Predicting reuse patterns of novel technologies is crucial for understanding technology diffusion and identifying high-value technologies. This study defines the combination of technology components (i.e., the International Patent Classification codes, IPCs) as "technology" and the invention patents that apply these technologies within the first year of their emergence as "early inventions". A framework was proposed to predict reuse patterns of novel technologies based on the characteristics of technology components and early inventions. A shape-based clustering analysis was conducted on the reuse trajectories of novel technologies that emerged from 1995 to 2020 to identify potential technology reuse patterns. Subsequently, the differences between technologies with diverse reuse patterns were compared. Finally, ten machine learning algorithms were employed to predict the reuse patterns of novel technologies. The findings indicate that the trajectories of 39,376 novel technologies exhibit four types of reuse patterns: s-shaped trajectory pattern, fleeting trajectory pattern, linear trajectory pattern, and exponential trajectory pattern. Furthermore, technologies with diverse reuse patterns substantially differ in their accessibility and similarity of technology components, as well as in the applicability and attention of early inventions. The Gradient Boosting Decision Tree algorithm (GBDT) yields the best performance in predicting the reuse patterns of novel technologies, and the applicability of early inventions serves as the most significant predictor.

Predicting technology convergence patterns can be a valuable tool for identifying cutting-edge technological opportunities and utilizing existing technical resources in industrial upgrading. Previous studies have predicted technology convergence based on the relationships between technologies extracted from patent documents, ignoring the connection between technologies generated by industrial application. This paper proposed a novel Multi-Layer Network Technology Prediction (MNTCP) method to predict technology convergence patterns in different segments by integrating connections generated form industrial application. We constructed a multi- layer network reflecting the different industry segments and applied an innovative approach to measure technology field similarities by combining intra- and inter-layer knowledge proximity with Evidence Theory. The effectiveness of the method was evaluated by the Area Under the Curve and the Ranking Score. Empirical research results in the Full Cell Vehicle industry indicate that the proposed method has better predictive ability compared to the baselines. This research reveales a tendency for technology convergence to occur more frequently between the upstream-midstream and midstream-downstream segments, with an increasing trend of upstream-downstream convergence as the industry matures. Distinct technology convergence patterns such as Intra-C-Section, Inter-F-Section, and B-Section-Centered convergence have been identified across different industry segments. These patterns highlight that technology convergence is driven not only by the importance for technology but also by adaptability and the potential to meet complex industry demands. The discovery of emerging topics showcases a wide range of focal areas, from material innovation in the upstream to systems integration in the downstream, presenting an evolving landscape of technology development in the industry. This study contributes to revealing the convergence patterns between technologies in a fine-grained way and improving the capabilities of technology management.

Scientific knowledge has been shown to be a key contributor to corporate technological innovation; hence, modern inventive firms place a strong emphasis on scientific research. However, little is known about how the knowledge linkage between science and technology (ST linkage) of corporations fosters their technological innovation. In an effort to fill this vacuum in the literature, we investigate how multiple properties of ST linkage influence corporate technological innovation. We conducted a Zero-inflated Negative Binomial regression using scientific publications, patents, and firm-level data from 671 pharmaceutical and 686 semiconductor corporations to test our hypotheses. We find that the higher the proportion of corporations citing their published scientific publications in patents, the more likely they are to produce more patents, and corporate technological innovation benefits from the utilization of scientific knowledge produced in the early stages. Furthermore, the positive effects of the aforementioned factors on the technological innovation performance of corporations are present in both scientific research strategies (e.g., independent vs. joint research). These findings contribute to the understanding of the underlying mechanism of corporate basic research facilitating technological innovation. This study also provides meaningful advice regarding how corporations can enhance their technological innovation through scientific research.

Technological opportunities are bred in intricate and interactive connections between science and technology (S&T). To identify these potential opportunities, lexical- or topic-based similarity approaches have been extensively applied to quantify S&T linkages; however, these lack consideration of different interaction patterns and lead-lag relationships between S&T. To this end, this study proposes a novel approach to detect technological opportunities within specific S&T topics by incorporating their structure-coupling patterns and temporal lead-lag distance. By transforming S&T knowledge systems into knowledge networks, a network coupling approach is employed to elaborate dynamic interaction patterns of S&T, and a time-lagged cross-correlation analysis is conducted to calculate their lead-lag distance under different time shifts. An evidence analysis from the energy conservation field demonstrates the feasibility and reliability of the proposed methodology in identifying technological opportunities implicit in S&T shared (exists in both S&T) and private topics (exists only in science or technology) from a topical dimension.

Measuring the knowledge linkage between science and technology (S&T) is crucial for understanding the interactions between S&T and assisting decision-makers in strategizing research and development investments. Conventional analyses of S&T knowledge linkage have frequently overlooked the semantic structure of knowledge elements thereby introducing biases in the measurements. To address this issue, this study introduces a novel method predicated on the tree semantic structure, which quantifies the S&T linkage by considering the hierarchy and category of knowledge elements within an ontological framework. In this method, knowledge trees are constructed to represent the core knowledge of S&T literature, incorporating hierarchically organized MeSH descriptors. These knowledge trees are subsequently utilized to measure the knowledge linkage between S&T by integrating intra-branch knowledge similarity and inter-branch knowledge distribution. An empirical analysis was conducted on a substantial corpus of scientific publications and patents within the biomedicine sector. The findings predominantly revealed a stronger knowledge linkage between S&T in recent years, relative to the early 2000 s. It was also observed that patents are more inclined to include broader concepts in their titles and abstracts, in contract to the more specific concepts found in scientific publications. S&T literatures have increasingly focused on knowledge related to diseases, equipment, and health care. To verify the reliability of the proposed method, validation was performed with alternative measurements of knowledge linkage. In comparison to single-feature-based linkage measurements and network-based approaches, our proposed method demonstrates superior adaptability in capturing S&T linkage, especially when there is a marked disparity in the sample sizes of S&T literature. This study not only enriches the measurements of S&T knowledge linkage, but also furnishes empirical insights into the evolving patterns of S&T linkage within the biomedical domain.

Detecting science–technology hierarchical linkages is beneficial for understanding deep interactions between science and technology (S&T). Previous studies have mainly focused on linear linkages between S&T but ignored their structural linkages. In this paper, we propose a network coupling approach to inspect hierarchical interactions of S&T by integrating their knowledge linkages and structural linkages. S&T knowledge networks are first enhanced with bidirectional encoder representation from transformers (BERT) knowledge alignment, and then their hierarchical structures are identified based on K‐core decomposition. Hierarchical coupling preferences and strengths of the S&T networks over time are further calculated based on similarities of coupling nodes' degree distribution and similarities of coupling edges' weight distribution. Extensive experimental results indicate that our approach is feasible and robust in identifying the coupling hierarchy with superior performance compared to other isomorphism and dissimilarity algorithms. Our research extends the mindset of S&T linkage measurement by identifying patterns and paths of the interaction of S&T hierarchical knowledge.

Detecting science–technology hierarchical linkages is beneficial for understanding deep interactions between science and technology (S&T). Previous studies have mainly focused on linear linkages between S&T but ignored their structural linkages. In this paper, we propose a network coupling approach to inspect hierarchical interactions of S&T by integrating their knowledge linkages and structural linkages. S&T knowledge networks are first enhanced with bidirectional encoder representation from transformers (BERT) knowledge alignment, and then their hierarchical structures are identified based on K-core decomposition. Hierarchical coupling preferences and strengths of the S&T networks over time are further calculated based on similarities of coupling nodes' degree distribution and similarities of coupling edges' weight distribution. Extensive experimental results indicate that our approach is feasible and robust in identifying the coupling hierarchy with superior performance compared to other isomorphism and dissimilarity algorithms. Our research extends the mindset of S&T linkage measurement by identifying patterns and paths of the interaction of S&T hierarchical knowledge.

科学与技术具有双向互惠、协同发展的特征.当前研究多从领域发展视阈探究科学与技术间的关联模式及相互作用,而较少从特定创新主体出发分析两者关联.论文和专利分别是科学与技术的代表性产出,本文以基因工程领域为例,构建以论文和专利为节点的创新型企业科学—技术关联网络,结合知识网络分析方法,整合节点语义特征与网络结构特征,揭示创新型企业科学与技术的关联性.结果表明:(1)在创新能力更强的企业中,科学向技术的转化程度更高,但科技规模过大会导致转化速度降低;(2)相比跨企业的科技关联,创新型企业内部的科技关联更具优势,科学到技术的知识流动速度更快,且创新能力更强的企业能够在更广泛的科技领域产生关联;(3)创新型企业科学与技术关联的领域多样性与领域平衡性和企业的异质性能力相关,而领域平衡性仅与领域多样性有关.本研究能够为创新型企业开展基础科学研究、促进科技创新提供管理启示.

探究新兴交叉学科知识元的生存特征及影响因素,有助于从微观层面揭示新兴交叉学科的形成与演进过程,提升对新兴交叉学科发展规律的科学认识.首先,构建多维测度指标量化新兴交叉学科知识元的学科来源和学科影响力属性;其次,采用Kaplan-Meier法构建新兴交叉学科知识元的生存曲线并剖析不同属性知识元生存特征差异;最后,利用Cox比例风险模型探究学科来源和学科影响力对知识元生存时间的影响机制.以医学信息学为例开展实证分析后发现,医学信息学知识元生存曲线呈现先陡降后缓降的趋势,平均生存时间为 4.41 年.医学信息学新产生的知识元生存时间显著低于原始学科归属为四个关联学科的知识元,关联学科中计算机科学的知识元在医学信息学中的生存风险最高.学科来源复杂度、新兴交叉学科使用频次和学科地位、关联学科热度均与知识元生存时间存在正相关关系.相较于关联学科热度,新兴交叉学科使用频次和新兴交叉学科地位对知识元生存时间影响程度更高.

[目的/意义]基于早期施引文献与科学论文的知识关联对科学论文扩散效果进行预测,有助于从价值反馈角度前瞻性识别高影响力学术论文,为科研人员建立科学研究成果早期学术影响力评估体系提供参考.[方法/过程]测度早期施引文献与目标科学论文在主题、期刊和作者 3 个层面的关联程度,采用线性回归与负二项回归模型,挖掘 3 种类型的知识关联度与目标科学论文扩散效果(即扩散速度、广度和强度)的内在关联机制;在此基础上引入机器学习算法对科学论文的扩散效果进行预测,剖析 3 类知识关联特征在预测任务中的重要性排序.[结果/结论]神经科学领域的实证分析显示,主题关联与目标科学论文的扩散速度呈正相关关系,与扩散广度和扩散强度呈倒U型关系;期刊关联会抑制目标科学论文的扩散速度,但能够正向影响其扩散强度与扩散广度;作者关联仅对扩散强度有稳定的正向影响;基于主题关联与期刊关联可以实现对科学论文扩散速度的有效预测,但难以预测扩散广度和扩散强度.随机森林模型在扩散速度预测中性能最佳,主题关联特征的重要性高于期刊关联.

Compared to previous studies that generally detect scientific breakthroughs based on citation patterns, this article proposes a knowledge entity-based disruption indicator by quantifying the change of knowledge directly created and inspired by scientific breakthroughs to their evolutionary trajectories. Two groups of analytic units, including MeSH terms and their co-occurrences, are employed independently by the indicator to measure the change of knowledge. The effectiveness of the proposed indicators was evaluated against the four datasets of scientific breakthroughs derived from four recognition trials. In terms of identifying scientific breakthroughs, the proposed disruption indicator based on MeSH co-occurrences outperforms that based on MeSH terms and three earlier citation-based disruption indicators. It is also shown that in our indicator, measuring the change of knowledge inspired by the focal paper in its evolutionary trajectory is a larger contributor than measuring the change created by the focal paper. Our study not only offers empirical insights into conceptual understanding of scientific breakthroughs but also provides practical disruption indicator for scientists and science management agencies searching for valuable research.

The citation-based approach has been applied extensively to explore the dynamic evolution and historical development of scientific research and technological advances, while it has rarely been used to analyze the dynamics of policy diffusion. Policy citations signify the flow of specific legislative knowledge in the legal system, which can provide traceable and measurable footprints for tracing the path of policy progress. To this end, this paper proposes a quantitative citation-based research framework for inspecting the patterns and evolution process of policy diffusion. First, time-varying policy citation networks at different stages are constructed by extracting citing/cited links of policies embedded in the textual content of policy documents. Pattern characteristics, topic dynamics, and historical trajectories of policy diffusion are then revealed by scrutinizing succinct structures of policy citation networks. An experimental study of large-scale new energy policies (NEPs) in China confirms the reliability of the citation-based approach in tracking policy diffusion. The introduced approach enriches the current methodology for policy research and provides valuable references for policymakers to judge their policymaking process.

随着数智技术的快速发展与广泛应用,数据资源成为推动社会经济发展的关键生产要素.如何开展数据治理工作并借助数据治理手段促进数据价值释放是政府、业界与学界共同关注的重要议题.目前,数据治理研究已成为热门研究领域,表现出政府与公共数据治理研究成果丰硕,不同企业与行业的数据治理研究进展差异明显,个人数据治理研究尚处于起步阶段的特征.面向多元场景的数据治理实践主要面临来自外部环境、治理主体以及数据属性与治理技术三个方面的挑战.未来应当着力完善数据治理理论体系、构建代表性数据治理框架、拓展企业与行业数据治理研究场景、强化个人数据治理研究创新、推动数据治理技术与方法研究以及探索数据治理落地实施方案.

Considerable research has focused on finding the optimum solution to reveal the role of intellectual capital in scientific breakthroughs, ignoring the fact that all roads lead to Rome. To this end, this study aims to explore the configuration of intellectual capital-comprising human and social capital-within research teams to trigger scientific breakthroughs. By identifying research teams of scientific breakthroughs and their control groups involved in gene editing, we initially determined the potential antecedents of scientific breakthroughs pertaining to intellectual capital through an integrative comparison of research teams with breakthroughs and non-breakthroughs. Subsequently, we employed a holistic approach grounded in configurational theory to investigate how the combination of human capital (within-domain experience and knowledge structure) and social capital (collaboration features within and beyond a team) facilitates scientific breakthroughs. A fuzzy-set qualitative comparative analysis (fsQCA) approach was used to empirically pinpoint seven configurations of human and social capital that explain scientific breakthroughs; these demonstrate that research teams making scientific breakthroughs are not all-around players in intellectual capital and that their success depends more on resourceful capital allocation. Our findings inspire team managers to rationally deploy critical set solutions to optimize team structure and resource allocation to pursue high-quality scientific innovation.

Compared to previous studies that generally detect scientific breakthroughs based on citation patterns, this article proposes a knowledge entity‐based disruption indicator by quantifying the change of knowledge directly created and inspired by scientific breakthroughs to their evolutionary trajectories. Two groups of analytic units, including MeSH terms and their co‐occurrences, are employed independently by the indicator to measure the change of knowledge. The effectiveness of the proposed indicators was evaluated against the four datasets of scientific breakthroughs derived from four recognition trials. In terms of identifying scientific breakthroughs, the proposed disruption indicator based on MeSH co‐occurrences outperforms that based on MeSH terms and three earlier citation‐based disruption indicators. It is also shown that in our indicator, measuring the change of knowledge inspired by the focal paper in its evolutionary trajectory is a larger contributor than measuring the change created by the focal paper. Our study not only offers empirical insights into conceptual understanding of scientific breakthroughs but also provides practical disruption indicator for scientists and science management agencies searching for valuable research.

Identifying widely disseminated papers (WDPs) on social media can help to understand dissemination mechanisms of scientific papers from academia to social media and assist in the formulation of public and science policy. This study applies machine learning methods to explore the possibility of identifying WDPs and to investigate the influence mechanisms of literature-related and social media-related features. A pre-task was first conducted to investigate whether the visibility of scientific papers on social media can be predicted, and the role of various features was analyzed. Then, we defined two predictive tasks for identifying WDPs before and after they are visible on social media. The performance of eight state-of-the-art algorithms was compared in three experiments against the dataset of the oncology field, and the contribution of literature-related and social media-related features in the tasks was explained based on the Shapley additional explanations (SHAP) value. The results show that XGBoost performs better than other algorithms, especially with an F1 score of 0.988 and AUC of 0.998 in the trend prediction task. Nearly all of the literature-related features have great effects on identifying long-term disseminated papers, and most social media-related features play more significant roles in identifying broadly mentioned papers. Moreover, journal features contribute more to identifying papers of social media visibility, while paper features, especially research topics, have a greater influence on identifying WDPs. The number and proportion of academic-related Twitter users have great impacts on the scale and duration of papers’ dissemination. The number and duration of first-generation tweets play critical roles in identifying broadly mentioned and long-term disseminated papers, respectively. This study provides profound insights into the influencing factors in the dissemination of papers from the scientific community to and across social media, and helps to understand the difference in knowledge propagation between academia and the public.

大数据资源的迅速累积与分析技术的快速发展不仅拓展智库研究范畴,促使智库研究更加重视数据洞察,同时对智库建设质量与创新水平提出更高要求.面对智库建设向现代化、创新化和科学化转变的发展需求,文章从信息链的角度阐释数据驱动智库研究变革,剖析数据变革环境下智库话语逻辑重塑的迫切需求,探讨数据驱动智库研究的多元互证体系构建方案,进而从重视相关与因果关系互补、融合技术理性与人文价值、引入三元世界分析视角、聚焦长知识链支撑、建构智库工程化服务模式等方面提出当前环境下智库建设的优化建议.

实现国家安全大数据的综合信息集成是支撑国家安全管理战略决策和突发事件有效应对的重要举措,也是响应国家安全大数据战略和"数字中国"建设的必然要求.在把握国家安全大数据综合信息集成的重大战略意义与社会应用价值的基础上,探索面向国家安全大数据综合信息集成的可行性方案,提出"大数据集成理论架构"→"城市数据画像建模"→"资源池构建与智能推演"→"数据蜂巢系统规划与开发"→"面向城市或区域的数据集成调研与示范研究"的具体实施路径,通过自下而上逐步实现国家安全大数据的综合信息集成,从而为国家安全事件管理与态势的科学研判提供信息支撑.

Collaboration and knowledge networks have been proved to play a crucial role in innovation. From a multilevel network perspective, this study integrates research on the two types of networks and investigates how city-level collaboration and knowledge networks influence innovation in the energy conservation field. To this end, we calculate a city's influence force in its collaboration network based on the weighted PageRank algorithm and propose a novel measurement method of network embedding to gauge the embedding depth and embedding breadth of a city's local knowledge network in the whole knowledge network. Empirical results suggest that a city's aggregation index and influential force in the collaboration network are positively related to its innovation, while geographical distance shows an inverted U-shaped effect. The embedding depth and embedding breadth of a city's local knowledge network have a positive effect, and the structural entropy of its knowledge network generates an inverted U-shaped effect on innovation. Our research contributes to a better understanding of the impact of city-level collaboration and knowledge networks on innovation and points to several general implications for innovation practice and complex network research.

Novel research drives scienti0ic breakthroughs but also has higher uncertainty of being recognized by citation count based metrics. This study proposed two indicators to measure the content novelty of a paper based on the knowledge entities it contains, and explored the relationship between content novelty and scienti0ic impact of papers. It is found that content novelty is negatively correlated with citation impact in our dataset. Our 0indings suggest that science policy in favor of citation count based impact may be biased against novel research.