Business process and rule integration approaches—An empirical analysis of model understanding. Issue 104 (February 2022) - Wang, Wei; Chen, Tianwa; Indulska, Marta; Sadiq, Shazia; Weber, Barbara
Abstract: Business process models are widely used in organizations by information systems analysts to represent complex business requirements. They are also used by business users to understand business operations and constraints. This understanding is extracted from graphical process models as well as business rules. Prior research advocated integrating business rules and business process models to improve the effectiveness of various organizational activities, such as developing a shared understanding of practices, process improvement, and mitigating risks of compliance and policy breaches. However, whether such integrated modeling can improve the understanding of business processes, which is a fundamental benefit of integrated modeling, has not been empirically evaluated. In this paper, first, we report on an experiment investigating whether rule linking, a representative integrated modeling method, can improve understanding performance. We use eye tracking technology to understand the cognitive process by which model readers use models to perform understanding tasks. Our results show that rule linking outperforms separated modeling in terms of understanding effectiveness, efficiency, perceived mental effort, and visual attention. Further, cognitive process analysis reveals that the form of rule representation does not affect the extent of deep processing, but rule linking significantly decreases the occurrence of rule scanning and screening processes. Moreover, our results show that rule linking leads to an increase of visual association suggesting improved information integration, leading to improved task performance. Highlights: We present the challenges and risks of information silos in the context of business process models and business rules. We empirically evaluate the effect of business rule and business process model integration on model understanding. Our study provides insights on the effect of integrated models on task performance, time efficiency, mental effort and visual attention.