Sustainable post-consumer textile waste management: Picture fuzzy set-based ORESTE approach

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The fashion industry negatively impacts air and water quality and contributes to landfill through its toxic chemical use, over-production of goods and inefficient shipping practices. Consumers' improper consumption of fashion exacerbates these environmental impacts. Post-consumer textile can be managed by consumers via deciding on the particular practices reducing the textile waste. Consumers play a significant role in shaping the fashion industry as their attitudes towards extending a garment's life cycle and supporting recycling impact the business practices of suppliers. This study analyzes post-consumer textile waste (post-consumer textile waste) management options for consumers and factors affecting the consumer attitudes. The study identifies consumer options, conducts a bibliometric analysis (BA) and detailed literature review to address the gap in multi-criteria decision making (MCDM) techniques; in doing so, it proves the originality of the selected ORESTE technique for the field of study and proposes a novel picture fuzzy sets approach based ORESTE methodology to sustainably manage PCTWs. The findings of the study reveals that the among the alternative sustainable PCTW management practices; extended producer responsibility actions is the most preferred action followed by donation, reuse, recycling, composting, second-hand shopping and swapping, respectively. The originally of this paper grounds on the fact that there is no comprehensive list of these practices with a consumer perspective, and no ORESTE approach or picture fuzzy set application in the literature. Thus, this paper contributes to the literature by highlighting the real preferences of consumers as practical implications, and by attracting the attention of industry representatives to take which practices into consideration most. Besides, this paper makes a theoretical contribution by proposing a novel fuzzy MCDM approach, and by offering an alternative to the Besson ranking procedure.