Identifying the Metaverse Value Recipe(s) Affecting Customer Engagement and Well-being in Retailing

Singh, Gaganpreet; Roy, Sanjit K.; Apostolidis, Chrysostomos; Quaddus, Mohammed; Sadeque, Saalem

Abstract

Following the increasing interest of retailers to engage with their consumers using digital channels and platforms, this study uses affordance theory and Leroi-Werelds's value typologies as a theoretical lens to identify recipes (i.e., combinations) of positive and negative affordances that facilitate or impede their interactions with the metaverse in the retail context. More specifically, the study aims to unveil the complex interplay between different value dimensions influencing customer engagement in the metaverse and their impact on customers' well-being. Fuzzy-set qualitative comparative analysis (fsQCA) was used to analyze data from Australian consumers. Unlike earlier studies that have focused on the identification of positive drivers of customer engagement, this research deviates and considers the trade-offs between positive and negative factors and investigates their impact on customer engagement and subjective well-being in a technology-centric context. The study reveals numerous pertinent 'value recipes' that contribute to our existing knowledge regarding the factors that affect customer engagement and subjective well-being in the metaverse. The theoretical contribution of this study lies in the development of several affordance combinations that can explain engagement and well-being in customer-metaverse interactions. From a practical standpoint, the findings suggest guidelines for successfully infusing the metaverse into the retail landscape.

Citation

Singh, G., Roy, S. K., Apostolidis, C., Quaddus, M., & Sadeque, S. (2025). Identifying the Metaverse Value Recipe(s) Affecting Customer Engagement and Well-being in Retailing. Technological Forecasting and Social Change, 210, Article 123870. https://doi.org/10.1016/j.techfore.2024.123870

• Journal Article Type: Article
• Acceptance Date: Oct 31, 2024
• Online Publication Date: Nov 12, 2024
• Publication Date: 2025-01
• Deposit Date: Nov 5, 2024
• Publicly Available Date: Nov 12, 2024
• Journal: Technological Forecasting and Social Change
• Print ISSN: 0040-1625
• Electronic ISSN: 1873-5509
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 210
• Article Number: 123870
• DOI: https://doi.org/10.1016/j.techfore.2024.123870
• Public URL: https://durham-repository.worktribe.com/output/3081602
• Publisher URL: https://www.sciencedirect.com/journal/technological-forecasting-and-social-change