IDEAS home Printed from https://ideas.repec.org/a/spr/svcbiz/v15y2021i1d10.1007_s11628-021-00437-w.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11628-021-00437-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11628-021-00437-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. Claudio Vitari & Elisabetta Raguseo, 2019. "Big data analytics business value and firm performance: Linking with environmental context," Post-Print hal-02293765, HAL.
    17. 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.
    18. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. del Val Núñez, Maria Teresa & de Lucas Ancillo, Antonio & Gavrila Gavrila, Sorin & Gómez Gandía, José Andrés, 2024. "Technological transformation in HRM through knowledge and training: Innovative business decision making," Technological Forecasting and Social Change, Elsevier, vol. 200(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
    2. Roßmann, Bernhard & Canzaniello, Angelo & von der Gracht, Heiko & Hartmann, Evi, 2018. "The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 135-149.
    3. Purva Grover & Arpan Kumar Kar, 2017. "Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 203-229, September.
    4. Raut, Rakesh D. & Mangla, Sachin Kumar & Narwane, Vaibhav S. & Dora, Manoj & Liu, Mengqi, 2021. "Big Data Analytics as a mediator in Lean, Agile, Resilient, and Green (LARG) practices effects on sustainable supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    5. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    6. Yaping Zhao & Zelong Yi, 2021. "Pricing of a Three-Stage Supply Chain with a Big Data Company," SN Operations Research Forum, Springer, vol. 2(4), pages 1-19, December.
    7. Pan Liu & Shu-ping Yi, 2018. "Investment decision-making and coordination of a three-stage supply chain considering Data Company in the Big Data era," Annals of Operations Research, Springer, vol. 270(1), pages 255-271, November.
    8. Claudio Vitari & Elisabetta Raguseo, 2019. "Big data analytics business value and firm performance: Linking with environmental context," Post-Print hal-02293765, HAL.
    9. Kozjek, Dominik & Vrabič, Rok & Eržen, Gregor & Butala, Peter, 2018. "Identifying the business and social networks in the domain of production by merging the data from heterogeneous internet sources," International Journal of Production Economics, Elsevier, vol. 200(C), pages 181-191.
    10. Wang, Hui & Gong, Qiguo & Wang, Shouyang, 2017. "Information processing structures and decision making delays in MRP and JIT," International Journal of Production Economics, Elsevier, vol. 188(C), pages 41-49.
    11. Shuihua Han & Yufang Fu & Bin Cao & Zongwei Luo, 2018. "Pricing and bargaining strategy of e-retail under hybrid operational patterns," Annals of Operations Research, Springer, vol. 270(1), pages 179-200, November.
    12. Ionica Oncioiu & Ovidiu Constantin Bunget & Mirela Cătălina Türkeș & Sorinel Căpușneanu & Dan Ioan Topor & Attila Szora Tamaș & Ileana-Sorina Rakoș & Mihaela Ștefan Hint, 2019. "The Impact of Big Data Analytics on Company Performance in Supply Chain Management," Sustainability, MDPI, vol. 11(18), pages 1-22, September.
    13. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Bryde, David J. & Giannakis, Mihalis & Foropon, Cyril & Roubaud, David & Hazen, Benjamin T., 2020. "Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations," International Journal of Production Economics, Elsevier, vol. 226(C).
    14. J. Piet Hausberg & Kirsten Liere-Netheler & Sven Packmohr & Stefanie Pakura & Kristin Vogelsang, 2019. "Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis," Journal of Business Economics, Springer, vol. 89(8), pages 931-963, December.
    15. Arunachalam, Deepak & Kumar, Niraj & Kawalek, John Paul, 2018. "Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 416-436.
    16. Nishikant Mishra & Akshit Singh, 2018. "Use of twitter data for waste minimisation in beef supply chain," Annals of Operations Research, Springer, vol. 270(1), pages 337-359, November.
    17. Pournader, Mehrdokht & Ghaderi, Hadi & Hassanzadegan, Amir & Fahimnia, Behnam, 2021. "Artificial intelligence applications in supply chain management," International Journal of Production Economics, Elsevier, vol. 241(C).
    18. Deepa Mishra & Angappa Gunasekaran & Thanos Papadopoulos & Stephen J. Childe, 2018. "Big Data and supply chain management: a review and bibliometric analysis," Annals of Operations Research, Springer, vol. 270(1), pages 313-336, November.
    19. Gang Wang & Angappa Gunasekaran & Eric W. T. Ngai, 2018. "Distribution network design with big data: model and analysis," Annals of Operations Research, Springer, vol. 270(1), pages 539-551, November.
    20. Lidong Wang & Cheryl Ann Alexander, 2015. "Big Data Driven Supply Chain Management and Business Administration," American Journal of Economics and Business Administration, Science Publications, vol. 7(2), pages 60-67, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:svcbiz:v:15:y:2021:i:1:d:10.1007_s11628-021-00437-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.