IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v161y2020ics0040162520311562.html
   My bibliography  Save this article

Potentials of blockchain technology in supply chain management: Long-term judgments of an international expert panel

Author

Listed:
  • Kopyto, Matthias
  • Lechler, Sabrina
  • von der Gracht, Heiko A.
  • Hartmann, Evi

Abstract

Blockchain technology offers numerous fields of application, especially for supply chain management (SCM), as it could supersede the middleman activities in many transaction-based processes along the supply chain. Blockchain technology has a disruptive impact on supply chain design and operations, making the exploration of future application scenarios of great importance. However, knowledge in this field remains scarce, despite the subject's strategical value. This empirical study addresses this gap by conducting an interdisciplinary Delphi survey. Long-term judgments from an international panel of 108 designated experts from academia, industry, and politics/associations with different context-related backgrounds (blockchain, SCM, hybrid functions) were systematically analyzed. The results reveal prospective scenarios how blockchains will be applied in SCM by 2035 and which SCM-specific obstacles need to be solved in advance. One key finding reveals that even though blockchain technology is said to enable transactions between untrusted parties, trust-related advantages of blockchain technology are not directly transferable to SCM without additional conditions. Counterintuitively, active trust management between supply chain partners will still be needed. Nonetheless, this research reveals that blockchain technology will be strongly applied in SCM by 2035 and thus provides beneficial orientation and stimulating perspectives for decision-makers in the field.

Suggested Citation

  • Kopyto, Matthias & Lechler, Sabrina & von der Gracht, Heiko A. & Hartmann, Evi, 2020. "Potentials of blockchain technology in supply chain management: Long-term judgments of an international expert panel," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    • Handle: RePEc:eee:tefoso:v:161:y:2020:i:c:s0040162520311562
    DOI: 10.1016/j.techfore.2020.120330
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162520311562
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2020.120330?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. Sikorski, Janusz J. & Haughton, Joy & Kraft, Markus, 2017. "Blockchain technology in the chemical industry: Machine-to-machine electricity market," Applied Energy, Elsevier, vol. 195(C), pages 234-246.
    2. Winkler, Jens & Kuklinski, Christian Paul Jian-Wei & Moser, Roger, 2015. "Decision making in emerging markets: The Delphi approach's contribution to coping with uncertainty and equivocality," Journal of Business Research, Elsevier, vol. 68(5), pages 1118-1126.
    3. Graefe, Andreas & Armstrong, J. Scott, 2011. "Comparing face-to-face meetings, nominal groups, Delphi and prediction markets on an estimation task," International Journal of Forecasting, Elsevier, vol. 27(1), pages 183-195, January.
    4. Micha Hirschinger & Alexander Spickermann & Evi Hartmann & Heiko Gracht & Inga-Lena Darkow, 2015. "The Future of Logistics in Emerging Markets—Fuzzy Clustering Scenarios Grounded in Institutional and Factor-Market Rivalry Theory," Journal of Supply Chain Management, Institute for Supply Management, vol. 51(4), pages 73-93, October.
    5. Kshetri, Nir, 2017. "Blockchain's roles in strengthening cybersecurity and protecting privacy," Telecommunications Policy, Elsevier, vol. 41(10), pages 1027-1038.
    6. Geels, Frank W. & Schot, Johan, 2007. "Typology of sociotechnical transition pathways," Research Policy, Elsevier, vol. 36(3), pages 399-417, April.
    7. Jiang, Ruth & Kleer, Robin & Piller, Frank T., 2017. "Predicting the future of additive manufacturing: A Delphi study on economic and societal implications of 3D printing for 2030," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 84-97.
    8. Armstrong, J. Scott & Overton, Terry S., 1977. "Estimating Nonresponse Bias in Mail Surveys," MPRA Paper 81694, University Library of Munich, Germany.
    9. von der Gracht, Heiko A. & Darkow, Inga-Lena, 2010. "Scenarios for the logistics services industry: A Delphi-based analysis for 2025," International Journal of Production Economics, Elsevier, vol. 127(1), pages 46-59, September.
    10. Daniel E. O'Leary, 2017. "Configuring blockchain architectures for transaction information in blockchain consortiums: The case of accounting and supply chain systems," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 24(4), pages 138-147, October.
    11. Rowe, Gene & Wright, George, 1999. "The Delphi technique as a forecasting tool: issues and analysis," International Journal of Forecasting, Elsevier, vol. 15(4), pages 353-375, October.
    12. Rowe, Gene & Wright, George, 1996. "The impact of task characteristics on the performance of structured group forecasting techniques," International Journal of Forecasting, Elsevier, vol. 12(1), pages 73-89, March.
    13. Geels, Frank W., 2002. "Technological transitions as evolutionary reconfiguration processes: a multi-level perspective and a case-study," Research Policy, Elsevier, vol. 31(8-9), pages 1257-1274, December.
    14. Norman Dalkey & Olaf Helmer, 1963. "An Experimental Application of the DELPHI Method to the Use of Experts," Management Science, INFORMS, vol. 9(3), pages 458-467, April.
    15. Peter Yeoh, 2017. "Regulatory issues in blockchain technology," Journal of Financial Regulation and Compliance, Emerald Group Publishing Limited, vol. 25(2), pages 196-208, May.
    16. Keller, Jonas & von der Gracht, Heiko A., 2014. "The influence of information and communication technology (ICT) on future foresight processes — Results from a Delphi survey," Technological Forecasting and Social Change, Elsevier, vol. 85(C), pages 81-92.
    17. Dinwoodie, John & Landamore, Melanie & Rigot-Muller, Patrick, 2014. "Dry bulk shipping flows to 2050: Delphi perceptions of early career specialists," Technological Forecasting and Social Change, Elsevier, vol. 88(C), pages 64-75.
    18. Förster, Bernadette & von der Gracht, Heiko, 2014. "Assessing Delphi panel composition for strategic foresight — A comparison of panels based on company-internal and external participants," Technological Forecasting and Social Change, Elsevier, vol. 84(C), pages 215-229.
    19. 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.
    20. Pilkington Marc, 2016. "Blockchain Technology: Principles and Applications," Post-Print halshs-01231205, HAL.
    21. Geist, Monica R., 2010. "Using the Delphi method to engage stakeholders: A comparison of two studies," Evaluation and Program Planning, Elsevier, vol. 33(2), pages 147-154, May.
    22. Melander, Lisa & Dubois, Anna & Hedvall, Klas & Lind, Frida, 2019. "Future goods transport in Sweden 2050: Using a Delphi-based scenario analysis," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 178-189.
    23. Spickermann, Alexander & Grienitz, Volker & von der Gracht, Heiko A., 2014. "Heading towards a multimodal city of the future?," Technological Forecasting and Social Change, Elsevier, vol. 89(C), pages 201-221.
    24. Winkler, Jens & Moser, Roger, 2016. "Biases in future-oriented Delphi studies: A cognitive perspective," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 63-76.
    25. Fritschy, Carolin & Spinler, Stefan, 2019. "The impact of autonomous trucks on business models in the automotive and logistics industry–a Delphi-based scenario study," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    26. von der Gracht, Heiko A., 2012. "Consensus measurement in Delphi studies," Technological Forecasting and Social Change, Elsevier, vol. 79(8), pages 1525-1536.
    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. Friedman, Nicola & Ormiston, Jarrod, 2022. "Blockchain as a sustainability-oriented innovation?: Opportunities for and resistance to Blockchain technology as a driver of sustainability in global food supply chains," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    2. Stumpf, Lukas & Schöggl, Josef-Peter & Baumgartner, Rupert J., 2023. "Circular plastics packaging – Prioritizing resources and capabilities along the supply chain," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    3. Juliet Orji, Ifeyinwa & Ojadi, Frank & Kalu Okwara, Ukoha, 2022. "The nexus between e-commerce adoption in a health pandemic and firm performance: The role of pandemic response strategies," Journal of Business Research, Elsevier, vol. 145(C), pages 616-635.
    4. Kumar, Devinder & Singh, Rajesh Kr & Mishra, Ruchi & Daim, Tugrul U., 2023. "Roadmap for integrating blockchain with Internet of Things (IoT) for sustainable and secured operations in logistics and supply chains: Decision making framework with case illustration," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    5. Minyi Xu & Shujian Ma & Gang Wang, 2022. "Differential Game Model of Information Sharing among Supply Chain Finance Based on Blockchain Technology," Sustainability, MDPI, vol. 14(12), pages 1-21, June.
    6. Paul, Tripti & Mondal, Sandeep & Islam, Nazrul & Rakshit, Sandip, 2021. "The impact of blockchain technology on the tea supply chain and its sustainable performance," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    7. Gebhardt, Maximilian & Spieske, Alexander & Birkel, Hendrik, 2022. "The future of the circular economy and its effect on supply chain dependencies: Empirical evidence from a Delphi study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    8. Kongmanas Yavaprabhas & Mehrdokht Pournader & Stefan Seuring, 2023. "Blockchain as the “trust-building machine” for supply chain management," Annals of Operations Research, Springer, vol. 327(1), pages 49-88, August.
    9. Gebhardt, Maximilian & Spieske, Alexander & Kopyto, Matthias & Birkel, Hendrik, 2022. "Increasing global supply chains’ resilience after the COVID-19 pandemic: Empirical results from a Delphi study," Journal of Business Research, Elsevier, vol. 150(C), pages 59-72.
    10. Santana, Carlos & Albareda, Laura, 2022. "Blockchain and the emergence of Decentralized Autonomous Organizations (DAOs): An integrative model and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    11. Ulpan Tokkozhina & Ana Lucia Martins & Joao C. Ferreira, 2023. "Uncovering dimensions of the impact of blockchain technology in supply chain management," Operations Management Research, Springer, vol. 16(1), pages 99-125, March.
    12. Mittal, Mandeep & Sarkar, Biswajit, 2023. "Stochastic behavior of exchange rate on an international supply chain under random energy price," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 205(C), pages 232-250.
    13. de Souza, Michele & Pereira, Giancarlo Medeiros & Lopes de Sousa Jabbour, Ana Beatriz & Chiappetta Jabbour, Charbel Jose & Trento, Luiz Reni & Borchardt, Miriam & Zvirtes, Leandro, 2021. "A digitally enabled circular economy for mitigating food waste: Understanding innovative marketing strategies in the context of an emerging economy," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    14. Bootz, Jean-Philippe & Michel, Sophie & Pallud, Jessie & Monti, Régine, 2022. "Possible changes of Industry 4.0 in 2030 in the face of uberization: Results of a participatory and systemic foresight study," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    15. Jacob Lohmer & Elias Ribeiro da Silva & Rainer Lasch, 2022. "Blockchain Technology in Operations & Supply Chain Management: A Content Analysis," Sustainability, MDPI, vol. 14(10), pages 1-88, May.
    16. Rijswijk, Kelly & de Vries, Jasper R. & Klerkx, Laurens & Turner, James A., 2023. "The enabling and constraining connections between trust and digitalisation in incumbent value chains," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    17. Zerbino, Pierluigi & Stefanini, Alessandro & Aloini, Davide, 2021. "Process Science in Action: A Literature Review on Process Mining in Business Management," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    18. Kazancoglu, Yigit & Sagnak, Muhittin & Mangla, Sachin Kumar & Sezer, Muruvvet Deniz & Pala, Melisa Ozbiltekin, 2021. "A fuzzy based hybrid decision framework to circularity in dairy supply chains through big data solutions," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    19. Sarker, Indranil & Datta, Bidisha, 2022. "Re-designing the pension business processes for achieving technology-driven reforms through blockchain adoption: A proposed architecture," Technological Forecasting and Social Change, Elsevier, vol. 174(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. Peppel, Marcel & Ringbeck, Jürgen & Spinler, Stefan, 2022. "How will last-mile delivery be shaped in 2040? A Delphi-based scenario study," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    2. Gebhardt, Maximilian & Spieske, Alexander & Birkel, Hendrik, 2022. "The future of the circular economy and its effect on supply chain dependencies: Empirical evidence from a Delphi study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    3. Tiberius, Victor & Gojowy, Robin & Dabić, Marina, 2022. "Forecasting the future of robo advisory: A three-stage Delphi study on economic, technological, and societal implications," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    4. 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.
    5. Di Zio, Simone & Bolzan, Mario & Marozzi, Marco, 2021. "Classification of Delphi outputs through robust ranking and fuzzy clustering for Delphi-based scenarios," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    6. Tobias Meyer & Heiko A. von der Gracht & Evi Hartmann, 2022. "Technology foresight for sustainable road freight transportation: Insights from a global real‐time Delphi study," Futures & Foresight Science, John Wiley & Sons, vol. 4(1), March.
    7. Fritschy, Carolin & Spinler, Stefan, 2019. "The impact of autonomous trucks on business models in the automotive and logistics industry–a Delphi-based scenario study," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    8. Merfeld, Katrin & Wilhelms, Mark-Philipp & Henkel, Sven & Kreutzer, Karin, 2019. "Carsharing with shared autonomous vehicles: Uncovering drivers, barriers and future developments – A four-stage Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 66-81.
    9. Frevel, Nicolas & Beiderbeck, Daniel & Schmidt, Sascha L., 2022. "The impact of technology on sports – A prospective study," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    10. Gebhardt, Maximilian & Spieske, Alexander & Kopyto, Matthias & Birkel, Hendrik, 2022. "Increasing global supply chains’ resilience after the COVID-19 pandemic: Empirical results from a Delphi study," Journal of Business Research, Elsevier, vol. 150(C), pages 59-72.
    11. Spickermann, Alexander & Zimmermann, Martin & von der Gracht, Heiko A., 2014. "Surface- and deep-level diversity in panel selection — Exploring diversity effects on response behaviour in foresight," Technological Forecasting and Social Change, Elsevier, vol. 85(C), pages 105-120.
    12. von Briel, Frederik, 2018. "The future of omnichannel retail: A four-stage Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 217-229.
    13. Förster, Bernadette & von der Gracht, Heiko, 2014. "Assessing Delphi panel composition for strategic foresight — A comparison of panels based on company-internal and external participants," Technological Forecasting and Social Change, Elsevier, vol. 84(C), pages 215-229.
    14. Tiberius, Victor & Hirth, Stefanie, 2019. "Impacts of digitization on auditing: A Delphi study for Germany," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 37(C).
    15. Delke, Vincent & Schiele, Holger & Buchholz, Wolfgang & Kelly, Stephen, 2023. "Implementing Industry 4.0 technologies: Future roles in purchasing and supply management," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    16. Shoukohyar, Sajjad & Seddigh, Mohammad Reza, 2020. "Uncovering the dark and bright sides of implementing collaborative forecasting throughout sustainable supply chains: An exploratory approach," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    17. Zeng, Michael A. & Koller, Hans & Jahn, Reimo, 2019. "Open radar groups: The integration of online communities into open foresight processes," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 204-217.
    18. Spickermann, Alexander & Grienitz, Volker & von der Gracht, Heiko A., 2014. "Heading towards a multimodal city of the future?," Technological Forecasting and Social Change, Elsevier, vol. 89(C), pages 201-221.
    19. Christoph Markmann & Alexander Spickermann & Heiko A. von der Gracht & Alexander Brem, 2021. "Improving the question formulation in Delphi‐like surveys: Analysis of the effects of abstract language and amount of information on response behavior," Futures & Foresight Science, John Wiley & Sons, vol. 3(1), March.
    20. Belton, Ian & MacDonald, Alice & Wright, George & Hamlin, Iain, 2019. "Improving the practical application of the Delphi method in group-based judgment: A six-step prescription for a well-founded and defensible process," Technological Forecasting and Social Change, Elsevier, vol. 147(C), pages 72-82.

    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:eee:tefoso:v:161:y:2020:i:c:s0040162520311562. 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: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    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.