Abstract. More and more organizations turn to the construction of process models to support strategical and operational tasks. At the same time, reports indicate quality issues for a considerable part of these models, caused by modeling errors. Therefore, the research described in this paper investigates the development of a practical method to determine and train an optimal process modeling strategy that aims to decrease the number of cognitive errors made during modeling. Such cognitive errors originate in inadequate cognitive processing caused by the inherent complexity of constructing process models. The method helps modelers to derive their personal cognitive profile and the related optimal cognitive strategy that minimizes these cognitive failures. The contribution of the research consists of the conceptual method and an automated modeling strategy selection and training instrument. These two artefacts are positively evaluated by a laboratory experiment covering multiple modeling sessions and involving a total of 149 master students at Ghent University.Keywords: modeling support, smart business process management, cognitive aspects of modeling, process of process modeling, process model quality.
IntroductionIn today's competitive markets with challenges in terms of globalization, mass-customization and risk control, it is considered important for organizations to manage and control their business processes thoroughly. Therefore, many organizations nowadays spend a great deal of effort to build and maintain a collection of business process models (or "process models" for short). These models represent various aspects of the business processes, such as the control, communication and information flows, while abstracting from individual process instances. The process models are used to support a diversity of process management tasks ranging from the strategical to the operational level: communication, documentation, analysis, (re)design, simulation, execution, etc. [1,2].Unfortunately, regardless of their importance and potential, case studies report many issues with the quality of process models in organizations [3][4][5]. Hence, researchers have built a large body of knowledge about the quality of conceptual (process) models [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Nevertheless, research about operational guidance on how to create high quality process models appears to be limited to the development of general guidelines about the ideal properties of produced models [22][23][24][25] and to the spontaneous description of best practices that emerged among process modeling experts [26,27]. Thus, there is a lack of sound operational support to help a modeler in translating her/his mental image of the real-world business process into a high quality process model [21].Considering on the one hand the importance of process modeling and on the other hand the reported quality issues and lack of operational support, the research objective addressed in this paper is to develop a practical method that...