Search results

This study aims to use social network analysis to (1) investigate public opinions on social media regarding apparel supply chain transparency and (2) identify key themes and the major communities discussing apparel supply chain transparency.

Data mining-based social network analysis was used to investigate the pattern of discussions regarding apparel supply chain transparency on Twitter and Instagram.

Both Instagram and Twitter networks exhibited high interest in environmental, working condition and community support in apparel supply chain as explained by the moral responsibility framework of corporate sustainability despite the intended theme of the campaign to promote transparency in terms of working conditions specifically. However, some inconsistencies were found regarding the importance of these communities in the network, suggesting that while survey methods hold significance in measuring user intension, the reaction-based user-generated data on social media can be useful to measure users' true behavior. Also, while Twitter network was dominated by knowledge-based messages, the Instagram network had emotion-driven messages.

By conducting this study, the authors will not only contribute to the existing literature of social media usage in the apparel industry but will also provide a foundation for the use of social network analysis to analyze user-generated data on social media, as this method is fairly new in the textile and apparel industry-related research. The authors also wish to help businesses and policy makers identify specific actionable areas where they are lagging and hence further improve their overall performance.