IDEAS home Printed from https://ideas.repec.org/h/spr/lnopch/978-981-97-4045-1_5.html
   My bibliography  Save this book chapter

Text Mining and Quantitative Analysis of Regional Public Brand Building Policies for Agricultural Products in China

In: Liss 2023

Author

Listed:
  • Yuling Sun

    (Nanjing University of Technology)

  • Xueying Hu

    (Nanjing University of Technology)

Abstract

The establishment of a regional common brand for agricultural products plays a crucial role in achieving agricultural and rural modernization and promoting the industrialization of agricultural products on a large scale. The branding of agricultural products enables the conversion of regional industrial advantages into market value, making it an essential tool for driving, integrating, and advancing the development of rural industries. The central government and various levels of government have implemented numerous policies aimed at fostering the development of regional public brands for agricultural products. Assessing and analyzing these policies holds significant importance in enhancing the formulation of effective policies. Therefore, this study employs a combination of quantitative and qualitative methods to examine the policy tools related to regional public branding of agricultural products. A coding system for these policy tools is developed using 30 policy documents as research samples, and content analysis is conducted to categorize and analyze the textual data. Based on the coding, semantic network analysis and co-occurrence analysis are done for different types of policy instruments. It is analyzed that the current policy instruments are mainly of coercive type, supplemented by hybrid type, and less of voluntary type. Additionally, the policy instruments exhibit a structural imbalance. It is observed that the coercive and hybrid policy instruments align more effectively with the policy objectives, whereas the voluntary policy instruments do not align as well with the policy objectives.

Suggested Citation

  • Yuling Sun & Xueying Hu, 2024. "Text Mining and Quantitative Analysis of Regional Public Brand Building Policies for Agricultural Products in China," Lecture Notes in Operations Research, in: Daqing Gong & Yixuan Ma & Xiaowen Fu & Juliang Zhang & Xiaopu Shang (ed.), Liss 2023, pages 67-77, Springer.
    • Handle: RePEc:spr:lnopch:978-981-97-4045-1_5
    DOI: 10.1007/978-981-97-4045-1_5
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:lnopch:978-981-97-4045-1_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.

    We have no bibliographic references for this item. You can help adding them by using 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.