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

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  • 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
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    Citations

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    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. Mulligan, Catherine & Morsfield, Suzanne & Cheikosman, Evîn, 2024. "Blockchain for sustainability: A systematic literature review for policy impact," Telecommunications Policy, Elsevier, vol. 48(2).
    4. 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.
    5. 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.
    6. 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.
    7. 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).
    8. 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.
    9. 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.

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