Search Results (458)

With the increasing prominence of sustainable consumption and the rising influence of Generation Z in the fashion market, secondhand fashion e-commerce platforms have become essential carriers of green fashion. Although AI-assisted recommendation mechanisms are widely embedded in these platforms, their psychological and behavioral effects on users’ continuous use and social engagement remain insufficiently examined. To address this gap, this study incorporates the Stimulus–Organism–Response (SOR) framework to investigate the psychological reaction pathways and behavioral intentions of Generation Z users within Human-AI Collaboration-enabled green e-commerce environments. Three AI-driven service stimuli—Human-AI Collaborative Recommendation Perception, AI Interaction Transparency, and Perceived Personalization—were conceptualized as stimulus variables; Psychological Immersion, Emotional Triggering, Cognitive Engagement, and Platform Trust were modeled as organism variables; and Continuous Use Intention and Social Sharing Intention served as behavioral response variables. Based on 498 valid samples analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), the results demonstrate strong empirical support for all proposed hypotheses. Specifically, AI-driven stimuli significantly and positively influence psychological responses, which subsequently strengthen users’ continuous usage and social sharing intentions. This research provides theoretical insights for developing Human-AI Collaboration-enabled service systems that balance efficiency and emotional resonance on green e-commerce platforms, and offers practical implications for promoting sustainable fashion values among younger consumers. Full article

This study examines the socio-economic and behavioral factors influencing sustainable consumption through secondhand clothing purchases among young consumers in Hanoi, Vietnam. By addressing the changing consumption patterns, this research contributes to understanding how youth behavior supports the transition toward sustainability in emerging urban markets. This research integrates the Theory of Planned Behavior (TPB) with additional constructs such as perceived economic benefits, environmental concern, perceived risk, shopping experience, and gender differences to provide an integrated socio-economic framework. Data were collected through a structured questionnaire administered to university students and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results indicate that perceived economic benefits and subjective norms are the strongest predictors of purchase intention across both general and luxury secondhand fashion segments, emphasizing affordability and social acceptance. Environmental concern and attitude also positively influence general secondhand purchase intentions, while perceived behavioral control notably impacts luxury secondhand purchases. Contrary to prior studies, perceived risk was found to be insignificant, and male consumers exhibited a higher engagement rate than females in this context. These findings underscore the complex interplay of economic, social, and environmental dimensions shaping sustainable fashion consumption among youth. This study suggests targeted marketing and policy strategies to promote sustainable consumption and supports the expansion of circular economy practices in emerging urban markets. Limitations related to sample scope and self-reported data warrant further research to generalize the findings and explore additional moderating variables. Full article

In practical deployments of decentralized federated learning (FL) in Internet of Things (IoT) environments, the non-independent and identically distributed (Non-IID) nature of client-local data limits model performance. Furthermore, concept drift further exacerbates complexity and introduces temporal uncertainty that significantly degrades convergence and generalization. Existing approaches, which mainly rely on model-level similarity or static clustering, struggle to disentangle inherent data heterogeneity from dynamic distributional shifts, resulting in poor adaptability under drift scenarios. This paper proposes FedPLC, a novel FL framework that introduces two mechanism-level innovations: (i) Prototype-Anchored Representation Learning (PARL), a strategy inspired by Learning Vector Quantization (LVQ) that stabilizes the representation space against label noise and distributional shifts by aligning sample embeddings with class prototypes; and (ii) Label-wise Dynamic Community Adaptation (LDCA), a fine-grained adaptation mechanism that dynamically reorganizes classifier heads at the label level, enabling rapid personalization and drift-aware community evolution. Together, PARL and LDCA enable FedPLC to explicitly disentangle static Non-IID heterogeneity from temporal concept drift, achieving robust and fine-grained adaptation for large-scale IoT/edge client populations. Our experimental results on the Fashion-MNIST, CIFAR-10, and SVHN datasets demonstrate that FedPLC outperforms the state-of-the-art federated learning methods designed for concept drift in both abrupt drift and incremental drift scenarios. Full article

Background: Posteriorly directed aneurysms at the internal carotid–posterior communicating artery (ICA–PCoA) junction concentrate technical risk at the posteromedial neck where the PCoA origin and perforators exist beneath the optic apparatus. Our aim was to describe, in a reproducible fashion, an anatomy-driven sequence in the management of a ruptured ICA–PCoA aneurysm that visualized the posterior wall and a closing line parallel to the PCoA axis and which is placed within contemporary practice. Case Presentation: This is a single case study employing predetermined surgical techniques demonstrating a reproducible method of anatomical microsurgery applied to a posterior projecting ICA-PCoA aneurysm. The authors describe a 62-year-old female who was stabilized by nimodipine and aggressive blood pressure control in the systolic range 140–160 mmHg after an aneurysmal subarachnoid hemorrhage. Diagnostic contrast catheter angiography showed a left ICA-PCoA aneurysm of 13.1 × 10.0 mm at the base with a neck of 4.3 mm projecting posteriorly into the carotid–optic cistern. Complete adherence to a protocol of staged techniques was employed for the operation, as detailed below. Step 1: Early cisternal decompression requiring total and immediate relaxation of the temporal lobe, rapidly opening up the carotid–optic anatomical window. Step 2: Circumferential dissection about the neck of the aneurysm permitting definition of the true posteromedial wall and definition of the perforator territories and anterior choroidal territories. Step 3: Brief but effective ICA proximal quiescence (58 s) permitting clipping under direct vision. Step 4: Staged closure of two clips with the closing line of the clips orientated parallel to the axis of the PCoA with maintenance of the diameter of all parent vessels, the origin of the PCoA and the integrity of the perforators. Urgent postoperative digital subtraction angiography (DSA) study showed complete exclusion of the aneurysm with no alteration in flow characteristics, and 3 months later DSA studies again showed permanent obliteration and patency of those branches. The immediate DSA demonstrated complete exclusion of the aneurysm with patent supraclinoid ICA caliber and PCoA ostium, the anterior choroidal artery was preserved; no angiographic vasospasm was identified. The postoperative course was uncomplicated; there was no hydrocephalus, seizure disorder or delayed ischemia. At discharge and three months postprocedure the patient was neurologically intact (Modified Rankin Scale 0). Non-contrast cranial CT (three months) demonstrated stable clip position and no hemorrhagic or ischemic sequelae. Conclusions: In posteriorly projecting ICA–PCoA aneurysms that are disturbed beneath the optic apparatus, an anatomy-guided strategy—early cisternal decompression, true posteromedial neck exposure, brief purposeful quieting of the proximal ICA and two-clip closure parallel to the PCoA in selected cases—may provide the opportunity for durable occlusion whilst the physiology of branching is preserved. We intend for this transparent description to be adopted, refined or discarded based on local anatomy and practice. Full article

The convergence of fashion and technology has created new opportunities for creativity, convenience, and sustainability through the integration of computer vision and artificial intelligence. This systematic review, following PRISMA guidelines, examines 200 studies published between 2017 and 2025 to analyze computational techniques for garment design, accessories, cosmetics, and outfit coordination across three key areas: generative design approaches, virtual simulation methods, and personalized recommendation systems. We comprehensively evaluate deep learning architectures, datasets, and performance metrics employed for fashion item synthesis, virtual try-on, cloth simulation, and outfit recommendation. Key findings reveal significant advances in Generative adversarial network (GAN)-based and diffusion-based fashion generation, physics-based simulations achieving real-time performance on mobile and virtual reality (VR) devices, and context-aware recommendation systems integrating multimodal data sources. However, persistent challenges remain, including data scarcity, computational constraints, privacy concerns, and algorithmic bias. We propose actionable directions for responsible AI development in fashion and textile applications, emphasizing the need for inclusive datasets, transparent algorithms, and sustainable computational practices. This review provides researchers and industry practitioners with a comprehensive synthesis of current capabilities, limitations, and future opportunities at the intersection of computer vision and fashion design. Full article

This study investigates how cultural and natural heritage can inform surface design for fashion, focusing on the development of a capsule collection of geoproducts in the UNESCO Global Geopark of Caçapava do Sul, Brazil. The purpose is to expand the scope of existing geoproducts, often limited to food and souvenirs, by introducing textile-based items that reflect local identity and contribute to sustainability. The research employed an applied, qualitative, and descriptive approach, including bibliographic review, questionnaires with local artisans, and the mapping of existing geoproducts. Data were analyzed through content analysis, and the creative process followed the method of cross-fertilization, which stimulates innovation by combining knowledge from design, geology, and craftsmanship. The design process was organized into four phases—preparation, generation of alternatives, selection, and realization—culminating in the capsule collection Aflora. The collection comprised two thematic lines: Cactaceae, inspired by endemic flora, and Geo, based on local geomonuments. The results demonstrate that surface design can mediate the relationship between fashion and heritage, producing identity-driven and innovative textile products. Three surface-design modules were produced, six product mockups, and two geoproduct prototypes, developed with materials such as wool, felt, sarja, and cotton fabrics. The study contributes theoretically by linking apparel design with heritage valorization, and practically by proposing a replicable model for geoproduct development. Limitations relate to the single case study and qualitative scope, suggesting future research on replication, eco-friendly printing, and market feasibility. Full article

The rapid rise of the pre-loved luxury fashion market in China reflects a unique shift in consumer behaviour, shaped by growing concerns for sustainability, affordability, and personal expression. While global scholarship on circular fashion has expanded, studies remain predominantly focused on Western consumers, leaving Chinese market dynamics underexplored. This study addresses this gap by examining the motivations and channel engagement of Chinese consumers purchasing pre-loved luxury fashion, including pre-owned, vintage, and collectors’ items. A sequential mixed-methods design was employed, integrating quantitative data from a survey of 438 Chinese consumers with qualitative insights from 21 semi-structured interviews. Structural equation modelling revealed that economic, individual, and social motivations significantly influenced perceived value, which in turn enhanced engagement with resale channels. Functional motivations, though present, played a less prominent role. Furthermore, engagement with online and offline channels, including social media platforms, livestream commerce, and luxury consignment boutiques, was found to mediate the relationship between perceived value and purchase intention. The study contributes to theory by adapting established luxury value frameworks to the pre-loved context and by introducing channel engagement as a mediating construct in the consumption of second-hand luxury fashion. The main theoretical frameworks that underpin the study, such as the Brand Luxury Index and the Four Value Dimensions, are used to provide a clearer understanding of its conceptual foundation. In particular, some key quantitative indicators, such as β-values or R², would make the summary more specific and informative. Practically, the findings provide actionable insights for platform operators and luxury brands seeking to build consumer trust and enhance experiential value in China’s rapidly evolving resale market. By situating the research within a culturally specific and digitally advanced retail environment, the study broadens understanding of circular luxury fashion consumption in non-Western contexts. Full article

This study investigates how Australian Zennials (born 1993–1999) navigate slow fashion consumption in a market dominated by fast fashion and affordability challenges. Using semi-structured interviews with 20 participants, it explores their motivations, barriers, and adaptive strategies. Findings reveal that Zennials are driven by ethical values, environmental awareness, and a preference for quality design, yet face constraints such as cost, limited access to sustainable brands, and skepticism toward greenwashing. Rather than a simple value–action gap, participants demonstrate creative solutions, most notably, strategic engagement with the second-hand market. This enables them to practice slow fashion ideals of durability, longevity, and mindful consumption in a cost-effective way. The study reframes the attitude–behavior gap by identifying Perceived Behavioral Control (PBC) as a key enabler, supported by knowledge, repair skills, and peer norms. These insights offer practical implications for brands, designers, and policymakers, positioning the second-hand economy as the central mechanism that operationalizes Zennial engagement with sustainable fashion. Full article

As the fashion industry accelerates its digital and sustainable transformation, the European Union’s policy development on Digital Product Passports (DPPs) has attracted growing attention. However, there is still a lack of systematic research into whether consumers, particularly those outside Europe, are willing to adopt this emerging technology for greater transparency. To address this, this study develops an extended Technology Acceptance Model (TAM) by integrating three individual-level consumer variables, Ethical–Sustainability Orientation (ESO), Circular Value Orientation (CVO), and Technological Awareness (TA), to examine how these factors work in concert to shape consumers’ intentions to accept Digital Product Passports (DPPs). Data were collected from US consumers through an online survey, yielding 425 valid responses. Participants were recruited from a professional consumer panel managed by a market research firm. Structural equation modeling was conducted to test the proposed research model and hypotheses. The results reveal that Perceived Usefulness (PU) emerges as the most influential determinant of consumers’ acceptance of Digital Product Passports. Both Ethical–Sustainability Orientation (ESO) and Circular Value Orientation (CVO) demonstrate significant direct effects on adoption intention and indirect impacts through PU. Technological Awareness (TA) exhibits only a modest direct effect, suggesting that its role in shaping adoption behavior is comparatively limited. This study broadens the geographic and cultural scope of existing research on Digital Product Passports (DPPs) by providing empirical evidence on consumer acceptance in a non-European context. The findings advance the theoretical understanding of DPP adoption while offering practical implications for fashion brands and policymakers seeking to facilitate the global implementation of DPP systems within the fashion industry. Full article

Pooling strategies are fundamental to convolutional neural networks, shaping the trade-off between accuracy, robustness to spatial variations, and computational efficiency in modern visual recognition systems. In this paper, we present and validate ECA110-Pooling, a novel rule-based pooling operator inspired by elementary cellular automata. We conduct a systematic comparative study, benchmarking ECA110-Pooling against conventional pooling methods (MaxPooling, AveragePooling, MedianPooling, MinPooling, KernelPooling) as well as state-of-the-art (SOTA) architectures. Experiments on three benchmark datasets—ImageNet (subset), CIFAR-10, and Fashion-MNIST—across training horizons ranging from 20 to 50,000 epochs show that ECA110-Pooling consistently achieves higher Top-1 accuracy, lower error rates, and stronger F1-scores than traditional pooling operators, while maintaining computational efficiency comparable to MaxPooling. Moreover, when compared with SOTA models, ECA110-Pooling delivers competitive accuracy with substantially fewer parameters and reduced training time. These results establish ECA110-Pooling as a principled and validated approach to image classification, bridging the gap between fixed pooling schemes and complex deep architectures. Its interpretable, rule-based design highlights both theoretical significance and practical applicability in contexts that demand a balance of accuracy, efficiency, and scalability. Full article

Due to the limitations of general-purpose generative artificial intelligence (GenAI) platforms in meeting the needs of fashion design, AI-based integrated fashion design platforms (AIIFDP) have emerged as a more suitable solution. As the next generation of designers, fashion design students play a pivotal role in shaping the optimization and promotion of AIIFDP. However, research on their continuance intention toward such platforms remains limited. This study constructs an integrated model by combining the Unified Theory of Acceptance and Use of Technology (UTAUT) with the Expectation-Confirmation Model (ECM), and extending it with variables such as personal innovativeness, habit, and perceived intelligence. Using a multi-stage SEM-ANN analysis, the study empirically analyzed data from 486 questionnaires completed by fashion design students in China. The results suggest that satisfaction is the most significant positive factor influencing continuance intention. Moreover, performance expectancy, social influence, perceived intelligence, and habit also exert significant effects. This study broadens the segmented perspective on the application of GenAI in design education and validates the applicability of the extended UTAUT-ECM model in the context of AIIFDP. It also provides theoretical foundations and multi-level strategic recommendations for optimizing AIIFDP products and guiding their integration into educational practices. Full article

The global secondhand apparel industry, valued at USD 256B in 2025, is expanding rapidly. The growing acceptance of secondhand fashion and advancements in retail technology have driven millions of individuals to resell, yet little research has analyzed the motivations behind these decisions. Guided by Consumption Values Theory and Goal-Framing Theory, this qualitative study uses ten in-depth interviews with experienced resellers to examine why individuals participate in apparel reselling. Analysis of the participants’ narratives indicates that financial gain is the dominant driver of participation, followed by the convenience provided by reselling platforms and channels, emotional satisfaction, and contributing to sustainability. Conceptually, the study integrates value-based and goal-based lenses to offer an extensive explanation of reseller motivations, shifting focus from the buyer perspective that has dominated prior research. Practically, the findings suggest that resale platforms can encourage participation by reducing visible fees, enabling faster payout, and simplifying the reselling process, while also making community and environmental benefits more visible. In all, these insights help retailers and sustainability advocates better design approaches that support individual resellers and sustain growth in apparel resale. Full article

The Metaverse—an evolving convergence of virtual and physical realities—has emerged as a transformative platform, particularly within the fashion and retail industries. Its immersive nature aligns closely with the principles of flow theory, which describes the optimal psychological state of deep engagement and enjoyment. This study investigates the dynamics of fashion retail shopping in the Metaverse through a novel multi-criteria decision-making (MCDM) framework. Specifically, it integrates the CRITIC and ARAS methods within a spherical fuzzy environment to address decision-making under uncertainty. Flow theory is employed as the theoretical lens, with its dimensions serving as evaluation criteria. By incorporating spherical fuzzy sets, the model accommodates expert uncertainty more effectively. The findings provide empirical insights into the relative importance of flow constructs in shaping immersive consumer experiences in Metaverse-based retail environments. This study offers both theoretical contributions to the literature on digital consumer behavior and practical implications for fashion brands navigating immersive virtual ecosystems. Sensitivity analyses and comparative validation further demonstrate the robustness of the proposed framework. Full article

The rapid rise of fashion resale platforms has created new pathways for sustainable consumption, yet little research has compared how different governance models, company-controlled versus seller-controlled, shape consumer trust and purchasing behavior. This study addresses that gap by applying the Stimulus–Organism–Response (SOR) framework to examine how information precision, authenticity, and risk aversion influence consumer trust and purchase intention within circular fashion markets. Drawing on an experimental design with 524 U.S. consumers randomly assigned to each platform type, multi-group structural equation modeling reveals that the three stimuli significantly enhance trust, which in turn drives purchase intention. Risk aversion exerted stronger effects in company-controlled contexts, whereas trust translated more directly into purchase intention on seller-controlled platforms. Theoretically, the research extends SOR applications to sustainability by identifying trust as the psychological bridge linking platform design to circular consumption. Practically, it offers actionable guidance for brands and peer-to-peer platforms on authentication, information transparency, and risk-reduction strategies that strengthen consumer confidence and promote environmentally responsible resale participation. The findings advance understanding of how governance structures can accelerate sustainable fashion retailing and contribute to the circular economy. Full article

This study examines and evaluates key performance indicators (KPIs) that impact user engagement on social media platforms, with a primary focus on fashion retail within seasonal tourism contexts. The primary objective is to determine which engagement metrics most accurately predict user interaction levels and to enhance strategic decision-making in digital marketing. Using a dataset of 2500 Facebook photos and videos from a women’s retail store, collected between 2016 and 2024, the study employs descriptive analysis and predictive modeling. Three KPIs—such as 3 s video views, reach from organic posts, and other clicks—are examined for their impact on user engagement. The posts are categorized into engagement levels, and classification models, including Random Forests (RF), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Naïve Bayes (NB), are evaluated. Results show that short video views and post reach are key predictors of user engagement. With XGBoost achieving a classification accuracy of 94.73%, the models perform effectively, and Cronbach’s alpha analysis confirms the consistency among the variables selected. The findings underscore the significance of KPI analysis in social media strategy and illustrate the value of data mining techniques in uncovering user behavior patterns that offer practical insights for optimizing digital marketing efforts. Full article