1 Introduction
With technological innovation entering an unprecedented phase of intense activity, a new wave of technological revolution and industrial transformation is underway. Disruptive technologies are reshaping human production and lifestyle. These technologies can swiftly alter existing market dynamics and social structures, posing significant challenges to traditional industries while also presenting fresh opportunities and valuable advancements. This upheaval is pivotal in breaking technological bottlenecks, fostering independent innovation, and enhancing national competitiveness. AI stands as one of the most influential and representative disruptive technologies today, attracting global attention and competition (Păvăloaia & Necula, 2023). Emphasizing the integrated and clustered development of strategic emerging industries, China’s 20th National Congress of the Communist Party (2022) underscores the importance of cultivating new growth engines such as next-generation information technology, AI, biotechnology, new energy, new materials, high-end equipment, and green industry. Despite China’s strides, it needs to accelerate AI development and application to bolster its international discourse and influence. While the Chinese government has issued several pertinent policies to enhance the fundamental research and innovative application of AI and foster its progressive development, including the Development Plan for New-Generation Artificial Intelligence, Measures to Promote Access to Open and Shared Data, and Guidance on Promoting the Sound Development for New-Generation Artificial Intelligence, the rapid advancement of AI technology has led to its increasingly pervasive utilization across diverse sectors. Consequently, this trend imposes heightened demands for the formulation of corresponding policies. The imperative task lies in crafting policies that not only foster technological innovation but also facilitate the profound integration of AI into diverse economic and social domains. Hence, this study endeavors to employ a quantitative analysis method of policy text to scrutinize the attributes, extant issues, and prospective enhancements of China’s AI disruptive technology policies, offering valuable insights for their formulation and refinement, thereby bearing significant theoretical and practical significance.
The remainder of this article is structured as follows: the second section provides a review of pertinent research regarding China’s policy on disruptive AI technology and policy evaluation. The third section develops a three-dimensional analysis framework for policy texts. In the fourth section, data sources are introduced, and the analysis quantifies China’s policy texts concerning disruptive AI technology across individual dimensions including policy actors, policy tools, and policy themes, as well as exploring multi-dimensional aspects such as the interplay between policy tools and themes, and between policy actors and themes. Finally, the fifth section elucidates the primary conclusions, offers pertinent suggestions, and outlines future prospects.
2 Literature review
Disruptive technology is first conceptualized by Clayton Christensen (2013), a Harvard University professor in the United States. It primarily denotes technologies that unexpectedly replace existing mainstream technologies, exerting destructive and transformative effects. The concept of disruptive technology has evolved over time, yet a unified definition remains elusive. Schumpeter’s theory of disruptive technology posits innovation as a pivotal driver of economic change. Each major innovation displaces old technologies and production systems, leading to the emergence of new production paradigms (Schumpeter, 2021). Nagy et al. (2016) defined disruptive technologies as radically innovative features, different technical standards, or novel forms of ownership. Paap and Katz (2004) viewed disruptive technology as the dismantling of outdated business models, proposing alternative technologies driven by user needs. Dahlin and Behrens (2005) contended that disruptive inventions should be novel, unique, and have significant future technological impacts. Thomond and Lettice (2002) characterized disruptive technological innovation as incremental or radical innovation, marked by high discontinuity, revolutionary nature, and innovativeness. Kenagy and Christensen (2002) suggested that disruptive technologies possess traits such as destructiveness, ease of being overlooked, develop-ability and user-friendliness. As disruptive technologies garner increasing academic attention, methods for their identification continuously evolve, transitioning from qualitative analysis to quantitative identification. Initially, due to limited identification techniques, scholars predominantly employ qualitative methods such as the Delphi method, questionnaires, scenario analysis, and technology roadmaps (Sommarberg & Mäkinen, 2019; Vojak & Chambers, 2004). With technological advancements, scholars have increasingly utilized quantitative analysis methods to delve deeper into and identify disruptive technologies. For instance, Buchanan et al. (2010) proposed a prediction system based on patent data, tailored for science-intensive disruptive technologies, to analyze and characterize these technologies. Momeni et al. (2016) employed patent technology pathways and topic models to identify disruptive technologies. Hughes (2017) introduced a time series prediction model based on big data to explore the application of big data technology in predicting disruptive technologies.
Policy evaluation constitutes both a professional activity and an academic discipline. As early as the 1970s, Lasswell (1970) introduced the concept of “policy science”, wherein policy evaluation emerged as a core component. Over time, policy evaluation theory has transitioned from positivism to constructivism and critical positivism. For instance, Fischer (1995) introduced the “Empirical Debate” framework within “Public Policy Evaluation”, stressing a cyclical evaluation process comprising narration, analysis, critique, narration, and reanalysis. Goldenberg (1983) introduced a theoretical framework for policy evaluation, suggesting that it should assess policy impacts relying on evaluator expertise and stakeholder perceptions, thereby enhancing policy efficacy. Mohr (1995) proposed the political tendency theory of evaluation, highlighting its inherent political nature in policy formulation. He noted that evaluation outcomes could serve as “ammunition” in political contests and that the evaluation process is rife with conflicts over interests and power, influenced by the values and interests of policymakers, implementers, beneficiaries, and other stakeholders. Simultaneously, numerous scholars have introduced various policy evaluation models, which can generally be categorized into three types: impact evaluation, econometric evaluation and empirical evaluation (House, 1978). Among these, the impact evaluation model is widely utilized and encompasses goal-oriented, side-effect, non-goal-constrained and audience-oriented evaluations, among others (Stufflebeam, 2001). Cerulli (2015) proposed the econometric evaluation model in Econometric evaluation of socio-economic programs Theory and applications. He scrutinized methods such as the double difference, synthetic control, and discontinuous methods across identification hypothesis, model type, and data structure dimensions. Additionally, he elaborated on the suitable contexts for point regression, instrumental variable, and regression correction methods. In empirical evaluation, scholars employ big data analysis techniques to assess policies. For instance, Prior (2012) employed text mining and semantic network analysis to extract core content elements from policy documents within the British medical and health domain. Du et al. (2021) developed two metrics, effectiveness and quantity, to measure policy intensity, analyzing 5,726 air pollution prevention and control policies in China. Wang et al. (2021) employed the PMC index model to comprehensively assess the pros and cons of 256 policies pertaining to new energy vehicles. Song et al. (2022) analyzed global food safety policies utilizing LDA topic modeling and K-Means clustering, identifying prevalent themes.
In summary, while there has been extensive research on the concept and technology identification of disruptive technologies, few scholars have focused on disruptive technology policies. Additionally, scholars have extensively researched quantitative analysis methods for policy texts, yet there are still some shortcomings: Firstly, most studies primarily focus on qualitative analysis, indicating the need for improved in-depth exploration and quantitative analysis of policy texts. Secondly, many studies begin from a specific perspective, such as policy content or effects, neglecting comprehensive multidimensional research. Therefore, this study focuses on AI disruptive technology policy texts, establishing a three-dimensional analysis framework from three different perspectives: policy tools, policy actors and policy themes. It utilizes social network analysis, policy tools, and LDA topic models to explore the characteristics and shortcomings of AI disruptive technology policies, particularly regarding policy tool usage, collaborative relationships among policy actors, and trends in policy themes, aiming to provide reference policy suggestions for government and relevant departments.
3 Research design
3.1 Construction of three-dimensional analytical framework
Harold Lasswell (2017), a founder of policy science, introduced the concept of public policy evaluation, encompassing various dimensions such as goals, values, and strategies. Lambin et al. (2014) highlighted that to attain effective goals and efficiency, policies must harness their synergistic potential, enabling policy subsystems to collaborate and support one another, thus fostering policy synergy. Policy synergy primarily involves the collaborative integration of policy tools, cooperative efforts among policy actors, and the evolving cooperation of policy themes (van den Bergh et al., 2021). However, such research often lacks in-depth discussion and analysis at the policy theme level. Therefore, this article focuses on Al disruptive technology policy texts, establishing a three-dimensional analysis framework that considers policy tools, policy actors, and policy themes. This framework aims to explore and analyze the attributes and limitations of policies concerning AI disruptive technology, considering multiple dimensions and granularities, to furnish a foundation for the formulation of policies in the realm of AI to offer valuable reference and guidance. This framework is applicable not only to the analysis of policy texts concerning AI disruptive technology but also to the examination of policy texts across various domains. The specific framework is depicted in Figure 1.
Figure 1. Framework for analyzing policies on disruptive AI technology. |
3.1.1 Dimension X: The policy tool
Policy tools are the means, methods, or measures adopted by policy actors to realize policy objectives (Howlett, 2017). Numerous studies have proposed various classifications for policy tools, among which the supply-side, environmental, and demand-side policy instruments proposed by Rothwell and Zegveld are the most widely used and operational models (Rothwell, 1985). Therefore, this study draws on Rothwell and Zegveld’s idea of categorizing policy tools, combines the own characteristics and implementation effects of AI disruptive technology policies, and constructs the AI disruptive technology policy tool dimension. Supply-side policy tools are mainly manifested in the government’s direct promotion of the sustainable development of AI, that is, the government provides basic protection for the development of AI through capital investment, talent cultivation, infrastructure, public services, and scientific and technology project support. Environmental policy tools are the external driving force of the government to promote the development of AI, mainly through goal programming, tax incentives, regulatory control, policy incentives, finance and other strategic measures to create a favorable social environment. Demand-side policy tools are the key driving force to promote the field of AI, mainly including government procurement, pilot demonstration, open cooperation, commercialization of scientific and research findings, scenario application and other strategic measures. The details are shown in Table 1.
Table 1. Classification and interpretation of policy tools. |
| Types | Names | Implications |
|---|---|---|
| Supply-side | Capital Investment | The government supports AI R&D and industrialization through the establishment of special funds |
| Talent Cultivation | The government strengthens AI education and training through the development of talent development program | |
| Infrastructure | The government provides data, computing power, platforms, and other resources through the establishment of AI infrastructure | |
| Public Services | The government promotes the application of AI in social governance, public security, healthcare, and other fields through the provision of public services | |
| Technology Project Support | The government encourages cross-border integration of AI with other fields through support for science and technology programs | |
| Environmental | Goal Programming | The government clarifies development objectives, key areas, and division of tasks through the formulation of AI development plans |
| Tax Incentives | The government reduces the burden on AI enterprises and individuals through the implementation of tax incentives | |
| Regulatory Control | The government regulates safety, ethics, privacy, and other aspects of AI through the formulation of regulations and controls | |
| Policy Incentives | The government rewards AI innovations and outstanding contributions through policy incentives | |
| Finance | The government guides social capital to invest in the AI industry through the provision of financial services | |
| Demand-side | Government Procurement | The government drives market demand through the procurement of AI products and services |
| Pilot Demonstration | The government promotes advanced applications of AI through pilot demonstrations | |
| Open Cooperation | The government promotes domestic and international AI exchanges and cooperation through openness and cooperation | |
| Commercialization of Scientific and Research Findings | The government accelerates the process of AI from lab to market through the promotion of the transformation of scientific and technological achievements | |
| Scenario Application | The government stimulates the innovation potential of AI through the creation of scenario applications |
3.1.2 Dimension Y: The policy actor
The policy actor is the primary participant involved in formulating, implementing, and overseeing public policies. The AI industry is a strategic emerging sector in China, encompassing various fields such as technical research and development, product development, market application, social services, and other aspects. Therefore, multi-departmental collaboration among the Ministry of Science and Technology, the Ministry of Industry and Information Technology, the Ministry of Education, the National Development and Reform Commission, and other relevant departments is necessary to enhance the scientific basis and relevance of policies and to promote the high-quality and sustainable development of the AI industry.
3.1.3 Dimension Z: The policy theme
The policy theme forms the core content of policy formulation and implementation, encapsulating the issues and objectives targeted by the policy (Isoaho et al., 2021). In China’s AI industry, policy themes extend across multiple domains, including the economy, society, safety, and ethics, indicating the government’s thorough attention and backing for AI industry advancement. Nevertheless, there is a need to enhance the coordination and relevance of policy themes. Certain policies demonstrate duplication or conflict, lack specific implementation details and assessment mechanisms, or do not align with market demand and technological advancements, thereby affecting policy effectiveness and efficiency. Thus, this study analyzes policy themes from three angles: content, evolution trend, and relationship evolution.
3.2 Data sources and preprocessing
As a disruptive technology, AI is profoundly forward-thinking and transformative. It presents representative characteristics and challenges in policy formulation and implementation. Additionally, AI technology is intricate and diverse, spanning various fields including society, economy, and daily life. Hence, choosing AI as a case study for disruptive technology policy research can assist relevant government departments in formulating and enhancing pertinent policies.
To ensure policy comprehensiveness and accessibility, this study focuses on AI disruptive technology policies issued by the central government and national ministries and commissions. Keyword searches for “artificial intelligence” and “AI” are performed on the websites of national ministries and commissions, as well as on Pkulaw.cn, covering both subject and content. After excluding informal documents like approvals, letters, and announcements, a total of 142 AI-related policy texts consisting of opinions, plans, notices, approaches, guidelines, etc., are identified. Statistical analysis reveals that the majority of AI disruptive technology policies are enacted after the issuance of the Development Plan for New-Generation Artificial Intelligence by the General Office of the State Council in 2017. Therefore, this study groups a few policies predating 2017 into the year 2017 for analysis purposes. The distribution of policies across each year is illustrated in Figure 2.
Figure 2. Number of policies each year. |
4 Quantitative analysis of policy texts on disruptive technologies of AI
4.1 Dimensional analysis of policy tools
The content of 142 AI disruptive technology policy texts is coded using Nvivo12 qualitative analysis software. Initially, the policy texts are coded sequentially following the format of “policy number- chapter number- article number”. For instance, 1-1-1 denotes the first article of the first chapter of policy document number 1. Subsequently, the analytical units are classified into their respective policy tools, resulting in a total of 1,230 analytical units extracted from the policy texts and categorized into the relevant policy tool system. The definitive classification of policy tools is presented in Table 2.
Table 2. Results of policy tool classification. |
| Types | Names | Reference Points | Sub-item share (%) | Total share (%) |
|---|---|---|---|---|
| Supply-side | Capital Investment | 89 | 18.54 | 39.02 |
| Talent Cultivation | 113 | 23.54 | ||
| Infrastructure | 127 | 26.46 | ||
| Public Services | 97 | 20.21 | ||
| Technology Project Support | 54 | 11.25 | ||
| Environmental | Goal Programming | 121 | 23.87 | 41.22 |
| Tax Incentives | 114 | 22.49 | ||
| Regulatory Control | 64 | 12.62 | ||
| Policy Incentives | 99 | 19.53 | ||
| Finance | 109 | 21.50 | ||
| Demand-side | Government Procurement | 48 | 19.75 | 19.76 |
| Pilot Demonstration | 41 | 16.87 | ||
| Open Cooperation | 32 | 13.17 | ||
| Commercialization of Scientific and Research Findings | 66 | 27.16 | ||
| Scenario Application | 56 | 23.05 | ||
| Total | - | 1,230 | - | - |
As a highly impactful disruptive technology, AI requires supply-side policy tools to drive essential technology research, development, and innovation. Simultaneously, environmental policy tools are necessary to foster the healthy growth of the AI industry. Additionally, demand-side policy tools are essential to stimulate market demand for AI products. However, the current AI disruptive technology policy in China exhibits an imbalance in tool utilization. While efforts are made to reinforce the use of supply-side and environmental policy tools, the utilization of demand-side policy tools remains notably insufficient. Against this backdrop, it becomes challenging to establish an innovation ecosystem for AI. Hence, there is a pressing need to enhance the utilization of policy tools in China’s AI disruptive technology policy.
4.2 Dimensional analysis of policy actors
The statistics indicate that 62 policy actors, including the Ministry of Industry and Information Technology, the Ministry of Science and Technology, and the Ministry of Education, are involved in the formulation of the 142 policies on disruptive AI technology. Among them, 52 policies involve joint issuance by two or more policy actors, with the highest number being 19. The multi-faceted nature of AI disruptive technology policies necessitates the collaborative engagement of numerous policy actors in their formulation. To delve deeper into the cooperative dynamics among policy actors, this study employs social network analysis to construct a network illustrating their collaboration. Social network analysis encompasses methodologies for examining the intricate social relationships and attributes among entities, offering insights into the relational structure between diverse subjects (Freeman, 2004). Initially, Python code is employed to generate the co-occurrence matrix of policy actors. Subsequently, Gephi social network analysis software is utilized to compute the density and degree centrality of the cooperative network. Finally, based on these calculations, the cooperative relationship network of policy actors is delineated. The specific network diagram is depicted in Figure 3.
Figure 3. Policy entities’ collaboration network. |
An in-depth analysis reveals that the Ministry of Industry and Information Technology and the Ministry of Science and Technology are crucial national departments in AI. They promote scientific and technological innovation and the dissemination of achievements, necessitating leadership in AI-related policy formulation and implementation. Additionally, as an emerging industry in the country, the AI field necessitates adjustments in leveraging and talent training from the Ministry of Finance, Ministry of Education, and other relevant departments to offer adequate financial support and foster a large pool of scientific and technological talent for its development. Hence, central entities like the Ministry of Industry and Information Technology, the Ministry of Science and Technology, and the Ministry of Finance establish numerous connections with other entities, serving as intermediary bridges. Nevertheless, in the field of AI, marginalized entities like the National Working Commission on Aging often serve as “information receivers” or “policy implementers”, indicating their significant involvement in the policy formulation and implementation process. Excessive dependence on central entities impedes effective information transmission and resource allocation among other entities, leading to a sparse cooperation network among AI policy actors. Overall, the AI disruptive technology policy cooperation network in China exhibits a “center-periphery” structure.
Policies like the Development Plan for New-Generation Artificial Intelligence, Artificial Intelligence Industry Innovation and Development Action Plan, Artificial Intelligence Standardization Innovation Action Plan emphasize the need for government departments to improve collaboration with diverse stakeholders, such as enterprises, academia, and social organizations. Collaborative endeavors and joint document issuance by multiple departments have become a new trend in shaping and enhancing AI disruptive technology policies in China. Therefore, there is an urgent requirement to further strengthen the collaborative cooperation mechanism among the policy actors engaged in AI disruptive technology.
4.3 Dimensional analysis of policy themes
By conducting policy theme analysis, a comprehensive understanding of policy content, goals, and tendencies can be achieved. LDA topic model analysis is employed to extract topics from policy texts, utilizing a Bayesian probabilistic model with a three-layer structure of words, topics, and documents, effectively uncovering latent themes within the analyzed texts (Gan & Qi, 2021). In the LDA topic model, the number of topics must be manually pre-set. To accurately extract the optimal topics, this study evaluates policy topics using the perplexity evaluation method. The calculation formula of perplexity is shown in equation (1):
$\text { Perplexity(D) }=\exp \left\{-\frac{\sum_{d=1}^{M} \log p\left(w_{d}\right)}{\sum_{d=1}^{M} N_{d}}\right\}$
Perplexity serves as a criterion for assessing the effectiveness of the topic model, lower perplexity values indicate better generalization ability of the model (Bastani et al., 2019). Typically, the optimal number of topics is extracted when perplexity is minimized. The optimal number of topics, ranging from 1 to 20, is tested to identify the minimum perplexity. The number of optimal AI disruptive technology policy topics for each year from 2017 to 2022 is determined from the perplexity values, with 11 in 2017, 13 in 2018, 14 in 2019, 16 in 2020, 16 in 2021, and 13 in 2022 as shown in Figure 4. To ensure the accuracy of policy theme content, the themes are manually screened to remove irrelevant or less relevant ones to the AI disruptive technology policy. The final selected themes are then named manually, and the results are presented in Table 3.
Figure 4. Perplexity of policy themes on disruptive AI technology across various time periods. |
Table 3. Distribution of policy topics across different time periods. |
| Time Windows | Optimal number of topics | Final number of topics | Topic Tags |
|---|---|---|---|
| 2017 | 11 | 8 | Core Technology of Manufacturing Industry; Intelligent Traffic; Transformation of Scientific and Technological Achievements of Universities; Intelligent Medical Construction; Product Testing Technology; Technical Talents in the Field of Unmanned Aerial Vehicles; Research and Development of Key Technologies and Information Security; Autonomous Driving Technology |
| 2018 | 13 | 9 | Intelligent Photovoltaic Industry; Rural Revitalization; Technological Innovation and Achievement Transformation; Virtual Reality Technology; Network and Information Security; Medical Informatization Energy Intelligent Supervision; Industrial Intelligent Management Platform; Education Informatization |
| 2019 | 14 | 10 | Intelligent Robotics; Elderly Health Products; New Generation Innovative Technology Research; Data Open Sharing; Health Care Aids; Product Quality Monitoring; Enterprise Technology Innovation Ecology; New Model of Shared Manufacturing; Digital Rural Development; AI Technician Training |
| 2020 | 16 | 13 | Energy Saving and Environmental Protection Development; Elderly Health Monitoring; Public Healthcare; Service-oriented Manufacturing Development; Digitization of Cultural Industry; Blockchain Technology Research; Talent and Technical Support; Intelligent Transportation Collaborative Innovation; Supporting and Guiding Enterprise Innovation; International Talent Cooperation and Exchanges; Scientific and Technological Achievement Transformation; Internet Industrial Technology; Data Resource Sharing |
| 2021 | 16 | 12 | Intelligent Community and Intelligent Elderly; Intelligent Medical Care; Intellectual Property Rights; Data Sharing Platform Construction; Digital Home; Data Industry Development; Intelligent Manufacturing; Data Security Management; Industrial Data Factor Development; Blockchain Industry Innovation; New Energy Technology; Internet Communication Technology |
| 2022 | 13 | 9 | Intelligent Capacity Technology Breakthrough;Intelligent Elderly; Emergency Hazardous Intelligent Devices; Data Security in Industrialization and Informatization; Intelligent Communities; AI Scenario Innovation; High-Quality Intelligent Development in Industry and Agriculture; Machine Learning and Algorithmic Models; Intelligence Talent Cultivation |
Based on the themes presented in Table 3 and the delineation of key areas in the Development Plan for New-Generation Artificial Intelligence, China’s policies regarding disruptive AI technology are classified into five categories: AI theoretical system, intelligent services, intelligent governance, high-tech talent cultivation, and intelligent sharing and mutual trust. The AI theoretical system primarily encompasses sub-themes such as machine learning and algorithmic models, blockchain technology, and internet communication technology. Intelligent services mainly encompass sub-themes like smart community, smart pension, smart medical care, smart health, and smart scene application. Intelligent governance primarily comprises sub-themes such as smart family, intelligent transportation, product quality monitoring, energy saving and environmental protection, new energy technology, and digital rural areas. Cultivating high-tech talents mainly entails sub-themes like talents and technical support, international talent cooperation and exchange, education informatization, and intellectual property rights. Intelligent sharing and mutual trust chiefly comprise sub-themes such as data open sharing and platform construction, as well as virtual reality technology. In 2017, marking a new era in AI development, the State Council mentioned it in the government work report for the first time. State departments have ramped up investments in “talents, technology, platforms, and funds” within the AI sector, resulting in a continuous release of policy dividends in disruptive AI technology. As evidenced by Table 3 , the themes encompassed in the 2017-2022 AI disruptive technology policies are rich and diverse, aligning closely with the national development strategy.
In order to delve deeper into the potential evolutionary connections among themes across various time frames, the theme evolutionary relationships are discerned through the calculation of theme cosine similarity. The specific formula for this calculation is depicted in equation (2):
$\cos (\theta)=\frac{\sum_{i=1}^{n}\left(x_{i} * y_{i}\right)}{\sqrt{\sum_{i=1}^{n}\left(x_{i}\right)^{2}} * \sqrt{\sum_{i=i}^{n}\left(y_{i}\right)^{2}}}$
The cosine similarity ranges from 0 to 1. As the value approaches 1, the content of the themes becomes more similar, indicating a higher likelihood of an evolutionary relationship between them. Conversely, as the value decreases, more differences emerge between the themes. To enhance the precision of the evolutionary path, we adopt a cosine similarity threshold of 0.3, based on the approach of previous scholars. Themes with cosine values exceeding 0.3 denote evolutionary relationships, while those below this threshold do not exhibit such relationships (Feng et al., 2018). The PyEcharts package in Python is used to generate Sankey diagrams illustrating the themes with evolutionary relationships across various time windows, as depicted in Figure 5.
Figure 5. Evolutionary trajectory of policy themes on disruptive AI technology. |
In Figure 5 , each node block represents a distinct theme, with connecting lines illustrating their evolutionary relationships. The thickness of these lines indicates the degree of similarity between the themes, thicker lines denote greater similarity. The figure reveals a clear evolutionary relationship among China’s AI disruptive technology policy themes, delineated into four modes: newborn, merger, split, and demise. Initially, the evolution of AI disruptive technology policy themes manifests “newborn” characteristics, exemplified by emerging policy themes like “Education Informatization” and “Rural Revitalization” in 2018. Additionally, theme evolution demonstrates the characteristic of “merging”. For instance, in 2017, four policy themes, “Intelligent Medical Construction” “Technical Talents in the Field of Drones” “Autonomous Driving Technology” and “Information Security of Key Technologies”, merged into the theme of “Network and Information Security” in 2018. Similarly, in 2019, the fusion of three policy themes, “Product Quality Monitoring” “New Generation Innovative Technology Research”and “New Mode of Shared Manufacturing”, formed the theme of “Internet Industrial Technology” in 2020, and so forth. Furthermore, theme evolution demonstrates the characteristic of “splitting”. For instance, the theme “Open Data Sharing” in 2019 splits into four themes: “Public Healthcare” “Intelligent Transportation Collaborative Innovation” “Digitization of the Cultural Industry” and “Digital Resource Sharing” in 2020. Similarly, the theme “Elderly Health Monitoring” in 2020 splits into three themes: “Intelligent Community and Intelligent Elderly Care” “Digital Home” and “Intelligent Medical Care” in 2021, and so forth. Lastly, the themes undergo a phase of “demise”. For instance, the theme “Talent and Technical Support”, present in 2020, vanishes in 2021.
Considering the “source-target” perspective of the evolutionary trajectory, China’s AI disruptive technology policy encompasses multiple thematic evolution paths.
(1) Intelligent Medical Construction (2017) → Medical Informatization (2018) → Health Care Aids (2019), Elderly Health Products (2019) → Public Healthcare (2020), Elderly Health Monitoring (2020) → Intelligent Medical Care (2021), Digital Home (2021), Intelligent Community and Intelligent Elderly (2021) → Intelligent Elderly (2022);
(2) Education Informatization (2018) → AI Technician Training (2019) → Talent and Technical Support (2020), Talent Cooperation and Exchanges (2020) → Intellectual Property Rights (2021) → Intelligence Talent Cultivation (2022);
(3) Key Technology Information Security (2017) → Network and Information Security (2018) → New Model of Shared Manufacturing (2019), Data Open Sharing (2019) → Data Resource Sharing (2020) → Data Sharing Platform Construction (2021), Data Security Management (2021) → Data Security in Industrialization and Informatization (2022);
(4) Transformation Of Scientific and Technological Achievements of Universities (2017) → Technological Innovation and Achievement Transformation (2018) → Enterprise Technology Innovation Ecology (2019) → Supporting and Guiding Enterprise Innovation (2020) → Blockchain Industry Innovation (2021) → AI Scenario Innovation (2022).
The analysis of the four primary evolutionary paths highlights the focal points of China’s AI disruptive technology policy, namely intelligent services, talent training, information security, and scientific and technological innovation. The 14th Five-Year Plan (2021-2025) and Long-Range Objectives for 2035 prioritize initiatives such as “establishing an intelligent service ecosystem for the convenience and benefit of the people” “fostering innovation among talents” “developing a robust digital security framework” and “enhancing mechanisms for scientific and technological innovation”. These objectives align closely with the evolving themes of China’s disruptive technology policy, suggesting that intelligent services, talent training, information security, and scientific and technological innovation will remain central areas of focus in China’s future AI policy endeavors.
4.4 Cross-analysis of policy tools, policy actors and policy themes
The dimensions of policy tools, policy actors, and policy themes form a complex system of policies for AI innovation and technology. Analyzing these dimensions aids in understanding the formulation and implementation of China’s policies on AI innovation and technology. Specifically, analyzing the relationship between policy tools and policy themes aids in understanding individual policy themes. The government can then select and adjust policy tools accordingly to achieve its policy objectives. Analyzing the relationship between policy actors and policy themes enhances understanding of the cooperative dynamics among diverse policy actors across different policy themes, thus facilitating more effective policy implementation.
4.4.1 Cross-analysis of “policy tools-policy themes”
Utilizing the coding outcomes of policy tools from Chapter 4.1 and the mined policy theme characteristics from Chapter 4.3 as data sources, a heatmap was generated to depict the cross-analysis results of policy tools and policy themes, as illustrated in Figure 6.
Figure 6. Cross-analysis of “policy tools-policy themes”. |
In Figure 6 , the color intensity of the heat map represents the number of policy tool codes under each policy theme. The darker the color, the more policy tools are utilized. Horizontally, the use of AI disruptive technology policies is relatively balanced for environmental and supply-side policy tools, but significantly imbalanced for demand-side tools. Vertically, the use of policy tools is balanced across the themes of “Intelligent Services” “Intelligent Governance” and “Intelligent Sharing and Mutual Trust”. However, the theme of “AI Theoretical System” lacks supply-side, environmental, and demand-side tools. Additionally, policies for “High-tech Talent Training” show limited use of supply-side and demand-side tools, mainly due to insufficient measures like capital investment, technological project support, government procurement, and scenario application. As for reasons, firstly, there are differences in priorities in policy formulation. Policymakers may favor immediate technology applications and industrial development, while neglecting long-term investment in basic theoretical systems and talent cultivation. Secondly, resource allocation has limitations. Economic resources and policy support may focus on existing successful models and fields, leading to insufficient support for research and talent cultivation in emerging fields like AI theoretical systems. The AI theoretical system is the cornerstone supporting scientific and technological innovation, inspiring new ideas, methods, and technologies. Therefore, future policies should promote theoretical research and cutting-edge development in AI. In addition, high-tech talent training is crucial for promoting technological innovation and industrial development. Future policies should focus on high-tech talent training, providing comprehensive support through both supply-side and demand-side policy tools. This will ensure a solid talent base and intellectual support for the country’s long-term development strategy.
4.4.2 Cross-analysis of “policy actors-policy themes”
Similarly, utilizing the quantitative collaboration of policy themes from Chapter 4.2 and the characteristics of policy themes from Chapter 4.3 as data sources, a heatmap is generated to illustrate the cross-analysis outcomes of policy actors and policy themes. This paper specifically presents the cross-analysis results of the top 20 policy actors demonstrating a relatively high collaboration density with policy themes, as depicted in Figure 7.
Figure 7. Cross-analysis of “policy actors-policy themes”. |
In Figure 7 , the color intensity of the heat map indicates the level of collaboration between policy entities under each policy theme. Darker colors represent closer collaboration. Horizontally, key entities in AI disruptive technology policies include the National Development and Reform Commission, the Ministry of Industry and Information Technology, and the Ministry of Science and Technology. In contrast, entities like the National Bureau of Statistics, the Ministry of Public Security, and the National Medical Security Administration show looser collaboration. Vertically, policy entities collaborate closely on “Intelligent Services” and “Intelligent Governance”, indicating their importance in AI development and their attention from various national departments. Policies on “Al Theoretical System” “High-Tech Talent Cultivation” and “Intelligent Sharing and Mutual Trust” urgently need more collaboration. The Ministry of Commerce and the State Administration of Taxation, in particular, lack coordination with other entities in these areas. The reasons can be summarized into three aspects: (1) Policy positioning and functions: The main roles of the Ministry of Commerce and the State Administration of Taxation may not directly involve promoting the development of AI theoretical systems and high-tech talent cultivation. (2) Resources and priorities: The Ministry of Commerce and the State Administration of Taxation may prioritize other urgent areas, such as economic development and tax collection. (3) Cross-departmental coordination challenges: Issues like bureaucracy, information asymmetry, and imperfect coordination mechanisms can lead to insufficient collaboration.
5 Conclusions
AI stands as a pivotal research domain within the sphere of new generation disruptive technology, progressively assuming a significant role across governance, manufacturing, and various other sectors. It has emerged as a potent catalyst for driving digital transformation and enhancing governance modernization. This study employs the three-dimensional analytical framework of “policy tools-policy actors-policy themes”. It focuses on subversive technology policies represented by AI. Firstly, it scrutinizes the synergistic and cooperative relationships among policy actors through social network analysis. Secondly, it examines the integration and utilization of policy tools employing a dedicated method. Lastly, it delves into the characteristics of the policy theme and its evolutionary trajectory using the LDA topic model and thematic similarity metrics, culminating in the following primary conclusions:
(1) Through analysis of the cooperation network among policy actors, it is evident that China’s AI disruptive technology policy actors form a collaborative network of size 62, with a network density of 0.353. However, the collaboration among these actors is not sufficiently close, exhibiting a “center-edge” network structure. Central entities such as the Ministry of Science and Technology, the Ministry of Industry and Information Technology, the Ministry of Finance, and the Ministry of Education serve as leaders and intermediaries, whereas marginal entities including the Ministry of Justice, the National Bureau of Statistics, and the National Committee on Aging exhibit limited participation in network cooperation. This imbalance leads to excessive dependence of marginal actors on central ones, hindering effective information transmission and resource allocation among entities. Consequently, there is a lack of effective collaborative mechanisms among policy actors.
(2) Analysis of the policy subject cooperation network reveals that AI disruptive technology policy tools in China are predominantly influenced by supply-side (46%) and environmental (37%) policies, with a significant deficiency in demand-side (17%) policies. Within supply-side policy tools, emphasis is placed on strategic initiatives such as capital investment, talent development, infrastructure, and public services. Environmental policies mainly foster AI innovation indirectly through target planning, tax concessions, and policy incentives, yet overlook measures related to legal regulations, posing challenges in adapting to the rapid advancement of new AI technologies. Conversely, demand-side policy tools primarily promote AI technology innovation through pilot demonstrations and open collaborations, but overlook strategic measures in government procurement, technology transfer, and scenario application, hindering the policy’s applicability to the diversification of AI applications and market demand.
(3) Analysis of policy themes reveals the diversity of China’s AI disruptive technology policies, covering a broad spectrum of areas, primarily summarized into five themes: AI theoretical system, intelligent services, intelligent governance, high-tech talent cultivation, and intelligent sharing and mutual trust. Additionally, examining the evolution of policy themes reveals four distinct characteristics: emergence, integration, divergence, and obsolescence. Analysis of the principal evolution trajectories indicates that China’s AI disruptive technology policies will continue to prioritize themes such as intelligent services, talent development, information security, and scientific and technological innovation, aligning with the key objectives of the 14th Five-Year Plan (2021-2025) and the Long-Range Objectives for 2035, and reflecting the national development strategy.
(4) Through cross-analysis of policy tools, policy actors, and policy themes, it is apparent that the utilization of policy tools related to the themes of “Intelligent Services” “Intelligent Governance” and “Intelligent Sharing and Mutual Trust” is relatively balanced. However, the theme of “AI Theoretical System” lacks comprehensive understanding and utilization of various tools. Additionally, while policy entities under the themes of “Intelligent Services” and “Intelligent Governance” exhibit close collaboration, the themes of “AI Theoretical System” and “High-tech Talent Training” necessitate greater collaboration among entities, particularly the Ministry of Commerce and the State Taxation Administration. These entities urgently need to enhance cooperation with other policy entities regarding the “AI Theoretical System” and “High-tech Talent Training”.
6 Policy recommendations
Building on the aforementioned research conclusions, this study proposes the following policy recommendations for China’s AI disruptive technology:
(1) Optimize the allocation of policy resources and increase the utilization of both supply-side and demand-side approaches
The sustainable advancement of disruptive technology sectors like AI in China relies on a balanced utilization of supply-side, environmental, and demand-side policy tools. China should address the gaps in integrating and applying policy tools to bolster their effectiveness across supply, environment, and demand domains. Leveraging demand-side policy tools can harness synergies within the “troika”. For instance, scarce utilization of supply-side tools like public services and scientific and technological project support calls for immediate attention. Given AI strategic significance as an emerging industry, establishing robust mechanisms for public services and technological projects is imperative to foster its integrated development with related sectors in China, thereby facilitating resource integration along the AI value chain. Similarly, underutilization of demand-side tools such as pilot demonstrations and development partnerships underscores the pressing need to bolster pilot projects for validating new technologies and models. Simultaneously, reinforcing policy backing for international collaboration, active engagement in setting global regulations and standards, and fostering an open, transparent, and inclusive international market environment for disruptive technologies like AI are vital. By establishing new platforms for international collaboration, these measures will inject fresh momentum into shared development efforts.
(2) Establish a collaborative governance mechanism for policy entities and foster collaborative innovation among multiple entities
AI disruptive technology policies involve various subjects, fields, and industries. Strengthening coordination mechanisms between different departments is crucial to establish innovative policies for disruptive technology, clarify the responsibilities and rights of policy entities, enhance policy coordination, and prevent policy duplication and conflicts, thus fostering policy synergy. “Core entities” like the Ministry of Industry and Information Technology, the Ministry of Science and Technology, and the Ministry of Finance, which exhibit close collaborative ties, should sustain their leading role in fostering disruptive technology industries such as AI. They should enhance collaboration with other entities, communicate, coordinate, monitor policy implementation, identify existing issues, and collectively devise solutions and improvement measures to establish an effective policy feedback loop. Conversely, “marginal entities” with limited collaborative relationships, such as the National Working Commission on Aging, the Ministry of Justice, and the National Bureau of Statistics, should leverage their roles in addressing development needs, providing legal protection, and offering data support for specific fields within the AI industry. Leveraging their professional and resource advantages, these entities should establish effective connections and collaboration with other stakeholders, thereby bolstering the innovative development of the AI industry. Specifically, the National Working Committee on Aging should enhance policy promulgation related to smart elderly care needs, the Ministry of Justice should collaborate on regulatory frameworks, and the National Bureau of Statistics should enhance data sharing. This collaborative approach among various entities facilitates the advancement of the AI disruptive technology industry.
(3) Emphasize the evolutionary trends of policy themes and enhance the balanced utilization of tools, as well as collaborative cooperation among entities within these themes
The evolution of AI policy themes in China reflects both the intrinsic dynamics of disruptive technology industries in AI and the shifts in the external environment. This provides crucial insights for guiding policy formulation and implementation. Consequently, it is imperative to enhance the monitoring and surveillance of policy themes. This involves timely identification of evolutionary patterns such as resurgence, fragmentation, consolidation, and obsolescence within these themes, understanding the trajectory of their evolution, and adapting policy content and priorities accordingly. Specifically, emphasis should be placed on policy themes like “Intelligent Services” “Talent Training” and “Information Security”. Additionally, attention should be directed towards emerging or potential policy themes. For instance, given the cessation of the “Talent and Technical Support” theme in 2021, there should be a concerted effort to nurture individuals with expertise in AI to foster sustainable growth within the industry. Moreover, it is crucial to enhance the equitable utilization of tools and foster collaborative cooperation among stakeholders within policy frameworks. Specifically, considering the relatively balanced tool utilization across themes like “Intelligent Services” “Intelligent Governance” and “Intelligent Sharing and Mutual Trust”, compared to the deficiency in tool application within the “AI Theoretical System” theme, China should persist in advancing initiatives such as smart communities, elderly care, and medical services, alongside intelligent governance encompassing transportation, energy efficiency, environmental conservation, and new energy technologies. This entails bolstering investments, talent cultivation, policy incentives, and scenario development within theoretical frameworks encompassing algorithmic models, blockchain technology, internet communication, and the practical deployment of tools. It is essential to clarify the role of each department in AI, optimize resource allocation, and enhance coordination and cooperation among policy entities, particularly the Ministry of Commerce and the State Administration of Taxation, in the themes of “AI Theoretical System” “High-tech Talent Training” and “Intelligent Sharing and Mutual Trust”. Specific measures include strengthening cooperation with core departments such as the Ministry of Science and Technology, the Ministry of Industry and Information Technology, and the Ministry of Education. Establish cross-department research projects and joint talent training mechanisms, encourage universities to expand AI professional programs as well as “intelligent+” talent cultivation, strengthen multi-disciplinary collaborative education, and jointly promote AI theory research and application.
Author contributions
Ying Zhou(yingzhou97@126.com): Thesis guidance, Framework formulation. Linzhi Yan(1757984016@qq.com): Data collection, Paper writing, Revision. Xiao Liu (2690270106@qq.com): Data collection, Paper writing, Revision.
Funding information
This study was supported by the National Social Science Foundation of China (Grant No. 22BTQ089).