Studies in Science of Science 科学学研究 is a comprehensive academic journal founded in 1983 and originally sponsored by the Chinese Society for Science of Science and S&T Policy Research. It is now jointly run by the Chinese Society for Science of Science and S&T Policy Research, the Institutes of Science and Development of the Chinese Academy of Sciences, and the Research Center for Science, Technology and Society of Tsinghua University. Studies in Science of Science is an interdisciplinary journal that examines science, technology, and innovation in close connection with economic, business, and management development. The Issues focuses on major national policies and widely debated events in science and technology innovation, especially their economic and industrial implications. It promptly presents academic viewpoints, the latest research progress, and policy recommendations including those related to industrial upgrading, innovation-driven growth, and science-and-technology governance that affects firms and markets. The “Theory and Methods” column takes science, technology, innovation activities and related economic and managerial phenomena as its core research objects. It highlights cutting-edge theoretical frameworks and methodological advances, encouraging contributions that integrate innovation studies with economics, management science, business strategy, industrial organization, and evaluation methods, thereby promoting theoretical and methodological innovation in these intersecting fields. The “Innovation Exploration” column is grounded in real-world practices of science and technology innovation at the levels of firms, industries, regions, and innovation systems. Using empirical research, it seeks to distill new insights from practice such as R&D management, entrepreneurship, digital transformation, technology transfer, and open innovation and to refine innovation theory with explicit attention to business models, organizational behavior, and performance outcomes. The “Frontiers and Perspectives” column takes global trends and frontiers in science and technology development as its background, examining new dynamics and their major impacts on economic structures, industrial competition, and business ecosystems. It focuses on strategies and policies for science, technology, and innovation development, and encourages authors to propose new ideas and forward-looking views whether at the level of national STI policy, regional innovation systems, or firm-level strategy as well as to engage in critique and debate that contribute to academic and practical progress. Studies in Science of Science is a monthly journal and can be subscribed to throughout China. International Standard Serial Number (ISSN): 1003-2053. Domestic Serial Number (CN): 11-1805/G3. Domestic postal distribution code: 82-315.
Editorial Department of Studies in Science of Science
How can the application of industrial robots improve the carbon productivityof enterprises?——Evidence from Chinese manufacturing listed companies
Industrial intelligence provides a feasible path for enterprises to develop new productive forces and achieve high-quality development. By incorporating carbon emissions as an input factor into the enterprise production function and using data from listed manufacturing companies, this study examines the impact of industrial robot applications on enterprise carbon productivity. The findings are: (1) Industrial robot applications significantly improve the carbon productivity of manufacturing enterprises, and this conclusion remains valid after a series of robustness tests and endogeneity treatments. (2) The mechanism of action shows that industrial robot applications not only promote the improvement of enterprise carbon productivity through resource efficiency mechanisms that improve labor productivity, capital utilization efficiency, and energy utilization efficiency, but also improve enterprise carbon productivity by using knowledge densification and enhancing enterprise adaptability. (3) Industrial robots are more effective in promoting enterprise carbon productivity in larger enterprises with lower financing constraints, enterprises with high internal control and no green M&A behavior, and enterprises in industries with higher competition and lower monopoly power. Therefore, enterprises should be encouraged to focus on the research and development of advanced industrial robots; enterprises should be encouraged to transform into learning organizations and strengthen enterprise agility management; and the carbon market should be promoted to further expand its coverage and improve the trading system.
Copyright (c) 2026 Studies in Science of Science https://creativecommons.org/licenses/by-nc/4.0
A dual case study of AI-based innovation process from a knowledge flow perspective
Artificial intelligence provides significant opportunities for enterprise technological innovation. This study, based on the perspective of knowledge flow, conducts a comparative case study of two AI startups with different industry backgrounds to explore the mechanism of AI-driven innovation. The study found that: (1) The AI-driven innovation process follows the path of "knowledge production - AI-driven market opportunity insight - knowledge acquisition - knowledge creation - knowledge application". Among them, the knowledge creation link achieves the embedded reorganization of AI and professional domain knowledge through the path of "training model - model prediction - experimental verification - iterative optimization"; (2) The internal factors affecting the innovation process include the founder's knowledge endowment, multi-skilled talents and organizational structure, while the external factors involve partner networks and technology, policies, and markets; (3) Enterprises with different industry backgrounds follow similar knowledge embedding paths, but form differentiated industry-specific models. Enterprises with different entrepreneurial backgrounds adopt differentiated knowledge acquisition strategies based on their own characteristics. This study helps to deepen the understanding of the mechanism of AI-driven innovation and provides practical insights for enterprises to accelerate AI-driven innovation.
Collaborative Innovation and Firm Appropriability: The Moderating Role of Knowledge Base
In the context of globalization and the knowledge economy, collaborative innovation has become a key path for enterprises to accelerate new product development and enhance competitiveness. Existing research mostly focuses on the value creation aspect of collaborative innovation, while the question of how enterprises should choose collaborative innovation models to ensure the value capture of innovative achievements remains to be explored. This paper examines the multiple impact paths of the breadth and depth of collaborative innovation on enterprise value exclusivity. Empirical analysis reveals that both the breadth and depth of collaborative innovation have a significant negative impact on primary exclusivity and a significant positive effect on secondary exclusivity. When the enterprise's knowledge base is large, the negative impact of collaborative innovation on primary exclusivity is weakened, but the positive effect of collaborative innovation on secondary exclusivity is suppressed; when the knowledge base is broad, the promoting effect of collaborative innovation on secondary exclusivity is enhanced. This study provides theoretical support for enterprises to choose appropriate collaborative innovation models, helping to better balance the tension between value creation and value capture.
How Can Traditional Craftsmanship Enterprises Achieve Breakthrough Innovation through Digital Intelligence? — A Case Study Based on Luzhou Laojiao
How traditional craft enterprises can leverage digitalization to achieve breakthrough innovation is of great significance for their adaptation to the digital wave. Based on the theory of digital innovation, this paper analyzes the mechanism of breakthrough innovation driven by digitalization in traditional craft enterprises through a case study of Luzhou Laojiao. The study found that: (1) Traditional craft enterprises mainly go through three core stages in achieving breakthrough innovation through digitalization: basic reshaping, inheritance-style translation, and extended innovation. Its essence is to take inheritance and innovation as the core and use digital technology and tools to help enterprises complete the innovative transformation of the traditional craft system into a digital craft system. (2) The "support-drive" mechanism of digital innovation is the key to achieving this innovative transformation. That is, digitalization, through the interaction of organizational support and technology drive, helps enterprises overcome the cognitive limitations and capability barriers of inheritance and innovation of traditional crafts in the digital economy era, and achieve breakthrough innovation. The research conclusions can provide new insights into the innovative development of traditional craft enterprises in the digital economy era and help expand the theoretical research on breakthrough innovation and digital innovation.
The Innovation Mechanism and Effects: Case Study of DeepSeek-R1 Model
DeepSeek-R1, developed by DeepSeek, has garnered global attention for its low-cost, high-performance, and open-source model. However, while existing research extensively discusses its innovative impact, it fails to reveal its underlying innovation mechanism. This paper employs a longitudinal single-case study, systematically detailing its innovation process, "business model," strategic management, and organizational structure to reveal its unique innovation mechanism. The study finds that DeepSeek's founders and team not only demonstrate a dual entrepreneurial spirit of arbitrage and innovation but also a strong sense of technological self-reliance, both contributing to outstanding achievements in independent and integrated innovation. Originating from its parent company, Magic Square Quantitative AI team, the typical application scenarios of quantitative trading, data science, artificial intelligence, and large-scale models enabled DeepSeek to establish a de facto demand-driven "business model" from its inception. Furthermore, the repeated reuse and optimization of single modules during the development of the DeepSeek-R1 model fostered internal economies of scale, while the synergy between multiple modules further realized an internal "flywheel effect." Its open-source model and application diffusion, while creating external economies of scale, also feed back into itself, forming a complementary innovation effect. This study fills a gap in existing research and provides a practical explanation and theoretical foundation for subsequent theoretical research and industrial policy and governance.
Beyond Nationalism: The Challenges and The Future Approach ofResponsible Research Innovation
Responsible Research and Innovation (RRI), as a global value, is gradually expanding from a framework dominated by the North to a broad international consensus. However, despite the increasing recognition of RRI's core concepts by more and more countries, its globalization practice still faces many challenges. These challenges stem not only from cross-cultural and cross-institutional heterogeneity but also from the impact of dramatic global geopolitical changes and rising nationalist sentiments, leading to increasingly serious ethical dumping. This paper explores, from theoretical, historical, and practical perspectives, how RRI can transcend the science and technology governance model dominated by the North and form a more globally adaptable framework. Through this exploration, this paper aims to prevent RRI from becoming a victim of political games and to provide theoretical basis and practical pathways for building a more equitable and inclusive global science and technology governance system.
Global Science and Technology Governance
Artificial Intelligence for Science: Enhancing the Application Value of Research Outcomes
This study focuses on the field of two-dimensional materials in materials science. A corpus of abstracts from papers in this field was constructed to examine the potential technological applications reflected in the abstracts. A multiple linear regression model was used to test how artificial intelligence (AI) empowers scientific research and influences the technological application value of the research results. Mediation effect analysis revealed that AI-enabled scientific research can improve research efficiency, better focus on technological needs, and reflect application orientation, thereby enhancing the technological application value of the results. This reveals the pathway by which AI technology enhances the application value of research findings. Moderating effect analysis showed that the maturity of AI technology has a positive moderating effect on the technological application value generated by AI-enabled scientific research. However, the researcher's academic influence and personal experience did not significantly affect the technological application value brought about by AI-enabled scientific research.
Governance of New Type of Data Security Risks in DeepSeek-like Generative Artificial Intelligence
Data security risk governance in generative artificial intelligence (AI) is a significant current challenge. Existing data security governance systems for generative AI fail to establish effective, categorized governance mechanisms for different data security risks, resulting in a disconnect from the realities of evolving generative AI, particularly with models like DeepSeek. DeepSeek models, featuring multi-head latent attention mechanisms, hybrid expert models, and pure reinforcement learning, exhibit novel characteristics that alter or transform existing data security risks, leading to new data leakage, data deviation, and data deletion risks with varying operational logics. To address data leakage risks, both technical and managerial prevention mechanisms should be established. For data deviation risks, emphasis should be placed on fine-tuning both data input and output, different from manual oversight, and establishing algorithmic interpretation rights for data output results. For data deletion risks, a shift from absolute data deletion to relative deletion with dynamic verification mechanisms should be implemented.
Legitimacy Construction of AI Tools: A Cognitive-Behavioral Interaction Perspective
This study, based on the perspective of employee cognition-behavior interaction, delves into the interactive relationships and mechanisms among employees' perceived professional identity threats, impression management behaviors, and AI legitimacy construction during the introduction of artificial intelligence (AI) tools into organizations. The study first identifies technological attributes, task characteristics, and organizational environment as key contextual factors triggering employees' perceived identity threats, and proposes three specific types of perceived identity threats: professional status threat, professional competence threat, and autonomy threat. It elaborates on how different threat perceptions drive employees to implement specific impression management behaviors and how these behaviors generate cognitive reinforcement. Furthermore, the study reveals that employees' impression management behaviors not only affect the legitimacy construction of AI tools within the organization at the cognitive, practical, and normative levels, but also that the degree of AI tool legitimacy construction conversely influences employees' perceived identity threats and impression management behaviors, thus forming an interactive feedback mechanism. The study theoretically integrates and deepens the understanding of identity theory, impression management theory, and organizational legitimacy theory, and provides clear strategic guidance for management practices, helping organizations effectively address the complex challenges of employee cognition, behavior, and legitimacy management during the application of AI tools.
From Science to Politics: Insights from the Climate Change Response Model for Artificial Intelligence Governance
The serious risks arising from the rapid development of artificial intelligence (AI) are becoming increasingly prominent, and its governance has become a major contemporary challenge of common concern to the global academic community, governments, and international organizations. However, a breakthrough governance solution has yet to be formed. By analyzing climate change as a typical case of global governance, this study finds similarities between AI and climate change in many aspects, such as the process of risk recognition and governance response. Relevant historical experiences can provide a reference paradigm for AI governance. Using comparative and historical research methods, this study traces and compares the development, evolution, and governance processes of both. The study finds that climate governance follows a gradual global collaborative governance path of "scientific consensus-driven - political agenda response"; while AI has accelerated the formation of a broad consensus from risk identification to global co-governance, and is gradually shifting towards the political agenda and taking related actions. In view of this, we can fully draw on the valuable experience of global climate change governance, negotiate the establishment of a similar international AI governance institution, reach a global AI agreement, and formulate a global governance framework and standards to unify action plans and effectively promote global AI governance.
How Can Indonesia Advance Its Economic Potential Through Digital Transformation and Business Resilience in The Local Digital Economy?
This study examines the role of digital transformation (DT) in enhancing the development of small medium enterprises (SMEs) within Indonesia’s local digital economy (LDE), emphasizing the mediating role of business resilience (BR). Using a quantitative approach, data were collected from 370 SMEs owners across Indonesia through structured questionnaires and analyzed using structural equation modeling (SEM). The results reveal that DT significantly improves LDE performance both directly and indirectly through BR. Firms that integrate digital technologies with adaptive capabilities demonstrate greater ability to manage uncertainty, maintain operational continuity, and sustain long-term competitiveness. These findings extend the literature on digital transformation by integrating dynamic capability and resource-based perspectives, highlighting resilience as a key mechanism that converts digital potential into economic value. The study also offers practical implications for policymakers and small business practitioners seeking to promote resilient digitalization strategies that foster inclusive and sustainable local economic development.
The Contribution of Green Finance and Digital Economic Development to Regional Carbon Emission Mitigation
This study examines the relationship between green finance, the digital economy, and efforts to reduce carbon emissions. The analysis employs a comprehensive dataset covering the period from 2010 to 2022, collected from thirty-one provinces, autonomous regions, and municipalities across China. The findings reveal several important insights. First, green finance plays a significant role in promoting regional carbon emission reduction. Similarly, the development of the digital economy also contributes to lowering regional carbon emissions. Furthermore, green technology innovation serves as a key moderating factor in the relationship between green finance and carbon emission mitigation. The results also indicate the presence of a threshold effect in the influence of green finance on regional carbon emission reduction, with varying impacts depending on regional population size. In addition, the contribution of green finance to carbon emission reduction differs across regions, with the strongest impact observed in western China, followed by the central region, while its effect in the eastern region is relatively weaker. Likewise, the digital economy demonstrates regional variations in its influence on carbon emission reduction, showing a stronger contribution in eastern regions, whereas its effect appears less pronounced in the central and western regions.
Assessing the Contribution of the Digital Economy to Rural Industrial Revitalization
The digital economy has emerged as a key driver of rural industrial development in China, contributing significantly to the country’s economic and social advancement. Using panel data from 30 Chinese provinces covering the period 2011–2021, this study examines the impact of the digital economy on rural industrial revitalization from a sustainable development perspective. The empirical results indicate that the digital economy significantly promotes rural industrial revitalization, while scientific and technological innovation acts as an important mediating mechanism in this relationship.The analysis further reveals notable regional heterogeneity, with the strongest impact observed in central regions, followed by western and eastern regions. Moreover, the influence of the digital economy varies across regions with different levels of rural employment. As the capacity for scientific and technological innovation improves, the positive effect of the digital economy on rural industrial development becomes increasingly pronounced. In addition, the digital economy generates spatial spillover effects, positively influencing local rural industries while exerting negative spillover effects on neighboring regions. Overall, these findings highlight the critical role of the digital economy in fostering sustainable development and promoting rural industrial revitalization in China.
Examining Smart Tourism Technology Effects on Usefulness, Service Experience, and Business Performance
Hotel employees play a crucial role in enhancing service experience and business performance through the adoption of smart tourism technology (STT). This study examines the relationships between STT attributes namely informativeness, accessibility, interactivity, personalization, and functionality and perceived usefulness, service experience evaluation, and business performance from the perspective of hotel employees. Using data collected from 274 hotel employees in Hong Kong, this study employs an empirical approach to analyze both direct and mediating effects within the proposed framework.The results reveal that personalization and functionality significantly enhance perceived usefulness, which in turn positively influences service experience evaluation and business performance. Moreover, perceived usefulness serves as a key mediating mechanism, particularly in linking personalization and functionality with service experience evaluation. These findings extend the Technology Acceptance Model (TAM) by incorporating STT attributes in the hospitality context. Practically, the study provides insights for hotel managers in leveraging smart tourism technologies to improve service quality and organizational performance.
Exploring the Mechanisms Through Which Sci-Tech Finance Drives China’s Low-Altitude Economy
This study examines the impact of science and technology finance on the development of China’s low-altitude economy, an emerging sector that plays a crucial role in the Fourth Transportation Revolution. Using a sample of 428 Chinese A-share listed firms in the industry from 2013 to 2023, this research constructs a comprehensive evaluation framework based on the structure–conduct–performance (SCP) paradigm. The empirical analysis employs fixed-effects models and mediation tests to investigate both direct and indirect effects. The results indicate that science and technology finance significantly promotes the development of the low-altitude economy, and this relationship remains robust after multiple robustness tests and endogeneity corrections. In addition, science and technology finance indirectly stimulates industry development by improving firms’ access to credit resources. The findings further show that regions with more developed financial ecosystems experience stronger promotion effects, while non-state-owned enterprises utilize financial resources more efficiently than state-owned enterprises. These results reveal the dual transmission mechanisms through which science and technology finance supports the growth of China’s low-altitude economy and provide empirical evidence for strengthening financial support policies for strategic emerging industries.
Can Generative AI Improve Corporate Values, Employee Perceptions, and Organizational Practices? A Human-Centric Case Study of Project Management SMEs in Indonesia
This study investigates the growing role of generative AI tools, particularly ChatGPT, in the context of Indonesian SMEs, with a focus on their influence on SMEs values, employee perceptions, and organizational practices. Adopting a mixed-methods approach, the study combines a literature review, two industry workshops, and a survey involving 52 professionals from various sectors. The analysis integrates thematic interpretation of qualitative data with an exploratory quantitative assessment. The findings reveal that 74% of respondents reported mixed or negative attitudes toward AI adoption, mainly due to concerns related to job security and data privacy, even though many acknowledged its potential to enhance productivity and support business automation. Furthermore, 42% of participants perceived positive changes in corporate values following the introduction of AI tools, while strong agreement emerged regarding the usefulness of generative AI in business planning, monitoring and control, and organizational integration. Although several statistically significant relationships were identified, including differences in AI use across types of organizations and work functions, these results should be interpreted cautiously because of the limited sample size. Overall, the findings underscore the need for Indonesian SMEs to balance the efficiency gains offered by generative AI with ethical considerations, human supervision, and targeted capability development. This study contributes to the growing discussion on digital transformation by offering early empirical evidence on how generative AI is beginning to reshape managerial practices, employee responses, and organizational culture within SMEs in Indonesia.
Assessing the Contribution of Green Finance and Digital Economic Development to Regional Carbon Emission Mitigation
Insurance Market Development, ICT Diffusion, and Economic Growth in Sub-Saharan Africa
This study extends the existing literature by examining the mediating role of information and communication technology (ICT) diffusion in the relationship between insurance development and economic growth in Sub-Saharan Africa (SSA). Using panel data from 36 SSA countries over the period 2007–2020 and applying the generalized method of moments (GMM), the results show that insurance penetration covering total, life, and non-life insurance along with ICT diffusion, significantly promotes economic growth in the region. The findings further indicate that ICT diffusion strengthens the growth-enhancing effect of insurance, highlighting its critical intervening role. Disaggregated ICT indicators reveal that fixed broadband, telephone subscriptions, and internet usage amplify the impact of total and life insurance penetration on economic growth, while fixed broadband plays a particularly strong role in enhancing the contribution of non-life insurance. Overall, the results suggest that although insurance development supports economic growth, its effectiveness is substantially reinforced through ICT infrastructure. Accordingly, the study underscores the importance for insurers to adopt digital technologies and leverage ICT diffusion to maximize their contribution to economic growth in SSA.
Key Drivers of Economic Growth in Developing Countries: A Comparative Study of Turkey and Bangladesh
This study examines the dynamic relationship between gross domestic product (GDP) growth and key macroeconomic indicators in Turkey and Bangladesh using panel data from the World Bank covering the period 1981–2020. The analysis focuses on population growth, inflation, literacy rate, natural resource utilization, investment, and foreign direct investment (FDI) to uncover their causal links with GDP growth. Employing the Dumitrescu–Hurlin panel causality test, the results reveal statistically significant causal relationships between GDP growth and the selected macroeconomic variables, with population growth emerging as the most influential determinant. The findings provide important insights into the distinct economic structures of the two emerging economies and offer policy-relevant implications for fostering sustainable and inclusive growth. Overall, the study contributes to the literature on economic development by highlighting the critical role of macroeconomic fundamentals in shaping long-term growth trajectories in emerging economies such as Turkey and Bangladesh.
SME Performance Outcomes: The Role of Technological Absorptive Capacity and E-Business Innovation
This study examines the relationship between technological absorptive capacity and SME business performance, with e-business innovation as a mediating mechanism. Using data from 179 sunflower oil–processing SMEs in Tanzania and applying PLS-SEM, the findings show that technological absorptive capacity significantly enhances business performance, both directly and indirectly through e-business innovation. The mediation effect is partial, indicating that absorptive capacity influences performance partly by fostering e-business innovation. Multigroup analysis reveals no statistically significant differences between SMEs in the Dodoma and Singida regions, although effect sizes are generally stronger in Dodoma, where the mediating role of e-business innovation is also more pronounced. Overall, the study provides empirical evidence of the importance of technological absorptive capacity and e-business innovation in improving SME performance and offers relevant implications for managers, policymakers, and researchers.
The Role of Research and Development in Fostering Innovation and Economic Growth in Developing Economies
This study investigates the mechanisms through which research and development (R&D) influences economic growth in developing countries. Using nine indicators drawn from three main categories in the World Economic Forum database, the analysis covers 32 lower-middle-income countries for the year 2019. Grounded in an R&D ecosystem framework, the study incorporates key components institutions, human capital, capital markets, R&D, and innovation that jointly shape economic development. Structural equation modeling (SEM) is employed to construct and test a path model capturing the interrelationships among these components. The results show that R&D activities and capital market institutions significantly enhance innovation performance; however, neither R&D nor innovation exhibits a direct or significant effect on economic growth. Consistent with a Schumpeterian institutional perspective, the findings suggest that innovation-driven growth mechanisms may be constrained in middle-income economies. The absence of a significant link between innovation and growth implies that, although R&D and capital markets foster entrepreneurial innovation, their contribution is insufficient to translate into sustained economic expansion. Overall, the results provide additional empirical support for the existence of a middle-income trap in developing economies.
Digital Infrastructure Development and Economic Growth: Firm-Level Evidence from Investment Efficiency
This study empirically investigates the impact of digital infrastructure development on corporate investment efficiency in the context of China’s Broadband China policy. Using a sample of Chinese listed firms from 2007 to 2021, the analysis employs a difference-in-differences approach, complemented by propensity score matching and instrumental variable techniques, to evaluate the policy’s effects on inefficient investment, overinvestment, and underinvestment. The results show that the Broadband China policy significantly improves corporate investment efficiency by alleviating inefficiencies, particularly those associated with excessive investment behavior. These effects are more pronounced in regions with relatively advanced infrastructure. Heterogeneity analysis further reveals substantial differences across locations, with firms situated in provincial capitals benefiting more from the policy than those in non-capital cities. Mechanism analysis indicates that improvements in managerial efficiency play a mediating role in the relationship between digital infrastructure development and corporate investment behavior. Overall, the findings provide both theoretical and empirical support for governmental initiatives aimed at strengthening digital infrastructure and promoting balanced regional economic development. Based on these results, the study proposes differentiated policy recommendations to enhance managerial efficiency through digital infrastructure development and to reduce regional disparities. Future research may extend the analysis by incorporating broader datasets and examining the effects of digital infrastructure on other firm-level outcomes, such as innovation performance and market competitiveness.
Does Financial Agglomeration Promote Balanced Growth? Evidence from Enterprise-Level Structural Change
This study investigates Chinese listed companies over the period 2013–2023 to systematically examine the relationship between financial agglomeration, enterprise structural upgrading, and economic growth disparities at the firm level. The empirical findings can be summarized as follows. First, a higher degree of financial agglomeration significantly reduces economic growth disparities among enterprises. Second, enterprise structural upgrading plays a crucial role in narrowing growth disparities across firms. Third, enterprise structural upgrading acts as an important mediating mechanism through which financial agglomeration influences economic growth disparities. Fourth, the disparity-reducing effect of financial agglomeration is more pronounced for large enterprises, while its impact on small and medium-sized enterprises is relatively weaker. Fifth, financial agglomeration is more effective in mitigating growth disparities in highly competitive industries, whereas its effect is less significant in industries characterized by lower levels of competition. Finally, in high-competition industries, the mediating role of enterprise structural upgrading between financial agglomeration and economic growth disparities is significantly stronger.
How Innovation Policies and Intergovernmental Relations Affect Cross-Regional Collaborative Innovation —A Case Study of the Artificial Intelligence Sector in the Yangtze River Delta Region
Cross-regional collaborative innovation is an important way to promote the emergence of new quality productivity, and governments at all levels guide enterprises to cross-regional collaborative innovation by formulating innovation policies. Therefore, it is of great significance to explore the influence mechanism of policy and intergovernmental relations on cross-regional collaborative innovation. The article constructs a cross-regional collaborative innovation impact analysis model based on the characteristics of innovation policies and inter-governmental relations and their differences, and conducts an empirical study on the artificial intelligence industry in the Yangtze River Delta region with the help of the TERGMs model. The study shows that: (1) the level of policy characteristics: the greater the innovation policy strength, the smaller the policy volatility, and the greater the financial support are favorable to cross-regional collaborative innovation; (2) the level of inter-governmental relations: the stronger the policy response is, the more favorable it is to cross-regional collaborative innovation; and (3) the level of policy differences: the greater the difference in the strength of innovation policies, the smaller the difference in the policy volatility, the greater the correlation of the policy content, and the greater the difference in the policy response is more favorable to cross-regional collaborative innovation. The conclusions of the study can provide a theoretical basis for the government to optimize the innovation policy to guide cross-regional collaborative innovation.
Copyright (c) 2025 Studies in Science of Science https://creativecommons.org/licenses/by-nc/4.0
Research on the mechanism and effect of digital transformation on the resilience of industrial chain——Comparison of resilience dimensions of manufacturing industrial chain
With the coordinated promotion of digital industrialization and industrial digitization, digital transformation continues to reshape the development process of traditional industries in China and has become an important driving force for building the resilience of manufacturing industry chain. This article empirically explores the impact of digital transformation on the resilience of manufacturing industry chain using the spatial Durbin model. The results show that digital transformation significantly enhances the resilience of the local manufacturing industry chain, but has a certain negative effect on neighboring regions, and the talent aggregation and the technological innovation play a mediating role in the relationship between the digital transformation and the resilience of the manufacturing industry chain. Heterogeneity analysis shows that digital transformation in the eastern and central regions has a significant promoting effect on the resilience of local manufacturing industry chains, and has a siphon effect on neighboring regions. But the digital transformation in the western region has a positive impact on the resilience of both local and neighboring manufacturing industry chains. Further analysis reveals that the digital transformation of the overall country has the most significant impact on the innovation dimension of the resilience of the manufacturing industry chain, and there are significant differences in the impact of digital transformation on the resilience dimensions of the manufacturing industry chain in different regions. The conclusion of this paper provides strong reference for accelerating the integration of digital economy and real economy and helping to enhance the resilience of manufacturing industry chain.
Who Can Gain More "Technological Dividends"?—A Study on the Heterogeneous Impact of Generative Artificial Intelligence on Researchers
With the iterative breakthroughs in technological applications of generative artificial intelligence (GAI), an increasing number of researchers have begun utilizing GAI to assist scientific work. This new wave of "technological dividends" and its potential "intelligence gap" warrant close attention. Based on empirical survey data from researchers at multiple Chinese universities, this study employs cutting-edge machine learning algorithms to systematically examine the impacts of GAI on researchers' innovative behaviors, working hours, and their heterogeneous treatment effects. The findings reveal:First, while GAI significantly stimulates researchers' innovative behaviors, it simultaneously leads to a corresponding increase in working hours, indicating that the current "technological dividends" of GAI are primarily manifested in the aspect of innovation incentives.Second, the effects of GAI demonstrate significant heterogeneity among researchers, with research output, age, and gender being critical factors influencing these differential treatment effects. Specifically, male researchers and high-output individuals exhibit more pronounced improvements in innovative behaviors under GAI's influence, while experiencing relatively limited negative impacts on working hours. Further verification confirms that high-performing researchers characterized by superior productivity constitute the main beneficiaries of GAI's "technological dividends" at the current stage.
Good Governance for Good Intelligence: Credible Governance of Artificial Intelligence Alienation
Improving the credible governance system of artificial intelligence is necessary to promote technological innovation and a legal guarantee to comprehensively respond to technological alienation and promote the development of new quality productive forces. Uncontrollable artificial intelligence has triggered a serious crisis of confidence in its application and even threatened social stability and national security. To balance development and security, the governance consensus of "credible development" should be condensed, and the flexibility and effectiveness of governance should be improved by following the theory of life cycle and technical governance. On the one hand, improves the legal compliance guidance framework before, during, and after the event, and reduces regulatory costs while improving regulatory deterrence; on the other hand, it is necessary to innovate the technology governance system, introduce regulatory technologies such as private cloud computing, supervision fine-tuning, and SAGE analysis, and improve the human-machine collaborative framework to fully implement data, algorithms, and content credible regulatory requirements. To improve the artificial intelligence safety supervision system, prevent the risk of alienation, and create a "mutually beneficial symbiosis" man-machine relationship.
R&D, Innovation, and Firm Outcomes: Market Value and Performance Evidence from the G7
This study provides the first empirical evidence on how innovation and firm growth influence performance across G7 economies, using a unique panel dataset of 252 firms observed from 2020 to 2024. Focusing on two key dimensions of firm performance labor productivity and asset turnover the analysis incorporates multiple innovation indicators, including R&D Intensity, R&D-to-Assets, and R&D Growth Rate. To mitigate potential endogeneity arising from reverse causality and omitted variable bias, the study employs a heteroskedasticity-based instrumental variable estimator that constructs internal instruments from the model’s error structure. The empirical results consistently show that innovation exerts a positive and significant causal effect on labor productivity, reinforcing its role as a critical driver of firm-level efficiency. Conversely, innovation demonstrates a negative and significant relationship with asset turnover, indicating short-term operational efficiency trade-offs, particularly among firms pursuing aggressive R&D strategies. Further analysis reveals that these innovation effects are moderated by firm profitability and industry-specific conditions, underscoring the strategic and contextual determinants of innovation outcomes. Overall, the findings highlight the dual nature of innovation simultaneously enhancing productivity while imposing transitional efficiency costs and provide important implications for corporate innovation strategy and public policy within advanced economies.
Open Source Innovation: Theoretical Connotations, Knowledge Framework and Future Prospects
With the rapid development of the digital economy and artificial intelligence, open innovation models have become increasingly widespread, emerging as a crucial driver of scientific and technological progress and economic growth. However, discussions in existing literature on aspects such as discussing the connotation of open source innovation, differentiating theoretical perspectives, and analyzing and engaging in dialogue on research topics are scattered, lacking systematic review and summary. This paper conducts a comprehensive review of 321 papers related to the theme of open source innovation that were included in SSCI-indexed journals from 2001 to 2023. Based on a review of the origin of open source innovation, it deeply analyzes the theoretical connotations of this concept and summarizes the dominant theoretical perspectives for distinguishing the existing connotations of open source innovation. Further, this paper constructs an “Element-Mechanism-Outcome (EMO)” knowledge framework for open source innovation. It summarizes four basic elements of open source innovation (open source developers, open source projects, open source communities, and open source licenses), three major mechanisms in the operation process of open source innovation (development collaboration mechanism, community regulation mechanism, and business operation mechanism), and three aspects of effectiveness in the value evaluation of open source innovation (scientific and technological effectiveness, industrial effectiveness, and social effectiveness). This paper also puts forward the future research prospects of open source innovation, thereby contributing to the expansion of knowledge in theoretical research on open source innovation and providing insights for the policy practice of open source innovation.
Does Efficiency Matter in M&A? Evidence from Indonesia’s Industrial Estate Group Companies
The consolidation of Industrial Estate companies in Indonesia into a single holding company is reshaping the structure, behaviour and business models of key actors in the country’s industrial development landscape. This study examines whether there is a relationship between the efficiency levels achieved by Industrial Estate companies and their degree of involvement and strategic role in mergers, acquisitions and consolidation processes within the holding. Because this relationship may operate in different directions, both highly efficient and poorly performing parks may be targeted for integration. The issue is relevant for a wide range of stakeholders, from policy-makers and the holding’s management to investors and regulators. We measure efficiency using two Stochastic Data Envelopment Analysis (SDEA) models. Based on a hand-collected data set of 113 Indonesian Industrial Estates, belonging to 48 corporate groups over the period 2022–2024, the results indicate that consolidation and M&A decisions essentially involve parks located at both ends of the efficiency spectrum, characterised by either relatively high or relatively low efficiency levels.