Exploring how users engage with hybrid process artifacts based on declarative process models: a behavioral analysis based on eye-tracking and think-aloud

Abstract

Process design artifacts have been increasingly used to guide the modeling of business processes. To support users in designing and understanding process models, different process artifacts have been combined in several ways leading to the emergence of the so-called "hybrid process artifacts". While many hybrid artifacts have been proposed in the literature, little is known about how they can actually support users in practice. Objective: To address this gap, this work investigates the way users engage with hybrid process artifacts during comprehension tasks. In particular, we focus on a hybrid representation of DCR graphs (DCR-HR) combining a process model, textual annotations and an in-Work supported by the Innovation Fund Denmark project EcoKnow (7050-00034A); the fourth author additionally by the Danish Council for Independent Research project Hybrid Business Process Management Technologies (DFF-6111-00337).

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

“…This approach provided more profound insights into participant behavior and their challenges during task performance. For example, it was shown that DCR Graphs (declarative process models based on directed graphs) helped users gain an overview of the business process [18]. In other fields, such as usability, the think-aloud method is widely used due to its benefits in revealing cognitive processes [19,20].…”

On the Semantic Transparency of Declarative Process Models: The Case of Constraints

“…overview in [14]). This research delves into various aspects associated with the representation of process models and process data (e.g., [2,3,4,7,15,17,33]), users' background and expertise (e.g., [29,35]), the tasks in which the models are used (e.g., [38,27]) and the tools supporting these tasks (e.g., [37,1,26]). Many of these aspects have been shown to have a significant impact on the comprehension of process models [14].…”