Sustainability and Social Responsibility

Understanding Consumers’ Online Fashion Renting Experiences: A Data-Mining Approach

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Abstract

With the increasing demand for fashion products, the textile and apparel industry is facing huge challenges in resource management and environmental regulations. Fashion renting provides an option to reuse clothing products and also fulfills an individual’s fashion needs while reducing the production of new clothes. The purpose of this study is to evaluate consumers’ actual renting experiences and to identify the motivations and barriers for those consumers to rent fashion products. A data-mining approach was applied in this study. Consumers’ comments on renting experiences from three fashion rental companies, Rent the Runway (RTR), Gwynnie Bee (GB), and Bag Borrow Steal (BBS), were collected as a reliable data source to dig into and identify consumers’ motivations and concerns. Based on the theory of customer value, both benefits and costs for fashion renting were discovered. In addition, a comparison of the three fashion rental companies was also discussed. This study is the first attempt to use a data-mining method to thoroughly investigate the benefits and costs of fashion online renting through real consumers’ feedback of three different types of rental companies. It provides an in-depth text analysis of online fashion renting consumers’ experiences through the use of LDA topic modeling and word co-occurrence networks.

Keywords: real experiences, fashion renting, data-mining, LDA modeling, benefits, costs

How to Cite:

Lang, C., Zhao, L. & Li, M., (2019) “Understanding Consumers’ Online Fashion Renting Experiences: A Data-Mining Approach”, International Textile and Apparel Association Annual Conference Proceedings 76(1). doi: https://doi.org/10.31274/itaa.8332