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Using sentiment analysis to improve supply chain intelligence

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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.

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Notes

  1. Some researchers (e.g. Granovetter 1983) used the term social network theory instead of social capital theory. Both terms are used interchangeably.

  2. While this study considered all of the three social capital dimensions, the context and the goal of the study is different from ours.

  3. Asymmetric information refers to the situation where different players in a supply chain having differing levels of specific information on various resources such as capacity, demand, and inventory status, related costs, supply chain operations and performance.

  4. As stated earlier, social capital theory states that the amount of social capital available to member depends on the strength and quantity of the network connections that the individual member can enlist, and the sum of the amount of capital that each network member possesses.

  5. Trust is a core concept in Putnam’s (2001) view of social capital. He claims that although social capital is created through an individual’s active participation in a collective, it is a set of features of social organizations – like trust, norms, and structures – that can help in creating a better society through coordinated actions.

  6. This search engine addresses the shortcomings of other currently used search engine technology.

  7. Limited to 140 characters by design.

  8. Cornell University is a pioneer in sentiment analysis, and has maintained a vocabulary of sentiment words (e.g., dislike, enjoy) even though it is originally derived from movie reviews. It is now regarded as the de facto sentiment vocabulary list for sentiment analysis.

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Authors and Affiliations

  1. Finance and Quantitative Management Department, Greehey School of Business, St. Mary’s University, San Antonio, Texas 78228, USA

    Ajaya Kumar Swain

  2. Management, Marketing, and Business Administration Department, Davies College of Business, University of Houston-Downtown, Houston, Texas 77002, USA

    Ray Qing Cao

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  1. Ajaya Kumar Swain
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  2. Ray Qing Cao
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Correspondence to Ajaya Kumar Swain.

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Swain, A.K., Cao, R. Using sentiment analysis to improve supply chain intelligence. Inf Syst Front 21, 469–484 (2019). https://doi.org/10.1007/s10796-017-9762-2

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