This study utilized the unified theory of acceptance and use of technology 2 (UTAUT2) to examine usage intentions associated with virtual fitting services. Six independent variables were examined: performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, and habit. The study collected responses from 445 participants who had utilized virtual fitting services. Regarding factors related to usage intentions associated with these services, performance expectancy and social influence were found to significantly influence the usage intentions associated with photo-based virtual fitting services. Furthermore, performance expectancy, social influence, and habit significantly influenced the usage intention of avatar-based virtual fitting services. This suggests that higher levels of performance expectancy and social influence positively impact the usage intentions associated with both types of virtual fitting services, while habit influences only avatar-based virtual fitting services. Moreover, the findings confirm that effort expectancy, facilitating conditions, and hedonic motivation from UTAUT2 do not significantly influence usage intentions associated with virtual fitting services. By analyzing factors influencing potential customers’ virtual fitting service usage intentions, this study can suggest effective strategies to increase usage intentions for companies providing virtual fitting services. Additionally, these findings can be utilized in the formulation of virtual fitting service marketing strategies.

This study focused on how retail tech promotes differentiated customer experiences in offline fashion stores. The purpose of this study is to determine the effects of the characteristics of fashion retail tech stores on consumers’ flow and satisfaction. We surveyed Koreans aged 10 to 50 who had experienced offline fashion retail tech stores. The survey was conducted from April 28, 2023, to May 21, 2023. The total number of survey respondents was 200. The quantitative data collected through questionnaires was analyzed using SPSS 25.0. To reveal the effects of fashion retail tech store characteristics on consumer’s flow and satisfaction, frequency analysis, we conducted frequency analysis, factor analysis, reliability analysis, correlation analysis, and regression analysis. The results of this study, figured out that fashion retail tech store’s characteristics, including playfulness, efficiency, interaction, and information provision, have a significant impact on behavior flow, emotional flow, and satisfaction. As a result of analyzing the influence of consumers’ flow led to satisfaction, it was confirmed that emotional flow positively influenced satisfaction, but behavioral flow had no meaningful effect on satisfaction. The results of our study can be used to make a successful marketing strategy and can serve as foundational data for consumer research on retail-tech-applied offline fashion stores.