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ORIGINAL RESEARCH article

Front. Mar. Sci.
Sec. Marine Affairs and Policy
Volume 11 - 2024 | doi: 10.3389/fmars.2024.1509523
This article is part of the Research Topic Challenges and Solutions in Forecasting and Decision-Making in Marine Economy and Management View all 20 articles

Spatiotemporal Characteristics and Influencing Factors of China's Knowledge Spillover Network of the Marine Industry

Provisionally accepted

The final, formatted version of the article will be published soon.

    Using social network analysis, spatial econometric method and structural equation model, based on the patent citation data of China's marine industry from 2008 to 2019, this paper analyzes the temporal-spatial characteristics and influencing factors of knowledge spillover network of marine Industry in China. The results show that: the knowledge spillover network with Qingdao, Beijing and Shanghai as the main distribution centers has expanded rapidly, and the network status of Zhoushan, Wuhan and other cities has improved significantly. The network space structure tends to be multi-core and complex, extending from coast to inland; There are significant differences in cyberspace. The central and western regions are low value areas, while the eastern region is the core area, and the core cities have built an "X" shaped spatial structure with Qingdao as the intersection；Knowledge proximity, social proximity, cognitive proximity and economic proximity are important factors that affect knowledge spillover networks. Geographic proximity has a reinforcing effect on knowledge proximity and economic proximity. This paper is beneficial in that it provides a reference and experience for the innovation of the marine industry and the high-quality development of the marine economy by effectively analyzing the spatio-temporal characteristics and influencing factors of China's marine knowledge diffusion network.

    Keywords: knowledge spillover network, Marine industry, Spatiotemporal characteristics, Influencing factors, social network analysis, structural equation model

    Received: 11 Oct 2024; Accepted: 12 Dec 2024.

    Copyright: © 2024 Liu, Zhang, Peng, Xie, Du and Tan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Kai Liu, Liaoning Normal University, Dalian, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.