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Stakeholder sentiment in service supply chains: big data meets agenda-setting theory

Author

Listed:
  • Ray Qing Cao

    (University of Houston Downtown)

  • Dara G. Schniederjans

    (University of Rhode Island)

  • Vicky Ching Gu

    (University of Houston Clear Lake)

Abstract

With growing reluctance to store and disseminate sensitive data throughout a supply chain network, there is a need to understand sentiment of big data and ways of control to achieve greater economic viability in the service-oriented supply chain which reflect a greater focus on knowledge sharing from traditional supply chains. Social network data were collected after referencing a focal corporate media (CM) document. This study provides causal inference by first conducting a CM document search and then a social network post web scrape of postings that reference the CM document while controlling for time and other demographic variables. This study finds salience of the big data topic positively impacts stakeholder sentiment but not when future applications are discussed.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:svcbiz:v:15:y:2021:i:1:d:10.1007_s11628-021-00437-w
    DOI: 10.1007/s11628-021-00437-w
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    1. Hélia Gonçalves Pereira & Maria Fátima Salgueiro & Paulo Rita, 2017. "Online determinants of e-customer satisfaction: application to website purchases in tourism," Service Business, Springer;Pan-Pacific Business Association, vol. 11(2), pages 375-403, June.
    2. Yong-Hong Kuo & Andrew Kusiak, 2019. "From data to big data in production research: the past and future trends," International Journal of Production Research, Taylor & Francis Journals, vol. 57(15-16), pages 4828-4853, August.
    3. Ai-Hsuan Chiang & Silvana Trimi, 2020. "Impacts of service robots on service quality," Service Business, Springer;Pan-Pacific Business Association, vol. 14(3), pages 439-459, September.
    4. Tan, Kim Hua & Zhan, YuanZhu & Ji, Guojun & Ye, Fei & Chang, Chingter, 2015. "Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph," International Journal of Production Economics, Elsevier, vol. 165(C), pages 223-233.
    5. 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.
    6. Shradha A. Gawankar & Angappa Gunasekaran & Sachin Kamble, 2020. "A study on investments in the big data-driven supply chain, performance measures and organisational performance in Indian retail 4.0 context," International Journal of Production Research, Taylor & Francis Journals, vol. 58(5), pages 1574-1593, March.
    7. Luvai Motiwalla & Amit V. Deokar & Surendra Sarnikar & Angelika Dimoka, 2019. "Leveraging Data Analytics for Behavioral Research," Information Systems Frontiers, Springer, vol. 21(4), pages 735-742, August.
    8. Shah, Naimatullah & Irani, Zahir & Sharif, Amir M., 2017. "Big data in an HR context: Exploring organizational change readiness, employee attitudes and behaviors," Journal of Business Research, Elsevier, vol. 70(C), pages 366-378.
    9. Michael Firth & Kailong (Philip) Wang & Sonia ML Wong, 2015. "Corporate Transparency and the Impact of Investor Sentiment on Stock Prices," Management Science, INFORMS, vol. 61(7), pages 1630-1647, July.
    10. Taewon Hwang & Sung Tae Kim, 2019. "Balancing in-house and outsourced logistics services: effects on supply chain agility and firm performance," Service Business, Springer;Pan-Pacific Business Association, vol. 13(3), pages 531-556, September.
    11. Noel Brown & Craig Deegan, 1998. "The public disclosure of environmental performance information—a dual test of media agenda setting theory and legitimacy theory," Accounting and Business Research, Taylor & Francis Journals, vol. 29(1), pages 21-41.
    12. Claudio Vitari & Elisabetta Raguseo, 2019. "Big data analytics business value and firm performance: Linking with environmental context," Post-Print hal-02293765, HAL.
    13. Chae, Bongsug (Kevin), 2015. "Insights from hashtag #supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research," International Journal of Production Economics, Elsevier, vol. 165(C), pages 247-259.
    14. Hsin Chang & Chung-Jye Hung & Kit Wong & Chin-Ho Lee, 2013. "Using the balanced scorecard on supply chain integration performance—a case study of service businesses," Service Business, Springer;Pan-Pacific Business Association, vol. 7(4), pages 539-561, December.
    15. Hazen, Benjamin T. & Boone, Christopher A. & Ezell, Jeremy D. & Jones-Farmer, L. Allison, 2014. "Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications," International Journal of Production Economics, Elsevier, vol. 154(C), pages 72-80.
    16. Na Rang Kim & Soon Goo Hong, 2020. "Text mining for the evaluation of public services: the case of a public bike-sharing system," Service Business, Springer;Pan-Pacific Business Association, vol. 14(3), pages 315-331, September.
    17. Harris, Irina & Wang, Yingli & Wang, Haiyang, 2015. "ICT in multimodal transport and technological trends: Unleashing potential for the future," International Journal of Production Economics, Elsevier, vol. 159(C), pages 88-103.
    18. Zhong, Ray Y. & Huang, George Q. & Lan, Shulin & Dai, Q.Y. & Chen, Xu & Zhang, T., 2015. "A big data approach for logistics trajectory discovery from RFID-enabled production data," International Journal of Production Economics, Elsevier, vol. 165(C), pages 260-272.
    19. Soo Chew & Richard Ebstein & Songfa Zhong, 2012. "Ambiguity aversion and familiarity bias: Evidence from behavioral and gene association studies," Journal of Risk and Uncertainty, Springer, vol. 44(1), pages 1-18, February.
    20. Erik Hofmann, 2017. "Big data and supply chain decisions: the impact of volume, variety and velocity properties on the bullwhip effect," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5108-5126, September.
    21. Opresnik, David & Taisch, Marco, 2015. "The value of Big Data in servitization," International Journal of Production Economics, Elsevier, vol. 165(C), pages 174-184.
    22. Max Finne & Saara Brax & Jan Holmström, 2013. "Reversed servitization paths: a case analysis of two manufacturers," Service Business, Springer;Pan-Pacific Business Association, vol. 7(4), pages 513-537, December.
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