IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v24y2024i2d10.1007_s10660-023-09686-5.html
   My bibliography  Save this article

Application of data elements in the coupling of finance and technology on the digital electronic platform

Author

Listed:
  • Wenjun Xie

    (Wuhan University of Technology)

  • Renxiang Wang

    (Wuhan University of Technology)

Abstract

With the continuous development of Internet technology, all areas of social economy have undergone profound changes. The circulation and application of data elements can significantly promote the construction of infrastructure such as big data centers and mobile base stations, and stimulate the market demand for digital consumption such as digital production, e-commerce and digital trade. Data mining technology will provide sustainable impetus for economic development. There is a clearer analysis of the role of data elements in the platform economy, but there is a lack of research in macroeconomics, especially in the coupling of finance and technology. This paper developed a coupling coordination and a coupling efficiency index system, using different methods to measure outcomes and outline the path to achieving high "quantity" and "quality" of finance-technology coupling development. Based on the data mining technology in e-commerce, the fuzzy set qualitative comparative analysis method is used to analyze the complex mechanisms and driving paths of data elements affecting the coordination degree and coupling efficiency of finance-technology coupling performance of China. We empirically document that data mining and data management are necessary to improve coupling coordination; in the absence of other conditions, data mining or data management can produce high coupling coordination, but not sufficient to improve coupling efficiency.

Suggested Citation

  • Wenjun Xie & Renxiang Wang, 2024. "Application of data elements in the coupling of finance and technology on the digital electronic platform," Electronic Commerce Research, Springer, vol. 24(2), pages 1435-1460, June.
  • Handle: RePEc:spr:elcore:v:24:y:2024:i:2:d:10.1007_s10660-023-09686-5
    DOI: 10.1007/s10660-023-09686-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-023-09686-5
    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/s10660-023-09686-5?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. Thomas Niebel & Fabienne Rasel & Steffen Viete, 2019. "BIG data – BIG gains? Understanding the link between big data analytics and innovation," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 28(3), pages 296-316, April.
    2. Xu, Ronghua & Shen, Yuxin & Liu, Meng & Li, Lu & Xia, Xuehua & Luo, Kaixin, 2023. "Can government subsidies improve innovation performance? Evidence from Chinese listed companies," Economic Modelling, Elsevier, vol. 120(C).
    3. Constantiou, Ioanna D & Kallinikos, Jannis, 2015. "New games, new rules: big data and the changing context of strategy," LSE Research Online Documents on Economics 63017, London School of Economics and Political Science, LSE Library.
    4. Maksim Malyy & Zeljko Tekic & Tatiana Podladchikova, 2021. "The value of big data for analyzing growth dynamics of technology based new ventures," Papers 2104.03053, arXiv.org.
    5. Kim Hua Tan & Guojun Ji & Chee Peng Lim & Ming-Lang Tseng, 2017. "Using big data to make better decisions in the digital economy," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 4998-5000, September.
    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. Erdsiek, Daniel & Rost, Vincent, 2022. "Datenbewirtschaftung in deutschen Unternehmen: Umfrageergebnisse zu Status-quo und mittelfristigem Ausblick," ZEW Expert Briefs 22-09, ZEW - Leibniz Centre for European Economic Research.
    2. Hassan Keshavarz & Akbariah Mohd Mahdzir & Hosna Talebian & Neda Jalaliyoon & Naoki Ohshima, 2021. "The Value of Big Data Analytics Pillars in Telecommunication Industry," Sustainability, MDPI, vol. 13(13), pages 1-36, June.
    3. Sundberg, Leif & Holmström, Jonny, 2023. "Democratizing artificial intelligence: How no-code AI can leverage machine learning operations," Business Horizons, Elsevier, vol. 66(6), pages 777-788.
    4. Irene Bertschek & Joern Block & Alexander S. Kritikos & Caroline Stiel, 2024. "German financial state aid during Covid-19 pandemic: Higher impact among digitalized self-employed," Entrepreneurship & Regional Development, Taylor & Francis Journals, vol. 36(1-2), pages 76-97, January.
    5. Tadas Limba & Andrejus Novikovas & Andrius Stankevičius & Antanas Andrulevičius & Manuela Tvaronavičienė, 2020. "Big Data Manifestation in Municipal Waste Management and Cryptocurrency Sectors: Positive and Negative Implementation Factors," Sustainability, MDPI, vol. 12(7), pages 1-14, April.
    6. Christian Rammer & Gastón P Fernández & Dirk Czarnitzki, 2021. "Artificial Intelligence and Industrial Innovation: Evidence from Firm-Level Data," Working Papers of Department of Economics, Leuven 674605, KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven.
    7. Gangopadhyay, Partha & Jain, Siddharth & Bakry, Walid, 2022. "In search of a rational foundation for the massive IT boom in the Australian banking industry: Can the IT boom really drive relationship banking?," International Review of Financial Analysis, Elsevier, vol. 82(C).
    8. Radicic, Dragana & Petković, Saša, 2023. "Impact of digitalization on technological innovations in small and medium-sized enterprises (SMEs)," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    9. Tan, Kim Hua, 2023. "Building Supply Chain Resilience with Digitalization," ADBI Working Papers 1389, Asian Development Bank Institute.
    10. Czarnitzki, Dirk & Fernández, Gastón P. & Rammer, Christian, 2023. "Artificial intelligence and firm-level productivity," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 188-205.
    11. Pratima (Tima) Bansal & Jury Gualandris & Nahyun Kim, 2020. "Theorizing Supply Chains with Qualitative Big Data and Topic Modeling," Journal of Supply Chain Management, Institute for Supply Management, vol. 56(2), pages 7-18, April.
    12. Koski, Heli & Fornaro, Paolo, 2024. "Digitalization and Resilience: Data Assets and Firm Productivity Growth During the COVID-19 Pandemic," ETLA Working Papers 113, The Research Institute of the Finnish Economy.
    13. Luigi M. De Luca & Dennis Herhausen & Gabriele Troilo & Andrea Rossi, 2021. "How and when do big data investments pay off? The role of marketing affordances and service innovation," Journal of the Academy of Marketing Science, Springer, vol. 49(4), pages 790-810, July.
    14. Seddon, Jonathan J.J.M. & Currie, Wendy L., 2017. "A model for unpacking big data analytics in high-frequency trading," Journal of Business Research, Elsevier, vol. 70(C), pages 300-307.
    15. Venturini, Francesco, 2022. "Intelligent technologies and productivity spillovers: Evidence from the Fourth Industrial Revolution," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 220-243.
    16. Pan, Qiaohong & Luo, Wenping & Fu, Yi, 2022. "A csQCA study of value creation in logistics collaboration by big data: A perspective from companies in China," Technology in Society, Elsevier, vol. 71(C).
    17. Barbara Brenner, 2018. "Transformative Sustainable Business Models in the Light of the Digital Imperative—A Global Business Economics Perspective," Sustainability, MDPI, vol. 10(12), pages 1-25, November.
    18. Delera, Michele & Pietrobelli, Carlo & Calza, Elisa & Lavopa, Alejandro, 2022. "Does value chain participation facilitate the adoption of Industry 4.0 technologies in developing countries?," World Development, Elsevier, vol. 152(C).
    19. Kim, Jaemin & Dibrell, Clay & Kraft, Ellen & Marshall, David, 2021. "Data analytics and performance: The moderating role of intuition-based HR management in major league baseball," Journal of Business Research, Elsevier, vol. 122(C), pages 204-216.
    20. Mohd Syaiful Rizal Abd Hamid & Nor Ratna Masrom & Nur Athirah Binti Mazlan, 2022. "The Key Factors of the Industrial Revolution 4.0 in the Malaysian Smart Manufacturing Context," International Journal of Asian Business and Information Management (IJABIM), IGI Global, vol. 13(2), pages 1-19, August.

    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:elcore:v:24:y:2024:i:2:d:10.1007_s10660-023-09686-5. 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.