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

Simon Barth (), Moritz Langner (), Peyman Toreini () and Alexander Maedche ()
Additional contact information
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)

A chapter in Information Systems and Neuroscience, 2022, pp 89-96 from Springer

Abstract: 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.

Keywords: Polychronicity; Machine Learning; Eye Tracking (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-031-13064-9_9

Ordering information: This item can be ordered from
http://www.springer.com/9783031130649

DOI: 10.1007/978-3-031-13064-9_9

Access Statistics for this chapter

More chapters in Lecture Notes in Information Systems and Organization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().