Detecting Flow Experiences in the Field Using Video-Based Head and Face Activity Recognition: A Pilot Study
Christoph Berger (),
Michael T. Knierim () and
Christof Weinhardt ()
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Christoph Berger: Karlsruhe Institute of Technology (KIT)
Michael T. Knierim: Karlsruhe Institute of Technology (KIT)
Christof Weinhardt: Karlsruhe Institute of Technology (KIT)
A chapter in Information Systems and Neuroscience, 2021, pp 120-127 from Springer
Abstract:
Abstract Flow represents a valuable daily life experience as it is linked to performance, growth, and well-being. As flow support is still a major challenge due to a lack of automatic and unobtrusive detection methods, NeuroIS scholars face the opportunity to devise measurement approaches for flow experience during IS use and, moreover, flow supporting, adaptive NeuroIS. This work presents the first results from a controlled experience sampling field study in which experiences were observed using video recordings during a week of scientific writing. Novel behavioral features (face and head activity) with negative flow-report correlations are identified. Together, the results contribute to the NeuroIS community by providing an extended concept of flow as a state of behavioral efficiency, the identification of novel correlates, and recommendations for economical and feasible extensions of the study approach.
Keywords: Flow experience; Field study; cESM; FaceReader; Emotion (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-030-88900-5_14
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DOI: 10.1007/978-3-030-88900-5_14
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