A 3-dimensional analysis for evaluating technology emergence indicators
Abstract Technology emergence has become a hot topic in R&D policy and management communities. Various methods of measuring technology emergence have been developed. However, there is little literature discussing how to evaluate the results identified by different methods. This research sharpens...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Liu, Xiaoyu [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
Technology emergence indicators |
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| Anmerkung: |
© Akadémiai Kiadó, Budapest, Hungary 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Scientometrics - Springer International Publishing, 1978, 124(2020), 1 vom: 04. Apr., Seite 27-55 |
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| Übergeordnetes Werk: |
volume:124 ; year:2020 ; number:1 ; day:04 ; month:04 ; pages:27-55 |
| Links: |
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| DOI / URN: |
10.1007/s11192-020-03432-6 |
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OLC2033225976 |
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| 520 | |a Abstract Technology emergence has become a hot topic in R&D policy and management communities. Various methods of measuring technology emergence have been developed. However, there is little literature discussing how to evaluate the results identified by different methods. This research sharpens a promising Technology Emergence Indicator (TEI) set by assessing alternative formulations on three distinct datasets: Dye-Sensitized Solar Cells, Non-Linear Programming, and Nano-Enabled Drug Delivery. Our TEIs derive from a conceptual foundation including three attributes of emergence: persistence, community, and growth that we systematically address through a 3-dimensional evaluation framework. Comparing TEI behavior through sensitivity analyses shows good robustness for the measures. The TEI serve to distinguish emerging R&D topics in the field under study. They can further be used to identify highly active players publishing on those topics. Importantly, results show that identified emerging terms and topics persist to a strong degree; thus, they serve to predict highly active R&D foci within the technical domain under study. | ||
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10.1007/s11192-020-03432-6 doi (DE-627)OLC2033225976 (DE-He213)s11192-020-03432-6-p DE-627 ger DE-627 rakwb eng 050 370 VZ 11 ssgn Liu, Xiaoyu verfasserin (orcid)0000-0003-2509-8457 aut A 3-dimensional analysis for evaluating technology emergence indicators 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2020 Abstract Technology emergence has become a hot topic in R&D policy and management communities. Various methods of measuring technology emergence have been developed. However, there is little literature discussing how to evaluate the results identified by different methods. This research sharpens a promising Technology Emergence Indicator (TEI) set by assessing alternative formulations on three distinct datasets: Dye-Sensitized Solar Cells, Non-Linear Programming, and Nano-Enabled Drug Delivery. Our TEIs derive from a conceptual foundation including three attributes of emergence: persistence, community, and growth that we systematically address through a 3-dimensional evaluation framework. Comparing TEI behavior through sensitivity analyses shows good robustness for the measures. The TEI serve to distinguish emerging R&D topics in the field under study. They can further be used to identify highly active players publishing on those topics. Importantly, results show that identified emerging terms and topics persist to a strong degree; thus, they serve to predict highly active R&D foci within the technical domain under study. Technology emergence indicators Technology forecasting Emerging technologies Technology emergence assessment R&D emergence Predictive indicators Porter, Alan L. aut Enthalten in Scientometrics Springer International Publishing, 1978 124(2020), 1 vom: 04. Apr., Seite 27-55 (DE-627)13005352X (DE-600)435652-4 (DE-576)015591697 0138-9130 nnns volume:124 year:2020 number:1 day:04 month:04 pages:27-55 https://doi.org/10.1007/s11192-020-03432-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-BUB SSG-OLC-HSW SSG-OPC-BBI GBV_ILN_4012 AR 124 2020 1 04 04 27-55 |
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10.1007/s11192-020-03432-6 doi (DE-627)OLC2033225976 (DE-He213)s11192-020-03432-6-p DE-627 ger DE-627 rakwb eng 050 370 VZ 11 ssgn Liu, Xiaoyu verfasserin (orcid)0000-0003-2509-8457 aut A 3-dimensional analysis for evaluating technology emergence indicators 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2020 Abstract Technology emergence has become a hot topic in R&D policy and management communities. Various methods of measuring technology emergence have been developed. However, there is little literature discussing how to evaluate the results identified by different methods. This research sharpens a promising Technology Emergence Indicator (TEI) set by assessing alternative formulations on three distinct datasets: Dye-Sensitized Solar Cells, Non-Linear Programming, and Nano-Enabled Drug Delivery. Our TEIs derive from a conceptual foundation including three attributes of emergence: persistence, community, and growth that we systematically address through a 3-dimensional evaluation framework. Comparing TEI behavior through sensitivity analyses shows good robustness for the measures. The TEI serve to distinguish emerging R&D topics in the field under study. They can further be used to identify highly active players publishing on those topics. Importantly, results show that identified emerging terms and topics persist to a strong degree; thus, they serve to predict highly active R&D foci within the technical domain under study. Technology emergence indicators Technology forecasting Emerging technologies Technology emergence assessment R&D emergence Predictive indicators Porter, Alan L. aut Enthalten in Scientometrics Springer International Publishing, 1978 124(2020), 1 vom: 04. Apr., Seite 27-55 (DE-627)13005352X (DE-600)435652-4 (DE-576)015591697 0138-9130 nnns volume:124 year:2020 number:1 day:04 month:04 pages:27-55 https://doi.org/10.1007/s11192-020-03432-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-BUB SSG-OLC-HSW SSG-OPC-BBI GBV_ILN_4012 AR 124 2020 1 04 04 27-55 |
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10.1007/s11192-020-03432-6 doi (DE-627)OLC2033225976 (DE-He213)s11192-020-03432-6-p DE-627 ger DE-627 rakwb eng 050 370 VZ 11 ssgn Liu, Xiaoyu verfasserin (orcid)0000-0003-2509-8457 aut A 3-dimensional analysis for evaluating technology emergence indicators 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2020 Abstract Technology emergence has become a hot topic in R&D policy and management communities. Various methods of measuring technology emergence have been developed. However, there is little literature discussing how to evaluate the results identified by different methods. This research sharpens a promising Technology Emergence Indicator (TEI) set by assessing alternative formulations on three distinct datasets: Dye-Sensitized Solar Cells, Non-Linear Programming, and Nano-Enabled Drug Delivery. Our TEIs derive from a conceptual foundation including three attributes of emergence: persistence, community, and growth that we systematically address through a 3-dimensional evaluation framework. Comparing TEI behavior through sensitivity analyses shows good robustness for the measures. The TEI serve to distinguish emerging R&D topics in the field under study. They can further be used to identify highly active players publishing on those topics. Importantly, results show that identified emerging terms and topics persist to a strong degree; thus, they serve to predict highly active R&D foci within the technical domain under study. Technology emergence indicators Technology forecasting Emerging technologies Technology emergence assessment R&D emergence Predictive indicators Porter, Alan L. aut Enthalten in Scientometrics Springer International Publishing, 1978 124(2020), 1 vom: 04. Apr., Seite 27-55 (DE-627)13005352X (DE-600)435652-4 (DE-576)015591697 0138-9130 nnns volume:124 year:2020 number:1 day:04 month:04 pages:27-55 https://doi.org/10.1007/s11192-020-03432-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-BUB SSG-OLC-HSW SSG-OPC-BBI GBV_ILN_4012 AR 124 2020 1 04 04 27-55 |
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Abstract Technology emergence has become a hot topic in R&D policy and management communities. Various methods of measuring technology emergence have been developed. However, there is little literature discussing how to evaluate the results identified by different methods. This research sharpens a promising Technology Emergence Indicator (TEI) set by assessing alternative formulations on three distinct datasets: Dye-Sensitized Solar Cells, Non-Linear Programming, and Nano-Enabled Drug Delivery. Our TEIs derive from a conceptual foundation including three attributes of emergence: persistence, community, and growth that we systematically address through a 3-dimensional evaluation framework. Comparing TEI behavior through sensitivity analyses shows good robustness for the measures. The TEI serve to distinguish emerging R&D topics in the field under study. They can further be used to identify highly active players publishing on those topics. Importantly, results show that identified emerging terms and topics persist to a strong degree; thus, they serve to predict highly active R&D foci within the technical domain under study. © Akadémiai Kiadó, Budapest, Hungary 2020 |
| abstractGer |
Abstract Technology emergence has become a hot topic in R&D policy and management communities. Various methods of measuring technology emergence have been developed. However, there is little literature discussing how to evaluate the results identified by different methods. This research sharpens a promising Technology Emergence Indicator (TEI) set by assessing alternative formulations on three distinct datasets: Dye-Sensitized Solar Cells, Non-Linear Programming, and Nano-Enabled Drug Delivery. Our TEIs derive from a conceptual foundation including three attributes of emergence: persistence, community, and growth that we systematically address through a 3-dimensional evaluation framework. Comparing TEI behavior through sensitivity analyses shows good robustness for the measures. The TEI serve to distinguish emerging R&D topics in the field under study. They can further be used to identify highly active players publishing on those topics. Importantly, results show that identified emerging terms and topics persist to a strong degree; thus, they serve to predict highly active R&D foci within the technical domain under study. © Akadémiai Kiadó, Budapest, Hungary 2020 |
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Abstract Technology emergence has become a hot topic in R&D policy and management communities. Various methods of measuring technology emergence have been developed. However, there is little literature discussing how to evaluate the results identified by different methods. This research sharpens a promising Technology Emergence Indicator (TEI) set by assessing alternative formulations on three distinct datasets: Dye-Sensitized Solar Cells, Non-Linear Programming, and Nano-Enabled Drug Delivery. Our TEIs derive from a conceptual foundation including three attributes of emergence: persistence, community, and growth that we systematically address through a 3-dimensional evaluation framework. Comparing TEI behavior through sensitivity analyses shows good robustness for the measures. The TEI serve to distinguish emerging R&D topics in the field under study. They can further be used to identify highly active players publishing on those topics. Importantly, results show that identified emerging terms and topics persist to a strong degree; thus, they serve to predict highly active R&D foci within the technical domain under study. © Akadémiai Kiadó, Budapest, Hungary 2020 |
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A 3-dimensional analysis for evaluating technology emergence indicators |
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