PaRUS: A Virtual Reality Shopping Method Focusing on Context between Products and Real Usage Scenes

TL;DR

PaRUS is a VR shopping method that reconstructs real usage scenes to improve user experience, trust, and satisfaction by providing more intuitive and contextually relevant virtual environments for product evaluation.

Contribution

This paper introduces PaRUS, a novel VR shopping approach that reconstructs real usage scenes to enhance contextual understanding and decision-making.

Findings

PaRUS significantly reduces perceived performance risk.

PaRUS improves user trust in virtual shopping.

PaRUS increases satisfaction with purchase outcomes.

Abstract

The development of AR and VR technologies is enhancing users' online shopping experiences in various ways. However, in existing VR shopping applications, shopping contexts merely refer to the products and virtual malls or metaphorical scenes where users select products. This leads to the defect that users can only imagine rather than intuitively feel whether the selected products are suitable for their real usage scenes, resulting in a significant discrepancy between their expectations before and after the purchase. To address this issue, we propose PaRUS, a VR shopping approach that focuses on the context between products and their real usage scenes. PaRUS begins by rebuilding the virtual scenario of the products' real usage scene through a new semantic scene reconstruction pipeline, which preserves both the structured scene and textured object models in the scene. Afterwards, intuitive visualization of how the selected products fit the reconstructed virtual scene is provided. We conducted two user studies to evaluate how PaRUS impacts user experience, behavior, and satisfaction with their purchase. The results indicated that PaRUS significantly reduced the perceived performance risk and improved users' trust and satisfaction with their purchase results.