of all syntax errors in the dataset are identified. This data is then used to collect a number of observations, which form a basis for future research

From Expert Discipline to Common Practice: A Vision and Research Agenda for Extending the Reach of Enterprise Modeling

The benefits of enterprise modeling (EM) and its contribution to organizational tasks are largely undisputed in business and information systems engineering. EM as a discipline has been around for several decades but is typically performed by a limited number of people in organizations with an affinity to modeling. What is captured in models is only a fragment of what ought to be captured. Thus, this research note argues that EM is far from its maximum potential. Many people develop some kind of model in their local practice without thinking about it consciously. Exploiting the potential of this “grass roots modeling” could lead to groundbreaking innovations. The aim is to investigate integration of the established practices of modeling with local practices of creating and using model-like artifacts of relevance for the overall organization. The paper develops a vision for extending the reach of EM, identifies research areas contributing to the vision and proposes elements of a future research Agenda