This study aims to investigate the key attributes of a steadily growing hotel sector (lifestyle hotels), which has shown great success in the global competitive market, by analyzing user-created content on Instagram. The dataset used in this study were prepared from a total of 20,999 lifestyle hotel posts and 24,262 boutique hotel posts created from 2013 to 2020 and retrieved using a Python web crawler. The locations, hashtags, and image data were analyzed based on frequency analysis using social network analysis methods and computer vision technology, after which they were visualized with a geographical information system and Gephi. The results demonstrated that lifestyle hotels share key attributes that differentiate them from others in terms of physical, geospatial, and experiential contexts. Design, location, and management type are the main attributes that comprise the distinct identity of each lifestyle hotel. Moreover, a lifestyle hotel is distinct from a boutique hotel in that staying in the former means consuming experiences with continuous changes. The information and knowledge gained from this research will contribute to bridging the gap between theoretical literature and the practical development of lifestyle hospitality.

Despite its efficiency, the structural integrity and geometric accuracy of artificial intelligence (AI)-generated imagery used in environmental psychology experiments have not been sufficiently validated. This study investigated the methodological validity and substitutability of generative AI-generated 360° images as experimental stimuli for indoor environmental research. Using a three-stage framework, we generated base panoramas with controlled structural parameters, integrated greenery via AI-based inpainting, and conducted multifaceted validation through objective quality metrics and expert assessments. Quantitative results confirmed high technical integrity, indicating that structural distortions at panoramic stitching points were effectively minimized. Furthermore, the AI-generated stimuli maintained stable visual quality across varying greenery densities. Expert evaluations confirmed that the AI-driven approach significantly outperforms conventional 3D modeling, particularly in terms of presence and realism. By achieving high usability and spatial integrity scores, we established a novel standard for employing generative AI to create high-fidelity virtual environments for architectural and psychological research.

Although metaverse model houses have recently emerged as an interactive alternative to traditional housing marketing tools, empirical research addressing users’ experiences within these environments remains limited. This study aimed to examine how three dimensions of user experience (UX)—operational, sensory, and exploratory—affect user satisfaction and behavioral intentions in metaverse model houses. A total of 83 participants explored a metaverse model house using a tablet PC and completed a questionnaire. Multiple linear regression analysis revealed that exploratory experience significantly influenced user satisfaction, while sensory experience was positively associated with all behavioral intentions, including the intention to revisit, recommend, reside, and purchase. These findings advance our understanding of UX in virtual housing environments and highlight the importance of immersive and exploratory elements in designing effective metaverse model houses. The results offer practical implications for improving digital housing marketing strategies and guiding the future development of metaverse-based architectural platforms.