Based on the new development pattern of Chinese technology transfer, in-depth investigation into the evolution and endogenous mechanisms of inter-provincial high-quality technology transfer networks is a key link in transforming high-quality technology into new quality productive forces. This paper analyzed the structural characteristics and endogenous mechanisms of the inter-provincial high-quality technology transfer network by using high-value patents to represent high-quality technology. The research findings are as follows: (1) Between 2001 and 2021, the high-quality technology transfer relationships gradually matured. The network as a whole exhibited typical "small-world" characteristic. Furthermore, with the shift of the network's core position towards the eastern region, the features of "regional agglomeration" and "polarization" became increasingly prominent, leading to a further deepening of the technological gap between regions. (2) The empirical results of the Time Exponential Random Graph Model (TERGM) indicated that China's high-quality technology transfer network exhibits significant reciprocity effect, Matthew effect, connectivity closure effect, and time dependence effect. The evolution of the network is accompanied by characteristics such as "path dependence" and "preference attachment". (3) Further analysis revealed that the implementation of National Intellectual Property Strategy Outline has significantly improved the structure of the high-quality technology transfer network and made transfer relationships more stable. (4) Additionally, the implementation of National Innovation-Driven Development Strategy Outline has effectively enhanced the degree of network integration and the efficiency of high-quality technology transfer, while also exacerbating the hierarchical effects between regions. This study has expanded the research perspective to the field of high-quality technology transfer, and filled the gap in existing research on the endogenous mechanisms of technology transfer networks. It will provide a theoretical basis and practical guidance for promoting the development of new quality productive forces under the background of high-level scientific and technological self-reliance and constructing a technology transfer big loop.

Intelligent connected vehicles (ICV) represent a major trend in the development of the automotive industry, and its key core technologies determine the success or failure of the next round of corporate competition in this industry. The Chinese automotive industry attaches great importance to the development of ICVs. Whether it can "overtake opponents by changing lanes" depends not on production volume, but on cutting-edge technology. Identifying the key core technologies in this industry, clarifying the international competitions and country gaps in core technologies, can provide certain empirical evidence. This paper applied machine learning algorithms to fuse structured data (patents) and unstructured data (scientific papers, fund projects, etc.) from the feature level; combined the LDA models to identify key core technology themes in the ICV industry, and based on the emerging characteristics of industrial technology, used the patent network analysis to identify cutting-edge key core technology themes. Based on this, country distribution and country gaps are analyzed in this paper. The study found that: (1) There are seven key core technology themes in the ICV industry, among which four are cutting-edge key core technology themes: trajectory planning technology, traffic control system technology, intelligent decision-making technology, and information security technology; (2) Although the new track of the Chinese automotive industry is large in scale, except for a few breakthroughs in key core technologies in the ICV industry, there is still a significant gap in overall technology themes compared to countries such as the United States, Japan, and Europe. To achieve "overtaking opponents by changing lanes", the industry still faces arduous tasks. This conclusion will not only promote the understanding of the actual situation of technological innovation in the ICV industry, but also have important guiding value for enterprises in this industry to strengthen the research and development of key core technologies, as well as reference significance for the government to issue relevant policies.

The breakthrough of key core technologies holds significant strategic importance for technological competition and national security. In the new phase of digital innovation for latecomer nations in technological catch-up, latecomer enterprises in intelligent manufacturing play a key innovative role in overcoming the "bottleneck" challenges of key core technologies. Based on the resource orchestration theory and from the perspective of data value chain, this paper systematically explored the development stages, evolutionary pathways,internal mechanisms and technological innovation models of Ansteel Group—as a latecomer enterprise in intelligent manufacturing—in the process of achieving breakthroughs in key core technologies. The findings revealed (1) the three-stage breakthrough process: Data element drives breakthroughs in key core technologies through three sequential phases—data element agglomeration effect phase, data element multiplier effect phase, and data element dual-wheel effect phase—resolving dilemmas in technology path selection, cross-scenario reuse of proprietary technologies and insufficient cross-domain innovation synergy; (2) the data value evolution path: Anchoring along the "front-end—back-end—full-stack" data value chain, an evolutionary pathway of "data resourceization→data productization→data factorization" has been formed, which underpinned corresponding approaches to data resource orchestration; and (3) the data resource orchestration mechanisms: Through three modes of data resource orchestration—gravitational,enabling,and feedback-based—the technological leaps of "integrated technological innovation, open-source technological innovation, and collaborative technological innovation" are driven, thereby overcoming foreign technology monopolies, scenario-specific technological constraints, and challenges in domestic substitution. The resulting theoretical framework has not only elucidated the intrinsic process mechanisms of breaking the "bottleneck" of technology challenges in latecomer nations in intelligent manufacturing but also generalized an "Ansteel Path" with broader implications for major national industrial innovation.

Data-driving is a typical feature of current economic development. Whether data elements can help enterprises achieve breakthroughs in key core technology fields has become a key issue to be explored. Based on the quasi-natural experiment of the national establishment of big data comprehensive experimental zones, this paper empirically examined the enabling effect of data elements on the enterprises' breakthroughs in key core technologies, and analyzed the underlying mechanisms. The study found that data elements significantly promoted the key core technology breakthroughs of enterprises. The mechanism analysis showed that data elements exhibited an effect of "internal and external co-governance". Through the four-dimensional mechanism of "optimizing the environment—reducing costs—accelerating sharing—intelligent R&D optimization", they play a role in improving the business environment, reducing information search costs, accelerating technology spillover, and optimizing R&D decision-making, thereby promoting breakthroughs in key core technologies of enterprises. The heterogeneity analysis revealed that for non-state-owned enterprises, high-tech enterprises, and enterprises in highly competitive industries, the promoting effect of data elements on key core technology breakthroughs was more significant. This paper has explored the enabling effect of data elements on breakthroughs in key core technologies, thus providing a theoretical and practical basis for unleashing the potential of data elements, enriching their application scenarios, and improving the basic system of data elements.

In the era of digital economy, the strategic value of data elements is becoming increasingly prominent, but the intrinsic mechanism by which they influence corporate risk-taking behavior remains unclear. Based on the dynamic capability theory, this paper constructed the core logical chain of "data elements—dynamic capabilities—risk-taking level" and empirically examined the influence mechanism of corporate data elements on the level of risk-taking using a sample of Chinese A-share listed companies from 2008 to 2022. The study found that corporate data elements can significantly promote an increase in the level of risk-taking, and this conclusion remains robust even after addressing endogeneity issues using instrumental variables and propensity score matching methods. The mechanism analysis showed that data elements improve the level of risk-taking by enhancing an enterprise's adaptability, absorptive capacity, and resource reconfiguration capability. The heterogeneity analysis indicated that the effect of data elements on enhancing the level of risk-taking is more pronounced in firms with high perceived uncertainty and those in competitive industries. This paper has innovatively applied the dynamic capability theory to the study of the economic consequences of data elements. It has revealed the micro-mechanisms that convert data elements into corporate risk-taking advantages, and will offer policy insights for better data element governance and enhanced risk-taking among firms.

Pseudo-digital trust caused by deviation between corporate digital trust proclamations and actual corporate constructions exerts a series of impacts on corporate reputation and corporate innovation. In this paper, a mediated moderating effect model was constructed to mitigate the impact of pseudo-digital trust on corporate reputation and corporate innovation from the perspective of corporate digital responsibility (CDR) and organizational resilience to enhance corporations' capacity to manage pseudo-digital trust. To test the hypotheses, a total of 437 data points were obtained from corporate managers and experienced grassroots employees through the questionnaire survey method. The results of the empirical study showed that pseudo-digital trust exerts a negative impact on corporate innovation. Corporate reputation plays a partial mediating role in the impact of pseudo-digital trust on corporate innovation. The implementation of CDR can help mitigate the negative impact of pseudo-digital trust on corporate reputation, and this moderating effect can influence corporate innovation through the mediating role of corporate reputation. Although organizational resilience moderates the negative impact of pseudo-digital trust on corporate reputation, this moderating effect cannot be transmitted to corporate innovation through the mediating role of corporate reputation. Our findings have further deepened our understanding of pseudo-digital trust, thereby providing firms with solutions to digital mistrust and trust traps in the context of the digital economy, improving corporate reputation, and promoting sustainable corporate innovation.

Identifying cutting-edge technologies is of crucial importance for enterprises' R&D strategic decision-making and formulation of governments' strategic plans for scientific and technological innovation. In response to the existing shortcomings in current research on cutting-edge technology identification, namely, the inability of traditional topic models to mine industry-specific technical terms, infer relationships between technological topics and potential application domains, and lack of in-depth analysis on the application scenarios of cutting-edge technology topics, this study proposed a cutting-edge technology identification method based on large language models (LLMs). Taking the intelligent wearables field as an example, the feasibility and effectiveness of this method were validated. The study revealed the following findings: (1) Compared to traditional topic models and clustering methods based on word similarity and word embeddings, LLMs can extract more professional technical information from patent text data and better uncover technical terms and cutting-edge technology topics. (2) The deep semantic parsing capabilities of LLMs can effectively reveal the potential application fields and future application scenarios of cutting-edge technology topics. The systematic correlation analyses of cutting-edge technology topics—potential application fields—future application scenarios provide a foundation for systematically identifying cutting-edge technologies with potential application domains and future application scenarios. (3) The cutting-edge technology identification method based on LLMs, constructed using a ternary coupling analysis approach of "comprehensive indicator evaluation - deep semantic parsing - application scenario analysis", can systematically identify cutting-edge technologies that are not only forward-looking, pioneering, and exploratory but also possess potential application fields and future application scenarios. The cutting-edge technology identification method based on large language models developed in this study has enriched existing approaches and will offers a novel research methodology for the identification of cutting-edge technologies.

Identifying the trends of emerging technology is crucial for nations and enterprises to seize future opportunities and maintain competitive advantages. This study constructed an emerging technology identification model based on the semantic and evolutionary perspectives, focusing on the characteristics of emerging technologies and patent texts. The model incorporates development potential into the evaluation indicators to assess technology development trends, forming an indicator system that includes novelty, persistence, community, growth, and development potential. By integrating the BERTopic model and entropy weight method to comprehensively screen emerging themes, the model addresses the limitations of traditional topic identification models in deep semantic mining. Furthermore, the study divided the patent data into three phases: foundation, development, and performance, and identifies emerging themes from both topic and keyword levels. An empirical analysis using the patent data from the industrial robot field from 2013 to 2022 demonstrated that the model can accurately identify emerging technologies. The eight identified themes revealed a high consistency between the identified themes and national policy documents, validating the accuracy and scientific nature of the method. This research will not only offer a new perspective for theoretical studies on emerging technologies but also provide strategic decision-making references for enterprises and governments in cutting-edge technological fields.

Whether and when firms in standards innovation ecosystem can benefit from cooperation and reap the expected returns in a highly uncertain environment is crucial, but this question has not been adequately answered by existing research. This paper discussed the impact of cooperative relationship breadth of firms in the standard innovation ecosystem on their value creation and the moderating role of asymmetric dependence, and empirically tested the research hypotheses using the fixed-effects multiple regression method with a sample of 132 listed firms participating in China Communications Standards Association (CCSA) from 2010 to 2020. The results of the study showed that: Firms cooperative relationship breadth in standard innovation ecosystem is conducive to value creation, mainly because as the number of cooperative relationship increases, the more technologically compatible complementary products there are with them, and the easier the network effect is stimulated and the more likely it is to win the market favor, thus facilitating the creation of value; The asymmetric dependence enhances the positive effect of cooperative relationship breadth on value creation. Asymmetric dependence can encourage focal firms and their partners to increase cooperative inputs and form dependent cooperative relationships, which is conducive to further improving the technological compatibility of them as well as the network effect, thus strengthening the contribution of cooperative relationship breadth to value creation, The expansion analysis revealed that: technological overlap between focal firms and cooperative firms enhances the positive effect of cooperative relationship breadth on value creation; and cooperative relationship breadth is more conducive to value creation for firms with a high degree of product diversification than for firms with a low degree of product diversification. This study has not only enriched the study of value creation from a micro perspective, but also provided practical references for firms to formulate cooperation strategies in standard innovation ecosystem and improve cooperation performance.

The innovation transition by the new-generation of innovative pharmaceutical companies represents a critical practice in breaking through key core technologies that cause bottlenecks and forging "pillars of a great power" in the biomedicine field. It is of great significance for safeguarding public health and national health security. However, few scholars have conducted research on this emerging phenomenon. Based on the IOLL theory, this paper employed a multiple-case research approach to systematically explore the innovation transition mechanisms of three new-generation of innovative pharmaceutical companies: BeOne Medicines, Innovent Biologics, and Shanghai Junshi Biosciences. The findings revealed that: (1) These companies leverage a dual advantage in knowledge and strategy to form a global resource orchestration mechanism, driven by the dual levers of "talent and policy", thereby accelerating technological accumulation. (2) Through a double helix continuous learning mechanism, it was demonstrated that bidirectional knowledge empowerment between individuals and organizations serves as a sustained engine for breakthroughs in complex technologies. (3) Through a nested "double-helix" combination mechanism of resource and learning, these companies exhibit three distinct innovation transition paths: technological radicalism, ecosystem integration and agile focus. This paper has unraveled the black box of how new generation enterprises in latecomer economies achieve technological transition in Schumpeter Mark II industries. It will also offer some theoretical and practical insights into how science-based firms can restructure their knowledge base to achieve high-quality development.

High-end disruptive innovation (HDI) alters the performance metrics of incumbent technological trajectories by introducing novel functionalities and discontinuous technical standards, thereby catalyzing breakthroughs in emerging technologies. Focusing on Haier's Oxygen-Controlled Freshness Refrigerator as a pivotal case study, this research employed a single-case methodology to investigate the processes and mechanisms through which user demands drive HDI. Key findings revealed: (1) The HDI process driven by user demands comprises three core phases: fuzzy front-end phase, R&D phase, and commercialization phase. (2) During the fuzzy front-end phase, user demand reinforces the organizational practices of technological innovation based on the logic of innovation for HDI. In the R&D phase, an equilibrium framework is established to balance the "Novelty-Feasibility-Economy" (NFE) triad, enabling the translation of scientific principles into technological products. At the commercialization phase, user demands operate through "embedding" and "realization" mechanisms, accelerating HDI's legitimacy attainment in market acquisition, diffusion, and eventual disruption. (3) The dual mechanisms underpin user demand-driven HDI: "technological breakthrough mechanisms" (encompassing core technology accumulation, innovation ecosystem orchestration, and brand catalysis) and "market responsiveness mechanisms" (manifested through organizational agility enhancement). On this basis, corresponding managerial implications are proposed: prioritizing economic viability in HDI, optimizing user data governance protocols, and instituting closed-loop management systems spanning from demand comprehension to realization.

Exploring how manufacturing enterprises take advantage of industrial Internet platforms to achieve business model innovation is of great significance to promote their transformation and upgrading. This study, based on the survey data of 376 manufacturing enterprises that have adopted industrial Internet platforms, and using multivariate regression, empirically explored the mechanism and boundary conditions that "user-platform" integration (i.e., function integration and subject integration) influences platform users' business model innovation. The results showed that: firstly, "user-platform" function integration and subject integration have positive impacts on platform users' business model innovation (i.e., value proposition innovation, value creation innovation and value capture innovation); Secondly, organizational agility (i.e., operational adjustment agility and market capitalizing agility) plays mediating roles in the relationship between "user-platform" integration and platform users' business model innovation; Thirdly, platform leadership and platform security positively moderate the positive effects of "user-platform" integration on the platform users' value creation and value capture innovation; Fourthly, platform users' digital readiness positively moderates the positive effect of "user-platform" function integration on their organizational agility, and the positive effects of organizational agility on value creation and value capture innovation. The conclusions have extended the theoretical framework of "user-platform" integration to promote platform users' business model innovation, and will provide theoretical reference for manufacturing enterprises to utilize the resources from industrial Internet platform to promote business model innovation.

With the increasing complexity of the world economic landscape, the impact of economic policy uncertainty on enterprises is increasingly important. The acceleration of corporate digital transformation represents a significant strategic initiative, and it is of strategic significance to explore the impact of economic policy uncertainty on corporate digital transformation. Based on the data of China's A-share listed enterprises from 2010-2019, this study explored the impact of economic policy uncertainty on corporate digital transformation from real options theory, and further developed a three-way interaction model from a government perspective. The results of this study showed that: (1) economic policy uncertainty can promote corporate digital transformation; (2) economic policy uncertainty reduces the effect of digital infrastructure policies on promoting corporate digital transformation; and (3) promoting the marketisation process is an effective way to mitigate the negative impact of economic policy uncertainty on digital infrastructure policies. This study has combined the perspectives of enterprises and governments to analyze the multidimensional impact of macroeconomic factors on corporate digital transformation and its related promotion policies, thereby providing a new theoretical and policy perspective for related research.

Technological innovation failure, as an unavoidable objective phenomenon in the process of enterprise technological innovation, exerts a significant impact on enterprises' innovation resilience. This paper selected the listed manufacturing enterprises in China from 2011 to 2021 as the research sample and employed the fixed effect and mediation effect models to examine the impact and underlying mechanisms of technological innovation failure on enterprises' innovation resilience. The study found that: (1) Technological innovation failure does not weaken enterprises' innovation resilience but enhances it; (2) Technological innovation failure strengthens enterprises' innovation resilience by increasing the risk preference of top management team; and (3) Faced with the impact of technological innovation failure, state-owned, large, and eastern and central enterprises will demonstrate stronger innovation resilience, while non-state-owned, small- and medium-sized, and western enterprises' innovation resilience is not significant. This paper will contribute to enriching the application of organizational resilience theory in research on enterprises' innovation resilience from the perspective of technological innovation failure and expanding the theoretical scope of prospect theory regarding managers' risk preference. It will also provide theoretical support and decision-making insights for reducing enterprises' "anti-failure" bias towards technological innovation, thus helping enterprises build efficient and reasonable top management team, and enhancing their innovation resilience.

Specialized, refined, differential and innovative enterprises (SRDI) are the backbone of high-quality economic development, and how to carry out carbon footprint management to effectively improve the quality of green innovation has become a new research concern. This study, based on the ‘Miles-Snow’ analysis framework, proposed seven conditions affecting the improvement of green innovation quality of SRDI enterprises from the three dimensions of carbon footprint management strategy, technological endowment and environmental regulation. We analyzed the data from 160 SRDI enterprises using the fsQCA method, revealing that: (1) the three paths—"tracking carbon tracing", "certifying carbon tracing" and "achieving the dual-effect of carbon tracing"— can lead to high green innovation quality of SRDI enterprises; (2) the above three paths enable SRDI enterprises to address external regulatory pressures from the market, public, and government, while enhancing green innovation quality through strategic choices and internal resource deployment; (3) based on the AMC theoretical model, we further revealed the dynamic process by which SRDI enterprises achieve high green innovation quality through "cognitive initiation -- motivation generation -- capability enhancement". These findings will provide useful management and policy insights for SRDI enterprises in China to implement carbon footprint strategies for improvement of their green innovation quality.

Developing new quality productive forces, stimulating innovation and development of enterprises requires intensive attention to the issue of enterprise labor cost stickiness. This paper empirically analyzed and tested the role mechanism of digital transformation in affecting the stickiness of enterprise labor cost from such three dimensions as technology, resources and cost, based on the sample data of A-share listed companies in Shanghai and Shenzhen from 2010 to 2022, and adopted the classical stickiness model and the intermediary mechanism model. It was found that digital transformation can significantly reduce labor cost stickiness of enterprises through technology substitution effect, resource allocation effect and adjustment cost effect; there are significant differences in the reduction of labor cost stickiness of enterprises with different property rights, different labor intensity, different industries and different sizes by digital transformation. Further analysis showed that digital transformation not only improves labor costs and promotes labor employment, but also reduces enterprise labor cost stickiness. The research in this paper is of great significance in encouraging digital transformation of enterprises, stimulating continuous innovation and development of enterprises, effectively reducing the labor cost stickiness of enterprises, and enhancing the market resilience and comprehensive competitiveness of enterprises.

Based on the theoretical analysis, this paper deconstructed the relationship between carbon emissions, green innovation and economic growth, and constructed the conjugate explanatory framework to promote economic growth in parallel with carbon emission regulation and green innovation. The empirical test with provincial panel data from 2011 to 2022 in China found that: Firstly, there is a significant dependence between economic growth and carbon emissions; green innovation could increase the economic contribution of unit carbon emissions effectively; and there is interactive spillover effect between them. Secondly, the interactive spillover effect of carbon emission and green innovation on economic growth has resonance characteristics. The higher the carbon emission scale, the stronger the endogenous driving force of green innovation on economic growth. Meanwhile, when the green innovation level is enhanced, the economic contribution of unit carbon emissions will be higher and higher. Finally, the spatial heterogeneity test also found that the interactive spillover effect of carbon emission regulation and green innovation on economic growth in the eastern region is most powerful, while the optimal spillover threshold of carbon emission regulation and green innovation to economic growth in the central and western areas is different. The above findings will provide inclusive path inspiration and a non-linear empirical basis for the scientific implementation of the "carbon peaking and carbon neutrality" target and for steady promotion of economic green transformation.

The introduction of the expert juror system provides professional knowledge to support case adjudication, offering enterprises a fair and impartial judicial environment. This paper employed the difference-in-differences method, using the data from listed companies from 2016 to 2020, to examine the impact of the expert juror system on corporate innovation and its action mechanisms. The research findings indicated that: (1) The expert juror system can enhance corporate innovation; (2) The expert juror system improves corporate innovation by alleviating corporate financing constraints and increasing the number of R&D personnel hired by enterprises; (3) The impact of the expert juror system on innovation is more pronounced when the enterprise belongs to a strategic emerging industry, operates in a highly competitive market, or is located in a region with a high level of intellectual property development; and (4) The expert juror system can enhance corporate value. The results of this study will contribute to understanding the action mechanism of the expert juror system on corporate innovation from the perspective of adjudication specialization, providing insights for improving China's judicial adjudication system, optimizing the business environment, and developing new quality productive forces tailored to local conditions.