Search Results (90)

The fashion industry represents a dynamic expression of cultural diversity and plays a crucial role in national economic health. This research designs strategic management guidance for BANU, a sustainable clothing startup in Senegal aimed at empowering local families to improve their lifestyles. Utilizing an exploratory research strategy, the study develops a comprehensive strategic plan for BANU as a natural textile dyes company, examining factors influencing its development at the macro, micro, and organization layers to identify key strategic issues and strategic options as a comprehensive strategic management plan for BANU to grow. A multifaceted strategic approach is recommended, including tailored operational strategies aligned with local traditions, sustainability, and customer engagement. Collaborations with local businesses, suppliers, and educational institutions are advised to strengthen BANU’s market presence. Additionally, differentiation through unique natural dye clothing and partnerships are encouraged. As BANU evolves, a shift towards corporate strategy, diversification, and international market expansion is suggested to enhance strategic management and ensure sustainable growth. Full article

Premium fashion brands are increasingly adopting Generative Artificial Intelligence (GenAI) to reduce costs and enhance creativity. However, consumers have mixed perceptions of clothing with AI-generated patterns (CAGPs) launched by premium fashion brands, especially in online shopping contexts where consumers cannot examine physical products firsthand. This study integrates the Theory of Planned Behavior (TPB) with Customer Perceived Value (CPV) to investigate Chinese Millennials’ attitudes and purchase intentions toward online purchases of CAGPs launched by premium fashion brands. Using a purposive sampling approach, the study collected 471 valid responses from Chinese Millennials. Structural equation modeling (SEM) was then employed to test the proposed model and hypotheses. The results reveal that perceived brand design effort and perceived price value are primary drivers of purchase intention for CAGPs from premium fashion brands, while perceived aesthetic value significantly shapes consumer attitudes. The subjective norm and attitude positively influence purchase intention. This study sheds light on the roles of aesthetic, emotional, monetary and social factors in driving purchase intention, offering practical suggestions for premium brands’ product design and marketing strategies. Full article

To address key challenges in clothing trademark quality inspection—namely, insufficient defect samples, unstable performance in complex industrial environments, and low detection efficiency—this paper proposes DLF-YOLO, an enhanced YOLOv11-based model optimized for industrial deployment. To mitigate the problem of limited annotated data, an unsupervised generative network, CycleGAN, is employed to synthesize rare defect patterns and simulate diverse environmental conditions (e.g., rotation, noise, and contrast variations), thereby improving data diversity and model generalization. To reduce the impact of industrial noise, a novel multi-scale dynamic synergy attention (MDSA) attention mechanism is introduced, which utilizes dual attention in both channel and spatial dimensions to focus more accurately on key regions of the trademark, effectively suppressing false detections caused by lighting variations and fabric textures. Furthermore, the high-level selective feature pyramid network (HS-FPN) module is adopted to make the neck structure more lightweight, where the feature selection sub-module enhances the perception of fine edge defects, while the feature fusion sub-module achieves a balance between model lightweighting and detection accuracy through the aggregation of hierarchical multi-scale context information. In the backbone, DWConv replaces standard convolutions before the C3k2 module to reduce computational complexity, and HetConv is integrated into the C3k2 module to simultaneously reduce computational cost and enhance feature extraction capabilities, achieving the goal of maintaining model accuracy. Experimental results on a custom-built dataset demonstrate that DLF-YOLO achieves an mAP@0.5:0.95 of 80.2%, with a 49.6% reduction in parameters and a 25.6% reduction in computational load compared to the original YOLOv11. These results highlight the potential of DLF-YOLO as a scalable and efficient solution for lightweight, industrial-grade defect detection in clothing trademarks. Full article

A dominant marketing paradigm has appeared in the form of live streaming e-commerce, which holds a significant user base and exhibits an upward trend in sales figures. Based on the technology acceptance model (TAM) and perceived value theory, combined with KANO and the analytic hierarchy process (AHP), this study extracts consumer demand characteristics through a questionnaire survey of 402 Chinese Douyin consumers who watch clothing live streaming online and participate in purchases. The study categorizes consumer demands into five dimensions with 17 indicators: perceived usefulness, perceived ease of use, perceived emotional value, perceived economic value, and e-commerce anchor characteristics, and a hierarchical framework for consumer demands in clothing-focused live streaming e-commerce is constructed. Four critical deep influence factors were indicated by the findings: information acquisition, after-sales support, smooth communication, and emotional experience. These indicators are the primary factors for future optimization of live streaming e-commerce platforms. The results of this study provide effective data analysis and suggestions related to consumer purchasing needs in clothing-based live streaming e-commerce platforms so as to improve customer satisfaction and thus turnover, as well as to provide a theoretical method for reference in subsequent consumer research on live streaming e-commerce platforms. Full article

Driven by the increasing awareness of environmental protection and the demand for personalized fashion, China’s second-hand clothing market is developing rapidly. Chinese consumers have begun to accept second-hand clothing, and online platforms such as Xianyu and Zhier have promoted the widespread trading of second-hand clothing. This study explored the key factors influencing customer satisfaction in China’s sustainable second-hand clothing market. Using a mixed research approach, factors such as pricing strategy, product quality, brand image, customer service, market environment and promotions were identified. The conclusion of grounded theory is that price, product quality, brand reputation, customer service quality, economic environment and platform promotions have a strong impact on customer satisfaction. The Kano model highlights the sensitivity of customer service quality, economic environment and promotions in improving satisfaction. Price is crucial, confirming the price sensitivity of customers. Brand reputation and product quality significantly increase satisfaction. Customer satisfaction significantly affects the amount of sustainable recycling. This study improves the theoretical framework and research hypotheses, provides valuable insights for future research and practical applications and contributes to the sustainable development of the second-hand clothing market. Full article

When filtering highly rugged terrain from dense point clouds (particularly in technical applications such as civil engineering), the most widely used filtering approaches yield suboptimal results. Here, we proposed and tested a novel ground-filtering algorithm, a multi-size voxel cube (MSVC), utilizing a deep neural network. This is based on the voxelization of the point cloud, the classification of individual voxels as ground or non-ground using surrounding voxels (a “voxel cube” of 9 × 9 × 9 voxels), and the gradual reduction in voxel size, allowing the acquisition of custom-level detail and highly rugged terrain from dense point clouds. The MSVC performance on two dense point clouds, capturing highly rugged areas with dense vegetation cover, was compared with that of the widely used cloth simulation filter (CSF) using manually classified terrain as the reference. MSVC consistently outperformed the CSF filter in terms of the correctly identified ground points, correctly identified non-ground points, balanced accuracy, and the F-score. Another advantage of this filter lay in its easy adaptability to any type of terrain, enabled by the utilization of machine learning. The only disadvantage lay in the necessity to manually prepare training data. On the other hand, we aim to account for this in the future by producing neural networks trained for individual landscape types, thus eliminating this phase of the work. Full article

Considering dialect areas as cultural areas has a long tradition in dialectology. Especially in the first half of the 20th century, researchers explored correspondences between dialect variation and other elements of everyday culture such as traditional clothing and customs. Since then, however, few studies have compared dialect variation with everyday culture, and virtually none have used quantitative methods. This study addresses this issue by employing a multivariate, dialectometric approach. It examines dialect variation in phonology and its relationship to non-linguistic aspects of everyday culture in Austria using two types of data: (a) dialect data from a recent dialect survey, and (b) ethnographic data published in the ‘Austrian Ethnographic Atlas’. Analyzing 90 phonetic-phonological and 36 ethnographic variables, statistical methods such as multidimensional scaling (MDS) and cluster analysis (CA) are employed. The results show only limited overlap between the linguistic and ethnographic data, with cultural patterns appearing more fragmented and small-scale. Geographical proximity is more indicative of cultural than linguistic similarity. MDS and CA reveal clear geographical patterns for the linguistic data that align with traditional dialect classifications. In contrast, the cultural data show less distinct clustering and only small-scale regions that do not coincide with the linguistic ones. This article discusses potential reasons for these differences. Full article

Although materials with infrared camouflage capabilities are increasingly being produced, few applications exist in clothing fabrics. Here, graphene/MXene-modified fabric with superior infrared camouflage, Joule heating, and electromagnetic shielding capabilities all in one was prepared by simply scraping a graphene slurry onto alkali-treated cotton fabrics, followed by spraying MXene. The functionality of the modified fabrics after different treatment times was then tested and analyzed. The results indicate that the mid-infrared emissivity of the modified fabric decreases with an increase in the coating times of graphene and MXene. When the graphene/MXene-modified fabrics are prepared at loads of 5 and 1.2 mg/cm², respectively, the modified fabrics have very low infrared emissivity in the 3–5 and 8–14 μm bands, and the surface temperature can be reduced by 53.1 °C when placed on a heater with a temperature of 100 °C (surface radiation temperature of 95 °C). The modified fabric also demonstrates excellent Joule heating capabilities; at 4 V of power, a temperature of 91.7 °C may be reached in 30 s. In addition, customized materials exhibit strong electromagnetic shielding performance. By simply folding the cloth, the electromagnetic interference shield effect can be increased to 64.3 dB. With their superior infrared camouflage, thermal management, and electromagnetic shielding performance, graphene/MXene-modified fabrics have found extensive use in intelligent wearables and military applications. Full article

In our investigation, we unveil a novel, eco-friendly, and cost-effective method for crafting a bio-derived electrode using discarded cotton fabric via a carbonization procedure, marking its inaugural application in a vanadium redox flow battery (VRFB). Our findings showcase the superior reaction surface area, heightened carbon content, and enhanced catalytic prowess for vanadium reactions exhibited by this carbonized waste cloth (CWC) electrode compared to commercially treated graphite felt (TT-GF). Therefore, the VRFB system equipped with these custom electrodes surpasses its treated graphite felt counterpart (61% at an equivalent current) and achieves an impressive voltage efficiency of 70% at a current density of 100 mA cm⁻². Notably, energy efficiency sees a notable uptick from 58% to 67% under the same current density conditions. These compelling outcomes underscore the immense potential of the carbonized waste cotton cloth electrode for widespread integration in VRFB installations at scale. Full article

The present paper studies the changes that occurred in the clothing trade during the COVID-19 pandemic and the influences exerted on business management. The pandemic sped up digitalization, pushing companies to quickly adapt to new economic conditions and marking a turning point for online commerce. In the fashion sector, where consumer behavior has shifted rapidly and digital technologies have transformed the business landscape, it is essential to examine these changes and their long-term impact. The research methodology involved an online questionnaire-based survey, targeting 153 respondents from various age groups. Descriptive statistics were used, such as the analysis of variation indicators, to explore patterns in the data and provide a clearer understanding of the phenomena studied. Additionally, a series of statistical tests were applied to validate the research questions. The aim was not to predict future behavior but to explain current trends and shifts, particularly the acceleration of digitalization during the pandemic. The findings highlight changes in customer behavior, the need for investments in technology and innovation, and the importance of adaptability, especially in marketing. There is a statistically significant relationship between income levels and spending on clothing, with higher incomes leading to increased expenditure. The pandemic amplified the role of online channels, particularly among higher-income groups. Promotional campaigns significantly impact purchase decisions, especially for lower-income consumers, serving as both purchase drivers and tools for customer retention. The shift toward online shopping, accelerated by the pandemic, highlights substantial growth potential for e-commerce in the fashion sector. Consumers favor platforms offering convenience, product diversity, and personalized experiences. A moderate interest in sustainable fashion was observed, with preferences leaning toward affordable and durable materials, underscoring the need for transparent and eco-friendly business practices. Full article

The integration of textile-based sensing and actuation elements has become increasingly important across various fields, driven by the growing demand for smart textiles in healthcare, sports, and wearable electronics. This paper presents the development of a small, smart dielectric elastomer (DE)-based sensing array designed for user control input in applications such as human–machine interaction, virtual object manipulation, and robotics. DE-based sensors are ideal for textile integration due to their flexibility, lightweight nature, and ability to seamlessly conform to surfaces without compromising comfort. By embedding these sensors into textiles, continuous user interaction can be achieved, providing a more intuitive and unobtrusive user experience. The design of this DE array draws inspiration from a flexible and wearable version of a touchpad, which can be incorporated into clothing or accessories. Integrated advanced machine learning algorithms enhance the sensing system by improving resolution and enabling pattern recognition, reaching a prediction performance of at least 80. Additionally, the array’s electrodes are fabricated using a novel sputtering technique for low resistance as well as high geometric flexibility and size reducibility. A new crimping method is also introduced to ensure a reliable connection between the sensing array and the custom electronics. The advantages of the presented design, data evaluation, and manufacturing process comprise a reduced structure size, the flexible adaptability of the system to the respective application, reliable pattern recognition, reduced sensor and line resistance, the adaptability of mechanical force sensitivity, and the integration of electronics. This research highlights the potential for innovative, highly integrated textile-based sensors in various practical applications. Full article

This research aims to assess consumer attitudes towards purchasing pre-loved luxury fashion items and explore how these attitudes influence their intention to buy such products. Luxury goods consumption is evolving into a multifaceted proposition where customers actively take on new responsibilities. In addition to being purchasers and users, they occasionally turn into luxury brand product dealers. Luxury fashion, which includes more expensive materials, apparel, and frequently new and limited-edition items, is unquestionably stylish. Luxury brands could draw clients and the attention of many audiences, becoming quite prominent, even though luxury fashion only makes up a small portion of the economy compared to other significant businesses. Using a convenience sampling technique, data were collected from 282 individuals in Cairo. The analysis was conducted through SPSS software v2023. Our findings show that consumers’ concerns about the environment have a big influence on their perceived value (PI) of used luxury fashion items, both directly and indirectly through the mediation of their desire for sustainability. Nonetheless, attitude strength has a moderating effect on this association. It is interesting to note that the relationship between environmental concern and sustainability is weakened under the influence of attitude strength. Furthermore, our findings indicate that modest levels of attitude strength make it easy to change how customers’ environmental concerns affect their previously owned luxury fashion items. High-end stores can also fight off counterfeit marketplaces by providing authentication services to consumers of pre-loved luxury clothing. This study emphasizes the role of consumer attitude as a mediator in shaping purchase intentions for pre-loved luxury fashion. However, its focus on one region and cross-sectional data collection presents limitations. Future studies should explore other markets and use longitudinal data for a deeper understanding. This research contributes to the existing literature by offering insights for consumers, marketers, and sellers promoting pre-loved luxury fashion. Full article

Numerous fashion brands, such as Patagonia, H&M, and Levi’s, offer take-back programs, encouraging customers to return used clothing for monetary incentives so that the brands can resell, recycle, or donate them. Drawing on social exchange theory, this study suggests that consumers are more likely to participate in a loyal brand’s take-back program as they own more items from loyal brands due to repeated purchases. Loyal consumers, viewing this as part of an ongoing relationship with the brand, may participate because they perceive greater benefits than non-loyal consumers. In turn, brands benefit by keeping loyal consumers engaged through product collection and future purchases using coupons. This study examines how brand loyalty affects the perceived benefits of take-back programs, shaping participation intention. It also explores how environmental concern moderates the mediating effect of perceived benefits between brand loyalty and participation intention. Data were collected from 467 U.S. consumers via an online survey. Results revealed that the more loyal consumers were, the greater they perceived economic, environmental, and convenience benefits to be, increasing their intention to participate. Economic benefits were more effective for consumers with low levels of environmental concern, while environmental benefits were more influential for those with high levels of environmental concern. Full article

One of the most fundamental values that businesses must ensure to enhance sustainable production and productivity is the satisfaction of their internal customers. It is anticipated that an increase in the satisfaction levels of internal customers will lead to improved work performance, creating a cycle linked to the services and values provided to employees by the organization. This study aims to uncover the impact of the quality of internal services offered by manufacturing companies, as well as the ethical leadership approach, on employee performance. The data obtained from surveys conducted with 412 employees of clothing and textile companies in Van, Turkey, were analyzed using the SPSS program. The results indicate that internal service quality, particularly its dimensions of responsiveness and assurance, positively affects employee performance. Furthermore, it was observed that ethical leadership generally has a negative effect on employee performance, but the ethicality and justice dimensions and task clarity dimensions of ethical leadership affect employee performance positively. Full article

Carbon nanomaterials are increasingly being integrated into modern research, particularly within the textile industry, to significantly boost performance and broaden application possibilities. This study investigates the impact of incorporating three distinct carbon-based nanofillers—carbon nanotubes (CNTs), carbon black (CB), and graphene (Gn)—into polyamide 6 (PA6) multifilament yarns. It explores how these nanofillers affect the physical, mechanical, and thermal properties of PA6 yarns and fabrics. By utilizing melt extrusion, the nanomaterials were uniformly distributed in the yarns, and knitted fabrics were subsequently produced for detailed analysis. The research offers critical insights into how each nanofiller improves the thermal behavior of PA6-based textiles, enabling the customization of their applications. FTIR spectroscopy revealed significant chemical interactions between polyamide and carbon additives, while DSC analysis showed enhanced thermal stability, particularly with the inclusion of graphene. The introduction of these nanomaterials led to increased absorbance and decreased transmittance in the UV-Vis-NIR spectrum. Additionally, Far-Infrared (FIR) emissivity and thermal effusivity varied with different concentrations, with optimal improvements observed at specific levels. Although thermal conductivity decreased with the addition of these nanomaterials, heat management experiments demonstrated varied effects on heat accumulation and cooling times, underscoring potential applications in insulation and cooling technologies. These findings enrich the existing knowledge on nanomaterial-enhanced textiles, providing valuable guidance for optimizing PA6 yarns and fabrics for use in protective clothing, sportswear, and technical textiles. The comparative analysis offers a thorough understanding of the relationship between carbon nanomaterials and thermal properties, paving the way for innovative advancements in functional textile materials. Full article