Does ChatGPT signal an "intelligent revolution"? It is urgent to make a systematic analysis. In this paper, starting from the evaluation of ChatGPT's innovation effects, the comparative analysis found that it belongs to progressive innovation and disruptive innovation in the series of OpenAI products; Compared with similar competitive products, it is indeed a breakthrough innovation and subversive technological innovation. In contrast to AI in China, ChatGPT has become an important part of the "bottleneck" technology system. It is found that ChatGPT, as a "production tool", forms a paradigm revolution in the supply of productivity tools in the field of artificial intelligence. At the same time, it also realizes the paradigm change of AI ethical governance. The organizational structural elements generated for ChatGPT can be traced back to the organizational mission of OpenAI, the flat project-oriented organization, the financing mechanism innovation with limited returns, and the efficient coupling of open source and crowdsourcing R&D organizational solution in the process of AI ModelOps. These findings lay a solid foundation for further research on ChatGPT's political, economic, and social impacts and related technological governance.
ChatGPT is a Natural Language Processing (NPL) Model driven by a new generation of artificial intelligence technology. The article discusses in depth the Causal Rules, Causal Representations and Causal Explanations of ChatGPT around causality. By analyzing the underlying logic in ChatGPT’s causal cognition and learning, the following three understandings are obtained: First, ChatGPT uses correlation as its underlying causal rule, driven by Deep Neural Networks(DNNs), Large Language Model（LLM）and statistical analysis methods, and realizes the embedding of causality in artificial intelligence in the form of probability. Second, to realize the simulation of human’s ability of causal cognition, causal language and causal inference, ChatGPT has a representation similar to human’s intelligence in the generation of natural language. Third, relying on the two paths of Computational Causal Explanation and Causal Emergent Explanation generated by intelligence, ChatGPT realizes the input from symbols and information to the output of "Data Knowledge", and acquires new capabilities in an emergent way, realizing artificial intelligence The "Cause and Effect Revolution" in the field of artificial intelligence constructs a blueprint for the future development of artificial intelligence.
Large language models like ChatGPT are expected to bring about a new round of knowledge revolution. ChatGPT has powerful language comprehension and learning abilities that enable it to function as a knowledge base. The open use of ChatGPT provides people with more equal opportunities to access knowledge and can help enrich people’s understanding of knowledge, resolve conflicts caused by differences in knowledge, and provide new ways to promote collaboration among groups. The relationship between humans and large language models should be one of collaboration to achieve knowledge building. To achieve this goal, companies should better shoulder their social responsibility for knowledge collaboration and establish trust relationships among diverse entities to promote human-machine collaboration. Multiple negotiation mechanisms should also be established to promote the active role of large language models in knowledge transformation and make knowledge production more innovative.
Rules of transparency are the basic principles of trustworthy AI, and obligation of transparency is the responsibility mechanism for designers, developers, and users of AI to achieve visual justice through technological and legal due process. As a transition from artificial narrow intelligence to artificial general intelligence, ChatGPT has the ability to generate structured content similar to that created by human. While improving academic capabilities, it also poses risks to academic integrity. ChatGPT's intervention in the creation of academic papers involves the underlying logic of copyright, the standard logic of academic integrity, and the top-level logic of trustworthy artificial intelligence. All three logics require authors to fulfill their obligation of transparency. Obligation of transparency is the proof of the performance of digital obligation, the display of digital fairness, and the expression of digital responsibility. It has the guarantee function of academic integrity, academic responsibility, and academic fairness.
Starting from the perspective of evidence-based decision-making, this paper explores the relative value of different types of evidence in scientific decision-making by analyzing the population-wide salt reduction. This paper argues that a simple linear relationship cannot be established between "evidence" and policy outcomes. First, the nature of "evidence" from the perspective of evidence-based decision-making is broader than that of traditional scientific evidence, and should include non-scientific evidence including values and civic cognition. Second, when the scientific evidence involved in the policy is controversial, two conflicting claims are often generated from the relevant scientific research, and the relative value of scientific evidence is weakened. Third, policymakers need to carefully consider the various evidentiary value of policy issues, recognize the role that non-scientific factors should play in evidence-based policy making, and avoid being bound by methodological constraints.
The term “patent” has dual meanings of “publicity” and “exclusivity”. Therefore, “public for protection” has become a classic expression of the patent system: the patent applicant discloses a technical solution to the society to obtain the protection of the patent law. If the technical information disclosed by the patent applicant is insufficient, the technical solution cannot be protected by the patent system. This is to ensure that the patented technical solution can be fully disclosed to the society to promote technological development.
In recent years, the number of patent applications for artificial intelligence (AI) algorithms has increased rapidly. In terms of the disclosure of AI algorithm patents, the following three factors lead to their mismatch with the existing patent disclosure standards - computer software patents and patents containing algorithm features: the black box attribute of AI algorithm, the low predictability of the algorithm technology field, and the easy hiding of useful technical information by patent applicants. In the field of AI algorithm patents, this mismatch causes a conflict between the “public interests corresponding to the full disclosure” and the “private interests directed by monopoly protection” pursued by the patent system: an AI algorithm technology is protected by both the patent system and trade secrets, which limits the public interests.
Reviewing the evolution of the patent system, disclosure theory has become one of the justifications of the patent system. For example, biotechnology is highly unpredictable, and in order to better realize the knowledge spillover effect of the patent system, the disclosure standard of biotechnology patents is high. Considering of the comparability between China’s biotechnology patents and AI algorithm patents, in order to better implement the “public for protection” concept required by the patent disclosure theory, the AI algorithm patent disclosure standards should be improved, and the technical information should be fully disclosed when the patent is granted. The AI algorithm patent applicant shall disclose the following four contents: (1) No less than 3 embodiments; (2) Disclosure of algorithm source code; (3) Disclose the algorithm training method; (4) Disclose algorithm training data.
Improving the disclosure standard of AI algorithm patents will lead to AI algorithm technology being protected as trade secrets, and increase the patent application cost of patent applicants. Incentive measures can be considered to eliminate the negative effects of changes in patent disclosure standards. And incentive measures can be divided into two types: inside the patent system and outside the patent system. The former includes reducing the patent application fee, reducing the annual patent fee, and giving priority to examination, while the latter mainly includes government grants, financial support, and tax relief. In view of the relatively low implementation cost of incentive measures in the patent system and the better incentive effect for patent applicants, it should be taken as a solution. Only in this way can patent applicants be encouraged to disclose algorithm source code, to train data and other information, and to better realize the social function of patent system to promote knowledge spillover and technology development.
In the process of innovation, the key to the success of innovation is to identify the matched partners. Partner identification from the perspective of technology convergence can organically combine the technology matching characteristics of partners with organizational innovation strategy, improve the success rate of cooperation and improve innovation performance. Link prediction in multi-layer networks can fully mine the link information in multi-layer networks, so as to identify potential cooperative links more accurately. By constructing the multi-layer network model of technology convergence-organization cooperation, the research considers the future technology convergence trend in the process of identifying partners, and proposes the potential characteristics of technology convergence among organizations based on the technology convergence trend, and realizes the prediction of partner links in the multi-layer network based on the structural indicators, partner diversity and technology convergence potential. Through the empirical study of patents in the field of artificial intelligence from 2005 to 2020, it is verified that the prediction accuracy of partner links in the multi-layer network is significantly higher than that in the single-layer network. The research results can provide scientific reference for enterprises to choose partners when they implement different innovation strategies.
This paper focuses on the demand-oriented technology transfer. By applying the theory of mass customization and its achievement examples, it analyzes the characteristics and core issues of mass customization of technology transfer.It proposes a theoretical model of mass customization of technology transfer. The case of technology transfer into mass customization is evidenced. This paper finds that the key links in the technology transfer mainly include obtaining common enterprise technical needs at low cost, analyzing common technical needs and compiling standardized technology packages. It should entrust the most suitable R&D organization to customize and develop standardized technologies, and synthesizing several standardized technologies into individualized technologies. In order to successfully implement the mass customization of technology transfer, technical experts and industry experts must participate in the whole process, and the participating enterprises and R&D teams must be trained in mass customization. And then,there must be a high-level professional organization for the technology transfer. They would be responsible for the management of the whole process of mass customization.
Abstract: [Purpose/significance] The value of patents is an important manifestation of AI patents' competitiveness. A comparative analysis of the value of AI patents between China and the United States is of great significance for understanding the current situation and status of China's industrial development and determining the future strategic planning and development direction. [Method/process] This study uses Himmpat database as the retrieval system, uses AHP-entropy weight method to calculate the weight of each index, analyzes the development status of artificial intelligence in China and the United States through the comparison of patent value, and analyzes the development path of China's AI industry. [Results/conclusion] The results show that in recent years, both China and the United States are in a rapid development stage in the field of artificial intelligence, but the United States has certain advantages at all levels with the help of "first mover advantage"; China has developed rapidly in the application layer, but there are still some weaknesses in the research and development of high-value patents. In the face of the gap between the industrial development of the two countries, China should give full play to the "advantage of backwardness" and solve the "stranglehold" problem in accordance with the existing problems. While paying attention to the application of technology, China should adopt the development strategy of "paying equal attention to both inside and outside", encourage the research and development of related patents at the basic and technical levels, promote interdisciplinary cooperation in the research and development of new technologies, "fan out from point to area", give play to the "head goose effect", and through cooperation between the dominant subjects, form a "preponderant polymer" to ensure the development of China's AI industry along a correct and efficient path.
The flow and circulation of low-carbon technology knowledge is an inherent requirement for building a new pattern of "dual circulation" and promoting sustainable economic growth under the "dual carbon" goal, but there is a lack of domestic policy research. Based on the balance panel data of 218 prefecture-level cities in China from 2006 to 2019, this paper reflects the overall flow of low-carbon technology knowledge with patent information, and uses the multi-point DID method to empirically test the impact and mechanism of low-carbon city pilot policies on low-carbon technology knowledge flow. The results show that: (1) The low-carbon city pilot policy can effectively promote the flow of low-carbon technology knowledge in general, but it is mainly manifested in accelerating the inflow of low-carbon technology knowledge. However, it has not shown the effect of the pilot policy on the spillover of low-carbon technology knowledge. This may be related to the insufficient demonstration effect of pilot policies. (2) Pilot policies can promote the knowledge flow of gray and clean technologies, but they have a more significant effect on gray technologies with more gradual and incremental characteristics, which is contrary to the reality that clean technology knowledge flows very quickly. (3) A higher level of human capital and industrial structure in pilot cities will help strengthen the promotion role of low-carbon city pilot policies, and the policy effects will be more obvious in the eastern and central regions, first- and second-tier and non-resource-based cities. Therefore, we should actively promote the demonstration and expansion of low-carbon city pilots, activate clean technology activities, and try to adopt differentiated pilot policies; The eastern and central regions and major cities should give full play to their existing talent and industry advantages, actively support the development of clean technology, and lead the deep low-carbon transformation; The western region, small and medium-sized cities, and resource-based cities should pay more attention to the coordination of pilot policies with other policies, so as to form a policy synergy to promote knowledge flow.
Industrial data governance has been the concentrated embodiment of the digital and intelligent transformation in the field of traditional industries. Based on the four core issues of industrial data, multi-source and heterogeneous, dividend release, value mining, system compatibility, this paper has researched and judged the internal causes and practical obstacles of governance. Further, from the perspective of comparison between China and Germany, the characteristics and laws of industrial data governance under the influence of different systems and mechanisms have been summarized. As a conclusion, this paper has found that the core feature of industrial data governance is value co-creation, and the theoretical basis could be constructed from three dimensions of strategic management, innovation management and industrial engineering management, which provided a theoretical reference for the practice and policy formulation of industrial data governance in China.
Abstract: China is experiencing a transition to the new normal economy, new product development (NPD) has become an important strategic tool for enterprises to maintain the leading position in the market. However, the scarcity of innovation resources and the uncertainty of institutional environment often limit the NPD activities of enterprises, which makes it common for enterprises to obtain complementary resources and reduce risks through cooperation. Among all kinds of cooperative relations, the cooperation with direct competitors has gradually attracted the attention of scholars, namely horizontal coopetition. Horizontal coopetition can not only create joint value, it is worth mentioning that enterprises themselves can also create additional profits through the resources and new technologies obtained in horizontal coopetition. However, the inherent contradictory logic of coopetition is also the source of potential risks such as opportunistic behavior, knowledge spillover and bargaining. Therefore, how to better play the value of horizontal coopetition to improve NPD advantage has become a key problem faced by enterprises. Based on the sample data of 229 enterprises, the relationship between horizontal coopetition and NPD advantage is tested, as well as the moderating effect of the two coopetition modes. The results show that horizontal coopetition has a positive effect on new product innovativeness (NPI) and new product development speed (NPDS). Equity coopetition strengthens the positive effect of horizontal coopetition on NPI but weakens the positive effect on NPDS, while contractual coopetition strengthens the positive effect of horizontal coopetition on NPDS but weakens the positive effect on NPI.
This study has important theoretical significance in three aspects. First, this study focuses on the specific type of horizontal coopetition, and refines the related research on the coopetition theory. Specifically, most empirical studies generalize coopetition, obscuring the particularities of different types of coopetition and their specific effects. This paper explores horizontal coopetition, a specific type of coopetition, not only responds to the call of scholars to refine the study of coopetition type, and broadens the research of horizontal coopetition in the field of new product development research, makes up for the shortage of research on the value of horizontal coopetition in improving NPI and NPDS, also supplements the important contextual factors that affect the value of horizontal coopetition.
Second, this study deepens the research on the antecedent variables of NPD advantages from the coopetition perspective. Existing studies have ignored the influence of horizontal coopetition on NPI and NPDS. This paper explores the mechanism of horizontal coopetition on NPI and NPDS under the background of NPD, and provides a new perspective for subsequent research on new product development based on the coopetition theory. In other words, this paper is a useful supplement to the research on the antecedent factors of NPI and NPDS.
Thirdly, this study integrates the coopetition theory and the transaction cost theory, and fully considers the importance of coopetition modes to the NPD advantage and the possible transaction cost differences of different coopetition modes when enterprises develop new products through horizontal coopetition. The cross-fusion of the two theories is conducive to further reveal the moderating effect of coopetition modes on the relationship between horizontal coopetition and NPD advantage.
Industrial technology innovation strategic alliance is a cooperative organization composed of enterprises, universities, research institutes and other organizations with complementary advantages and joint development. Its goal is to enhance the key common technological innovation capability in the industry. The industrial technology innovation strategic alliance is becoming more and more important in the increasingly fierce external competition environment, but it faces various challenges in the process of pursuing the stable development of the alliance. There are many reasons, including opportunism, improper governance mechanism, asymmetric resource dependence and so on. In the aspect of alliance synergy, member conflict and dependence are two important dimensions that affect the stability, and they are also important risk points of alliance collaborative innovation. The key problem is how to manage the possible risks in the process of alliance operation. With the deepening of alliance cooperation, the influences of different factors on alliance stability are intertwined, and multiple and complex factors affect alliance stability through linkage matching. These factors are often interdependent and act together, and the combination of different influencing factors will produce chemical reactions, which will lead to the difference of result. In particular, the matching influence of alliance membership and alliance governance mechanism is difficult to be independent, and the influence of linkage matching on alliance results is often difficult to reflect due to the limitations of traditional linear regression.
Based on the integration perspective, and using a cross-sectional questionnaire of 56 pilot industrial technological innovation strategic alliances evaluated by China's Ministry of Science and Technology in 2012, as well as dynamic tracking data of alliance activity from 2013 to 2021, this paper employs the configuration method to investigate the path of alliance handling membership relationships and improving stability. It is found that: Firstly, a single condition does not constitute a necessary condition for high alliance stability, but the coordination mechanism plays a key role when conflicts occur. Secondly, we summarized four paths of alliance membership and governance mechanism to achieve high alliance stability, which helped to provide a comprehensive and unified perspective, and explained why a single contradictory result might be true on the whole. Thirdly, we reveal the hidden asymmetric path between high stability and non-high stability of alliances and its mechanism, and find two configurations that produce non-high alliance stability, that is, the alienation of members and the lack of alliance governance mechanism will both cause alliance instability. This asymmetric path provides new insights for the literature of alliance success and failure. In a word, different from previous cross-sectional studies, we use cross-sectional questionnaire and longitudinal tracking design to break through the time limit, provide new empirical support for alliance research, and have important significance for showing the dynamic evolution of alliance network construction and operation process. On this basis, we summed up three practical inspirations of alliance operation.
With the global economic recession, entrepreneurial activities have become a powerful driver to promote economic development, and entrepreneurial failure has become an inevitable phenomenon of entrepreneurial activities. Entrepreneurial behavior is the product of the interaction between the entrepreneur's own characteristics and specific situations, and its emotional experience and cognitive judgment can affect the entrepreneurial process. The role models can bring spiritual inspiration and experience guidance to entrepreneurs, especially the resilience, lessons and learning generated in the adversity of entrepreneurial failure, so as to lay the necessary emotional and cognitive foundation for the success of entrepreneurs. Therefore, the sharing and learning of lessons learned from failure and successful experience summary and the burst of passion have become the only way to the success of entrepreneurship, which depends on the sharing of other people's events, especially positive events， that is interpersonal capitalization.
Social interpersonal capitalization means that individuals can gain value beyond the positive events themselves by processing the information shared by others in the society, such as learning from experience, learning from lessons, example demonstration, motivation stimulation and emotional promotion. The existing researches on interpersonal capitalization mostly focus on the disclosing party, while the researches focusing on the responding party are rare, and the existing researches only focus on the workplace situation. However, entrepreneurship success requires not only their own reflection, but also external benchmarking. Therefore, the sharing of positive entrepreneurial events by others in society is not only the source of motivation for entrepreneurs to persevere in entrepreneurship, but also the learning benchmark for their success in overcoming difficulties.
The study points out that resource bricolage is the key response to the resource dilemma faced by newly established enterprises. Based on the dual system theory, individuals will form intuitive and logical psychological processes when facing external incentives and clues. When positive events are disclosed to entrepreneurs, the information itself will complete the mechanism from resource to emotion, and then to cognition. As information resources, positive events will help improve the resource situation and play a key role in entrepreneurs' understanding of the resources at hand. As we all know, entrepreneurs need to spend much more financial, material and energy resources to carry out entrepreneurial activities in the plight of lack of resources, but entrepreneurial passion can make entrepreneurs have the courage and motivation to face challenges. At the same time, entrepreneurial learning is the key source of entrepreneurial bricolage, which further affects entrepreneurial bricolage by influencing individual cognitive thinking and knowledge structure. Therefore, entrepreneurial passion and entrepreneurial learning may be an important intermediary for social interpersonal capitalization to affect entrepreneurial bricolage. In addition, with the increasing volatility, uncertainty, complexity, ambiguity of the external environment, environmental uncertainty continues to be intensified, which brings great opportunities to entrepreneurs, but also brings great challenges. All these greatly affects the access and utilization of resources for entrepreneurs. Therefore, environmental dynamics may be an important scenario for social interpersonal capitalization mechanism to play.
Therefore, it is of great practical significance to pay attention to the emotional and behavioral reactions of entrepreneurs as responders in the process of social interpersonal capitalization, further expand the process of interpersonal capitalization to entrepreneurs in the entrepreneurial environment, and consider the role of boundary conditions of environmental dynamics. Based on above, the study aimed to conceive the conceptual model of the impact of social interpersonal capitalization on entrepreneurial bricolage through entrepreneurial passion and entrepreneurial learning according to dual- system theory. At the same time, the study examined the boundary effect of environmental dynamics. Therefore, the research will make a certain theoretical contribution to opening the black box of pre-factors that influence entrepreneurial bricolage behavior. Through multi-stage data collection, this research finally uses sample data of 340 entrepreneurs to conduct empirical analysis. The results show the follows. Firstly, social interpersonal capitalization positively affects individual entrepreneurial bricolage behavior. Secondly, entrepreneurial passion and entrepreneurial learning mediates the effect of social interpersonal capital on individual entrepreneurial bricolage behavior. Finally, environmental dynamics negatively regulates not only the direct impact of social interpersonal capitalization on entrepreneurial passion and learning, but also the indirect effect of social interpersonal capitalization on individual entrepreneurial bricolage behavior through entrepreneurial passion and entrepreneurial learning.
The innovative contributions of this study lie in the follows. Firstly, the research scientifically defined the connotation of social interpersonal capitalization, supplemented and improved the social interpersonal capitalization measurement scale based on Chinese entrepreneurial situation, thus will provide a measurement tool for future empirical research on social interpersonal capitalization. Secondly, the research revealed the dual mechanism of emotion and cognition of the impact of social interpersonal capitalization on entrepreneurial patchwork, thus will enrich the research on antecedents and mechanisms of entrepreneurial bricolage. Finally, the research clarified the boundary condition role of environmental dynamics in the influence of social interpersonal capitalization on entrepreneurial patchwork, thus will enrich the theoretical research on environmental dynamics and expand the situational conditions under which social interpersonal capitalization plays its role.
In the complex and changeable digital environment, capability reconfiguration has become an effective mechanism for digital enterprises to make full use of emerging digital technologies to achieve digital integration product innovation. However, how to perform capability reconfiguration to accelerate digital integration product innovation has not been fully revealed by existing research. DigiBird is selected as the case study object, to analyze the process and mechanism of enterprise capability reconstruction under different stages of digital integration product innovation. The results show that: In the initiating digital innovation stage, digital sensing capabilities reconfiguration is realized, which plays accelerated insight for potential digital innovation opportunities. In the developing digital innovation stage, digital learning capabilities reconfiguration is realized, which plays orchestrating enhancement for digital technology integration performance. In the implementing digital innovation stage, digital agility capabilities reconfiguration is realized, which plays remodeling support for value creation process transforming. The research results extend the literature on capacity reconfiguration and digital integration product innovation, and provide guidance for digital enterprises to accelerate the process of digital integration product innovation through capacity reconfiguration.
Under the driving force of digital innovation, the ecological competition trend of emerging industries is deepening. Building emerging industry innovation ecosystems and conducting scientific and effective strategic management is of great significance to enhance the international competitiveness of China’s industries. However, the existing strategic management theories lack sufficient attention to China’s industrial context and digital context. Based on the comprehensive advantage theory, this study takes China’s new energy vehicle industry as a longitudinal case and explores the formation mechanism of the comprehensive advantage of emerging industry innovation ecosystem. The results show that: (1) the ecosystem experiences the three evolution stages of digital innovation “competence storage period – empowerment periods – competence expansion period”, and has formed the comprehensive advantages with different characteristics through “strategic change – strategic upgrading – strategic transition” at each evolution stage; (2) in terms of the driving forces and strategic paths, the formation of the comprehensive advantage is driven collectively by China’s unique institutional, technological and market contexts, and develops cyclically along the strategic paths of “dominant advantage selection – core competence cultivation – comprehensive advantage formation”; (3) accompanied with the dynamic selection of dominant advantages between “digital resources – digital platforms – digital ecosystems”, the ecosystem has cultivated the core competence of “distributed innovation competence”, “recombination innovation competence” and “cross-border integration innovation competence” in turn, and has promoted the synchronous formation and continuous evolution of comprehensive advantage manifested in a “multi-point breakthrough – platform empowerment – ecological integration” overall trend. Based on the above conclusions, this study finally constructs the theoretical framework of the formation mechanism of the comprehensive advantage of emerging industrial innovation ecosystem. Meanwhile, considering that the industrial digital reformation is still in a dynamic wave, future research should continue to track the strategic changes of emerging industries and refine a new round of comprehensive advantage strategy cycle, thus improving the theoretical framework with higher contextual adaptability and strategic guidance value. In terms of the theoretical contributions, this study helps to facilitate the cross integration of multiple theoretical fields, enrich the competitive strategy research of innovation ecosystems at the industry-level, advance the indigenous strategic management theories in the digital context, and realize the multi-dimensional expansion of the comprehensive advantage theory at the levels of “enterprise – cluster – industry – industry innovation ecosystem”. In a practical sense, this study may provide three aspects of implications for China’s digital innovation practice, industrial strategic planning, and policy system optimization. First, the industrial management departments should implement differentiated comprehensive advantage strategies according to the specific types of emerging industries. The three strategic paths revealed in this study may provide valuable reference for the strategic planning of emerging industries in different regions, industrial types and evolution stages. Second, innovative enterprises, as core members of the emerging industry innovation ecosystem, should take advantage of the digital resource endowment and platform empowerment to accelerate the cooperation and integrated innovation with more digital ecological partners. Third, the government should speed up the construction of the digital innovation policy system, and enhance the complementarity, context adaptability and process coherence of the innovation policy mixes.
Regional Eco-innovation covers the level of regional economic development, innovation capabilities and environmental sustainability. It is a combination of innovation-driven development strategy and ecological civilization construction. This paper divides the Eco-innovation process into technology research and development stage and achievement transformation stage, and incorporates waste recycling, energy consumption and undesired output into the innovation process, and constructs a closed-loop network structure of the Eco-innovation process. On the above basis, combined Super-efficiency model and SBM model, proposed an improved network DEA model that considers waste recycling and undesired output, which is used to measure the overall efficiency of regional Eco-innovation and the efficiency of each sub-stage, further establishes a measurement model of Eco-innovation scale efficiency to explore the scale efficiency of each decision-making unit. Finally collect the relevant panel data of China's regional innovation to conduct an empirical analysis, and the results show that the proposed method is effective and feasible. This paper expands the research on the Eco-innovation efficiency base on the network DEA structure, from the perspective of closed-loop Eco-innovation of waste recycling, it provides a certain theoretical reference and practical enlightenment for the optimal allocation of regional innovation system resources.
Marine ranching is an important part of China's marine economic development. This paper explores the technological innovation strategy of marine ranching based on the perspective of collaborative innovation network. From three dimensions of cooperation projects, patent applications and published papers, the paper selects the relevant data of China's marine ranching technology innovation collaboration from 2012 to 2021. First of all, based on the analysis of the existing collaborative network, the actor network theory is used to build the marine ranching technology innovation collaborative network, and the functions and roles of various actors in the marine ranching technology innovation collaborative network are analyzed; Secondly, the multi case study method is used to clarify the operation mechanism of the collaborative innovation network. The government policy promotion mechanism is the driving force for network expansion, the trust cooperation mechanism is the basis for the stable operation of the network, the interactive feedback mechanism is the way for the high-quality operation of the network, and the organization coordination mechanism is the guarantee for the stable operation of the network; Then, the UCINET software is used to analyze the data, calculate the network density, average path length, and the core edge structure evolution shape of the network centrality at each stage of the evolution of China's marine ranching technology collaborative innovation network, and make corresponding analysis. Using the UCINET software plug-in NETDRAW, the network evolution map of the five stages of China's marine ranching technology collaborative innovation network was drawn, and the characteristics of network evolution and the evolution of the relationship between actors were analyzed. Finally, the research conclusions and views of the paper are drawn.
The study found that the overall number of technological innovation in the field of marine ranching is not large, and the main innovation subjects are mainly scientific research institutes, universities and high-tech enterprises. Take patent application as an example. At present, the research and development of a single institution is the main form, and the number of cooperative innovations is relatively low. The cooperation mode is mainly in the form of scientific research institutes universities and scientific research institutes scientific research institutes. Among the actors in the marine ranching technology innovation collaboration network, the government and leading enterprises are the core actors of the network, and universities, research institutes and technology suppliers are the indispensable main actors. Small and medium-sized enterprises in marine ranching can actively or passively join the network. In addition, various service institutions, enterprises and customers related to marine products, marine industry associations and public welfare organizations, media, etc. are common actors in the marine ranching technology innovation collaboration network. At this stage, the marine ranching technology innovation collaboration network is still in a period of rapid development and turbulence. Collaborative innovation has not been separated from the primary mode of government industry university research cooperation, and science and technology intermediaries, customers, industry associations, etc. are less involved. But on the whole, the number of network innovation subjects is still increasing, the network structure is becoming increasingly complex, the cooperation path within the network is becoming longer, and the cooperation accumulation of innovation subjects is declining. The current collaborative innovation network is low density.
The research viewpoints in this paper can provide referential ideas and basis for major technological breakthroughs and research and development of marine ranching in the future, and help promote the technology supply of modern marine ranching construction, which is of great significance for the healthy development of marine ranching in China.
