Understanding how consumers have shifted in clothing consumption in the midst of the global COVID-19 pandemic is critical for fashion clothing brands and businesses to identify what value means to consumers to locate growth opportunities. This exploratory study intends to provide a picture of consumers’ clothing consumption evolution while going through the pandemic crisis. We take a viewpoint that integrates the perspectives of life status changes and stress coping to examine consumers’ responses to clothing consumption during the COVID-19 global pandemic. A total of 68,511 relevant tweets were collected from January 1, 2020, through September 31, 2020. Sentiment and content analysis identified five themes which are revealed by 16 topics associated with clothing consumption over the phases of pre-lockdown, lockdown, and reopening. Pent-up demand for clothing products and changed clothing consumption habits were identified. Our findings provide evidence that consumption change is the fundamental mechanism of stress coping.

PurposeThis 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.Design/methodology/approachA 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.FindingsThe 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.Originality/valueThis 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.

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Return rates for e-retail fashion companies are significantly higher than in-store sales. Twenty to fifty percent of online clothing sales are returned. Apparel retailers are haunted by returns based on sizing issues, with $62.4 billion in returns attributed to poor choices by the consumer in the USA. However, over the next ten years online sales are predicted to double, compounding the problem exponentially. Garment sizing and knowing your correct size for a particular garment or brand while online shopping is part of the problem. It is the combinations of body measurements that determine sizing and sizing labels in clothing not usually one measurement. Most consumers don't know their body measurements when attempting to determine the size of a garment that they would like to purchase when shopping online and can have significant difficulty attempting to take their own measurements. This can lead to frustration and an incomplete sale or shopping cart abandonment. Many customers even resort buying a garment in two or more sizes and return the ones that do not fit, as they do not want to waste their time trying to determine which would be a perfect size. This adds to cost and waste affecting profitability. By the time these garments are returned to the vendor or manufacture they are out of season and usually not resalable at the original price because of the time lag and subsequent repackaging problems. This research focuses on creating a fast-personal garment apparatus, system, and method for measuring body dimensions extracted from two-dimensional (2D) images captured by a consumer. Measurements of the individual are taken from captured pictures or photographs from their smart phones while wearing one or more coded dimensioning garments that have markings at specific locations that can be aligned with characteristic body features and key measurement areas. Computer vision is used to track these markings and extract key body dimensions. TensorFlow, a machine learning software application, is incorporated for object detection can be used to recognize colors and patterns on the garment allowing the garment to act as a measurement device for the body. The extracted dimensions could further used to predict additional body information such as; size growth and fit information, for example with fitness apps and workout appeal, or simply predicting children's wear and maternity wear needs as the body grows.

There are long-standing arguments that challenge the resale business as a circular fashion model. Considering the whole fashion industry and market, luxury resale is still quite small. To scale the industry, it is critical to attract more consumers to embrace fashion resale and circular fashion. However, many of the customers most likely to embrace resale might have already opted into the market, indicating that online or in-store resale businesses are competing for a limited pool of customers. As a result, it is challenging for the industry to scale. Therefore, it is imperative to understand how consumers engage with luxury resale platforms, what value they are looking for, and to what degree resale customers’ desires for fashion clothing and sustainability can be met in a reconciling manner. Such understanding will facilitate luxury resale platforms to grow their customer base and scale up the industry. This exploratory study focuses on understanding online luxury platform customers and their consumption experience to determine what key attributes affect customer value and engagement. The research explores customer experience using a text-mining approach to provide answers to identified research questions: (1) How do online luxury resale platforms provide customer value to buyers and sellers? What are the driving values for consumers to buy or sell pre-owned products? (2) Are there any issues regarding buying and selling pre-owned products using online luxury resale platforms? (3) Does sustainability play a role in individuals’ consumption using online luxury resale platforms? The article discusses the implications of the study, its limitations, and future research directions.