MV-QUAL: A tool for understanding decisions to purchase virtual products in the metaverse

Abstract

This paper develops the metaverse quality (MV-Qual) scale and explores the various factors that may impact consumers' perceptions of quality and value when purchasing virtual products in a metaverse world. Utilizing Flow Theory and SERVQUAL as theoretical lenses to conduct our research, a framework was developed and tested, demonstrating how various factors are interconnected and impact consumers' shopping experiences in the metaverse. Data were collected from a national sample of 322 metaverse users in the United States and analyzed using partial least squares structural equation modeling in SmartPLS, complemented by a neural network causal analysis in Neusrel. The results demonstrated that several factors impact metaverse store quality and perceived virtual product value, including control, vividness, avatar design, rarity, and resale value. Subsequently, metaverse store quality and perceived virtual product value positively impact the intention to purchase virtual products in metaverse. This research contributes to the advancement of understanding consumer behavior in the metaverse and adds to the emerging marketing literature on the metaverse. Furthermore, the MV-QUAL scale developed serves as a practical tool that both marketing researchers and practitioners can use to enhance understanding and discover new ways to improve the metaverse consumer experience.