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.
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.
Public acceptance of artificial intelligence (AI) not only determines the scope of its applications but also forms the social foundation for its sustainable and healthy development. The widespread adoption of AI technologies must be built upon broad societal acceptance. AI acceptance can be understood as the public's willingness to use or purchase AI-enabled products or services. With the rise of generative AI, growing societal concerns persist regarding AI's simulation of human intelligence, potential replacement of human functions, and even threats to human agency. The proliferation of such controversies may undermine the social basis for AI's healthy development, making it imperative to investigate the mechanisms shaping public acceptance of AI.
This study develops an extended model based on the Theory of Planned Behavior (TPB), incorporating trust mechanisms to systematically examine key factors influencing AI acceptance. The findings reveal a stratified acceptance pattern: (1) For weak AI, cognitive attitudes, subjective norms, and perceived behavioral control all demonstrate significant positive effects; (2) Regarding advanced AI (encompassing strong AI and super AI), cognitive attitudes show no significant impact, subjective norms only positively affect strong AI acceptance, while perceived behavioral control remains influential for both advanced AI categories.
Mechanism analysis further identifies that trust in AI products, operational processes, regulatory frameworks, and R&D practices mediates the acceptance process. Crucially, this trust mechanism persists even in advanced AI evaluation—when individuals perceive advanced AI as reliable, beneficial, controllable, and aligned with their expectations, they exhibit greater acceptance. These findings deepen our understanding of AI's complex acceptance mechanisms and provide theoretical and policy insights for responsible AI development.
From the perspective of value rationality, the development of artificial intelligence should establish clear ethical boundaries to mitigate the tension between unlimited technological advancement and limited human rationality. If technological development is allowed to dominate the future unchecked, people may no longer benefit from its promised advantages, and resistance to AI in the form of a "new-Luddite movement" could reemerge in the near future. Therefore, it is essential to adopt forward-looking policy frameworks that address both present needs and future challenges.
To guide current AI development toward enhancing human well-being, enterprises should strengthen internal safety controls and risk prevention mechanisms, improving technical standards while enhancing both the intelligence and autonomy of AI systems, as well as their controllability. Meanwhile, government agencies must establish robust dynamic monitoring and evaluation mechanisms for AI-related risks, appropriately updating laws, regulations, and ethical guidelines to ensure AI progresses along a beneficial and responsible path. For advanced AI, which possesses disruptive potential, relevant authorities must develop proactive governance strategies. Given the significant risks and uncertainties associated with its development, these strategies should employ more "imaginative" approaches to risk regulation and comprehensive governance. In line with the principles of anticipatory governance, the framework for advanced AI must incorporate reflexive value considerations and responsible innovation paradigms. Ultimately, all stakeholders should collaborate to construct a "communication-participation-trust-acceptance" pathway for AI adoption. Technology innovators, regulators, and industry practitioners must consistently disseminate information about new technological risks to the public and establish efficient risk communication channels. This will help achieve a balanced approach to AI governance, foster societal consensus on developing safe, trustworthy, controllable, and ethically aligned AI systems, and promote scientifically viable governance solutions.
The growing importance of the interplay between fundamental research and technological innovation has marked a shift from the era of “small science,” characterized by individual scientific inquiry, to the era of “big science,” where national leadership and broader societal collaboration jointly drive progress. It has become essential for governments to strengthen the management of research organizations by concentrating key resources on advancing critical fields and core technologies to address significant strategic demands, thereby promoting organized research.
As China’s science and technology policy undergoes dynamic adjustments, there is an emerging focus on reinforcing the organized approach to scientific research. While the term “organized research” has only recently entered the discourse of Chinese national policy, Western countries have a long history of conducting extensive theoretical studies and practical explorations into the organized phenomenon of scientific research activities and institutions. These efforts have resulted in relatively sophisticated institutional frameworks and governance practices.
This paper revisits the historical evolution of organized research in the West, dividing it into four stages – the “germination stage,” the “national security-oriented stage,” the “market-oriented transformation stage,” and the “future-oriented stage.” Situated at a pivotal juncture in history, the study endeavors to delineate key concepts from significant historical events within these stages. Over time, the organized research model has adapted to societal needs and technological trends, increasingly characterized by a mission-driven nation, the promotion of interdisciplinary collaboration, and the close integration with the market. The roles of governments, academic institutions, and enterprises have continuously evolved and become more interwoven, collectively advancing the systematization and institutionalization of scientific organizations.
In addition, this paper examines the theoretical underpinnings of organized research, especially using the “connected science” model exemplified by the U.S. Defense Advanced Research Projects Agency (DARPA) as a case study, seeks to identify pathways for implementing organized research in contemporary times. The establishment of DARPA has facilitated the conceptualization of “connected science” – an innovative organizational model that bridges institutional and individual interfaces within scientific organizations while driving economic growth and technological innovation. This model certifies a flexible, innovation-oriented system distinguished by its commitment to radical innovation, deep collaboration, and a flat, tightly integrated structure among participants. It also tackles specific technological challenges by leveraging both internal and external resources, aligning national missions with individual interests, and transcending basic and applied research. The wealth of practical experience accumulated points to the effectiveness of this model in organizing and managing scientific research.
The paper concludes by proposing three theoretical logics to conceptualize the “connected science” model for understanding organized research activities. The first emphasizes the integration of technological and talent elements. Within the connected science model, it is insufficient to simply possess R&D resources and talent. These elements need to work together and be underpinned by institutional mechanisms that foster the seamless integration of diverse innovation components within organizations. The second involves bridging the gap between institutional and individual interests and aligning organizational and project visions. Innovative organizations must connect the capabilities of institutional frameworks with contributions of individual actors. This multi-level connection ensures institutions effectively harness individual expertise while aligning it with broader organizational goals. The third pertains to the necessity of coordinating organizational and project visions. Harmonizing these visions is critical to overcoming the technological path dependence that can arise from vague national missions and rigid networks. This alignment also encourages the generation of new ideas, creates innovative networks, and empowers organizations and project teams to move beyond established patterns, thus pushing forward novel developments. Taken together, these theoretical logics provide a foundation for advancing organized research to higher levels of productivity and impacts.
Organized interdisciplinary research in universities is a key form of organized scientific research, focusing mainly on the intersection of similar disciplines in natural and engineering disciplines. Under the perspective of expected governance, the promotion of organized cross-disciplinary research in universities has formed a progressive four-part process, which aims to foresee the diversified prospects of cross-disciplinary research in the light of collective interests, to advocate the integration of multiple subjects into the rationale and practical transformation chain of cross-disciplinary research, to integrate cross-disciplinary projects and educational activities in the face of the complex challenges of reality, and to build an open and holistic framework based on a unified vision, collective wisdom, and cultural infiltration. Looking to the future, universities should organize themselves to enhance their capacity and create a strategic foresight network for cross-disciplinary research; encourage diversified participation and build a cross-domain scientific and technological decision-making community; strengthen internal integration and promote cross-disciplinary projects and educational activities; cultivate a culture of openness, and carry forward the cross-disciplinary research governance experience with Chinese characteristics, so as to become an effective framework for the integration of education, science and technology, and human resources in China. It will also foster an open culture and promote the experience of cross-disciplinary research governance with Chinese characteristics, so as to make it an effective aid for the integration of education, science and technology, and talents in China.
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.
A historical examination of the institutionalization of modern science reveals deep and sustained interactions, as well as shifting balances of power, between scientific societies and universities. The seventeenth century witnessed the vigorous organizational growth of scientific societies, while traditional universities, mired in the confines of scholasticism, experienced a relative decline in their intellectual authority. In the eighteenth century, scientific societies were gradually institutionalized under state patronage and played a central role in disseminating enlightenment rationality and practical knowledge. Notably, this period also witnessed the nascent research function within universities, exemplified by the University of Halle and the University of G?ttingen, which sought to integrate critical inquiry with teaching and thereby laid the groundwork for the transformation of the university in subsequent generations. The nineteenth century marked a major turning point. On the one hand, scientific societies accelerated the process of specialization amid an unprecedented expansion of knowledge, and disciplinary boundaries became increasingly rigid; on the other hand, the locus of scientific research shifted decisively to the university, which, with its systematic system of personnel training and an increasingly pronounced research orientation, emerged as the primary driving force in the production of scientific knowledge. The impact of societies on universities can be seen in three main areas: disciplinary differentiation within universities, the organizational structure of universities, and university research activities. Scientific societies fostered the early internal structuring of scientific disciplines, thereby establishing the disciplinary framework. The development and differentiation of disciplines laid the groundwork for the lecture system in universities, while the differentiation of knowledge and the institutionalization of disciplines drove the rise of the departmental system. Furthermore, the recognition of new knowledge through journal publications, the reward mechanisms of societies, and the construction of systematically operational academic networks all effectively promoted scientific research in universities.
Through in-depth interviews with 15 innovators in the metaverse sector, this study investigates effective pathways for digital economy industry development. Integrating collaborative governance and interactive governance theories, it examines the "Meta-academy" initiative in W City as a case study. The findings reveal that collaborative governance under the Accompanied Leadership framework effectively resolves market and administrative failures through tripartite coordination (state-market-society). This approach facilitates structural reorganization of party-society relations and enhances social mobilization of innovators. The Accompanied Leadership model operationalizes multi-stakeholder collaboration through three mechanisms: legal institutionalization of community organizations, identity recognition and regulatory compliance frameworks, and relational contract-based cooperation. These mechanisms expand operational spaces for youth innovation, stimulate endogenous industry growth, and provide theoretical insights for digital-era governance practices.
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.
As a critical means to promote organizational strategy renewal and develop new business, intrapreneurship has attracted extensive attention from both practical and theoretical circles. How organizations can enhance the effectiveness of intrapreneurial activities to achieve sustained success has become the core issue. Among the various factors influencing intrapreneurial success, the growth aspirations of intrapreneurs play a pivotal role. Intrapreneurial growth aspirations (IGAs), often manifesting as a vision for scaling and expanding new business initiatives, are instrumental in driving proactive behaviors and shaping the trajectory of intrapreneurial ventures. Despite this, academic inquiry into IGAs remains conspicuously limited, with the majority of studies disproportionately focused on growth aspirations within the context of independent entrepreneurship. Such a focus has left significant gaps in understanding the distinct dynamics of IGAs, particularly given the structural and operational differences between intrapreneurship and independent entrepreneurial endeavors. Increasingly, scholars have emphasized the necessity of differentiating these two forms of entrepreneurial activity. To address these gaps, there is an urgent need to prioritize the exploration of IGAs. Specifically, identifying ambitious intrapreneurs—those who possess a strong drive to achieve growth—and investigating the contextual and individual factors shaping their aspirations are crucial steps.
Intrapreneurs, as key agents in driving organizational innovation and fostering new business development, have increasingly captured the attention of scholars. Among the various individual characteristics, intrapreneurs’ entrepreneurial experience has been identified as particularly critical, often surpassing other individual characteristics in relevance to intrapreneurial activities. This prior experience profoundly influences how individuals perceive, confront, and adapt to their current organizational context, enabling them to integrate their functional roles seamlessly into their existing knowledge structures. More interestingly, differences in entrepreneurial experience imply different antecedent identities for intrapreneurs. Individuals with entrepreneurial experience often undergo a significant identity shift—from that of an independent entrepreneur to an intrapreneur—which can profoundly shape their behaviors and attitudes in intrapreneurial endeavors. To better understand these dynamics, this study examines IGAs by comparing former entrepreneurs to intrapreneurs without prior entrepreneurial experience through the lens of role identity theory. Moreover, this research focuses on two key contextual dimensions: task context, represented by the innovativeness of intrapreneurial programs, and social context, illustrated by the presence and influence of intrapreneurship role models.
Hypotheses in this study were tested using data from 11921 intrapreneurs spanning 50 countries. The empirical results find that intrapreneurs with entrepreneurial experience exhibit lower levels of IGAs compared to their no-entrepreneurial counterparts, and this negative relationship is weakened when the intrapreneurial project is of higher innovativeness and when the intrapreneurial role model holds higher scale. The theoretical contributions of this study are manifold. First, this study addresses the lack of intrapreneurial research in terms of IGAs, thereby providing useful additions and expansions to the research in intrapreneurial field. Second, this study profoundly reveals the entrepreneurial identity of intrapreneurs who differ in terms of their entrepreneurial experience, which helps to advance and deepen role identity research in the field of intrapreneurship from an identity perspective. Lastly, this study integrates contextual research in the fields of entrepreneurship and organizational behavior, which can enrich and expand contextualized research in the field of intrapreneurship. In summary, this study provides fresh perspectives on leveraging the entrepreneurial skills of former entrepreneurs within the corporate context and offers valuable insights into the potential pathways to intrapreneurial success.
Modern industrial system serves as the material and technological foundation of a modern nation. With the rapid advancement of the new round of technological revolution and industrial transformation, China’s modern industrial system building presents enormous opportunities and remarkable achievements. However, it still faces numerous problems and challenges. Globally, international political conflicts such as the Russia-Ukraine war and Middle Eastern tensions have erupted in multiple regions, while profound changes in the global economic environment, including U.S. trade sanctions against China and Europe's "de-risking" policies, have introduced significant uncertainties to China's modern industrial system building. Domestically, challenges persist, such as low added value and high energy consumption in traditional industries, as well as insufficient core technological capabilities in emerging industries and reliance on foreign supply chain. China remains in a critical phase of overcoming obstacles in its modern industrial system building. The industrial structure transformation is a core variable in understanding the economic development disparities between developing and developed nations, while also represents an essential requirement for latecomer countries to accelerate their economic growth. From the perspective of industrial structure, a modern industrial system is characterized by a higher proportion of industries that are intensive in advanced knowledge and technology, exhibit strong innovation capabilities, and boast high value-added rates.
Therefore, this article proposes an analytical framework for industrial structural transformation in modern industrial system building. By comparing the evolution trends of industrial structures in the U.S., Germany, the U.K., Japan, and China, the study examines three dimensions: service-oriented level of industrial structure, technological level of industrial structure and digital economy export level. Among these, the service-oriented level of industrial structure refers to the degree of transformation toward a service-based economy, measured by the formula (Primary industry value-added share of GDP × 1) + (Secondary industry value-added share of GDP × 2) + (Tertiary industry value-added share of GDP × 3); the technological level of industrial structure indicates the proportion of medium- and high-tech industries within manufacturing industry, those with higher technological barriers and added value, measured by the ratio of medium- and high-tech manufacturing revenue to total industrial revenue; the digital economy export level reflects the share of digital economy-related exports in total exports, measured by the proportion of ICT (Information and Communication Technology) manufacturing and service industries exports in total export volume. Given disparities in economic development stages across countries, the analysis defines comparable periods based on per capita income levels, with a focus on structural transformations after reaching high-income status.
The study yields three key insights: first, a high-level service industry is a robust driver of national economic growth and a vital indicator of competitive advantage. Balancing the proportions of the three industries and optimizing the relationship between service industry and manufacturing industry are crucial for sustainable development. Second, high-quality manufacturing industry is central to economic stability and industrial competitiveness. Sustained innovation, flexible industrial policies, and robust infrastructure investment are essential for structural upgrading. Third, digital economy development must be prioritized, leveraging the service sector to facilitate the transition from production-oriented to consumption-driven economies, while tailoring structural upgrading pathways to national conditions.
The history of the aviation industry reveals that core and key technologies cannot be acquired through external means such as requests, purchases, or negotiations. For latecomers, achieving technological advancement requires a steadfast commitment to autonomous innovation. Most existing research provides in-depth case studies of breakthroughs in individual core and key technologies or products, implicitly assuming that the overall systemic knowledge at the overall product level is exogenously given. This approach makes it challenging to clearly identify the sources of industrial systematic growth. In fact, systemic knowledge is the foundation and key to achieving breakthroughs in core and key technologies and enabling latecomers to catch up. However, existing research has not given it sufficient attention, creating a significant knowledge gap in the theoretical framework of autonomous innovation.
To address this theoretical gap, this study takes four representative aircraft models from the history of China's aviation industry, JH-7, MA60, ARJ21, and C919, as case studies to explore the generation, accumulation, and value of systemic knowledge. The study also delves into the intrinsic mechanisms through which systemic knowledge drives the systematic growth of the aviation industry, aiming to clarify the development roadmap for China’s aviation industry and other critical fields, while supporting the nation's transition from a large manufacturing country to a world manufacturing power. The results show that, first, the generation of systemic knowledge is rooted in independent product development, emphasizing autonomous research and development to master and understand the underlying principles of technology rather than relying on surveying and mapping or imitation for technological accumulation. Second, the accumulation of systemic knowledge is achieved through product development platforms that enable technological iteration and upgrading, highlighting the strategic and leading role of systemic knowledge as critical to accelerating technological progress and achieving high-quality domestic substitution. Third, the value of systemic knowledge lies in enhancing national industrial autonomy and driving profound transformations in the industrial foundation. Furthermore, leading enterprises at the "pinnacle" of the industrial system generate and accumulate systemic knowledge through independent product development. By employing "long-arm innovation," these enterprises drive iterative advancements in component knowledge across the supply chain, enabling high-quality domestic substitution. In addition, collaboration with leading users facilitates the critical process of "learning by using," fostering user-driven knowledge innovation. This synergy establishes a market-oriented "positive cash flow development cycle," propelling systematic growth throughout the industry.
Based on these findings, China should implement mechanisms such as " horse-racing" and " open competition mechanism to select the best" in strategic emerging industries and future-oriented industries to accelerate the cultivation of leading enterprises capable of mastering systemic knowledge, thereby gaining a competitive edge in development. Simultaneously, the government should maximize its role in guiding scientific and technological innovation by fostering a "pair-share" collaborative mechanism that connects major strategic product development enterprises with leading users. Additionally, an industrial innovation system should be established, centered on the development of "pillars of a great power," which integrates basic research, breakthroughs in core technologies, and systemic knowledge innovation.
Since the 21st century, artificial intelligence (AI) technologies, particularly those exemplified by deep learning, have significantly accelerated the interdisciplinary integration of various fields. This integration has led to a surge of AI-driven scientific research, especially in the application of AI within the realm of biology (AI4B). Over the past seven decades, AI and biology have mutually influenced one another; while AI has enabled the precise resolution of complex biological problems, it has simultaneously fostered its own continuous iteration and advancement. AI4B has established a transformative model for the production of scientific knowledge through human-machine collaboration, which can be delineated across three dimensions: in the tool empowerment dimension, AI enhances task efficiency; in the deep embedding dimension, AI reshapes the scientific innovation chain; and in the general platform dimension, AI addresses multimodal tasks. The development of a human-machine symbiotic AI4B knowledge ecosystem is of paramount importance for effectively mitigating AI-related risks, formulating pertinent policies, and shaping the "emerging science and technology" that AI represents.
Against the backdrop of intensifying global technological competition and shifting geopolitical landscapes, U.S. research integrity and research security policies have undergone a pronounced strategic transformation, aiming to address heightened competition and security challenges in scientific research. To elucidate the underlying logic and evolutionary trajectory of these policies, this study analyzed 72 core federal-level policy documents issued from 2000 to 2024 using an LDA-based topic modeling approach. The findings indicate that the United States has established a three-layered policy framework—encompassing global governance, institutional regulation, and implementation support—that comprises eight principal themes, including global research governance and risk mitigation, misconduct investigation, foreign influence and conflict of interest, and peer review and ethical standards.
Over time, U.S. research integrity and security policies have shifted from a singular focus on regulating misconduct toward more multifaceted governance. Initially, policy efforts centered on addressing frequent misconduct cases and strengthening enforcement mechanisms. Since 2011, the policy landscape has expanded to emphasize a range of issues such as compliance, disclosure, peer review, and conflicts of interest. In the most recent phase, policy priorities have become more balanced, highlighting the necessity of long-term strategies that reconcile academic openness with robust security oversight. Furthermore, the study reveals notable functional and thematic variations among federal agencies responsible for formulating and implementing these policies, reflecting an increasingly specialized and collaborative approach to research governance. These insights offer a new perspective on the driving forces behind U.S. policy adjustments and serve as a valuable reference for China and other nations seeking to play a more proactive role in global research governance.
In the context of a new round of technological revolution, where corporate innovation paradigms are undergoing significant transformation, leaders can leverage paradoxical leadership to contribute managerial wisdom to technology insight. Despite its potential, empirical research exploring this relationship from an enterprise perspective remains limited. This study addresses this gap by introducing consolidation innovation as a mediating variable and the diversity of R&D teams as a moderating variable, constructing a moderated mediation model grounded in consolidation innovation theory and information decision-making theory. Empirical analysis based on data from 388 enterprises within strategic emerging industries reveals that (1) paradoxical leadership positively influences the formation of technology insight; (2) consolidation innovation partially mediates the relationship between paradoxical leadership and technology insight, indicating that part of the impact is exerted indirectly through fostering an innovative organizational environment; (3) the diversity of R&D teams positively moderates the effect of paradoxical leadership on both consolidation innovation and technology insight, suggesting that diverse teams enhance the effectiveness of paradoxical leadership; and (4) team diversity significantly strengthens the mediating role of consolidation innovation between paradoxical leadership and technology insight, highlighting the importance of leveraging diverse perspectives for enhanced innovation outcomes. These findings elucidate the mechanisms by which paradoxical leadership fosters technology insight, providing valuable insights for enterprises aiming to cultivate their capacity for technological innovation and application. The study contributes to China’s strategic objective of becoming a global leader in science and technology by highlighting the importance of paradoxical leadership and the amplifying role of diverse R&D teams. Enterprises can develop effective leadership strategies that harness these benefits, enhancing innovation capabilities and competitiveness in rapidly evolving technological landscapes. Moreover, fostering an inclusive work environment that encourages diverse perspectives and collaborative problem-solving is critical for driving innovation and achieving sustainable competitive advantage. By embracing paradoxical thinking, leaders can balance stability with change, tradition with innovation, and short-term performance with long-term vision, nurturing a culture conducive to continuous exploration and exploitation of novel technologies. The research underscores the value of investing in leadership development and promoting team diversity to yield significant returns in terms of enhanced technology insight and innovation capability, positioning enterprises to better compete in the global market.
Recent years have witnessed a surge of ethical controversies surrounding science and technology—spanning gene editing, artificial intelligence (AI), biosafety, and climate change, among others. While early “deficit models” assumed that merely increasing public understanding of science would lead to greater support for innovation, this notion proves inadequate when people confront complex and value-laden dilemmas. Increasing evidence suggests that individuals with similar scientific understanding can arrive at vastly different stances if their psychological dispositions (interest in science, trust in science, and life satisfaction) differ. This study aims to deepen the critique of the deficit model by exploring how multiple knowledge dimensions (scientific knowledge vs. general education) and psychological factors collectively influence public attitudes toward seven techno-ethical issues.
Using nationally representative data from the 2023 “Science, Technology, and Society Barometer Survey,” we examine Chinese adults’ attitudes toward gene editing, genetically modified foods, biosafety, AI, assisted reproduction, animal experimentation, and climate change. The survey measures both scientific knowledge (focused on factual, concept-based items) and general knowledge (proxied by formal educational attainment), while also assessing participants’ level of trust in science, interest in cutting-edge research, and perceived life satisfaction. Regression analyses test whether distinct types of knowledge correlate with more supportive or cautious positions on each issue, and whether psychological factors moderate these relationships.
Our findings reveal divergent effects of scientific knowledge across issues. On assisted reproduction, animal experimentation, biosafety, and AI, individuals with higher science scores exhibit more supportive or less cautious attitudes, suggesting they see the benefits and weigh risks more analytically. Yet for climate change, greater scientific knowledge heightens anxiety, likely because it sharpens awareness of long-term, large-scale impacts. In contrast, general educational background exerts weaker or inconsistent effects. Moreover, psychological factors significantly moderate these knowledge–attitude links. People who strongly trust scientists may rely less on their own educational credentials, weakening or strengthening the effect of knowledge depending on context. High interest in science can lead to selective information processing, sometimes weakening the usual positive association between knowledge and support for new technologies. Meanwhile, those with higher life satisfaction may adopt a more cautious stance, motivated by a desire to safeguard their well-being against potential disruptions.
These results underscore that public attitudes toward science are shaped by much more than informational “deficits.” Rather than merely disseminating facts, policymakers and communicators must address trust-building, support varying levels of interest, and acknowledge the role of life satisfaction in shaping how people respond to emerging technologies. This approach has the potential to foster more constructive dialogues and balanced risk-benefit assessments, facilitating alignment between scientific advancements and public values. By integrating multi-dimensional knowledge measures with psychological frameworks, this study expands our theoretical and practical understanding of why some technologies gain acceptance while others trigger concern. Ultimately, effective governance of techno-ethical controversies hinges on a nuanced approach that recognizes both the cognitive and emotional components of people’s judgments, reinforcing the need for inclusive, trust-centered, and empathetic science communication.
The business environment and industrial transfer and undertaking are important factors that regulate the high-quality development of a region. This article conducts a survey on 9 provinces (3 each in the eastern, central, and western regions) and 32 cities (7 provincial capitals and 25 prefecture level cities) in the eastern, central, and western regions of China. Based on literature data such as the Statistical Yearbook and Statistical Abstract from 2006 to 2024, 2998 survey samples were combined for interactive correlation analysis and regression analysis, which to some extent analyzes and presents the inherent connections between the three. Research has found that the business environment has a significant positive impact on regional high-quality development, and industrial transfer has a significant effect on the relationship between the business environment and regional high-quality development. There are differences and discrete characteristics in different regions, industry types, and development categories. Therefore, it is necessary to create a more accurate, scientific and systematic business environment, build a regional industrial development plan with orderly gradient transfer and reasonable echelon development, so as to promote the regional high-quality development of difference, coordination and sustainability, and accelerate the development process of Chinese path to modernization.
