Fashion or apparel refers to a topic discussed publicly as an indispensable discipline on a day-to-day basis, which has aroused rising attention from academic sessions over the past two decades. However, since the topic of fashion design covers knowledge in extensive ranges and considerable information, scholars have not fully grasped the research field of fashion design, and the research lacks directional guidance. To gain more insights into the existing research status and fronts in the fashion design field, this study conducts a quantitative literature analysis. The research of this study is conducted by employing CiteSpace technology to visualize and analyze 1388 articles regarding “fashion design” in the Web of Science (WOS) Core Collection. To be specific, the visualization and the analysis concentrate on the annual number of articles, author collaboration, institutional collaboration, literature citations, keywords clustering, and research trend evolution of the mentioned articles. As highlighted by this study, the effect of the US and the UK on academic research in fashion design is relatively stronger and extensive. Sustainable fashion refers to the research topic having aroused more attention since 2010, while new research topics over the past few years consist of “wearable fashion”, “transgender fashion” and “medical fashion”. The overall research trend of fashion design is developing as interdisciplinary cross research. This study systematically reviews the relevant literature, classifies the existing research status, research hotspots and frontier trends in the academic field of “fashion design”, and presents the knowledge map and information of literature for researchers in relevant fields.

Digital technologies represented by AR (Augmented Reality), VR (Virtual Reality), and digital twins, along with the expansion of metaverse platforms and digital marketing concepts, have attracted the attention of numerous sports fashion product consumers and brands, particularly in the category of sports shoes. Therefore, in the context of digital technologies, understanding the factors that affect consumer experience and the preferences in the online purchasing process of sports shoes is very important. This study employs Latent Dirichlet Allocation topic analysis to analyze 44,110 online user posts and comments from social platforms, extracting thematic elements of consumer experience needs for purchasing sports shoes online. The information obtained is further encoded and designed into a questionnaire, which is then utilized alongside the Kano model to analyze the overall preferences of consumer experience needs. The results indicate that webpage design and basic product information are considered as Must-be attributes for user experience needs; providing information on after-sales service policies and product comment, products’ special feature information, and online size testing are recognized as Performance attributes. Additionally, high-tech interaction methods, visual presentation, personalized customization, virtual try-on, apparel matching recommendations, and dressing scenario recommendations are identified as Attractive attributes. The study reveals that in the context of new digital technology development, the online shopping experience for sports shoes is enhanced across four dimensions: platform experience augmentation, product experience augmentation, user demand augmentation, and interactive experience augmentation. These four dimensions collectively constitute the holistic experience design for the online retail platform. Therefore, this research provides case references and theoretical insights for researchers and developers in the fields of brand marketing, experience design, and product service innovation.

Digital fashion is reshaping clothing and apparel research by changing how garments are designed, represented, experienced, and circulated across physical–digital environments. Existing studies have examined digital fashion through technological innovation, virtual garments, consumer experience, sustainability, and platform branding, but these perspectives remain fragmented. This paper argues that digital fashion should be understood as an apparel product–service system (PSS) in which design processes, user participation, platform infrastructures, and mediated value creation are increasingly integrated. Drawing on an integrative conceptual review and a structured illustrative scan of selected fashion enterprises, the study develops a relational framework for interpreting digital fashion transformation. The analysis shows that digital technologies reconfigure apparel design into adaptive and feedback-driven practices; users participate as operant resources through styling, sharing, remixing, and feedback; and platforms coordinate garments, digital assets, interfaces, data, content, and commercial interaction. The paper contributes by reframing digital fashion as a platform-mediated apparel PSS.

The rise of virtual models in the digital age presents a new frontier for cosmetic advertising. Nevertheless, the comparative effectiveness of “idealized” versus “naturalistic” facial features in these models remains a topic of debate and an area of development. This study examines the impact of “idealized” and “naturalistic” facial features in virtual models on consumers’ cognitive and affective responses. Using eye-tracking and a structural equation model, we analyzed visual attention patterns and the roles of affective resonance, trustworthiness, likability, and expertise perception. The results indicate that non-homogeneous or defective naturalistic features increase visual attention and purchase intention, with consumers focusing on imperfections such as freckles. In contrast, idealized facial features mainly draw attention to areas such as the eyes and nose. Mediation analysis reveals that likability and affective resonance are primary influences on purchase intention, while expertise perception and trustworthiness are secondary. This experiment suggests that consumers prioritize socio-emotional connections over professional authority when evaluating naturalistic designs. Our findings provide a framework for virtual model design, helping brands balance aesthetics with psychological optimization, and offer insights into the interplay between visual stimuli and human cognitive and emotional processes in decision-making.

Social-media imagery provides a scalable source for quantifying visual changes in apparel, but its use in ski sportswear color research remains limited. This study develops a transparent workflow for analyzing temporal color patterns in ski sportswear images collected from Instagram and Rednote between 2023 and 2025. The final analytical sample comprised 9,829 images across six semiannual periods. After foreground segmentation, dominant colors were extracted using K-means clustering (K=5) and mapped to hue, saturation, and value (HSV) categories. The choice of K was examined through a stratified sensitivity analysis of 60 images using K=2–8. Temporal changes were assessed with one-versus-rest grouped binomial logistic regression with Benjamini–Hochberg correction, while six-point linear models were used only for temporal trend analysis to 2026. Black–white–gray (BWG) remained the predominant hue category, while blue showed a significant decrease over time (OR = 0.953, adjusted p=0.018). Low-saturation colors increased (OR = 1.090, adjusted p<0.001), while high-saturation colors declined. Medium-value colors increased (OR = 1.092, adjusted p<0.001), while low-value colors decreased modestly (OR = 0.951, adjusted p<0.001). The exploratory six-point models did not support robust hue forecasts and should be interpreted only as directional scenarios. Overall, temporal change in the sampled social-media imagery was more evident in saturation and value than in hue. The proposed workflow provides a transparent supplementary tool for monitoring platform-level visual color patterns, while broader market generalization requires independent validation.