Geography of Technology Transfer in China

The following sections are included:

• PREFACE
• ABOUT THE AUTHOR
• CONTENTS
• LIST OF FIGURES
• LIST OF TABLES

The following sections are included:

• Rising Discussions and Challenges
• Objective and Motivation
• Overview of the Chapters

The following sections are included:

• Classic Innovation Theories
• Framework of Technology Transfer Network

The following sections are included:

• Research Flowchart
• Network Construction
• Social Network Analysis
• Technology Space Proxies
• Network Dynamics Models
• Research Data

In the context of globalized knowledge and technology (Audretsch et al., 2014), there is an increasing interest in the flow of technology between China and other countries (Deng & Lu, 2022; Liu & Yan, 2022). Transnational technology flows, viewed as a lens through which globalization can be understood, exhibit complex networking patterns that develop over time. In this vein, a network of international technology transfer is conducted by social network analysis to showcase technology relatedness between China and the rest of the world. Furthermore, this chapter will comprehensively analyze the spatial evolution of China’s transnational technology transfer network from two network perspectives: geographical and technological, such as transfer intensities, nodal centralities, pipeline hierarchies, technology categories, diversities and relatedness, etc.

In the era of a knowledge-based economy, cities are increasingly recognizing the importance of acquiring external knowledge as a key driver of economic growth. In China, the government has implemented measures to stimulate technology transfer between cities and promote the exchange of ideas. By analyzing patent transaction data from China’s State Intellectual Property Office (SIPO) spanning from 2008 to 2015, researchers have been able to map out the landscape of technology transfer. The findings reveal a trend toward increasing intercity patent transfer, with a growing number of cities and city pairs involved over time. Two notable features of this network are the dominant role played by certain key cities, resulting in a “diamond-shaped” pattern that extends geographically, and the hub-and-spoke structure of the network with Beijing, Shanghai, and Shenzhen acting as first-tier hubs for technology transfer (Gui et al., 2019a)…

This chapter delves into the spatial evolution of technology transfer networks (TTN) across typical regions in China, including the Beijing–Tianjin–Hebei (BTH) urban agglomeration, Yangtze River Delta (YRD) urban agglomeration, Pearl River Delta (PRD) urban agglomeration, and the three northeastern provinces (including Helongjiang, Jilin, and Liaoning). By utilizing social network analysis and negative binomial regression model, we aim to explore the spatial heterogeneity of TTNs in different regions in terms of nodal centrality and linkage intensity, as well as their influencing factors. Our findings reveal that these technology transfer networks exhibit a core–peripheral structure with hierarchies driven by multidimensional proximity mechanisms, which are common features across different regions. Furthermore, intercity technology flows in the three major urban agglomerations play a crucial role in sustaining their innovation vitalities and enhancing their innovative capacities (Liu et al., 2019). However, we also identify a key issue hindering innovative performance in the three northeastern provinces, namely the strong nationalization and weak localization of their technology flows (Liu & Niu, 2019).

Innovation is considered a crucial factor in accelerating economic growth (Hesse & Fornahl, 2020), and knowledge is widely acknowledged as the key to regional innovation (Tavassoli & Carbonara, 2014). As a function of complexity, the emergence of new knowledge is increasingly dependent on the integration and recombination of various kinds of knowledge from different sources (Bresnahan et al., 2001; Breschi & Lenzi, 2015). The process of knowledge generation and combination is not only triggered through the interaction in the local knowledge base (Tavassoli & Carbonara, 2014), it can also be accomplished via establishing cross-regional linkages such as trade exchanges, foreign direct investment, technology alliances, incorporation into knowledge networks, and participation in global production networks among others (Boschma & Iammarino, 2009). Therefore, the creation of new knowledge can also be conceptualized as the outcome of integration between adjacent and distant interactions (Bathelt et al., 2004). However, due to its character of high localization, knowledge communication between local actors may lose its value over time on account of knowledge redundancy and limited chances for recombination (Boschma & Frenken, 2010; Breschi & Lenzi, 2015). As aconsequence, the entry of external knowledge is crucial to knowledge creation and innovation capacity development within the region (Tavassoli & Carbonara, 2014; Lee & Sohn, 2019), and knowledge flow in turn interconnects with innovation in distant places in the background of current globalized knowledge economy (Corpataux et al., 2009; Binz & Truffer, 2017)…

As China’s economy transitions into a new era, it must confront the challenges of transformation and upgradation of traditional as well as emerging new industries. To promote the transfer and transformation of scientific and technological achievements, there is an urgent need for a new and powerful impetus. Against this background, expediting technology transfer emerges as an imperative choice to facilitate China’s economic restructuring and transition toward an innovation-driven development path. As a post-developmental economy, China displays conspicuous non-heterogeneity in its regional innovation resources and industrial structure. Consequently, interregional technology transfer has assumed a critical role in enabling the flow and dissemination of innovative resources, thereby advancing technological progress and propelling economic growth in underserved areas…

In the era of knowledge economy, innovation happens to be the primary element and key support for national and regional economic growth (Mankiw, 1992; Dunning, 2002). Cities are facing a fresh round of scientific and technical revolution, that is, actively taking part in the global innovation network to improve their global competitiveness. This phenomenon recognizes “global pipeline” as an important path to improving the regional innovation capacity for absorbing external advanced technology. From the perspective of “Space of Flows” (Castells, 1996), cities can enhance their innovation performance through knowledge flow at different scales, and their innovation development increasingly relies on the physical and virtual networks they involve in. The urban transportation can then alleviate the restriction that the geographical distance has on knowledge spillover of the innovation actors (Basile et al., 2012), which is a significant variable to influence the position of the cities in the global innovation network. In this way, the positive externality of transportation can be understood as a basis of innovation promotion in the cities due to its indirect effect on the spillover of technology innovation. Therefore, the research on the mechanism of how transportation affects technology transfer can not only enrich the theory of how physical space influences innovation space under the background of innovation geography but also help to further understand the basic law of innovation geography and the effect of transportation…

The following sections are included:

• National Perspective
• Regional and Local Perspective
• Actor Perspective

The following sections are included:

• Conclusions
• Discussions

The following sections are included:

• References
• Index