Measuring the Cognitive Complexity in the Comprehension of Modular Process Models

Abstract

Modularization in process models is a method to cope with the inherent complexity in such models (e.g., model size reduction). Modularization is capable to increase the quality, the ease of reuse, and the scalability of process models. Prior conducted research studied the effects of modular process models to enhance their comprehension. However, the effects of modularization on cognitive factors during process model comprehension are less understood so far. Therefore, this paper presents the results of two exploratory studies (i.e., a survey research study with N = 95 participants; a follow-up eye tracking study with N = 19 participants), in which three types of modularization (i.e., horizontal, vertical, orthogonal) were applied to process models expressed in terms of the Business Process Model and Notation (BPMN) 2.0. Further, the effects of modularization on the cognitive load, the level of acceptability, and the performance in process model comprehension were investigated. In general, the results revealed that participants were confronted with challenges during the comprehension of modularized process models. Further, performance in the comprehension of modularized process models showed only a few significant differences, however, the results obtained regarding the cognitive load revealed that the complexity and concept of modularization in process models were misjudged initially. The insights unraveled that the attitude towards the application and the behavioral intention to apply modularization in process model is still not clear. In this context, horizontal modularization appeared to be the best comprehensible modularization approach leading to a more finegrained comprehension of respective process models. The findings indicate that alterations in modular process models (e.g., change in the representation) are important to foster and enable their comprehension. Finally, based on our results, implications for research and practice as well as directions for future work are discussed in this paper.

Measuring ancient technological complexity and its cognitive implications using Petri nets

“…The structuredness metric measures the effort required to understand the information behind the process 43 by deconstructing the Petri net models into components similar to programming constructs such as sequences, selections, and iterations 35 , 53 . These constructs are given a weight based on their structural complexity, which is a reported factor that reduces the comprehension of conceptual models 43 , 54 . In Petri nets, these constructs are sets of places, transitions, and arcs.…”

Exploring Comprehension Strategies of Modular Process Models: A Combined Eye-Tracking and Concurrent Think-Aloud Study

“…For instance, flattened process models have been shown to be more comprehensable, useful, and effective compared to models with subprocesses in a study involving large real-life process models. Next to the comparison of the mentioned modularization types, the modularization techniques horizontal, vertical, and orthogonal were considered in terms of process models [13,14]. Ref.…”

“…Ref. [14] outlined that horizontal modularization appears to be more comprehensible than vertical and orthogonal modularization. This is in contrast to the results of [13,15], where horizontal modularization led to negative effects on comprehensibility.…”

“…Ref. [14] showed that in terms of the modeling element interactivity such as the presentation, the load is on a low up to medium level. But, when focusing on the mental effort, the load is medium up to high.…”

Automated conceptual model clustering: a relator-centric approach

“…Techniques for model clustering, relevance, and summarization have been developed for more than three decades in conceptual modeling, from earlier approaches such as [16] in the mid-1980s, to [1,12] in the second half of the 1990s, to [47,48] in the first decade of the 2000s, to recent approaches such as [11]. Recently, the problem of complexity management of large symbolic models has gained significant interest also in the areas of ontology engineering [2,6,13,14,20,33], enterprise models [34], and process models [53,55].…”