Despite the growing adoption of augmented reality (AR) in online fashion retail, limited understanding exists regarding how specific AR features influence different dimensions of customer engagement behavior (CEB). Addressing this gap, this study examines the effects of key AR features—realism, interactivity, personalization, and usability—on participation, advocacy, and co-creation behaviors. Drawing on the stimulus–organism–response framework and flow theory, this study investigates the mediating role of psychological immersion and the moderating role of self-regulatory focus. Using survey data from 865 consumers in China, and Partial Least Squares–Structural Equation Modeling, the findings reveal that AR features significantly enhance consumers’ psychological immersion, which in turn strengthens advocacy and co-creation behaviors. While AR features also directly influence multiple engagement dimensions, their effects vary across behavioral outcomes. Psychological immersion serves as a key explanatory mechanism linking AR features to engagement. This study contributes by conceptualizing CEB as a multidimensional construct and by demonstrating that AR features operate through differentiated psychological pathways. By integrating psychological immersion and self-regulatory focus, the findings provide a more nuanced understanding of how and under what conditions AR drives customer engagement in online fashion retail.
With the rapid diffusion of AIGC technologies, the fashion industry has entered a new phase of technology-enabled transformation across multiple application scenarios. Using a Systematic Literature Review approach, this study analyzes 163 publications retrieved from Web of Science, EBSCO, IEEE Xplore, and Scopus to examine the research landscape, technological pathways, thematic focuses, and future directions of AIGC in fashion. The results indicate that related research has grown rapidly since 2022, forming an overall international trend of multi-center distribution and broad participation; relevant studies are widely distributed in interdisciplinary journals, and empirical case studies and quantitative research are currently the dominant paradigms. The technological evolution reveals a gradual paradigm shift from GAN-based approaches toward Diffusion Models and Large Language Models (LLMs). Research themes primarily focus on Intelligent Design Customization, Trend Forecasting Consumer Behavior, AI Fashion Education, Law Ethics, and Sustainable Fashion Design; furthermore, based on qualitative analysis, a three-layer framework of "Application–Design–Attribute" is constructed to elucidate the advantages and risks of AIGC in the fashion industry. Finally, this study proposes future research agendas such as advancing the three-layer architecture and building multi-agent collaborative systems. This research contributes to a deeper understanding of how AIGC empowers the fashion industry and provides a reference for related research.
This study examines how the status symbolism associated with vegan fashion materials (vegan leather vs. vegan fur) influences consumer responses and how vegan material authenticity moderates these effects in women’s handbag advertising. Drawing on the Value–Attitude–Behavior framework, schematic processing theory, and cognitive dissonance theory, we conducted a 2 × 2 between-subjects experiment using fictitious handbag advertisements featuring vegan leather and vegan fur. Data were collected from 487 U.S. women and analyzed using ANCOVA and SPSS Process Macro model 4 and 8, with fashion involvement included as a covariate. The results reveal three key findings. First, vegan leather handbags elicited a more favorable advertising attitude and higher purchase intention than vegan fur handbags. Second, perceived vegan material authenticity moderated these responses: higher authenticity amplified favorable responses to vegan leather but attenuated favorable responses to vegan fur. This interaction was most pronounced for advertising attitude, whereas the effect on purchase intention was only marginally significant. Third, advertising attitude mediated the effect of status symbolism on purchase intention, with this indirect effect emerging only under high vegan material authenticity but not under low authenticity. Taken together, vegan material authenticity does not uniformly enhance consumer responses in vegan fashion contexts; rather, its effects depend on the symbolic congruence between a vegan material and its product category. The study extends prior research on vegan fashion by demonstrating that material symbolism and authenticity shape consumer responses alongside the ethical and functional considerations emphasized in earlier work, offering context-specific implications for vegan handbag advertising.
Pressure sensitivity plays a critical role in determining the comfort, fit, and functionality of compression garments and wearable textile products. This study investigated pressure pain threshold (PPT) and pressure pain tolerance (PPTo) across the lower limbs of 20 healthy young adults (10 males, 10 females) using a pressure algometer at multiple anatomical sites. Significant gender differences were observed: males generally exhibited higher PPT and PPTo values, whereas females showed greater pressure sensitivity across the tested lower-limb sites. Pressure sensitivity also varied with limb circumferential orientation. The medial and posterior regions of the gastrocnemius tended to be the most sensitive, whereas the anterior and lateral regions generally exhibited greater tolerance. Longitudinal variation was also evident, with the proximal sub-popliteal area being relatively more sensitive and the distal mid- and lower-shank regions showing greater tolerance. These findings indicate that pressure perception in the lower limbs is not uniform but follows distinct anatomical and gender-related patterns. The results indicate the importance of considering both body location and gender in pressure sensitivity analysis and provide actionable insights for optimizing pressure magnitude and distribution, material selection, and structural design in compression garments. Integrating such ergonomic evidence into product design and development may enhance user comfort, wearability, and functional performance in health-related wearable textile applications.
Abstract Translating abstract design intent into text-based prompts presents a practical barrier in generative AI–mediated design, particularly for novice users who lack stable design vocabulary. This study proposes a structured interaction framework that replaces free-form prompting with attribute-based design variable selection, implemented as a web-based fashion image generation system. The system organizes six core fashion design attributes into a taxonomy-driven variable space and converts user selections into prompts through a rule-based translation pipeline. To examine the proposed framework, the study combined exploratory image-generation analysis with a user study. The evaluation investigated whether structured prompt construction could preserve attribute-level interpretability across diffusion-based image generation conditions and how users perceived the interaction structure during fashion ideation tasks. The results suggest that the proposed interaction framework supports relatively stable attribute reflection and enables users to explore design variations through more structured and interpretable interaction processes. The study contributes to human–AI interaction design by demonstrating how domain-specific structure can be embedded into generative AI interaction architectures to reduce reliance on prompt-writing skills and support more transparent relationships between user input and generated output.
Abstract The rapid proliferation of AI-generated images in the fashion industry has created a need for systematic evaluation methods to assess garment consistency between original and AI-generated images. Conventional quantitative metrics often fail to capture fine-grained garment attributes, while human evaluation, though accurate, is costly and difficult to scale. This study proposes an automated evaluation method leveraging Vision–Language Models (VLMs) to assess garment consistency in AI-generated images. To enable systematic evaluation, we developed a garment-specific evaluation framework by operationalizing DeLong’s visual definers into 20 attributes, which were embedded into the prompt to guide the VLMs. To validate the proposed method, experiments were conducted using a real-world dataset of original fashion images paired with AI-generated ghost mannequin photography. While traditional quantitative metrics failed to effectively capture garment consistency, the proposed method demonstrated substantial alignment with human evaluation on overall garment consistency. Compared to human evaluation, the proposed method successfully identified inconsistencies across the defined attributes; however, at the attribute level, it showed higher sensitivity to color and texture but lower sensitivity to shape and line dimensions. These findings suggest that VLM-based evaluation can effectively complement existing evaluation methods by providing scalable and theoretically grounded insights.
Understanding how breast morphology changes with age and body mass index (BMI) is essential for developing ergonomically designed breast-related fashion products that ensure better fit and comfort. However, creating accurate 3D aging models is challenging due to the scarcity of comprehensive data, particularly for specific populations like Chinese women. This paper presents a novel framework for modeling 3D aging patterns in Chinese female breasts by integrating both age and BMI as key variables. We constructed a 3D statistical shape model (SSM) from scans of 305 Chinese women (aged 20–70 years, BMI 16.1–36.9 kg/m2) to capture core geometric variations. Moving beyond traditional linear methods, we employed a Gaussian kernel-based locally weighted partial least squares regression (LW-PLSR) to model the complex, nonlinear relationships between demographic and anthropometric factors and breast shape. To our knowledge, this is the first study to generate a 3D breast aging model that explicitly incorporates BMI alongside age for any population. The resulting model accurately captures changes in breast morphology and provides objective, data-driven tools for the fashion and textiles industries. It also offers practical geometric insights to inspire designers, manufacturers, and researchers to advance inclusive fashion design for women across a comprehensive range of ages and body sizes, thereby improving apparel comfort, fit, and functionality.
This study presented a genetic programming (GP) model for predicting solar-radiation reflectance of cotton woven fabrics as protective materials. The influence of three structural parameters, namely, yarn fineness (14, 25, 36 tex), weave type (plain, twill, satin), and relative fabric density (55–85
Abstract Silk is susceptible to deterioration induced by light, heat, and moisture, which leads to reduced structural stability and progressive strength loss. However, because destructive analysis is fundamentally unsuitable for silk textile cultural heritage, residual tensile strength is typically inferred through subjective evaluation. This study aims to quantitatively analyze deterioration behavior and examine the feasibility of nondestructive prediction of the residual tensile strength of silk textile cultural heritage by employing indicators that exhibit significant correlations with tensile strength reduction. As indicators of tensile strength, protein secondary structure, pH, moisture regain, yellow index, and K/S value were measured in artificially aged silk textiles. Results showed that tensile strength was not significantly influenced by differences in deterioration environments but correlated with crystallinity and protein secondary structure composition. Although linear regression demonstrated limited explanatory power (R2 = 0.576), XGBoost demonstrated improved predictive performance (R2 = 0.654–0.751) by capturing complex multivariate interactions, suggesting the potential for nondestructive estimation of residual tensile strength in textile cultural heritage. The prediction model using crystallinity, protein secondary structure contents, pH, yellow index, and K/S value showed the highest predictive accuracy. Further, with the accumulation of data encompassing textiles with diverse properties and the refinement of relevant indicators, the development of a machine learning–based nondestructive precision prediction model is anticipated. Such an approach can overcome the limitations of subjective assessment and contribute to systematic conservation management through the quantitative prediction of residual tensile strength in textile cultural heritage.
Compression garments are used by athletes for post-exercise recovery and injury prevention, yet their effectiveness depends on the interaction between textile properties, garment construction, and user-specific biomechanics. Despite growing participation of women in collegiate sports, few studies have examined recovery-focused compression engineered specifically for female athletes. This pilot investigation combined user-centered design methodology with textile performance testing to develop and evaluate a hamstring compression sleeve for female NCAA athletes. Survey data from competitive athletes (N = 34) identified durability, stretch, and thermal comfort as primary design priorities. Five candidate fabrics were evaluated for thickness, mass, elongation, and air permeability, leading to selection of a spacer knit fabric that provided high extensibility with sufficient stability for localized compression. A prototype sleeve incorporating elastomeric striping aligned with hamstring musculature was produced and evaluated during four repeated sprint testing sessions with NCAA athletes (N = 8). Functional performance measures included isometric strength, jump performance, and power output, alongside perceived soreness and wearability assessments. No statistically significant differences were detected between the compression and control limbs across biomechanical performance variables. However, participants consistently reported positive perceptions related to comfort, usability, and recovery support. These findings suggest that perceived benefits of compression garments may not always be reflected into short-term performance metrics, but remain relevant to athlete experience. The study demonstrates the feasibility of integrating textile engineering, garment design, and athlete feedback within a single development process and provides a framework for future optimization of compression systems tailored to female athletic populations.
Guided by the Technology-Organization-Environment (TOE) framework, this study explores how firefighting gear manufacturers perceive 3D apparel visualization software and how these perceptions shape their intentions to adopt the technology. Semi-structured interviews were conducted with 12 experts from six companies producing wildland and structural turnout gear, including pattern makers, designers, technical designers, and a business manager. Content analysis revealed that manufacturers viewed 3D apparel visualization software as a useful tool for pattern optimization, visualizing air gaps, and improving communication with municipal customers. However, participants also highlighted significant technical limitations, particularly in simulating composite materials, multilayer garment structures, and substantial ease and air gap behavior in structural turnout gear. They also noted constraints related to organizational slack, including financial and human resource demands and disruptions to established workflows. Adoption decisions were further shaped by key industry characteristics, such as stringent National Fire Protection Association (NFPA) standards, unchanging garment style requirements, conservative industry norms, and varying levels of business partner readiness. The findings extend TOE research to a specialized functional clothing context by highlighting sector-specific constraints and the importance of distinguishing between current capabilities and perceived future value. Practical implications are offered for software developers, regulators, and manufacturers seeking to align digital design investments with the technological and regulatory realities of firefighting gear innovation.
This study introduces a novel approach for producing zero-waste dress shirts (ZWSs) through size development by proposing a method that transforms a base pattern into a rectangle, effectively reducing fabric waste while maintaining garment functionality. To minimize fabric waste, we developed ZWSs by marking a graded conventional shirt pattern within a rectangle that has a large seam allowance. Moreover, we proposed a sewing method for handling the large seam allowance. The produced ZWSs were evaluated against conventional shirts for appearance, comfort, satisfaction, and purchase intention. In addition, we investigated the difference between consumer evaluations before and after explaining zero-waste fashion design. Although the ZWSs were slightly more wrinkled than the conventional shirts, their overall appearance was comparable. At the same time, the method successfully achieved zero waste. In terms of comfort, ZWSs required more ease allowance around curved areas like the neck and armholes owing to the stiffness of the seam allowance. This could be addressed using thin fabric. Although the ZWSs initially scored lower in comfort and satisfaction, their overall satisfaction and purchase intention improved when participants were informed about their zero-waste design. This highlights the importance of consumer awareness of sustainable practices in increasing the acceptance and appeal of zero-waste fashion design.
The integration of Artificial Intelligence (AI) with wearable smart textiles is transforming passive fabrics into active systems capable of sensing, responding, and adapting to environmental and user-specific stimuli. Despite growing interest, a comprehensive quantitative analysis of this interdisciplinary field remains lacking. This study conducts a bibliometric and visual analysis of publications from 2000 to 2025 from the Web of Science (WoS) Core Collection, utilizing Visualization of Similarities viewer (VOSviewer), CiteSpace, and Bibliometrix to examine publication trends, collaboration networks, thematic evolution, and intellectual structures. Results indicate rapid growth in publications, with China, the United States, and South Korea leading a highly collaborative international network. The field exhibits strong interdisciplinary connections among materials science, nanotechnology, electronic engineering, and AI-based data analytics. Key research themes include fiber-based nanogenerators, while emerging trends focus on AI-sensor integration, sustainable materials, and multifunctional applications. This study constructs a knowledge framework and identifies future directions such as AI algorithm miniaturization, self-powered sensing, eco-friendly material industrialization, and deeper scenario-based applications. These findings address the gap in macro-level analysis and offer strategic guidance to advance research and innovation in AI-enhanced wearable smart textiles.
Abstract This study investigates how visual transformative (VT) smart clothing, garments that dynamically change color, pattern, or silhouette through integrated materials or embedded technological mechanisms, influences consumer adoption by examining its effects on perceived usefulness (PU), aesthetics (AES), hedonic motivation (HM), and social influence (SI). Drawing on TAM, UTAUT, and UTAUT2, we modeled VT as the independent variable, perceived aesthetics (AES), perceived usefulness (PU), hedonic motivation (HM), and social influence (SI) as mediators, and age (AGE), socio-cultural experience (SCE), technological experience (TE), and individual innovativeness (II) as moderators. An online survey of 350 Korean women aged 20–59 evaluated function-oriented versus expression-oriented smart clothing stimuli. Data were analyzed using correlation analysis, hierarchical regression, and PROCESS macro models. VT significantly increased AES but decreased PU, producing a negative total effect on intention to use via a suppression pattern in which the adverse PU pathway outweighed aesthetic gains. AES and PU significantly mediated the VT–intention relationship, whereas HM and SI did not, indicating that adoption of VT garments is driven primarily by functional and aesthetic judgments rather than hedonic or social processes. Moderated mediation analyses showed that AGE, SCE, and TE strengthened the PU–intention path, amplifying the negative indirect effect of VT among younger, culturally exposed, and technologically experienced consumers, while II exerted only a limited influence. These findings conceptualize visually transformative smart clothing as a distinct category whose acceptance depends on resolving tensions between expressive appearance and functional credibility, offering guidance for the design and communication of technology-embedded fashion.
Abstract Clothing involves a complex production process and long-term usage, during which garments undergo repeated maintenance. Consequently, the use-phase plays a critical role in assessing the overall environmental impacts of apparel. While life cycle assessment (LCA) is widely applied in the fashion and textile industry, many studies prioritize cradle-to-gate stages. This review highlights two key aspects. First, it establishes the importance of the use-phase within the entire life cycle. Second, it delves into a detailed analysis of the use-phase, examining major elements affecting environmental burdens, including garment lifespan, washing frequency, drying method, and regional differences. The use-phase often accounts for the second-highest environmental impact after production, and in certain cases, represents the most significant contributor. Extending garment lifespan is a highly effective strategy for mitigating overall impacts by avoiding the need for new production. Washing frequency and the choice between machine and air drying are also confirmed as important factors. Regional differences, which include consumer behavior and the carbon intensity of electricity grids, lead to substantial differences in use-phase impacts. In summary, this review highlights the central role of the use-phase in apparel LCA and emphasizes its key factors. While extensive LCA studies have focused on washing and drying processes, other common practices such as ironing and dry cleaning have received limited academic attention. This oversight constitutes a notable gap in the current literature, suggesting a need for further research to achieve a comprehensive understanding of the environmental impacts of apparel.
Recommendation explanations are crucial in helping users make informed and confident decisions, especially in domains such as fashion, where personal style and preferences play an important role. While previous studies have predominantly used review data for explanations, the review-based method requires the availability and quality of a good number of reviews. To address this issue, we investigate the effectiveness of content-based recommendation explanations in fashion recommender systems. Using a Large Language Model (LLM) and deep learning techniques trained on fashion attribute data, we developed a framework that extracts essential visual information from product images and generates user-tailored explanations. This approach allows us to generate customized explanations at various levels—basic, simple, and detailed—for each recommendation. We developed a My Own Style (MOS) interface that displays fashion products, recommendations, and explanations. Our user study with 211 participants showed that detailed explanations, especially when combined with diversity-based algorithms, significantly improved user satisfaction and trust in fashion recommendations. This study contributes to clothing and textile research by providing guidelines for fashion-specific LLM prompts and demonstrating the effectiveness of LLM-generated explanations in fashion e-commerce. Our findings point the way to more personalized and transparent AI-driven fashion recommender systems that improve user experience and style exploration in fashion e-commerce.
Lattice structures, best known for their high strength-to-weight ratio, flexibility, and tunable mechanical properties, have garnered increasingly more attention across various industries that require advanced performance and material efficiency for their products. The advent of three-dimensional (3D) printing technologies has further expanded design possibilities and manufacturing accuracy of such structures, thus enabling the fabrication of products with complex geometries that are difficult or not possible to achieve through traditional production methods. In the textile and apparel industries, the integration of 3D-printed lattice structures offers unprecedented opportunities to create lightweight, adaptive, and highly customizable wearable devices. These innovations have paved the way for emerging applications that range from sportswear and protective gear to smart textiles and personalized healthcare solutions. This review examines the key 3D printing technologies and materials commonly employed in lattice fabrication, which highlight their role in producing wearable lattice textiles across different performance scales. The study further categorizes 3D-printed lattice geometries based on how they address specific requirements in textile applications, such as breathability, energy absorption, and wear comfort. Additionally, the review discusses current challenges, including material limitations, structural design complexities, and scalability issues, while proposing future directions for research and development. This review paper serves as a comprehensive resource for researchers, engineers, and designers who wish to leverage 3D-printed lattice structures for the next generation of functional textiles and apparel.
As the fashion industry undergoes rapid digital transformation, designer brands have gained increasing visibility through expanded creative and communication opportunities. However, many still face significant challenges in brand development, including limited resources and insufficient consumer understanding. This study proposes an AI-driven branding framework that integrates the Persona/Scenario (P/S) methodology to assist designers in identifying visual style features, understanding target consumer profiles, and formulating strategic brand positioning. The framework incorporates multimodal AI components, including mixture-of-experts convolutional neural networks (MoE-CNNs) for visual style classification, large language models (LLMs) for consumer text analysis, and k-nearest neighbors (KNN) for brand similarity mapping in semantic space. Based on in-depth interviews and simulation-based evaluation procedures, the study demonstrates the potential of AI to enhance the traditional P/S approach and support brand strategy development for early-stage designer brands. The framework provides a scalable and flexible pathway to facilitate systematic and consumer-oriented brand growth. By proposing a structured yet adaptable decision-support system, this research contributes to the interdisciplinary integration of AI technologies and fashion branding and enables a strategic branding path that fuses user orientation with design-driven thinking.
Abstract Hijabs, niqabs, and burqas are widely worn by Muslim women in hot climates, however, comparative studies on their thermo-physiological effects remains scarce. This study investigated the physiological and subjective responses to wearing a Hijab, niqab, and burqa in a hot-dry environment. Ten healthy Indonesian females (28.8 ± 3.0 y, 160.6 ± 4.6 cm, and 53.4 ± 4.4 kg) completed three trials (hijab, niqab, and burqa) during exercise at an air temperature of 36 °C and an air humidity of 30%RH with radiation on the ceiling (globe temperature at 70 °C, wet-bulb globe temperature at 36.5 °C). Rectal, auditory canal, mean skin temperature, total sweat rate, and heart rate did not differ significantly among the three headwear types. However, the maximum temperature at the back of the neck tended to be lower with the niqab than with the burqa, but the difference was not significant (p = .053). The microclimate temperature inside the chest was significantly higher with the burqa than with the other two conditions (p < .001). Remarkably, the microclimate temperature at the top of the head rose to maximum 71 ± 9, 72 ± 6, and 73 ± 8 °C for the hijab, niqab, and burqa, respectively, with no significant differences. Both the niqab and burqa increased breathing resistance and often caused facial discomfort due to fabric contact with the nose and eyes. While the niqab provided more neck ventilation than the hijab or burqa, both the niqab and burqa impose additional breathing burdens during exercise in hot environments.
