Virtual reality in retailing: a meta-analysis to determine the purchase and non-purchase behavioural intention of consumers
Industrial Management & Data Systems
ISSN: 0263-5577
Article publication date: 7 November 2023
Issue publication date: 2 January 2024
Abstract
Purpose
The purpose of this meta-analysis is to encapsulate the outcomes and generate meaningful conclusions by examining the factors that influence consumers' purchase and non-purchase behaviour intention in a virtual reality retailing context.
Design/methodology/approach
This study integrates the outcomes from 52 studies, including 403 relationships involving 19,188 samples. The analysis was conducted using R-metafor and AMOS software.
Findings
The findings indicate that key factors that influence purchase and non-purchase behavioural intentions are virtual reality (VR)characteristics, virtual reality experience and consumer attitudes. VR experience is the strongest predictor for purchase decisions in virtual environment ,while consumer attitude towards VR most strongly influences the non-purchase behaviour of the consumers. Furthermore, the age of the respondents, cultural backgrounds (high vs low power distance) and gender moderate the relationship between consumers' attitudes and purchase and behaviour intentions.
Practical implications
Marketers can positively influence consumer attitudes and behavioural intentions by prioritizing the design of the virtual environment and facilitating unique experiences (by manipulating different sensory stimuli) in virtual retailing.
Originality/value
The current meta-analysis reconciles and reinforces the findings in the extant literature and provides a robust empirical generalization of the critical factors that influence consumers' purchase or behavioural intentions in a virtual retailing context.
Keywords
Citation
Mishra, S., Mishra, A., Dubey, A. and Dwivedi, Y.K. (2024), "Virtual reality in retailing: a meta-analysis to determine the purchase and non-purchase behavioural intention of consumers", Industrial Management & Data Systems, Vol. 124 No. 1, pp. 212-252. https://doi.org/10.1108/IMDS-05-2023-0336
Publisher
:Emerald Publishing Limited
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