Tracking direct and indirect impact on technology and policy of transformative research via ego citation network

IF 1.5 3区管理学 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE Journal of Data and Information Science Pub Date : 2024-07-17 DOI:10.2478/jdis-2024-0018

Xian Li, Xiaojun Hu

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引用次数: 0

Abstract

Purpose The disseminating of academic knowledge to nonacademic audiences partly relies on the transition of subsequent citing papers. This study aims to investigate direct and indirect impact on technology and policy originating from transformative research based on ego citation network. Design/methodology/approach Key Nobel Prize-winning publications (NPs) in fields of gene engineering and astrophysics are regarded as a proxy for transformative research. In this contribution, we introduce a network-structural indicator of citing patents to measure technological impact of a target article and use policy citations as a preliminary tool for policy impact. Findings The results show that the impact on technology and policy of NPs are higher than that of their subsequent citation generations in gene engineering but not in astrophysics. Research limitations The selection of Nobel Prizes is not balanced and the database used in this study, Dimensions, suffers from incompleteness and inaccuracy of citation links. Practical implications Our findings provide useful clues to better understand the characteristics of transformative research in technological and policy impact. Originality/value This study proposes a new framework to explore the direct and indirect impact on technology and policy originating from transformative research.

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通过自我引用网络跟踪变革性研究对技术和政策的直接和间接影响

目的向非学术受众传播学术知识在一定程度上依赖于后续引用论文的转化。本研究旨在基于小我引用网络，探讨变革性研究对技术和政策产生的直接和间接影响。设计/方法/途径基因工程和天体物理学领域的关键诺贝尔奖获奖论文（NPs）被视为变革性研究的代表。在这篇论文中，我们引入了引用专利的网络结构指标来衡量目标文章的技术影响力，并将政策引用作为衡量政策影响力的初步工具。研究结果研究结果表明，在基因工程领域，NPs 对技术和政策的影响高于其后续引用代，但在天体物理学领域则不然。研究局限诺贝尔奖的选择并不均衡，而且本研究使用的数据库《维度》存在不完整和引文链接不准确的问题。实际意义我们的研究结果为更好地理解变革性研究在技术和政策影响方面的特点提供了有用的线索。原创性/价值本研究提出了一个新的框架，用于探索变革性研究对技术和政策产生的直接和间接影响。

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来源期刊

Journal of Data and Information Science INFORMATION SCIENCE & LIBRARY SCIENCE-

CiteScore

3.50

自引率

6.70%

发文量

495

期刊介绍： JDIS devotes itself to the study and application of the theories, methods, techniques, services, infrastructural facilities using big data to support knowledge discovery for decision & policy making. The basic emphasis is big data-based, analytics centered, knowledge discovery driven, and decision making supporting. The special effort is on the knowledge discovery to detect and predict structures, trends, behaviors, relations, evolutions and disruptions in research, innovation, business, politics, security, media and communications, and social development, where the big data may include metadata or full content data, text or non-textural data, structured or non-structural data, domain specific or cross-domain data, and dynamic or interactive data. The main areas of interest are: (1) New theories, methods, and techniques of big data based data mining, knowledge discovery, and informatics, including but not limited to scientometrics, communication analysis, social network analysis, tech & industry analysis, competitive intelligence, knowledge mapping, evidence based policy analysis, and predictive analysis. (2) New methods, architectures, and facilities to develop or improve knowledge infrastructure capable to support knowledge organization and sophisticated analytics, including but not limited to ontology construction, knowledge organization, semantic linked data, knowledge integration and fusion, semantic retrieval, domain specific knowledge infrastructure, and semantic sciences. (3) New mechanisms, methods, and tools to embed knowledge analytics and knowledge discovery into actual operation, service, or managerial processes, including but not limited to knowledge assisted scientific discovery, data mining driven intelligent workflows in learning, communications, and management. Specific topic areas may include: Knowledge organization Knowledge discovery and data mining Knowledge integration and fusion Semantic Web metrics Scientometrics Analytic and diagnostic informetrics Competitive intelligence Predictive analysis Social network analysis and metrics Semantic and interactively analytic retrieval Evidence-based policy analysis Intelligent knowledge production Knowledge-driven workflow management and decision-making Knowledge-driven collaboration and its management Domain knowledge infrastructure with knowledge fusion and analytics Development of data and information services