Coordinated regional economic development is a fundamental requirement for achieving high-quality and sustainable growth, with innovation serving as its primary driving force. In traditional development paradigms, high-end production factors such as skilled labor, advanced technology, and capital tend to concentrate in regions characterized by strong industrial agglomerations and rich innovation resources. This uneven spatial distribution of key inputs places underdeveloped and peripheral regions at a significant disadvantage in accessing essential production factors and participating in innovation-driven growth processes. Consequently, regional disparities are exacerbated, hindering inclusive national development. However, the deep integration of data elements and artificial intelligence (AI) technologies has begun to reshape this landscape. This integration has effectively reduced spatial and institutional barriers to the dissemination of knowledge and technologies, dramatically lowering the costs of cross-regional technology diffusion and enhancing the fluidity of innovation resources. In doing so, it contributes to more efficient allocation of resources across regions and elevates regional innovation capacities, thereby offering new and potentially transformative pathways for narrowing long-standing economic development gaps between regions. In addition, the productive value of data elements cannot be realized independently. Their economic potential is maximized only through synergistic interaction with other production factors. Particularly, the convergence of data with AI technologies generates multiplier and complementary effects that significantly enhance innovation efficiency and technological productivity. These effects have drawn growing attention in both theoretical and empirical studies, becoming a focal point in discussions of digital transformation and spatial equity. Against this backdrop, the present study adopts the Regional Innovation System (RIS) theoretical framework to investigate the role of data-intelligence integration in shaping regional economic disparities. The RIS framework emphasizes the systemic and interactive nature of innovation, underscoring the importance of collaboration among multiple actors within a region,including governments, firms, universities, and research institutions. Such multi-agent collaboration facilitates knowledge exchange, accelerates technological diffusion, and enhances the integration of innovation resources. Thus, it provides a solid theoretical basis for understanding the regionalized effects of data-AI integration.
To conduct this analysis, the study constructs a multidimensional indicator system to systematically evaluate the development and application levels of data elements and AI technologies across Chinese provinces. A coupling coordination model is applied to measure the extent and quality of integration, which is then introduced into the empirical model as a key explanatory variable. This methodological approach helps address two notable gaps in the literature: the insufficient attention to the synergistic relationship between emerging digital factors and traditional inputs, and the lack of empirical research on how digital transformation influences regional economic convergence. Empirical analysis is conducted using panel data from 30 Chinese provinces (excluding Xizang, Hong Kong, Macao, and Taiwan) covering the period from 2012 to 2023, yielding 360 valid observations. Regional economic disparity is quantified using average income gap indicators. The study employs both multiple linear regression and threshold effect models to assess the impact of data-AI integration on regional disparities. Furthermore, four potential mechanisms are analyzed: industrial upgrading, resource misallocation, regional innovation capacity, and innovation infrastructure.
The results demonstrate that higher levels of data-AI integration significantly reduce regional economic disparities. Regional innovation capacity, industrial structure upgrading, and improvements in resource allocation function as mediating pathways, while innovation infrastructure exerts a moderating influence. Heterogeneity analysis reveals that these effects are more pronounced in the eastern and western regions, but are not statistically significant in the central region. Additionally, the threshold effect model identifies a critical financial development level (6.173), beyond which the positive impact of data-AI integration on narrowing disparities reverses, possibly due to diminishing marginal returns or crowding-out effects in overly mature financial systems.
In sum, by integrating insights from the RIS framework, Schumpeterian innovation theory, and industrial structural evolution theory, this study provides a systematic investigation of how emerging production factors—especially data and AI—interact to influence regional economic coordination. Unlike previous studies that focus narrowly on digital technologies or innovation inputs in isolation, this research emphasizes the systemic value of factor integration. The findings offer not only theoretical contributions but also practical implications for policymakers seeking to promote high-quality, balanced, and innovation-driven regional development in China.

The issue of climate change is becoming increasingly severe, and countries around the world have recognized the urgency of reducing carbon emissions. China has also expressed the ambition to achieve the dual carbon goals and regards it as an important development task at present. However, achieving this long-term goal within the tight deadline is a daunting task. As a vital strategic technology in the new era, artificial intelligence technology (AIT) has been extensively applied in the fields of energy and environment, significantly influencing the achievement of dual-carbon goals. Although numerous studies have been conducted in this area, several gaps remain. Firstly, while there is abundant research on AIT and dual-carbon goals individually, studies that specifically explore their relationship are relatively rare. Secondly, existing research tends to address carbon reduction or carbon neutrality from a singular perspective, lacking a comprehensive approach. Moreover, there is still a gap in studies that accurately assess the urban dual-carbon practice process and analyze the empowering role of AIT.
In view of this, this paper comprehensively considers carbon reduction and carbon absorption, scientifically constructs an indicator system to measure the urban dual carbon practice performance (DCP), and calculates the DCP level of 279 cities in China from 2006 to 2022. Since existing research on the impact of artificial intelligence technology (AIT) on urban dual carbon practice (DCP) is limited, and achieving dual carbon goals is a long-term process requiring a precise understanding of local carbon-governance dynamics and potential, the study develops an indicator system from two dimensions: low-carbon status (LCS) and low-carbon potential (LCP). LCS reflects current low-carbon status and sustainability, while LCP assesses the supportiveness of policy, technology, and human resources for low-carbon transition and the potential for further transition. It uses the entropy method to measure DCP at the urban level in China. The core explanatory variable is the application level of AIT, measured by AI patent applications, as they indicate regional AI technology and output. By comprehensively using the panel econometric model, mediation effect model, moderation effect model, this study explores the impact and underlying mechanisms of AIT on DCP, in order to find effective ways for cities to achieve their dual carbon goals.
The research results show that there is a U-shaped relationship between AIT and DCP. When the level of AIT exceeds 6.702, the impact on DCP changes from inhibitory effect to promoting effect. In terms of sub-dimensions, the promotive effect of AIT on low-carbon potential is highlighted first. The mediating effect analysis shows that factor upgrading, industrial upgrading and power upgrading play important mediating roles. The moderation effect analysis shows that digital infrastructure, digital industry development, and digital innovation capability all positively moderate the U-shaped relationship between the two, shifting the turning point to the left. Heterogeneity analysis shows that in regions with high low-carbon potential and poor low-carbon status, AIT has a stronger empowering effect on DCP. There is a U-shaped relationship between the two in the eastern and central regions, while the relationship is not significant in the western region. The greater impact stems from central cities rather than peripheral cities. The empowering effect of AIT is stronger in regions with high innovation and entrepreneurship activity, high information infrastructure level, and strong policy planning orientation.
Unlike previous studies, the possible marginal contributions of this paper include four aspects. Firstly, this study constructs a scientifically sound evaluation system for DCP from the dimensions of low-carbon status and low-carbon potential, objectively evaluating the process of urban dual carbon practices. Secondly, this study reveals the non-linear impact of AIT on DCP, to accurately grasp the application degree and scope of AIT. Thirdly, it explores the impact mechanisms of AIT on DCP from the perspectives of factor upgrading, power upgrading, and industrial upgrading. Finally, this study reveals the differential impacts of AIT on DCP in different types of cities from multiple dimensions, providing the bases for government differentiated policies and promoting the achievement of dual carbon goals. Overall, this study enriches AI-related research and provides new empirical evidence for leveraging AIT in achieving the dual-carbon goals.

Digital innovation is recognized as a crucial pathway for enterprises to escape competitive dilemmas, build sustainable competitive advantages, and develop new-quality productivity. Distinct from traditional innovation, digital innovation is characterized by its focus on how organizations leverage digital technologies to develop new products, improve production processes, transform organizational models, and upgrade business models. These capabilities enable enterprises to adapt to emerging digital-era trends, including rapid product iteration cycles and blurred innovation boundaries. Most existing studies are limited to discussing the impact of artificial intelligence on enterprises' overall innovation. This body of research suffers from two key limitations:on the one hand, they lack attention to digital innovation specifically; on the other hand, they fail to distinguish between heterogeneous ambidextrous innovation types. Furthermore, regarding mechanisms, existing studies are confined to analyzing the mediating effect of broad cross-industry search, overlooking the knowledge boundary issues inherent in AI-enabled cross-industry search under different learning modes.
Addressing these gaps, this paper follows the logical framework of "construction-bundling-utilization" from resource orchestration theory to investigate how AI applications influence exploitative and exploratory digital innovation.The analysis examines the cross-mediating roles of different cross-industry search types, alongside the moderating roles of knowledge integration capabilities and dual environments. The goal is to clarify the action mechanisms and contextual mechanisms through which AI applications influence corporate ambidextrous digital innovation via intra-industry and extra-industry cross-industry search. This endeavor seeks to expand the theoretical landscape of digital innovation determinants and provide both theoretical insights and practical guidelines for enterprises aiming to enhance heterogeneous digital innovation capabilities.
The study yields four findings. First, AI applications positively promote both exploitative and exploratory digital innovation. Second, intra-industry and extra-industry boundary-spanning search play cross-mediating roles between AI applications and ambidextrous digital innovation. Specifically, intra-industry cross-industry search serves as a stronger mediator between AI applications and exploitative digital innovation, while extra-industry cross-industry search has a stronger mediating role between AI applications and exploratory digital innovation. Third, knowledge integration capabilities and dual environments exert moderating effects at distinct stages. Knowledge integration capabilities positively moderate the relationship between cross-industry search and ambidextrous digital innovation, representing the "capability-utilization" stage. Dual environments positively moderate the relationships between AI applications and cross-industry search, and between AI applications and ambidextrous digital innovation, representing the "construction-capability" stage.
Potential research contributions of this study are fourfold. First, this study expands the research on determinants of ambidextrous digital innovation. By examining the relationship between AI applications and digital ambidexterity through resource orchestration theory, it extends the technology-driven resource management paradigm and deepens the understanding of AI-mediated innovation mechanisms. Second, it refines the measurement dimensions and provides nuanced insights into the mediating role of cross-industry search. Within the resource orchestration framework, the study highlights the multiple mediating roles of AI-enabled cross-industry search capabilities, which facilitate ambidextrous innovation by integrating heterogeneous knowledge across industry boundaries. Unlike prior research, it subdivides boundary-spanning search into intra-industry and extra-industry types, explaining how distinct knowledge systems drive innovation—thereby expanding the theory’s applicability. Third, it enriches contextual discussions on knowledge integration capabilities in digital innovation. The paper reveals that knowledge integration capabilities optimize the interactive processing efficiency of AI-enabled cross-industry knowledge resources, highlighting their crucial synergistic role in innovation resource transformation and utilization. Fourth, it enriches the contextual analysis of dual environmental contexts (i.e., technological turbulence and market dynamism). The paper finds that stronger dual environments amplify the effect of AI applications in empowering resource orchestration, thereby enriching contextual discussions on the digital innovation process within the resource orchestration framework.
The present investigation is subject to certain scope constraints that merit discussion. First, its reliance on questionnaire surveys for variable measurement means respondent subjectivity could compromise objectivity. Future research could consider quantitative indicators. Second, while resource orchestration theory provides a framework, practical digital innovation may also be influenced by factors such as institutional environment and corporate culture. Future studies could explore these issues from other theoretical perspectives.

In the era of digital economy, data has become the most crucial production factor and plays a vital role in innovation and entrepreneurship. Innovation is both the essence and means of entrepreneurship, and innovation-driven entrepreneurship has always been a hot topic of academic discussion. In the new context of digital economy, more and more large enterprises have become the main body of innovation and entrepreneurship; while the logic of behavioral choices and the process mechanism of innovation-driven entrepreneurship have also changed. In recent years, the rapid rise and popularization of the application of digital technologies such as big data, cloud computing, artificial intelligence, etc. have created and extended a huge number of application scenarios. Enterprises have set off a new round of innovation and entrepreneurship to meet diversified scenario needs, especially the increasing product innovation and brand extension activities. It has been shown that digital technology has improved the enterprise's information acquisition and resource development abilities, and reshaped the enterprises' value creation process.
Therefore, this paper centers on the theme of "innovation-driven entrepreneurship", focuses on the important issue of how large enterprises in the digital economy can identify and grasp the opportunities of innovation and entrepreneurship so as to enhance their competitive advantages, with an attempt to open the "black box" of innovation-driven entrepreneurship of large enterprises in the digital economy. The study constructs a process model of innovation-driven entrepreneurship in large enterprises in the digital economy era through literature review and sorting. First, it reviews the current research status of scenario-based innovation and brand extension, and the association between the two and Corporate-related entrepreneurship. Then, it explains how scenario-based innovation and brand extension drive the entrepreneurial activities associated with enterprises and constructs a process model of innovation-driven entrepreneurship in large enterprises in the era of digital economy. Finally, it draws the research conclusions, theoretical contributions, managerial insights and future perspectives of this paper.
The results of the study show that, firstly, digital technological innovation and upgrading of consumer demand give rise to a large number of scenario-based innovation activities, which realize scenario-based innovation of products and services and scenario-based reconstruction of the enterprise value chain, so as to satisfy users' personalized needs and create new value for the enterprises. Secondly, brand extension is a natural result of the mature development of scenario-based innovation, which helps to promote the incubation of new businesses. Thirdly, through scenario-based innovation and brand extension, large enterprises develop associated new businesses, gradually break through industry boundaries and organizational boundaries, build systematic product series and modular business portfolios, create innovation and entrepreneurship platforms and ecosystems, and realize innovation-driven entrepreneurship.
Against the backdrop of digital economy, this study deeply analyzes the process mechanism of innovation-driven entrepreneurship of large enterprises, and refines the management theory innovation of innovation-driven entrepreneurship by exploring and constructing the intrinsic connection between scenario-based innovation, brand extension and corporate-related entrepreneurship, and forms a theoretical framework of innovation-driven entrepreneurship with large enterprises as the main body. It enriches and contributes to the theory of corporate entrepreneurship, and contributes to the new path of "scenario-based innovation, brand extension, and corporate-related entrepreneurship" for large enterprises to enhance their competitive advantages through innovation and entrepreneurship, and opens up the "black box" of innovation-driven entrepreneurship for large enterprises in the era of digital economy. Meanwhile, it also provides certain theoretical reference value for how to realize innovation-driven entrepreneurship of large enterprises. The large enterprises should attach great importance to innovation and entrepreneurship, especially user demand- oriented scenario-based innovation, actively seize the transformation and upgrading opportunities brought by digital technology, fully utilize existing brand reputation and influence to promote new business development, and on this basis, they are warranted to promote organizational change, build platform and ecosystem based organizations, and further deepen the innovation and corporate-related entrepreneurship.

With the new round of scientific and technological revolution and industrial transformation in the world, new technologies such as AI, big data, cloud computing, and V2X are developing rapidly. These new technologies are gradually integrated with the automotive industry, giving birth to historic changes in the automotive industry, and opening a new stage of intelligent and networked competition in the automotive industry. In this context, autonomous vehicles are gradually becoming the new highland of global competition.
In recent years, China′s autonomous vehicle industry has developed rapidly under the two-wheel drive of policy and market. However, commercialization above Level 3 is not easy, facing many technical and policy obstacles. As a disruptive and innovative technology, autonomous vehicles have the characteristics of intersectionality, integration, complexity, and interdisciplinaryness, and it is difficult to rely on the resources of a single enterprise to solve all the problems in technology, commercial landing and other industrial development. Cross-border integration and collaborative innovation have become the key and inevitable choices for the innovation and development of the autonomous vehicle industry. The collaborative innovation mode of autonomous vehicles has important significance and plays a role in promoting technological innovation and industrial landing, accelerating industrial development and upgrading. Therefore, this study focuses on the collaborative innovation mode of China′s autonomous vehicle industry and its impact on collaborative innovation performance.
First, by searching the patents of China′s autonomous vehicle industry, this study extracts 27 networks with large network scales in the Yangtze River Delta, Beijing-Tianjin-Hebei and Pearl River Delta city clusters by using social network tools. Then it analyzes each cooperation relationship in the network from four aspects: kinship, geography, industry and learning relationship, and the proportion of each cooperative relationship in the total number of cooperative relationships is calculated. If the proportion of the two relationships is similar, it is defined as a hybrid mode. Otherwise, the relationship with a higher proportion is the dominant mode. Through the analysis of 27 samples, the collaborative innovation mode of China′s autonomous vehicle industry is divided into four categories: kinship-related collaborative innovation mode, geo-related collaborative innovation mode, industry-related collaborative innovation mode, and hybrid collaborative innovation mode.
Furthermore, there are different network embedding characteristics in different collaborative innovation networks, which, together with the collaborative innovation mode, affect the collaborative innovation performance. On this basis, a network embedding perspective is introduced, and 27 collaborative innovation network samples are further analyzed by the fsQCA method to explore the configuration matching between different collaborative innovation modes and network embedding features in the industry, as well as their impact on collaborative innovation performance.
The study concludes that (1) there are five paths to high collaborative innovation performance. Combined with the characteristics of network embeddings, the collaborative innovation modes to achieve high collaborative innovation performance in the autonomous vehicle industry can be further summarized into four categories. G1: The kinship-related collaborative innovation mode with extensive and diversified cooperation; G2: The kinship-related collaborative innovation mode with long-term and deep cooperation; G3: The hybrid collaborative innovation mode with extensive and deep cooperation; G4: The geo-related collaborative innovation mode with extensive cooperation. Among them, the kinship-related and kinship-geo hybrid collaborative innovation modes have the greatest impact on the collaborative innovation performance of the autonomous vehicle industry. Some geo-related collaborative innovation modes can also produce higher collaborative innovation performance. But at present, it is difficult to achieve high collaborative innovation performance in the industry-related collaborative innovation mode. (2) There are six paths to achieve low collaborative innovation performance, which can also be summarized into four categories combined with the characteristics of network embeddedness. D1: The geo-related collaborative innovation mode with extensive, diversified and low-intensity cooperation; D2: The geo-related collaborative innovation mode with small-scale, diversified and high-intensity cooperation; D3: The kinship-related collaborative innovation mode with small-scale, diversified and low-intensity cooperation; D4: The collaborative innovation mode is dominated by industry relationship with low cooperation intensity. There are several obvious characteristics of low collaborative innovation performance, such as low cooperation intensity, small cooperation scale, high partner heterogeneity, and high industry relationship.

Patient capital, characterized by long-term investment, strategic investment, and stable returns, has the potential to guide capital toward the development of new and high-quality productive forces. Venture capital, as the primary vehicle for patient capital, should fully leverage its role as a catalyst for technological innovation and as an accelerator for economic growth. The venture capital cooperation network is a complex structure formed by multiple venture capital institutions through joint investment syndicates, partnerships, and strategic alliances. Within this cooperative network, venture capital institutions occupying structural holes act as intermediaries, obtaining information about investment opportunities through different syndicate partners, thereby playing a crucial role in enhancing individual performance and ensuring the sustainable growth of invested projects. Existing research has increasingly focused on the "structural hole paradox" in inter-organizational cooperation networks. While occupying structural holes can provide organizations with greater access to resources and opportunities, it also leads to more conservative behavioral strategies and poorer cooperative performance. Current studies on the impact of structural holes on venture capital institutions primarily build single-level analytical frameworks, exploring the effects of structural hole positions on venture capital institutions' behavior, the role of structural holes in the selection of venture capital partners, and the impact of structural holes on investment performance. However, there is limited discussion on the existence and mechanisms of the "structural hole paradox" within venture capital cooperation networks. Therefore, this paper, grounded in social network theory and structural hole theory, delves into the manifestations and effects of the "structural hole paradox" within venture capital cooperation networks. It innovatively incorporates this issue into the research scope of structural hole theory and, by clarifying the role of venture capital institutions occupying structural hole positions, lays a foundation for future research on the "structural hole paradox".
The study selects investment and exit events of venture capital institutions in China from 2001 to 2022 as samples. By defining variables and constructing a multiple linear regression model, the hypothesis is tested at both the individual level of venture capital institutions and the syndicate level. It then examines the relationship between the number of structural holes occupied by individual venture capital institutions and their investment performance, as well as the relationship between the overall structural hole level of the venture capital syndicate and syndicate performance.
The research results indicate the following: First, the more structural holes a venture capital institution occupies, the more investment opportunities it acquires; the higher the overall level of structural holes among syndicate members, the greater the total financing raised for syndicate investment projects. Second, the number of structural holes occupied by a venture capital institution positively affects its investment performance, but as the average structural hole level of the syndicate increases, the likelihood of a successful exit for the syndicate decreases. Third, venture capital institutions with a high level of structural holes tend to collaborate with other venture capital institutions that also possess high structural hole levels, but the performance of syndicates formed by such "strong partnerships" is actually worse. Fourth, venture capital institutions with more structural holes tend to invest in early-stage and high-tech industries, which positively mediate investment performance.
This paper integrates social network theory, structural hole theory, and related research findings. By constructing a dual-level network analysis framework, it explores the existence and impact of the "structural hole paradox" in venture capital cooperation networks, enriching the application scenarios and explanatory power of structural hole theory. Unlike previous studies, this study innovatively connects and couples the individual and syndicate levels of venture capital institutions, deepening the theoretical understanding of the "structural hole paradox" and providing empirical support for the complex role of structural holes in multi-level networks. Additionally, the research unveils new mechanisms behind the cooperation strategies and investment behaviors of venture capital institutions, offering new empirical support for the application of structural hole theory in the venture capital field.

With the rapid development of the digital economy and the real economy, the industrial internet platform has become a new organizational form in this context. This type of platform brings together numerous participants in the industrial chain and achieves value proposition by coordinating the needs of participants and corresponding products and services. The existing research on platform value proposition mainly includes two parts. The first part emphasizes that the activity systems carry out the interaction of participants at the real level, especially the impact of centrality and openness on the value proposition. The second part emphasizes that digital artifacts carry the interaction at the digital level, especially the impact of reprogrammability and recombinability on the value proposition. However, these studies are still relatively independent and have not explored their configuration effects. It has been noted that the study of complex objects needs to be viewed from an integrated perspective of interaction and technology; thus, this study holds that, as the key carrier of the digital economy and the real economy, there is a need to explore the configuration paths of the value proposition of activity systems and digital artifacts on the Industrial Internet platform.
This study selects the digital service projects provided by the industrial internet platform as samples. These projects involve numerous participants and are important scenarios for observing value propositions. At the same time, these projects involve a large number of activities and artifacts, making them very suitable for exploring the configurations of activity ecosystems and digital artifacts. For this purpose, twenty five projects are selected for research. This study uses fuzzy set qualitative comparative analysis (fsQCA) to explore the configurations of the centrality and openness of activity systems and the reprogrammability and recombinabilty of digital artifacts. Data is collected from semi-structured in-depth interviews and secondary channels, and interviewees are a number of key figures, including chairmen, founders, vice-chairmen, general managers, deputy general managers, and digital service project managers of Industrial Internet platforms. In terms of variable measurement, since the academic community has not yet formed a unified measuring method for value propositions, this study combines the connotation of value propositions and adopts a textual analysis method to portray value propositions. Eigenvector centrality is invoked to measure centrality, and openness is measured by calculating the ratio of the number of activities with no interdependencies within the system to the number of interdependencies of all activities.
The NCA results show that none of the four conditional variables are necessary for the value proposition, which supports the necessity of using the QCA. First, centrality limits the interaction between participants across activities, this needs reprogrammability or recombinabilty to break this constraint. Reprogrammability or recombinabilty has a substitution relationship. Second, openness and non-centrality can form a configuration to carry out the interaction of participants and achieve the value proposition. At the same time, openness, reprogrammablility, and recombinabilty can form a configuration that provides more digital interaction scenarios for participants to achieve the value proposition of the platform. Reprogrammability and recombinabilty complement each other to jointly compensate for the non-centrality.
The research conclusions make theoretical contributions in achieving value propositions for the Industrial Internet platform from the perspective of integration of activity systems and digital artifacts, deepening the internal complex relationships between the activity systems and digital artifacts, and enriching the research methods of the industrial internet platform value proposition. At the same time, it also provides rich practical insights into how industrial internet platforms carry out multi-agent interaction in the context of the digital economy and the real economy. On one hand, an open and non-centralized activity system is the mode of construction of most industrial Internet platforms, which is more conducive to facilitating the rapid calibration of needs and the realization of value propositions of participating subjects in the open activity system. Therefore, industrial Internet platforms need to build appropriate activity systems. On the other hand, it is necessary to utilize digital artifacts to transform the industrial activity flow and realize the efficient linkage between offline and online scenarios.

Future industries are the key for China to seize the commanding heights of the new round of scientific and technological development.It is of great significance to clarify the mechanism of convergence cluster development of strategic emerging industries driven by digital transformation, so as to promote the upgrading of strategic emerging industries to future industries, and to enhance China's global discourse power.However, current research faces the following issues:(1)The factors and mechanisms of strategic emerging industry integration cluster development are still in the discussion stage; (2)Research on digital transformation focuses on influencing factors and mechanisms, mainly observing short-term and significant phenomena, and there is a need to enrich the research on long-term and underlying phenomena; (3) The research on future industries is currently still at the strategic design stage, and it is urgent to conduct in-depth qualitative and quantitative studies to clarify their development mechanisms.
This paper introduces the digital wave theory, builds a wave-undercurrent research framework based on major node policies and enterprise opportunities and challenges, combines data cleaning with Python software, and comprehensively applies factor ontology and process ontology to reveal the mechanism of the development of convergence clusters in strategic emerging industries driven by digital transformation.It further explores the mechanism of the digital wave, the undercurrent on industries with different attributes, and the mechanism for future development of convergence clusters under the goal of industrialization,and puts forward corresponding management insights.
It is found that, (1) on the basis of the significant stage characteristics, the digital transformation driving the strategic emerging industry integration cluster process is comprised of digital waves and digital undertows, with a conduction relationship between the two.In the three stages of digital development, explosion, and reconstruction, both micro-level short-term significant influences (digital waves) and macro-level long-term underlying influences (digital undertows) exist.Driven by national key policies, enterprises undergo technological and organizational transformations, forming digital waves; these transformations further radiate to industries, leading to the formation of digital undertows, ultimately driving the digital surge, clustering, and reconstruction of strategic emerging industries, and promoting their integration cluster development.(2) Within the TOE framework, the transmission and transformation mechanisms of the three stages of digital transformation driving the strategic emerging industry integration cluster development are the same.The wave-undertow transformation mechanisms in each stage are based on environmental factors, which promote micro-level technological and organizational digitalization and radiating to industries, forming new environmental factors.As the development stage progresses, the digitalization degree of technological and organizational elements deepens, and environmental factors at the digital wave level become increasingly refined, while those at the digital undertow level continue to iterate, driving the evolution of digital undertows to digital waves in the next stage.(3) To achieve the goal of integration cluster development and cultivating future industries, strategic emerging industries need to make differentiated strategic choices based on their main business during the digital transformation process.Digital native enterprises should adapt to the waves and focus on the undertows, while digital non-native enterprises should ride the undertows and focus on the waves.To drive the surge of future industries through strategic emerging industry integration cluster development, it is necessary to adopt scenario-driven and data-enabled strategies, waiting for digital reconstruction to be completed, and then driving the next wave of digital waves and undercurrents, thereby catalyzing the future industry revolution.
This paper makes the following contributions: theoretically, it enriches the research perspective on digital transformation and deepens the understanding of the mechanisms of digital transformation's influence on the process of strategic emerging industry integration cluster development; practically, it can provide a certain realistic basis for the strategic positioning and planning of related industries, and offer reference for relevant managers and policymakers involved in digital transformation, strategic emerging industries, and future industries.

The Chinese government has been endeavoring to have carbon dioxide emissions peak before 2030 and achieve carbon neutrality before 2060, which has heightened governmental and societal attention to corporate ESG (Environmental, Social, and Governance) performance. Agricultural listed companies, as the core carriers of the green technology innovation system, not only construct mechanisms for enhancing multidimensional environmental benefits through green technology research and application innovation but also create synergies along the industrial chain via innovation. In this context, it is essential for agricultural listed companies to prioritize ESG performance, enhance the quality and transparency of ESG disclosures, ensure standardized and sustainable high-quality development pathways, and continuously improve green technology innovation capabilities. Existing research indicates that ESG practices in China remain nascent, with no consensus on how its three dimensions (environmental performance, social responsibility, and corporate governance) impact corporate performance. Few studies have directly linked corporate ESG performance, green technology innovation, and corporate performance, particularly exploring the moderating role of green technology innovation in the "ESG-performance" nexus. Notably, specialized research on agricultural listed enterprises is scarce. Therefore, this study focuses on agricultural listed companies to investigate three core relationships: the impact of ESG performance on corporate performance, the effect of green technology innovation on corporate performance, and the moderating role of green technology innovation in the relationship between ESG performance and corporate performance.
This paper examines ESG performance among China′s agricultural listed companies, exploring the interplay between ESG performance, green technology innovation, and corporate performance. The study employs initial samples of agricultural firms listed on China′s A-share market from 2013 to 2023, selecting 821 valid observations following rigorous screening. It utilizes descriptive statistical analysis, correlation analysis, and regression modeling to test the hypotheses. Additionally, robustness checks are conducted to address potential endogeneity issues, omitted control variables, and alternative sample intervals. Further analysis examines the distinct impacts of the environmental, social, and governance dimensions of ESG on firm performance. Heterogeneity tests are also performed based on ownership structure (state-owned versus non-state-owned) and regional distribution (eastern versus non-eastern regions).
According to the empirical analysis, it is concluded that (1) agricultural listed companies with superior ESG performance exhibit significantly higher corporate performance; (2) green technology innovation exerts a positive impact on corporate performance; however, this facilitating effect not only exhibits a time-lagged characteristic but also demonstrates a progressively declining trend over time; (3) investments in green technology innovation act as a significant moderator, amplifying the positive influence of ESG performance on corporate performance; (4) heterogeneity analysis further reveals that non-state-owned and eastern-region agricultural listed companies demonstrate stronger ESG-driven performance enhancements, particularly under the moderating effect of green technology innovation. Thus, in regions with relatively weaker economic foundations and imperfect market mechanisms, it is necessary to formulate and actively utilize ESG strategies to promote sustainable agricultural development and modernization in these areas. In contrast, in the eastern regions, the focus should be on strengthening ESG disclosure and regulatory oversight, and driving agricultural sustainability to a higher level. Non-state-owned enterprises should closely monitor their performance in environmental protection, social responsibility, and corporate governance on an ongoing basis; while state-owned enterprises should gradually increase their investment in the ESG domain, continuously optimize their internal control mechanisms, and improve efficiency levels to resolve existing issues and achieve sustainable development.
The theoretical contributions of this study are threefold. First, it uncovers the significant positive impact of ESG performance on agricultural listed companies' corporate performance, enriching ESG-related research and providing empirical evidence for understanding how agricultural enterprises can optimize ESG practices to enhance economic benefits within a sustainable development framework. Second, it highlights the dynamic, time-decaying nature of green technology innovation's performance-enhancing effects, deepening scholarly insights into the temporal evolution of green innovation's economic consequences. This offers policymakers and corporate managers theoretical guidance for designing innovation incentives and strategic investments with temporal considerations. Third, it underscores the catalytic role of green technology innovation in advancing corporate sustainability strategies, opening new theoretical perspectives for research in related fields.

In the process of promoting carbon neutrality and peak carbon emissions, tackling heavy-polluting enterprises is of utmost importance. With the intensification of resource constraints, stakeholders are not only concerned about the operational performance of these enterprises but also pay close attention to their environmental performance. With policy support for building innovation alliances among enterprises, if positive green innovation linkage exists among heavy-polluting enterprises, it will significantly improve their green innovation efficiency, promote green innovation within these enterprises, enhance their carbon performance, and elevate the overall green innovation capabilities of the heavy-polluting industry. Undoubtedly, this will be a tremendous driving force for achieving China's low-carbon transformation and "dual carbon" goals.
Therefore, in accordance with the "Guidelines for Environmental Information Disclosure of Listed Companies" published by the Ministry of Environmental Protection, this study categorizes 16 industries as heavy-polluting industries. It utilizes data from heavy-polluting listed companies on the Shanghai and Shenzhen A-shares exchanges from 2010 to 2021 as samples. Through empirical methods such as panel regression and rolling regression, it examines the evidence, mechanisms, and economic consequences of green innovation linkage among heavy-polluting enterprises. Specifically, by constructing a linkage model and conducting panel regressions, it explores the performance of green innovation linkage among heavy-polluting enterprises in the current period and lagged periods. By grouping them based on median values and conducting regressions, the study investigates the heterogeneity characteristics of green innovation linkage among heavy-polluting enterprises. Through a three-step model, it examines the mechanisms behind green innovation linkage among heavy-polluting enterprises from the perspectives of "knowledge spillover" and "risk avoidance".By employing rolling regression, it obtains the linkage coefficient of green innovation among heavy-polluting enterprises. This coefficient is then used as an explanatory variable, while the manually collected and calculated carbon performance of enterprises, specifically the ratio of carbon emissions per unit of revenue, is used as the explained variable. The benchmark regression model includes appropriate new control variables to examine the economic consequences of linkage.
The research indicates that there is negative linkage in green innovation among heavy-polluting enterprises in the current period, but positive linkage in the two lagged periods. This result is robust even after undergoing a series of robustness tests. Heterogeneity analysis shows that when the digitization level of enterprises and the level of information flow in their respective regions are high, the negative linkage in green innovation among heavy-polluting enterprises is mitigated, leading to enhanced positive linkage in lagged periods. Mechanism analysis reveals that green innovation linkage among heavy-polluting enterprises is mainly achieved through knowledge spillover and risk avoidance mechanisms. Specifically, knowledge spillover acts as a mediating effect in the lagged periods, while risk avoidance acts as a mediating effect in the current period and a masking effect in the lagged periods. Examination of the economic consequences of linkage shows that when there is positive green innovation linkage among heavy-polluting enterprises in the lagged periods, it significantly improves their carbon performance.
According to the conclusions drawn from the above research, policy recommendations should be formulated from the perspectives of both enterprises and the government. For enterprises, firstly, they should enhance the construction of internal and external information exchange and sharing platforms, and increase the breadth and depth of communication and cooperation with peer enterprises. Secondly, enterprises should raise their environmental awareness, shoulder the responsibility of environmental protection, and actively improve their carbon performance levels. Thirdly, enterprises should enhance their risk control capabilities and strike a balance between resource allocation and risk-taking. Finally, enterprises should prioritize the development of digital technologies and formulate digital transformation plans tailored to their own needs. As for the government, firstly, it should attach importance to infrastructure construction, build advanced transportation networks, and utilize the convenience brought by knowledge flow and innovation resource spillovers for enterprises. Secondly, it should promote the deep integration of digitization and enterprise green innovation, guiding enterprises to establish green innovation management systems characterized by digitization. Thirdly, the government should strengthen its supervisory role, create a fair competition environment, and encourage enterprises to actively fulfill social responsibilities and engage in green innovation activities.

Against the backdrop of “carbon peak and carbon neutrality”, China is accelerating the construction of a green, low-carbon and circular development economic system.This effort is designed to catalyze a shift in economic development paradigms through the lens of green innovation. While corporate investment in green innovation has seen a sustained increase, and the volume of green innovations has surged, and there are strategic green innovation behaviors funded by patent policies, resulting in enterprises focusing on the quantity of innovation while ignoring the improvement of innovation quality. Therefore, exploring how enterprises can improve the quality of green innovation and realize the "win-win" of economic and environmental benefits has become the focus of current research.
Environmental, social and governance (ESG), as a comprehensive framework for evaluating the sustainable development of enterprises, can significantly enhance investor confidence, improve consumer loyalty, and then bring economic benefits and social influence to enterprises. Its disclosure level is also increasing year by year. Does enterprise ESG performance have a positive impact on the quality of green innovation? Is the impact heterogeneous among different types of enterprises? What is the underlying mechanism of action? Answering the above questions is helpful to grasp the mechanism of ESG performance of enterprises as a whole, and improve the quality of green innovation of enterprises.
Following stakeholder theory, this study selects panel data of Chinese A-share listed companies from 2010 to 2021 as the research sample, and uses a two-way fixed effects model to empirically analyze the impact and mechanism of enterprise ESG performance on the quality of green innovation. It employs the patent knowledge breadth method to measure the quality of green innovation as the dependent variable enterprises, and uses the Huazhong ESG rating index to reflect the ESG performance of Chinese listed companies as the explanatory variable. The results show that enterprise ESG performance can significantly promote the green innovation quality. After a series of robustness tests such as instrumental variable method and PSM test, the conclusion still holds true. Moreover, the impact of enterprise ESG performance on green innovation varies across enterprises, industries, ESG levels, and green innovation quality. Corporate ESG performance has a more significant promoting effect on large-scale, non-state-owned enterprises, non heavy polluting enterprises, low ESG levels, and high green innovation quality enterprises. Furthermore, the ESG performance of enterprise promotes the improvement of green innovation quality by increasing internal funding acquisition, talent agglomeration, and strengthening external social supervision. Among them, the transmission effect of social supervision is the strongest, followed by fund acquisition, while the role of talent agglomeration is the weakest. Environmental regulations have a significant negative moderating effect on promoting the improvement of green innovation quality in ESG performance.
Drawing from the conclusions presented, this study offers a series of strategic recommendations. Enterprises are encouraged to, firstly, establish a sustainable development oriented ESG disclosure system, an ESG data collection and monitoring systems based on international standards, a special ESG management committee, and an internal evaluation mechanism. Secondly, differentiated strategies should be adopted according to the characteristics of different enterprises. For example, small-scale enterprises should strengthen cooperation with universities and research institutions; state-owned enterprises can further clarify property rights and management responsibilities; heavy polluting enterprises should develop and apply advanced emission reduction technologies and clean energy; enterprises with high ESG levels can participate in technical cooperation R&D projects and technical forums; low green innovation quality enterprises should establish an environmental and social responsibility office or team to focus on monitoring and improving ESG performance. The government needs to strengthen the synergy of environmental policies and encourage enterprises to improve their ESG performance. Finally, in order to improve the quality of green innovation, enterprises can increase environmental protection awareness to foster a culture of environmental stewardship, use multi-channel financing to reduce costs, and emphasize the utilization of talent agglomeration effect.

In the rapidly evolving technological landscape and increasingly competitive market environment, enterprises have become acutely aware that relying solely on internal R&D is no longer sufficient to meet the ever-changing market demands. Faced with this challenge, there is an urgent need for enterprises to transform their innovation methods by tapping into external sources of innovation. Users, not only as the ultimate consumers of products but also as vital participants in innovation, provide invaluable insights and directions for innovation through their needs and feedback during the product usage process. Moreover, the innovative consciousness of users serves as an essential driver for enhancing corporate innovation capabilities. Therefore, it is necessary to investigate the innovation value, specifically the contributions in idea implementation by users within open innovation communities. Existing research has predominantly focused on the impact of the characteristics of ideas themselves, yet it lacks a systemic logical framework at the individual level that can represent individual characteristics. This study utilizes personality traits to characterize individual differences among users and introduces a framework grounded in the theory of cognitive-affective personality systems to examine the impact pathways and boundary conditions of user personality traits on implementation contributions.
This study focuses on users within the representative open innovation community, specifically the Xiaomi MIUI community, as its research sample. The MIUI community is an open and successful innovation community and has attracted a large number of Xiaomi fans. Xiaomi users are free to comment and provide feedback on product innovation and development, such as creative solicitation, version testing, or marketing. The effective integration of user ideas and internal resources of Xiaomi has driven the iterative innovation of MIUI mobile operating system. Research data from a total of 48 354 Xiaomi users was obtained for the study. Utilizing text mining techniques to extract data from user comments, the study constructs scores for users' personality traits using the Linguistic Inquiry and Word Count (LIWC) dictionary method. Additionally, natural language processing techniques are employed to obtain scores for users' emotional inclinations. It then validates the chain mediating effect of cognitive ability, emotional orientation, and user engagement behavior on the contribution of different personality traits to idea implementation contributions. Furthermore, the study investigates the moderating effect of peer recognition by defining variables and constructing a negative binomial regression model.
The findings reveal that users' openness significantly and positively influences idea implementation contributions, whereas neuroticism has a significant negative impact. Cognitive ability and user engagement behavior act as chain mediators in the relationship between openness and idea implementation contributions, while negative emotions and user engagement behavior mediate the relationship between neuroticism and idea implementation contributions. Moreover, peer recognition significantly and positively moderates the mediating role of cognitive ability in the link between openness and idea implementation contributions. Peer recognition also significantly and positively moderates the mediating role of negative emotions and user engagement behavior in the relationship between neuroticism and idea implementation contributions.
This study constructs an interdisciplinary theoretical framework to explain how personality traits influence users’ idea implementation contributions. Drawing upon the cognitive-affective personality system theory, it introduces a chain-mediated effect of cognition/emotion and user engagement behavior, while also considering the moderating effect of peer recognition within community contexts. In order to enhance innovation capabilities through self-built open innovation communities, enterprises should give priority to incentivizing users with open personality traits, while managers should adopt more refined and personalized strategies to activate users' cognitive abilities, emotional expressions, and engagement behaviors, and the innovative value in users' negative emotions should be explored. This study contributes significantly to understanding the mechanisms underlying users’ idea implementation contributions, and offers insights for optimizing user management in open innovation communities for enterprises. Future research can further validate the accuracy of measuring personality traits by conducting comparative verification through the distribution of questionnaires, and more investigations and studies can be conducted in other open innovation communities to further enhance the reliability of the research. In addition, the study concludes that the mediation results show only partial mediation effects, therefore, there are other mediation effects that have not been confirmed and could be further explored in future research.

Guided by the comprehensive deepening of the innovation-driven development strategy, the manufacturing industry is transitioning from a traditional development model that is oriented toward efficiency and driven by quantity to a new model characterized by innovation, quality, and intelligent manufacturing. "To C" intelligent manufacturing enterprises driven by digital technologies can only remain competitive in the fiercely competitive market by continuously generating new ideas and converting them into new products that are both novel and valuable. However, current research on intelligent manufacturing primarily focuses on macro topics like technical architecture, often overlooking the driving mechanisms of internal human resource allocation on team creativity in new product development. To address this gap, this study proposes a novel construct of intelligent manufacturing informational faultlines, providing a fresh theoretical foundation and research perspective for companies to stimulate team creativity in new product development from the lens of team work mindset.
This paper integrates the social information processing theory with the integrated model of individual growth at work to explore the impact mechanism of intelligent manufacturing informational faultlines on team creativity in new product development. It analyzes and empirically tests the mediating role of team thriving at work (team learning and team vitality) and the moderating role of human-AI collaboration. The empirical analyses of the hypotheses based on the data from 306 samples reveal that intelligent manufacturing informational faultlines positively promote team creativity in new product development; team learning and team vitality serve as mediators in the relationship between intelligent manufacturing informational faultlines and team creativity in new product development; additionally, human-AI collaboration not only reinforces the positive relationships between intelligent manufacturing informational faultlines and both team learning and team vitality, but also amplifies the mediating pathways through which intelligent manufacturing informational faultlines affect team creativity in new product development via team learning and team vitality.
Compared to existing literature, this study makes the following theoretical contributions: First, it advances the application and development of informational faultlines in the context of intelligent manufacturing and validates the theoretical model of the influence of intelligent manufacturing informational faultlines, human-AI collaboration, and team thriving at work on team creativity in new product development. This enriches the understanding of the theoretical implications of informational faultline theory and fills the gap in research on the impact of intelligent manufacturing informational faultlines on team creativity in new product development. Second, it uncovers the mediating role of team thriving at work (team vitality, team learning) in the relationship between intelligent manufacturing informational faultlines and team creativity in new product development, thereby further opening the "black box" of how intelligent manufacturing informational faultlines influence new product creativity. Third, it reveals the moderating role of human-AI collaboration in the relationship between "intelligent manufacturing informational faultlines—team thriving at work (team vitality, team learning)—new product creativity", providing a novel and important perspective for understanding the boundary conditions under which intelligent manufacturing informational faultlines affect team creativity in new product development.
This study offers three practical implications: First, when constructing innovation-oriented R&D teams, enterprises should actively incorporate intelligent technologies such as artificial intelligence and big data analytics. It is essential to recruit members who can complement the team's tasks with diverse expertise, functions, and proficiency in AI technologies. Furthermore, fostering cross-disciplinary and interdepartmental communication can be encouraged through measures such as implementing a cross-departmental job rotation system. Second, organizations should regularly organize professional development opportunities for members through specialized training, online courses, or short-term visiting scholar programs to facilitate the acquisition and assimilation of new knowledge and information. Additionally, team cohesion and emotional communication among members can be enhanced through team-building activities, quality development exercises, or creative workshops. Material incentives may also be employed to boost employee motivation. Third, organizations should periodically provide training related to collaborating with AI, including teaching employees to effectively integrate and utilize knowledge and resources from different subgroups through AI tools, as well as to enhance communication with other organizational members via AI systems.

Although the number of patent applications and patent authorizations in our country has increased steadily, a large number of patents have not been implemented to gain technical advantages or economic benefits, which not only wastes social resources but also hinders the creation of a positive innovation atmosphere across society. Currently, our country is focusing on high-quality development as the thread of work, vigorously developing new quality productivity. This background underscores the importance and urgency of patent transformation and application, as the implementation of patents can supply technological achievements to the market and promote societal industrial transformation. The fourth amendment to the Patent Law of the People's Republic of China added a patent open licensing system. After preliminary pilot experiments, the implementation of the open patent licensing system is now being fully advanced.
The patent open licensing system is a normative system supported by administrative authorities to encourage patentees to actively participate in the transformation and application of patents through licensing, to complete the matching of technology supply and demand, and to achieve the goal of market-oriented allocation of factors. The system incentivizes patentee participation with reductions in annual fees, increases transaction efficiency with standardized licensing conditions, and ensures the smooth operation of the system through the intervention of public power. Universities and research institutes have great potential for patent technology innovation but a low rate of patent transformation, so they are a key target for incentives under the patent open licensing system. The establishment of the patent open licensing system is not only to meet the current needs of patent transformation, but also reflects China's determination to improve the quality of patents and further enhance the national overall innovation level.
In order to ensure the patent open licensing system can fully exert its incentive effects and at the same time make the system consistent with the national long-term development goals, this paper conducts a comprehensive and in-depth analysis of the content of the system: (1) The provisions regarding licensing fees do not conform to patent transaction practices, which may suppress the enthusiasm of domestic and international entities to participate. (2) Reductions in annual fees are an important means to attract patentees to make patent open licensing declarations, but legal provisions are still uncertain and may induce moral hazard. (3) The implementation of the patent open licensing system at every stage cannot be separated from the support and guidance of administrative authorities, but currently, the functions of administrative authorities lack clear stipulations, which may lead to improper government intervention in the market. To reduce conflicts between specific measures within the patent open licensing system, it is necessary to adjust and optimize the patent open licensing system from the perspective of systems theory, specifically focusing on three aspects: the system's philosophy, structure, and specific measures.
This paper advocates the following conclusions: First, the patent open licensing system undertakes the task of promoting the transformation and application of patents in the short term, but it should also aim for long-term goals of improving patent quality and fostering an ecosystem of high-quality innovation. Second, during the implementation of the system, administrative authorities should respect the laws of market operation, avoid interfering with the autonomy of the parties, and retain space for autonomous negotiation for the parties. There should be a formal review of open licensing declarations, an information disclosure system for ex post supervision, and a strengthening of the connection with the patent examination and patent invalidation systems. Third, the proportion of annual fee reductions should be set within a certain range, with differentiated reductions based on the implementation of patents. To prevent open licensing parties from speculating and arbitraging, and to filter out low-quality patents, a mechanism for the return of annual fees should be added, comprehensively examining the subject's subjective intentions, objective actions, and the implementation of patents, setting different amounts of annual fee returns, and pairing them with other types of punitive measures. Fourth, administrative authorities should strengthen their service functions and guide patentees to pay attention to the benefits through patent use through diversified measures, and better balancing the interests of patentees and licensees.

The study embarks on a nuanced exploration aiming to unravel the intricate mechanism governing the relationship between the hierarchy of patented technological convergence (HPTC) and patent market value (PMV).Its overarching objective is to furnish the intellectual landscape with novel theoretical paradigms and methodological frameworks that can effectively nurture high-value patents, thereby propelling the quality-driven evolution of intellectual property rights in China.The paramount significance of enhancing patent market value is duly acknowledged, serving as a cornerstone in the endeavor to establish a resilient intellectual property regime.This imperative is underscored by the relentless march of scientific and technological progress, a phenomenon characterized by escalating complexity and the pervasive influence of technological convergence on global innovation trends.In this context, interdisciplinary research and patent activities emerge as pivotal features, emphasizing the criticality of cultivating high-value cross-disciplinary patents as a means to fortify the foundations of a robust intellectual property ecosystem.
The HPTC is an innovative conceptual framework crafted through the meticulous synthesis of patent metrics and insights garnered from the realm of technological convergence.The present study endeavors to undertake a comprehensive synthesis and refinement of the HPTC concept.This hierarchical convergence framework, marked by its nuanced classification of patents across diverse domains, not only offers a fresh theoretical perspective but also proffers a methodological framework poised to foster the cultivation of high-value patents.
Informed by the pioneering insights of Genrich S.Altshuller and his Theory of Inventive Problem Solving (TRIZ), the study further delves into the interdisciplinary nature of technologies across different domains.Altshuller's seminal theory posits inherent patterns in the innovation process, advocating for the analytical scrutiny of periodicity patterns as a means to efficaciously assess new technologies and address innovation challenges.Given that emerging technologies often crystallize through the fusion of existing ones, the interdisciplinary scopes may vary, potentially imparting differential impacts on the value of inventions.To operationalize this theoretical framework, the study integrates Altshuller's approach with the Cooperative Patent Classification (CPC) system, thus offering a universally compatible framework anchored on tangible products rather than functional biases.This culminates in the proposition of five levels of patented technology convergence - sections, classes, subclasses, main groups, and subgroups, thereby enriching the discourse on technological convergence in patents.
This study constructs the theoretical model to explore the impact of the hierarchical convergence of patented technologies on patent market value, encompassing both a direct effects model and an intermediate effects model, with patent technology quality serving as the mediating variable.Then, it conducts empirical research using sustainability technologies (ST) patent data from Chinese companies spanning the years 1985 to 2019.The results show that higher-level patented technological convergence has a greater positive impact on the market value of patents, and it generates a greater positive impact by influencing the quality of patent technology.
This research conclusion provides new theoretical perspectives and logical approaches for cultivating high-value patents in China and also provides decision support for sustainable technological innovation in China.First, against the backdrop of multi-disciplinary cross-convergence and multi-technical cross-border integration, we have fully realized that stepping up the strategic direction of multi-disciplinary cross-convergence is not only the need to cope with changes and open up new situations but also the need to face the future and win the future.However, in this process, one cannot ignore the fact that technological convergence is evolving into more complex and heterogeneous forms.Exploring new characteristics and convergence mechanisms of technology convergence requires a more fine-grained perspective.Second, optimize high-value patent research and development and patent commercialization strategies, and strengthen strategic planning and management of cross-level patent technology convergence.For China's ST patents, there is no significant difference in the number of patents arising from higher-level technological convergence and patents arising from lower-level technological convergence, but the impact of the two on the patent market value is somewhat different.Therefore, there is an urgent need to accelerate the construction of interdisciplinary teams and promote collaboration and knowledge exchange among different fields to more effectively deal with complex technical challenges.