Evaluation of biomedical industry technological innovation ability based on a grey panel clustering model

Pin Lv; Pin Lv

doi:10.3934/mbe.2023070

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 1: 1538-1557. doi: 10.3934/mbe.2023070

Previous Article Next Article

Research article Special Issues

Evaluation of biomedical industry technological innovation ability based on a grey panel clustering model

Pin Lv ^,

Wuxi Vocational College of Science and Technology, Wuxi, Jiangsu 214028, China

Academic Editor: Jeffrey Yi-Lin Forrest

Received: 26 May 2022 Revised: 24 June 2022 Accepted: 26 June 2022 Published: 03 November 2022

Technological innovation in the biomedical industry is the basis for improving the core competitiveness of the biomedical industry and promoting the high-quality development of the industry. However, the technological innovation capacity of China's biomedical industry is not balanced, and there are great differences between regions. Therefore, accurately grasping the status quo of technological innovation in China's biomedical industry and assessing regional differences are of great significance and effect for the country to formulate targeted policies and systems. In view of this, this paper designs a two-stage biomedical industry technological innovation capability evaluation index system from the perspective of the innovation value chain. According to the panel data of China's biomedical industry from 2012 to 2018, a grey relational clustering model based on panel data is constructed and used to evaluate the technological innovation capability of China's biomedicine industry from two dimensions: the level of technological research and development and the ability to transform technological achievements.
Citation: Pin Lv. Evaluation of biomedical industry technological innovation ability based on a grey panel clustering model[J]. Mathematical Biosciences and Engineering, 2023, 20(1): 1538-1557. doi: 10.3934/mbe.2023070

Related Papers:

Abstract

Technological innovation in the biomedical industry is the basis for improving the core competitiveness of the biomedical industry and promoting the high-quality development of the industry. However, the technological innovation capacity of China's biomedical industry is not balanced, and there are great differences between regions. Therefore, accurately grasping the status quo of technological innovation in China's biomedical industry and assessing regional differences are of great significance and effect for the country to formulate targeted policies and systems. In view of this, this paper designs a two-stage biomedical industry technological innovation capability evaluation index system from the perspective of the innovation value chain. According to the panel data of China's biomedical industry from 2012 to 2018, a grey relational clustering model based on panel data is constructed and used to evaluate the technological innovation capability of China's biomedicine industry from two dimensions: the level of technological research and development and the ability to transform technological achievements.

References

[1]	X. Wen, H. Huang, X. Wang, W. Gao, S. O. Management, Construction of biomedical industry innovation index system, Sci. Technol. Manage. Res., 36 (2016), 42–46+52.
[2]	H. Andreas, Value chain orchestration in action and the case of the global agrochemical industry, Long Range Plann., 35 (2002), 615–635. https://doi.org/10.1016/S0024-6301(02)00160-7 doi: 10.1016/S0024-6301(02)00160-7
[3]	K. Xu, L. H. Sun, Development status and problems of biomedical industrial parks in China, Chin. J. New Drugs, 28 (2019), 2440–2446.
[4]	Y. Y. Li, S. X. Gao, Biomedical industry's developing situation based on statistical data from 1995 to 2015, China Sci. Technol. Forum, 12 (2016), 42–47+97.
[5]	J. Y. Lee, C. L. Kim, G. Choi, Exploring data envelopment analysis for measuring collaborated innovation efficiency of small and medium-sized enterprises in Korea, Eur. J. Oper. Res., 278 (2019), 533–545. https://doi.org/10.1016/j.ejor.2018.08.044 doi: 10.1016/j.ejor.2018.08.044
[6]	C. H. Kim, W. S. Shin, Natural resource dependence and economic growth: A TOPSIS/DEA analysis of innovation efficiency, Asian J. Shipp. Logist., 35 (2019), 70–76. https://doi.org/10.1016/j.ajsl.2019.03.010 doi: 10.1016/j.ajsl.2019.03.010
[7]	X. Q. Huang, The competitiveness of Singapore's biomedical industry: An analysis based on diamond model, Southeast Asia, 2 (2011), 31–36.
[8]	Z. Yin, Analysis of various factors affecting China's biomedical innovation strategy, Sci. Res., 1 (2008), 13–18.
[9]	Y. Cao, L. Zhu, N. Y. Mao, An empirical study on the innovation ability of biomedical industry cluster based on multi-level grey evaluation model, Bus. Econ., 31 (2012), 94–97.
[10]	Y. M. Zhu, X. Y. Xi, S. Z. Chu, Factors impacting formation and development of bio-pharmaceutical cluster in China, Chin. J. New Drugs, 22 (2013), 900–904.
[11]	X. M. Fu, S. Q. Wang, X. H. Lin, Y. Liu, Innovation efficiency and influencing factors of China's Marnie biomedical industry based on SFA method, Sci. Technol. Manage. Res., 40 (2020), 202–208.
[12]	K. Xu, L. H. Sun, Empirical study on the dynamic efficiency of China's biomedical medicine industrial park based on Malmquist model, J. Shenyang Pharm. Univ., 36 (2019), 534–538.
[13]	K. Xu, L. H. Sun, Efficiency evaluation of China's biomedical medicine industrial parks based on three-stage DEA, Chin. J. New Drugs, 28 (2019), 646–650.
[14]	F. Zhang, J. Z. Chen, Design and calculation of evaluation index system of regional industrial development potential, Stat. Decis., 14 (2013), 31–34.
[15]	L. Q. Zhang, Z. C. He, Y. Wu, Evaluation of strategic emerging industries based on grey relational analysis: Empirical analysis on biology and medicine industry, Econ. Math., 27 (2010), 79–84.
[16]	Y. W. Chen, Y. Li, J. Xu, Empirical study by factor analysis in biomedical industry development in China, Chin. Pharm., 22 (2011), 3844–3846.
[17]	J. Q. Ding, J. Y. Bai, X. F. Lin, Performance evaluation of Chinese bio-industry listed companies based on DEA model, Progress Biochem. Biophys., 1 (2020), 1–12.
[18]	Z. C. He, M. Zhang, Y. Wu, Research on efficiency of service towards generic technology in biophrmaceutical industry, China Soft Sci., 2 (2014), 130–139.
[19]	T. E. Stuart, S. Z. Ozdemir, W. W. Ding, Vertical alliance networks: The case of university–biotechnology-pharmaceutical alliance chains, Res. Policy, 36 (2007), 477–498. https://doi.org/10.1016/j.respol.2007.02.016 doi: 10.1016/j.respol.2007.02.016
[20]	B. Achilladelis, N. Antonakis, The dynamics of technological innovation: The case of the pharmaceutical industry, Res. Policy, 30 (2001), 535–588. https://doi.org/10.1016/S0048-7333(00)00093-7 doi: 10.1016/S0048-7333(00)00093-7
[21]	C. J. Chen, C. C. Huang, A multiple criteria evaluation of high-tech industries for the science-based industrial park in Taiwan, Inf. Manage-Amster, 41 (2004), 839–851. https://doi.org/10.1016/j.im.2003.02.002 doi: 10.1016/j.im.2003.02.002
[22]	Y. Yang, H. J. Xue, Study on regional diversity of industrial technology independent innovation capability, China's Ind. Econ., 11 (2011), 68–76.
[23]	J. Q. Zhang, The comparative study on technology innovation ability of high technology industry under two phase angle—the empirical analysis from the industry data, Sci. Technol. Prog. Policy, 33 (2016), 72–77.
[24]	C. Dou, X. Xiong, G. H. Chen, Muti-dimension study on SME's innovation efficiency from the perspective of innovation value Chain—based on two-stage additive decomposition DEA Model, Sci. Technol. Prog. Policy., 36 (2019), 77–85. https://doi.org/10.6049/kjjbydc.2018040237 doi: 10.6049/kjjbydc.2018040237
[25]	S. Y. Yin, L. T. Chen, Research on technological innovation efficiency of Chinese pharmaceutical enterprises based on two-stage SFA model, Soft Sci., 30 (2016), 54–58.
[26]	Y. Zheng, Z. H. Dong, L. J. Dong, Research on technological innovation efficiency and influencing factors of listed pharmaceutical companies in my country, Mod. Comm. Ind., 40 (2019), 12–16.
[27]	S. X. Zhou, S. X. Yu, Y. Z. Wang, Evaluation and suggestions on innovation efficiency in the stage of technology R&D and achievement transformation of China's pharmaceutical manufacturing industry, Jiangxi Soc. Sci., 42 (2022), 145–153.
[28]	Z. J. Yu, S. Z. Chu, Analysis of the correlation between the competitiveness of my country's pharmaceutical manufacturing industry and the ability of innovative drug research and development, China J. New Drugs, 27 (2018), 279–284.
[29]	P. Z. Liu, A. X. Ma, A comparative study on the technological innovation capability of the pharmaceutical industry and its influencing factors, China Drug Eval., 32 (2015), 59–64.
[30]	K. Y. Wan, B. X. Du, J. H. Wang, Z. Guo, D. Feng, X. Gao, et al., Deep learning-based intelligent management for sewage treatment plants, J. Cent. South Univ., 29 (2022), 1537–1552. https://doi.org/10.1007/s11771-022-5036-3 doi: 10.1007/s11771-022-5036-3
[31]	Z. A. Gao, X. C. Wang, Research on the influencing factors of money laundering risk in my country's free trade zones based on fuzzy cognitive map, Oper. Res. Manage., 31 (2022), 177–182.
[32]	S. Liu, Y. Yang, Y. Cao, N. Xie, A summary on the research of GRA models, Grey Syst.: Theory Appl., 3 (2013), 7–15. https://doi.org/10.1108/20439371311293651 doi: 10.1108/20439371311293651
[33]	Y. G. Dang, X. Y. Zhu, S. Ding, J. J. Wang, Panel data clustering method based on grey correlation degree and its application in air pollution analysis, Cont. Decis., 32 (2017), 2227–2232.
[34]	X. M. Li, Y. G. Dang, J. J. Wang, Grey index correlation clustering model and application under panel data, Cont. Decis., 30 (2015), 1447–1452.
[35]	T. B. Jia, S. F. Liu, Research on organizational power allocation model based on grey relational clustering, Pract. Understand. Math., 46 (2016), 31–37.
[36]	B. J. Frey, D. Dueck, Clustering by passing messages between data points, Science, 315 (2007), 972–976. https://doi.org/10.1126/science.1136800 doi: 10.1126/science.1136800

Reader Comments

Your name:*

Email:*
© 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)