Semi-Automatic Inductive Derivation of Reference Process Models that Represent Best Practices in Public Administrations
In the course of business process management, reference models are widely used for process improvement or as starting point for the creation of individual process models. Current scientific literature mainly offers deductive approaches to construct reference models. Although there are some approaches that inductively develop a reference model from a set of individual process models, these approaches focus on the derivation and representation of common practices. However, there is no inductive method to detect best practices and represent them in a reference model. This paper addresses this research gap by applying design science research to develop an approach for the semi-automatic and inductive derivation of reference process models that represent best practices in public administrations. The approach creates a merged model to keep the structure of the source models and detect identical parts in the process models. It identifies best practices using query constructs and ranking criteria to group process model elements and evaluate these groups. The contribution of this paper is a conceptualization of the approach and a demonstration of its functionality with an example. The implementation and evaluation is subject of future work.