A spatial-temporal network analysis of patent transfers from US universities to firms

被引:14
|
作者
Hu, Tao [1 ,2 ]
Zhang, Yin [3 ]
机构
[1] Harvard Univ, Ctr Geog Anal, Cambridge, MA 02138 USA
[2] Wuhan Univ, Geocomputat Ctr Social Sci, Wuhan 430079, Hubei, Peoples R China
[3] Kent State Univ, Sch Informat, Kent, OH 44242 USA
关键词
Patent transfer; University technology transfer; Spatial– temporal analysis; Network analysis; Firms; U; S; Patent and Trademark Office (USPTO); TECHNOLOGY-TRANSFER OFFICES; KNOWLEDGE TRANSFER; UNITED-STATES; INNOVATION; PERFORMANCE;
D O I
10.1007/s11192-020-03745-6
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Universities play an important role in innovation development and are being recognized as a critical element for the global competitiveness of firms. However, there have been very few large-scale empirical studies using public patent transfer datasets to examine patent transfers from universities to firms. This study proposes a workflow that maps and integrates U.S. Patent and Trademark Office issued patent records with patent assignment datasets to result in the study data covering patents and their transfer transactions from 1990 to 2016. This study focuses on patent transfers from U.S. universities for a spatial-temporal analysis at three levels: institutional, state, and national. In addition, the study identifies a technology-oriented network among universities, firms, and technological areas and supports the notion that patent transfers coincide with the development and change of a local region and are affected and driven by policies, economic development, and cultural factors. This study reveals that the geographical distance of patent transfers has been shortened over time, suggesting more local and regional collaborations among universities and businesses. The results of the study can help identify emerging development fields in a given region, potentially leading to policy applications for research and development, strategic planning, and building effective collaboration networks between universities and businesses.
引用
收藏
页码:27 / 54
页数:28
相关论文
共 50 条
  • [31] Spatial-Temporal Evolution Analysis of Carbon Emissions Embodied in Inter-Provincial Trade in China
    Wang, Tianrui
    Chen, Yu
    Zeng, Leya
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2022, 19 (11)
  • [32] Effects of high-tech industrial agglomeration and innovation on regional economic development in China: Evidence from spatial-temporal analysis and Spatial Durbin Model
    Huang, Donglan
    Xu, Guoteng
    Li, Chengjiang
    Yang, Shu
    ECONOMIC ANALYSIS AND POLICY, 2025, 86 : 692 - 712
  • [33] A multi-level spatial-temporal model for freight movement: The case of manufactured goods flows on the US highway networks
    Shen, Guoqiang
    Zhou, Long
    Aydin, Saniye Gizem
    JOURNAL OF TRANSPORT GEOGRAPHY, 2020, 88
  • [34] Ship energy consumption analysis and carbon emission exploitation via spatial-temporal maritime data
    Chen, Xinqiang
    Lv, Siying
    Shang, Wen -long
    Wu, Huafeng
    Xian, Jiangfeng
    Song, Chengcheng
    APPLIED ENERGY, 2024, 360
  • [35] Water use efficiency evolution in the Yellow River Basin: an integrated analysis of spatial-temporal decomposition
    Sun, Siao
    HYDROLOGICAL SCIENCES JOURNAL, 2023, 68 (01) : 119 - 130
  • [36] Integrating deep learning, satellite image processing, and spatial-temporal analysis for urban flood prediction
    Mohamadiazar, Nasim
    Ebrahimian, Ali
    Hosseiny, Hossein
    JOURNAL OF HYDROLOGY, 2024, 639
  • [37] Attention-based spatial-temporal adaptive dual-graph convolutional network for traffic flow forecasting
    Xia, Dawen
    Shen, Bingqi
    Geng, Jian
    Hu, Yang
    Li, Yantao
    Li, Huaqing
    NEURAL COMPUTING & APPLICATIONS, 2023, 35 (23) : 17217 - 17231
  • [38] Spatial-Temporal Enhancement of ACO-Based Selection Schemes for Adaptive Routing in Network-on-Chip Systems
    Hsin, Hsien-Kai
    Chang, En-Jui
    Wu, An-Yeu
    IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2014, 25 (06) : 1626 - 1637
  • [39] Deep Spatial-Temporal Feature Fusion From Adaptive Dynamic Functional Connectivity for MCI Identification
    Li, Yang
    Liu, Jingyu
    Tang, Zhenyu
    Lei, Baiying
    IEEE TRANSACTIONS ON MEDICAL IMAGING, 2020, 39 (09) : 2818 - 2830
  • [40] Spatial-temporal distribution of air-pollution-intensive industries and its social-economic driving mechanism in Zhejiang Province, China: a framework of spatial econometric analysis
    Ding, Lei
    Fang, Xuejuan
    ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY, 2022, 24 (02) : 1681 - 1712