IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v56y2018i13p4447-4466.html
   My bibliography  Save this article

Data supply chain (DSC): research synthesis and future directions

Author

Listed:
  • Konstantina Spanaki
  • Zeynep Gürgüç
  • Richard Adams
  • Catherine Mulligan

Abstract

In the digital economy, the volume, variety and availability of data produced in myriad forms from a diversity of sources has become an important resource for competitive advantage, innovation opportunity as well as source of new management challenges. Building on the theoretical and empirical foundations of the traditional manufacturing Supply Chain (SC), which describes the flow of physical artefacts as raw materials through to consumption, we propose the Data Supply Chain (DSC) along which data are the primary artefact flowing. The purpose of this paper is to outline the characteristics and bring conceptual distinctiveness to the context around DSC as well as to explore the associated and emergent management challenges and innovation opportunities. To achieve this, we adopt the systematic review methodology drawing on the operations management and supply chain literature and, in particular, taking a framework synthetic approach which allows us to build the DSC concept from the pre-existing SC template. We conclude the paper by developing a set of propositions and outlining an agenda for future research that the DSC concept implies.

Suggested Citation

  • Konstantina Spanaki & Zeynep Gürgüç & Richard Adams & Catherine Mulligan, 2018. "Data supply chain (DSC): research synthesis and future directions," International Journal of Production Research, Taylor & Francis Journals, vol. 56(13), pages 4447-4466, July.
  • Handle: RePEc:taf:tprsxx:v:56:y:2018:i:13:p:4447-4466
    DOI: 10.1080/00207543.2017.1399222
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2017.1399222
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/00207543.2017.1399222?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.

    Citations

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


    Cited by:

    1. Thanos Papadopoulos & Uthayasankar Sivarajah & Konstantina Spanaki & Stella Despoudi & Angappa Gunasekaran, 2022. "Editorial: Artificial Intelligence (AI) and Data Sharing in Manufacturing, Production and Operations Management Research," Post-Print hal-03766170, HAL.
    2. Chatterjee, Sheshadri & Chaudhuri, Ranjan & Gupta, Shivam & Sivarajah, Uthayasankar & Bag, Surajit, 2023. "Assessing the impact of big data analytics on decision-making processes, forecasting, and performance of a firm," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    3. H. Kava & K. Spanaki & T. Papadopoulos & S. Despoudi & O. Rodriguez Espindola & M. Fakhimi, 2024. "Data analytics diffusion in the UK renewable energy sector: an innovation perspective," Post-Print hal-04478933, HAL.
    4. Ranjan Chaudhuri & Sheshadri Chatterjee & Demetris Vrontis & Sumana Chaudhuri, 2022. "Innovation in SMEs, AI Dynamism, and Sustainability: The Current Situation and Way Forward," Sustainability, MDPI, vol. 14(19), pages 1-19, October.
    5. Harkaran Kava & Konstantina Spanaki & Thanos Papadopoulos & Stella Despoudi & Oscar Rodriguez-Espindola & Masoud Fakhimi, 2021. "Data Analytics Diffusion in the UK Renewable Energy Sector: An Innovation Perspective," Post-Print hal-03781046, HAL.
    6. Wamba, Samuel Fosso & Dubey, Rameshwar & Gunasekaran, Angappa & Akter, Shahriar, 2020. "The performance effects of big data analytics and supply chain ambidexterity: The moderating effect of environmental dynamism," International Journal of Production Economics, Elsevier, vol. 222(C).
    7. Konstantina Spanaki & Uthayasankar Sivarajah & Masoud Fakhimi & Stella Despoudi & Zahir Irani, 2022. "Disruptive technologies in agricultural operations: a systematic review of AI-driven AgriTech research," Annals of Operations Research, Springer, vol. 308(1), pages 491-524, January.
    8. Alnoor Bhimani, 2020. "Digital data and management accounting: why we need to rethink research methods," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 31(1), pages 9-23, April.

    More about this item

    Statistics

    Access and download statistics

    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:taf:tprsxx:v:56:y:2018:i:13:p:4447-4466. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TPRS20 .

    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.