Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.3963
Publisher URL: https://www.epubli.de/shop/buch/Adapting-to-the-Future-Christian-M-Ringle-Thorsten-Blecker-Wolfgang-Kersten-9783754927700/121489
Title: Application of social media data in supply chain management : a systematic review
Language: English
Authors: Chen, Xi 
Wong, T. C. 
Editor: Kersten, Wolfgang  
Ringle, Christian M.  
Blecker, Thorsten 
Keywords: Business Analytics
Issue Date: 1-Dec-2021
Publisher: epubli
Source: Hamburg International Conference of Logistics (HICL) 31: 499-523 (2021)
Part of Series: Proceedings of the Hamburg International Conference of Logistics (HICL) 
Volume number: 31
Abstract (english): 
Purpose: Recently, big data has received considerable industrial and academic attention. Social media(SM) are becoming reliable big data sources that include various information such as customer’s opinions, product reviews and trends. However, the supply chain management (SCM) field has been lagging behind other industries in adopting SM. Hence, this paper aims to explore the value of SM and its application in SCM with recommendation for future work.
Methodology: This paper reviews the existing literature systematically to highlight major research works and trends by using bibliometric analysis.
Findings: Our review results show that the research on SM and SCM has attracted significant attention over the decade. SM data has been used together with different analytical tools (e.g. text mining, sentiment analysis) to manage different supply chain activities (e.g. demand forecasting, production). However, the potential of SM has not been thoroughly investigated due to the inherent nature of SM data. Therefore, this study field is in its infancy. We suggest some directions can be considered for future research, e.g. sentiment indicators for SC related posts.
Originality: This paper is the first attempt to systematically analyse the interaction of SM data and SCM and to highlight the new approaches of adopting SM data for improving SCM.
Conference: Hamburg International Conference of Logistics (HICL) 2021 
URI: http://hdl.handle.net/11420/11178
DOI: 10.15480/882.3963
ISBN: 978-3-754927-70-0
ISSN: 2365-5070
Document Type: Chapter/Article (Proceedings)
Peer Reviewed: Yes
License: CC BY-SA 4.0 (Attribution-ShareAlike 4.0) CC BY-SA 4.0 (Attribution-ShareAlike 4.0)
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