Journal of Informetrics最新文献

{"title":"Collaborating with top scientists may not improve paper novelty: A causal analysis based on the propensity score matching method","authors":"Linlin Ren ,&nbsp;Lei Guo ,&nbsp;Hui Yu ,&nbsp;Feng Guo ,&nbsp;Xinhua Wang ,&nbsp;Xiaohui Han","doi":"10.1016/j.joi.2024.101609","DOIUrl":"10.1016/j.joi.2024.101609","url":null,"abstract":"<div><div>In previous collaboration studies, a majority of them concentrate on examining cooperation models, often overlooking the pivotal role played by a Top Scientist (TS) in scientific advancements. As far as my knowledge extends, only one relevant work delves into the correlation between innovation and collaboration with TSs, and no research has explored this relationship from a causal perspective. More precisely, previous studies suffer from several limitations in their examination of this topic: 1) Existing studies on Papers' Novelty (PN) primarily focus on calculating methods, with limited exploration of its relationship with scientific cooperation. 2) Research that has explored the link between collaboration with TSs and output innovation often adopts a correlational perspective, lacking a causal analysis that could correct for potential confounding factors. 3) Previous methodologies overlook the attributes of citation networks as potential confounding factors, a crucial consideration in identifying identical papers in causal analyses. 4) The impact of disciplinary diversity of papers on the innovation output when collaborating with TSs is often overlooked in prior research. To address these limitations, we conduct a causal analysis of publications in three subfields of computer science from the Web of Science (WoS) database to demonstrate the impact of collaborating with TSs on PN. Specifically, to tackle Limitations 1) and 2), we employ PN as a metric to assess the quality of academic output and explore its causal relationship with collaborating with TSs using the Propensity Score Matching (PSM) method. To address Limitation 3), we comprehensively consider potential confounding factors influencing PSM matching by further incorporating the attributes of citation networks, thereby minimizing selection bias. To deal with Limitation 4), we not only focus on the overall treatment effect but also delve into the treatment effect of intra-disciplinary and interdisciplinary collaboration modes. The research findings indicate that the papers collaborating with TSs exhibit lower PN compared to those without the participation of TSs. This suggests that collaboration with TSs may come at the cost of reduced novelty. This discovery prompts profound reflections on scientific collaboration, emphasizing the challenges and trade-offs that may exist in collaboration.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101609"},"PeriodicalIF":3.4,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inter- and intra-domain knowledge flows: Examining their relationship with impact at the field level over time","authors":"Giovanni Abramo ,&nbsp;Ciriaco Andrea D'Angelo","doi":"10.1016/j.joi.2024.101614","DOIUrl":"10.1016/j.joi.2024.101614","url":null,"abstract":"<div><div>Just as innovations often succeed in fields beyond their original domains, this study explores whether the same applies to scientific discoveries. We investigate the flow of knowledge across scientific disciplines by analyzing connections between 2015 cited publications indexed in the Web of Science and their citing counterparts. Specifically, we measure the rates of knowledge dissemination within and across different fields. Our study addresses key questions about disparities between inter- and intra-domain dissemination rates, the relationship between dissemination types and scholarly impact, and the evolution of these patterns over time. These findings enhance our understanding of knowledge flows and provide practical insights with significant implications for evaluative bibliometrics.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101614"},"PeriodicalIF":3.4,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scientific knowledge role transition prediction from a knowledge hierarchical structure perspective","authors":"Jinqing Yang ,&nbsp;Jiming Hu","doi":"10.1016/j.joi.2024.101612","DOIUrl":"10.1016/j.joi.2024.101612","url":null,"abstract":"<div><div>There are several potential patterns in the evolution of scientific knowledge. In order to delve deeper into the changes in function and role during the evolution of knowledge, we have proposed a research framework that examines the transition of scientific knowledge roles from the perspective of a hierarchical structure. We constructed two classification models of transition possibility and transition type to predict whether one undergoes a role transition and which type of role transition it belongs to. Several datasets were constructed by utilizing the entire corpus of publications available in <em>PubMed</em> and the history records of <em>MeSH</em>. Among the tasks of transition type prediction and transition possibility prediction, the <em>Gradient Boosting</em> classifier performed the best. The binary classification model of transition possibility achieved a precision of 72.58 %, a recall of 71.04 %, and an F1 score of 71.78 %. The multi-classification model of transition possibility had a macro-F1 score of 61.29 %, a micro-F1 score of 84.07 %, and a weighted-F1 score of 82.90 %. Further, we found that the knowledge genealogy features contribute the most to the prediction of transition possibility while knowledge attribute and network structure features have a significantly greater influence on the prediction of transition type. Most features have an obvious effect on the role transition of the <strong><em>Content-change type</em></strong>, followed by <strong><em>Child-generation</em></strong> and <strong><em>Localization-shift types.</em></strong></div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101612"},"PeriodicalIF":3.4,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early identification of breakthrough technologies: Insights from science-driven innovations","authors":"Dan Wang ,&nbsp;Xiao Zhou ,&nbsp;Pengwei Zhao ,&nbsp;Juan Pang ,&nbsp;Qiaoyang Ren","doi":"10.1016/j.joi.2024.101606","DOIUrl":"10.1016/j.joi.2024.101606","url":null,"abstract":"<div><div>Identifying breakthrough technologies is crucial for advancing technological innovation and, in this sense, the innovation patterns driven by science are considered to be key pathways for forming breakthrough technologies. Building on this premise, this paper presents a framework for identifying breakthrough technologies that starts with these signals of scientific innovation. The first step in the method is to construct a science-technology knowledge network based on papers and patents. Then a two-stage selection method funnels the scientific innovation signals, filtering out those with the potential to trigger technological breakthroughs. Next, a machine learning-based link prediction model, integrating three types of features, identifies new links between science-driven signals and existing technologies. A community detection algorithm then identifies sub-networks of technologies formed around these new links. Finally, a structural entropy index is used to evaluate these sub-networks to determine potential breakthrough technologies. By systematically characterizing the content and core features of scientific innovation signals, this study reveals the driving sources of technological breakthroughs and sheds light on the absorption and diffusion processes of scientific innovation. We validated the method through a use case in the field of artificial intelligence. Those who manage technological innovation should find the insights of this research particularly valuable.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101606"},"PeriodicalIF":3.4,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantification and identification of authorial writing style through higher-order text network modeling and analysis","authors":"Hongzhong Deng,&nbsp;Chengxing Wu,&nbsp;Bingfeng Ge,&nbsp;Hongqian Wu","doi":"10.1016/j.joi.2024.101603","DOIUrl":"10.1016/j.joi.2024.101603","url":null,"abstract":"<div><div>Determining the true author of anonymized texts has important applications ranging from text classification and information extraction to forensic investigations. Despite substantial progress, current authorship identification solutions are limited to extracting straightforward semantic relationships in writing styles, lacking consideration for higher-order features among multiple vocabulary, phrases, or sentences in language structure. Here, we propose a novel approach based on hypernetwork theory to encode higher-order text features into a unified text hyper-network and investigate whether the hyper-order topological features of the text hyper-network contribute to revealing the author's stylistic preferences. Our results indicate that metrics of the text hyper-network, such as hyperdegree, average shortest path length, and intermittency, can capture more information about the author's writing styles. More importantly, in the author identification task of 170 novels, our method accurately distinguished the authorship of 81% of the novels, surpassing the accuracy of the method of using paired word relationships. This further highlights the importance of higher-order features in text analysis, beyond mere pairwise interactions of words.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101603"},"PeriodicalIF":3.4,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge substitutability and complementarity in scientific collaboration","authors":"Kexin Lin ,&nbsp;Beibei Hu ,&nbsp;Zixun Li ,&nbsp;Yi Bu ,&nbsp;Xianlei Dong","doi":"10.1016/j.joi.2024.101601","DOIUrl":"10.1016/j.joi.2024.101601","url":null,"abstract":"<div><div>Understanding the substitutability and complementarity in scientific collaboration is of great significance to reduce the costs of team building and enhance the team's research performance. In this paper, knowledge substitutability in scientific collaboration characterizes similar properties shared during the (re)combination process, and knowledge complementarity describes the synergistic effect created by different knowledge combinations. This paper aims to explore the influence of knowledge substitutability and complementarity on research performance based on the American Physical Society dataset. Overall, we find that knowledge substitutability negatively influences scientists’ research performance, while knowledge complementarity has a positive effect. However, the analysis reveals that the positive correlation between knowledge complementarity and research performance only exists for scientists with small-sized teams, while scientists with large-sized teams are not significantly influenced by the complementarity. This paper provides a new perspective and practical insights into team formation and management.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101601"},"PeriodicalIF":3.4,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Technological recombinant strategy and breakthrough innovation of team: The moderating role of science linkage","authors":"Tao Wang,&nbsp;Jiajie Wang,&nbsp;Jing Shi,&nbsp;Jianjun Sun,&nbsp;Lele Kang","doi":"10.1016/j.joi.2024.101613","DOIUrl":"10.1016/j.joi.2024.101613","url":null,"abstract":"<div><div>Why can some knowledge production activities help teams achieve significant innovation breakthroughs while others go unnoticed? Technological recombinant is considered an important way for teams to gain an innovative edge in the era of big science. However, few empirical studies have revealed the role of differentiated technological recombinant strategies in team breakthrough innovation. This study develops an approach to identify technical teams from the full-domain cooperation network and investigates the differentiated impact of technological recombinant creation and reuse strategies on team breakthrough innovation, considering the moderating role of science linkage. Using biopharmaceutical patent data from 2000 to 2019 and building empirical models to estimate, the study reveals that both recombinant strategies distinctly affect technological novelty and impact, while science linkage enhancing these effects. By elucidating the nuanced roles of technological recombinant strategies in team-based breakthrough innovations, this study offers targeted guidance for optimizing innovation processes within organizations.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101613"},"PeriodicalIF":3.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effects of scientific collaboration network structures on impact and innovation: A perspective from project teams","authors":"Zhifeng Liu ,&nbsp;Chenlin Wang ,&nbsp;Jinqing Yang","doi":"10.1016/j.joi.2024.101611","DOIUrl":"10.1016/j.joi.2024.101611","url":null,"abstract":"<div><div>Scientific collaboration is critical in tackling complex research challenges, necessitating optimized configurations of research teams. While existing research primarily examines the impact of collaboration network characteristics on the impact and innovation of individual papers, there is less focus on these characteristics within the context of research projects. To bridge this gap, this study adopts the perspective of project teams and explores the influence of scientific collaboration network structures on the impact and innovation of research outputs. By employing ordinary least squares regression and negative binomial regression methods on a dataset encompassing 21,618 NSF grants and their associated 351,550 publications, we rigorously analyze how specific network characteristics impact the innovation and impact of the research outputs. The results reveal a negative correlation between the count of structural holes and both the impact and conventionality of the team's papers. Meanwhile, the small world of a project team positively correlates with the papers' impact and displays an inverted U-shaped relationship with innovation. Further analysis confirms that there is no interactive effect between structural holes and small world. A series of robustness checks have been conducted, demonstrating that these findings are robust. This study contributes valuable insights for scholars, institutions, and policymakers aiming to enhance research team effectiveness. It underscores the nuanced impacts of network properties on research outputs, offering a new perspective by focusing on project-based team structures rather than individual paper collaborations.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101611"},"PeriodicalIF":3.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conclusions need to follow from supporting results","authors":"Robin Haunschild ,&nbsp;Lutz Bornmann","doi":"10.1016/j.joi.2024.101610","DOIUrl":"10.1016/j.joi.2024.101610","url":null,"abstract":"","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101610"},"PeriodicalIF":3.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The disruption index suffers from citation inflation: Re-analysis of temporal CD trend and relationship with team size reveal discrepancies","authors":"Alexander Michael Petersen ,&nbsp;Felber J. Arroyave ,&nbsp;Fabio Pammolli","doi":"10.1016/j.joi.2024.101605","DOIUrl":"10.1016/j.joi.2024.101605","url":null,"abstract":"<div><div>Measuring the rate of innovation in academia and industry is fundamental to monitoring the efficiency and competitiveness of the knowledge economy. To this end, a disruption index (CD) was recently developed and applied to publication and patent citation networks (<span><span>Wu et al., 2019</span></span>; <span><span>Park et al., 2023</span></span>). Here we show that CD systematically decreases over time due to secular growth in research production, following two distinct mechanisms unrelated to innovation – one behavioral and the other structural. Whereas the behavioral explanation reflects shifts associated with techno-social factors (e.g. self-citation practices), the structural explanation follows from ‘citation inflation’ (CI), an inextricable feature of real citation networks attributable to increasing reference list lengths, which causes CD to systematically decrease. We demonstrate this causal link by way of mathematical deduction, computational simulation, multi-variate regression, and quasi-experimental comparison of the disruptiveness of PNAS versus PNAS Plus articles, which differ primarily in their lengths. Accordingly, we analyze CD data available in the SciSciNet database and find that disruptiveness incrementally increased from 2005-2015, and that the negative relationship between disruption and team-size is remarkably small in overall magnitude effect size, and shifts from negative to positive for team size ≥ 8 coauthors.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101605"},"PeriodicalIF":3.4,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}