Acknowledging the potential of NFTs (non-fungible tokens) to enhance consumer-brand relationships, major luxury fashion brands continue to enter the virtual NFT market, releasing exclusive collectibles. However, this emerging market poses unique challenges for luxury fashion brands in crafting virtual NFTs that successfully convey consistent and integrated brand meanings of luxury. Given the visually oriented nature of NFTs, this study aims to empirically examine how visual design features of virtual luxury NFTs, including brand visibility and visual quality, interact with perceived prototypicality of products (fashion NFTs vs. art NFTs) in generating consumers’ perceived essence of the brand, resulting in consumers’ purchase intention. This study enriches the understanding of how visual design features impact consumer perceptions and purchase intentions toward virtual luxury NFTs, identifying brand visibility, visual quality, and prototypicality as the critical factors.
Open access
Aesthetic Perception and Analysis
Consumer Behavior in Brand Consumption and Identification
With the development of digital technology, the fashion industry has also begun a digital transformation. Non-Fungible Token (NFT) clothing, as a new fashion trend, has always been a new direction pursued by brands. At the same time, the main group of consumers is also gradually shifting from the previous Generation Y to Generation Z. Generation Z is the future consumption potential group, they deserve brands to adjust their consumption strategies. By exploring the consumption behaviour of Generation Z and its attitude towards virtual fashion, this paper analyzed the fitness between NFT clothing and Generation Z. And it used the literature analysis method to analyze and summarize the papers related to virtual fashion, NFT clothing and the consumption view of Generation Z. Based on the result, it can be seen that the characteristics of NFT clothing are in line with the consumption psychology of Generation Z. Although NFT clothing is still in its early stage, it can be predicted that Generation Z will be the main consumer group of virtual fashion in the future based on their interest in virtual fashion. The future of brands focusing on NFT clothing is bright. By studying Generation Z consumption psychology, this paper hopes to contribute to brand marketing strategy for the future.
Open access
Consumer Retail Behavior Studies
Consumer Behavior in Brand Consumption and Identification
Tie Qiu, Min Zhang, Xize Liu, Jing Liu · 6 authors
As the application of the industrial Internet of Things (IIoT) becomes more widespread, the IIoT is being combined with social networks. Nodes in the network can be users, machines, and so on. Using the sensing detection technology of the IIoT, industrial machines can realize real-time informatization, which is convenient for users to perform remote management. Nodes can communicate with each other and make ratings. These ratings can be modeled as directed weighted edges between nodes and form directed weighted networks (DWNs). The edge weight represents the “strength” of relationship and the direction of edge points from the edge generator to the edge receiver. Predicting edge weights in DWNs is critical to predicting unknown ratings or recovering lost data. In this article, we propose a directed edge weight prediction model (DEWP) using decision tree ensembles. It extends the local similarity indices to DWNs and extracts a series of similarity indices between nodes as features of each edge. These features are used to construct a blended regression model of random forest, gradient boost decision tree, extreme gradient boosting, and light gradient boosting machine. The proposed algorithm was evaluated experimentally with the Bitcoin OTC and Bitcoin Alpha datasets by removing 10% to 90% of edges in the original network. Compared with other classical algorithms, DEWP has higher prediction accuracy and robustness.
The aim of the paper was to develop a prototype of smart textile material with shape memory elements that give variable thermal insulation dependent on the emission-absorption of heat. Shape memory elements were made in the form of spirals of two-way action from nitinol (NiTi) one-way wire. Two groups of samples were made: active and non-active. The active spirals expand at temperatures lower than the characteristic inner state transition temperature and contract as the temperature becomes higher than the transient temperature, which was about 45℃. The non-active spirals do not change dimensions under the influence of heat supply. The material of the layered structure was prepared. The first layer consisted of cotton woven fabric and the second layer featured a system of NiTi spiral elements, while the final layer was made of a thin Teflon foil. The behavior of samples during absorption-emission of heat was studied. Temperature measurements were conducted using an infrared camera; samples were placed on a heater to ensure contact between the Teflon layer and the base, and the temperature was recorded at the sample surface (woven fabric) as a function of the heating time for both active and non-active samples. A theoretical model that makes it possible to determine the time variable thermal parameters of the smart textile material was developed. Good agreement between the experimental and theoretical results was received. The temperature on the surface of the active sample was approximately 10℃ higher at the end of heating than the temperature of the non-active sample after the same heating pattern.