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Using sentiment analysis to improve supply chain intelligence

Author

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  • Ajaya Kumar Swain

    (St. Mary’s University)

  • Ray Qing Cao

    (University of Houston-Downtown)

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.

Suggested Citation

  • Ajaya Kumar Swain & Ray Qing Cao, 2019. "Using sentiment analysis to improve supply chain intelligence," Information Systems Frontiers, Springer, vol. 21(2), pages 469-484, April.
    • Handle: RePEc:spr:infosf:v:21:y:2019:i:2:d:10.1007_s10796-017-9762-2
    DOI: 10.1007/s10796-017-9762-2
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    2. Liu, Ying & Huang, Hongyun & Mbanyele, William & Li, Xin & Balezentis, Tomas, 2025. "Harnessing supply chain digital innovation for enhanced corporate environmental practices and sustainable growth," Energy Economics, Elsevier, vol. 142(C).
    3. Ajaya K. Swain & Valeria R. Garza, 2023. "Key Factors in Achieving Service Level Agreements (SLA) for Information Technology (IT) Incident Resolution," Information Systems Frontiers, Springer, vol. 25(2), pages 819-834, April.
    4. Ray Qing Cao & Dara G. Schniederjans & Vicky Ching Gu, 2021. "Stakeholder sentiment in service supply chains: big data meets agenda-setting theory," Service Business, Springer;Pan-Pacific Business Association, vol. 15(1), pages 151-175, March.
    5. Fredström, Ashkan & Parida, Vinit & Wincent, Joakim & Sjödin, David & Oghazi, Pejvak, 2022. "What is the Market Value of Artificial Intelligence and Machine Learning? The Role of Innovativeness and Collaboration for Performance," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    6. An, Suxia & Gu, Yu & Pan, Lu & Yu, Ying, 2024. "Supply chain digitalization and firms' green innovation: Evidence from a pilot program," Economic Analysis and Policy, Elsevier, vol. 84(C), pages 828-846.
    7. Singh, Amit & Jenamani, Mamata & Thakkar, Jitesh J. & Rana, Nripendra P., 2021. "Propagation of online consumer perceived negativity: Quantifying the effect of supply chain underperformance on passenger car sales," Journal of Business Research, Elsevier, vol. 132(C), pages 102-114.
    8. Hyunsun Park & Shan Jiang & One-Ki Daniel Lee & Younghoon Chang, 2024. "Exploring the Attractiveness of Service Robots in the Hospitality Industry: Analysis of Online Reviews," Information Systems Frontiers, Springer, vol. 26(1), pages 41-61, February.

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