Predicting the research output/growth of selected countries: application of Even GM (1, 1) and NDGM models
Abstract The study aims to forecast the research output of four selected countries (USA, China, India and Pakistan) using two models of Grey System Theory—Even Model GM (1, 1) and Nonhomogeneous Discrete Grey Model (NDGM). The study also conducts publication growth analysis using relative growth rat...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Javed, Saad Ahmed [verfasserIn] Liu, Sifeng [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Scientometrics - Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978, 115(2017), 1 vom: 25. Nov., Seite 395-413 |
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| Übergeordnetes Werk: |
volume:115 ; year:2017 ; number:1 ; day:25 ; month:11 ; pages:395-413 |
| Links: |
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| DOI / URN: |
10.1007/s11192-017-2586-5 |
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| Katalog-ID: |
SPR017616352 |
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| 520 | |a Abstract The study aims to forecast the research output of four selected countries (USA, China, India and Pakistan) using two models of Grey System Theory—Even Model GM (1, 1) and Nonhomogeneous Discrete Grey Model (NDGM). The study also conducts publication growth analysis using relative growth rate (RGR) and the doubling time (Dt). The linear and exponential regression analyses were also performed for comparison. The study also proposes and successfully tests two novel synthetic models for RGR and Dt that facilities the comparison of the countries’ performance when actual data and forecasted data produce different sequences of performance in the given period of time. The data of documents published by the four countries from 2005 to 2016 was collected from SJR/Scopus website. Performance criterion was Mean Absolute Percentage Error. The study confirms that NDGM is a better model for forecasting research output as its accuracy level is higher than that of the Even Model GM (1, 1) and statistical regression models. The results revealed that USA is likely to continue leading in research output at least till 2025 however the research output difference between USA and China is likely to reduce. The study reveals that the less developed countries tend to possess higher relative growth rate in publications whereas the more developed countries tend to possess lower relative growth rate. Further, the more developed countries need more time for publications to double in numbers for a given relative growth rate and less developed countries need less time to do so. The study is original in term of its analysis of the problem using the models involved in the study. The study suggests that the strategies of USA and China to enhance the research output of their respective countries seem productive for the time being however in long run less developed countries have greater competitive advantage over the more developed countries because of their publication growth rate and time required to double the number of publications. The study reported nearly linear trend of growth in research output among the countries. The study is primarily important for the academic policy makers and encourages them to take corrective measures if the growth rate of their academic/publishing sector is not reasonable. | ||
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| 650 | 4 | |a Research output |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Research growth |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a China |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Pakistan |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a India |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Synthetic relative growth rate doubling time |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Liu, Sifeng |e verfasserin |4 aut | |
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10.1007/s11192-017-2586-5 doi (DE-627)SPR017616352 (SPR)s11192-017-2586-5-e DE-627 ger DE-627 rakwb eng 050 370 ASE 31.00 bkl Javed, Saad Ahmed verfasserin aut Predicting the research output/growth of selected countries: application of Even GM (1, 1) and NDGM models 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The study aims to forecast the research output of four selected countries (USA, China, India and Pakistan) using two models of Grey System Theory—Even Model GM (1, 1) and Nonhomogeneous Discrete Grey Model (NDGM). The study also conducts publication growth analysis using relative growth rate (RGR) and the doubling time (Dt). The linear and exponential regression analyses were also performed for comparison. The study also proposes and successfully tests two novel synthetic models for RGR and Dt that facilities the comparison of the countries’ performance when actual data and forecasted data produce different sequences of performance in the given period of time. The data of documents published by the four countries from 2005 to 2016 was collected from SJR/Scopus website. Performance criterion was Mean Absolute Percentage Error. The study confirms that NDGM is a better model for forecasting research output as its accuracy level is higher than that of the Even Model GM (1, 1) and statistical regression models. The results revealed that USA is likely to continue leading in research output at least till 2025 however the research output difference between USA and China is likely to reduce. The study reveals that the less developed countries tend to possess higher relative growth rate in publications whereas the more developed countries tend to possess lower relative growth rate. Further, the more developed countries need more time for publications to double in numbers for a given relative growth rate and less developed countries need less time to do so. The study is original in term of its analysis of the problem using the models involved in the study. The study suggests that the strategies of USA and China to enhance the research output of their respective countries seem productive for the time being however in long run less developed countries have greater competitive advantage over the more developed countries because of their publication growth rate and time required to double the number of publications. The study reported nearly linear trend of growth in research output among the countries. The study is primarily important for the academic policy makers and encourages them to take corrective measures if the growth rate of their academic/publishing sector is not reasonable. Nonhomogeneous (dpeaa)DE-He213 NDGM (dpeaa)DE-He213 GM (1, 1) (dpeaa)DE-He213 Research output (dpeaa)DE-He213 Research growth (dpeaa)DE-He213 Regression (dpeaa)DE-He213 USA (dpeaa)DE-He213 China (dpeaa)DE-He213 Pakistan (dpeaa)DE-He213 India (dpeaa)DE-He213 Synthetic relative growth rate doubling time (dpeaa)DE-He213 Liu, Sifeng verfasserin aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 115(2017), 1 vom: 25. Nov., Seite 395-413 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:115 year:2017 number:1 day:25 month:11 pages:395-413 https://dx.doi.org/10.1007/s11192-017-2586-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 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 ASE AR 115 2017 1 25 11 395-413 |
| spelling |
10.1007/s11192-017-2586-5 doi (DE-627)SPR017616352 (SPR)s11192-017-2586-5-e DE-627 ger DE-627 rakwb eng 050 370 ASE 31.00 bkl Javed, Saad Ahmed verfasserin aut Predicting the research output/growth of selected countries: application of Even GM (1, 1) and NDGM models 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The study aims to forecast the research output of four selected countries (USA, China, India and Pakistan) using two models of Grey System Theory—Even Model GM (1, 1) and Nonhomogeneous Discrete Grey Model (NDGM). The study also conducts publication growth analysis using relative growth rate (RGR) and the doubling time (Dt). The linear and exponential regression analyses were also performed for comparison. The study also proposes and successfully tests two novel synthetic models for RGR and Dt that facilities the comparison of the countries’ performance when actual data and forecasted data produce different sequences of performance in the given period of time. The data of documents published by the four countries from 2005 to 2016 was collected from SJR/Scopus website. Performance criterion was Mean Absolute Percentage Error. The study confirms that NDGM is a better model for forecasting research output as its accuracy level is higher than that of the Even Model GM (1, 1) and statistical regression models. The results revealed that USA is likely to continue leading in research output at least till 2025 however the research output difference between USA and China is likely to reduce. The study reveals that the less developed countries tend to possess higher relative growth rate in publications whereas the more developed countries tend to possess lower relative growth rate. Further, the more developed countries need more time for publications to double in numbers for a given relative growth rate and less developed countries need less time to do so. The study is original in term of its analysis of the problem using the models involved in the study. The study suggests that the strategies of USA and China to enhance the research output of their respective countries seem productive for the time being however in long run less developed countries have greater competitive advantage over the more developed countries because of their publication growth rate and time required to double the number of publications. The study reported nearly linear trend of growth in research output among the countries. The study is primarily important for the academic policy makers and encourages them to take corrective measures if the growth rate of their academic/publishing sector is not reasonable. Nonhomogeneous (dpeaa)DE-He213 NDGM (dpeaa)DE-He213 GM (1, 1) (dpeaa)DE-He213 Research output (dpeaa)DE-He213 Research growth (dpeaa)DE-He213 Regression (dpeaa)DE-He213 USA (dpeaa)DE-He213 China (dpeaa)DE-He213 Pakistan (dpeaa)DE-He213 India (dpeaa)DE-He213 Synthetic relative growth rate doubling time (dpeaa)DE-He213 Liu, Sifeng verfasserin aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 115(2017), 1 vom: 25. Nov., Seite 395-413 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:115 year:2017 number:1 day:25 month:11 pages:395-413 https://dx.doi.org/10.1007/s11192-017-2586-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 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 ASE AR 115 2017 1 25 11 395-413 |
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10.1007/s11192-017-2586-5 doi (DE-627)SPR017616352 (SPR)s11192-017-2586-5-e DE-627 ger DE-627 rakwb eng 050 370 ASE 31.00 bkl Javed, Saad Ahmed verfasserin aut Predicting the research output/growth of selected countries: application of Even GM (1, 1) and NDGM models 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The study aims to forecast the research output of four selected countries (USA, China, India and Pakistan) using two models of Grey System Theory—Even Model GM (1, 1) and Nonhomogeneous Discrete Grey Model (NDGM). The study also conducts publication growth analysis using relative growth rate (RGR) and the doubling time (Dt). The linear and exponential regression analyses were also performed for comparison. The study also proposes and successfully tests two novel synthetic models for RGR and Dt that facilities the comparison of the countries’ performance when actual data and forecasted data produce different sequences of performance in the given period of time. The data of documents published by the four countries from 2005 to 2016 was collected from SJR/Scopus website. Performance criterion was Mean Absolute Percentage Error. The study confirms that NDGM is a better model for forecasting research output as its accuracy level is higher than that of the Even Model GM (1, 1) and statistical regression models. The results revealed that USA is likely to continue leading in research output at least till 2025 however the research output difference between USA and China is likely to reduce. The study reveals that the less developed countries tend to possess higher relative growth rate in publications whereas the more developed countries tend to possess lower relative growth rate. Further, the more developed countries need more time for publications to double in numbers for a given relative growth rate and less developed countries need less time to do so. The study is original in term of its analysis of the problem using the models involved in the study. The study suggests that the strategies of USA and China to enhance the research output of their respective countries seem productive for the time being however in long run less developed countries have greater competitive advantage over the more developed countries because of their publication growth rate and time required to double the number of publications. The study reported nearly linear trend of growth in research output among the countries. The study is primarily important for the academic policy makers and encourages them to take corrective measures if the growth rate of their academic/publishing sector is not reasonable. Nonhomogeneous (dpeaa)DE-He213 NDGM (dpeaa)DE-He213 GM (1, 1) (dpeaa)DE-He213 Research output (dpeaa)DE-He213 Research growth (dpeaa)DE-He213 Regression (dpeaa)DE-He213 USA (dpeaa)DE-He213 China (dpeaa)DE-He213 Pakistan (dpeaa)DE-He213 India (dpeaa)DE-He213 Synthetic relative growth rate doubling time (dpeaa)DE-He213 Liu, Sifeng verfasserin aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 115(2017), 1 vom: 25. Nov., Seite 395-413 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:115 year:2017 number:1 day:25 month:11 pages:395-413 https://dx.doi.org/10.1007/s11192-017-2586-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 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 ASE AR 115 2017 1 25 11 395-413 |
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10.1007/s11192-017-2586-5 doi (DE-627)SPR017616352 (SPR)s11192-017-2586-5-e DE-627 ger DE-627 rakwb eng 050 370 ASE 31.00 bkl Javed, Saad Ahmed verfasserin aut Predicting the research output/growth of selected countries: application of Even GM (1, 1) and NDGM models 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The study aims to forecast the research output of four selected countries (USA, China, India and Pakistan) using two models of Grey System Theory—Even Model GM (1, 1) and Nonhomogeneous Discrete Grey Model (NDGM). The study also conducts publication growth analysis using relative growth rate (RGR) and the doubling time (Dt). The linear and exponential regression analyses were also performed for comparison. The study also proposes and successfully tests two novel synthetic models for RGR and Dt that facilities the comparison of the countries’ performance when actual data and forecasted data produce different sequences of performance in the given period of time. The data of documents published by the four countries from 2005 to 2016 was collected from SJR/Scopus website. Performance criterion was Mean Absolute Percentage Error. The study confirms that NDGM is a better model for forecasting research output as its accuracy level is higher than that of the Even Model GM (1, 1) and statistical regression models. The results revealed that USA is likely to continue leading in research output at least till 2025 however the research output difference between USA and China is likely to reduce. The study reveals that the less developed countries tend to possess higher relative growth rate in publications whereas the more developed countries tend to possess lower relative growth rate. Further, the more developed countries need more time for publications to double in numbers for a given relative growth rate and less developed countries need less time to do so. The study is original in term of its analysis of the problem using the models involved in the study. The study suggests that the strategies of USA and China to enhance the research output of their respective countries seem productive for the time being however in long run less developed countries have greater competitive advantage over the more developed countries because of their publication growth rate and time required to double the number of publications. The study reported nearly linear trend of growth in research output among the countries. The study is primarily important for the academic policy makers and encourages them to take corrective measures if the growth rate of their academic/publishing sector is not reasonable. Nonhomogeneous (dpeaa)DE-He213 NDGM (dpeaa)DE-He213 GM (1, 1) (dpeaa)DE-He213 Research output (dpeaa)DE-He213 Research growth (dpeaa)DE-He213 Regression (dpeaa)DE-He213 USA (dpeaa)DE-He213 China (dpeaa)DE-He213 Pakistan (dpeaa)DE-He213 India (dpeaa)DE-He213 Synthetic relative growth rate doubling time (dpeaa)DE-He213 Liu, Sifeng verfasserin aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 115(2017), 1 vom: 25. Nov., Seite 395-413 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:115 year:2017 number:1 day:25 month:11 pages:395-413 https://dx.doi.org/10.1007/s11192-017-2586-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 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 ASE AR 115 2017 1 25 11 395-413 |
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10.1007/s11192-017-2586-5 doi (DE-627)SPR017616352 (SPR)s11192-017-2586-5-e DE-627 ger DE-627 rakwb eng 050 370 ASE 31.00 bkl Javed, Saad Ahmed verfasserin aut Predicting the research output/growth of selected countries: application of Even GM (1, 1) and NDGM models 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The study aims to forecast the research output of four selected countries (USA, China, India and Pakistan) using two models of Grey System Theory—Even Model GM (1, 1) and Nonhomogeneous Discrete Grey Model (NDGM). The study also conducts publication growth analysis using relative growth rate (RGR) and the doubling time (Dt). The linear and exponential regression analyses were also performed for comparison. The study also proposes and successfully tests two novel synthetic models for RGR and Dt that facilities the comparison of the countries’ performance when actual data and forecasted data produce different sequences of performance in the given period of time. The data of documents published by the four countries from 2005 to 2016 was collected from SJR/Scopus website. Performance criterion was Mean Absolute Percentage Error. The study confirms that NDGM is a better model for forecasting research output as its accuracy level is higher than that of the Even Model GM (1, 1) and statistical regression models. The results revealed that USA is likely to continue leading in research output at least till 2025 however the research output difference between USA and China is likely to reduce. The study reveals that the less developed countries tend to possess higher relative growth rate in publications whereas the more developed countries tend to possess lower relative growth rate. Further, the more developed countries need more time for publications to double in numbers for a given relative growth rate and less developed countries need less time to do so. The study is original in term of its analysis of the problem using the models involved in the study. The study suggests that the strategies of USA and China to enhance the research output of their respective countries seem productive for the time being however in long run less developed countries have greater competitive advantage over the more developed countries because of their publication growth rate and time required to double the number of publications. The study reported nearly linear trend of growth in research output among the countries. The study is primarily important for the academic policy makers and encourages them to take corrective measures if the growth rate of their academic/publishing sector is not reasonable. Nonhomogeneous (dpeaa)DE-He213 NDGM (dpeaa)DE-He213 GM (1, 1) (dpeaa)DE-He213 Research output (dpeaa)DE-He213 Research growth (dpeaa)DE-He213 Regression (dpeaa)DE-He213 USA (dpeaa)DE-He213 China (dpeaa)DE-He213 Pakistan (dpeaa)DE-He213 India (dpeaa)DE-He213 Synthetic relative growth rate doubling time (dpeaa)DE-He213 Liu, Sifeng verfasserin aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 115(2017), 1 vom: 25. Nov., Seite 395-413 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:115 year:2017 number:1 day:25 month:11 pages:395-413 https://dx.doi.org/10.1007/s11192-017-2586-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 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 ASE AR 115 2017 1 25 11 395-413 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR017616352</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111052932.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2017 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11192-017-2586-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR017616352</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11192-017-2586-5-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">050</subfield><subfield code="a">370</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">31.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Javed, Saad Ahmed</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Predicting the research output/growth of selected countries: application of Even GM (1, 1) and NDGM models</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The study aims to forecast the research output of four selected countries (USA, China, India and Pakistan) using two models of Grey System Theory—Even Model GM (1, 1) and Nonhomogeneous Discrete Grey Model (NDGM). The study also conducts publication growth analysis using relative growth rate (RGR) and the doubling time (Dt). The linear and exponential regression analyses were also performed for comparison. The study also proposes and successfully tests two novel synthetic models for RGR and Dt that facilities the comparison of the countries’ performance when actual data and forecasted data produce different sequences of performance in the given period of time. The data of documents published by the four countries from 2005 to 2016 was collected from SJR/Scopus website. Performance criterion was Mean Absolute Percentage Error. The study confirms that NDGM is a better model for forecasting research output as its accuracy level is higher than that of the Even Model GM (1, 1) and statistical regression models. The results revealed that USA is likely to continue leading in research output at least till 2025 however the research output difference between USA and China is likely to reduce. The study reveals that the less developed countries tend to possess higher relative growth rate in publications whereas the more developed countries tend to possess lower relative growth rate. Further, the more developed countries need more time for publications to double in numbers for a given relative growth rate and less developed countries need less time to do so. The study is original in term of its analysis of the problem using the models involved in the study. The study suggests that the strategies of USA and China to enhance the research output of their respective countries seem productive for the time being however in long run less developed countries have greater competitive advantage over the more developed countries because of their publication growth rate and time required to double the number of publications. The study reported nearly linear trend of growth in research output among the countries. The study is primarily important for the academic policy makers and encourages them to take corrective measures if the growth rate of their academic/publishing sector is not reasonable.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Nonhomogeneous</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">NDGM</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">GM (1, 1)</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Research output</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Research growth</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Regression</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">USA</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">China</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Pakistan</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">India</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Synthetic relative growth rate doubling time</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Sifeng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Scientometrics</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978</subfield><subfield code="g">115(2017), 1 vom: 25. 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Javed, Saad Ahmed |
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Javed, Saad Ahmed Liu, Sifeng |
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Elektronische Aufsätze |
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Javed, Saad Ahmed |
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predicting the research output/growth of selected countries: application of even gm (1, 1) and ndgm models |
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Predicting the research output/growth of selected countries: application of Even GM (1, 1) and NDGM models |
| abstract |
Abstract The study aims to forecast the research output of four selected countries (USA, China, India and Pakistan) using two models of Grey System Theory—Even Model GM (1, 1) and Nonhomogeneous Discrete Grey Model (NDGM). The study also conducts publication growth analysis using relative growth rate (RGR) and the doubling time (Dt). The linear and exponential regression analyses were also performed for comparison. The study also proposes and successfully tests two novel synthetic models for RGR and Dt that facilities the comparison of the countries’ performance when actual data and forecasted data produce different sequences of performance in the given period of time. The data of documents published by the four countries from 2005 to 2016 was collected from SJR/Scopus website. Performance criterion was Mean Absolute Percentage Error. The study confirms that NDGM is a better model for forecasting research output as its accuracy level is higher than that of the Even Model GM (1, 1) and statistical regression models. The results revealed that USA is likely to continue leading in research output at least till 2025 however the research output difference between USA and China is likely to reduce. The study reveals that the less developed countries tend to possess higher relative growth rate in publications whereas the more developed countries tend to possess lower relative growth rate. Further, the more developed countries need more time for publications to double in numbers for a given relative growth rate and less developed countries need less time to do so. The study is original in term of its analysis of the problem using the models involved in the study. The study suggests that the strategies of USA and China to enhance the research output of their respective countries seem productive for the time being however in long run less developed countries have greater competitive advantage over the more developed countries because of their publication growth rate and time required to double the number of publications. The study reported nearly linear trend of growth in research output among the countries. The study is primarily important for the academic policy makers and encourages them to take corrective measures if the growth rate of their academic/publishing sector is not reasonable. |
| abstractGer |
Abstract The study aims to forecast the research output of four selected countries (USA, China, India and Pakistan) using two models of Grey System Theory—Even Model GM (1, 1) and Nonhomogeneous Discrete Grey Model (NDGM). The study also conducts publication growth analysis using relative growth rate (RGR) and the doubling time (Dt). The linear and exponential regression analyses were also performed for comparison. The study also proposes and successfully tests two novel synthetic models for RGR and Dt that facilities the comparison of the countries’ performance when actual data and forecasted data produce different sequences of performance in the given period of time. The data of documents published by the four countries from 2005 to 2016 was collected from SJR/Scopus website. Performance criterion was Mean Absolute Percentage Error. The study confirms that NDGM is a better model for forecasting research output as its accuracy level is higher than that of the Even Model GM (1, 1) and statistical regression models. The results revealed that USA is likely to continue leading in research output at least till 2025 however the research output difference between USA and China is likely to reduce. The study reveals that the less developed countries tend to possess higher relative growth rate in publications whereas the more developed countries tend to possess lower relative growth rate. Further, the more developed countries need more time for publications to double in numbers for a given relative growth rate and less developed countries need less time to do so. The study is original in term of its analysis of the problem using the models involved in the study. The study suggests that the strategies of USA and China to enhance the research output of their respective countries seem productive for the time being however in long run less developed countries have greater competitive advantage over the more developed countries because of their publication growth rate and time required to double the number of publications. The study reported nearly linear trend of growth in research output among the countries. The study is primarily important for the academic policy makers and encourages them to take corrective measures if the growth rate of their academic/publishing sector is not reasonable. |
| abstract_unstemmed |
Abstract The study aims to forecast the research output of four selected countries (USA, China, India and Pakistan) using two models of Grey System Theory—Even Model GM (1, 1) and Nonhomogeneous Discrete Grey Model (NDGM). The study also conducts publication growth analysis using relative growth rate (RGR) and the doubling time (Dt). The linear and exponential regression analyses were also performed for comparison. The study also proposes and successfully tests two novel synthetic models for RGR and Dt that facilities the comparison of the countries’ performance when actual data and forecasted data produce different sequences of performance in the given period of time. The data of documents published by the four countries from 2005 to 2016 was collected from SJR/Scopus website. Performance criterion was Mean Absolute Percentage Error. The study confirms that NDGM is a better model for forecasting research output as its accuracy level is higher than that of the Even Model GM (1, 1) and statistical regression models. The results revealed that USA is likely to continue leading in research output at least till 2025 however the research output difference between USA and China is likely to reduce. The study reveals that the less developed countries tend to possess higher relative growth rate in publications whereas the more developed countries tend to possess lower relative growth rate. Further, the more developed countries need more time for publications to double in numbers for a given relative growth rate and less developed countries need less time to do so. The study is original in term of its analysis of the problem using the models involved in the study. The study suggests that the strategies of USA and China to enhance the research output of their respective countries seem productive for the time being however in long run less developed countries have greater competitive advantage over the more developed countries because of their publication growth rate and time required to double the number of publications. The study reported nearly linear trend of growth in research output among the countries. The study is primarily important for the academic policy makers and encourages them to take corrective measures if the growth rate of their academic/publishing sector is not reasonable. |
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| score |
7.403097 |