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A two-dimensional (2D) body measurement system was implemented to study the application of sportswear design in measurement garment development. A total of 50 participants were recruited. The basic demographic information and sportswear preference data were collected through a survey to understand consumer preferences and acceptance of the new designs. The body measurements were collected through both the selected 2D measurement system and a commercial three-dimensional scanning booth to evaluate measurement accuracy.

Finding the right size has been a long-existing problem for clothing consumers. Size problem is the most common reason for e-commerce returns and adds a high cost for retailers. One possible solution is to offer consumers an easy-to-use method that extracts accurate body measurements to be used for clothing size selection. The purpose of this research is to apply sportswear design elements on measurement garments to see if consumers’ interest in using the 2D measurement system can be increased without influencing the measurement accuracy.

The results showed that the added design features increased consumers’ interest in using 2D body measuring technology without significantly influencing measurement accuracy.

This research applied sportswear elements to convert a 2D measurement bodysuit to a fashionable clothing product. The solution resolved users’ privacy concerns and increased their acceptance and use of the technology. Other studies have not focused on using aesthetic features to improve the 2D measurement technology.

Two-dimensional (2D) measurement technology has become more popular than before, thanks to the widespread availability of smartphones and smart devices. However, most existing 2D body measurement systems have background constraints and may raise privacy concerns. The purpose of this research was to test the idea of designing a 2D measurement system that works with a color-coded measurement garment for background removal and privacy protection. Clothing consumers can use the proposed system for daily apparel shopping purposes.

A 2D body measurement system was designed and tested. The system adopted a close-fitted color-coded measurement garment and used neural network models to detect the color-code in the garment area and remove backgrounds. In total, 78 participants were recruited, and the collected data were split into training and testing sets. The training dataset was used to train the neural network and statistical prediction models for the 2D system. The testing dataset was used to compare the performance of the 2D system with a commercial three-dimensional (3D) body scanner.

The results showed that the color-coded measurement garment worked well with the neural network models to process the images for measurement extraction. The 2D measurement system worked better at close-fitted areas than loose-fitted areas.

This research combined a color-coded measurement garment with neural network models to solve the privacy and background challenges of the 2D body measurement system. Other researchers have never studied this approach.

Concerns over environmental degradation, alongside the global legalization of hemp cultivation, have prompted applications of hemp-derived textile products. However, research examining consumers’ acceptance of such products is lacking. It is critical to understand how young consumers adopt these products as they prioritize sustainability and lead the future purchasing power. Thus, this study aims to investigate young consumers’ perceptions, attitudes, and purchase intentions regarding hemp fiber textiles and clothing based on the Theory of Consumption Value and the Spiral of Silence Theory.

A US-based research company was hired to recruit survey participants, yielding 314 valid data. Data analysis, including Exploratory Factor Analysis and Partial Least Squares Structural Equation Modeling, was conducted using the R programming language and RStudio software.

Purchase intentions of hemp-based clothing and textiles among young consumers are influenced by attitude, perceived social value and future trend perception. Perceived social value also impacts attitude and future trend perception. High environmental involvement correlates with positive attitudes. Consumers’ subjective knowledge of hemp and objective knowledge regarding hemp fiber impact their perceptions of future trends in such products.

This research represents a seminal consumer study focusing on the resurgence of industrial hemp fiber textiles and clothing. This study empirically validates the significant role of social factors in young consumer acceptance of such products at both personal and societal levels. This study expands current knowledge and provides actionable insights for practitioners.

This study aims to discover clothing consumption shifts and explore the effect of consumer resilience in changing clothing consumption patterns or establishing new clothing consumption routines after experiencing disruptions caused by the Covid-19 pandemic.

A two-phase multi-method approach was employed. The first phase of qualitative inquiry was conducted to identify clothing consumption shifts using a content mining approach on a text data sample of 17,195 tweets posted from 1 January to 30 September 2020. The second phase of the quantitative study was conducted to explore consumer resilience's effect on clothing consumption shifts based on a collected national sample of 418 respondents through an online survey.

This study identified clothing consumption changes including value shifts, style shifts and spending shifts. It was also found that resilient consumers care about a company's ethical practices and value the quality instead of the number of items in their wardrobe. Consumers spend more time wearing comfortable clothing and are used to the working-from-home lifestyle.

This study explored approaches to making use of social media data for a better understanding of consumers' clothing behaviour. Also, this study attempted to explore and understand clothing consumption practices during and post the Covid-19 global pandemic, focusing on identifying shifts that might last longer for fashion businesses to explore growth opportunities.

This study intends to examine consumers' fashion customization experiences through a web content mining (WCM) approach. By applying the theory of customer value, this study explores the benefits and costs of two levels of mass customization (MC) to identify the values derived from style (i.e. shoe customization) and fit customization experiences (i.e. apparel customization) and further to compare the dominating dimensions of value derived across style and fit customization.

A WCM approach was applied. Also, two case studies were conducted with one focusing on style customization and the other focusing on fit customization. The brand Vans was selected to examine style customization in study 1. The brand Sumissura was selected to examine fit customization in study 2. Consumers' comments on customization experiences from these two brands were collected through social networks, respectively. After data cleaning, 394 reviews for Vans and 510 reviews for Sumissura were included in the final data analysis. Co-occurrence plots, feature extraction and grouping were used for the data analysis.

The emotional value was found to be the major benefit for style customization, while the functional value was indicated as the major benefit for fit customization, followed by ease of use and emotional value. In addition, three major themes of costs, including unsatisfied service, disappointing product performance and financial risk, were revealed by excavating and evaluating consumers' feedback of their actual clothing customization experiences with Sumissura.

This study initiates the effort to use web mining, specifically, the WCM approach to thoroughly investigate the benefits and costs of MC through real consumers' feedback of two different types of fashion products. The analysis of this study also reflects the levels of customization: style and fit. It provides an in-depth text analysis of online MC consumers' feedback through the use of feature extraction analysis and word co-occurrence networks.