On the analysis of collaboration networks between industry and academia: the Mexican case of the innovation incentive program
Abstract The responsible for proposing public policies have to decide how to allocate economic resources to boost Research & Development in target industrial areas. Typically, the government supports R &D projects from universities, companies, or collaborations between them. Thus, it is impo...
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| Autor*in: |
Montes-Orozco, Edwin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© Akadémiai Kiadó, Budapest, Hungary 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Scientometrics - Springer International Publishing, 1978, 129(2024), 3 vom: 19. Jan., Seite 1523-1544 |
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| Übergeordnetes Werk: |
volume:129 ; year:2024 ; number:3 ; day:19 ; month:01 ; pages:1523-1544 |
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| DOI / URN: |
10.1007/s11192-023-04903-2 |
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SPR055288219 |
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| 520 | |a Abstract The responsible for proposing public policies have to decide how to allocate economic resources to boost Research & Development in target industrial areas. Typically, the government supports R &D projects from universities, companies, or collaborations between them. Thus, it is important to obtain insights about the dynamics of resource allocation. In this work, we propose to study the Mexican R&D public policy called the Innovation Incentive Program (PEI) through a social networks analysis. We use real data publicly available to model the program as three distinct networks, then, use structural metrics (clustering coefficient, average degree, average path length, diameter of the network, and density) to assess the robustness of such networks; finally, we identify the most significant nodes in the networks, which help to understand what industrial areas were benefited and what sectors should be considered in future public policies. We show that two networks correspond to the scale-free complex network model and one follows the small-world complex network model suggesting that the top Mexican higher education institutions and research centers indeed are a key element to set-up collaborations. | ||
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10.1007/s11192-023-04903-2 doi (DE-627)SPR055288219 (SPR)s11192-023-04903-2-e DE-627 ger DE-627 rakwb eng 050 370 VZ 31.00 bkl Montes-Orozco, Edwin verfasserin (orcid)0000-0001-8594-7006 aut On the analysis of collaboration networks between industry and academia: the Mexican case of the innovation incentive program 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The responsible for proposing public policies have to decide how to allocate economic resources to boost Research & Development in target industrial areas. Typically, the government supports R &D projects from universities, companies, or collaborations between them. Thus, it is important to obtain insights about the dynamics of resource allocation. In this work, we propose to study the Mexican R&D public policy called the Innovation Incentive Program (PEI) through a social networks analysis. We use real data publicly available to model the program as three distinct networks, then, use structural metrics (clustering coefficient, average degree, average path length, diameter of the network, and density) to assess the robustness of such networks; finally, we identify the most significant nodes in the networks, which help to understand what industrial areas were benefited and what sectors should be considered in future public policies. We show that two networks correspond to the scale-free complex network model and one follows the small-world complex network model suggesting that the top Mexican higher education institutions and research centers indeed are a key element to set-up collaborations. Complex systems (dpeaa)DE-He213 Complex networks (dpeaa)DE-He213 Social networks (dpeaa)DE-He213 R&D (dpeaa)DE-He213 Miranda, Karen (orcid)0000-0001-8554-2631 aut García-Nájera, Abel (orcid)0000-0002-3220-0782 aut López-García, Juan-Carlos (orcid)0000-0002-4815-3116 aut Enthalten in Scientometrics Springer International Publishing, 1978 129(2024), 3 vom: 19. Jan., Seite 1523-1544 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:129 year:2024 number:3 day:19 month:01 pages:1523-1544 https://dx.doi.org/10.1007/s11192-023-04903-2 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 VZ AR 129 2024 3 19 01 1523-1544 |
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10.1007/s11192-023-04903-2 doi (DE-627)SPR055288219 (SPR)s11192-023-04903-2-e DE-627 ger DE-627 rakwb eng 050 370 VZ 31.00 bkl Montes-Orozco, Edwin verfasserin (orcid)0000-0001-8594-7006 aut On the analysis of collaboration networks between industry and academia: the Mexican case of the innovation incentive program 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The responsible for proposing public policies have to decide how to allocate economic resources to boost Research & Development in target industrial areas. Typically, the government supports R &D projects from universities, companies, or collaborations between them. Thus, it is important to obtain insights about the dynamics of resource allocation. In this work, we propose to study the Mexican R&D public policy called the Innovation Incentive Program (PEI) through a social networks analysis. We use real data publicly available to model the program as three distinct networks, then, use structural metrics (clustering coefficient, average degree, average path length, diameter of the network, and density) to assess the robustness of such networks; finally, we identify the most significant nodes in the networks, which help to understand what industrial areas were benefited and what sectors should be considered in future public policies. We show that two networks correspond to the scale-free complex network model and one follows the small-world complex network model suggesting that the top Mexican higher education institutions and research centers indeed are a key element to set-up collaborations. Complex systems (dpeaa)DE-He213 Complex networks (dpeaa)DE-He213 Social networks (dpeaa)DE-He213 R&D (dpeaa)DE-He213 Miranda, Karen (orcid)0000-0001-8554-2631 aut García-Nájera, Abel (orcid)0000-0002-3220-0782 aut López-García, Juan-Carlos (orcid)0000-0002-4815-3116 aut Enthalten in Scientometrics Springer International Publishing, 1978 129(2024), 3 vom: 19. Jan., Seite 1523-1544 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:129 year:2024 number:3 day:19 month:01 pages:1523-1544 https://dx.doi.org/10.1007/s11192-023-04903-2 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 VZ AR 129 2024 3 19 01 1523-1544 |
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10.1007/s11192-023-04903-2 doi (DE-627)SPR055288219 (SPR)s11192-023-04903-2-e DE-627 ger DE-627 rakwb eng 050 370 VZ 31.00 bkl Montes-Orozco, Edwin verfasserin (orcid)0000-0001-8594-7006 aut On the analysis of collaboration networks between industry and academia: the Mexican case of the innovation incentive program 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The responsible for proposing public policies have to decide how to allocate economic resources to boost Research & Development in target industrial areas. Typically, the government supports R &D projects from universities, companies, or collaborations between them. Thus, it is important to obtain insights about the dynamics of resource allocation. In this work, we propose to study the Mexican R&D public policy called the Innovation Incentive Program (PEI) through a social networks analysis. We use real data publicly available to model the program as three distinct networks, then, use structural metrics (clustering coefficient, average degree, average path length, diameter of the network, and density) to assess the robustness of such networks; finally, we identify the most significant nodes in the networks, which help to understand what industrial areas were benefited and what sectors should be considered in future public policies. We show that two networks correspond to the scale-free complex network model and one follows the small-world complex network model suggesting that the top Mexican higher education institutions and research centers indeed are a key element to set-up collaborations. Complex systems (dpeaa)DE-He213 Complex networks (dpeaa)DE-He213 Social networks (dpeaa)DE-He213 R&D (dpeaa)DE-He213 Miranda, Karen (orcid)0000-0001-8554-2631 aut García-Nájera, Abel (orcid)0000-0002-3220-0782 aut López-García, Juan-Carlos (orcid)0000-0002-4815-3116 aut Enthalten in Scientometrics Springer International Publishing, 1978 129(2024), 3 vom: 19. Jan., Seite 1523-1544 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:129 year:2024 number:3 day:19 month:01 pages:1523-1544 https://dx.doi.org/10.1007/s11192-023-04903-2 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 VZ AR 129 2024 3 19 01 1523-1544 |
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10.1007/s11192-023-04903-2 doi (DE-627)SPR055288219 (SPR)s11192-023-04903-2-e DE-627 ger DE-627 rakwb eng 050 370 VZ 31.00 bkl Montes-Orozco, Edwin verfasserin (orcid)0000-0001-8594-7006 aut On the analysis of collaboration networks between industry and academia: the Mexican case of the innovation incentive program 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The responsible for proposing public policies have to decide how to allocate economic resources to boost Research & Development in target industrial areas. Typically, the government supports R &D projects from universities, companies, or collaborations between them. Thus, it is important to obtain insights about the dynamics of resource allocation. In this work, we propose to study the Mexican R&D public policy called the Innovation Incentive Program (PEI) through a social networks analysis. We use real data publicly available to model the program as three distinct networks, then, use structural metrics (clustering coefficient, average degree, average path length, diameter of the network, and density) to assess the robustness of such networks; finally, we identify the most significant nodes in the networks, which help to understand what industrial areas were benefited and what sectors should be considered in future public policies. We show that two networks correspond to the scale-free complex network model and one follows the small-world complex network model suggesting that the top Mexican higher education institutions and research centers indeed are a key element to set-up collaborations. Complex systems (dpeaa)DE-He213 Complex networks (dpeaa)DE-He213 Social networks (dpeaa)DE-He213 R&D (dpeaa)DE-He213 Miranda, Karen (orcid)0000-0001-8554-2631 aut García-Nájera, Abel (orcid)0000-0002-3220-0782 aut López-García, Juan-Carlos (orcid)0000-0002-4815-3116 aut Enthalten in Scientometrics Springer International Publishing, 1978 129(2024), 3 vom: 19. Jan., Seite 1523-1544 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:129 year:2024 number:3 day:19 month:01 pages:1523-1544 https://dx.doi.org/10.1007/s11192-023-04903-2 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 VZ AR 129 2024 3 19 01 1523-1544 |
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10.1007/s11192-023-04903-2 doi (DE-627)SPR055288219 (SPR)s11192-023-04903-2-e DE-627 ger DE-627 rakwb eng 050 370 VZ 31.00 bkl Montes-Orozco, Edwin verfasserin (orcid)0000-0001-8594-7006 aut On the analysis of collaboration networks between industry and academia: the Mexican case of the innovation incentive program 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The responsible for proposing public policies have to decide how to allocate economic resources to boost Research & Development in target industrial areas. Typically, the government supports R &D projects from universities, companies, or collaborations between them. Thus, it is important to obtain insights about the dynamics of resource allocation. In this work, we propose to study the Mexican R&D public policy called the Innovation Incentive Program (PEI) through a social networks analysis. We use real data publicly available to model the program as three distinct networks, then, use structural metrics (clustering coefficient, average degree, average path length, diameter of the network, and density) to assess the robustness of such networks; finally, we identify the most significant nodes in the networks, which help to understand what industrial areas were benefited and what sectors should be considered in future public policies. We show that two networks correspond to the scale-free complex network model and one follows the small-world complex network model suggesting that the top Mexican higher education institutions and research centers indeed are a key element to set-up collaborations. Complex systems (dpeaa)DE-He213 Complex networks (dpeaa)DE-He213 Social networks (dpeaa)DE-He213 R&D (dpeaa)DE-He213 Miranda, Karen (orcid)0000-0001-8554-2631 aut García-Nájera, Abel (orcid)0000-0002-3220-0782 aut López-García, Juan-Carlos (orcid)0000-0002-4815-3116 aut Enthalten in Scientometrics Springer International Publishing, 1978 129(2024), 3 vom: 19. Jan., Seite 1523-1544 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:129 year:2024 number:3 day:19 month:01 pages:1523-1544 https://dx.doi.org/10.1007/s11192-023-04903-2 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 VZ AR 129 2024 3 19 01 1523-1544 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The responsible for proposing public policies have to decide how to allocate economic resources to boost Research & Development in target industrial areas. Typically, the government supports R &D projects from universities, companies, or collaborations between them. Thus, it is important to obtain insights about the dynamics of resource allocation. In this work, we propose to study the Mexican R&D public policy called the Innovation Incentive Program (PEI) through a social networks analysis. We use real data publicly available to model the program as three distinct networks, then, use structural metrics (clustering coefficient, average degree, average path length, diameter of the network, and density) to assess the robustness of such networks; finally, we identify the most significant nodes in the networks, which help to understand what industrial areas were benefited and what sectors should be considered in future public policies. 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Montes-Orozco, Edwin |
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on the analysis of collaboration networks between industry and academia: the mexican case of the innovation incentive program |
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On the analysis of collaboration networks between industry and academia: the Mexican case of the innovation incentive program |
| abstract |
Abstract The responsible for proposing public policies have to decide how to allocate economic resources to boost Research & Development in target industrial areas. Typically, the government supports R &D projects from universities, companies, or collaborations between them. Thus, it is important to obtain insights about the dynamics of resource allocation. In this work, we propose to study the Mexican R&D public policy called the Innovation Incentive Program (PEI) through a social networks analysis. We use real data publicly available to model the program as three distinct networks, then, use structural metrics (clustering coefficient, average degree, average path length, diameter of the network, and density) to assess the robustness of such networks; finally, we identify the most significant nodes in the networks, which help to understand what industrial areas were benefited and what sectors should be considered in future public policies. We show that two networks correspond to the scale-free complex network model and one follows the small-world complex network model suggesting that the top Mexican higher education institutions and research centers indeed are a key element to set-up collaborations. © Akadémiai Kiadó, Budapest, Hungary 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract The responsible for proposing public policies have to decide how to allocate economic resources to boost Research & Development in target industrial areas. Typically, the government supports R &D projects from universities, companies, or collaborations between them. Thus, it is important to obtain insights about the dynamics of resource allocation. In this work, we propose to study the Mexican R&D public policy called the Innovation Incentive Program (PEI) through a social networks analysis. We use real data publicly available to model the program as three distinct networks, then, use structural metrics (clustering coefficient, average degree, average path length, diameter of the network, and density) to assess the robustness of such networks; finally, we identify the most significant nodes in the networks, which help to understand what industrial areas were benefited and what sectors should be considered in future public policies. We show that two networks correspond to the scale-free complex network model and one follows the small-world complex network model suggesting that the top Mexican higher education institutions and research centers indeed are a key element to set-up collaborations. © Akadémiai Kiadó, Budapest, Hungary 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract The responsible for proposing public policies have to decide how to allocate economic resources to boost Research & Development in target industrial areas. Typically, the government supports R &D projects from universities, companies, or collaborations between them. Thus, it is important to obtain insights about the dynamics of resource allocation. In this work, we propose to study the Mexican R&D public policy called the Innovation Incentive Program (PEI) through a social networks analysis. We use real data publicly available to model the program as three distinct networks, then, use structural metrics (clustering coefficient, average degree, average path length, diameter of the network, and density) to assess the robustness of such networks; finally, we identify the most significant nodes in the networks, which help to understand what industrial areas were benefited and what sectors should be considered in future public policies. We show that two networks correspond to the scale-free complex network model and one follows the small-world complex network model suggesting that the top Mexican higher education institutions and research centers indeed are a key element to set-up collaborations. © Akadémiai Kiadó, Budapest, Hungary 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| title_short |
On the analysis of collaboration networks between industry and academia: the Mexican case of the innovation incentive program |
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https://dx.doi.org/10.1007/s11192-023-04903-2 |
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| author2 |
Miranda, Karen García-Nájera, Abel López-García, Juan-Carlos |
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Miranda, Karen García-Nájera, Abel López-García, Juan-Carlos |
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2024-07-03T14:37:45.310Z |
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| score |
7.4021244 |