In the age of digital transformation, generative AI, exemplified by technologies like ChatGPT, serves as both a catalyst for technological innovation and a potential harbinger of various societal risks. A nuanced and exhaustive understanding of this groundbreaking technology is therefore imperative to harmonize the dual imperatives of technological advancement and effective governance. Employing text analysis techniques to meticulously categorize public comments and formulate theoretical constructs, this research not only offers an incisive look into the inherent complexities and probable societal ramifications of generative AI but also establishes an empirical foundation for crafting governance strategies that are both responsive and responsible.
Utilizing a corpus of public commentary on the Reddit platform related to ChatGPT, this study undertakes a multifaceted examination of generative AI. It employs a suite of analytical tools, including Latent Dirichlet Allocation (LDA) models, sentiment analysis, social network analysis, and a bespoke Technology-Technology, Organization, Environment (T-TOE) analytical framework. This comprehensive approach illuminates the various dimensions of generative AI's societal impact. Specifically, the study finds that public discourse largely centers around six core themes, including but not limited to, the technological underpinnings and diverse application domains of generative AI. Among these, issues related to technological transformation and the nuances of human-AI interaction attract heightened attention. Sentiment analysis corroborates a generally optimistic public outlook towards this technology, particularly with regard to its capacity to usher in transformative technological changes.
Integrating empirical insights with theoretical extrapolation, the study enables a granular understanding of generative AI. Through the lens of the T-TOE model, the multi-dimensional impact of generative AI is rigorously assessed. In the Technology-Technology dimension, generative AI is posited as a lynchpin for digital and intelligent transformation. Nonetheless, it is accompanied by endogenous technological risks, such as data security vulnerabilities and algorithmic biases, which necessitate vigilant governance. In the Technology-Organization axis, generative AI is expected to substantially enhance organizational efficiency and decision-making prowess. However, the potential for discord between technological adoption and organizational culture—manifesting as managerial missteps or cultural incongruities—raises concerns that warrant careful attention. In the Technology-Environment sphere, generative AI is seen as exerting a pervasive influence on various societal domains through its intelligent capabilities. Yet, lurking beneath are latent risks, including but not limited to, the erosion of privacy norms and the exacerbation of social inequalities, that require preemptive governance measures.
In light of these insights, the study concludes by delineating governance strategies across three critical dimensions: technological, organizational, and environmental. In the technological realm, the study advocates for a robust discourse among experts across disciplines to enhance the understanding of inherent risks, coupled with the development of comprehensive technical standards and liability frameworks. Organizationally, it underscores the need for directional guidance from governmental agencies and advocates for a symbiotic collaboration across different sectors, encouraging public-private partnerships for a nuanced approach to governance. Environmentally, the study suggests the construction of a multi-faceted goal system, significant investments in digital infrastructures, and a sustained focus on ensuring both cultural and algorithmic fairness. Taken together, these recommendations offer an integrated governance blueprint, adept at balancing the often competing demands of innovation, societal value, and regulatory order.
Based on techniques such as autoregressive generative modeling, pre-training, and reinforcement learning with human feedback, ChatGPT has gained powerful natural language processing capabilities.The emergence of ChatGPT marks a significant shift in the traditional conception of generative AI. However, it also poses various types of risks in terms of model training, generating content and applications. Therefore, it is imperative to find ways to mitigate these potential risks while guid-ing the rapid development of generative AI to prevent a "pollute first, regulate later" scenario. Before proceeding with governance, several key governance premises must be established. The first is to uphold human-centered values. Humanism should be a fundamental value of governance, ensuring that AI technologies prioritize human well-being and ethical considerations. The second is to uphold the concept of inclu-sive and prudent agile governance. One is to balance safety and innovation. The second is to optimize the relationship between governing and being governed. Opti-mize the relationship between governing and being governed, strengthen the interac-tion between the two sides, and form synergy in governance. The third is to enhance the flexibility of governance. Governance should pay more attention to the foresight of governance, and shift from outcome governance to process governance. Once again, it is to insist on multi-party participation combining point and surface. Effec-tive governance requires the active participation of all stakeholders, but multi-party participation in governance does not mean equal responsibility, it should be clear that the government and service providers are the main duty-bearers, and need to take more responsibility in the governance process. Finally, a systematic governance model with multiple measures should be adopted. On the one hand, it is necessary to classify governance and adopt different means of governance for different risks, which is an inevitable requirement for multi-pronged governance, with technical is-sues being referred to technical governance and legal issues being referred to legal governance; on the other hand, for comprehensive risks, it is a multi-pronged ap-proach to adopting a systematic governance scheme.
The specific governance path around these principles can be summarized in thefollowing five areas:
First, establish standardized training datasets. The government and relevant in-dustry organizations should jointly lead the construction of standard training da-tasets according to the type of AI generation and different training stages, establish a sound training data evaluation and supervision system, and determine theupdate cy-cle of standard training datasets. Monitor the quality of standard training datasets, eliminate false and harmful information, and control the whole process of standard training datasets.
Second, implement the professional qualification certification of AI trainers. On the one hand, change the current professional qualification certification of AI trainers, which is mainly based on skill identification, to qualification access to match their important role in the process of generative AI training and maintenance; on the other hand, set up AI trainer access qualifications in a graded manner, i.e., according to the different scenarios of the task, set up AI trainer access qualifica-tions in a graded manner.
Third, strengthen the algorithmic supervision technology. First, enterprises de-veloping and using generative AI should strengthen internal algorithm regulation technology to achieve effective internal standardized governance. Second, encourage the third party to supervise the development and application of algorithms to realize effective external regulation. Finally, increase the user feedback system. User feed-back algorithms for generative AI should be established to give users the right to judge and annotate the output information of generative AI, and user feedback should be screened as part of the new training dataset to further train and optimize generative AI.
Fourth, strengthen the end-to-end ethical governance framework. First, it needs to be made clear that the ethical basis for development and use is the promotion of human well-being. At the same time, a specialized ethics training and review institu-tion should be established to conduct ethics training and regular ethics review for generative AI. Second, value-sensitive design is utilized to implant ethical concepts into AI generators so that they can discern unethical information and reject the out-put. Once again, the ethical connotations of generative AI should be discussed regu-larly and an ethical declaration should be formed to address new ethical risks in a timely manner. Finally, a user code of ethics should be formulated in time to develop AI ethics.
Fifth, optimize the legal framework and responsibility allocation. On the one hand, it is to optimize the relevant legislative system. One of them is to rationalize the legal governance system of generative artificial intelligence. Although, there are a wide variety of laws and regulations on artificial intelligence in China, and there are also special departmental regulations on generative AI governance. However, the phenomenon of multiple departments still exists. Secondly, for the new problems brought by generative AI, a legal response needs to be made as soon as possible. Adapting to the unique challenges posed by AI includes clarifying the responsibili-ties and obligations of relevant AI systems. On the other hand, it is necessary to rea-sonably allocate the subject of legal responsibility. Specifically: first, distinguish the specific infringement scenarios of generative AI, and set the legal liability of the corresponding subjects according to different liability scenarios; second, in scenari-os such as infringement disputes arising from the application of generative AI, the developers and service providers of generative AI shall be jointly and severally lia-ble. Then, after completing the relief for the infringed person, the secondary distri-bution of the corresponding responsibility shall be carried out specifically. Thirdly, in the case where a user's illegal use of generative AI causes damage to the rights of a third party, a "notification-disposal" safe haven mechanism should be established to protect the provider of generative AI and avoid the expansion of joint and several liability.
The issue of neck-jamming technology is crucial to the development of science and technology, however, there are many conceptual ambiguities in the understanding of neck-jamming technology. This paper clarifies the concept of neck-jamming technology, which is conducive to clearing the root cause and promoting the solution. First, the endogenous contradiction between the old model and the new stage is the root cause, and the political behavior of the technology supply side to launch technology restriction on the technology demand side is the external fuse. Second, under the premise that the neck-jamming technology is state-based, this paper analyzes the definition and characteristics of neck-jamming technology. In order to avoid the waste of resources caused by the generalization of the concept, the scope of neck-jamming technology is further clarified. Finally, disruptive technology and countermeasures are discussed, and the role of neck-jamming technology in supporting high-level scientific and technological self-reliance and self-improvement is proposed.
The theory of technology parasitism points out that parasitism between technologies contributes to the symbiotic development of two or more technologies. The disproportionate growth of sub-technologies in host technology will promote the development of complex technology systems. Host technology with more daughter technologies develops faster than host technology with fewer daughter technologies, and the evolution of host technology is not independent of its associated daughter technologies. In the initial stage of parasitism, the influence of external environment is the external condition of parasitism, and the difference within technology is the internal limit of parasitism, which shows that parasitism is selective. In the growth stage, daughter technology and host technology promote each other and coevolve, indicating that parasitism has reciprocity. In the ascendant phase, subtechnologies alternate parasitism with host technologies, indicating that parasitism involves substitution. In the final effect stage, the sub-technologies is alternately parasitic in host technology shows that parasitism has substitutability. Combined with the general law of technological evolution, "Double S curve" can reflect the evolution law of technology from parasitism to symbiosis from the inside of technology. The theory of technological parasitism should abandon the description of evolutionary phenomena and pay more attention to the explanation of bonding mechanism. In the early stage of innovation management, diversity and foundation should be emphasized, while flexibility and orientation should be emphasized in the middle and late stage.
The development of human-animal chimeras has consistently been accompanied by ethical controversies. For this groundbreaking technique to advance scientifically and systematically, it is imperative to urgently anticipate, analyze, evaluate, and clarify the associated ethical issues. As the technological barriers of this field are gradually overcome in the future, more pronounced ethical controversies and risks will emerge. This is particularly true when chimeras involve non-human primates, as these controversies become even more profound. The current state of human-animal chimera development necessitates a thorough contemplation of the ethical challenges within this domain. However, previous examinations of the ethical issues and governance of human-animal chimeras have primarily occurred in ethical dialogues that are disconnected from the practical context of technological advancement, resulting in what can be termed as “ivory tower” research. A comprehensive integration of technological considerations into discussions regarding the ethical dimensions of human-animal chimeras research becomes both pressing and indispensable.
Explorations into the frontiers of transformative technology should be grounded in research teams that possess a leading position within specific fields, supported by philosophical contemplation that is deeply informed by the current intricacies of technological development. This paper, rooted in the technological barriers and risks within the realm of human-animal chimera research, draws upon the theoretical foundations of Husserl’s formal ontology, Kant’s philosophy, theory of misperception and Heideggerian existentialism. It aims to analyze the associated ethical issues systematically and comprehensively, anticipating the ethical risks of this field based on the dynamic interplay between technological advancements and ethical considerations. Through comprehensive analysis, the ethical complexities inherent in the current stage of human-animal chimera research encompass several critical facets. These include a lack of well-defined concepts and standardized frameworks, ethical hazards intertwined with human consciousness concerns, the potential encroachment upon human dignity (currently latent but foreseeable), disputes concerning the moral status of human-animal chimeras, regulatory intricacies, ethical concerns regarding reproduction (requiring further theoretical exploration), issues regarding the rights of research participants, challenges associated with public opinion and instinctual aversion, safeguarding the welfare of experimental animals, and biosafety concerns.
Moreover, by closely examining the dynamic interplay between technological advancements and ethical considerations, it becomes evident that precise oversight, monitoring, and tracking of the differentiation trajectory of human stem cells assume paramount significance in mitigating the most pressing ethical risks. A mere escalation in the proportion of human cells within chimeras is liable to exacerbate a multitude of ethical challenges; meaningful progress necessitates the collective resolution of other concurrent technological bottlenecks. The mastery of technical obstacles pertaining to transplantation also holds the potential to alleviate specific ethical quandaries.
In short, incorporating the ongoing advancements in tangible technological progress into the ethical investigation of human-animal chimeras, coupled with rigorous philosophical scrutiny, serves to illuminate, analyse, and pinpoint the authentic ethical hazards within this domain. This endeavour not only aids in elucidating intricacies and solidifying a robust ethical underpinning at the vanguard of transformative life sciences but also propels the trajectory of human-animal chimeras technology toward a constructive and methodical trajectory. As technology continues to evolve, the prospect of new ethical challenges arising in the future remains plausible. Consequently, the ethical framework surrounding human-animal chimeras research necessitates further enhancement and refinement to ensure its alignment with evolving circumstances.
Academics have thoroughly researched the causes of cross-border M&A. Most of these studies show that markets, the search for resources, and the acquisition of technology impact enterprises' outbound investment activities. The range of factors influencing cross-border M&A has been studied more recently. Some researchers have discovered that national strategy is also a key factor and that government support for outward foreign direct investment has substantial explanatory power. The socialist path with Chinese characteristics establishes the government's crucial role in the growth of businesses. The industrial policy, notably the "five-year plan," embodies the national strategic purpose, which is responsible for directing the flow and reallocating resources and factors as China is undergoing industrial transformation. There is minimal research contrasting domestic M&A and cross-border M&A together, even though researchers have focused on the industrial policy as a significant factor affecting cross-border M&A. Comparing domestic M&A and cross-border M&A offers a clearer picture of how industrial policy affects Chinese companies' propensity for cross-border M&A and makes it easier to comprehend the mechanisms involved.
The study uses Chinese A-share listed companies from 2008 to 2018 as its research subjects and empirically investigates the influence of industrial policy on enterprises' inclination to engage in cross-border M&A. The study's findings revealed that industrial policy contributed to differences in the propensity of businesses to engage in cross-border mergers and acquisitions. Businesses supported by the industrial policy were more likely to choose cross-border M&A because these policies could assist businesses in obtaining more government subsidies and help them improve their commercial credit, increasing their propensity to do so. According to heterogeneity research, regions with more competitive industries and less market-oriented regions significantly impact the likelihood of enterprises engaging in cross-border M&A. Furthermore, it is discovered that corporate innovation capacity, environmental uncertainty, and CEO compensation stickiness can significantly increase the positive impact of industrial policy on firms' inclination for cross-border M&A. In addition, industrial policy raises the equity level and completion rate of cross-border M&A but does not affect the transaction value. At the same time, strategic asset-seeking motivation can significantly enhance the impact of industrial policy on the transaction value and equity level of cross-border M&A.
This study's contributions include (1) confirming that industrial policy significantly impacts the cross-border M&A of Chinese companies, broadening the cross-border M&A research paradigm, and completing the gaps in the current literature. (2) It demonstrates how industrial policy impact businesses' inclination for international mergers and acquisitions by cutting from the viewpoints of government subsidies and commercial credit. (3) The analytical framework concurrently incorporates the motive conditions of cross-border M&A to answer the related but unaddressed questions of why Chinese enterprises participate in cross-border M&A and how they might accomplish effective cross-border M&A. (4) The interplay between strategic asset-seeking motivation and industrial policy is investigated, as is the influence of industrial policy on various transaction value, equity level, and completion rate characteristics. (5) This article provides a reference for decision-making to further promote a high degree of international opening by objectively assessing the implementation effect of industrial policy in cross-border M&A.
Granting researchers the ownership or long-term use right of scientific and technological achievements by their duties plays an important role in removing the institutional barriers that hinder the free flow of technological factors. However, after empowerment, the transformation of scientific and technological achievements is still constrained by a series of factors such as unfree decision-making and transformation process. In order to promote the transformation of scientific and technological achievements centered on scientific and technological personnel and make technologies circulate freely within a certain space and time range, the concept of building a "technology free island" for the transformation of scientific and technological achievements is proposed in this study. In the "technology freedom island", full ownership of technology is granted to scientific researchers, so as to break the constraints of state assets supervision and existing income mechanism to the maximum extent. Through improving the current legislation and system in the transformation of scientific and technological achievements, a systematic "technology free island" guarantee mechanism for transformation of job-related scientific and technological achievements is developed, so as to realize the promotion and management of transformation of job-related scientific and technological achievements.
Based on the quasi-natural experiment of the implementation of government-guided fund fault-tolerance mechanism, this paper applies the multi-period DID method to conduct policy evaluation. Our main findings are as follows. First, fault-tolerance mechanism promotes the governmental-guidance fund to invest early and early technology start-ups in the first year, and decreases the investment next year, the opposite policy effects are robust. Second, the influencing mechanism shows that the fault-tolerant mechanism improves the risk-bearing level and use efficiency of the guide fund in the first year, but reduces the risk-bearing level of and has no significant effect on the use efficiency of guiding fund next year. Third, we study the heterogeneity to show that experienced fund management institution reduces the positive effect of policies in the first year and next year, while the fund with limited partnership promotes the positive effect of policies in the first year. Last, we further show that the fault tolerance mechanism improves the investment in the active venture capital areas and early-technology entrepreneurs in the other province; The mechanism depresses the investment in early start-ups that is in the active venture capital areas in the following year. We conclude that the incentive effect of fault-tolerant mechanism is a "flash in the pan". Therefore, local government is necessary to optimize and adjust fault-tolerance mechanisms to make the implementation of policy effective, and formulate more precise and effective measures to ensure that policy implementation is consistent with policy intentions.
The evolution of science communication models has progressed from the ‘deficit model’ to the ‘Public Engage in Science’. Both of them reflects how science communicators share scientific information with the public. As artificial intelligence (AI) technology is being applied in the media industry, intelligent media has established a new science communication system. This paper analyzes the features of science communication of intelligent media, which is interactivity, dynamics, precision, and self-learning, along with the pluralism of communicators as well as the multiplicity of transmission channels and spaces. The interactive science communication model within intelligent media is designed on this basis. Subsequently, a survey is conducted and 205 valid questionnaires are collected. These questionnaires analyze the current situation of entrepreneurs implementing new media supported by AI technologies to gain scientific knowledge. Finally strategic suggestions are proposed to communicate science knowledge to entrepreneurs in an intelligent media context.
Contemporary science and technology have shown unprecedented institutionalized characteristics. A new notion of “institutionalized science research” is introduced in the context of policy necessary, and its distinctive characteristics are explored from historical logic, theoretical logic, and empirical logic. This study demonstrates that the history of scientific development is also the history of scientific knowledge production and scientific institutional evolution. Improving the efficiency of scientific research is the intrinsic motivation for the evolution of institutionalized science. Knowledge production mode II cannot be simply used as a basis for the transformation of research institutes. Knowledge innovation advances by interaction in the two-way as highly split and integration of scientific disciplines, which reflects the evolution of institutionalized science research. The knowledge system shapes matching research organization. The knowledge domains that play a leading role in the knowledge system architecture are different, forming a macro dynamic evolution of institutionalized science research history. As revealed by the experience of some famous scientific research institutions, scientific research innovation is not achieved in isolation, but requires an interaction between science, technology, and instrument development. Mutual support between traditional and cutting-edge disciplines is also important. Organizational design should take into account both the concentration and integration of scientific research. These structural resource allocations are the irreplaceable value of the institutionalized science research. This article provides a new perspective for research on science and technology policies, and has theoretical and practical reference value for the future reform of China’s science and technology system.
After three decades of practice and exploring, the National Natural Science Foundation of China has accumulated successful experiences for the management of the interest-oriented research funding. However, it still needs to further explore the management of organized innovation facing to the nation’s major needs. To lead the scientists to combine the interest-oriented research and the one facing to the nation’s major needs, solving the problem of “stuck neck” in the science and technology, the department of Management Sciences had implemented reforms through launching and management of a series of special projects in recent years. It had explored the mechanism for proposing the scientific questions for the national major needs, i.e. “Six steps”, and the mode for the organized innovation, so that to produce research achievements that could be effectively employed in practice. These serve for the aim of basic research effectively promoting the high-quality development of China.
One of the most difficult issues in research project management is the delegation of authority between funding bodies, research institutions, and researchers, i.e., the principal-agent problem in institutional economics. If not handled properly, the principal-agent problem can lead to many issues such as improper matching of funding for research project management, deviation of funding use from research objectives, and disconnection between research practice and assessment mechanism. This paper starts with a comparative study of the whole process of research project management in the Mainland and Hong Kong as well as systematically analyses the manifestations and coping mechanisms of the principal-agent problem in research management. This paper finds that there are significant differences between Hong Kong and the Mainland in terms of project submission, funding use, supervision and management of research activities, project completion, and acceptance. Meanwhile, the extent and nature of the manifestation of the principal-agent problem in research project management also vary greatly. Hong Kong's research management system is better able to cope with several specific issues in research project management, such as adverse selection in project submission and evaluation, moral hazard in project execution, and uncertainty in research activities. Based on the findings of the paper, the authors put forward policy recommendations such as the expansion of the experts database, the establishment of a performance-oriented appraisal mechanism, the enhancement of flexibility in the allocation of funds, and the construction of a credibility information system for researchers, to effectively address the principal-agent problem in research management in terms of mechanism design.
China has caught up with and even surpassed some developed countries in the industrial sector of complex product systems such as telecommunication equipment, power generation control and grid protection systems, high-speed railways, large engineering equipment, large passenger aircraft in the past 20 years. This has challenged the popular view that enterprises in developing countries could never achieve technological catch-up given the high technical complexity and high organization and coordination requirements in the ?eld. Moreover, China has not used global product networks that once helped western enterprises gain and maintain their leading edge in the ?eld of complex product systems and became the dominate multi-national company pattern. Instead, local Chinese enterprises have adopted a pattern of engineer-centered enterprises. However, there is little literature to analyze this in depth. The literature in the field of multinational corporations and global value chains analyzes the organizational form and innovation models of global production networks, and why they fail in some industrial and technological contexts. The literature in the field of complex product systems focuses on some of the technical characteristics of complex product systems and their requirements for organizational forms. However, neither of the two literature analyzes what kind of organizational form and innovation model of multinational corporations is conducive to innovation competition in the industrial sectors of complex product systems, how it operates and what is its institutional basis. This article takes Huawei in the telecom-equipment industry and Nanrui Electric in the grid equipment industry as the cases and collects evidences through interviews, ?eld observation and document analysis. This article explores the growth path and sources of competitiveness of engineer-centered enterprises and finds that: with the “home-front” system, engineer-centered enterprises send engineering teams to local markets, acquire context-based knowledge through interactions with local users, and establish communications between the front line and formal R&D forces at the headquarters, which helps effectively respond to the problems and demands emerging in application scenarios, and promote organizational learning and technological capabilities. The “home-front” system is enabled by a set of organizational conditions. First, enterprises need to acquire enough high-quality engineers and organize them organically. Second, enterprises need to give engineers the leading power in strategic decision-making to ensure the long-term commitment to technical competence rather than utilitarianism and short-term profit. At the same time, only by fully empowering front-line engineers can resource allocation serve the identification and solution of technical problems in practice more effectively, and promote the formation of context-based knowledge. Third, enterprises need to develop a learning organization with high integration, and encourage collectivism and collaboration to the maximum extent in the core system and incentive mechanism. This helps promote collective learning and enables enterprises to effectively acquire and apply context-based knowledge. This article develops the theory of multinational corporations and the theory of complex product system, and provides inspiration for improving technological capabilities of enterprises through R&D-oriented corporate governance, and building a technological innovation system with enterprises as the main body, markets as the guidance and deep integration of industry, research and application.
This paper differentiates the two stages of COVID-19 pandemic in 2020, uses big data on firms’ registration from a big data platform, and empirically examines the loss and resilience of entrepreneurship in China under COVID-19 pandemic. The results show that: (1) the entrepreneurial activities decreased significantly in the COVID-19 emergency phase, which is 28.59% lower than that in the same period of 2019, cities with more COVID-19 cases and longer emergency response suffer the greater the decline of entrepreneurial activities. (2) the entrepreneurial activities in the COVID normal phase rose and rebounded significantly, with an increase of 25.62% in the second quarter compared with the same period of 2019. Cities with more increase of firms’ exit rate and higher decline of entrepreneurial activities in the COVID-19 emergency phase also have more increase of entrepreneurial activities. (3) Overall, COVID-19 did not cause a significant decrease in entrepreneurial activities in 2020, but there are significant differences in entrepreneurial activities in different regions. This paper not only enriches the comprehensive understanding of the economic impact of COVID-19 pandemic, but also identifies the important source of China's economic resilience under the impact of the epidemic.
Scientific and technological innovation is the first driving force for the construction of "Chinese path to modernization". It is of great significance to explore whether digital industry innovation can lead the modernization of industrial structure to promote the high-quality development of China's economy. Based on the logical framework of "factor chain industry chain value chain", this paper analyzes the internal mechanism of digital industry innovation promoting the modernization of industrial structure, constructs the measurement indicators of industrial structure modernization from three dimensions of service level, industrial structure upgrading and service industry structure upgrading, and empirically tests the specific impact of digital industry innovation on industrial structure modernization using panel data from 30 provinces in China from 2009 to 2017. The research finds that the innovation level of China's digital industry is on the rise year by year, with both "quality" and "quantity" keeping abreast, but the gap between regions is large, resulting in a "digital divide" phenomenon; Before 2012, China's economic structure had a tendency of "breaking away from reality to emptiness". After entering the new normal, the industrial structure continued to transform to a modern service economy where the proportion of advanced manufacturing and productive services continued to rise; The innovation of China's digital industry has significantly promoted the modernization of industrial structure. This conclusion is still valid after using tool variables, adjusting the sample range, replacing the main explanatory variables, and considering the missing variables; The impact of digital industry innovation on the modernization of industrial structure has significant regional heterogeneity and temporal heterogeneity; Digital industry development and regional productivity enhancement are important ways for digital industry innovation to promote the modernization of industrial structure. The above research conclusions provide empirical evidence for China to seize the development opportunities of digital economy and build a modern industrial system.
Small and medium-sized supporting enterprises play a key role in the innovation of core technologies and key components, which is crucial to enhance industrial and supply chain security. However, Small and medium-sized supporting enterprises struggle with technological innovation because of the restriction of resources and capabilities. Thus, in this context, the collaboration between host manufacturers and supporting enterprises provides an important way for the technological innovation breakthrough of supporting enterprises. As suggested by some researchers, firms could generate innovation through organizational learning, resource acquisition, and social networks. While these research streams make important and unique contributions to the literature on firm innovation, the lack of attention devoted to the two-way flow of innovation resources represents a significant limitation in prior research. It is the goal of this paper to address this gap.
Building on the motivation-opportunity-ability (MOA) framework, the authors propose and test a series of hypotheses using a survey of 511 samples. The research results show that the collaborative innovation participated by host manufacturers is an effective way for supporting enterprises to improve their technological innovation performance. While the collaborative innovation participated by host manufacturers provides an opportunity for enhancing the supporting enterprises' technological innovation performance, the degree to which this opportunity is realized depends upon the supporting enterprises' innovativeness(motivation), long-term cooperation orientation, and technical competence (ability).
This study contributes to the literature in two ways. Firstly, from the perspective of two-way interaction, this paper reveals how the collaborative innovation participated by host manufacturers promotes technological innovation of supporting enterprises, which broadens the research horizon of technological innovation and also provides new insights and evidence for the research on technological innovation of supporting enterprises; Secondly, this paper introduces the MOA theory into the field of technological innovation, and finds that the supporting enterprises' innovativeness, long-term cooperation orientation and technical competence play an important moderating role, which enriches the research of MOA theory and expands the application scope of MOA theory in the field of technological innovation.
This study provides two implications for practice. First of all, facing the challenge of interruption on core technology and key components, host manufacturers should actively create opportunities for the collaboration between host manufacturers and supporting enterprises, such as sharing their demand information with supporting enterprises to accelerate the formation of collaborative relationships, and working together with supporting enterprises to achieve better technological innovation performance. Besides, host manufacturers could also evaluate the motivation and ability conditions of supporting enterprises, and select supporting enterprises with stronger innovativeness, long-term cooperation orientation, and technical competence for technological innovation. Secondly, as the main engine of core technologies, key components, and important links, supporting enterprises should maintain a high sensitivity to the external environment in the face of new innovation needs, fully identify and utilize opportunities to obtain market information and innovation resources and promote innovation in core technologies and key components by deepening communication and feedback during the process of collaboration. Supporting enterprises need to be more proactive and continue to enhance their innovativeness, long-term cooperation orientation, and technical competence to meet innovation needs.
In the era of digital economy, the innovation process of firms not only involves the Research and Development of new technologies and products but also involves the allocation of R&D resources by the new technologies and products. The Chinese government attaches great importance to the development of big data. Under the dual impetus of technological revolution and policy support, can Chinese enterprises apply big data to decrease misallocation in firm innovation? Big data refers to the integrated information assets derived from the circulation data containing massive information after being analyzed and processed by digital technology. Less literature directly studies how big data affects misallocation in firm innovation. Drawing on the ideas of Hsieh and Klenow (2009)’s productivity misalignment model, this paper constructs a model of enterprise innovation resources misallocation, expounded the impact of big data application on innovation resources misallocation from the theoretical level, and verified the inferences of the theoretical model by using the data of China listed firms from 2011 to 2020. It is found that 52.83% of industries in China have an insufficient allocation of R&D capital and 62.26% of industries have an insufficient allocation of R&D staff. The results show that big data significantly restrains the misallocation of R&D capital and R&D staff, and this inhibition effect is more obvious when R&D resources are insufficient. The possible reason is that big data application contributes to the information collection, information processing, information integration, and information analysis between the internal R&D process and the external market environment of the enterprise, thus transferring to the allocation of R&D factors. For example, in the capital market, big data application can solve the problem of insufficient supply of traditional finance by expanding financing channels and accurately allocating credit resources, and breaking the inefficiency of resource allocation in traditional financial markets. In the labor market, big data application can eliminate the incompleteness of information to a certain extent to make the supply and demand information of employees in enterprise R&D services more accurate. The conclusion still holds after a series of robustness tests. The mechanism test shows that big data application affects the misallocation of firm R&D resources mainly through the knowledge flow effect and the technology barrier effect. On the one hand, big data application can produce a knowledge flow effect, accelerate knowledge absorption and knowledge diffusion between enterprises, and improve the mismatch of innovation resources. On the other hand, although big data application can break through the technical barrier effect of myopic investment by management, it is not conducive to improving the allocation of innovation resources for enterprises with low employee skills. Furthermore, the resource complementary effect indicates that the effectiveness of data elements also needs the support of technical conditions. When enterprises effectively promote the construction of digital infrastructure, the operational efficiency of enterprises can be improved, and the information analysis and decision-making optimization of the innovation process can be realized at a smaller cost, resulting in the efficient allocation of innovation resources. The conclusion of this paper provides useful enlightenment to promote the rational allocation of R&D resources and speed up the construction of an innovative country with data resources as the key element under the background of new scientific and technological revolution.
The current complex and severe internal and external environment brings unprecedented impact to enterprise development, and how to improve enterprise innovation resilience has become a realistic problem to be solved. This paper clarifies the connotation and characteristics of enterprise innovation resilience and its measurement method, discusses the factors affecting enterprise innovation resilience, and analyzes the theoretical mechanisms affecting enterprise innovation resilience from several perspectives of providing redundant resources, alleviating financing constraints, and optimizing resource allocation. For further validation, venture capital, an important variable influencing firm innovation resilience, is selected for empirical analysis. Based on the quarterly data of listed companies in GEM from 2009 to 2020, this paper combines the patent data, venture capital data and venture capital organization data. It is found that venture capital support significantly enhances firms' innovation resilience, and this effect is heterogeneous by firm characteristics. In addition, the type, nature, and location of venture capital play a significant moderating role on this effect. Based on the research results, relevant policy recommendations are proposed, which provide useful references for firms to survive and grow in turbulent times.
The science and technology innovation base (STIB) is the first echelon of national strategic science and technology force. Universities are the main supporting units of STIB. However, few studies have answered the question of whether establishing STIB can promote the development of science and technology innovation in universities by empirical analysis. Based on the mixed cross-sectional data of 394 universities in China from 2008 to 2017 and the typical STIB data, this paper examines the influence of the establishment of STIB on science and technology innovation in universities by using the differences-in-differences (DID) method. In addition, the heterogeneity test is carried out according to the different regions and administrative departments. The empirical results show that establishing STIB significantly improves the science and technology innovation in universities. Moreover, the establishment of STIB in the western region has the strongest influence on the level of scientific and technological innovation in universities, followed by the central region, while the eastern region has no significant influence. In addition, the degree of positive influence of STIB on the science and technology innovation of universities in the different administrative departments is similar. Therefore, the administrative departments should take policy guidance to give full play to the guiding role of STIB for the science and technology innovation in universities. At the same time, the construction of STIB of universities in the western region should be given more attention. In addition, various measures should be taken to ensure the science and technology innovation activities in the STIBs, thereby driving the development of science and technology innovation in universities.
