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Since 2015, China has made efforts to reform its intellectual property rights (IPR) system to better protect and stimulate innovation. These reforms are a result of the demand for more stringent intellectual property (IP) protection from China’s domestic, innovative industries and a measure to ease the pressure exerted by its foreign trading partners, particularly against the background of the US-China trade dispute that started at the beginning of 2018. This paper summarizes these reforms and their implications.

This paper combines a variety of sources, including academic articles, government websites, news reports, industry surveys and expert opinions, to offer insights in China’s IPR system and its recent reforms.

This paper summarizes and discusses (1) the state’s law amendments, including the 2015 amendment of the “Law on Promoting the Transformation of Scientific and Technological Achievements”, the second amendment of the “Anti-Unfair Competition Law” with regard to trade secret protection, the fourth amendment of the “Patent Law”, and the legislations and regulations addressing the criticisms of the US administration over China’s so-called “forced” technology transfer policies; (2) the establishment of the specialized IP courts and tribunals since 2014; (3) the restructuring of the State IP Office; and (4) the issuing of an “Outline for Building an IPR Powerhouse (2021–2035)”.

This paper highlights China’s efforts to make its IPR system stronger and more just. It also discusses international observers’ reactions and pinpoints specific areas for further improvement.

Firms may suffer differently from the patent thickets in a particular technology field. This paper explores how patent thickets affect the financial performance of firms with different patent propensities and technological leadership.

From the perspective of patent strategy, the authors study how patent propensity, the possibility that a firm applies for patents, affects the patent thickets and financial performance. Additionally, this paper uses patent stock to measure technological leadership, the degree to which a firm can develop, maintain and enhance technology and product innovation, to study the impact of patent propensity on firms. A three-way interaction model is used to explore the relationship among patent thickets, patent propensity, technological leadership and financial performance based on an unbalanced panel of 69 Chinese telecommunication equipment firms from 2008 to 2019.

The authors find that patent propensity positively moderates patent thickets and financial performance. Notably, technological leadership negatively moderates the moderating effect of patent propensity.

This paper enriches the heterogeneous literature of patent thickets and financial performance. It sheds light on the fact that firms with different technological leadership may use different patent strategies to cut through patent thickets.

This paper aims to provide a complete analysis framework and prediction method for the construction of the patent securitization (PS) basic asset pool.

This paper proposes an integrated classification method based on genetic algorithm and random forest algorithm. First, comprehensively consider the patent value evaluation model and SME credit evaluation model, determine 17 indicators to measure the patent value and SME credit; Secondly, establish the classification label of high-quality basic assets; Then, genetic algorithm and random forest model are used to predict and screen high-quality basic assets; Finally, the performance of the model is evaluated.

The machine learning model proposed in this study is mainly used to solve the screening problem of high-quality patents that constitute the underlying asset pool of PS. The empirical research shows that the integrated classification method based on genetic algorithm and random forest has good performance and prediction accuracy, and is superior to the single method that constitutes it.

The main contributions of the article are twofold: firstly, the machine learning model proposed in this article determines the standards for high-quality basic assets; Secondly, this article addresses the screening issue of basic assets in PS.

While the growth of emerging technologies like Blockchain has created significant market opportunities and economic incentives for firms, it is valuable for both researchers and practitioners to understand their creation mechanisms. This paper aims to discuss the aforementioned objective.

Based on the knowledge search perspective, this study examines the impact of search boundary on innovation novelty and quality. Additionally, innovation targets, namely R&D innovation and application innovation, are proposed as the moderator of the knowledge search effect. Using a combination of machine learning algorithms such as natural language processing and classification models, the authors propose new methods to measure the identified concepts.

The empirical results of 3,614 Blockchain patents indicate that search boundary enhances both innovation novelty and innovation quality. For R&D innovation, the positive impact of search boundary on innovation quality is enhanced, whereas for application innovation, the positive effect of search boundary on innovation novelty is improved.

This study mainly contributes to the growing literature on emerging technologies by describing their creation mechanisms. Specifically, the exploration of R&D and application taxonomy enriches researchers' understanding of knowledge search in the context of Blockchain invention.

This study aims to investigate the structural characteristics, spatial evolution paths and internal driving mechanisms of the knowledge transfer (KT) network in China’s patent-intensive industries (PIIs). The authors' goal is to provide valuable insights to inform policy-making that fosters the development of relevant industries. The authors also aim to offer a fresh perspective for future spatiotemporal studies on industrial KT and innovation networks.

In this study, the authors analyze the patent transfer (PT) data of listed companies in China’s information and communication technology (ICT) industry, spanning from 2010 to 2021. The authors use social network analysis and the quadratic assignment procedure (QAP) method to explore the problem of China’s PIIs KT from the perspectives of technical characteristics evolution, network and spatial evolution and internal driving mechanisms.

The results indicate that the knowledge fields involved in the PT of China’s ICT industry primarily focus on digital information transmission technology. From 2010 to 2021, the scale of the ICT industry’s KT network expanded rapidly. However, the polarization of industrial knowledge distribution is becoming more serious. QAP regression analysis shows that economic proximity and geographical proximity do not affect KT activities. The similarity of knowledge application capacity, innovation capacity and technology demand categories in various regions has a certain degree of impact on KT in the ICT industry.

The current research on PIIs mainly focuses on measuring economic contributions and innovation efficiency, but less on KT in PIIs. This study explores KT in PIIs from the perspectives of technological characteristics, network and spatial evolution. The authors propose a theoretical framework to understand the internal driving mechanisms of industrial KT networks.

How prospective or emerging technologies can be supported through government-funded research projects has gradually received global attention. However, previous studies have primarily focused on the effects of government funding on subsequent technological development, the overall economy or social welfare of a country or corporate research and development (R&D) activities. These studies have not examined the technology distribution and development trends of government-funded research from a comprehensive technology perspective. In addition, previous measurements of the influence of government-funded R&D projects faced the difficulty of transferring the research achievements of government-funded research to the commercial market.

Patents can provide a preliminary understanding of the collaboration, development focus and status of market technologies. Accordingly, the purpose of this study was to examine the development directions of patented technologies engendered from government-funded research projects. Analyzing the network of government-funded patented technologies helped identify the current status and location of specific technologies in a patent network as well as the hotspot technologies in government-funded research projects that correspond to the market.

The results of this study indicated that the technologies obtaining government-funded patents mainly consist of advanced materials and semiconductors and that the technological focus has shifted over the years. Nanotechnology, pharmaceutical technology and sanitary technology have gradually become the technologies receiving most of government-funded patents. The trend of development of these technologies also corresponds to the emerging technologies advocated by countries worldwide in recent years.

This study provided a comprehensive verification of the government-funded patented technologies from a macro perspective by identifying key technologies using technology network analysis. The findings of this study can serve as a reference for the allocation of governmental R&D resources and the promotion of novel technologies in the private sector.

This study aims to propose a combined effect framework to explore the relationship between research and development (R&D) team networks, knowledge diversity and breakthrough technological innovation. In contrast to conventional linear net effects, the article explores three possible types of team configuration within enterprises and their breakthrough innovation-driving mechanisms based on machine learning methods.

Based on the patent application data of 2,337 Chinese companies in the biopharmaceutical manufacturing industry to construct the R&D team network, the study uses the K-Means method to explore the configuration types of R&D teams with the principle of greatest intergroup differences. Further, a decision tree model (DT) is utilized to excavate the conditional combined relationships between diverse team network configuration factors, knowledge diversity and breakthrough innovation. The network driving mechanism of corporate breakthrough innovation is analyzed from the perspective of team configurations.

It has been discerned that in the biopharmaceutical manufacturing industry, there exist three main types of enterprise R&D team configurations: tight collaboration, knowledge expansion and scale orientation, which reflect the three resource investment preferences of enterprises in technological innovation, network relationships, knowledge resources and human capital. The results highlight both the crowding-out effects and complementary effects between knowledge diversity and team network characteristics in tight collaborative teams. Low knowledge diversity and high team structure holes (SHs) are found to be the optimal team configuration conditions for breakthrough innovation in knowledge-expanding and scale-oriented teams.

Previous studies have mainly focused on the relationship between the external collaboration network and corporate innovation. Moreover, traditional regression methods mainly describe the linear net effects between variables, neglecting that technological breakthroughs are a comprehensive concept that requires the combined action of multiple factors. To address the gap, this article proposes a combination effect framework between R&D teams and enterprise breakthrough innovation, further improving social network theory and expanding the applicability of data mining methods in the field of innovation management.

The intelligent connected vehicle (ICV) is an important trend in automobile development, but little research has been conducted on the technological differences in the ICV industry across countries. In this regard, the authors select China, the United States (US) and the European Union (EU) as countries with developed ICV industries to reveal these differences based on the perspective of subdivision technology.

The authors use logistic regression to fit lifecycles at technology level and country level based on ICV-related patents from China, the US and the EU, then use the Revealed Technological Advantage (RTA) index, Fast-Growing Specialization Index (FGSI) and International Patent Classification (IPC) numbers to conduct comparison of national technology advantages, finally use the social network analysis to investigate the evolution of characteristics and intermediate nodes of each technology innovation network.

Technology lifecycles vary according to the subdivision technology and country. The global development of the ICV industry has reached the mature stage, and 2030 may be a watershed moment, ushering in a wave of new technology iterations. In various subdivision technologies, China and the US have more leading RTAs, and China and the EU have more leading FGSIs. Innovation networks in different countries expand with technology lifecycles, with that in China being the fastest. China's Universities, the US's enterprises and the EU's research institutes are active in cooperative innovation as intermediaries.

This is the first study to compare the development of the ICV industry in major countries from the perspective of subdivision technology and reveal characteristics of innovation networks in each.

This paper aims to focus on the characteristics of a two-tier network featuring internal subject cooperation and external embedded cooperation in the context of regional innovation systems (RISs) and explore the influence of network characteristics on knowledge emergence.

Using social network analysis, a two-tier internal and external cooperation network of a RIS is constructed. A negative binomial regression method is used to explore the effects of the characteristics of these two-tier internal and external networks on knowledge emergence, the moderating effect of the cooperation knowledge base in this context is investigated and grouping and quantile regressions are used to conduct heterogeneity analysis.

The scale of the internal cooperation network has a positive effect on knowledge emergence, and the betweenness centralization of the internal cooperation network has an inverted U-shaped effect on knowledge emergence. The scale and structural holes of the external embedded network have an inverted U-shaped effect on knowledge emergence. Furthermore, the internal cooperation knowledge base weakens the influence of the external embedded network on knowledge emergence.

This research may enlighten policymakers with respect to improving the scale and structure of the RIS cooperation network and matching the embedded network based on the internal cooperation knowledge base to promote knowledge emergence.

This research contributes to the study of knowledge emergence by exploring the influence of a two-tier network structure and scale characteristics on knowledge emergence in RISs. This paper also extends the framework of relevant research by integrating the internal cooperation knowledge base into the analysis of externally embedded cooperation and knowledge emergence.

The purpose of this study is to investigate whether repeated alliances between two members of a patent pool boost enterprise innovation. Furthermore, this paper intends to determine whether the innovation performance becomes higher or lower based on the partnership characteristics.

In this empirical study, hierarchical regression is used to analyze the longitudinal data obtained from 12 patent pools managed by MPEG LA during a time period ranging from 2006 to 2018. The members of patent pools comprise research institutions, firms and universities. Research analyses are performed based on a sample of 68,400 member pairs who had established repeated alliances. The information regarding such pairs is gathered from public databases.

Repeated alliances positively correlate with enterprise innovation performance in patent pools; this performance is higher when the two enterprises have exploratory collaborations. Conversely, the performance is lower when the partners have a similar technology base and are engaged in a technological competition (competitive learning and patent litigation). Moreover, the performance is lower when one partner demonstrates higher network centrality and richer structural holes than the other partner.

Patent pools play an instrumental role in eliminating patent-licensing barriers, thereby allowing mutual acquisition of complementary technologies, and cooperatively strengthening technology development. From the perspective of theories of coopetition, knowledge management and social network, this study explores the impact of patent pools on enterprise innovation performance and ascertains the moderating roles of technology coopetition, technology similarity and network position, thereby expanding the scope of innovation effect in the context of patent pools.