While building trustworthy AI has become a consensus worldwide, the rationality of trustworthy AI concept has always been a source of controversy. Critics challenge the concept by insisting that AI cannot satisfy trust conditions, while advocates attempt to classify AI or stratify trust to open up a space for trustworthy AI, but fail to provide a sound rationale. Restricted by the traditional interpersonal trust model centered on the trustor, critics and defenders are unable to conduct a comprehensive investigation from the dual perspective of human and AI, leading to critics making unilateral judgments and defenders providing failed defenses. Focusing on the trustworthiness and exploring the trustworthiness traits of AI would provide new perspective and feasible ideas to defense.

Measuring and analyzing individual risk perception of artificial intelligence is the basis of studying its risk and social impact. In this study, a multi-index and multi-factor structural equation model (MIMIC) was constructed to measure youth's risk perception level of artificial intelligence from four aspects, and the impact of eight risk factors on it was investigated. The results show that youth's evaluation of social, ethical, legal, national security, political and other risk factors positively enhances their risk perception of artificial intelligence, while the evaluation of economic, technological, personal and property factors has no significant impact. Compared with other factors, concerns about ethics, national security and law significantly increase the risk perception of AI among young people. It shows that the contemporary youth's risk perception of artificial intelligence is mainly characterized by "Foresight" rather than "Short-Term Concern", which shows that the youth group has a deep sense of worry about the overall, long-term and legal aspects of technology. In view of these measurement and analysis results, it is suggested that in the process of comprehensively developing AI risk governance, more efforts should be made to strengthen the governance of AI hidden risks from the perspective of national strategy and legal ethics, so as to eliminate people's concerns to the greatest extent and consolidate the social foundation for the implementation of emerging technologies.

Many studies have proved that source credibility has a significant positive impact on the propagation effect. However, does science popularization in the era of social media also follow this rule? Traditional Persuasion Communication researches focus on all aspects of the complete communication path that may cause attitude changes, including source, information, channel, and destination. In recent years, researchers have paid more and more attention to understanding how the audience completes information decision-making behavior from the perspective of social cognition, emphasizing that persuasion is a process and that different information processing methods change their attitudes and behaviors. Based on the Heuristic-Systematic Model theory from the socio-cognitive perspective, this study samples 743 science short videos from 20 representative accounts on the “Douyin” platform. In the content-coding stage, researchers first establish the coding category table, and then three coders encode the samples. In the data analysis stage, the reliability of the coding results is tested first, and then the hypotheses of the heuristic cue and systematic cue and their sub-hypotheses are verified by the parameter test method. To verify the effect mechanism, factor analysis is used to calculate the argument richness score, heuristic cue score, and systematic cue score of each sample, then the sample grouping of argument richness and consistency is carried out with the median, and finally, correlation analysis and regression analysis are used to verify whether there is bias, additivity, or attenuation effect. The results of content analysis show: 1) Authoritative sources can’t play a better propagation effect and have a significant negative impact, reversing the positive cognition of authoritative sources in the traditional media environment. Science communication has transferred from the "top-down" mode to the "equal dialogue" mode. 2) The concentration of content, narrative mode, the use of music, and expression mode are important systematic cues that affect the propagation effect of science short videos. Only those science short videos with "temperature" can stimulate emotional resonance. 3)When the evidence provided by the science short video is insufficient, the heuristic cue score is not related to the propagation effect, indicating that there is no bias effect. 4)Under the condition of sufficient evidence provided by the science short video, when the heuristic cue score is congruent with the systematic cue score, the additivity effect of both on the propagation effect is not significant. In contrast, the scores of the two cues are incongruent, and the influence of systematic cues on the propagation effect is more significant than that of heuristic cues, that is, an attenuation effect occurs. It suggests that we should pay attention to the expression and emotional narration in the process of science popularization. However, there are still many imperfections in this study. From the actual samples, there is some uneven distribution of variables, such as music use and expression mode; In addition, because the content analysis is manually encoded, there may make the encoding no objective. These may have a certain impact on the results of statistical analysis. Future research can also include text analysis, such as qualitative analysis and emotional analysis of the comment text in the sample.

The intensity of basic research funds has always been a key issue of concern to all walks of life, and the determination and comparison of the intensity of China's basic research funds should be based on the premise that the statistical caliber of China and foreign countries is consistent. Taking China's basic research funding and statistical caliber as the research object, this paper analyzes the concept of basic research, the status quo of investment, existing problems, and statistical caliber. The study found that there are three problems in Basic Research in China: there is room for optimization of the execution structure; Local governments do not pay enough attention to basic research; The statistical caliber of basic research and R&D funding is not completely consistent with foreign countries, which affects the accuracy of science and technology statistics, and there is a possibility that funding is underestimated. In order to further improve the investment mechanism of basic research and ensure financial support, three suggestions are put forward: guide enterprises to attach importance to basic research investment; Encourage local governments with the capacity to attach importance to investment in basic research, and form a mechanism for the central and local governments to jointly support the development of basic research; Standardize the methods and caliber of science and technology statistics.

Building and improving collaborative innovation relationships between difference subjects is an important part of constructing the national innovation system. Under the impetus of new round of scientific and technological revolution, the boundaries between multiple innovation subjects are blurred. As a result, Industry-University-Research Collaborative Innovation (IURCI) has become increasingly important for the realization of S&T innovation, thus serves as a feasible way leading to the high-quality development of economy. Although the government itself cannot carry out innovation activities directly, it can play a supporting role in policy coordination, resource guarantee and other aspects to promote collaborative innovation. In the context of China’s economic transition which characterized by "big market - big government", government to provide investments and fundings for various innovation subjects is common phenomenon. As the essential measurements to promote collaborative innovation, Government Fundings (GF) have been proved to have positive effect on IURCI. However, less attention has been paid on how the GF works so that the exact functioning mechanism of GF hasn’t been revealed. S&T Social Organization (STSO) is an indispensable component of national innovation system. They can promote IURCI effectively by playing the role of "lubricant" and "glue" between the two forces of government and market. To reveal the impact and possible functioning mechanism of GF on IURCI, this paper focused on the direct effect of GF on IURCI as well as the mediation effect of S&T Social Organization. Based on the provincial panel data of 31 provinces in mainland China from 2010 to 2018, we calculated the collaborative innovation level of industry, university and research by using the collaborative degree model of compound collaborative system. Empirical study was also conducted using regression models, and the results has been tested with bootstrap method. The findings are as follows. First of all, the GF can effectively promote IURCI, but it doesn’t have direct effect on it. STSO serves as an intermediary mechanism and has fully transmitted the function of GF. Secondly, GF can promote the development of three types of STSO, but only the private non-enterprise units play the major role and completely mediate the impact of GF on IURCI. The findings above have revealed the mechanism of GF on IURCI by empirically testing the mediating effect of STSO during the process of government sponsoring the collaborative innovation. Those findings provide a possible explanation for the unstable empirical results of the role of government in previous studies. To meet the policy requirements in the 14th Five-year Plan, we further make the following two suggestions. First, establishing platform funding pattern to enhance the synergistic effect of GF. Second, strengthening the policy support for STSO to provide impetus for IURCI. The limitation of this paper mainly exists in the constraint of macro statistics. On the one hand, the lag of data makes it impossible to better represent the rich policy practice in recent years. On the other hand, the inaccessibility of data makes it difficult to subdivide the specific forms of GF. For future study, we can enrich the analysis of the mechanism of GF in promoting IURCI by exploring the heterogeneity of different types of government funding based on micro-data such as project funding.

National scientific research institutions are the important scientific and technological force of the country and the source of innovation for social progress. In the new period, the construction of national laboratories has become an important starting point for the construction of national scientific research system. In this construction process, the scientific and technological capacity is important, but it is also very important to grasp the participants and their relations. In order to explore the relationship between national scientific research institutions and enterprises and relevant scientific and technological policies in line with China's national conditions, this paper, based on Alexander Gerchenkron's advantage of backwardness theory, combined with Peter Hall and David Soskice's research on the varieties of capitalism in developed countries, selects the cooperation paradigms of three types of national scientific research institutions and enterprises represented by the United States, Germany and China, and analyzes the characteristics and differences of their cooperation fields. Based on the comparative analysis, it is proposed that China should give play to the government's control ability and macro-control ability in the current period of peaceful competition and the external environment with economic development as the primary task, implement the rights and responsibilities of all participants in the construction of national scientific research institutions through scientific and technological policies, increase basic research investment, improve the ownership of intellectual property rights, and smooth the two-way output of technology to promote the construction of national laboratory system.

The research in the field of social-technological system framework shows that the growth of an enterprise depends on the development of its technical system and administrative system. Therefore, how to improve the development level of enterprise subsystem is the key for latecomers to achieve sustainable capacity accumulation and sustainable catch-up. In view of this, this study uses the case study method to analyze the mechanism for latecomers to improve their subsystems and achieve sustainable catch-up by tracking the catch-up process of three Chinese manufacturing enterprises. It is found that: first, the catch-up action of latecomers is restricted by the development of its subsystems, and the slow development of its internal technical system and administrative system leads to the lag of technology and management; second, latecomers can improve their subsystems by carrying out technological learning and managerial learning so as to achieve capability accumulation. Third, the technical system and the administrative system influence each other, and their coordinated development is the key for latecomers to achieve capability accumulation and sustainable catch-up. The research conclusions have the following three practical implications for latecomers to implement sustainable catch-up action: first, latecomers should balance their investment in technological learning and managerial learning in order to improve their technical system and administrative system; second, latecomers should pay attention to the coordinated development of their technical system and administrative system, so as to realize the development and capability improvement of the whole enterprise system. Third, late-developing countries should provide a favorable policy environment for enterprises to carry out managerial learning, so as to encourage enterprises to pay attention to managerial learning as early as possible in order to build the management basis needed to achieve sustainable catch-up.

The exploration of transformation from intellectual property management to intellectual property governance is not only the demand traction in the practice of China's intellectual property system, but also the bottleneck to be broken in the theory of intellectual property governance. From the perspective of process management, this study divides intellectual property governance into four links: creation, application, protection and service. Through policy text and multi-case grounded analysis, the interaction mechanism of intellectual property governance subjects in each link is explored. The results show that the creation, application, protection and service of intellectual property governance are not isolated, but interrelated and nested, and there are significant differences in the functional positioning and interaction of intellectual property governance subjects in different links, and the heterogeneity is dynamic.

Clarifying the diversified evolution mechanism of technological innovation in China’s regional strategic emerging industries is of great significance to promoting the supply-side reform of China’s economy. In this paper, a pioneering analysis framework is constructed to characterize different “novelty” characteristics of technological paths, taking technological path of strategic emerging industries in Chinese cities as the research object, aiming at the evolutionary principle in “Break-through Technological Paths” and “Adjacent Technological Path” in the process of urban technological diversification. It is found that the development of break-through and adjacent technology paths is dominated by different evolutionary mechanisms. The development of adjacent technology paths has the characteristics of path dependence while depending on the local knowledge base, but the development of breakthrough technology paths present random contingency that does not depend on the historical structure of urban technology, and especially is prominent in stage of break-through technology path creation. The research provides a new perspective for observing Chinese experience of technological development in China’s strategic emerging industries

Industrial intelligence with "intelligent manufacturing” as the core has provided a crucial driving force for China to achieve the goal of "carbon peak and carbon neutrality." As the world's largest emerging economy, China is accelerating the construction of emerging infrastructure such as 5G, cloud computing, and artificial intelligence. At the same time, as global warming continues to accelerate, China stresses the need to protect the global environment and achieve a harmonious coexistence between man and nature. In 2021, for the first time, China included "carbon peak and carbon neutrality" in its government work report. The vigorous development of industrial intelligence characterized by "machine for man" can not only reduce production costs of enterprises to improve production efficiency through the application of intelligent machines but also reshape economic geographic patterns by changing factor endowment conditions, realize resource sharing among enterprises, and reduce carbon emissions. Therefore, studying the relationship between industrial intelligence and carbon productivity and its mechanism is of great theoretical and practical significance. Based on the panel data of 30 provinces in China from 2011 to 2019, this paper measures the industrial intelligence indicators from three aspects: the basic conditions of intelligence, the degree of intelligent application, and the achievements of intelligent technology, and empirically tests the spatial-temporal evolution characteristics and influence relationship between industrial intelligence and carbon productivity. This paper includes industrial agglomeration into the research framework and expounds on the nonlinear relationship between industrial intelligence and carbon productivity based on the life cycle theory of industrial agglomeration to analyze the influencing mechanism between industrial intelligence, industrial agglomeration, and carbon productivity. In addition, this paper also takes resource dependence, industrialization level, and environmental regulation intensity into consideration to explore whether it has a heterogeneous impact on the relationship between industrial intelligence and carbon productivity, and improve the theoretical framework of the relationship between industrial intelligence and carbon productivity. The results show that industrial intelligence significantly improves carbon productivity. And this conclusion remains valid after a series of robustness tests including the introduction of instrumental variables, the replacement of the regression method, and the replacement of the calculation method of explanatory variables. Mechanism analysis shows that industrial intelligence promotes diversified agglomeration and specialized agglomeration. But it mainly indirectly improves carbon productivity through diversified agglomeration. Due to the existence of the life cycle of industrial agglomeration, the influence of industrial intelligence on carbon productivity presents a nonlinear feature of "first increase and then decrease." Spatial econometric analysis shows that industrial intelligence has a spatial spillover effect. Industrial intelligence can improve carbon productivity not only in local areas but also in neighboring areas, which will be conductive to shape a spatial pattern of coordinated green development among regions. Heterogeneity analysis shows that the role of industrial intelligence in promoting carbon productivity is more significant in regions with non-resource dependence, high industrialization levels, and low environmental regulation intensity. The results of this study provide a feasible path to increase carbon productivity and achieve the "double-carbon" goal and also provides a beneficial reference for government departments to plan the strategic layout of regional industrial intelligence energetically.

As the largest CO2 emitter country, It’s so important for China to reduce CO2 emissions while maintaining economic growth. Industrial linkage analyses can be applied to identify key emission reduction sectors and provinces. Based on the multi-regional input-output model, we use the method of inter-sectoral correlation analysis to analyze the forward and backward correlation of production and CO2 emissions between industrial sectors combined with marginal and absolute indicators. In the backward linkage, Guangdong is the province with the largest CO2 inflow, the electricity, heat production and supply sector, as well as construction sector are the largest CO2 inflow industries, and electricity and heat sectors in Ningxia, Xinjiang and Inner Mongolia are the key emission reduction industries. In the forward linkage, Shandong is the province with the largest CO2 outflow, and the tertiary industry has become the main driving force for CO2 emissions. The key emission reduction industries include coal mining products in Shanxi and Inner Mongolia, and service industries in developed provinces such as Beijing, Shanghai and Guangdong. The key emission reduction provinces of both supply and demand are Hebei, Shanxi, Inner Mongolia and Shandong.

Facing the opportunities and challenges brought by the escalating trade frictions between China and the United States, it is vital for Chinese enterprises to achieve the transformation from "Following" to "Leading" through disruptive innovation breakthroughs. Disruptive innovation has become the key to the development of enterprises and the implementation of national strategic goals. Meta-knowledge development capability is a new concept in knowledge management that may become disruptive innovation's power source. However, the effect and mechanism of this role are not evident in the existing literature. Based on the theory of knowledge transfer and dynamic capability, and with the construction of resources to capabilities as the clue, this study establishes a multi-stage theoretical model for transforming meta-knowledge development capability into enterprise disruptive innovation. Data were collected from 331 relevant Chinese enterprises through a questionnaire survey. PLS-SEM and bootstrapping were used to test the hypothesis. In order to further verify the results of the empirical analysis, the researchers interviewed top managers of 10 enterprises engaged in disruptive innovation activities, with an average interview time of 38 minutes. The research results show that: (1) Meta-knowledge development capability has a positive and significant impact on the disruptive innovation of enterprises, and this effect is realized through the chain mediation of knowledge transfer and dynamic ability. Specifically, the meta-knowledge development capability broadens the scope of knowledge search, enables enterprises to locate and obtain the required knowledge accurately, and improves the efficiency of knowledge transfer. Furthermore, the accumulation and interactive configuration of knowledge resources have improved the dynamic capabilities of enterprises and promoted their disruptive innovation. (2) Social capital can strengthen the influence of meta-knowledge development ability and its path, and it plays a significant role in promoting meta-knowledge development ability and knowledge transfer. This is mainly because high social capital can provide rich and stable access to knowledge and help enterprises acquire new knowledge with both quality and quantity. (3) Dynamic capability is essential in the path from meta-knowledge development capability to enterprise disruptive innovation. It plays a complete intermediary role between knowledge transfer and disruptive innovation. In other words, the indirect impact of knowledge transfer on disruptive innovation is achieved by improving enterprises' perception, responsiveness, and resource allocation through knowledge transfer. Based on the management practice in China, the research results enrich the relevant theories of knowledge management and innovation management. The research links disruptive innovation and the frontier concept of knowledge management, meta-knowledge development capability, and clarifies the significance of meta-knowledge development capability in promoting enterprises to achieve disruptive innovation, expanding the research on the impact of knowledge management on disruptive innovation and providing suggestions for the systematic construction of the theoretical system in the field of disruptive innovation. At the same time, managers of enterprises need to recognize the importance of knowledge management when conducting disruptive innovation. Moreover, they should pay more attention to meta-knowledge development capabilities and actively take measures such as expanding social capital, strengthening knowledge transfer, and constantly cultivating dynamic capabilities to achieve the goal of disruptive innovation.

The development of new energy vehicles in China is rapid, but the technological innovation ability is still need to be improved. In order to promote its long-term sustainable development of them, the Dual-Credit policy came into being. Sixteen A-share listed companies of new energy vehicles were selected as research samples, and the DID model was applied to study the impact of Dual-Credit policy on technological innovation. The results show that the "double points" policy can effectively improve the innovation performance of enterprises. At the same time, enterprises can promote their own technological innovation development by adjusting the proportion of talents and actively expanding research investment. Therefore, in the future, new energy enterprises can increase investment in R&D, and add more talents to promote the breakthrough and development on core technologies of new energy vehicles.

From the perspective of "symbiosis theory", combined with the four core ecological elements of "innovation community - resources - technology - environment", this paper uses the DEMATEL method to rank the importance of 30 measurement indicators, delete the key condition factors, and then use the fsqca method to explore the key path of ries system energy level improvement from the perspective of configuration by mining the antecedent configurations that affect the ries system energy level improvement in the case area. It is found that: (1) innovation subject, innovation resources, innovation environment, collaborative innovation and system opening will have an important impact on the energy level of ries system, which is an important factor determining the energy level of ries system; (2) The four paths that drive the high-energy level are the balanced drive path of "innovative population resource niche environmental niche", "high environmental niche advantageous innovative population" dual drive path, "high resource niche high technology niche" collaborative drive path, "innovative population resource niche technology niche environmental niche" comprehensive coordination path, The above four paths further illustrate that the energy level rise of ries system has "multiple concurrent causality", which promotes the study of ries system from the overall perspective to a certain extent.

Design thinking has effectively created a new business, strategic transformation, and organizational culture change. Still, little is known about its impact on the value of innovation as it changes with the innovation context. Facing the dual uncertainty of process and outcome, the study unfolds innovation as an interactive iterative process of trial and error of value-creating solutions and value-capturing benefit negotiation under the constraints of limited rationality faced by subjects, constructs a process framework focusing on stakeholder and multi-cluster collaboration and retrospective reasoning and embracing failure to influence the value of design thinking, compares the value differences of design thinking in technological innovation and business model innovation, and To reveal the mechanisms by which design thinking affects the value of innovation. The study shows that: first, design thinking has a positive effect on the value of both types of innovation, except for the negative impact of embracing failure on the value of business model innovation; second, focusing on stakeholders and embracing failure have a greater impact on the value of technological innovation than business model innovation; finally, multicluster collaboration and retrospective reasoning have a more significant effect on the value of business model innovation than technological innovation. On the one hand, the study reveals the differences in the value of design thinking in the two types of innovation contexts. On the other hand, it explores the differences in the application of design thinking in different innovation contexts, which provides new ideas for companies to apply design thinking to optimize innovation in response to the digital intelligence environment in a customized manner.

Artificial Intelligence Ethical Standards (AIES) should precede the construction of a unified market for the emerging digital economy. By surveying the present state of artificial intelligence ethical research, this paper identifies leading trends in Europe and America, which establish a seminal discourse, yet fall short of a more rigorous theoretical foundation for ethical standards and system frameworks. This paper takes the fundamental concept of artificial intelligence ethical standards as a breakthrough point, adheres to the guidance of Marxism, and through full use of historical investigation and logical deduction methods in response to the question of whether artificial intelligence ethical standards are indeed even possible, and if so, by what means? First, the discussion begins through careful analysis of existing standards, theories, and standards law, this paper argues that artificial intelligence ethical standards are possible both from a theoretical and practical viewpoint. Second, issues pertaining to Western models of standard are examined through the use of a Marxist critical method of political economy, whereby a Marxist mechanism of artificial intelligence ethical standards is proposed. This paper introduces the operating rule system of human society in western standard theory, i.e. “Norm-Directive-Standard”, and extracts “Wissenschaft” to complement the former by reviewing the important discussion of Marxist political economy theory on the First Industrial Revolution and machine invention. Therefore, this paper forms a new Marxist operating rule system of human society, i.e. “Wissenschaft-Norm-Directive-Standard”. Third, by the observance of the dynamic interaction between technology, ethics, economy, and standards since the industrial revolution, this paper analyzes the operating principle for relevant rules which inform the construction of ethical standards in human social systems, and aims to demonstrate the principle of societal value within the artificial intelligence field, through its potential conversion into similar principles of technical performance and/or economic efficiency - suggesting a conceptual model of “Water Molecule Model”, i.e. Model E2T, for Marxist artificial intelligence ethical standards. In the process of constructing a unified market in China, the AIES model is helpful for ensuring the transfer of principles that help realize the value of Chinese modernization such as “common prosperity” from a bottom-up view of technology, in order to provide ethical justification for the R & D of AI technology and industrial implementations, as well as strengthening the competitiveness of agents in the socialist market economy, and coordinated developments between AI and human society. Altogether, this paper’s contribution can be classified into three main areas; philosophical, methodological, and theoretical. Regarding the first, by comparing the historical background of unified markets at home and abroad, this paper proposes the idea of development, i.e. “unified market, artificial intelligence ethical standards first”. Second, by using the Marxist critical method of political economy, this paper brings to the fore important discussions on the industrial revolution and mechanical innovation, providing empirical support for a “knowledge system” which complements certain Western standard theory of human society operating rules system and their principles of operation. Third, the “Water Molecule Model” of Marxist artificial intelligence ethical standards is established by analyzing the operating mechanism of “Wissenschaft-Norm-Directive-Standard”, which offers a new ontological basis for contemporary Chinese artificial intelligence ethical standards and discourse system.

With the institutionalized development of science and technology (S&T) incentives and rolling out of major awards in China, the fairness and validity of the selection of such awards are of increasing concern for various communities. Eyeing the customary approach of peer review in the selection process, the research questions are: (1) When multiple experts need to score or rank multiple nominees to select the winners, to what extent can the peer review results reflect the real differences of the evaluands? (2) For the judgments of experts of the same group, can we get the same shortlist when scoring or sorting methods are used? If the selection processes of science and technology awards can be regarded as experiments, the scoring results to be examined are the test data. In this way, we can build a two factor ANOVA model to examine the uncontrollable factors such as the level of achievements, expert preferences, and the controllable factors of selection criteria, decision-making rules, and so on. Thus the study builds an evaluation model of reliability based on two-way ANOVA, to conduct a sample analysis of the ratio data and ordinal data generated respectively by anonymous online appraisal and assessment of expert panels in four different disciplines between year 2017 and 2019. The result shows that, summarizing expert scoring data and determining the winners based on the total score of the projects will produce large systematic errors. If the shortlisting is based on majority voting rule, the average reliability of the expert group’s evaluation is only 28.6%, with maximum value of 60% and minimal value of 6.5%, which means the selection of winner depends more on other factors than the project itself. Thus in the previous years, among the 115 award winners, at least 8 winners should have been eliminated. To solve the problem, the study carries on a quantitative evaluation and comparison regarding the selection of awards between different years, disciplines, phases of selection, and methods of selection, suggests that systematic errors can be reduced by methods of rank sum or independent voting of experts to increase the reliability of peer review. Besides, the study also takes reference from the features and latest development of renowned international S&T awards to examine the organization of selection, evaluation indicators, and decision-making procedures of domestic awards, discusses the possibility of canceling oral defense, adopting anonymous network evaluation instead of panel meeting.