Eye-Tracking Analysis of Gender-Specific Online Information Research and Buying Behavior

Zaharia, Silvia; Kauke, Daniela; Hartung, Ella

doi:10.1007/978-3-319-58484-3_12

Cited by 2 publications

(1 citation statement)

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“…Another study investigated how gaze shifts affected person perception (Mason et al, 2005). Previous studies prove that females and males do have different gaze behaviors, i.e., online shopping (Hwang & Lee, 2017;Zaharia et al, 2017), picture viewing (Abdi Sargezeh et al, 2019, video courses (Zhang & Yang, 2022). This present study aims at investigating gaze pattern differences between females and males when forming person perception.…”

Section: Introductionmentioning

confidence: 93%

Understanding Gender-Based Gaze Behaviors in Person Perception: An Eye-Tracking Study

Chen¹

2022

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Section: Introductionmentioning

confidence: 93%

Understanding Gender-Based Gaze Behaviors in Person Perception: An Eye-Tracking Study

Chen¹

2022

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Recognizing shopper demographics from behavioral responses in a virtual reality store

et al. 2023

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The use of virtual reality (VR) technology in the context of retail is a significant trend in current consumer research, as it offers market researchers a unique opportunity to measure purchase behavior more realistically. Yet, effective methods for assessing the virtual shopping experience based on consumer’s demographic characteristics are still lacking. In this study, we examine the validity of behavioral biometrics for recognizing the gender and age of customers in an immersive VR environment. We used behavior measures collected from eye-tracking, body posture (head and hand), and spatial navigation sources. Participants (n = 57) performed three tasks involving two different purchase situations. Specifically, one task focused on free browsing through the virtual store, and two other tasks focused on product search. A set of behavioral features categorized as kinematic, temporal, and spatial domains was processed based on two strategies. First, the relevance of such features in recognizing age and gender with and without including the spatial segmentation of the virtual space was statistically analyzed. Second, a set of implicit behavioral features was processed and demographic characteristics were recognized using a statistical supervised machine learning classifier algorithm via a support vector machine. The results confirmed that both approaches were significantly insightful for determining the gender and age of buyers. Also, the accuracy achieved when applying the machine learning classifier (> 70%) indicated that the combination of all metrics and tasks was the best classification strategy. The contributions of this work include characterizing consumers in v-commerce spaces according to the shopper’s profile.

show abstract

Eye-Tracking Analysis of Gender-Specific Online Information Research and Buying Behavior

Cited by 2 publications

References 28 publications

Understanding Gender-Based Gaze Behaviors in Person Perception: An Eye-Tracking Study

Understanding Gender-Based Gaze Behaviors in Person Perception: An Eye-Tracking Study

Recognizing shopper demographics from behavioral responses in a virtual reality store

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