Insights from hashtag #supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research

Chae, Bongsug (Kevin)

International Journal of Production Economics, 2015, vol. 165, issue C, 247-259

Abstract: Recently, businesses and research communities have paid a lot of attention to social media and big data. However, the field of supply chain management (SCM) has been relatively slow in studying social media and big data for research and practice. In these contexts, this research contributes to the SCM community by proposing a novel, analytical framework (Twitter Analytics) for analyzing supply chain tweets, highlighting the current use of Twitter in supply chain contexts, and further developing insights into the potential role of Twitter for supply chain practice and research. The proposed framework combines three methodologies – descriptive analytics (DA), content analytics (CA) integrating text mining and sentiment analysis, and network analytics (NA) relying on network visualization and metrics – for extracting intelligence from 22,399 #supplychain tweets. Some of the findings are: supply chain tweets are used by different groups of supply chain professionals and organizations (e.g., news services, IT companies, logistic providers, manufacturers) for information sharing, hiring professionals, and communicating with stakeholders, among others; diverse topics are being discussed, ranging from logistics and corporate social responsibility, to risk, manufacturing, SCM IT and even human rights; some tweets carry strong sentiments about companies׳ delivery services, sales performance, and environmental standards, and risk and disruption in supply chains. Based on these findings, this research presents insights into the use and potential role of Twitter for supply chain practices (e.g., professional networking, stakeholder engagement, demand shaping, new product/service development, supply chain risk management) and the implications for research. Finally, the limitations of the current study and suggestions for future research are presented.

Keywords: Supply chain management; Twitter; Data analytics; Network analytics; Content analytics; Big data; Social media analytics; Application Programming Interface (API) (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (47)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:165:y:2015:i:c:p:247-259

DOI: 10.1016/j.ijpe.2014.12.037

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