The rapid development of technology is changing the experience of the way people purchase goods. With the rise of three-dimensional holographic technology, the e-tailing market is also facing a transition from two-dimensional to three-dimensional. A representation of 3D technology is extended reality (XR), which is gradually penetrating the entertainment and leisure sector as it continues to evolve. Extended reality includes virtual reality, augmented reality and mixed reality. Virtual reality (VR) represents immersive, interactive, multi-sensory, user-centred, three-dimensional computer-generated environments and the technologies that construct such environments (Aukstakalnis & Blatner, 1992; Cruz-Neira, 1998). Augmented reality (AR) is a range of technologies integrating real-world and virtual information to enhance a particular reality (Lamantia, 2009). Mixed reality (MR) is defined as an existing display system that displays both real and virtual objects (Milgram and Kishino, 1994). Subsequently, Microsoft (2022) defined mixed reality as an experience that can be transformed between augmented reality and virtual reality. The adoption of extended reality in the retail industry has contributed to a more diverse shopping experience for consumers, reduced retail costs, and increased mobility, availability, and sustainability for retail (Verhoef et al. 2017; Reinartz et al. 2019; van Esch et al. 2019; Bulearca and Tamarjan, 2010). This study was a pilot study using a questionnaire to quantitatively analyse and predict the prospects and opportunities for using extended reality technology in the retail market to provide a guide and reference for a future sustainable and equitable new digital retail market. We utilise Davis (1993) 's TAM model as a framework and identify additional factors to construct our conceptual model. In conclusion, this work provides a guideline and perspectives on consumer XR technology acceptance to contribute to adopting extended reality technologies in the retail industry. The study findings (n=807) suggest the implications of gender, age, previous use experience and consumer intentions on user technology acceptance. Suggestions were provided for retailers to match three types of technology to different customer groups.