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University–industry R&D linkage metrics: validity and applicability in world university rankings
In September 2015 Thomson Reuters published its Ranking of Innovative Universities (RIU). Covering 100 large research-intensive universities worldwide, Stanford University came in first, MIT was second and Harvard in third position. But how meaningful is this outcome? In this paper we will take a cr...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5065891/ https://www.ncbi.nlm.nih.gov/pubmed/27795591 http://dx.doi.org/10.1007/s11192-016-2098-8 |
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author | Tijssen, Robert J. W. Yegros-Yegros, Alfredo Winnink, Jos J. |
author_facet | Tijssen, Robert J. W. Yegros-Yegros, Alfredo Winnink, Jos J. |
author_sort | Tijssen, Robert J. W. |
collection | PubMed |
description | In September 2015 Thomson Reuters published its Ranking of Innovative Universities (RIU). Covering 100 large research-intensive universities worldwide, Stanford University came in first, MIT was second and Harvard in third position. But how meaningful is this outcome? In this paper we will take a critical view from a methodological perspective. We focus our attention on the various types of metrics available, whether or not data redundancies are addressed, and if metrics should be assembled into a single composite overall score or not. We address these issues in some detail by emphasizing one metric in particular: university–industry co-authored publications (UICs). We compare the RIU with three variants of our own University–Industry R&D Linkage Index, which we derived from the bibliometric analysis of 750 research universities worldwide. Our findings highlight conceptual and methodological problems with UIC-based data, as well as computational weaknesses such university ranking systems. Avoiding choices between size-dependent or independent metrics, and between single-metrics and multi-metrics systems, we recommend an alternative ‘scoreboard’ approach: (1) without weighing systems of metrics and composite scores; (2) computational procedures and information sources are made more transparent; (3) size-dependent metrics are kept separate from size-independent metrics; (4) UIC metrics are selected according to the type of proximity relationship between universities and industry. |
format | Online Article Text |
id | pubmed-5065891 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-50658912016-10-28 University–industry R&D linkage metrics: validity and applicability in world university rankings Tijssen, Robert J. W. Yegros-Yegros, Alfredo Winnink, Jos J. Scientometrics Article In September 2015 Thomson Reuters published its Ranking of Innovative Universities (RIU). Covering 100 large research-intensive universities worldwide, Stanford University came in first, MIT was second and Harvard in third position. But how meaningful is this outcome? In this paper we will take a critical view from a methodological perspective. We focus our attention on the various types of metrics available, whether or not data redundancies are addressed, and if metrics should be assembled into a single composite overall score or not. We address these issues in some detail by emphasizing one metric in particular: university–industry co-authored publications (UICs). We compare the RIU with three variants of our own University–Industry R&D Linkage Index, which we derived from the bibliometric analysis of 750 research universities worldwide. Our findings highlight conceptual and methodological problems with UIC-based data, as well as computational weaknesses such university ranking systems. Avoiding choices between size-dependent or independent metrics, and between single-metrics and multi-metrics systems, we recommend an alternative ‘scoreboard’ approach: (1) without weighing systems of metrics and composite scores; (2) computational procedures and information sources are made more transparent; (3) size-dependent metrics are kept separate from size-independent metrics; (4) UIC metrics are selected according to the type of proximity relationship between universities and industry. Springer Netherlands 2016-08-13 2016 /pmc/articles/PMC5065891/ /pubmed/27795591 http://dx.doi.org/10.1007/s11192-016-2098-8 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Article Tijssen, Robert J. W. Yegros-Yegros, Alfredo Winnink, Jos J. University–industry R&D linkage metrics: validity and applicability in world university rankings |
title | University–industry R&D linkage metrics: validity and applicability in world university rankings |
title_full | University–industry R&D linkage metrics: validity and applicability in world university rankings |
title_fullStr | University–industry R&D linkage metrics: validity and applicability in world university rankings |
title_full_unstemmed | University–industry R&D linkage metrics: validity and applicability in world university rankings |
title_short | University–industry R&D linkage metrics: validity and applicability in world university rankings |
title_sort | university–industry r&d linkage metrics: validity and applicability in world university rankings |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5065891/ https://www.ncbi.nlm.nih.gov/pubmed/27795591 http://dx.doi.org/10.1007/s11192-016-2098-8 |
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