Segmentation of Areas of Interest Inside a Virtual Reality Store

Moghaddasi, Masoud; Khatri, Jaikishan; Llanes-Jurado, José; Spinella, Luciano; Marín‐Morales, Javier; Guixeres, Jaime; Alcañíz, Mariano

doi:10.1007/978-3-030-50729-9_13

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…The first involves general features, which are not related to any specific zones but rather the whole shopping period. The second focuses on zonal features, which are related to a particular period in the task when the shopper is inside a zone on the floor plan (known as a zone of interest or ZOI) or is interacting with or looking at a specific area at the shelf level, known as an area of interest or AOI ( Moghaddasi et al, 2020 ). Additionally, these characteristics can be sub-divided into features related to space, time and kinematics inside the VR environment.…”

Section: Literature Reviewmentioning

confidence: 99%

Recognizing Personality Traits Using Consumer Behavior Patterns in a Virtual Retail Store

et al. 2022

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Virtual reality (VR) is a useful tool to study consumer behavior while they are immersed in a realistic scenario. Among several other factors, personality traits have been shown to have a substantial influence on purchasing behavior. The primary objective of this study was to classify consumers based on the Big Five personality domains using their behavior while performing different tasks in a virtual shop. The personality recognition was ascertained using behavioral measures received from VR hardware, including eye-tracking, navigation, posture and interaction. Responses from 60 participants were collected while performing free and directed search tasks in a virtual hypermarket. A set of behavioral features was processed, and the personality domains were recognized using a statistical supervised machine learning classifier algorithm via a support vector machine. The results suggest that the open-mindedness personality type can be classified using eye gaze patterns, while extraversion is related to posture and interactions. However, a combination of signals must be exhibited to detect conscientiousness and negative emotionality. The combination of all measures and tasks provides better classification accuracy for all personality domains. The study indicates that a consumer’s personality can be recognized using the behavioral sensors included in commercial VR devices during a purchase in a virtual retail store.

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Section: Literature Reviewmentioning

confidence: 99%

Recognizing Personality Traits Using Consumer Behavior Patterns in a Virtual Retail Store

et al. 2022

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“…Using a genetic algorithm (GA) with a modified Fisher criterion as a cost function, the optimal widths of Adjacent and Near were set at 18 and 13 cm, respectively [19]. Note that the lengths of these two zones are fixed and equal to the lengths of the shelves.…”

Section: Feature Extractionmentioning

confidence: 99%

“…As an instance, Bigné et al [16] compared subjects' gaze patterns by presenting a 360 • video in an HMD and a 3D display. This relatively new technology has been used in several studies of consumer behavior [17][18][19][20][21][22][23]; however, extensive research should still be performed to shed light on every aspect of consumer behavior in VEM.…”

Section: Introductionmentioning

confidence: 99%

“…This property can be applied to data gathered from several types of physiological or behavioral sensors such as eye-tracking, electroencephalography (EEG), heart rate variability (HRV), and functional magnetic resonance imaging (fMRI) [63]. Moreover, these methods can be applied to low-cost, noninvasive signals, which can be accessible through VR headgear such as 3D eye movement, gaze information [16], eye blink rate [64], head movement and orientation, controller (hand) movement, and orientation [19]. These signals can be highly informative.…”

Section: Introductionmentioning

confidence: 99%

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Recognition of Customers’ Impulsivity from Behavioral Patterns in Virtual Reality

et al. 2021

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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.

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Virtual Reality Shopping-Insights: A data-driven framework to assist the design and development of Virtual Reality shopping environments

Grande,

Albusac,

Vallejo

et al. 2024

SoftwareX

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Segmentation of Areas of Interest Inside a Virtual Reality Store

Cited by 3 publications

References 5 publications

Recognizing Personality Traits Using Consumer Behavior Patterns in a Virtual Retail Store

Recognizing Personality Traits Using Consumer Behavior Patterns in a Virtual Retail Store

Recognition of Customers’ Impulsivity from Behavioral Patterns in Virtual Reality

Virtual Reality Shopping-Insights: A data-driven framework to assist the design and development of Virtual Reality shopping environments

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