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Business process and rule integration approaches—An empirical analysis of model understanding
Journal
Information Systems
Type
journal article
Date Issued
2022
Author(s)
Abstract (De)
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.
Language
English
Keywords
Business process modeling
Business rule modeling
Eye-tracking
Cognitive process
Model understanding
Controlled experiment
HSG Classification
contribution to scientific community
HSG Profile Area
None
Refereed
Yes
Publisher
Elsevier
Volume
104
Start page
101901
Subject(s)
Division(s)
Eprints ID
264691