Motivation
In today’s digital economy, Information Systems (IS) are a significant investment for companies and constitute an indispensable part of employees daily work [1]. Due to technological developments such as multi-media-rich user interfaces, IS are increasingly able to induce highly engaging, interactive, and holistic experiences [1]. One such experience called flow - defined as “the holistic sensation that people feel when they act with total involvement” [2, p. 36] - is considered to be of theoretical and practical significance as this phenomenon is expected to explain a considerable amount of well-being and performance at work [3–5].
However, despite increasing interest of IS scholars in flow [6], a central challenge is the limited knowledge about real-time measurement. Researchers typically rely on self-report scales which are administered post-task (e.g., [7, 8]). As flow occurs during task execution, post-task self-reported measures cannot assess parameters like the length or depth of flow during task execution, and are subject to reporting inaccuracies [9]. The recent rise of the NeuroIS field with the inclusion and development of psychophysiological measures therefore provides new possibilities for objective and continuous measurements of psychological constructs in the context of IS [10, 11].
Goal of the Thesis
The overall goal of this thesis project is the following:
- Extract features which are specific for a Flow-state out of a data set of physiological data
- Develop and train a Neural Network on this physiological data to identify a state called "Flow".
- As mentioned, the training data for this project are available
Skills
- Very good time management and self-organization skills
- Good development skills (python is the preferred language, but all other languages are also possible)
- Good knowlege about ML-Procedures
- Interest in physiological data analysis is beneficial
- Good english skills (as the language of the thesis is english)
Contact
References
2. Csikszentmihalyi, M.: Beyond Boredom and Anxiety. Jossey-Bass, San Francisco, CA (1975).
3. Webster, J., Trevino, L.K., Ryan, L.: The dimensionality and correlates of flow in human-computer interactions. Comput. Human Behav. 9, 411–426 (1993).
4. Ghani, J.A., Supnick, R., Rooney, P.: The Experience of Flow in ComputerMediated and in Face-to-Face Groups. In: Proceedings of the 12th International Conference on Information Systems. pp. 229–237 (1991).
5. Mahnke, R., Benlian, A., Hess, T.: Flow experience in information systems research: Revisiting its conceptualization, conditions, and effects. In: Proceedings of the 35th International Conference on Information Systems. pp. 1–22 (2014).
6. Rissler, R., Nadj, M., Adam, M.T.P.: Flow in Information Systems research: Review, integrative theoretical framework, and future directions. In: International Conference on Wirtschaftsinformatik. pp. 1051–1065 (2017).
7. Jackson, S.A., Eklund, R.C.: Assessing flow in physical activity: The flow state scale-2 and dispositional flow scale-2. J. Sport Exerc. Psychol. 24, 133– 150 (2002).
8. Engeser, S., Rheinberg, F.: Flow, performance and moderators of challengeskill balance. Motiv. Emot. 32, 158–172 (2008).
9. Peifer, C.: Psychophysiological Correlates of Flow-Experience. In: Engeser, S. (ed.) Advances in Flow Research. pp. 139–164. Springer Science, New York, NY (2012).
10. Riedl, R., Léger, P.-M.: Fundamentals of NeuroIS Information Systems and the Brain. Springer Berlin Heidelberg (2016).
11. Riedl, R., Davis, F.D., Hevner, A.R.: Towards a NeuroIS Research Methodology: Intensifying the
