Application of Computational Techniques in the Textile and Clothing (T&C) Industry

A special issue of Stats (ISSN 2571-905X).

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 2423

Special Issue Editors


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Guest Editor
Högskolan i Borås, Borås, Sweden
Interests: deep learning; data mining; textile and clothing; supply chain management; modelling

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Guest Editor
Ecole Nationale Supérieure des Arts et Industries Textiles, Roubaix, France
Interests: artificial intelligence; fashion digitalization; modelling; optimization; decision support systems; wearable management
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Special Issue Information

Dear Colleagues,

Today’s supply chain operations are experiencing a transition from an industrial age to an information age. The textile and clothing (T&C) sector is no exception to this. While the complexity of operations in the T&C sector has increased due to the increasing demand for faster lead times, reduced cycle times, customized products, tailored experiences, and an improvement in overall transparency and sustainability, following the technological advancements in collecting, storing, and transmitting data, the concerns associated with a lack of data have been replaced with the issue of too much data. As a result, the T&C sector has become a cornerstone for computational research, including artificial intelligence, machine learning, data mining, etc., that aims to harness the potential of data collection and large datasets in identifying the hidden patterns, trends, and correlations that are critical for market analysis, designing products, and tackling various manufacturing and supply chain challenges. In this context, the application of computational techniques is becoming prominent in a range of investigations, ranging from modelling, simulation and optimization in manufacturing and distributions to improving operating characteristics.

This Special Issue of Stats focuses on investigations regarding the applications of computational techniques that aim to leverage the power of (large) datasets to study or tackle various challenges in the T&C sector, ranging from design, manufacturing, and supply chain operations to market analysis, and beyond. We invite authors to contribute original research work to this peer-reviewed Special Issue of Stats.

Dr. Vijay Kumar
Prof. Dr. Xianyi Zeng
Guest Editors

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Keywords

  • textile and clothing
  • fashion
  • computational analysis
  • machine learning
  • artificial intelligence
  • deep learning
  • data mining
  • market analysis
  • supply chain management
  • manufacturing
  • design
  • modelling
  • decision support systems

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Published Papers (1 paper)

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Research

19 pages, 579 KiB  
Article
Exploring the Consumer Acceptance of Nano Clothing Using a PLS-SEM Analysis
by Andreea-Ionela Puiu, Rodica Ianole-Călin and Elena Druică
Stats 2023, 6(4), 1095-1113; https://doi.org/10.3390/stats6040069 - 19 Oct 2023
Cited by 1 | Viewed by 2285
Abstract
We use an extended framework of the technology acceptance model (TAM) to identify the most significant drivers behind the intention to buy clothes produced with nano fabrics (nano clothing). Based on survey data, we estimate an integrated model that explains this intention as [...] Read more.
We use an extended framework of the technology acceptance model (TAM) to identify the most significant drivers behind the intention to buy clothes produced with nano fabrics (nano clothing). Based on survey data, we estimate an integrated model that explains this intention as being driven by attitudes, perceived usefulness, and perceived ease of use. The influences of social innovativeness, relative advantage, compatibility, and ecologic concern on perceived usefulness are tested using perceived ease of use as a mediator. We employ a partial least squares path model in WarpPLS 7.0., a predictive technique that informs policies. The results show positive effects for all the studied relationships, with effect sizes underscoring perceived usefulness, attitude, and compatibility as the most suitable targets for practical interventions. Our study expands the TAM framework into the field of nano fashion consumption, shedding light on the potential drivers of the adoption process. Explorations of the topic hold the potential to make a substantial contribution to the promotion of sustainable fashion practices. Full article
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