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

Yuling Sun () and Xueying Hu ()
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Yuling Sun: Nanjing University of Technology
Xueying Hu: Nanjing University of Technology

A chapter in LISS 2023, 2024, pp 67-77 from Springer

Abstract: 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.

Keywords: Regional public brands of agricultural products; Policy tools; Semantic network analysis; Co-occurrence analysis (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-981-97-4045-1_5

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