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  • Reshaping Future:Research Progress and Future Prospects of Disruptive Technology Topics
    DENG Sanhong, GUO Jianming, SHI Yujie,
    2025, 7(2): 1-12. https://doi.org/10.19809/j.cnki.kjqbyj.2025.02.001
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    [Purpose/significance]Disruptive technology is regarded as a revolutionary force that "changes the rules of the game" and "reshapes the future pattern", and has gradually become a hot and difficult issue in interdisciplinary research. This paper summarizes the literature related to disruptive technologies, clarifies the concepts and characteristics of disruptive technologies, subdivides research topics and directions, summarizes research focuses and looks forward to future development trends, and provides reference for relevant personnel. [Method/process]On the basis of sorting out the latest related research on disruptive technologies, this paper clarifies the research progress of disruptive technologies from three aspects: the concept and characteristics of disruptive technologies, common identification methods, and the evolution and prediction analysis of disruptive technologies, and grasps the research status and development trend of this field. [Result/conclusion]This paper comprehensively summarized the current research status of disruptive technology, and analyzed the existing problems and shortcomings.
  • Research on Quantitative Evaluation of Artificial Intelligence Policy Texts in the Yangtze River Delta Region
    ZHOU Ying, YANG Danjie, JIANG Mei, ZHAO Xiaochun
    2025, 7(2): 13-22. https://doi.org/10.19809/j.cnki.kjqbyj.2025.02.002
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    [Purpose/significance]The quantitative evaluation of existing effective artificial intelligence (AI) policies aims to provide reference for government department to formulate scientific and reasonable AI policies and promote the development of AI. [Method/process]Taking 10 AI policies in the Yangtze River Delta region from 2015 to 2024 as the research samples, the text mining method is used to construct the evaluation index system of AI policies in the Yangtze River Delta region, and conduct quantitative evaluation by combining the PMC index model. [Result/conclusion]The study found that from a macro policy text perspective, the average PMC index of the 10 AI policy samples in the Yangtze River Delta region was 7.11, indicating that the overall policy design was scientifically rational. From a micro policy text perspective, there were significant differences in the levels of AI policy texts in the Yangtze River Delta region. Based on the research conclusions, targeted policy improvement suggestions are proposed in terms of expanding the scope of policy targets, establishing a policy evaluation system, adjusting policy directions, and implementing policies tailored to local conditions.
  • Comparative and Evaluative Research on the Data Infrastructure Policies of Central and Local Governments in China Before and#br# After the Implementation of the "Twenty Measures for Data"
    MA Haiqun, YU Tongtong, WANG Hangong, ZHANG Tao
    2025, 7(2): 23-34. https://doi.org/10.19809/j.cnki.kjqbyj.2025.02.003
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    [Purpose/significance]To explore the direction for provincial governments to further plan data policies, with the aim of providing support for the establishment and improvement of data infrastructure systems in various regions.[Method/process] This article adopts the method of text similarity calculation to compare the policy texts of 22 provinces' data policies released before 2023 with the "Twenty Data Articles", and selects the "Twenty Provincial Version Data Articles" of 8 provinces for policy evaluation research. [Result/conclusion]The data shows that in terms of comparing data infrastructure systems, the data policies introduced by 22 provincial governments before 2023 show a relatively high overall similarity level compared to the "20 Data Policies". Although the specific sub projects of the "Four Major Tasks" have a high overall similarity level, the average similarity level of each sub project is not high. In terms of policy evaluation for the "Twenty Provincial Data Articles", research has found that the maximum and average similarity values of texts have overall improved; In terms of data property rights and revenue distribution, the maximum similarity value is relatively scattered; In terms of circulation transactions and security governance, the performance of maximum similarity is relatively concentrated. Overall, the data infrastructure systems developed by each province have areas for mutual learning.
  • Analysis of Collaborative Networks of Distinguished Scientists Based on Strong and Super Relationships：A Case Study of Nobel#br# Prize Winners#br#
    LIU Junwan, ZHANG Rui, SUI Hongzhe
    2025, 7(2): 35-47. https://doi.org/10.19809/j.cnki.kjqbyj.2025.02.004
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    [Purpose/significance]Scientific cooperation has become an important feature to promote scientific and technological breakthroughs and original innovation. Exploring the laws and patterns of scientific cooperation among distinguished scientists can help provide decision-making references for the country to cultivate leading talents and establish high-performance teams. [Method/process]In this paper, we take 145 Nobel Prize winners in natural sciences from 1990 to 2010 as research objects, identify the strong and super relationships in their scientific cooperation and construct their cooperation networks. It further applies social network analysis and network recursive decomposition to analyse the quantitative characteristics of strong and super-relationships of Nobel Laureates and their impact on scientific research performance, and to investigate whether there are structural changes in the strong and super-relationships networks before and after the award. [Result/conclusion]It is found that 99% of Nobel Laureates have strong relationship partners and 82% have super relationship partners, and the stability and durability of super relationships are higher than those of strong relationships. Strong relationship cooperation is usually carried out in the form of a team of 3-4 people; super relationship cooperation has a higher information transfer efficiency between members, and the cooperation relationship is closer, mainly in the form of two-by-two cooperation. An increase in the number of strong and super-relationships in a collaborative network can promote the productivity of outstanding scientists, but has no significant effect on the quantity of their first-authored publications; excessively high network density can inhibit scientific productivity. In addition, it was found that the number of entries and exits of members in strong-relationship co-operative networks produced significant structural changes after the award, but this phenomenon was not found in super-relationship co-operative networks.
  • Research on the Influencing Factors of Dropout Behavior among Users of Ancient Language Models from the Perspective of#br# Resilience Theory
    ZHANG Hai, FANG Jiping, WANG Dongbo
    2025, 7(2): 48-57. https://doi.org/10.19809/j.cnki.kjqbyj.2025.02.005
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    [Purpose/significance]The deep integration of ancient book digitization and large language models is the future development trend. In order to clarify the influence factors and formation mechanisms of dropout behavior among users of domain ancient book large language models, mobilize the willingness of users to use large language models, achieve high-quality user retention, and promote the non-human high-quality development of domain large language models. [Method/process]This study takes users in the field of ancient books as an example, based on resilience theory, and draws on grounded theory research methods to code and deconstruct first-hand data obtained from in-depth interviews with 30 users of ancient book large language models. The focus is on extracting influencing factors and conceptual categories from the perspective of resilience theory, and then constructing a research model on the mechanism of dropout behavior formation among users of ancient book large language models from the perspective of resilience theory. [Result/conclusion]The research results show that resilience factors, psychological resilience, cognitive factors, and situational factors are important factors affecting the dropout behavior of users of the ancient book large language model.Resilience factors include three dimensions:information resilience, technological resilience, and environmental resilience. Psychological resilience mainly goes through three stages: emotional stress, emotional resilience, and cognitive resilience.The research results provide necessary reference for effectively preventing users of ancient language models from dropout and achieving high-quality user retention.
  • Relationship Between Academic Influence and Institutional Cooperation in Specific Fields：Evidence from the Computer Science Domain
    YANG Yukai, ZHAO Yi, ZHANG Chengzhi
    2025, 7(2): 58-71. https://doi.org/10.19809/j.cnki.kjqbyj.2025.02.006
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    [Purpose/significance]In scientific collaboration, institutions are the primary driving units of scientific research. Compared to intra-institutional collaboration, inter-institutional collaboration often has the potential to produce high-impact papers. Therefore, studying fine-grained collaboration at the institutional level holds significant importance. [Method/process]To explore the relationship between different types of institutional cooperation and academic influence, this paper classifies institutions and defines various types of cooperation. Using network analysis methods, it investigates the relationship between network indicators of different types of institutional cooperation and academic influence. [Result/conclusion]Taking the computer science domain as an example, the analysis of the relationship between network indicators of different types of institutional cooperation and academic influence reveals that degree centrality and closeness centrality are positively correlated with academic influence, while betweenness centrality is negatively correlated with academic influence. Additionally, there are significant differences in the correlation between different centrality indicators and academic influence across various subfields, differing markedly from the overall regression results of the computer science field.
  • Analysis of Public Data Authorization Operation Policies Based on Structural Topic Model
    SHI Zhuorun, ZHAO Yupan, YAN Ruyu
    2025, 7(2): 72-81. https://doi.org/10.19809/j.cnki.kjqbyj.2025.02.007
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    [Purpose/significance]The public data authorization and operation policy plays an important guiding role in further guiding and regulating the practice of public data authorization and operation， as well as promoting the development and utilization of public data. Analyzing the existing public data authorization and operation policies can provide important references for the development of public data authorization and operation practices， as well as for policy formulation and improvement. [Method/process]This article selects relevant policies on public data authorization and operation issued by various levels of government in China， and uses the structural topic model to analyze and evaluate the existing policy texts. [Result/conclusion]The research shows that existing public data authorization and operation policies focus on three main themes： data security， data sharing and openness， and operational supervision. Among these， there is more emphasis on the supervision of public data operation platforms， while there is less attention paid to data security and data development. Based on this， the article proposes targeted countermeasures and suggestions， aiming to provide references for the practice of public data authorization and operation and policy formulation.
  • Simulation Research on Financial Competitive Intelligence Early Warning Mechanism of New Ventures Based on Big Data Thinking
    WANG Keping, ZHOU Jingyi, CHE Yao, QIAO Zhen
    2025, 7(2): 82-94. https://doi.org/10.19809/j.cnki.kjqbyj.2025.02.008
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    [Purpose/significance]Financial competitive intelligence is a core element in promoting the stable development of new ventures. Based on big data thinking, exploring the construction process and simulation effects of financial competitive intelligence warning mechanisms for new ventures is of great significance for improving their competitiveness and promoting healthy development. [Method/process]The article takes startups as the research object, constructs a financial competitive intelligence early warning mechanism for startups based on big data thinking, and uses system dynamics methods to construct causal relationship diagrams and system flow diagrams, and simulates them using Vensim PLE software. [Result/conclusion]The system dynamics model constructed in the article can well fit the financial competitive intelligence warning process and reveal the positive effects of factors such as the application rate of big data technology, key financial indicators, intelligence analysis methods, internal financial personnel participation rate, and professional audit personnel participation rate on financial competitive intelligence warning. This provides new ideas for new startups to smoothly carry out financial competitive intelligence warning work.
  • Research on the Path to Realize the Digital Transformation of Listed Specialized and New "Little Giant" Enterprises：From the Perspective of Linkage Between Internal and External Factors Within the Organization
    LIU Jianfeng, TANG Jin, XU Xukan, ZHOU Haowen
    2025, 7(2): 95-108. https://doi.org/10.19809/j.cnki.kjqbyj.2025.02.009
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    [Purpose/significance]In the post pandemic era of global economic downturn, the deep integration of digital economy and real economy is the key to promoting economic recovery and growth. As an important support for the resilience of the national economy, specialized, refined, and new "little giant" enterprises urgently need to pay attention to the value of data elements, actively participate in the construction and trading of data markets, and drive business innovation and value creation with data. Therefore, exploring the digital transformation path of specialized, refined, and new "little giant" enterprises is particularly urgent. [Method/process]Based on the TOE framework,this article uses fsQCA and NCA methods to select 6 elements at the "technology organization environment" level from 95 listed specialized and innovative "little giant" enterprises as samples. From the perspective of internal and external factor linkage, this article explores the configuration path combination required for enterprises to achieve digital transforma⁃tion paths. [Result/conclusion]The research findings indicate that none of the six antecedent variables alone can promote high-level digital transformation in enterprises. The driving mechanisms for high-level digital transformation can be divided into technology driven digital transformation, government support technology innovation dual wheel drive, and high-quality development driven digital transformation. Among them, enhancing enterprise innovation capabilities is the core condition for enterprises to achieve high-level digital transformation.
  • Intelligence Process of Marine Security:Model Construction and Case Deduction
    WANG Xuehui, HU Feng, WANG Peiwen
    2025, 7(2): 109-121. https://doi.org/10.19809/j.cnki.kjqbyj.2025.02.010
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    [Purpose/significance]In recent years, our country has been increasingly threatened by security threats from the maritime direction. In this regard, it is necessary to strengthen the intelligence construction in the maritime security system and improve the effectiveness of intelligence in maritime security governance/decision-making.[Method/process]Comprehensively applying the methods of literature research, comparative analysis and case demonstration, on the basis of clarifying the connotation of relevant concepts, sorting out and evaluating the existing intelligence process models in the academic community, combined with the actual scenarios of maritime security incidents, construct an intelligence process model for maritime security assurance and take Fukushima nuclear sewage discharged into the sea as a case. [Result/conclusion]The model is composed of the target layer, the demand layer, the business layer, the service layer, the feedback layer, and the support layer, which is not only suitable for the current complex scenarios of ensuring maritime security, but also conducive to improving the work efficiency of marine security intelligence.
  • Research on Emergency Intelligence Risk Traceability from the Perspective of Three Dimensional Space
    LIU Yizhou, HUANG Wei
    2025, 7(2): 122-132. https://doi.org/10.19809/j.cnki.kjqbyj.2025.02.011
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    [Purpose/significance]How to leverage the high value of intelligent intelligence technology and services for risk traceability analysis in the context of large-scale emergency situations is an urgent challenge that needs to be addressed in the governance of risks in an intelligent society. [Method/process]Based on the theory of ternary space, the risk of emergencies is divided into three dimensions: physical space, social space, and information space. Intelligent intelligence technologies such as large model causal relationship extraction, complex network analysis, and natural language processing are applied to collect and analyze emergency intelligence. A risk traceability model is constructed for seven elements including event risk sources and environmental risk sources, and empirical research is conducted. [Result/conclusion]Physical spatial risk sources play a crucial role in the process of risk transmission to other spaces, with a relatively dense distribution in the early stages of risk outbreaks, while the distribution of risk sources in social and information spaces exhibits a phase lag effect with similar periodicity over time. The emotional risk sources in the information space are mostly located at the core of the node network, which is an important factor in triggering irrational behavior between nodes. The social spatial risk characteristics of information risk sources are not obvious.
  • Exploration of Big Data-Enabled Approaches in Social Science Research
    XU Anming, PENG Tianhuan
    2025, 7(2): 133-142. https://doi.org/10.19809/j.cnki.kjqbyj.2025.02.012
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    [Purpose/significance]The integration of big data into various fields and aspects of social science research has facilitated the transformation of research paradigms.To this end, this article analyzes the motivations and logic behind big data empowering social science research and explores the implementation pathways for promoting the application of big data in social science research. [Method/process]Based on an analysis of the dilemmas faced by traditional social science research methods, the article delves into the impact mechanisms of big data on the entire process of social science research,including topic selection,literature review,research framework design, data collection, data analysis and visualization, dissemination of results, and evaluation of outcomes.Subsequently,it proposes leveraging the opportunity of "new liberal arts" development to promote the construction of philosophy and social science laboratories from multiple dimensions, including enhancing awareness,fostering differentiated development,strengthening computational support,developing tools and platforms,cultivating interdisciplinary talents,building data resource libraries, and improving supporting systems. [Result/conclusion]The study reveals that big data exerts a comprehensive and pervasive influence on social science research.The construction of philosophy and social science laboratories that integrate various innovative resources has become an effective way to promote the application of big data in social science research.