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Identifying the metaverse value recipe(s) affecting customer engagement and well-being in retailing

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

Abstract

Following the increasing interest of retailers in engaging with their consumers using digital channels and platforms, this study uses affordance theory and Leroi-Werelds' 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 analyse data from Australian consumers. This research deviates from earlier studies that have focused on the identification of positive drivers of customer engagement, considering instead the trade-offs between positive and negative factors and investigating 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.

Suggested Citation

  • Singh, Gaganpreet & Roy, Sanjit K. & Apostolidis, Chrysostomos & Quaddus, Mohammed & Sadeque, Saalem, 2025. "Identifying the metaverse value recipe(s) affecting customer engagement and well-being in retailing," Technological Forecasting and Social Change, Elsevier, vol. 210(C).
    • Handle: RePEc:eee:tefoso:v:210:y:2025:i:c:s0040162524006681
    DOI: 10.1016/j.techfore.2024.123870
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