In recent years E-commerce platforms recommend some products for consumers based on their shopping history and user persona. However, sometimes, unfamiliar products or styles would be recommended to consumers unintentionally or intentionally. Curiosity drives consumers to try but this idea would be declined with the consideration of product fit uncertainty. Augmented Reality (AR) is the integration of digital information with the user‘s environment in real-time (Hilken et al. 2018), it can deal with issues related to physical apprehension that hinder consumers’ online shopping (i.e, clothes, cosmetics), especially for the unfamiliar style. This study aims to investigate whether AR technology could improve consumers' purchase likelihood. Augmented Reality (AR) can enhance customer experiences in a multichannel environment (Hilken et al. 2017). AR Integrates online experiences into the offline experience (Hilken et al. 2018), such as virtual try-on or magic mirrors. Customers often find it difficult to imagine how firms’ products and services fit them personally or fit with their environment (Hilken et al. 2018). Drawn on AR, consumers can easily evaluate the fitness between themselves and the selected products. Prior studies have explored the different underlying processes of why AR technology could improve consumer purchase intention and customer experience. For instance, according to the situated cognition theory, AR creates a feeling of spatial presence (Hilken et al. 2017). The usage of AR benefits mental imagery, improving decision comfort (Heller et al. 2019). AR can compensate for consumers’ need for touch and offer hedonic and/or utilitarian benefits (Gatter et al. 2022). Based on media richness theory, AR offers more information for customers (Hoffmann et al. 2022), which represents a fitting concept for customers to evaluate the product (Javornik 2016).

• Anqi Hu(University of Nottingham Ningbo China, China)
• Ruizhi Yuan(University of Nottingham Ningbo China, China)
• Martin J. Liu(University of Nottingham Ningbo China, China)
• Ruolan Chen(University of Nottingham Ningbo China, China)