Analyzing Twitter and Instagram Social Networks to Trace the Consumer Opinion Regarding Transparency in the Apparel Supply Chain

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2018-01-01
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Modi, Dipali
Zhao, Li
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International Textile and Apparel Association (ITAA) Annual Conference Proceedings
Iowa State University Conferences and Symposia

The first national meeting of textile and clothing professors took place in Madison, Wisconsin in June 1959. With a mission to advance excellence in education, scholarship and innovation, and their global applications, the International Textile and Apparel Association (ITAA) is a professional and educational association of scholars, educators, and students in the textile, apparel, and merchandising disciplines in higher education.

This site provides free, public access to the ITAA annual conference proceedings beginning in 2015. Previous proceedings can be found by following the "Additional ITAA Proceedings" link on the left sidebar of this page.

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Heightened demand for apparel supply-chain transparency and increased usage of social media in sustainability campaigns has created new opportunities to understand public’s perspective toward sustainability issues. Through social network analysis, this study aims to utilize large-scale user-generated data on social media to gain a deeper understanding of public’s views concerning apparel supply-chain transparency. Grounded in small-world theory, this study utilizes #whomademyclothes campaign on Twitter and Instagram as research context. The analysis of social networks formed by the hashtags in 17,030 Instagram posts and 4,530 tweets revealed that public associates sustainability with working condition improvements, environmental protection, community development, and transparency enhancement in the apparel supply-chain. The findings revealed that both Twitter and Instagram users considered working condition improvement as the primary duty to fulfill. This study contributes to the literature by providing a foundation for the use of social network analysis to analyze user-generated social-media data.

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