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A text mining approach to explore factors influencing consumer intention to use metaverse platform services: Insights from online customer reviews

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  • Kumari, Vandana
  • Bala, Pradip Kumar
  • Chakraborty, Shibashish

Abstract

Metaverse technology has recreated real-world surroundings into a virtual setting that has caught the attention of many consumers and brands. Metaverse platforms are facilitating brands to sell physical and digital versions of their products, and consumers are eagerly purchasing these products. Owing to this dramatic shift, it is crucial to understand the factors impacting user adoption of metaverse platform services. The present study leverages an LDA-based topic modeling approach to analyze 17136 online user reviews of the Highrise Virtual Metaverse mobile platform. The study revealed ten factors impacting consumer intention to use the metaverse platform services that are asset buy-sell experience, availability of virtual assets, visually appealing virtual environment, enjoyment from virtual events, social interaction, social activity, positive experience with the metaverse ecosystem, mobile app-related issues, user account-related issues, and the feature of watch ad-to earn. Building upon the uses and gratification theory and flow theory, the study explained that consumers gain three major gratifications and experience a flow state while using metaverse platform services. Furthermore, it explores the emotions expressed by users within the metaverse platform. Correspondence analysis is performed to understand the factors leading to positive and negative user experiences with the platform services. The study presents crucial practical implications for brand marketers and metaverse platform developers.

Suggested Citation

  • Kumari, Vandana & Bala, Pradip Kumar & Chakraborty, Shibashish, 2024. "A text mining approach to explore factors influencing consumer intention to use metaverse platform services: Insights from online customer reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    • Handle: RePEc:eee:joreco:v:81:y:2024:i:c:s0969698924002637
    DOI: 10.1016/j.jretconser.2024.103967
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