Artificial intelligence (AI) technology has become an essential tool for deepening the theoretical research on corporate innovation, thus increasingly embedding itself into enterprise innovation practices and significantly impacting innovation efficiency. Grounded in a corporate lifecycle perspective, this study leveraged the data from 1,103 Chinese high-tech manufacturing enterprises listed between 2016 and 2021, by employing the decision tree analysis and multiple machine learning methods to examine AI's heterogeneous effects on enterprise innovation across different lifecycle stages. The findings are as follows: (1) Most machine learning models demonstrate superior predictive performance in forecasting firms' innovation performance (patent authorizations) compared to traditional linear regression models, highlighting machine learning's capability in capturing non-linear relationships among variables. (2) Enhancing AI capability and increasing AI patent filings significantly boost innovation capacity in mature-stage firms, while the innovation performance of growth- and decline-stage firms depends less on AI technology. (3) The decision tree analysis further indicated that innovation in growth and maturity stages primarily relies on AI patent activities, whereas innovation in decline-stage firms depends more heavily on the size of employees and technical personnel. Overall, while AI technology positively correlates with enterprise innovation performance, this relationship varies significantly across corporate lifecycle stages. This study will extend the lifecycle theory and provide practical guidance for enterprises on strategically deploying AI technology according to their developmental stages.

Accelerating artificial intelligence (AI) technological innovation is crucial for China to gain the strategic advantages in the new round of global competition and to promote the development of new productive forces. Intellectual property (IP) protection is an essential institutional arrangement for stimulating innovative vitality. However, given the typical characteristics of AI innovation, the effectiveness of IP protection in facilitating innovation remains unclear. Based on the identification criteria of AI patents released by WIPO, we constructed a dataset of over 330,000 AI patent applications from 273 Chinese cities from 2010 to 2021. On this basis, the research employed the intellectual property demonstration city policy as a quasi-natural experiment framework and applies a multi-period DID model to examine the impact of IP protection on AI innovation. The results indicated that the IP demonstration city policy significantly promotes urban AI innovation through mechanisms such as the agglomeration of scientific and digital talents, enhanced innovation-oriented public expenditure, and introduction of venture capital. The heterogeneity analysis showed that the incentive effect of IP protection is pronounced in collaborative patents, foundational technology patents, and the output of invention patents. Further research revealed that IP protection not only increases the quantity of urban AI innovation but also contributes to improving innovation quality. This study will provide detailed evidence for the impacts of IP protection on AI innovation and offer valuable implications for fully leveraging the incentive effects of IP policies.

An appropriate exploratory innovation pace is crucial for enhancing corporate innovation efficiency and success rates, and it is of great significance for seizing innovation opportunities and realizing innovation-driven development. In the era of digital intelligence, the development and application of artificial intelligence (AI) technology have brought many opportunities for a new round of technological revolution and industrial transformation. However, existing research is insufficient in examining the relationship between the AI application and exploratory innovation pace at the firm level. Based on the organizational information processing theory and the data from Chinese A-share listed companies from 2011 to 2020, this paper conducted an empirical analysis on the relationship between AI application and exploratory innovation pace. The results showed that: (1) AI application has an inverted U-shaped impact on exploratory innovation pace; (2) Top management team regulatory focus moderates the relationship between AI application and exploratory innovation pace; (3) TMT promotion focus and prevention focus have heterogeneous regulatory effects on the relationship between AI application and exploratory innovation pace; and (4) The inverted U-shaped impact of AI application on exploratory innovation pace is stronger in non-state-owned enterprises, mature enterprises, low-tech industries, and enterprises in low-competition market environments. By doing so, this paper has deepened the understanding of the impact of AI application on exploratory innovation pace at the micro-corporate level and it will provide profound insights into the impact mechanism of top management team preference differences on corporate information processing and matching processes.

Improving the structural resilience of innovation networks is an important theoretical and practical issue facing the high-quality development of the AI industry. This paper adopted the AI industry in Shanghai as an example, constructed a cooperative innovation network through text mining, and then used the ERGM to reveal the impact of endogenous factors of the innovation network on its structural resilience. The research found that: (1) The evolution of the structural resilience of the innovation network in Shanghai's AI industry can be divided into three main stages: slow growth, rapid growth, and maintaining stability. The continuous improvement of network heterogeneity is the key driver for the transformation of the innovation network from an assortative core network to a resilient network; (2) The intermediary structure negatively affects the structural resilience of the artificial intelligence industry's innovation network; (3) The expansive structure negatively affects the structural resilience of the AI industry's innovation network in general, but the three-star structure positively affects its structural resilience. This study has addressed the gap in research on the resilience of innovation networks in the artificial intelligence industry, and it will help to deepen the theoretical understanding of innovation network resilience, thus having positive implications for enriching the data sources of innovation network research.

The rapid development of artificial intelligence (AI) has injected new vitality into China's smart manufacturing transformation. How to integrate AI with smart manufacturing to achieve transformation is a critical challenge for Chinese manufacturing enterprises. From the perspective of "cognitive-behavior" integration, this paper explored the process of AI-driven smart manufacturing transformation in manufacturing enterprises through a longitudinal case study of BOE Technology Group. The findings indicated that: (1) The AI-driven transformation process of manufacturing enterprises experiences three stages: automation, integration and autonomy, culminating in a leap from basic intelligence to ecological intelligence; (2) Manufacturing enterprises employ three meaning construction approaches— "technology demand-driven", "strategic awakening-driven", and "cultural reshaping-driven"—which influence the three resource orchestration patterns of "utilization-oriented → exploration-oriented → dynamic-oriented", thus facilitating the integration of AI and smart manufacturing transformation; (3) The role of meaning construction in resource orchestration evolves dynamically in multiple stages, and presents a process of "guidance → reshaping → reinforcement". The results have not only provided a theoretical framework for manufacturing enterprises to achieve smart manufacturing transformation but also enriched the application of resource orchestration theory and managerial cognition theory. The study will offer valuable insights for strategic decision-making in smart manufacturing transformation within manufacturing enterprises.

With the deepening development of a new round of scientific and technological revolution and industrial transformation, artificial intelligence (AI) has become an important driving force for the transformation and high-quality economic development of enterprises. This paper selected A-share listed companies from 2004 to 2021 as samples, and adopted the dual machine learning method to evaluate the impact and mechanism of artificial intelligence on enterprise growth. The econometric results showed that: (1) AI significantly promotes firm growth and the conclusions remain valid after a series of robustness tests; (2) The heterogeneity analysis revealed that the promotion effect of AI on enterprise growth is more significant in non-state-owned enterprises, small and medium-sized enterprises, and traditional industries; (3) The mechanism test based on the perspective of AI's technical-economic characteristics showed that AI mainly exerts its synergy, innovation and substitutability to promote enterprise growth by improving enterprise productivity, reducing management costs, promoting enterprise innovation, and optimizing the structure of labor employment. The paper has revealed the important role and internal mechanism of AI in promoting enterprise growth, and it will provide some important supplements and references for accelerating the development of AI and promoting enterprise transformation and upgrading.

The high-quality development of the manufacturing industry is the top priority of the high-quality development of China's economy. As the core driving force of new quality productivity, artificial intelligence has a strong ability to influence the economy and society, and it is of great significance to study whether its introduction can improve the innovation performance of manufacturing enterprises. Based on the data of China's manufacturing A-share listed enterprises from 2011 to 2019, this paper used the difference-in-differences method to systematically study the impact and mechanism of the application of artificial intelligence technology on the innovation performance of manufacturing enterprises. The results showed that: (1) the application of artificial intelligence technology significantly improves the innovation performance of manufacturing enterprises, and it is still valid after a series of robustness tests; (2) from the perspective of the mechanism, ai technology can improve the innovation performance of enterprises by influencing factors such as government subsidies, the total labor force and labor structure of enterprises; and (3) there is heterogeneity in the application of artificial intelligence technology in the innovation performance of manufacturing enterprises with different property rights and different life cycles. The study held that promoting the integration and application of artificial intelligence technology can not only promote the high-quality development of the manufacturing industry and expand the innovation space, but also become a strong driving force for the realization of China's manufacturing industry "going from big to strong".

The development of new quality productive forces (NQPF) hinges on the scientific and technological innovation, with a focus on industrial upgrading. The current wave of scientific and technological revolutions, driven by artificial intelligence (AI), is providing new momentum for China's sustained economic growth. Based on the micro-data from Chinese-listed companies from 2009 to 2022, this study constructed an NQPF index system and empirically tested the impact of AI applications on NQPF and its mechanisms via the "innovation" and "industry" pathways by using the two-way fixed effect model. The findings indicated that: (1) AI applications significantly enhance NQPF through multidimensional new quality factors of production; (2) the mechanism test showed that AI applications foster substantial innovation rather than strategic innovation, with substantial innovation positively mediating and moderating the impact of AI on NQPF; (3) the internal specialization and external supply chain efficiency also positively mediate and moderate this impact, while the AI-driven industrial chain integration stimulates NQPF; (4) the heterogeneity analysis revealed that AI's impact on NQPF is more pronounced in the private, manufacturing, and competitive industry firms. This study has clarified the differences in innovation motivation of AI application effectiveness, elucidated the mechanism of AI-induced industrial fission and chain upgrading, and enriched the research on NQPF influencing factors and the economic consequences of AI applications, which will provide important policy insights for leveraging AI to drive NQPF development.

Exploring the integration of artificial intelligence (AI) with the real economy is an important issue for reconstructing industrial development paradigms and fostering the emergence of intelligent economic forms. Existing research primarily examines the micro-level impacts of AI from perspectives such as production efficiency and employment structure, but lacks a comprehensive discussion on whether enterprises can leverage AI to enhance their competitive advantage. Based on this, this study used manufacturing listed companies in the Shanghai and Shenzhen A-share markets from 2007 to 2022 as the research sample to empirically test the impact of AI adoption on enterprise competitive advantages using a fixed effects model. The results showed that AI adoption contributes to enhancing enterprise competitive advantage, and the conclusion remains robust even after addressing issues such as endogeneity, substitution variables, and other robustness tests. The contextual analysis indicated that enterprises can effectively improve their competitive advantage only when AI is embedded in specific application scenarios, complemented by appropriate assets, and supported by enhanced technological capabilities. The mechanism tests revealed that AI adoption enhances competitive advantage through improvements in production efficiency and innovation efficiency. Further research suggested that the positive impact of AI on competitive advantage is more evident in latecomer enterprises and those with faster adoption speeds. The conclusion of this study has not only addressed the fundamental question of whether AI, as a general-purpose technology, contributes to improving enterprise competitive advantage but also will provide certain empirical evidence and managerial insights on how traditional enterprises can leverage AI to create unique value and enhance their competitive position.

Artificial intelligence has become a crucial force in enabling entrepreneurial development, playing an increasingly important role in the chance of entrepreneurial opportunity discovering and value creating. Although the entrepreneurial field has gradually paid attention to artificial intelligence, the current research on artificial intelligence-enabled entrepreneurship is still fragmented and lacks systematic integration. This paper adopted the unsupervised machine learning methods, combined machine coding with manual coding, and used structure topic models to analyze 122 relevant literatures collected by Web of Science. This study sorted out the current research status from the aspects of the antecedents, scenarios, processes, results, theoretical perspectives, and tools and methods of AI-enabled entrepreneurship. This study found that the research on the driving mechanism of AI-enabled entrepreneurship needs to be further deepened, the research on scenario fields needs to be further enriched, the research on path mechanisms needs to be further explored, the research on dual impacts needs to be further discussed, the underlying logic needs to be further broken through, and the technical instruments need to be further integrated. On this basis, it proposed a future research framework and topics, and revealed that we should deepen the research on the driving force of AI-enabled entrepreneurship at the macro-, meso-, micro- and even cross-level levels, promote the research on the multi-scenario ecology of AIenabled entrepreneurship in regions, industries and fields, strengthen the research on the process path of AI-enabled entrepreneurship from a cross-level and dynamic perspective, pay full attention to the possible positive results of AI-enabled entrepreneurship, especially the negative impact, and further explore the unique theoretical system of AI-enabled entrepreneurship based on new practices, provide solid data, tools and methods to support the research on AI-enabled entrepreneurship. By combing through existing literature and constructing a future research framework, this paper will provide a guideline for enriching and deepening research on artificial intelligence-enabled entrepreneurship.

    Artificial intelligence (AI) is reshaping the structural form of the innovation ecosystems and cooperation modes between innovation subjects, bringing disruptive changes to enterprise innovation, and posing new challenges to the innovation ecosystem theory. However, current research lacks insight into how AI participates in building the innovation ecosystem. This paper focused on the theories related to innovation ecosystems, and analyzed the structure and construction process of the innovation ecosystems with AI participation based on the exploratory case study of Huawei′s HarmonyOS. The research found that: (1) AI, as the innovation subject, plays such multiple identities as enabler, supporter, connector and driver, and the innovation ecosystem with AI participation presents the characteristics of coexistence of human and AI dual innovation subjects; (2) AI, as the innovative technology, promotes multi-level and all-round interaction among innovation subjects, and promotes the diversification and decentralization of innovation ecosystems; (3) AI co-evolved and co-existed with the innovation ecosystem it participated in, and in different stages of the construction of the innovation ecosystem, the innovation mechanism evolve from being expert-led, to partnerships and collaborations, and finally to collaborative sharing. This study has further enriched and developed the theory of innovation ecosystems, and it will provide guidance for enterprises to build innovation ecosystem under the new situation to enhance innovation vitality and sustainable development ability. 

     The AI patent is an important means to promote manufacturing enterprises to transform traditional production methods and achieve intelligent development. Exploring the mechanism by which AI patent networks affect the intelligent development of enterprises can help reveal external effective paths to enhance intelligent development. By combining the open innovation theory and knowledge base theory, and based on the "structure-resources-capability" framework, this paper used a modified gravity model to construct innovation networks with 2010-2022 AI patents of manufacturing enterprises and used the bidirectional fixed effects model to explore the impact of the position of AI patent networks on the intelligent development of enterprises. The theoretical and empirical research results indicated that the position of AI patent networks has a significant promoting effect on the intelligent development of enterprises, that is, having high centrality and structural holes is conducive to the intelligent development of enterprises. AI patent network position is beneficial for improving knowledge breadth and depth of enterprises, but it mainly indirectly promotes the intelligent development of enterprises through knowledge depth. The intelligent development of enterprises has a network peer effect, and it positively moderates the positive relationship between the position of AI patent networks and the intelligent development of enterprises. In technology-intensive industries and developed cities, the position of AI patent networks has a more significant positive effect on promoting the intelligent development of enterprises. This paper took the AI patents as the foundation for constructing networks, studied the external factors of intelligent development from the perspective of network position, and clarified the important path for improving the development of enterprise intelligence through the position of AI patent networks, which has enriched the research on the antecedents of enterprise intelligence development; it also extended the peer effect to the activities of enterprise intelligent development to reveal the decision-making motives for enterprises to choose intelligent development, thereby further enriching the research framework on the impact of the position of AI patent networks on the intelligent development of enterprises. The study has expanded the research on the antecedents of intelligent development and provided some theoretical guidance for manufacturing enterprises to actively plan intelligent manufacturing strategies.

    With the release of Chat GPT and the advent of New Bing, especially the emergence of new war modes in the Russia-Ukraine Conflict, AI technology has played an important role in the future development of competitions. How to break through the bottleneck of artificial intelligence technology, effectively enhance the competitiveness of the artificial intelligence industry is currently an unavoidable problem. This article focused on improving the competitiveness of the artificial intelligence industry, with the main focus on solving the problem of industrial innovation efficiency. In this paper, through literature analysis and system analysis methods, relevant analysis was conducted on the innovation ecosystem of the artificial intelligence industry, the following main conclusions was made: (1) the overall trend of artificial intelligence has become the core hub of industrial development, social change, and international competition has been clarified, and the viewpoint that the development of artificial intelligence is the development of the future has been formed; (2) we constructed a scientific and efficient artificial intelligence industry innovation ecosystem model, which will provide an effective analytical tool for effectively solving the problem of innovation environment barriers and innovation subject opposition, and for the macro regulatory layout and micro precise policy implementation of the artificial intelligence industry; (3) we established a multi-disciplinary, multi industry talent cultivation and output mechanism with intelligent education as the core, thus providing strategic support for the long-term development of the artificial intelligence industry.

   With the implementation of regional coordinated development and new urbanization strategy, as a special region of China′s regional plate, the metropolitan economic circle has developed rapidly and has become an important national economic growth pole. Yangtze River Delta Economic Circle, Pan-Pearl River Delta Economic Circle and Bohai Economic Circle represent the highest level of China′s economic development, shouldering the responsibility of taking the lead in achieving modernization. In this context, it is of great practical significance to study and judge the stage characteristics and spatial-temporal pattern of the science and technology innovation ability of artificial intelligence industry in the three economic circles, identify the evolution law of the science and technology innovation ability of artificial intelligence industry in the three economic circles, and explore the collaborative promotion path of the science and technology innovation ability of artificial intelligence industry in the three economic circles.

     Based on the internal mechanism of the artificial intelligence industry, this paper constructs the comprehensive measurement index system of the science and technology innovation ability of the artificial intelligence industry by drawing on the innovation triple helix structure framework. Taking the three major economic circles in China as the spatial scale, the science and technology innovation ability of the artificial intelligence industry is scientifically measured, and the stage characteristics, spatial imbalance and distribution dynamic evolution trend of the science and technology innovation ability of the artificial intelligence industry are deeply explored. Firstly, the G1-CRITIC weighting method is used to dynamically measure the science and technology innovation ability of artificial intelligence industry in the Yangtze River Delta, the Pan-Pearl River Delta and the Bohai Economic Circle, and then reveals the evolution process of the science and technology innovation ability of artificial intelligence industry. Secondly, Kernel density estimation method is used to describe the spatial differentiation characteristics and time evolution path of the science and technology innovation ability of artificial intelligence industry in the three economic circles as a whole and each economic circle. Thirdly, traditional and spatial Markov chain analysis methods are used to examine the endogenous interaction mechanism and spatio-temporal evolution law of the science and technology innovation ability of artificial intelligence industry in the three economic circles.

      Through the measurement and trend evolution of the science and technology innovation ability of artificial intelligence industry in each provincial-level region of China′s three major economic circles from 2009 to 2018, the findings are as follows: First, the science and technology innovation ability of the artificial intelligence industry in the three major economic circles is on the rise, but its gradient characteristics are obvious. The science and technology innovation ability of the artificial intelligence industry in the Yangtze River Delta Economic Circle is in an absolute leading position, and the Pan-Pearl River Delta and Bohai Rim Economic Circles are at a relatively low level. Second, the "polarization effect" of the science and technology innovation ability of the artificial intelligence industry in the three major economic circles has intensified. The Yangtze River Delta, Pan-Pearl River Delta, and Bohai Rim Economic Circles all have a trend of multi-level differentiation, and they have a clear hierarchical structure with significant spatial agglomeration effects. Third, due to the spatial interaction and positive spillover effects between provinces within the economic circle, the "club convergence" phenomenon and the "Matthew effect" of the science and technology innovation ability of the artificial intelligence industry have been alleviated, and the convergence process is not Independence, the heterogeneity of spatial spillover effects persists. 

     A comprehensive investigation of the development trend, trend evolution and spatial imbalance of the science and technology innovation ability of the artificial intelligence industry in China′s three major economic circles can build a domestic and international dual cycle for the three major economic circles of the Yangtze River Delta, the Pan-Pearl River Delta and the Bohai Rim, and explore regional integrated development provide decision-making reference.

    Artificial intelligence (AI) is the most important core content in the fourth Industrial Revolution. More and more governments and enterprises have realized the importance of it and have made strategic adjustments one after another, actively deploying their own artificial intelligence development strategies. In recent ten years, the related literature of artificial intelligence research presents a blowout trend. The literature review of these research results based on traditional methods has encountered many problems, such as the wide is too range, the quantity is too much and the difficulty in objective analysis. Therefore, several scholars have used knowledge mapping to conduct quantitative research on literature in the field of artificial intelligence, but none of them has studied the development in China from a global perspective. In this context, it is of great significance for the development of AI fields in China to analyze the current appearance and trend of AI research in the world and the characteristics of those in China. In order to solve these problems, this paper summarizes the characteristics of relevant research on AI from the perspective of global and China, and analyzes the research hotspots and evolution trends in this field in China. It is hoped to further fill in the gaps in existing research, provide valuable reference information for the development of AI in China, and thus facilitate its future construction and development.

     Based on the research method of knowledge mapping, this paper uses the literatures related to AI in Web of Science and CNKI as the data source, and utilizes CiteSpace software to make a visual analysis of the development, characteristics, and evolution process of China′s AI research in the recent ten years from a global perspective. First, through the function of dual-map overlay, this paper analyzes the characteristics of AI research from a macro perspective, and also analyzes China′s role in AI research from the perspective of countries and institutions. Then, by using the co-word analyze of the subject words, this paper compares the research hotspots in the world and analyzes the characteristics of relevant researches in China. At the same time, the evolution trend of artificial intelligence research in China is analyzed by using burst term analysis. Finally, the conclusions of the research and analysis are summarized, and some suggestions are put forward.

       It is found that the research of artificial intelligence has shown a trend of multi-discipline and interdisciplinary integration. China′s development in the field of artificial intelligence has achieved results, but there is still a gap between the top. China′s research on the application of artificial intelligence is more abundant, which is related to China′s rich data resources and application scenarios. In the future, we should pay more attention to the cultivation of talents, long-term support and investment in basic technologies, and expect that artificial intelligence technology can bring more profound changes.

     Accelerating the deep integration of AI and the real economy is an important engine for China to achieve leapfrog development of science and technology, industrial optimization and upgrading, and supply-side structural reform. How to use AI for the transformation and upgrading of traditional industries and the transformation of economic development mode has become an important issue in China′s economic development. Based on the perspective of cost stickiness, this paper empirically examines the impact and mechanism of AI on enterprise cost stickiness by taking A-share listed companies in Shanghai and Shenzhen from 2011 to 2018 as research samples.

     The main conclusions are as follows: First, AI has a restraining effect on enterprise cost stickiness and can play a role in reducing costs. Second, in the enterprises with higher adjustment cost and more serious agency problem, AI has a more significant inhibition effect on cost stickiness, indicating that AI can affect cost stickiness through reducing adjustment cost and alleviating agency problem. Third, the inhibition effect of AI on enterprise cost stickiness mainly comes from the commercial application of AI, and there is no significant relationship between AI research and development and cost stickiness. Fourth, after distinguishing different cost factors, AI will restrain the cost stickiness of material resources and increase the cost stickiness of human resources, indicating that AI will increase the demand for high-end technical talents while reducing the cost of materials.

     The possible research contributions of this paper are as follows: First, it innovatively adopts the method of combining text mining and artificial research and judgment to construct enterprise-level AI data, and distinguishes AI research and development from AI application mode, so as to provide reference for AI research at enterprise micro-level. Second, existing literature has explored the impact of AI on labor market and economic development from the macro level, while some literatures have focused on the impact of AI on micro enterprises. Different from previous studies, this paper discusses the cost reduction effectiveness and path of AI enabling real enterprises from the perspective of cost stickiness, which not only provides new ideas for promoting the rapid development of real enterprises, but also extends the relevant research on the economic consequences of AI. Third, cost reduction is an important part of the transformation and upgrading of real enterprises in the context of big data. Different from the perspective of internal governance mechanism and external environment, this paper explores the impact of AI on cost stickiness from the perspective of micro enterprises, which not only enriches the research on the influencing factors of cost stickiness, but also adds new evidence for scientific and technological progress to promote the reduction of cost, improvement of quality and efficiency of real enterprises.

     Based on the above research conclusions, this paper puts forward the following countermeasures and suggestions for accelerating the deep integration of AI and physical enterprises:

    At the national level, we will, on the one hand, continue to strengthen the promotion of AI research and development and commercial implementation, improve the penetration rate of AI, use AI and other new information technologies to drive high-quality economic development, and create a good innovation environment for the high-quality development of traditional enterprises. On the other hand, we should establish and improve AI incentive policies, give enterprises preferential policies related to AI, encourage enterprises to speed up the process of AI research and development and commercial landing, and promote the transformation and upgrading of traditional enterprises, so as to transform the mode of economic development and promote high-quality economic development.

      At the enterprise level, we can see that firstly, according to their own development needs, traditional entities can speed up the deep integration with AI, especially the application of AI in production, operation and management activities, so as to help them optimize cost management, accelerate transformation and upgrading and improve the quality of development. Secondly, in the process of accelerating the deep integration with AI, traditional enterprises should continue to increase the research and development of AI to maintain their core competitiveness. Thirdly, the research and development and application of AI cannot be separated from the support of high-end information technology talents. Therefore, enterprises should focus on cultivating and recruiting high-level talents related to AI to provide talent support for the entity enterprises with AI empowerment.

    Artificial intelligence not only affects the production and life style of human society profoundly, but also causes many ethical dilemmas and challenges. These problems cannot be solved completely by technology. As the fast iterative of technology and business models, the legislation and regulation is also difficult to keep up with the pace of technology development. Therefore, setting up new ethics of science and technology to promote better artificial intelligence service is the subject of the whole society. From the perspective of science and technology ethics, this paper reviews and analyzes relevant researches, focusing on the ethical issues in artificial intelligence fields, such as robotics, algorithms, big data and unmanned vehicle. 
     The research on machine ethics focuses on moral responsibility, realization of machine ethics and robot ethics. It is the core of robot ethics to realize the harmony among robot, human and nature. The emergence and governance of algorithm discrimination in algorithm ethics has become the focus of academic research and public attention. As algorithms have more and more life attributes, algorithm discrimination is the most likely ethical risk in the algorithmic decision-making process. Unmanned vehicles are faced with such ethical issues as the determination of "moral algorithm", the legal responsibility of traffic accidents and the protection of passenger privacy. Ethics has become one of the main obstacles to commercializing unmanned vehicle. Big data technology has produced a series of ethical problems, such as insufficient autonomy of data subject, vanishing boundary of individual data right, and violation of information privacy, which are generated from the source of subject, objective reasons and social background and need to be solved from the establishment of ethical principles, ethical governance system and ethical policies of big data. In many cases, the ethical issues of big data technology are essentially the ethical contradictions between users as data producers and users of big data technology. Artificial intelligence technology brings a series of ethical risks. The ethical governance of artificial intelligence technology needs to reshape ethical strategies from the perspectives of government management, technology, public and relationship, and to be based on the concept of "responsible innovation", the participation of multiple subjects and the ethical embedding of the whole process of technology development. In general, the study of artificial intelligence ethics is still in its early stage of development, and there are still deficiencies in the localization, systematization, comprehensiveness and implementability of the study, which need to be further explored with the development of artificial intelligence technology. 
    In the future, first of all, we should establishing the ethics principle and management system according to Chinese situation to promote the high-quality development of artificial intelligence in China. Secondly, we should promote interdisciplinary cooperation in the study of artificial intelligence ethics. Thirdly, we need to take into account the context in which different cases occur, and integrate theory analysis into practice cases to improve the pertinence and operability of artificial intelligence ethics. Fourthly, scholars generally believe that only the government, the market, social organizations, academia and other diverse subjects to participate, we should make clear the responsibility division and cooperation of multiple subjects in ethical governance. At present, how to realize the responsibility division and cooperation of multiple subjects still needs to be discussed and continuously promoted.

   The rapid development of artificial intelligence (AI), while is bringing convenience to human life and improving productivity, also gradually affecting the various existing social and economic conditions. Based on the advance of the technology of machine learning, the creativity of AI has gradually made it possible for AI to create and invent. Originated from the Industrial Revolution, the IP regime is designed to stimulate innovation and protect the intellectual achievements, and shall always keep up with times in order to follow the process of technology.
    This article explores the degree of adaptability of the important aspects of the patent and copyright systems such as the objects of protection, qualification of the right holder, ownership of patent or copyright, judgment of infringement, and subjects of liability, in a new AI era when AI algorithms independently make inventions and create literary and artistic works based on their deep learning ability. Based on the exploration above, several conclusions are drawn, including (a) the present IP regime shall be designed to show an optimistic and encouraging attitude towards the inventions and creations by AI, excluding inappropriate subject matters, while the examination or authorization standard of the inventions and creations by AI shall differ from those by human, which means a special criteria including novelty standard, inventiveness standard as well as practical applicability for AI created inventions in the field of patent, and when AI plays the role of author in the creative process as well as the final work fulfils the requirement of originality and replicability in the field of copyright; (b) at present the rightsholder of AI inventions or creations shall be relevant natural person or legal entity, e.g. programmer or the owner of AI, rather than AI itself, and when the "Strong AI era" comes, we cannot deny the possibility for AI to have the legal status of inventor or author; (c) due to the complex implementing process of patent, indirect patent infringement as well as "induced infringement" shall also be defined as a kind of infringement and bear relative liability in the field of AI inventions; and (d) a specialized Registration-Authorization system for AI works shall be taken into consideration, as well as taking the AI artwork as a kind of derivative work in order to make an adjudgment of copyright infringement.
    More increasingly, this article explores the role of the patent and the copyright law system as to the extent that they accord with the principle of fairness, both for the current "Weak AI era" and the future "Strong AI era". Based on case studies, this article analyses a series of challenges brought by the development of AI technology to the current patent law and copyright law, try to suggest some possible solutions, including (a) the solution of "public property", which means AI created inventions or works should belong to the public and could be used by everyone free of charge in "Strong AI era"; (b) "the right of data processor" as a novel neighboring right, which means "the right of data processor" should be enjoyed by the owner of the right to use the program or equipment that generates the data, which functions as the key element in the application of AI technologies; (c) Sui Generis rights for AI created inventions or works as a transitional plan when the "Strong AI era" just comes and the legal status of AI are not fully accepted by the society then; and (d) changing the regulations on primary rights in patent and copyright law, e.g. protection period, etc.
    From the practical point of view, the research in this paper provides systematic suggestions on IP regime for present "weak AI era", as well as in the future "strong AI era".

 In the context of digital economy, the application of artificial intelligence (AI) technology is deeply affecting the transformation of firm management, the expansion of business boundaries, and the change of management models. Although more and more firms are accelerating business model innovation through AI, we should see that the expansion of enterprise boundaries also brings challenges to firm management. Based on the nature of AI technology, our paper aims to explore and study how AI application capabilities and AI management capabilities act in promoting firm innovation so as to facilitate firms in making changes in accordance with AI technology. Combining the complementary assets view and the organizational learning theory, we propose an analytical framework based on firm′s application and management capabilities on AI technologies, emphasize the necessity of combining AI with human intelligence, and explain the functions and influences of the two capabilities with their complementary effect on the costs and benefits of firm innovation; the complementary effect of the two capabilities can help firms effectively acquire and control the complementary assets required for innovation, reduce the costs of digital innovation, improve operational efficiency and promote corporate innovation performance.In terms of the benefits of AI application, it helps firms automate the business processes, analyze data, coordinate the communication with customers, and facilitate the interaction between employees. With the development of AI application capabilities, firms can increase efficiency in processing structured and unstructured data. The cognitive abilities of AI can greatly reduce the cost of coordination and communication with customers and employees. Meanwhile, AI application have also brought new problems such as increasing costs in maintaining data security and privacy, and therefore it is important to establish AI management capabilities ( the ability to continuously learn in the process of applying AI to control the risks and costs brought by AI).We found that AI application capabilities can reduce cost in labor, communication and coordination, and data search, but will induce new cost in building digital infrastructure, dealing with ethical issues, securing critical data, and managing organizational structure change. More importantly, firms must establish AI management capabilities to effectively deal with the continuous innovation of big data, digital technology, AI technology, and to manage the internal organizational structure change as well as the external environment risks brought about by the technologies; the effective combination of the AI capabilities benefit firms in coordinating digital technology enabled resources, acquiring complementary assets, and controlling the cost and risks of AI. We also propose that with the interaction of AI application capabilities and management capabilities, firms will be able to reduce cost in labor, communication and coordination, and data search; meanwhile, firm will be able to control costs in building digital infrastructure, deal with ethical issues, secure critical data, and manage organizational structure change. Finally, this paper provides new insight for firm′s digital innovation strategies. We suggest firms not only should focus on the application of AI, but also need to establish management capabilities of AI, such that firms can take advantage of AI application while avoiding the risks and costs, form business models and operating models that are hard to imitate and maintain the benefits and competitive advantages brought by technology innovation.