Obtaining Semi-Formal Models from Qualitative Data: From Interviews Into BPMN Models in User-Centered Design Processes. Issue 3 (7th February 2023)

• Record Type: Journal Article
• Authors: Law, Yuen C.; Wehrt, Wilken; Sonnentag, Sabine; Weyers, Benjamin
• Is Part Of: International journal of human-computer interaction. Volume 39:Issue 3(2023)
• Page Start: 476
• Page End: 493
• Publication Date: 2023-02-07
• Subjects: Human-computer interaction -- Periodicals; 004.01905
• DOI: 10.1080/10447318.2022.2041899
• Languages: English
• ISSNs: 1044-7318

Abstract

Abstract: Gathering qualitative user data in a user-centered design process is one of the very early steps to create interactive systems. However, generating structured models from qualitative data towards descriptions that can be used for the implementation of interactive systems and prototypes raises various challenges, such as a strong influence of the modeler's knowledge and their interpretation of the gathered qualitative data. Introducing the modeler's bias may result in a system implementation which does not fully represent the information provided in the original qualitative data, generating an unwanted gap between what the user needs and what the system provides. To address this challenge, in this paper we present a structured and manual transformation method, which enables a modeler to create BPMN models from interview data by reducing the modeler's individual influence on the resulting BPMN model. We evaluate this approach in the context of the implementation of persuasive systems, which should support changing unwanted work-related habits. Therefore, we conducted unstructured interviews with office workers, thinking aloud interviews, in which we asked office workers to imagine a situation where they showed an unwanted work-related habit and to describe this habit together with an alternative behavior. In a quantitative experimental study, we then asked study participants to create BPMN models either with or without our new transformation method. Our analyses showed that when using our method, different participants created very similar BPMN models of the habits, even with little training. We conclude that the major contribution of our work is that the presented method can be applied to the creation of structured models from unstructured interview data. This method that makes use of rich interview data is suitable for the design and implementation of interactive systems.