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author:

Lin, L. (Lin, L..) [1] | Chen, Y. (Chen, Y..) [2]

Indexed by:

Scopus

Abstract:

Original-innovation talent significantly influences a nation’s competitive edge. Consequently, supportive policies towards original-innovation talents are critical for the enhancement of national innovation governance. This study utilised a Latent Dirichlet Allocation (LDA) model to conduct a comprehensive analysis of the development and evolution of 334 original-innovation talent policies enacted in China from 1978 to 2022, where we scrutinised the features and progression of China’s original-innovation talent policies and recapitulated the course of their maturation. Semantic analysis was employed to pinpoint the keyword characteristics of these policies, while the LDA model was utilised to extract relevant information, study the evolution of topic intensity, and dissect the fluctuation of topic intensity across various periods. The results indicated that China’s original-innovation talent policies have metamorphosed in alignment with the nation's development objectives, which now focus predominantly on the support of breakthroughs in key core technologies and basic research. The paper concludes with suggestions for strengthening the top-level design of policies, optimising incentive policies, and creating a supportive environment for original innovation. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Keyword:

content analysis LDA Talents policy topic evolution

Community:

  • [ 1 ] [Lin L.]School of Economics and Management, Fuzhou University, Fuzhou, China
  • [ 2 ] [Chen Y.]School of Economics and Management, Fuzhou University, Fuzhou, China

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Source :

Technology Analysis and Strategic Management

ISSN: 0953-7325

Year: 2023

Issue: 12

Volume: 36

Page: 4128-4143

2 . 9

JCR@2023

2 . 9 0 0

JCR@2023

ESI HC Threshold:29

JCR Journal Grade:2

CAS Journal Grade:4

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 3

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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