IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v157y2020ics0040162520309422.html
   My bibliography  Save this article

A bibliometrics-based research framework for exploring policy evolution: A case study of China's information technology policies

Author

Listed:
  • Yang, Chao
  • Huang, Cui
  • Su, Jun

Abstract

Qualitative methods for analyzing policy evolution are often unequipped to process high volumes of policy texts that involve many domains and long timespans. This makes it difficult to take full advantage of the semantic information contained in policy literature. It is also difficult to use traditional qualitative methods to systematically analyze the characteristics of a complex policy mix network, such as the locations, evolution, and relationships between policy actors/targets. In order to address these issues, we propose a bibliometrics-based research framework for exploring policy evolution. We first collect all relevant policy documents from a target domain. We then construct networks of policymaker based on co-occurrence relationships in policy promulgation, in order to determine core policymakers, as well as changes in their status in the networks over time. Lastly, we use semantic analysis to identify policy targets and construct policy target keyword co-occurrence networks for discrete time periods. The evolution of a specific policy domain can then be examined based on changes in network centrality. Information technology policies in China were used as a case study to demonstrate the reliability of our method. The results reflect the practical value of using this method for the quantitative analysis of policy documents.

Suggested Citation

  • Yang, Chao & Huang, Cui & Su, Jun, 2020. "A bibliometrics-based research framework for exploring policy evolution: A case study of China's information technology policies," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    • Handle: RePEc:eee:tefoso:v:157:y:2020:i:c:s0040162520309422
    DOI: 10.1016/j.techfore.2020.120116
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162520309422
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2020.120116?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. Cui Huang & Jun Su & Xiang Xie & Xuanting Ye & Zhang Li & Alan Porter & Jiang Li, 2015. "A bibliometric study of China’s science and technology policies: 1949–2010," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(2), pages 1521-1539, February.
    2. Lutz Bornmann & Robin Haunschild & Werner Marx, 2016. "Policy documents as sources for measuring societal impact: how often is climate change research mentioned in policy-related documents?," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1477-1495, December.
    3. Cui Huang & Jun Su & Xiang Xie & Jiang Li, 2014. "Basic research is overshadowed by applied research in China: a policy perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(3), pages 689-694, June.
    4. Marja-Liisa Niinikoski & Stefan Kuhlmann, 2015. "In discursive negotiation: Knowledge and the formation of Finnish innovation policy," Science and Public Policy, Oxford University Press, vol. 42(1), pages 86-106.
    5. Laver, Michael & Benoit, Kenneth & Garry, John, 2003. "Extracting Policy Positions from Political Texts Using Words as Data," American Political Science Review, Cambridge University Press, vol. 97(2), pages 311-331, May.
    6. Niinikoski, Marja-Liisa & Moisander, Johanna, 2014. "Serial and comparative analysis of innovation policy change," Technological Forecasting and Social Change, Elsevier, vol. 85(C), pages 69-80.
    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. Wang, Jiaxi & Zhang, Jingjing, 2023. "The impact of policy collaboration networks and policy topic networks on policy diffusion: Empirical evidence from the energy field," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    2. Safdar, Sarah & Ren, Minglun & Chudhery, Muhammad Adnan Zahid & Huo, Jiazhen & Rehman, Hakeem-Ur & Rafique, Raza, 2022. "Using cloud-based virtual learning environments to mitigate increasing disparity in urban-rural academic competence," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    3. Yang, Chao & Huang, Cui, 2022. "Quantitative mapping of the evolution of AI policy distribution, targets and focuses over three decades in China," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    4. Planells-Artigot, Enrique & Ortigosa-Blanch, Arturo & Martí-Sánchez, Myriam, 2021. "Bridging fields: A comparative study of the presence of think tanks," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    5. Meng, Jia-Hui & Wang, Jian, 2023. "The policy trajectory of dual-use technology integration governance in China: A sequential analysis of policy evolution," Technology in Society, Elsevier, vol. 72(C).
    6. Ba, Zhichao & Ma, Yaxue & Cai, Jinyao & Li, Gang, 2023. "A citation-based research framework for exploring policy diffusion: Evidence from China's new energy policies," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    7. Zeba, Gordana & Dabić, Marina & Čičak, Mirjana & Daim, Tugrul & Yalcin, Haydar, 2021. "Technology mining: Artificial intelligence in manufacturing," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    8. Yiwen Liu & Jian Li & Yi Xu, 2022. "Quantitative Evaluation of High-Tech Industry Policies Based on the PMC-Index Model: A Case Study of China’s Beijing-Tianjin-Hebei Region," Sustainability, MDPI, vol. 14(15), pages 1-17, July.

    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. Yang, Chao & Huang, Cui, 2022. "Quantitative mapping of the evolution of AI policy distribution, targets and focuses over three decades in China," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    2. Cui Huang & Chao Yang & Jun Su, 2018. "Policy change analysis based on “policy target–policy instrument” patterns: a case study of China’s nuclear energy policy," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 1081-1114, November.
    3. Huang, Cui & Yang, Chao & Su, Jun, 2021. "Identifying core policy instruments based on structural holes: A case study of China’s nuclear energy policy," Journal of Informetrics, Elsevier, vol. 15(2).
    4. Ba, Zhichao & Ma, Yaxue & Cai, Jinyao & Li, Gang, 2023. "A citation-based research framework for exploring policy diffusion: Evidence from China's new energy policies," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    5. Armèn Hakhverdian, 2009. "Capturing Government Policy on the Left–Right Scale: Evidence from the United Kingdom, 1956–2006," Political Studies, Political Studies Association, vol. 57(4), pages 720-745, December.
    6. Simon Hug & Tobias Schulz, 2007. "Referendums in the EU’s constitution building process," The Review of International Organizations, Springer, vol. 2(2), pages 177-218, June.
    7. Pongsak Luangaram & Yuthana Sethapramote, 2016. "Central Bank Communication and Monetary Policy Effectiveness: Evidence from Thailand," PIER Discussion Papers 20, Puey Ungphakorn Institute for Economic Research.
    8. Rybinski, Krzysztof, 2020. "The forecasting power of the multi-language narrative of sell-side research: A machine learning evaluation," Finance Research Letters, Elsevier, vol. 34(C).
    9. Hamza Bennani, 2012. "National influences inside the ECB: an assessment from central bankers' statements," Working Papers hal-00992646, HAL.
    10. Kenneth Benoit & Michael Laver & Christine Arnold & Paul Pennings & Madeleine O. Hosli, 2005. "Measuring National Delegate Positions at the Convention on the Future of Europe Using Computerized Word Scoring," European Union Politics, , vol. 6(3), pages 291-313, September.
    11. Weiss, Max & Zoorob, Michael, 2021. "Political frames of public health crises: Discussing the opioid epidemic in the US Congress," Social Science & Medicine, Elsevier, vol. 281(C).
    12. Matthew Gentzkow & Jesse M. Shapiro & Matt Taddy, 2019. "Measuring Group Differences in High‐Dimensional Choices: Method and Application to Congressional Speech," Econometrica, Econometric Society, vol. 87(4), pages 1307-1340, July.
    13. Shengli Dai & Weimin Zhang & Jiamin Zong & Yingying Wang & Ge Wang, 2021. "How Effective Is the Green Development Policy of China’s Yangtze River Economic Belt? A Quantitative Evaluation Based on the PMC-Index Model," IJERPH, MDPI, vol. 18(14), pages 1-17, July.
    14. Ralf Meinhardt & Sebastian Junge & Martin Weiss, 2018. "The organizational environment with its measures, antecedents, and consequences: a review and research agenda," Management Review Quarterly, Springer, vol. 68(2), pages 195-235, April.
    15. Torsten J. Selck, 2005. "Improving the Explanatory Power of Bargaining Models," Journal of Theoretical Politics, , vol. 17(3), pages 371-375, July.
    16. Cory Koedel & Jiaxi Li & Matthew G. Springer & Li Tan, 2018. "Teacher Performance Ratings and Professional Improvement," Working Papers 1808, Department of Economics, University of Missouri.
    17. Kayla N. Jordan & James W. Pennebaker & Chase Ehrig, 2018. "The 2016 U.S. Presidential Candidates and How People Tweeted About Them," SAGE Open, , vol. 8(3), pages 21582440187, July.
    18. Michael Evans & Wayne McIntosh & Jimmy Lin & Cynthia Cates, 2007. "Recounting the Courts? Applying Automated Content Analysis to Enhance Empirical Legal Research," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 4(4), pages 1007-1039, December.
    19. Diana Hicks & Julia Melkers & Kimberley R. Isett, 2019. "A characterization of professional media and its links to research," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 827-843, May.
    20. Hayo, Bernd & Henseler, Kai & Rapp, Marc Steffen, 2019. "Estimating the monetary policy interest-rate-to-performance sensitivity of the European banking sector at the zero lower bound," Finance Research Letters, Elsevier, vol. 31(C).

    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:157:y:2020:i:c:s0040162520309422. 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.