Fashion Supply Chains and Social Media: Examining the Potential of Data Analysis of Social-Media Texts for Decision Making-Processes in Fashion Supply Chains

• Published in: Business Models and ICT Technologies for the Fashion Supply Chain pp. 271 - 281
• Main Authors: Beheshti-Kashi, Samaneh, Hribernik, Karl, Lützenberger, Johannes, Arabsolgar, Dena, Thoben, Klaus-Dieter
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Electrical Engineering
• Subjects: Fashion blogs; Fashion buying; Fashion supply chains; Social media; Social media text data analysis
• ISBN: 9783319485102; 3319485105
• ISSN: 1876-1100; 1876-1119
• DOI: 10.1007/978-3-319-48511-9_22

Fashion companies often face challenges in meeting the demand of consumers since often production plans have to be placed before exact knowledge of the demand is available. Since the industry is a highly consumer- and trend-oriented industry, predicting the customers demand is crucial for the company’s success. Nowadays, these customers have been empowered through the Web 2.0 and are able to publish opinions and experiences on various social-media applications. At the same time, these consumers are members of the fashion supply chain. This paper considers a typical fashion supply chain and focusses on the role of the buyer, whose function resides with the retailer. The buyer plays a crucial role since she or he is responsible for the trend monitoring and selection of future fashion collections. The objective of this paper is to examine if social-media text data shared by means of fashion blogs contains color information and if these color comments correspond to real-world customer demand. For this purpose, 232 blog posts were collected, analyzed, and compared to qualitative information on colors provided by a real-world clothing company. The analysis shows that it is indeed possible to discover color information from fashion blogs. Moreover, it revealed that the information identified in the blogs correspond with real-world customer demand.