User acceptance and adoption of circular offerings in the fashion sector: insights from user-generated online reviews
Journal article, Peer reviewed
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Original versionJournal of Cleaner Production. 2019, 231 928-939. https://doi.org/10.1016/j.jclepro.2019.05.162
Recent research on circular offerings has indicated that the lack of consumer and user acceptance of circular offerings is one of the primary barriers for the transition to a circular economy. Available studies on this topic have used data from hypothetical scenarions, reducing their explanatory potential. Today, established circular businesses such as fashion subscription services open new research opportunities to address such limitation. In this context, this paper assesses the suitability of user-generated online reviews as a novel source of information for investigating factors and conditions of acceptance and adoption of circular offerings in the fashion sector. Based on the data analysis, it was found that user-generated online reviews were credible, contributed with new insights regarding factors and conditions of acceptance and allowed us to analyse a significant number of observations. The reviews provided significant insights into economic factors and the impacts this type of offering had on the daily life of users, an aspect that has been studied rarely, so far. Nevertheless, this type of data sources also presented some limitations. On the one hand, and because of their nature, they did not offer significant insights into other consumer and user acceptance factors identified in the literature such as demographic, psycho-social and cultural factors. On the other hand, they only provided a snapshot of the user experience in a given moment of time. To address these limitations, we suggest future research on user and consumer acceptance of circular offerings should investigate ways to integrate user-generated online reviews with more traditional tools and methods to gain insigths into cultural and psychosocial factors, explore additional sources that can help capture the dynamics of service use such as social media, online communities and customer service chats, and expand data collection to other market segments.