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

Beheshti-Kashi, Samaneh; Hribernik, Karl; Lützenberger, Johannes; Arabsolgar, Dena; Thoben, Klaus‐Dieter

doi:10.1007/978-3-319-48511-9_22

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Such examples show how the insights about public and consumer sentiment can be derived from textual content collected from Web 2.0 (Rousidis et al , 2020). However, relatively little scholarly attention has been paid to the ways text mining analytics for attribute detection and abstraction can provide insights about markets for fashion (Beheshti-Kashi et al , 2017). With growing interest in refining business analytics to meet current needs, researchers have focused on techniques such as clothing attribute detection to help apparel companies assess new trends.…”

Section: Literature Review and The Research Modelmentioning

confidence: 99%

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Trend tracking tools for the fashion industry: the impact of social media

Rudniy,

Rudna,

Park

2023

JFMM

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PurposeThis paper seeks to demonstrate the value of using social media to capture fashion trends, including the popularity of specific features of clothing, in order to improve the speed and accuracy of supply chain response in the era of fast fashion.Design/methodology/approachThis study examines the role that text mining can play to improve trend recognition in the fashion industry. Researchers used n-gram analysis to design a social media trend detection tool referred to here as the Twitter Trend Tool (3Ts). This tool was applied to a Twitter dataset to identify trends whose validity was then checked against Google Trends.FindingsThe results suggest that Twitter data are trend representative and can be used to identify the apparel features that are most in demand in near real time.Originality/valueThe 3Ts introduced in this research contributes to the field of fashion analytics by offering a novel method for employing big data from social media to identify consumer preferences in fashion elements and analyzes consumer preferences to improve demand planning.Practical implicationsThe 3Ts improves forecasting models and helps inform marketing campaigns in the apparel retail industry, especially in fast fashion.

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Section: Literature Review and The Research Modelmentioning

confidence: 99%

“…In an example of attribute research, Beheshti-Kashi et al , 2017 defined a range of popularity for trending colors. However, this study had the same limitation of an insubstantial sample size.…”

Section: Literature Review and The Research Modelmentioning

confidence: 99%

Trend tracking tools for the fashion industry: the impact of social media

Rudniy,

Rudna,

Park

2023

JFMM

View full text Add to dashboard Cite

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Text Mining for Supply Chain Risk Management in the Apparel Industry

2021

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Text mining tools are now widely used for the efficient management of information and resources in business, academic and research organizations. This paper provides a comprehensive overview of research articles on the application of text mining techniques in the field of Supply Chain Risk Management and the apparel industry. Research articles published between 2000 and 2020, were obtained from various journals through two online databases, i.e., SCOPUS and IEEE Xplore. Through a systematic approach following PRISMA guidelines, 370 research papers were screened, filtered and finally classified into three main areas: Supply Chain Risk Management and outsourcing in the apparel industry, application of text mining in Supply Chain Risk Management and application of text mining in the apparel industry. In this study, we have identified a comprehensive list of various available data sources for text mining, methodologies and risks associated with outsourcing in the apparel industry. We classify the gaps in expanding the application of text mining in the apparel industry’s Supply Chain Risk Management. Extracting useful information from online newspapers through text mining could vividly enhance the ability to monitor supply chain risks and provide the ability to link data to provide decision makers with the right information at the right time.

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

Cited by 2 publications

References 7 publications

Trend tracking tools for the fashion industry: the impact of social media

Trend tracking tools for the fashion industry: the impact of social media

Text Mining for Supply Chain Risk Management in the Apparel Industry

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