This article explores how the widespread adoption of generative artificial intelligence, GAI, is reshaping the temporal fabric of postdoctoral research work, a segment of the scientific workforce that combines exceptionally high exposure to emerging automation with limited employment security and strong performance surveillance. Building on the theory of technological change, the study links substitution and complementarity channels to both objective and subjective measures of working time. Empirically, it draws on individual-level data from the 2023 Nature Global Post?Doctoral Survey, which collected responses from 3?715 postdoctoral researchers in ninety?three countries. To address the endogeneity of GAI uptake, the study employs two?stage least squares estimation, using cross?national variation in OpenAI regional access restrictions as an instrument, and triangulates the results with causal forest and Deep IV estimators in order to accommodate non?linear effects and complex heterogeneity. Across all methods, intensive GAI use is associated with statistically and substantively significant increases in weekly non?contract hours and overtime hours, averaging between 0.6 and 1.3 additional hours, along with discernible declines in reported time satisfaction and work–life balance. Mechanism analyses indicate that these outcomes arise primarily through a process of deskilling and task fragmentation rather than through productivity?induced task creation or wage gains. Specifically, the automation of routine scholarly functions, such as text drafting and coding, transfers effort toward engineering, iterative proofreading, and compliance checking, thereby reducing professional autonomy and increasing temporal fragmentation. Complementarity channels deliver few short?run benefits, as indicated by lower effective hourly earnings and muted growth in opportunities to initiate self?directed projects. Institutional context modulates the magnitude of these effects. In laboratories that exhibit entrenched overtime norms, the GAI-induced time burden is forty to seventy per cent larger, whereas in departments that report difficulty recruiting postdoctoral fellows, an indicator of stronger local bargaining power, the negative effects on both hours and time satisfaction are attenuated. Further heterogeneity analysis reveals an emergent distributional divide. Male researchers and individuals with above?median prior productivity incur smaller time penalties and, in some cases, modest efficiency gains, implying that current technological dividends accrue disproportionately to already advantaged actors and may widen inequality in early academic careers. The study contributes to debates on automation and academic labor by demonstrating that cognitive technologies can intensify rather than relieve time pressure even among high?skill workers, extending the insight of the industrious revolution literature to contemporary knowledge production. It also shows that organizational governance and labor market structure critically shape whether potential efficiency gains are converted into genuine leisure. Policy implications include the need to recalibrate performance metrics that encourage perpetual acceleration, to strengthen postdoctoral bargaining positions through transparent career pathways and collective representation, and to ensure inclusive access to high?quality AI resources and training.
Traditional regulatory models have become increasingly inadequate in addressing the complexity and rapid evolution of artificial intelligence (AI) technologies. Regulatory sandbox have gradually expanded into the AI domain, emerging as tools to balance security and development by offering flexible regulatory spaces.These sandboxes have been adopted by numerous countries worldwide.At present, mechanism design of the AI regulatory remains in the exploratory stage, with divergent practices across countries in key aspects such as legal authorization, regulatory boundaries, and governance models.This paper reviews the origin, evolutionary path and international application of AI regulatory sandboxes, and adopts the framework of "experimentalist governance" to guide the construction and operation of regulatory sandboxes.We summarize the operation processes of artificial intelligence regulatory sandboxes in 9 countries around the world, and extract the mechanisms of framework goal setting, independent experimental execution, supervision and feedback, evaluation and adjustment.This mechanism illustrates the applicability of regulatory sandboxes in enabling dynamic policy adjustment, fostering multi-stakeholder collaboration, and promoting regulatory innovation. It facilitates the continuous optimization of policy objectives by encouraging effective interactions among regulators, innovators, and consumers through real-time feedback and coordinated efforts, thereby fostering a positive relationship between technological innovation and regulatory compliance.The development of AI regulatory sandboxes should focus on sector-specific regulatory approaches, the formulation of measurable application criteria, and the enhancement of international cooperation and mutual recognition mechanisms to address global governance challenges posed by AI technologies. By promoting the localized evolution of sandbox systems and actively participating in the formulation of international standards, China can enhance its AI governance capacity between domestic and international regulatory systems.
The heightened verisimilitude of Artificial Intelligence (AI) technology is a double-edged sword, simultaneously empowering societal progress and harboring risks that undermine the authenticity of information. This paper focuses on the cutting-edge issue of Deceptive AI, systematically constructing an innovative classification framework based on the mechanism of technical verisimilitude and risk dimensions to address the increasingly severe challenge to authenticity. Transcending fragmented and isolated studies of phenomena such as deepfakes and social bots, this paper pioneeringly defines the concept of “Deceptive AI” within the Chinese context. It profoundly distinguishes and elucidates the differences and connections between “Deceptive AI” and “deception by AI,” underscoring that its core lies in the technical verisimilitude of AI in simulating intelligent behavior and the induced cognitive distortion effect. This research dissects the conceptual connotation and intrinsic mechanisms of technical verisimilitude, and categorizes five types of deceptive AI technologies—Generative Adversarial, Behavior Simulation, Semantic Manipulation, Cognitive Intervention, and System Contamination—revealing their spectrum-like evolutionary features. The study constructs a two-dimensional, four-quadrant classification framework based on “Technical Verisimilitude - Deceptive Intent,” delineating the deception mechanisms and differentiated risk spectra of Fraudulent, Simulation, Functional, and Inducing AI. High-verisimilitude AI is not merely a technical tool, but rather deeply embeds itself within the human cognitive system. It triggers profound psychological mechanisms such as cognitive shortcuts, emotional resonance, echo chambers, and authority bias, inducing collective cognitive distortion and consequently posing systemic threats to the information ecology, social trust, and even public safety. This research not only provides an original theoretical framework for understanding and addressing the authenticity risks of deceptive AI, but also aims to alert society to the cognitive risks inherent in technical verisimilitude, offering forward-looking perspectives and risk response strategies for the healthy development of a future era of human-AI coexistence.
In the era of artificial intelligence, the traditional latent advantages of backwardness are eroding, while the catch-up cycle for latecomer nations is compressing toward its theoretical limit within the economic convergence framework. Consequently, resolving the catch-up dilemma faced by these nations in the wave of AI development has become a critical agenda. This study, therefore, focuses on two pivotal questions. First, has the traditional latecomer advantage undergone a structural transformation in the AI era? Do developing countries, particularly emerging economies, retain the potential for leapfrogging development through technological catch-up? Second, how can a systematic theoretical framework be constructed to refine and expand the explanatory power of latecomer advantage theory in this new context?
Addressing these questions, this research investigates the adaptability of latecomer advantage theory in the AI age. Integrating a case study on the deconstruction of the CHIPS and Science Act of 2022, it innovatively develops a tripartite analytical framework encompassing factor competition, techno-economic paradigm, and geopolitics. This framework systematically elucidates the reconstruction logic of latecomer advantages across three dimensions: capability evolution, dynamic transition, and power realignment. Firstly, AI is shifting the driver of economic development from the industrial era’s dual engines of “energy and capital” to a synergistic triad of “data, algorithms, and computing power” in the intelligent economy. Secondly, AI not only expands the technological possibility frontier of production and innovation but also triggers a systemic shift in the economic feasibility frontier, thereby reshaping the patterns of innovation-driven growth and the genesis of comparative advantage. Finally, AI has become both a central arena for national strategic competition and a critical variable reshaping the global landscape of scientific and technological power.
Furthermore, drawing on the successful exploration of China’s Deepseek, this study reveals that latecomers should identify stable fulcrums and plan development priorities amid economic uncertainty, thereby accelerating the iterative upgrading and dynamic evolution of their latecomer advantages to foster sustained productivity growth. Firstly, enhancing the abundance and diversity of high-quality production factors and promoting the deep integration of total factor resources are crucial for cultivating high-level competitive advantages and clarifying the logic of sustained economic growth. Latecomer economies must prioritize the optimization of their factor endowment structures and the cultivation of distinctive factor advantages, a foundational strategy for enabling a shift toward higher value-added creation. Secondly, transitioning from passively receiving technology spillovers to actively nurturing endogenous innovation ecosystems serves as the core driver for the adaptive reconstruction and sustained realization of latecomer advantages. Latecomer economies should pursue indigenous innovation in critical core technologies with the strategic objectives of enhancing effectiveness, improving precision, and expanding depth. Finally, a nonlinear transition mechanism involving institutional rule-building, opportunity capture, and advantage reconstitution emerges as a novel pathway to unlocking latecomer advantages in the intelligent age.Latecomer economies should forge first-mover advantages in critical fields such as technical standards, thereby securing greater discourse power within global value networks.
In summary, the theory of latecomer advantage urgently requires adjustment, innovation, activation, extension, and consolidation at both theoretical and practical levels in the AI era. Implementing asymmetric leapfrogging strategies and pioneering differentiated competitive tracks represent effective means for latecomer nations to break free from path dependency and circumvent technological blockades. This study not only broadens the explanatory boundaries of latecomer advantage theory in the age of the intelligent economy but also provides empirical insights and practical references for the strategic choices of latecomer nations against the backdrop of a profoundly adjusting world order.
In the era of globalization, China has emerged as a pivotal player in global science and technology (S&T) governance. However, despite its significant advancements in scientific and technological capabilities, China still faces substantial challenges in constructing international technoscientific discourse power. These challenges stem from internal structural weaknesses and external pressures exerted by Western hegemony. This study, grounded in Optimal Distinctiveness Theory (ODT), proposes a novel "legitimacy-distinctiveness" dynamic equilibrium framework to elucidate the dual logic of constructing China's international technoscientific discourse power.
The study identifies two core mechanisms through which China has been able to enhance its international techno-scientific influence. First, legitimacy accumulation is achieved through active participation in existing international institutions. By strictly adhering to international rules and norms in mature governance regimes, such as fulfilling financial obligations in the International Thermonuclear Experimental Reactor (ITER) project and complying with the World Health Organization (WHO) protocols during the COVID-19 pandemic, China has been able to build a solid foundation of procedural legitimacy. This compliance not only enhances China's international credibility but also mitigates the potential risks of being marginalized by the existing Western-dominated governance structures.
Second, distinctiveness creation is realized through institutional innovation in emerging and contested technological spaces. China has been proactive in pioneering new initiatives and platforms that reflect its unique strengths and values. For instance, the establishment of the Alliance of International Science Organizations (ANSO) under the Belt and Road Initiative has facilitated South-South research collaborations and provided a new paradigm for international S&T cooperation. Additionally, China's launch of the Global Artificial Intelligence Governance Initiative has positioned it as a leader in shaping the ethical and normative frameworks for emerging technologies.
The study further reveals that China's transition from a peripheral participant to a central agenda-setter in global S&T governance is facilitated by the synergistic optimization of compensatory and integrative strategies. Compensatory strategies enable dynamic partitioning between regulatory conformity in core domains and disruptive innovation in emerging fields. For example, while strictly adhering to the Paris Agreement in climate governance, China has simultaneously pursued unique technological innovations in its BeiDou Navigation Satellite System. This approach effectively resolves the "compliance-innovation dilemma" that often plagues emerging economies.
Integrative strategies, on the other hand, foster alignment between practical-technical innovations and conceptual-normative leadership. The Digital Silk Road infrastructure projects not only enhance technological connectivity but also embody the narrative of "technology for a shared future." This alignment generates compound influence by combining hard technological capabilities with soft normative power.
In response to the growing threats of techno-nationalism and decoupling, China has deployed three evidence-based solutions. Domain complementarity involves strategic allocation of compliance and innovation efforts across different technological maturity spectra. This approach has been validated in semiconductor diplomacy cases, resulting in a 37% reduction in R&D internationalization costs. Resource orchestration emphasizes the convergence of hard and soft power, exemplified by China's leadership in 5G Standard Essential Patents (SEPs) and its ethical AI frameworks. This strategy has increased China's bargaining leverage by 43% in international standard-setting bodies such as the International Organization for Standardization (ISO) and the International Telecommunication Union (ITU). Risk hedging through institutionalized buffering via multilateral coalitions has reduced the impacts of decoupling by 83% through BRICS technology pools.
This study makes several significant contributions to the field. First, it extends the application of Optimal Distinctiveness Theory from organizational to geopolitical contexts, providing a new analytical lens for understanding power transitions in global S&T governance. Second, it re-conceptualizes techno-scientific discourse power as co-constituted by legitimacy and distinctiveness, thereby transcending traditional paradigms that focus solely on discourse power. Finally, it offers an operational blueprint for emerging economies to escape the "dependent discourse" trap and enhance their international influence in the realm of science and technology.
In conclusion, this study not only provides a theoretical framework for understanding China's pathway toward constructing international techno-scientific discourse power but also offers practical insights for other emerging economies seeking to navigate the complex landscape of global S&T governance. By leveraging the "legitimacy-distinctiveness" dynamic equilibrium framework, this study underscores the importance of balancing compliance with innovation and highlights the potential for emerging economies to shape a more inclusive and equitable global technoscientific order.
Pillar industries are the main battleground for developing new productive forces. Against the backdrop of the global industrial pattern reconstruction and the in-depth integration and accelerated evolution of a new round of scientific and technological revolutions, systematically understanding and accelerating the cultivation of new pillar industries to create a new strategic engine for the development of new productive forces has become a significant theoretical and policy issue for advancing new-type industrialization and achieving high-quality development during the "15th Five-Year Plan" period. Based on a systematic deconstruction of the evolution of pillar industry policies and academic research paradigms, this paper defines the connotation and prominent characteristics of new pillar industries, constructs a theoretical framework for the development of new pillar industries, and further proposes a "Strategy-Stage-Capability-Policy" (SSC-P) mechanism for the cultivation and development of new pillar industries. It also explores the strategic pathways for cultivating and building new pillar industries. The study provides an important theoretical basis and strategic decision-making support for exploring a Chinese-characteristic industrial policy theoretical system, building a new pillar industry system to support new-type industrialization, and accelerating innovation-led development of new productive forces.
Amidst the accelerating evolution of unprecedented global transformations unfolding over a century and the profound recalibration of international power structures, the imperative of technological security as a bulwark against national security vulnerabilities has attained critical significance; technological risk, constituting a fundamental determinant impacting the integrity of national technological ecosystems, renders the establishment of a resilient and comprehensive technological risk assessment architecture fundamentally indispensable for the preemptive identification, mitigation, and ultimate resolution of major systemic threats; effectively neutralizing technological hazards performs an irreplaceable function in the overarching mission of national security preservation, serving not only to shield national technological sovereignty from external coercion and undue influence but also to strategically foster the judicious allocation of resources and the optimization of innovation trajectories across the technological landscape; this pressing context consequently generates an urgent, non-deferrable necessity for the concurrent advancement of indigenous innovation capacities and the meticulous construction of a sophisticated technological risk assessment and preemptive warning framework. Accordingly, anchored firmly in the established tenets of risk management theory, this research erects a holistic technological risk assessment framework structured upon the interconnected triad of "Risk Source - Risk Occurrence Probability - Risk Hedging Resilience," introduces a pioneering tripartite analytical paradigm—the Technology Dependence-Control-Gap (T-DCG) Model—predicated on the critical vectors of Technology Dependence (TD), Technology Control (TC), and Technology Gap (TG), and rigorously elucidates the complex causal pathways and synergistic mechanisms through which these three constitutive elements collectively modulate and amplify the aggregate technological risk exposure; proceeding from this conceptual and methodological groundwork, a systematic quantitative apparatus for technological risk measurement is meticulously engineered, commencing with the deployment of the Armington elasticity of substitution model to compute the Technology Dependence Index (TDI), subsequently defining and operationalizing the Technology Control Index (TCI) by leveraging the regulatory scope and restriction severity codified within the U.S. Commerce Control List (CCL), then introducing and applying the Relative Technological Comparative Advantage (RTCA) index to derive the Technology Gap Index (TGI), culminating in the algorithmic synthesis of these three cardinal metrics to yield the Comprehensive Technical Risk Value (TRV), which facilitates the stratification of technological risk severity into four discrete, ordinally ranked classifications: Extremely High Risk, High Risk, Medium Risk, and Low Risk; a rigorous empirical validation exercise concentrating on the strategically pivotal semiconductor domain—encompassing fabrication equipment, foundational materials, and finished integrated circuits—robustly corroborates the methodological feasibility and discriminatory power of the proposed system, with the application of the T-DCG model yielding the following granular risk categorizations: lithography machines, photolithography chemicals, logic processors and cutting-edge memory devices assessed at the Extremely High Risk level; radio frequency (RF) components, fourth-generation semiconductor substrates ion implantation systems, and photomask substrates classified as High Risk; silicon wafer substrates, atomic layer deposition (ALD) apparatus, plasma etching tools, and metrology/inspection equipment designated as Medium Risk; while chemical mechanical planarization (CMP) polishers, CMP slurry compounds, physical vapor deposition (PVD) sputtering targets, advanced packaging substrates, and lead frame components evaluated as Low Risk; this investigation consequently furnishes a novel theoretical vantage point for conceptualizing and dissecting technological risk, delivers an innovative, quantitatively grounded methodology for the ongoing evaluation and vigilant monitoring of technological vulnerabilities, and substantially augments the granular, micro-level scholarship pertaining to security risk appraisal within the intricate and dynamic realm of technology.
From a knowledge perspective, a product constitutes a knowledge set. For a long time, academic research has conflated knowledge production in the product R&D phase with that in the product usage phase. Focusing on the issue of knowledge production during new product usage, this paper selects three cases—Comet airliner, lithography machine, and intelligent driving vehicles—and through comparative research, finds that:
First, from a temporal dimension, the knowledge set of a new product comprises laboratory knowledge from the R&D phase and user knowledge from the usage phase. User knowledge, historically overlooked, refers to new knowledge generated during the usage phase that extends beyond the scope of laboratory R&D. This new knowledge arises from unforeseen actual usage scenarios and the discrepancies between designed and real-world scenarios. The production of user knowledge embodies the technological iteration of new products: in the iterative process during the usage phase, each instance of user knowledge produced serves as a knowledge reference for the next round of laboratory-based product R&D. User knowledge and laboratory knowledge are non-substitutable and complementary.
Second, unlike laboratory knowledge production—where both “problem identification” and “problem solving” are undertaken by product manufacturers—user knowledge production follows the technological iteration logic of lead users raise problems—manufacturers solve problems. As new products may expose multiple issues during use, a small subset of users (i.e., lead users) bear the uncertainties, risks, and costs associated with new product adoption. Once a new product completes user knowledge production, it matures, and the broad base of ordinary users then becomes the foundation supporting the evolution of the new product into a new industry.
Third, generative artificial intelligence (AI) has transformed the mode of user knowledge production. Manufacturers and users can now synchronously perceive the usage environment and user behavior, enabling manufacturers to achieve vertically integrated user knowledge production. Generative AI narrows and blurs the boundary between ordinary users and lead users by “injecting the wisdom of predecessors into users’ minds,” reducing users’ uncertainties, risks, and costs, and empowering ordinary users to become lead users involved in user knowledge production. This assists manufacturers in producing user knowledge more rapidly and with higher quality, generating “intelligent solutions” for technological iteration.
Based on these findings, three implications are proposed. Firstly, a lead user supply mechanism must be established. Without lead users’ environmental perception and problem identification, manufacturers cannot complete technological iteration of new products, thereby impeding the formation of new development trajectories and industries. Secondly, the development of generative AI must be accelerated. Generative AI-empowered new products are capable of perceiving usage scenarios and collecting user behavior data more comprehensively, accurately, and rapidly. This capability enables broader participation of ordinary users in user knowledge production while promoting exponential improvements in corporate efficiency for user knowledge generation. Thirdly, the data asset management system must be rapidly improved. User knowledge production for new products—whether or not generative AI is employed—involves the use of user data. Accelerating the improvement of user data legislation and establishing a sound data property rights system will facilitate the advancement of user knowledge production.
The importance of artificial intelligence algorithms in various decision-making scenarios has become increasingly prominent, yet they simultaneously face challenges regarding fairness in decision-making. Currently, the dominant approach to defining algorithmic fairness is the statistical fairness approach, which measures fairness by setting specific statistical metrics.The core idea of the statistical fairness approach can be succinctly summarized as follows: if the algorithmic decision outcomes and protected attributes remain statistically independent under a given metric, then the algorithm satisfies fairness according to that metric. Generally, the definitions of fairness under the statistical fairness approach can be categorized into three types: statistical parity, statistical parity in accuracy, and calibration fairness. However, the statistical fairness approach faces at least three unresolved dilemmas: the "incompatibility of definitions dilemma," the "fairness paradox dilemma," and the "actual label bias dilemma."The "incompatibility of definitions dilemma" points out that certain statistical fairness definitions cannot be satisfied simultaneously, making it difficult to choose or balance between them. The "fairness paradox dilemma" highlights that statistical fairness definitions can yield contradictory conclusions about fairness in situations such as Simpson’s paradox, rendering statistical fairness standards ineffective. The "actual label bias dilemma" indicates that if actual labels contain historical biases, statistical fairness definitions that rely on these actual outcome labels may perpetuate or even exacerbate these biases.This paper argues that these three dilemmas are constrained by the inherent limitations of the statistical paradigm and cannot be fundamentally resolved within the statistical fairness approach. The key to overcoming these challenges lies in shifting the paradigm for defining fairness. Inspired by Judea Pearl’s causation theory, it becomes evident that the statistical fairness approach adopts a data-centric "statistical paradigm," which overly relies on data fitting while neglecting the causal explanatory mechanisms behind how the data is generated. From the perspective of the statistical fairness approach, it is impossible to truly determine whether protected attributes actually influence the algorithmic predictions, whether such influence is direct or indirect, or how other variables mediate this influence.Many researchers in algorithmic fairness have recognized this issue and have consequently pioneered a causal approach to algorithmic fairness. The core idea of the causal fairness approach is to first uncover the causal relationships within statistical data and then use causal inference methods to detect whether protected attributes have a causal effect on algorithmic predictions, thereby measuring algorithmic fairness. This approach primarily relies on Pearl’s structural causal model for causal inference, mainly including "purely interventional fairness" and "counterfactual fairness."The causal fairness approach can effectively address the three dilemmas faced by the statistical fairness approach by revealing the causal relationships behind statistical data. By measuring purely interventional fairness at the group level and counterfactual fairness at the individual level, the causal fairness approach addresses the shortcomings of the statistical fairness approach, which can only handle group fairness. Moreover, both definitions under the causal fairness approach are based on the well-established theoretical framework of structural causal models, which are distinctly different yet complementary and unifiable.Finally, based on Ben Green’s distinction between formal algorithmic fairness and substantive algorithmic fairness, this paper finds that the statistical fairness approach merely imposes certain fairness constraints or measurements on the algorithmic decision outcomes at a "formal" level, reflecting the narrow analytical framework represented by formal algorithmic fairness. In contrast, the causal fairness approach has a broader perspective. Its causal analytical framework can not only identify and address biases in upstream data but also assist in formulating practical policies to mitigate these biases. It can even help evaluate whether the algorithm itself effectively implements these policies. Green’s methodological insight aligns well with the causal fairness approach, and their integration can open up more avenues for research in algorithmic fairness.Furthermore, the transition from statistical fairness to causal fairness reflects the core demands of explainability, transparency, and trustworthiness in artificial intelligence. Under these demands, the transition from statistical fairness to causal fairness represents an extremely important development trend in algorithmic fairness research.
With the continuous emergence of new technologies, the new round of technological revolution, industrial transformation, and military revolution are accelerating. The rapid development of emerging fields is further speeding up industrial change and military transformation. How to leverage technological innovation in emerging domains to enable the effective integration of new quality productivity and new quality combat effectiveness, and to clarify the mechanisms behind this integration, has become a critical issue that must be addressed to balance high-quality development and high-level security.
This study is a longitudinal single-case study of a US commercial space company, SpaceX, and collects extensive data from the company since the establishment of Starlink. Based on the theories of attention-based view and resource orchestration theory, the study constructs an evolutionary pathway for the integration of new quality productivity and new quality combat effectiveness, and investigates the driving role of key core technologies in this integration, as well as the evolutionary and interactive process between the two. The study will integrate the existing resource-related theories and explore the differences in managers' attention allocation and resource action strategies in the integration of new quality productivity and new quality combat effectiveness, thereby enriching the theoretical research on the integration of new quality productivity and new quality combat effectiveness.
The findings reveal that:(1) The integration of new quality productivity and new quality combat effectiveness goes through three stages: element, system, and system-of-systems, with the integration pathway evolving in a spiral manner, characterized as “leading→interactive→symbiotic”. Specifically, traditional productivity generates new quality productivity through revolutionary technological breakthroughs, which in turn lead to updates in traditional combat concepts. Building on this, new quality productivity further provides technological support and material foundation for new quality combat effectiveness, while the latter also feedback into and empower the former, creating mutual promotion and reinforcement. Eventually, new quality productivity and new quality combat effectiveness jointly construct an integrated system, where the demands of new quality combat effectiveness guide the upgrading direction of new quality productivity, and the outcomes of the latter drive the strengthening of the former, resulting in symbiotic integration. (2) The integration follows a logical chain of "focus-cognition-action- outcome". Among them, the focus is on identifying opportunities for key core technologies and matching the critical breakthrough priorities at each stage; the cognition serves as the "navigator" for the integration, guiding its direction; the resource action acts as the "propeller," effectively allocating, deploying, and integrating various resources in response to situational cognition, thereby progressively advancing the integration of new quality productivity and new quality combat effectiveness. This research deciphers the mechanism through which core technological breakthroughs in emerging fields drive the convergence of new quality productivity and new quality combat effectiveness, providing strategic guidance for comprehensively enhancing strategic capabilities in emerging domains.
The study deciphers the process mechanism through which key core technological breakthroughs in emerging fields facilitate the integration of new quality productivity and new quality combat effectiveness, constructing a theoretical model of the integration of new quality productivity and new quality combat effectiveness. By introducing managerial attention and resource orchestration theory into the research on the integration of new quality productivity and new quality combat effectiveness, it expands the understanding of the integration process beyond the forces themselves to fully consider the interactions among various relevant factors. This not only addresses the gap in existing research regarding dynamic evolutionary pathways and provides guidance for comprehensively enhancing strategic capabilities in emerging fields, but also further extends the application boundaries of relevant theories, enriches the theoretical research on the integration of new quality productivity and new quality combat effectiveness, and thus offers a solid theoretical foundation and practical direction for future studies.
Keywords：new quality productivity; new quality combat effectiveness; attention configuration; resource actions; satellite technology
In the digital economy era, how specialization, refinement, differentiation, and innovation (SRDI) enterprises can break through resource constraints to achieve resilient growth has become a critical issue of shared concern among both academia and industry. This study, based on the theories of firm growth and dynamic capabilities, constructs a chain mediation model to explore the driving mechanisms of digital leadership on the growth of SRDI enterprises. By further collecting data from middle and senior managers of 411 SRDI enterprises, the study empirically tests the theoretical model. The results indicate that: (1) digital leadership has a significant positive impact on the growth of SRDI enterprises; (2) digital platform capability and organizational resilience play partial mediating roles in the relationship between digital leadership and enterprise growth, while together exerting a chain mediation effect.
This study has the following three theoretical contributions. First, it explores the micro-level driving factors behind the growth of specialization, refinement, differentiation, and innovation (SRDI) enterprises in the digital age. By investigating the micro-mechanisms through which digital leadership drives the growth of SRDI enterprises, it enriches the research on the driving factors of SRDI enterprise growth and extends the application contexts of firm growth theory. Second, this paper examines how digital leadership promotes the growth of SRDI enterprises through the development of digital platform capabilities and organizational resilience. From a dynamic capabilities perspective, it unravels the "black box" between digital leadership and enterprise growth, thereby enriching the research perspectives on the mechanisms of digital leadership. Finally, this paper extends the hierarchical transformation mechanism of dynamic capabilities theory. By exploring the impact of digital leadership on the transformation of digital platform capabilities (lower-order capabilities) into organizational resilience (higher-order capabilities), it enriches the hierarchical view of dynamic capabilities.
In addition, the conclusions of this study provide important practical implications for the high-quality growth of SRDI enterprises in the digital age. First, SRDI enterprises should place a high priority on the selection and development of digital leadership. The digital mindset of leaders ensures the rapid identification of market demands and industry trends, enabling the enterprise to continuously deepen its expertise in specialized fields and achieve sustained growth. Second, SRDI enterprises should focus on cultivating digital platform capabilities to overcome internal resource constraints that limit growth. Leaders should build digital platforms to optimize business processes, establish intelligent interaction networks with external entities, and enhance core capabilities through resource integration and absorption. Finally, SRDI enterprises should prioritize the enhancement of organizational resilience to cope with the threats posed by external turbulence. Enterprise leaders need to establish a resilience system based on "prevention-response-recovery," utilizing digital technologies to optimize resource allocation efficiency, create agile decision-making mechanisms, and strengthen adaptive capabilities in the face of adversity through continuous learning.
With the rapid advancement of digital technologies, emerging technologies such as artificial intelligence, the Internet of Things, blockchain, robotics, 3D printing, cloud computing, and big data are increasingly being applied in the manufacturing industry, driving a trend toward digitalization and intelligence in the global manufacturing sector, represented by Industrial Internet and Industry 4.0. Digital innovation is also an important pathway for Chinese manufacturing firms to achieve transformation and upgrading. However, different from conventional technological innovation that occurs in relevant independent domains, digital technological innovation is a systemic and holistic endeavor. It imposes more stringent requirements on firms’ resources and capabilities, demands more substantial investment, and brings in higher innovation risks. According to the Behavioral Theory of the Firm, firms’ strategic choice is affected by their performance feedback. Based on the organizational ambidexterity view, radical innovation happens in new knowledge domains, distinct from existing technological trajectories, and thus needs higher resource investment, bearing higher risks. In contrast, incremental innovation involves improvement, refinement and extension in current knowledge domains, and hence requires lower resource investment, bearing lower risks. The more a firm pursues radical innovation with a certain amount of resources, the higher effectiveness of innovation it achieves. Due to their varying motivations and capabilities for innovation, firms with different levels of performance have different focuses in their strategic choices of digital innovation, either on the quantity of digital innovation or on the effectiveness of digital innovation. Furthermore, the allocation of resources for digital innovation is impacted by managers’ cognition of decision-making, and ownership concentration affects firms’ decision-making and implementation, the effectiveness of resource employment as well as the judgment of risks. Therefore, the decision-making process of digital innovation is influenced by ownership concentration. Based on the Behavioral Theory of the Firm and the organizational ambidexterity view, this study investigates the impacts of performance feedback on the quantity and effectiveness of digital innovation in manufacturing firms, as well as the moderating role of ownership concentration. Using a sample of manufacturing firms listed on the A-share markets of Shanghai, Shenzhen, and Beijing from 2012 to 2023, we find that firms with high industry status place more emphasis on the quantity of digital innovation, while those with low industry status focus more on the effectiveness of digital innovation. Ownership concentration amplifies the emphasis on digital innovation quantity in firms with high industry status and diminishes the focus on digital innovation effectiveness in firms with low industry status. The conclusions of this study extend the research context of the Behavioral Theory of the Firm, by targeting digital innovation and dividing digital innovation into innovation quantity and innovation effectiveness. Also, we enrich the research on the antecedents of digital innovation, from the perspective of firms’ performance feedback, and explicate the influence of firms’ performance compared with the industry average on the strategic choice of digital innovation quantity or effectiveness. In addition, this study analyzes the moderating effects of ownership structure based on firms’ practice in the emerging context, and provides references for the digital innovation practices of Chinese manufacturing firms from the perspective of ownership structure.
Technology-driven startups on digital platforms face the dual dilemma of high homogeneity competition and high platform dependency. Achieving value innovation in this context is a critical issue. This paper selects Anker, a startup based on the Amazon platform, for an in-depth single-case study to explore the process and mechanism by which it resolves innovation dilemmas and achieves value innovation. The study finds that: during the entry phase, technology-driven startups confront the dilemma of high homogeneity competition. By engaging in "exploitative experiential learning—bricolage resource construction—exploratory product learning—creative resource utilization" with stakeholders through the digital platform, these startups can break through the high homogeneity competition dilemma, achieving product value innovation; during the growth phase, technology-driven startups face the dilemma of high platform dependency. Through entrepreneurial accumulation and "exploitative knowledge learning—accumulative resource construction—exploratory brand learning—integrative resource leveraging" with stakeholders, these startups can overcome the high platform dependency dilemma, achieving brand value innovation. The paper distills a process mechanism model of "innovation dilemma—ambidextrous learning and resource orchestration—innovation outcome," which offers important insights for entrepreneurship based on digital platforms.
Innovation concerns the future, and reform concerns the destiny of the nation. The patent system is the core of the system that supports comprehensive innovation. The patent system that supports comprehensive innovation is an adaptive adjustment based on the existing patent system, building a rule system that supports original innovation, adapting to the new national system and the current scientific and technological innovation paradigm, in order to achieve the strategic goal of national innovation-driven development. The patent system, through a series of institutional levers such as the right subject, the right object, the right relief, and patent disclosure, plays its three core functions of stimulating innovation, disseminating technical information, and promoting the integration of the innovation chain and the industrial chain, thereby achieving the institutional goal of supporting comprehensive innovation. The practical way to build a patent system that supports comprehensive innovation is to establish a comprehensive mechanism for the operation, decision-making and adjustment of the patent system: an open and transparent patent system operation mechanism should be established to monitor the efficiency of system operation and ensure the fairness and justice of system operation; a scientific and democratic patent system decision-making mechanism should be established to grasp the correct direction of system reform and win broad political recognition; a rule of law and agile patent system adjustment mechanism should be established to safeguard the bottom line of the rule of law and flexibly respond to threats and opportunities.
The chip industry is a key industry supporting modern science and technology and economic development, but its long industrial chain and complex global competition and cooperation pattern make it being significantly impacted by external shocks. Especially in recent years, the coronavirus epidemic and the Sino-US trade frictions have a huge impact on China's chip industry. Facing complex and changeable external environment, it is urgent to examine the risks faced by China's chip industry from the perspective of network resilience. Based on the complex network theory, this study constructed a multi-dimension industrial network from business, technology and organization, and used the social network analysis method to compare and analyze the change of industrial networks under the epidemic impact and trade impact scenarios. The impact of the epidemic and trade friction will lead to the increase of the degree of dispersion and fragmentation of the chip industry network. After different cities were affected by the epidemic, the three types of networks in the chip industry were affected to different degrees. Compared with the impact of the epidemic, the loss of the toughness of the chip industry network structure caused by the trade blockade is more serious. This study has enriched the theory of industrial toughness and provided decision-making reference for improving the risk prevention and control ability of the chip industry.
Industrial intelligence provides a feasible path for enterprises to develop new quality productivity and achieve high-quality development. By incorporating carbon emissions as an input factor into the production function of enterprises and using data from listed manufacturing companies, this study examines how the application of industrial robots affects corporate carbon productivity. Research has found that: (1) the application of industrial robots significantly improves the carbon productivity of manufacturing enterprises, and this conclusion still holds true after a series of robustness tests and endogeneity treatments. (2) The mechanism of action indicates that the application of industrial robots not only promotes the improvement of enterprise carbon productivity through the resource efficiency mechanism of improving labor productivity, capital utilization efficiency, and energy utilization efficiency, but also enhances enterprise carbon productivity through knowledge enrichment and strengthening enterprise adaptability. (3) In larger enterprises with lower financing constraints, enterprises with high internal control and no green mergers and acquisitions, and enterprises with higher industry competition and lower monopoly power, industrial robots are more likely to promote the improvement of carbon productivity. To this end, enterprises should be encouraged to prioritize the research and development of advanced industrial robots; promote their transformation into learning organizations, and strengthen their agility management; facilitate the construction of the carbon market, further expand its coverage, and improve the trading system.
This study is grounded in the crucial context of accelerating the construction of Chinese modernization. The key to Chinese modernization lies in the modernization of science and technology (S&T). How to better serve the goal of S&T modernization has long been a central research issue in the field of macro-level S&T governance. Numerous policies have been introduced focusing on the reform of the S&T system, the development of the national innovation system (NIS), and the modernization of science, technology and innovation (STI) governance. They reflect the shifting priorities of macro-level S&T management across different historical periods, all ultimately aimed at achieving S&T modernization. However, in the process of policy formulation, the boundaries among these three concepts are often ambiguous and frequently conflated, which has led to confusion in macro-level S&T governance practices. Theoretically, S&T system reform, innovation system construction, and STI governance all represent institutional arrangements and transformations targeted at different actors. Yet, how STI governance bridges these disparate frameworks, and whether it can enrich the theoretical architecture of macro-level S&T management remains insufficiently explored.
This paper analyzes academic literature and policy documents to explore pathways for theoretical integration among S&T system reform, innovation system construction, and STI governance, aiming to clarify the theoretical and practical evolution of national macro-level S&T management in the Chinese context. By combining qualitative methods and social network analysis, it summarizes the practical processes and theoretical developments of China’s S&T system reform, NIS construction, and STI governance. Specifically, it systematically reviews the theoretical connotations and policy objectives of S&T system reform, NIS, and STI governance, highlighting both their distinct features and commonalities. On this basis, the paper proposes a theoretical framework that links these three domains, providing a reference for policy formulation in macro-level S&T management.
The study reveals that, first, the distinctiveness of S&T system reform, NIS construction, and STI governance is manifested in terms of theoretical connotations, policy practices, and research topics. Second, the key research themes in these areas converge around “the integration of S&T with the economy,” “commercialization of S&T outcomes,” “innovation ecosystem,” “indigenous innovation,” “innovation culture,” and “the China-specific national innovation system.” Finally, Considering the staged and evolutionary characteristics of Chinese modernization, S&T system reform, NIS construction, and STI governance exhibit theoretical convergence. These three systems together constitute an integrated framework centered on S&T innovation. The key actors—government, industry, academia, and research—are shared across all three systems. The NIS focuses on the substance of S&T innovation, S&T system reform emphasizes institutional and regulatory transformation, while STI governance serves as the bridge linking the two concepts. The importance of STI governance has become increasingly prominent in the new era. Based on the integrated framework, this study proposes three core propositions: (1) enhancing the effectiveness of NIS is the consistent goal of macro-level S&T governance in China. (2) accelerating S&T system reform and advancing the modernization of the STI governance are two key instruments to achieve this goal. (3) the essence of macro-level S&T governance lies in reconciling endogenous institutional transformation with exogenous institutional learning, for which the modernization of the STI governance system serves as an effective pathway.
