The serious risks derived from the rapid development of artificial intelligence have become increasingly prominent, and its governance issue has developed into a major problem of The Times that is of common concern to the global academic community, governments and international organizations, but no breakthrough governance plan has yet been formed. Through the analysis of climate change, a typical case of global governance, it is found that the two have convergence in many aspects such as risk understanding process and governance response path, and relevant historical experience will provide a reference paradigm for AI governance. Using the comparative research method and the historical research method, the development, evolution and governance process of the two are sorted out and compared. The study found that climate governance follows the path of progressive global collaborative governance driven by scientific consensus and responsive to political agenda. Artificial intelligence has accelerated the formation of a broad consensus, from risk identification to global co-governance, and is gradually moving to the political agenda and related actions. In view of this, we can fully learn from the valuable experience of global governance on climate change, negotiate the establishment of a similar artificial intelligence international governance body, reach a global agreement on artificial intelligence, formulate a global governance framework and standards, and unify action plans to effectively promote artificial intelligence global governance.
This study develops a theoretical framework to explain the dynamic interplay among employees' professional identity threats, impression management behaviors, and the legitimacy construction of artificial intelligence (AI) tools within organizational contexts. As AI tools such as DeepSeek and ChatGPT become widely integrated into organizational practices due to their remarkable capabilities in innovation generation, decision-making support, and information analysis, understanding employees' responses and adaptations to these technological advancements becomes essential. Despite the notable improvements in efficiency and output quality facilitated by AI tools, employees frequently engage in impression management behaviors aimed at strategically modifying or obscuring the contributions of these technologies. Such behaviors reflect deeper anxieties regarding threats to their professional identity, particularly concerning perceived risks of diminished professional status, competence, and decision-making autonomy.
Addressing existing theoretical gaps, this research integrates identity theory, impression management theory, and organizational legitimacy theory into a comprehensive, interactive theoretical framework. Prior studies often examined these theories independently, neglecting the nuanced interplay at the micro-level between individual cognition, behavior, and broader organizational legitimacy processes. This study systematically addresses this gap by posing four critical research questions: (1) Under what specific organizational and task-related circumstances do employees perceive threats to their professional identity upon the introduction of AI tools? (2) How do distinct perceptions of identity threats—status threats, competence threats, and autonomy threats—influence employees' specific impression management behaviors? (3) How do these impression management behaviors, in turn, influence the cognitive, pragmatic, and normative legitimacy construction of AI tools within organizations? (4) How does the legitimacy constructed around AI tools reciprocally influence employees' ongoing identity threat perceptions and subsequent behaviors, thereby creating a continuous feedback loop?
To address these questions, the study first identifies and elaborates on key contextual factors that evoke perceptions of identity threats among employees. These factors are categorized into three main dimensions: technological attributes, task characteristics, and organizational contexts. Technological attributes include AI tools' decision transparency and autonomous decision-making capabilities, directly influencing employees' perceptions of control and their professional roles. Task characteristics, such as task specialization, uniqueness, and clarity of performance evaluation criteria, also significantly impact employees' feelings of professional competence and security. Meanwhile, organizational contexts—particularly managerial communication strategies emphasizing AI's efficiency and potential substitutability, alongside the organization's information feedback mechanisms—play critical roles in shaping employees' identity threat perceptions.
Subsequently, the study explicates how these perceived identity threats motivate employees toward specific impression management strategies, including downplaying the contributions of AI tools, extensively modifying AI-generated outcomes, and concealing detailed information about tool usage. These impression management behaviors are strategic responses aimed at safeguarding professional status, competence perception, and autonomy within organizational social evaluations. Importantly, the research highlights that these behaviors do not merely stem from identity threats but also reinforce the very perceptions of threat that initiated them, resulting in a reinforcing negative feedback loop.
Further, the analysis elucidates how employees' impression management behaviors directly affect the construction of AI legitimacy within organizations. Through deliberate distortion and concealment of information, employees' behaviors adversely influence colleagues' accurate understanding and evaluation of AI tools' cognitive legitimacy (perceived necessity and value), pragmatic legitimacy (actual utility and effectiveness in practice), and normative legitimacy (alignment with organizational culture and ethical standards). Consequently, these behaviors significantly obstruct the institutionalization and effective integration of AI technologies.
The reciprocal dynamics between AI legitimacy construction and employees' identity threat perceptions are thoroughly explored, emphasizing that insufficient legitimacy enhances employees' perceptions of professional vulnerability, thus exacerbating further impression management behaviors and creating a persistent negative feedback cycle.
In conclusion, this research contributes significantly to theoretical development by providing an integrative, interactive theoretical model that bridges the micro-level psychological and behavioral responses of employees with macro-level organizational legitimacy processes. By explicitly articulating how identity threats, impression management behaviors, and legitimacy construction dynamically interact, the study advances identity theory, impression management theory, and legitimacy theory, offering a more nuanced understanding of technology integration within organizational contexts. Practically, this study provides actionable strategies for organizational leaders to effectively manage the intricate psychological and behavioral challenges accompanying AI adoption, facilitating smoother institutionalization and long-term acceptance of transformative technologies.
The governance of data security risks in generative artificial intelligence is a critical issue currently facing us. The existing data security governance system for generative artificial intelligence remains in its embryonic stage, failing to establish effective typed governance mechanisms for different types of data security risks, and is out of sync with the practical development of DeepSeek-like generative artificial intelligence that has iterated to an advanced stage. The new technical features of the DeepSeek series of models—including multi-head latent attention mechanisms, mixture of experts models, and pure reinforcement learning—have led to quantitative changes or alienation of original data security risks, giving rise to data leakage security risks, data deviation security risks, and data deletion security risks with distinct operational logics. These risks require the exploration of matching typed governance mechanisms.
First, data leakage security risks stem from external attacks, simple internal configuration errors, or human negligence. To address these risks, preventive mechanisms at both the technical and management levels should be established respectively. Technical governance should take a leading role here, requiring the systematic application of technologies such as data masking and privacy-preserving computing, federated learning and distributed training optimization, as well as data encryption storage and isolation. Preventive mechanisms at the management level consist of pre-defined management matters that are implemented in real time, including basic elements such as domain warning mechanisms, data classification and grading protection mechanisms, data security organization and evaluation mechanisms, and data leakage incident emergency response mechanisms.
Second, data deviation security risks refer to various risks that deviate from reasonable expectations during the use of artificial intelligence systems, encompassing both data quality issues caused by machine hallucinations and invasive issues in data processing behaviors. In response, adjustments at both the input and output ends of data should be emphasized—distinct from the manual fine-tuning model—and the right to algorithmic explanation of data output results should be established. Here, the two-end adjustment mechanism does not mean reverting to the large-scale model development path of supervised fine-tuning; rather, it achieves adjustment effects through special mechanisms constructed outside the model while maintaining DeepSeek’s current development model. For example, to address machine hallucination security risks, synthetic content detection tools can be introduced to identify and classify data or content generated or modified by large models, or high-quality data such as synthetic data can be used to reduce the impact of poor-quality data on DeepSeek. To address data infringement security risks, content filters can be embedded at data output ports to directly block the output of harmful instructions such as hate speech, pornography, and violence, or value alignment mechanisms can be embedded at data output ports. Meanwhile, the right to algorithmic explanation of data output results serves as a reasonable intermediate path to balance human rights protection and legal interest protection, particularly suitable for scenarios involving unauthorized information, uncertain data sources, and unreasonable data processing during DeepSeek’s data output process.
Finally, regarding the data deletion security risks faced by DeepSeek—including objective impossibility of deletion or deletion-induced harm—de-absoluteizing data deletion and using dynamic verification mechanisms to counter logical reproduction phenomena are relatively scientific measures. After de-absoluteizing data deletion, the relative deletion obligation targeting specific data becomes factually fulfillable. On this basis, fulfilling DeepSeek’s data deletion obligations involves a process of confirming the scope of data to be deleted based on deletion requests and carrying out deletion after comprehensive evaluation.
Assessing the technological application value inherent in papers is important for understanding their role in the innovation chain. This study focuses on the field of two-dimensional (2D) materials science, a domain witnessing widespread Artificial Intelligence (AI) adoption, to empirically evaluate the impact of AI technology use within the research process on the technological application value of these papers. To identify AI adoption in research, we referenced established practices, performing precise keyword searches within paper abstracts to determine if AI techniques were utilized. To define this technological application value, we first constructed a corpus of paper abstracts in the field of 2D materials. We then examined the tendency towards technological application manifested in the abstract content, defining this tendency as the paper's technological application value. This involves leveraging text analysis techniques to quantify the prominence of technological application-oriented themes within each paper’s abstract, thereby constructing an index of its inherent technological application value. Our empirical findings provide evidence for the impact of AI use on the technological application value of papers. Firstly, using AI technology significantly enhances the technological application value of papers in the field of 2D materials. Secondly, mediation analysis suggests that using AI technology enhances the potential for technological application of papers. This enhancement is achieved by improving research efficiency (e.g., via high-throughput screening or simulation), better focusing on technological demands, and strengthening application-oriented objectives (e.g., via inverse design targeting specific properties). Thirdly, while the overall technological maturity of AI employed significantly and positively moderates its impact on the technological application value of research, this moderation exhibits a bidirectional nature when analyzed across different developmental stages. Specifically, during phases of accelerated AI technological maturation, the enhancing effect on application value becomes progressively stronger with advancing years, indicating greater value gains from more advanced AI applications. Conversely, in the early adoption stages of AI, a different trend emerges: the positive impact on application value tends to diminish over successive years, potentially due to the trial-and-error costs and uncertainties associated with nascent technologies. Furthermore, employing the H-index as a proxy for researchers’ academic influence and experience, our statistical analysis did not find a significant moderating effect of this factor on the relationship between AI adoption and the enhancement of papers’ technological application value. This finding suggests that while AI might potentially impact research productivity differently across different researchers as suggested by other studies, its capacity to strengthen the technological application value of papers in the 2D materials field appears less contingent on the researchers’ established academic influence and experience in this specific context. By defining and quantifying the technological application value of papers based on their abstract content, this study empirically reveals the specific pathways and moderating mechanisms through which AI technology influences this value. The findings indicate that AI not only impact research productivity but also significantly enhance the technological application value of papers.
Responsible Research and Innovation (RRI), as a global value, is gradually expanding from the framework dominated by the Global North to a widely accepted international consensus. However, despite the increasing recognition of RRI’s core principles by more countries, its path toward global implementation still faces numerous challenges. These challenges stem not only from cross-cultural and cross-institutional heterogeneity but also from the impacts of dramatic changes in global geopolitics and the rise of nationalism, which have led to the increasing phenomenon of ethical dumping. This paper explores, from theoretical, historical, and practical perspectives, how RRI can transcend the technology governance model constructed by Global North countries and form a framework that is more globally adaptable. Through this exploration, the paper aims to prevent RRI from becoming a casualty of political maneuvering and contribute to the creation of a more equitable and inclusive global science governance system.
This paper is based on the discourses by Xi Jinping, general secretary of the Communist Party of China Central Committee, on the important role of citizens' scientific literacy in innovation and development. A theoretical and practical analysis is conducted of how improving the scientific literacy of different groups of people promotes innovation and development from three dimensions: the source, process and end of scientific and technological innovation. It holds that citizens' scientific literacy is a "booster" or "accelerator" that acts on the whole process of scientific and technological innovation in different ways. It influences scientific and technological innovation and ultimately promotes innovation and development by strengthening the "source" at the front end of scientific and technological innovation, smoothing the "flow" of the process and expanding the "pool" at the end. On this basis, adhering to the principle of "placing science popularization on an equal footing with scientific and technological innovation", and focusing on promoting the collaborative interaction and resonance between scientific and technological innovation and the improvement of citizens' scientific literacy, this paper puts forward policy and work suggestions on integrating the construction of citizens' scientific literacy and scientific and technological innovation into the target system, responsibility system, organization system and evaluation system of innovative development.
Building upon recent theoretical developments in science communication that extend beyond the transmission view, this study integrates the ritual view of communication into the theoretical framework of science communication, providing a detailed explanation of its conceptual foundation. Within the ritual view, science communication is framed as a process where diverse participants share experiences related to scientific topics and jointly construct meaning based on these shared experiences. Additionally, a comprehensive comparison between the ritual and transmission views of science communication is presented, highlighting key differences across multiple dimensions. Building on the above elaboration, systematic theoretical approaches for the ritual view of science communication are developed at three levels: the intrapersonal, interpersonal/intragroup, and intergroup levels. The ritual view of science communication complements the transmission view of science communication, offering a novel theoretical lens to bridge gaps among diverse participants in science communication and alleviate cultural conflicts between scientific and non-scientific domains. Additionally, the theoretical approaches for the ritual view of science communication can provide systematic and practical guidance for future research and practices in science communication.
The relationship between technocracy and the rule of law is a new consideration for the legalization of governance in an intelligent society. In the intelligent age, technocracy, which is based on algorithms and data, affects the rule of law in social governance, leading to changes in the language structure of legal practice, legal judgment, and the form of justice. However, technocracy has limitations in its involvement in legal practice. These limitations are specifically manifested in the following ways: the codification of legal text language does not promote the emergence of good laws; legal judgment overemphasizes instrumental rationality while ignoring value rationality; and objective, quantitative data justice is hard to truly realize legal justice. Technocracy and the rule of law are not two absolutely isolated subsystems. Instead, they have a dialectical relationship of mutual causation. The trend of their mutual embedding and coupling is unstoppable. It is necessary to reflect on how the legal system of an intelligent society can respond to the embedding and coupling of technocracy to realize the generative update of the legal system. To this end, we should adhere to the regulatory position of “promotive regulation” that responds to science and technology, speed up the value alignment of artificial intelligence with humans, and realize algorithmic justice through the legal reservation and technical due process.
Generative artificial intelligence technology has been embedded in various aspects of social science knowledge production research, not only reshaping the basic process of social science knowledge production, but also leading the intelligent research paradigm to become one of the mainstream research paradigms in the field of social science knowledge production. However, while generative artificial intelligence expands the field of social science knowledge production, it also brings challenges such as unclear data ownership, data bias, data illusion, and data arrogance in the process of processing complex data generation content, eroding the common virtue of social science knowledge production. To enhance the efficiency and quality of generative artificial intelligence in knowledge production, improve the public, fair, objective, and critical character of the social science knowledge production community, and build an ideal collaborative vision of “knowledge co creation, human-machine symbiosis”.
This study utilizes the evolutionary game theory to construct a tripartite game model among platform owners, complementors, and users within the digital platform ecosystem. Through simulation experiments, the equilibrium points were validated, and the states of each party corresponding to different equilibrium points were explored, thereby elucidating the multi-agent strategic interaction logic. Additionally, the dynamic evolution of equilibrium states was analyzed using a three-dimensional dynamical system, and the evolution paths of the dynamic equations and the stability of their equilibrium points were demonstrated during the modeling process. Based on the simulation results, the conclusions are as follows：(1) During the unstable development phase of the platform, the platform, complementors, and users tend to adopt strategies of balanced resource allocation, multi-homing, and non-purchase, respectively. (2) In the platform improvement phase, the strategic game between the platform and complementors becomes prominent. A high proportion of resource allocation by the platform incentivizes complementors to provide high resource feedback, and complementors adopting a customization strategy encourage the platform to allocate more resources to them.(3) During the platform maturity phase, increasing resource investment by platform owners, appropriately reducing product pricing, and strengthening market regulation will increase the probability of users choosing to purchase.(4) The dynamic evolution of the digital platform ecosystem is highly sensitive to various parameters, manifested as the equilibrium point dynamically evolving with changes in parameters. This indicates that strategic interactions among entities are significant and easily influenced by the strategic decisions of other entities. This study expands the research focus of platform governance from a single-agent perspective to the strategic interactions among multiple agents, and achieves a transition of the dynamic model from an internal/equilibrium model to an external/balance model. It reveals the core governance logic of the digital platform ecosystem, enriches the existing platform governance theory, and provides profound practical insights.
The DeepSeek-R1 big model of DeepSeek corporation has drawn global attention with its low-cost, high-performance, and open-source model. However, while existing research has extensively discussed its innovative impact, it has not revealed the underlying innovation mechanism. This paper adopts a longitudinal single-case study approach. Based on a systematic and detailed description of its innovation process, "business model", strategic management, and organizational structure, it reveals a special innovation mechanism. The research finds that the founders and team of DeepSeek corporation not only demonstrate a dual entrepreneurial spirit of arbitrage and innovation, but also have an innovation consciousness of technological self-reliance and self-strengthening. These two factors jointly lead to outstanding achievements in independent and integrated innovation. Originating from the AI team of its parent company, Phantoms Quant, and being a typical application scenario of quantitative trading, data science, artificial intelligence, and large models, DeepSeek has formed a de facto strong demand-driven "business model" at the beginning of its entrepreneurship. During the development process of the DeepSeek-R1 model, the multiple reuse and optimization of single modules have given rise to internal economies of scale, and the interaction among multiple modules has further achieved an internal "flywheel effect". Meanwhile, its open-source model and application diffusion have formed external economies of scale and, in turn, have complemented its own innovation. This study fills the existing research gap and provides a realistic explanation and theoretical basis for subsequent theoretical research and industrial policies and governance.
How traditional craftsmanship enterprises can achieve breakthrough innovation through the power of digital intelligence is of great significance for their adaptation to the digital wave. Based on the theory of digital innovation, this paper analyzes the mechanism of the breakthrough innovation process of traditional craft enterprises driven by digital intelligence through a case study of Luzhou Laojiao. The research finds that: (1) Traditional craft enterprises achieve breakthrough innovation through digital intelligence primarily by undergoing three core phases: foundational reshaping, heritage-oriented translation, and extension-oriented renewal. In essence, this process centers on heritage-driven innovation, leveraging digital–intelligent technologies and tools to facilitate the enterprise’s transition from a traditional craft system to a digital–intelligent craft system. (2) The "support-drive" mechanism of digital innovation is the key mechanism for digital intelligence to drive the breakthrough innovation of traditional craft enterprises. That is, digital intelligence promotes enterprises to break through the cognitive limitations and capability barriers of traditional crafts in the digital economy era through the interactive action of organizational support and technological drive, achieving breakthrough innovation. The research conclusions can provide new insights for the innovative development of traditional craft enterprises in the digital economy era and help to expand the theoretical research on breakthrough innovation and digital innovation.
In the context of globalization and the knowledge economy, collaborative innovation has emerged as a critical pathway for enterprises to accelerate new product development and enhance competitiveness. However, existing research on this topic exhibits three primary gaps. First, while much of the literature focuses on the value creation process jointly achieved by firms and external partners, insufficient attention has been paid to value capture in collaborative innovation. In particular, prior studies have overlooked the differential effects of the breadth and depth of collaborative innovation on a firm’s ability to profit from innovation. Second, current research often treats innovation appropriability as a unitary construct, neglecting the distinct temporal focuses and antecedent factors of primary appropriability and generative appropriability. Given the conceptual complexity of innovation appropriability, further exploration of its antecedents and boundary conditions is warranted. Third, although collaborative innovation emphasizes knowledge flow and sharing across organizational boundaries, the role of a firm’s knowledge base in facilitating value capture from collaborative innovation remains underexplored.
To address these gaps, this study investigates the following research questions: How do different dimensions of collaborative innovation influence a firm’s value appropriability? How does a firm’s knowledge base moderate this relationship? Using a sample of listed companies in the computer, communications, and electronic equipment manufacturing sectors, we conduct empirical analyses to these questions. Our findings indicate that both the depth and breadth of collaborative innovation have significant negative effects on primary appropriability but significant positive effects on generative appropriability. Furthermore, this study reveals that when the scale of a firm’s knowledge base is large, it can mitigate the negative effect of collaborative innovation on primary appropriability, while attenuating its positive effect on generative appropriability effect. Besides, a broad knowledge base enhances the positive impact of collaborative innovation on generative appropriability.
This study has several theoretical and practical contributions. Firstly, it echoes scholars’ discussions on value capture of emerging-economy firms and clarifies the asymmetric effects of collaborative innovation on primary versus generative appropriability. By doing so, this study overcomes the limitation of prior research that treated innovation appropriability as a unitary construct, thereby advancing the integration of innovation appropriability and collaborative innovation literatures. Secondly, this study contributes to the literature by examining the relationship between collaborative innovation and firm performance from an integrative perspective—considering both the direction of knowledge flows during collaboration and the characteristics of a firm’s accumulated knowledge base. By elucidating how knowledge base attributes influence the link between collaborative innovation and appropriability, this research expands understanding of the antecedents and boundary conditions of innovation appropriability. Regarding to practical implications, while collaborative innovation can significantly enhance a firm’s innovation efficiency, firms should recognize the distinct roles of the breadth and depth of collaboration. To strengthen their innovation appropriability, firms must develop capabilities to acquire, assimilate, and exploit external knowledge, thereby facilitating the capture of generative value from innovation iterations. Furthermore, when selecting collaborative innovation strategies, firms should align their approach with the characteristics of their knowledge base. If a firm possesses a large-scale knowledge base, it can extract immediate returns from current collaborative outcomes; if a firms owns a broader knowledge base, it can to ensure sustained gains through leveraging iterative innovation in the future.
Overall, this study can provide high-tech enterprises with theoretical guidance on effectively capturing value in collaborative innovation settings. Additionally, it seeks to enhance understanding of the value creation-appropriability paradox and promote a dynamic balance between the two.
Artificial Intelligence (AI) has created significant opportunities for technological innovation in enterprises. From the perspective of knowledge flow theory, this study conducts a comparative case analysis of two AI startups from different industrial backgrounds to explore the mechanisms of AI-driven innovation processes. Research findings: (1) The AI-driven innovation process follows the path of “knowledge production—AI-driven market opportunity insight—knowledge acquisition—knowledge creation—knowledge application”,wherein the knowledge creation stage achieves the chimeric recombination of AI and domain-specific knowledge through the path of “training model—model prediction—experimental verification—iterative optimization”; (2) The innovation process is influenced by both internal and external factors: internal factors include founders’ knowledge endowment, compound talents, and organizational structure, while external factors encompass partnership networks and innovation environment factors such as technology, policy, and market conditions; (3) Enterprises with different industrial backgrounds follow similar knowledge chimeric paths but develop differentiated industry-specific models, while enterprises with different entrepreneurial backgrounds adopt differentiated knowledge acquisition strategies based on their characteristics.This study is helpful to deepen the understanding of the mechanism of AI-driven innovation process and provide practical enlightenment for enterprises to accelerate AI-driven innovation.
General-purpose technologies and technology transfer in universities is a crucial way to overcome the bottleneck of being "stuck" by core technologies and to address the disconnection between innovation chain and industrial chain. Technology transfer in universities is not only a bridge that connect scientific research and production, but also a core driving force to promote high-quality development. However, in this process, a series of challenges occur, including technology islands, lack of market orientation and capital. To search for solutions, relevant studies usually focus on the process of technology transfer, demonstrating that it is affected by internal and external factors, such as the capabilities of R&D practices, policy regulation, academic entrepreneurship activities, etc. These studies also investigate the channels to realize technology transfer, including patent licensing, the establishment of spin-of and U-I innovation collaboration. Unfortunately, these studies often neglect the inherent nature of technological emergence in universities: 1) the technical characteristics of the endogenous emergence of universities; 2) the internal mechanism of how universities utilize knowledge reorganization via general-purpose technologies to realize efficient technology transfer.
To fill above research gaps and enrich current studies on university technology transfer, this paper investigates how universities reorganize knowledge through general-purpose technologies, and the intrinsic mechanisms enabling them to bridge both the "subject" and "domain" gaps in technology transfer from knowledge reorganization perspective. To empirically verifies above hypotheses, we obtain information of 72,944 patents from 284 Chinese universities from IncoPat database, then using the Logit model to perform estimation. We find that: (1) Different university general-purpose technologies vary in their transfer—higher applied generality of university technologies increases the likelihood of technology transfer, while higher basic generality decreases this likelihood. (2) Government subsidies enhance the relationship between applied generality of university technologies and technology transfer but weaken the relationship between basic generality and technology transfer. (3) The maturity of knowledge enhances the relationship between applied generality of university technology and technology transfer, without significant moderating effect on the relationship between basic generality and technology transfer.
This study decomposes a unique attribute (generality) of university technology from an endogenous perspective, thereby enriching the research on the relationship between general-purpose technology and technology transfer, so that providing insights for universities to achieve "double-leap". Our contributions are twofold: (1) rather than macro and exogenous theoretical perspectives. we investigate technology transfer from a micro perspective of "general-purpose technology", which enriches the research on the micro mechanism of the technology transfer in universities. (2) we introduce the knowledge reorganization theory to explore internal mechanism of "subject" and "domain" double gap in university technology transfer, so that effectively promoted the new understanding of the technology transfer in universities. (3) we empirically examine the moderating role of government subsidies and knowledge maturity, which reveals the influencing mechanism of external factors on universities’ technology transfer and enriches the studies on innovation policy and knowledge management.
This study provides practical implications as well. First, universities should build an interdisciplinary research platform to deeply integrate fundamental theories and practical knowledge, bridge social needs and technological frontiers, and improve the efficiency and quality of knowledge reorganization. Second, based on effective knowledge reorganization, universities should actively seek and deepen tripartite collaboration (university-industry-government) to accelerate their technology output and transfer. Third, government should facilitate university-industry cooperation by building a platform for information exchange and resource sharing, which accelerates technology transfer; meanwhile, policy instruments could be used to encourage and support innovative and practical research in universities, realizing the self-reliance on science and technology development.
Under the background of China vigorously promoting the construction of open innovation ecology, how enterprises can use the Internet platform to accelerate global technology exchange and promote patents overseas has become an important content of high-quality ‘going out’ for Chinese enterprises. On the basis of clarifying the intrinsic mechanism of the influence of corporate internetisation on overseas patent applications, this paper empirically examines the influence of internetisation on corporate patents going abroad by applying the data of overseas patent applications filed by Chinese GEM listed companies from 2009 to 2021. It is found that the Internetisation of enterprises has a positive incentive effect on patent technology going overseas, which significantly increases the number of overseas patent applications by enterprises, and the conclusion still holds after a series of robustness tests. The mechanism test shows that enterprise internetisation helps its patents to go overseas by improving its collaborative innovation ability and reducing its operating costs. Further research finds that Internetisation has a more obvious effect on promoting overseas patent applications of enterprises with a higher degree of market competition and larger scale. The conclusion of this paper provides a useful reference for promoting Chinese enterprises to actively layout their patents overseas and then improve the international influence of technology.
With the ongoing development of the digital economy, digital technology innovation has emerged as a key driver for businesses to enhance profitability and increase market value. The Chinese government has implemented a range of incentive policies to support enterprises in overcoming challenges related to digital innovation. However, many enterprises still face cognitive limitations in understanding and applying digital technology, leading to fragmented innovation efforts that fail to generate a systematic advantage. Additionally, the interplay of multiple government policies can result in negative effects, such as conflicts, resource mismatches, and strategic behavior by firms, ultimately undermining the effectiveness of these policies or creating perverse incentives.
Existing research has often overlooked a detailed analysis of the relationship between government incentives and firms' digital technology innovation, particularly from a fine-grained perspective. There is a need for empirical studies that differentiate between various types of incentivizing policies and innovations, and examine the complementarity or substitutability between variables. Furthermore, a theoretical analysis that integrates multiple policy instruments, innovation behaviors, and the resulting benefits is essential for explaining the diverse innovation paths of firms.
This study seeks to investigate the impact of mixed government incentives on firms' digital technology innovations and the subsequent economic outcomes, using regression analysis of data from China's A-share listed firms from 2014 to 2020. In particular, the paper introduces the concept of multi-dimensional digital technology innovation, focusing on three key dimensions: degree (overall level of digital investment), breadth (expansion across different technological domains), and depth (intensity of focus on a specific technological path). The study also constructs a conceptual framework for "mixed government incentives", incorporating both financial and institutional incentives, to offer a more comprehensive understanding of how government policies influence firms' multi-dimensional innovation behaviors and the resulting benefits.
The findings reveal that: (1) government mixed incentive policies positively influence diversified digital technology innovations, with financial and institutional incentives complementing each other; (2) while the breadth and degree of innovation negatively impact firm earnings, the depth of innovation has a significant positive effect on earnings, and there is a substitution effect between breadth and depth; (3) financial incentives mitigate the negative impact of innovation degree and breadth on earnings, while enhancing the positive effect of depth on earnings, whereas institutional incentives exhibit the opposite moderating effect; (4) while the response to government incentives is consistent across digital industries, the impact on firm earnings varies.
Theoretical contributions include: (1) constructing a logical framework linking government incentives, multi-dimensional innovation, and innovation benefits, which challenges the traditional linear analysis of "single incentive - single innovation"; (2) advancing the understanding of policy mix effects by exploring the complementarity between fiscal and institutional incentives; (3) enriching the theoretical framework by incorporating alternative relationships between innovation dimensions and the moderating effect of policies.
Empirically, this study addresses how firms can optimize innovation behavior and value creation in a complex policy environment in the digital economy, offering both theoretical insights and empirical evidence to guide the formulation of more targeted, coordinated, and effective digital technology policies. By analyzing the policy-innovation-benefit relationship, the paper aims to uncover the underlying mechanisms linking macro-level policy decisions with micro-level firm behaviors, ultimately promoting policy synergy and strategic pathways for high-quality business development in the digital economy.
Customer-led front desk service strategy and product-led back office service strategy are important ways to transform and upgrade manufacturing industry and improve its performance in the era of digital economy. However, there is little research on the role of the two service strategies in the incentive effect of digital technology integration on enterprise performance. This paper selects 371 high-end equipment manufacturing enterprises in Shanghai and Shenzhen A-shares from 2013 to 2022 as research samples, and explores the mechanism of digital technology integration on the performance of high-end equipment manufacturing enterprises by constructing A model of "digital technology integration - service strategy - enterprise performance". The results show that: (1) Digital technology integration can positively improve the long-term performance of enterprises; (2) Service-oriented strategy plays an intermediary role between digital technology integration and enterprise performance. Digital technology integration can improve short-term performance of enterprises through front-office service-oriented strategy and long-term performance through back-office service-oriented strategy. This paper has enriched the theoretical vision of the incentive effect of existing digital technology on enterprise performance, and provided theoretical support and practical reference for further promoting the integration of digital technology and promoting the high-quality development of high-end equipment manufacturing enterprises.
The exploratory project rooted in breakthrough innovation, as an innovative project management practice in complex situations such as emerging technologies and new product development, promotes innovative development in terms of exploring the unknown and verifying new ideas. Based on the related research of self-determination theory and work gamification, exploratory projects are used as the research situation to explore the influence mechanism of work gamification on the innovative behavior of project members. The study founds that: first, the achievement elements, immersion elements and social elements have a positive influence on the innovation behavior of the exploratory project members; and second, the harmony work passion explains the mechanism of "self-comparison" and "social comparison", which has a significant intermediary effect on the innovation behavior; third, the competitive characteristics play a positive regulating effect between harmonious work passion, workplace jealousy and innovative behavior. This paper constructs the influence mechanism of the innovative behavior of exploratory projects, and provides the situation-matching work gamification management mode for exploratory projects, so as to improve the innovative behavior of project members and provide some reference for the management practice of exploratory projects.
