Obtaining Semi-Formal Models from Qualitative Data: From Interviews Into BPMN Models in User-Centered Design Processes

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.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is supported by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) under Grant No.:318151256, WE 5408/2-1 and SO 295/10-1.

Notes on contributors

Yuen C. Law

Yuen C. Law obtained his PhD in computer Science from the RWTH Aachen University in 2016 and currently works at the Computer Science department at the Costa Rica Institute of Technology as docent and researcher. His interests include virtual and augmented reality applications, visualization, and human-computer interaction.

Wilken Wehrt

Wilken Wehrt is a Postdoctoral Researcher in Work and Organizational Psychology at the University of Mannheim, Germany. His research interests include habits, motivation, and self-regulation.

Sabine Sonnentag

Sabine Sonnentag holds a doctoral degree from the Technical University of Braunschweig, Germany, and is currently a full professor of Work and Organizational Psychology at the University of Mannheim, Germany. Her research addresses the question of how individuals can stay healthy, energetic, and productive on the job.

Benjamin Weyers

Benjamin Weyers obtained his PhD from University of Duisburg-Essen, Germany in 2011. He is currently assistant professor for Human-Computer Interaction at University of Trier, Germany. His research interest lies in development and investigation of interactive systems in work-related context with a specific focus on virtual and augmented reality as well as persuasive systems.