Based on Text Mining and Analysis of Beijing’s Achievement Transformation Policy Research

Zihao Cao (), Xinfeng Wang () and Jian Ma ()
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Zihao Cao: North China University of Technology
Xinfeng Wang: North China University of Technology
Jian Ma: North China University of Technology

A chapter in Proceedings of the 2023 5th International Conference on Economic Management and Cultural Industry (ICEMCI 2023), 2024, pp 480-485 from Springer

Abstract: Abstract This study utilizes text mining to analyze Beijing’s technology transfer policies from 2006–2023. Frequency and semantic network analysis provide an in-depth investigation of the regional policy landscape. The study found frequent keywords like innovation, development, enterprise, reflecting policies aim to support enterprise innovation. Network shows continuous promotion across financial support, tax incentives, IP protection to integrate R&D with economic and social development. Beijing’s policies have achieved remarkable results and exemplify nationwide leadership. Suggestions proposed to further improve policies include increasing fiscal support, establishing evaluation incentives, simplifying approvals, strengthening industry-academia ties, and cultivating intermediaries.

Keywords: technology transfer policies; text mining; word frequency analysis; semantic network analysis (search for similar items in EconPapers)
Date: 2024
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DOI: 10.2991/978-94-6463-368-9_56

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