IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v88y2023icp1151-1172.html

Evolution of digital economy research: A bibliometric analysis

Author

Listed:
  • Xia, Yan
  • Lv, Gongming
  • Wang, Huijuan
  • Ding, Lin

Abstract

As a new economic form, the digital economy has become an important manifestation of national comprehensive strength, which is an absolute key force to enhance international competitiveness and reshape the international economic landscape in the digital era. Hundreds of schools of thought contend on the digital economy, and a consensus theory and framework has not yet been formed. In order to clarify the development venation and research status of the digital economy, and highlight the key points of digital economy research field in the future, this paper conducts bibliometric analysis and visualization by using Cite Space on digital economy connotation and extension mainly from four respects: the period characteristics of published articles, the distribution characteristics of articles, the characteristics of keyword changes, and the evolution characteristics of research directions. We take the WOS Core Collection as the database, and sets the subject headings with the digital economy connotation and extension as the retrieval target, and finally obtained 918 and 10,735 articles respectively as of 2022. We find that (a) the research on the connotation of the digital economy has experienced a long incubation and germination period, while the denotation period has maintained a long period of popularity, and both have ushered in a research climax in recent years; (b) the research team on the connotation of the digital economy is relatively scattered, and there is no unified consensus on the connotation of the digital economy, while the connection between the denotation period teams is relatively close; (c) from the perspective of keywords, “internet” and “big data” have caused a local upsurge in digital economy research. Otherwise, the denotation period hotspot of the digital economy is about 15 years earlier than the connotation research on average, which provides fertile soil for the formation, development and maturity of the digital economy connotation; (d) judging from the citation frequency of references, the total number of research articles on the connotation of the digital economy published after 2017 is relatively high and relatively concentrated, and a consensus on the understanding of the connotation of the digital economy has begun to form; (e) from the perspective of the evolution of research directions, “Information Science Library Science”, “Computer Science” and “Government Law” are the research hotspots in recent years, and “Public Administration” and “Engineering” may be the research growth points in the next few years.

Suggested Citation

  • Xia, Yan & Lv, Gongming & Wang, Huijuan & Ding, Lin, 2023. "Evolution of digital economy research: A bibliometric analysis," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 1151-1172.
  • Handle: RePEc:eee:reveco:v:88:y:2023:i:c:p:1151-1172
    DOI: 10.1016/j.iref.2023.07.051
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1059056023002599
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.iref.2023.07.051?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    2. Romeo Turcan & Anita Juho, 2014. "What happens to international new ventures beyond start-up: An exploratory study," Journal of International Entrepreneurship, Springer, vol. 12(2), pages 129-145, June.
    3. Xie, Mengmeng & Ding, Lin & Xia, Yan & Guo, Jianfeng & Pan, Jiaofeng & Wang, Huijuan, 2021. "Does artificial intelligence affect the pattern of skill demand? Evidence from Chinese manufacturing firms," Economic Modelling, Elsevier, vol. 96(C), pages 295-309.
    4. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The skill content of recent technological change: an empirical exploration," Proceedings, Federal Reserve Bank of San Francisco, issue nov.
    5. Acemoglu, Daron & Autor, David, 2011. "Skills, Tasks and Technologies: Implications for Employment and Earnings," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 12, pages 1043-1171, Elsevier.
    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. Tian, Lichuan & Sun, Kai & Yang, Jie & Zhao, Yang, 2024. "Does digital economy affect corporate ESG performance? New insights from China," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 964-980.
    2. Guan, Kaolei & Fu, Mengting & Zhu, Haining, 2024. "Opportunistic behaviour behind corporate digitalization disclosure: The moderating role of economic policy uncertainty," Finance Research Letters, Elsevier, vol. 66(C).
    3. Xiaohui Xin & Ruoyu Zhu & Guoli Ou, 2025. "Does the opening of high-speed rail speed up the development of urban digital economy?," Economic Change and Restructuring, Springer, vol. 58(2), pages 1-24, April.
    4. Jinqi Su & Changhong Dong & Ke Su & Lin He, 2023. "Research on the Construction of Digital Economy Index System Based on K-means-SA Algorithm," SAGE Open, , vol. 13(4), pages 21582440231, December.
    5. Ma, Bianjing & Chen, Lei & Wang, Xiaohui & Ding, Song, 2024. "Who benefits more from the digital economy: (Non-)Cognitive ability and the labor income premium," International Review of Economics & Finance, Elsevier, vol. 96(PB).
    6. Peng, Zhen & Bai, Fan & Zhao, Feng, 2024. "Digital finance, life cycle, and enterprise mergers and acquisitions," Finance Research Letters, Elsevier, vol. 67(PB).
    7. Wen, Ting & Qi, Sinan & Qian, Yue, 2024. "Index measurement and analysis on spatial-temporal evolution of China's new economy based on the DPSIR model," International Review of Economics & Finance, Elsevier, vol. 90(C), pages 252-264.
    8. D. Doncheva & V. Zheleva & M. Karaboytcheva, 2025. "The Information Economy in the Age of Digitalization: Key Characteristics, Distinctions and Development Trends," Journal of Risk & Control, SCIENPRESS Ltd, vol. 12(1), pages 1-2.
    9. Wang, Yu & Dong, Fan, 2025. "How intellectual property protection enhances data capitalization: The mediating effects of resources and willingness to capitalize on data," International Review of Financial Analysis, Elsevier, vol. 107(C).
    10. Liu, Xuyi & Cui, Wentian & Zhang, Shun, 2025. "Better e-commerce less carbon emissions in China?," Energy, Elsevier, vol. 318(C).
    11. Li Sun & Wenjun Cui & Yang Li & Yueli Luo, 2025. "Understanding the energy poverty in China: chronic measurement and the effect of the digital economy," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(12), pages 29439-29470, December.

    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. Ari Hyytinen & Petri Rouvinen & Mika Pajarinen & Joosua Virtanen, 2023. "Ex Ante Predictability of Rapid Growth: A Design Science Approach," Entrepreneurship Theory and Practice, , vol. 47(6), pages 2465-2493, November.
    2. Lyu, Wenjing & Liu, Jin, 2021. "Artificial Intelligence and emerging digital technologies in the energy sector," Applied Energy, Elsevier, vol. 303(C).
    3. Jean-Philippe Deranty & Thomas Corbin, 2022. "Artificial Intelligence and work: a critical review of recent research from the social sciences," Papers 2204.00419, arXiv.org.
    4. Tommaso AGASISTI & Geraint JOHNES & Marco PACCAGNELLA, 2021. "Tasks, occupations and wages in OECD countries," International Labour Review, International Labour Organization, vol. 160(1), pages 85-112, March.
    5. Loebbing, Jonas, 2018. "An Elementary Theory of Endogenous Technical Change and Wage Inequality," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181603, Verein für Socialpolitik / German Economic Association.
    6. Rabensteiner, Thomas & Guschanski, Alexander, 2022. "Autonomy and wage divergence: evidence from European survey data," Greenwich Papers in Political Economy 37925, University of Greenwich, Greenwich Political Economy Research Centre.
    7. Aleksandra Parteka & Joanna Wolszczak-Derlacz, 2020. "Wage response to global production links: evidence for workers from 28 European countries (2005–2014)," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 156(4), pages 769-801, November.
    8. David Hémous & Morten Olsen, 2022. "The Rise of the Machines: Automation, Horizontal Innovation, and Income Inequality," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(1), pages 179-223, January.
    9. Battisti, Michele & Gatto, Massimo Del & Parmeter, Christopher F., 2022. "Skill-biased technical change and labor market inefficiency," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    10. Antje Mertens & Laura Romeu-Gordo, 2023. "Retirement in Western Germany – How Workplace Tasks Influence Its Timing," Work, Employment & Society, British Sociological Association, vol. 37(2), pages 467-485, April.
    11. Matthew Ross, 2021. "The Effect of Intensive Margin Changes to Task Content on Employment Dynamics over the Business Cycle," ILR Review, Cornell University, ILR School, vol. 74(4), pages 1036-1064, August.
    12. Francesco Vona & Giovanni Marin & Davide Consoli, 2019. "Measures, drivers and effects of green employment: evidence from US local labor markets, 2006–2014," Journal of Economic Geography, Oxford University Press, vol. 19(5), pages 1021-1048.
    13. Daniele Angelini, 2023. "Aging Population and Technology Adoption," Working Paper Series of the Department of Economics, University of Konstanz 2023-01, Department of Economics, University of Konstanz.
    14. Caitlin Allen Whitehead & Haroon Bhorat & Robert Hill & Tim Köhler & François Steenkamp, 2021. "The Potential Employment Implications of the Fourth Industrial Revolution Technologies: The Case of the Manufacturing, Engineering and Related Services Sector," Working Papers 202106, University of Cape Town, Development Policy Research Unit.
    15. Kudoh, Noritaka & Miyamoto, Hiroaki, 2025. "Robots, AI, and unemployment," Journal of Economic Dynamics and Control, Elsevier, vol. 174(C).
    16. Daron Acemoglu & Gino Gancia & Fabrizio Zilibotti, 2015. "Offshoring and Directed Technical Change," American Economic Journal: Macroeconomics, American Economic Association, vol. 7(3), pages 84-122, July.
    17. Christoph Riedl & Eric Bogert, 2024. "Who Benefits from AI? Self-Selection, Skill Gap, and the Hidden Costs of AI Feedback," Papers 2409.18660, arXiv.org, revised Apr 2026.
    18. T. Gries & R. Grundmann & I. Palnau & M. Redlin, 2017. "Innovations, growth and participation in advanced economies - a review of major concepts and findings," International Economics and Economic Policy, Springer, vol. 14(2), pages 293-351, April.
    19. Speer, Jamin D., 2016. "How bad is occupational coding error? A task-based approach," Economics Letters, Elsevier, vol. 141(C), pages 166-168.
    20. Vahagn Jerbashian, 2019. "Automation and Job Polarization: On the Decline of Middling Occupations in Europe," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(5), pages 1095-1116, October.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:eee:reveco:v:88:y:2023:i:c:p:1151-1172. 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.elsevier.com/locate/inca/620165 .

    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.