While artificial intelligence (AI) is reshaping China’s manufacturing industry, its implementation in the labour-intensive apparel manufacturing sector remains constrained by limited digitalisation and relatively low technological readiness. To address this gap, this study examined how employees’ behavioural intentions mediate the relationships between key technological, organisational, environmental, and social predictors and organisations’ decisions to adopt AI in the Chinese apparel manufacturing sector. We combined the unified theory of acceptance and use of technology with the technological-organisational-environmental framework and evaluated the resulting model using partial least squares structural equation modelling with paired responses from 200 managers and 200 technicians representing 200 Chinese small- and medium-sized apparel manufacturing firms. The results showed a significant negative indirect effect of performance expectancy (β = −0.081, p = .037) and significant positive indirect effects of peer influence (β = 0.232, p = .002) and regional context (β = 0.501, p < .001) on organisational AI adoption decisions through employees’ behavioural intentions. For organisational AI adoption decisions, the model accounted for 71.4
This study evaluated the manufacturing process and mechanical performance of fully 3D-printed shoes fabricated using polylactic acid (PLA), lightweight PLA (LW-PLA), thermoplastic polyurethane (TPU), and lightweight TPU (LW-TPU). The filaments were characterized through rheological, differential scanning calorimetry (DSC), and morphological analyses, and fully 3D-printed shoes were fabricated using dual-nozzle fused deposition modeling (FDM) with three body/support material configurations: LW-PLA/PLA, TPU/LW-PLA, and LW-TPU/PLA. The printed shoes were evaluated for morphology, compressive properties, impact attenuation behavior, and preliminary plantar pressure analysis. LW filaments maintained porous structures after extrusion and printing, while TPU shoe and LW-TPU shoe showed greater deformability than the rigid LW-PLA shoe. The TPU shoe showed strong impact absorption through large deformation, whereas LW-TPU shoe exhibited a balanced response with low stiffness, high compressive deformability, controlled impact attenuation, and lightweight structure. A pilot plantar-pressure evaluation with two participants was conducted to obtain preliminary pressure distribution observations. The peak pressure results showed a tendency toward reduced pressure in selected metatarsal and heel regions for the TPU shoe and LW-TPU shoe, while LW-TPU shoe showed increased contact area in the midfoot and heel regions. Overall, LW-TPU showed potential as a promising material candidate for fully 3D-printed shoes, and this study provides foundational data for future material selection, process optimization, and standardized evaluation of fully 3D-printed footwear.
Digital image-based color evaluation has emerged as a promising approach for non-contact, large-area color assessment in textile manufacturing. However, its applicability across fundamentally different dyeing systems remains insufficiently investigated. This study presents a calibrated stereo-photometric digital image-based framework for comprehensive color characterization of polyethylene terephthalate (PET) fabrics dyed using conventional aqueous and supercritical carbon dioxide (scCO₂) systems. Digital images acquired under controlled illumination were processed through image fusion, color calibration, and region-of-interest analysis to quantify color depth, spatial uniformity, and perceptual color attributes in the CIE LCh color space. Image-derived measurements showed high correlations with conventional spectrophotometric measurements for the CIE tristimulus Y value and Kubelka–Munk color strength (K/S). Linear relationships between image-derived and spectrophotometric CIE Lab* values demonstrated consistent relative color trends between the two methods across both dyeing systems, although differences in absolute colorimetric values remained. Beyond comparison with conventional spectrophotometry, the framework enabled simultaneous full-field color characterization, providing richer color information than conventional point-based measurements. These results highlight the potential of the framework as a complementary approach for quantitative textile color evaluation and provide a foundation for future computer vision- and AI-assisted textile quality inspection.
Conductive bicomponent yarns suitable for apparel manufacturing were fabricated under various twisting conditions, and the correlation between their internal structures and electrical properties was systematically investigated. Three types of conductive bicomponent yarns with identical linear densities were prepared by varying the combinations of first- and second-twist levels using PET yarn and Ag-coated polyamide filaments. Their mechanical, morphological, and electrical properties were evaluated, and non-destructive X-ray computed tomography (X-ray CT) was used to analyze the three-dimensional internal fiber assemblies. X-ray CT analysis revealed pronounced differences in packing density, helical structure, and turn length depending on the twist combination and sequence. Bicomponent yarns with higher internal packing density and shorter turn length exhibited increased inter-filament contact, resulting in a significant reduction in linear resistance. In particular, the PET-AP70/PET-AP70 structure exhibited the densest internal configuration and the lowest electrical resistance with minimal variation. This paper for the first time, quantitatively establishes a structure–property relationship between twist-induced internal architecture and electrical performance in conductive bicomponent yarns using X-ray CT. The findings provide design guidelines for durable and reliable smart textile applications.
This study examines how sustainability communication in luxury retail shapes brand trust and behavioral intentions, focusing on consumers' post-exposure perceptions of information quality and provider type. Building on perceived objectivity and perceived persuasiveness, the study explores how consumers evaluate identical sustainability information attributed to either a human agent or an artificial intelligence (AI) chatbot, and how these perceptions influence brand trust, information acceptance intention, and purchase intention. A scenario-based experiment with 346 participants was conducted, and the data were analyzed using PLS-SEM. Perceived objectivity and perceived persuasiveness were positively associated with brand trust, which inturn mediated their relationships with information acceptance intention and purchase intention. The indirect effect of perceived objectivity was significant only under AI communication, suggesting that algorithmic provider cues may strengthen the trust-mediated role of objectivity, whereas perceived persuasiveness remained consistently associated with trust and behavioral intentions across provider types. Subgroup analyses further suggest a conditional pattern: among consumers with lower perceived brand luxury, the links between brand trust and behavioral intentions were stronger under AI communication, whereas no significant provider differences emerged among those with higher perceived brand luxury. As these results derivefrom split-sample multi-group comparisons rather than a formal interaction test, they should be regarded as subgroup-based evidence. These findings extend luxury branding research by showing how AI provider cues shape perceived information quality in sustainability communication and by highlighting the need to align provider strategy with consumers' perceptions of luxury brands.
