This study explores industrial application cases of digital twin-based beauty services and examines the possibility of expanding such services to women with disabilities from the perspective of inclusive welfare. As digital transformation accelerates across industries, the beauty sector is actively adopting artificial intelligence, big data, and virtual simulation technologies to provide personalized and non-face-to-face services. Among these technologies, digital twin has emerged as a key infrastructure enabling real-time data-based analysis, predictive simulation, and hyper-personalized service delivery. This study adopts a qualitative case analysis approach. Major global and domestic beauty companies applying digital twin-based technologies were selected and analyzed according to technological characteristics, service structure, level of personalization, and potential for social expansion. Based on the derived common characteristics, the applicability of these services for women with disabilities was examined in terms of accessibility, autonomy, psychological safety, and welfare implications. The results indicate that digital twin-based beauty services share key structural features, including non-face-to-face interaction, data-driven personalization, virtual simulation, and iterative feedback systems. These features have significant potential to reduce physical and psychological barriers faced by women with disabilities in conventional beauty service environments. Furthermore, such services can enhance self-esteem, self-efficacy, and psychological well-being by enabling independent appearance management and self-expression. This study is significant in that it extends the discussion of digital twin technology beyond industrial productivity to the domain of human-centered welfare services. It proposes a conceptual framework for technology-based inclusive beauty services and provides a theoretical foundation for future empirical research and policy development.
Choi et al. (Tue,) studied this question.
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