Recognition of Customers’ Impulsivity from Behavioral Patterns in Virtual Reality

Masoud Moghaddasi*, Javier Marín-Morales, Jaikishan Khatri, Jaime Guixeres, Irene Alice Chicchi Giglioli, Irene Alice Margherita Chicchi Giglioli, Mariano Alcañiz

*Autore corrispondente per questo lavoro

Risultato della ricerca: Contributo in rivistaArticolo in rivistapeer review

Abstract

Featured ApplicationThe results of this study can provide promising insights for the retailers both in physical and future virtual stores.Virtual reality (VR) in retailing (V-commerce) has been proven to enhance the consumer experience. Thus, this technology is beneficial to study behavioral patterns by offering the opportunity to infer customers' personality traits based on their behavior. This study aims to recognize impulsivity using behavioral patterns. For this goal, 60 subjects performed three tasks-one exploration task and two planned tasks-in a virtual market. Four noninvasive signals (eye-tracking, navigation, posture, and interactions), which are available in commercial VR devices, were recorded, and a set of features were extracted and categorized into zonal, general, kinematic, temporal, and spatial types. They were input into a support vector machine classifier to recognize the impulsivity of the subjects based on the I-8 questionnaire, achieving an accuracy of 87%. The results suggest that, while the exploration task can reveal general impulsivity, other subscales such as perseverance and sensation-seeking are more related to planned tasks. The results also show that posture and interaction are the most informative signals. Our findings validate the recognition of customer impulsivity using sensors incorporated into commercial VR devices. Such information can provide a personalized shopping experience in future virtual shops.
Lingua originaleEnglish
pagine (da-a)N/A-N/A
Numero di pagine15
RivistaAPPLIED SCIENCES
Volume11
DOI
Stato di pubblicazionePubblicato - 2021

Keywords

  • V-commerce
  • consumer behavior
  • machine learning
  • signal processing
  • virtual reality

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