{"title":"Exploring the potential of disruptive innovation in the social sciences: A quantitative study of its impact on societal visibility","authors":"Yingqun Li , Ningyuan Song , Yu Shen , Lei Pei","doi":"10.1016/j.joi.2024.101584","DOIUrl":null,"url":null,"abstract":"<div><p>Scientific innovation serves as the driving force behind societal progress. In contrast to conservative innovation, disruptive innovation reshapes scientific paradigms and trajectories, significantly influencing both the scientific community and societal development. This study employs an extensive empirical dataset to explore the potential of disruptive innovation to enhance the societal visibility of scientific research. Our research reveals that disruptive innovation significantly enhances societal visibility, increasing it by 11.96% compared to consolidating innovation. Furthermore, disruptive innovation does not directly lead to early-stage \"breakthroughs\" in scientific endeavors, but it does have a notable \"acceleration\" effect on societal visibility. Particularly striking is its ability to promote visibility of scientific research on social media platforms such as Twitter and blogs. However, its influence is insignificant in news articles and policy documents. This phenomenon may be attributed to the high-risk nature of disruptive innovation, which conflicts with the high level of trust, professionalism, and certainty sought in news and policy. This study carries essential implications for selecting innovative directions, the channels through which innovation is disseminated, and the formulation of science policies.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 4","pages":"Article 101584"},"PeriodicalIF":3.4000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Informetrics","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1751157724000968","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Scientific innovation serves as the driving force behind societal progress. In contrast to conservative innovation, disruptive innovation reshapes scientific paradigms and trajectories, significantly influencing both the scientific community and societal development. This study employs an extensive empirical dataset to explore the potential of disruptive innovation to enhance the societal visibility of scientific research. Our research reveals that disruptive innovation significantly enhances societal visibility, increasing it by 11.96% compared to consolidating innovation. Furthermore, disruptive innovation does not directly lead to early-stage "breakthroughs" in scientific endeavors, but it does have a notable "acceleration" effect on societal visibility. Particularly striking is its ability to promote visibility of scientific research on social media platforms such as Twitter and blogs. However, its influence is insignificant in news articles and policy documents. This phenomenon may be attributed to the high-risk nature of disruptive innovation, which conflicts with the high level of trust, professionalism, and certainty sought in news and policy. This study carries essential implications for selecting innovative directions, the channels through which innovation is disseminated, and the formulation of science policies.
期刊介绍:
Journal of Informetrics (JOI) publishes rigorous high-quality research on quantitative aspects of information science. The main focus of the journal is on topics in bibliometrics, scientometrics, webometrics, patentometrics, altmetrics and research evaluation. Contributions studying informetric problems using methods from other quantitative fields, such as mathematics, statistics, computer science, economics and econometrics, and network science, are especially encouraged. JOI publishes both theoretical and empirical work. In general, case studies, for instance a bibliometric analysis focusing on a specific research field or a specific country, are not considered suitable for publication in JOI, unless they contain innovative methodological elements.