Pub Date : 2025-11-01DOI: 10.1016/j.joi.2025.101745
Fang Han , Yanqing Ren , Ruhao Zhang , Lingzi Feng , Lixue Wang , Junpeng Yuan
This study quantitatively analyzes 373 researchers with retracted papers from 20 leading medical institutions in China and examines their characteristics, retraction drivers, and career impacts based on their publication histories. The results show that: (1) young researchers with retractions show weaker academic performance than their non-retracted peers, while senior researchers exhibit higher productivity, influence, and larger collaboration networks; (2) output-driven incentives strongly correlate with misconduct-related retractions, and younger researchers face higher misconduct risks; (3) peer pressure among researchers within the same institute does not significantly influence the institute’s overall retraction frequency; and (4) retractions significantly reduce citations (–41.5%), collaborations, and career mobility, with early career researchers being the most affected. Midcareer researchers suffer primarily from citation decline. (5) Retractions due to scientific error have a greater negative impact on the authors’ subsequent career development. Their annual citation numbers decrease by 61.8%, and the number of co-authors decreases by 23.6%, which are 1.6 times and 1.4 times the decreases in the academic misconduct group, respectively. These findings provide critical insights into current retraction trends.
{"title":"Drivers and penalties of retraction: An empirical study of Chinese medical researchers","authors":"Fang Han , Yanqing Ren , Ruhao Zhang , Lingzi Feng , Lixue Wang , Junpeng Yuan","doi":"10.1016/j.joi.2025.101745","DOIUrl":"10.1016/j.joi.2025.101745","url":null,"abstract":"<div><div>This study quantitatively analyzes 373 researchers with retracted papers from 20 leading medical institutions in China and examines their characteristics, retraction drivers, and career impacts based on their publication histories. The results show that: (1) young researchers with retractions show weaker academic performance than their non-retracted peers, while senior researchers exhibit higher productivity, influence, and larger collaboration networks; (2) output-driven incentives strongly correlate with misconduct-related retractions, and younger researchers face higher misconduct risks; (3) peer pressure among researchers within the same institute does not significantly influence the institute’s overall retraction frequency; and (4) retractions significantly reduce citations (–41.5%), collaborations, and career mobility, with early career researchers being the most affected. Midcareer researchers suffer primarily from citation decline. (5) Retractions due to scientific error have a greater negative impact on the authors’ subsequent career development. Their annual citation numbers decrease by 61.8%, and the number of co-authors decreases by 23.6%, which are 1.6 times and 1.4 times the decreases in the academic misconduct group, respectively. These findings provide critical insights into current retraction trends.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101745"},"PeriodicalIF":3.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465730","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}
Pub Date : 2025-11-01DOI: 10.1016/j.joi.2025.101733
Jonas Lindahl , Rickard Danell , Kaylee Litson , David F. Feldon
{"title":"Corrigendum to “Sex differences in research productivity among doctoral students in Sweden: A quantile regression approach” [Journal of Informetrics 19 (2025) 101702]","authors":"Jonas Lindahl , Rickard Danell , Kaylee Litson , David F. Feldon","doi":"10.1016/j.joi.2025.101733","DOIUrl":"10.1016/j.joi.2025.101733","url":null,"abstract":"","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101733"},"PeriodicalIF":3.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145680937","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}
Pub Date : 2025-11-01DOI: 10.1016/j.joi.2025.101748
Chun-Chieh Wang , Szu-Chia Lo , Mu-Hsuan Huang , Dar-Zen Chen
This study presents a matrix-based framework for tracking and classifying researcher affiliation drifting, with a particular focus on multi-country co-affiliations. By structuring author-affiliation data into time-sequenced matrices, the method captures both the persistence and configuration of institutional ties within individual publications. Each paper is categorized based on the types of co-affiliated countries, and researchers are subsequently classified into field-independent typologies reflecting the degree and structure of their institutional mobility. Applied to a dataset of Highly Cited Researchers (HCRs) in mathematics, the framework reveals notable affiliation patterns—most prominently, a high concentration of researchers exhibiting simultaneous affiliations across multiple countries without transitional or exploratory affiliation types. These observations demonstrate the method’s utility in surfacing affiliation structures that may not be visible through conventional bibliometric indicators. While the mathematics domain serves only as an implementation example, the results echo broader concerns about the strategic use of multi-affiliations in certain fields. The proposed approach contributes a replicable, scalable tool for analyzing affiliation dynamics, with implications for bibliometric research, institutional evaluation, and science policy.
{"title":"Tracking author affiliation drift: A matrix-based method for identifying temporal patterns","authors":"Chun-Chieh Wang , Szu-Chia Lo , Mu-Hsuan Huang , Dar-Zen Chen","doi":"10.1016/j.joi.2025.101748","DOIUrl":"10.1016/j.joi.2025.101748","url":null,"abstract":"<div><div>This study presents a matrix-based framework for tracking and classifying researcher affiliation drifting, with a particular focus on multi-country co-affiliations. By structuring author-affiliation data into time-sequenced matrices, the method captures both the persistence and configuration of institutional ties within individual publications. Each paper is categorized based on the types of co-affiliated countries, and researchers are subsequently classified into field-independent typologies reflecting the degree and structure of their institutional mobility. Applied to a dataset of Highly Cited Researchers (HCRs) in mathematics, the framework reveals notable affiliation patterns—most prominently, a high concentration of researchers exhibiting simultaneous affiliations across multiple countries without transitional or exploratory affiliation types. These observations demonstrate the method’s utility in surfacing affiliation structures that may not be visible through conventional bibliometric indicators. While the mathematics domain serves only as an implementation example, the results echo broader concerns about the strategic use of multi-affiliations in certain fields. The proposed approach contributes a replicable, scalable tool for analyzing affiliation dynamics, with implications for bibliometric research, institutional evaluation, and science policy.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101748"},"PeriodicalIF":3.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519447","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}
Pub Date : 2025-11-01DOI: 10.1016/j.joi.2025.101735
Heng Zhang , Chengzhi Zhang
{"title":"Corrigendum to “Automated generation of research workflows from academic papers: a full-text mining framework” [Journal of Informetrics, 19 (2025) 101732]","authors":"Heng Zhang , Chengzhi Zhang","doi":"10.1016/j.joi.2025.101735","DOIUrl":"10.1016/j.joi.2025.101735","url":null,"abstract":"","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101735"},"PeriodicalIF":3.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145680936","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}
Pub Date : 2025-11-01DOI: 10.1016/j.joi.2025.101741
Yujie Zhang , Guoxiu He , Zhuoren Jiang
Current studies predominantly highlight the growing intersections among disciplines but lack insights into more nuanced aspects of science communication. This work investigates disciplinary communication through two metrics: interactivity, defined as the product of knowledge absorption and diffusion, capturing the overall breadth of knowledge interaction; and radiation, the ratio of outward diffusion to absorption, reflecting the relative tendency to export knowledge. To achieve this, we encode the disciplinary information of each paper as a continuous vector by pre-trained graph neural networks on extensive academic data. The metrics are derived from the distances computed using the paper vectors. We categorize the disciplines into four quadrants: “exposed,” “absorptive,” “service,” and “hermetic”, based on the two metrics. Our findings indicate that life-related sciences (medicine, neuroscience) are “exposed,” with open characteristics. Formal sciences (mathematics, physics and astronomy) are “hermetic,” with limited interaction breadth and radiation capacity. Chemistry, business and management are “absorptive,” focusing on knowledge absorption with limited dissemination. Engineering and Energy are “service-oriented,” centered on transformation and connecting. Our findings and computational methods could contribute to a better understanding of scientific communication systems.
{"title":"Mapping science and revealing disciplinary communication modalities via pre-trained graph neural networks","authors":"Yujie Zhang , Guoxiu He , Zhuoren Jiang","doi":"10.1016/j.joi.2025.101741","DOIUrl":"10.1016/j.joi.2025.101741","url":null,"abstract":"<div><div>Current studies predominantly highlight the growing intersections among disciplines but lack insights into more nuanced aspects of science communication. This work investigates disciplinary communication through two metrics: interactivity, defined as the product of knowledge absorption and diffusion, capturing the overall breadth of knowledge interaction; and radiation, the ratio of outward diffusion to absorption, reflecting the relative tendency to export knowledge. To achieve this, we encode the disciplinary information of each paper as a continuous vector by pre-trained graph neural networks on extensive academic data. The metrics are derived from the distances computed using the paper vectors. We categorize the disciplines into four quadrants: “exposed,” “absorptive,” “service,” and “hermetic”, based on the two metrics. Our findings indicate that life-related sciences (medicine, neuroscience) are “exposed,” with open characteristics. Formal sciences (mathematics, physics and astronomy) are “hermetic,” with limited interaction breadth and radiation capacity. Chemistry, business and management are “absorptive,” focusing on knowledge absorption with limited dissemination. Engineering and Energy are “service-oriented,” centered on transformation and connecting. Our findings and computational methods could contribute to a better understanding of scientific communication systems.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101741"},"PeriodicalIF":3.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415757","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}
Pub Date : 2025-10-22DOI: 10.1016/j.joi.2025.101739
Avner Peleg
We develop a method for evaluating the productivity and research diversity of an individual scientific research paper. The method is based on the productivity and research diversity indicator (PRDI), which measures the value of the paper based on a combination of the number of descendant papers of the evaluated paper, , and the number of research directions emerging from it, . We define the basic PRDI P by , and develop a detailed procedure for calculating it. We then describe an informative generalization of the basic PRDI calculation, which is based on perturbing two of the main steps in the basic calculation. The perturbation generates statistics of the basic PRDI value, and the generalized PRDI, , is defined as the mean value of P for the generalized calculation. We demonstrate the method and the insights that it can provide by applying it for three example papers, which started new research directions within the general research area of the evaluated author. We explain how the method and its ingredients can be used as additional in-depth tools in decisions on tenure, promotion, and hiring in academic institutions, in peer-review of scientific papers, and in analysis of the development of scientific concepts in various areas of science and engineering. Furthermore, our study introduces a number of mathematical methods, such as coarse-graining, perturbation-induced statistics, and coarse-grained genealogical charts, which can be useful in data science applications in general.
{"title":"A method for evaluating the productivity and research diversity of an individual scientific research paper","authors":"Avner Peleg","doi":"10.1016/j.joi.2025.101739","DOIUrl":"10.1016/j.joi.2025.101739","url":null,"abstract":"<div><div>We develop a method for evaluating the productivity and research diversity of an individual scientific research paper. The method is based on the productivity and research diversity indicator (PRDI), which measures the value of the paper based on a combination of the number of descendant papers of the evaluated paper, <span><math><msub><mrow><mi>n</mi></mrow><mrow><mi>d</mi></mrow></msub></math></span>, and the number of research directions emerging from it, <span><math><msub><mrow><mi>n</mi></mrow><mrow><mi>r</mi></mrow></msub></math></span>. We define the basic PRDI <em>P</em> by <span><math><mi>P</mi><mo>=</mo><mi>ln</mi><mo></mo><mo>(</mo><msub><mrow><mi>n</mi></mrow><mrow><mi>d</mi></mrow></msub><mo>+</mo><mn>1</mn><mo>)</mo><mo>+</mo><msub><mrow><mi>n</mi></mrow><mrow><mi>r</mi></mrow></msub></math></span>, and develop a detailed procedure for calculating it. We then describe an informative generalization of the basic PRDI calculation, which is based on perturbing two of the main steps in the basic calculation. The perturbation generates statistics of the basic PRDI value, and the generalized PRDI, <span><math><msup><mrow><mi>P</mi></mrow><mrow><mo>(</mo><mi>g</mi><mo>)</mo></mrow></msup></math></span>, is defined as the mean value of <em>P</em> for the generalized calculation. We demonstrate the method and the insights that it can provide by applying it for three example papers, which started new research directions within the general research area of the evaluated author. We explain how the method and its ingredients can be used as additional in-depth tools in decisions on tenure, promotion, and hiring in academic institutions, in peer-review of scientific papers, and in analysis of the development of scientific concepts in various areas of science and engineering. Furthermore, our study introduces a number of mathematical methods, such as coarse-graining, perturbation-induced statistics, and coarse-grained genealogical charts, which can be useful in data science applications in general.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101739"},"PeriodicalIF":3.5,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145361873","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}
Pub Date : 2025-10-19DOI: 10.1016/j.joi.2025.101737
Bo Yang, Hong Jiao, Qingqing Fan, Jiawen Chen, Jiaxue Liu
Data reuse is increasingly advocated as a strategy to enhance research reproducibility, accelerate project progress, and reduce research costs. Although few dispute the principle of data reuse, its effect on citation performance in experiment-based or data-intensive studies remains uncertain. To dispel concerns about the impact of data reuse on research, researchers require clear evidence of its benefits. This study employs informetric analysis, analysis of variance, and multiple linear regression to conduct a large-scale investigation of scientists’ dataset (re)use behavior, providing direct evidence of the citation performance of their research. The results show that: (i) The volume of released data in biomedical and life sciences continues to grow steadily; however, tracking the (re)use of Gene Expression Omnibus datasets over time shows that actual utilization and reuse have not kept pace with; (ii) Papers that declare the reuse of released datasets, especially those reusing their own data (self-reuse), garner more citations, indicating that dataset reuse does not negatively impact citation performance and may even enhance it; (iii) Our co-citation model predicts that, owing to the “sheep flock effect,” data reuse could increase the exposure of reusers’ related works and subsequently enhance the citation performance of their other publications.
{"title":"Does reusing scientific datasets reduce the impact of the papers?","authors":"Bo Yang, Hong Jiao, Qingqing Fan, Jiawen Chen, Jiaxue Liu","doi":"10.1016/j.joi.2025.101737","DOIUrl":"10.1016/j.joi.2025.101737","url":null,"abstract":"<div><div>Data reuse is increasingly advocated as a strategy to enhance research reproducibility, accelerate project progress, and reduce research costs. Although few dispute the principle of data reuse, its effect on citation performance in experiment-based or data-intensive studies remains uncertain. To dispel concerns about the impact of data reuse on research, researchers require clear evidence of its benefits. This study employs informetric analysis, analysis of variance, and multiple linear regression to conduct a large-scale investigation of scientists’ dataset (re)use behavior, providing direct evidence of the citation performance of their research. The results show that: (i) The volume of released data in biomedical and life sciences continues to grow steadily; however, tracking the (re)use of Gene Expression Omnibus datasets over time shows that actual utilization and reuse have not kept pace with; (ii) Papers that declare the reuse of released datasets, especially those reusing their own data (self-reuse), garner more citations, indicating that dataset reuse does not negatively impact citation performance and may even enhance it; (iii) Our co-citation model predicts that, owing to the “sheep flock effect,” data reuse could increase the exposure of reusers’ related works and subsequently enhance the citation performance of their other publications.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101737"},"PeriodicalIF":3.5,"publicationDate":"2025-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320001","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}
Pub Date : 2025-10-15DOI: 10.1016/j.joi.2025.101738
Zhesi Shen , Robin Haunschild , Lutz Bornmann
{"title":"Document types make the difference","authors":"Zhesi Shen , Robin Haunschild , Lutz Bornmann","doi":"10.1016/j.joi.2025.101738","DOIUrl":"10.1016/j.joi.2025.101738","url":null,"abstract":"","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101738"},"PeriodicalIF":3.5,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320000","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}
Pub Date : 2025-10-13DOI: 10.1016/j.joi.2025.101736
Fangjie Xi , Yu Wang , Chenchen Li , Ying Huang , Xiaojun Hu
Identifying emerging technological convergence is essential for anticipating future innovation trajectories. Existing approaches typically rely on either International Patent Classification (IPC) co-occurrence networks, which capture general combination frequencies, or association rule networks, which emphasize statistically significant and often higher-order relationships. However, these two structural views are rarely integrated, limiting their effectiveness in representing both the breadth and depth of technological linkages. To address this gap, we propose a Multi-Channel Graph Convolutional Network (MC-GCN) that treats IPC co-occurrence and association rule networks as structurally distinct inputs. While co-occurrence data reflect raw interaction patterns, association rules—derived via data mining—serve as a refined signal that highlights meaningful and potentially multi-IPC convergence patterns. Our model encodes each view through separate GCN channels and fuses their embeddings within a unified representation space. To establish a comprehensive evaluation, we also include topological link prediction baselines such as Common Neighbors, Adamic–Adar, and Preferential Attachment in our comparative analysis. Applied to CRISPR-related patent data, the MC-GCN significantly outperforms single-channel models, achieving an AUC of 0.973 when combined with XGBoost. Furthermore, five predicted IPC combinations were validated in newly granted patents in early 2025, demonstrating the model’s practical utility in forecasting real-world technological convergence.
{"title":"Predicting technological convergence with multi-channel graph neural networks: A case study of CRISPR","authors":"Fangjie Xi , Yu Wang , Chenchen Li , Ying Huang , Xiaojun Hu","doi":"10.1016/j.joi.2025.101736","DOIUrl":"10.1016/j.joi.2025.101736","url":null,"abstract":"<div><div>Identifying emerging technological convergence is essential for anticipating future innovation trajectories. Existing approaches typically rely on either International Patent Classification (IPC) co-occurrence networks, which capture general combination frequencies, or association rule networks, which emphasize statistically significant and often higher-order relationships. However, these two structural views are rarely integrated, limiting their effectiveness in representing both the breadth and depth of technological linkages. To address this gap, we propose a Multi-Channel Graph Convolutional Network (MC-GCN) that treats IPC co-occurrence and association rule networks as structurally distinct inputs. While co-occurrence data reflect raw interaction patterns, association rules—derived via data mining—serve as a refined signal that highlights meaningful and potentially multi-IPC convergence patterns. Our model encodes each view through separate GCN channels and fuses their embeddings within a unified representation space. To establish a comprehensive evaluation, we also include topological link prediction baselines such as Common Neighbors, Adamic–Adar, and Preferential Attachment in our comparative analysis. Applied to CRISPR-related patent data, the MC-GCN significantly outperforms single-channel models, achieving an AUC of 0.973 when combined with XGBoost. Furthermore, five predicted IPC combinations were validated in newly granted patents in early 2025, demonstrating the model’s practical utility in forecasting real-world technological convergence.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101736"},"PeriodicalIF":3.5,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145319999","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}
Pub Date : 2025-10-08DOI: 10.1016/j.joi.2025.101734
Jiangyang Fu , Xin Liu , Chenwei Zhang , Jiang Li
Scientists may not fulfill the objectives delineated within their research proposals subsequent to the receipt of funding. The extent to which unfinished grants enhance scientific knowledge remains an open question. Drawing upon a dataset from the Research Grants Council of Hong Kong (RGC) that encompasses the years 2010 to 2020, and is distinguished by its inclusion of self-reported grant completion rates, this study seeks to assess the potential contributions of research grants that were not fully completed to the progress of scientific knowledge. The analysis is conducted by leveraging the RGC's detailed records of project completion rates. The results indicate that, notwithstanding a relative lack in productivity and impact, there is no evidence that unfinished grants generate knowledge that is less disruptive than that produced by completed grants. Consequently, it is suggested that funding bodies should consider revising their assessment criteria to recognize the intrinsic merit of grants that are traditionally labeled as unfinished, thus providing more flexibility for the exploration of novel research domains within the grant allocation process.
{"title":"Unfinished grants, unending progress: The impact of unfinished research grants on scientific innovation","authors":"Jiangyang Fu , Xin Liu , Chenwei Zhang , Jiang Li","doi":"10.1016/j.joi.2025.101734","DOIUrl":"10.1016/j.joi.2025.101734","url":null,"abstract":"<div><div>Scientists may not fulfill the objectives delineated within their research proposals subsequent to the receipt of funding. The extent to which unfinished grants enhance scientific knowledge remains an open question. Drawing upon a dataset from the Research Grants Council of Hong Kong (RGC) that encompasses the years 2010 to 2020, and is distinguished by its inclusion of self-reported grant completion rates, this study seeks to assess the potential contributions of research grants that were not fully completed to the progress of scientific knowledge. The analysis is conducted by leveraging the RGC's detailed records of project completion rates. The results indicate that, notwithstanding a relative lack in productivity and impact, there is no evidence that unfinished grants generate knowledge that is less disruptive than that produced by completed grants. Consequently, it is suggested that funding bodies should consider revising their assessment criteria to recognize the intrinsic merit of grants that are traditionally labeled as unfinished, thus providing more flexibility for the exploration of novel research domains within the grant allocation process.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101734"},"PeriodicalIF":3.5,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265439","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}
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