Understanding consumers’ online fashion renting experiences: A text-mining approach

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. Besides that, it also fulfills an individual's fashion needs while reducing the production of new clothes. Though some studies have been done to examine consumers’ opinions of fashion renting, most of these studies employed survey methods and did not utilize actual data of consumers’ real renting experiences. 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 consumers’ online fashion renting experiences through the use of Latent Dirichlet Allocation (LDA) topic modeling and word co-occurrence networks.