KIT | KIT-Bibliothek | Impressum | Datenschutz

Eye-Tracking-Based Classification of Information Search Behavior Using Machine Learning: Evidence from Experiments in Physical Shops and Virtual Reality Shopping Environments

Pfeiffer, Jella; Pfeiffer, Thies; Meißner, Martin; Weiß, Elisa

Classifying information search behavior helps tailor recommender systems to individual customers’ shopping motives. But how can we identify these motives without requiring users to exert too much effort? Our research goal is to demonstrate that eye tracking can be used at the point of sale to do so. We focus on two frequently investigated shopping motives: goal-directed and exploratory search. To train and test a prediction model, we conducted two eye-tracking experiments in front of supermarket shelves. The first experiment was carried out in immersive virtual reality; the second, in physical reality—in other words, as a field study in a real supermarket. We conducted a virtual reality study, because recently launched virtual shopping environments suggest that there is great interest in using this technology as a retail channel. Our empirical results show that support vector machines allow the correct classification of search motives with 80% accuracy in virtual reality and 85% accuracy in physical reality. Our findings also imply that eye movements allow shopping motives to be identified relatively early in the search process: our models achieve 70% prediction accuracy after only 15 seconds in virtual reality and 75% in physical reality. ... mehr

Open Access Logo

Verlagsausgabe §
DOI: 10.5445/IR/1000125534
Veröffentlicht am 30.10.2020
DOI: 10.1287/isre.2019.0907
Zitationen: 2
Web of Science
Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 09.2020
Sprache Englisch
Identifikator ISSN: 1047-7047, 1526-5536
KITopen-ID: 1000125534
Erschienen in Information systems research
Band 31
Heft 3
Seiten 675–691
Vorab online veröffentlicht am 13.05.2020
Nachgewiesen in Scopus
Web of Science
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
KITopen Landing Page