A framework for social media analytics in textile business circularity for effective digital marketing
In the contemporary era of digital transformation, organizations are increasingly aligning their operations with sustainability objectives, particularly within the framework of circular economy (CE) principles in production and consumption systems. While the concept of circularity has been extensive...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Journal of Open Innovation: Technology, Market and Complexity |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2199853125000794 |
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| Summary: | In the contemporary era of digital transformation, organizations are increasingly aligning their operations with sustainability objectives, particularly within the framework of circular economy (CE) principles in production and consumption systems. While the concept of circularity has been extensively explored through theoretical research, a notable gap remains in empirical studies that analyze user-generated content related to circularity in the textile industry. This study aims to bridge the gap by proposing a novel Instagramⓒ analytics framework that seamlessly integrates content and network analyses. A mixed-method approach is adopted, merging qualitative insights derived from unstructured data with quantitative techniques. To analyze content, unsupervised machine learning methods, including topic modeling and sentiment analysis, are employed. In parallel, Social Network Analysis (SNA) and hashtag co-occurrence analysis are applied to investigate the dynamics within the network. The findings demonstrate a significant level of interest and engagement in discussions surrounding Textile Circularity (TC). Moreover, consumer responses to sustainability initiatives show considerable variation, underscoring the necessity of strategies that foster meaningful interactions. Notably, content emphasizing positive sentiments and tangible benefits, such as cost savings and environmental improvements, consistently achieves higher engagement levels. This paper contributes to the field by integrating social media data with advanced data analytics techniques. Together, these approaches offer an unparalleled opportunity to investigate customer drivers within the context of TC. Additionally, the study presents a comprehensive analytical model and delivers actionable insights. These findings hold the potential to refine digital marketing strategies and enhance customer engagement, particularly by deepening the understanding of factors that motivate consumers to TC. |
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| ISSN: | 2199-8531 |