Yang Zhang , Yang Wang , Haifeng Du , Shlomo Havlin
{"title":"Delayed citation impact of interdisciplinary research","authors":"Yang Zhang , Yang Wang , Haifeng Du , Shlomo Havlin","doi":"10.1016/j.joi.2023.101468","DOIUrl":null,"url":null,"abstract":"<div><p>Interdisciplinary research increasingly fuels innovation, and is a key input for future breakthroughs. Yet the timing of when interdisciplinary research achieves its highest citation impact remains unclear. Here, we use the time of a paper to reach its citation peak to quantify citation dynamics, and examine its relationship with paper interdisciplinarity. Using large scale publication datasets spanning over 37 years, our results suggest that interdisciplinary papers show significant delayed citation impact both at the individual paper level and collectively, as it takes longer for highly interdisciplinary papers to reach their citation peak as well as their half citations. Such relationships are nearly universal across various scientific disciplines and time periods. Furthermore, we study the underlying forces behind this delayed impact, finding that the effect goes beyond the Matthew effect (i.e., the rich-get-richer effect). Although team size and content conventionality are partly related to the citation delay, they cannot fully explain this effect. Overall, our results suggest that governments, research administrators, and funding agencies should be aware of this general feature of interdisciplinary science, which may have broad policy implications.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1751157723000937/pdfft?md5=f3acbb2b2ed2fd6688e92267a9f35dd8&pid=1-s2.0-S1751157723000937-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Informetrics","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1751157723000937","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
Interdisciplinary research increasingly fuels innovation, and is a key input for future breakthroughs. Yet the timing of when interdisciplinary research achieves its highest citation impact remains unclear. Here, we use the time of a paper to reach its citation peak to quantify citation dynamics, and examine its relationship with paper interdisciplinarity. Using large scale publication datasets spanning over 37 years, our results suggest that interdisciplinary papers show significant delayed citation impact both at the individual paper level and collectively, as it takes longer for highly interdisciplinary papers to reach their citation peak as well as their half citations. Such relationships are nearly universal across various scientific disciplines and time periods. Furthermore, we study the underlying forces behind this delayed impact, finding that the effect goes beyond the Matthew effect (i.e., the rich-get-richer effect). Although team size and content conventionality are partly related to the citation delay, they cannot fully explain this effect. Overall, our results suggest that governments, research administrators, and funding agencies should be aware of this general feature of interdisciplinary science, which may have broad policy implications.
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.