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Recognizing Polychronic-Monochronic Tendency of Individuals Using Eye Tracking and Machine Learning

In: Information Systems and Neuroscience

Author

Listed:
  • Simon Barth

    (Karlsruhe Institute of Technology (KIT), Institute of Information Systems and Marketing (IISM))

  • Moritz Langner

    (Karlsruhe Institute of Technology (KIT), Institute of Information Systems and Marketing (IISM))

  • Peyman Toreini

    (Karlsruhe Institute of Technology (KIT), Institute of Information Systems and Marketing (IISM))

  • Alexander Maedche

    (Karlsruhe Institute of Technology (KIT), Institute of Information Systems and Marketing (IISM))

Abstract

Eye tracking technology is a NeuroIS tool that provides non-invasive and rich information about cognitive processes. Recently, it has been demonstrated that eye movement analysis using machine learning algorithms also represents a promising approach to recognize user characteristics and states as a foundation for designing neuro-adaptive information systems. Polychronicity, an individual’s attitude towards multitasking work, is a user characteristic tightly related to cognitive processes and therefore a potential candidate to be recognized with eye tracking technology. However, existing research to the best of our knowledge did not yet investigate automatic recognition of the user’s polychronic-monochronic tendency. In this study, we leverage eye movement data analysis and machine learning to recognize the user’s level of polychronicity. In a lab experiment, eye tracking data of 48 participants was collected and subsequently the users’s polychronic-monochronic tendency was predicted.

Suggested Citation

  • Simon Barth & Moritz Langner & Peyman Toreini & Alexander Maedche, 2022. "Recognizing Polychronic-Monochronic Tendency of Individuals Using Eye Tracking and Machine Learning," Lecture Notes in Information Systems and Organization, in: Fred D. Davis & René Riedl & Jan vom Brocke & Pierre-Majorique Léger & Adriane B. Randolph & Gernot (ed.), Information Systems and Neuroscience, pages 89-96, Springer.
    • Handle: RePEc:spr:lnichp:978-3-031-13064-9_9
    DOI: 10.1007/978-3-031-13064-9_9
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