IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v187y2023ics0040162522007077.html
   My bibliography  Save this article

The impact of technology on the future of football – A global Delphi study

Author

Listed:
  • Beiderbeck, Daniel
  • Evans, Nicolas
  • Frevel, Nicolas
  • Schmidt, Sascha L.

Abstract

The speed of technological innovation in football has drastically increased in recent years. Therefore, we examine the impact of (digital) technologies on the future of association football up until the year 2026. In this regard, we also take into account diverging socio-economic circumstances in different parts of the world. We use a two-round sequential Delphi method to gather both quantitative and qualitative data from an expert panel consisting of 85 technical directors from official FIFA member associations. In total, we test ten future-oriented projections and collect information about participants' work experience, sentiments, as well as their individual attitude towards technology. While experts generally agree that the importance of technology in football will continue to increase, we find differences in perception among sub-groups of experts (e.g., smaller associations express an even higher desire for technologies to be implemented) and identify two distinct future scenarios. Thus, our study sheds light on multifaceted opinions towards technology diffusion in football. This unprecedented global perspective allows governing bodies to understand the industry's expectations, desires, and reservations, which helps building a better understanding for the needs within the global football ecosystems with respect to emerging technology innovations.

Suggested Citation

  • Beiderbeck, Daniel & Evans, Nicolas & Frevel, Nicolas & Schmidt, Sascha L., 2023. "The impact of technology on the future of football – A global Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    • Handle: RePEc:eee:tefoso:v:187:y:2023:i:c:s0040162522007077
    DOI: 10.1016/j.techfore.2022.122186
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162522007077
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2022.122186?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. Vanessa Ratten, 2019. "Sports Technology and Innovation," Springer Books, Springer, number 978-3-319-75046-0, November.
    2. Wladimir Andreff, 2001. "The Correlation between Economic Underdevelopment and Sport," Post-Print halshs-00274657, HAL.
    3. Barrios, Maite & Guilera, Georgina & Nuño, Laura & Gómez-Benito, Juana, 2021. "Consensus in the delphi method: What makes a decision change?," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    4. Saha Atanu & H. Alan Love & Robert Schwart, 1994. "Adoption of Emerging Technologies Under Output Uncertainty," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 76(4), pages 836-846.
    5. Warth, Johannes & von der Gracht, Heiko A. & Darkow, Inga-Lena, 2013. "A dissent-based approach for multi-stakeholder scenario development — The future of electric drive vehicles," Technological Forecasting and Social Change, Elsevier, vol. 80(4), pages 566-583.
    6. Steve J. Bickley & Ho Fai Chan & Sascha L. Schmidt & Benno Torgler, 2021. "Quantum-Sapiens: The Quantum Bases for Human Expertise, Knowledge, and Problem-Solving (Extended Version with Applications)," CREMA Working Paper Series 2021-14, Center for Research in Economics, Management and the Arts (CREMA).
    7. Aengenheyster, Stefan & Cuhls, Kerstin & Gerhold, Lars & Heiskanen-Schüttler, Maria & Huck, Jana & Muszynska, Monika, 2017. "Real-Time Delphi in practice — A comparative analysis of existing software-based tools," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 15-27.
    8. 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.
    9. Lago-Peñas Carlos & Rey Ezequiel & Kalén Anton, 2019. "How does Video Assistant Referee (VAR) modify the game in elite soccer?," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 19(4), pages 646-653, July.
    10. 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.
    11. Flostrand, Andrew & Pitt, Leyland & Bridson, Shannon, 2020. "The Delphi technique in forecasting– A 42-year bibliographic analysis (1975–2017)," Technological Forecasting and Social Change, Elsevier, vol. 150(C).
    12. 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.
    13. Beiderbeck, Daniel & Frevel, Nicolas & von der Gracht, Heiko A. & Schmidt, Sascha L. & Schweitzer, Vera M., 2021. "The impact of COVID-19 on the European football ecosystem – A Delphi-based scenario analysis," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    14. 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.
    15. 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.
    16. 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.
    17. Ngo, Liem Viet & O'Cass, Aron, 2013. "Innovation and business success: The mediating role of customer participation," Journal of Business Research, Elsevier, vol. 66(8), pages 1134-1142.
    18. Devansh Patel & Dhwanil Shah & Manan Shah, 2020. "The Intertwine of Brain and Body: A Quantitative Analysis on How Big Data Influences the System of Sports," Annals of Data Science, Springer, vol. 7(1), pages 1-16, March.
    19. von der Gracht, Heiko A., 2012. "Consensus measurement in Delphi studies," Technological Forecasting and Social Change, Elsevier, vol. 79(8), pages 1525-1536.
    20. Fariborz Damanpour & Kathryn A. Szabat & William M. Evan, 1989. "The Relationship Between Types Of Innovation And Organizational Performance," Journal of Management Studies, Wiley Blackwell, vol. 26(6), pages 587-602, November.
    21. Bonaccorsi, Andrea & Apreda, Riccardo & Fantoni, Gualtiero, 2020. "Expert biases in technology foresight. Why they are a problem and how to mitigate them," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    Full references (including those not matched with items on IDEAS)

    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. 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).
    2. 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).
    3. Beiderbeck, Daniel & Frevel, Nicolas & von der Gracht, Heiko A. & Schmidt, Sascha L. & Schweitzer, Vera M., 2021. "The impact of COVID-19 on the European football ecosystem – A Delphi-based scenario analysis," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    4. 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).
    5. Berbel-Vera, Jose & Barrachina Palanca, Mercedes & Gonzalez-Sanchez, Maria Beatriz, 2022. "Key CDO functions for successful digital transformation: Insights from a Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    6. 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.
    7. 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.
    8. 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).
    9. 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).
    10. 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).
    11. 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).
    12. Akartuna, Eray Arda & Johnson, Shane D. & Thornton, Amy, 2022. "Preventing the money laundering and terrorist financing risks of emerging technologies: An international policy Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    13. 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.
    14. 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.
    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. Xu, Jinou & Pero, Margherita & Fabbri, Margherita, 2023. "Unfolding the link between big data analytics and supply chain planning," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    17. Menéndez-Caravaca, Eloísa & Bueno, Salvador & Gallego, M. Dolores, 2021. "Exploring the link between free and open source software and the collaborative economy: A Delphi-based scenario for the year 2025," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    18. Prommer, Lisa & Tiberius, Victor & Kraus, Sascha, 2020. "Exploring the future of startup leadership development," Journal of Business Venturing Insights, Elsevier, vol. 14(C).
    19. Meissner, Philip & Brands, Christian & Wulf, Torsten, 2017. "Quantifiying blind spots and weak signals in executive judgment: A structured integration of expert judgment into the scenario development process," International Journal of Forecasting, Elsevier, vol. 33(1), pages 244-253.
    20. 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.

    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:187:y:2023:i:c:s0040162522007077. 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.