Using sentiment analysis to improve supply chain intelligence

Ajaya Kumar Swain () and Ray Qing Cao ()
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Ajaya Kumar Swain: St. Mary’s University
Ray Qing Cao: University of Houston-Downtown

Information Systems Frontiers, 2019, vol. 21, issue 2, No 13, 469-484

Abstract: Abstract Analysis of comments and opinions expressed in social media can be used to gather additional intelligence via market research information to better predict consumer behavior. The area of “opinion mining”, particularly sentiment analysis, aims to find, extract, and systematically analyze people’s opinions, attitudes and emotions towards certain topics. Performance of a supply chain is closely associated with the level of trust, collaboration, and information sharing among its members. In this paper, using textual “sentiment analysis”, we explore the relationship between elements of social media content generated by supply chain members and performance of supply chain. In particular, we identify specific elements of member generated supply chain related content on social media such as: information sharing, collaboration, trust, and commitment to determine their association with supply chain performance. We find information sharing and collaboration to be positively associated with supply chain performance, and these findings are consistent with previous reports in supply chain literature. In addition, ours is one of the first attempts to use sentiment analysis to analyze social media content in a supply chain context. The findings indicate that supply chain members value the sharing of relevant information and collaborative contents on social media as such efforts improve individual and overall supply chain performance. The results of this study should prove useful to other studies that utilize social media in a supply chain context, and to improve supply chain management strategies.

Keywords: Social media; Intelligent decision making; Social network analysis; Sentiment analysis; And supply chain management (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10796-017-9762-2

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