Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 -- 1876 2022-07-21 13:40:11 |
2 format correct -2 word(s) 1874 2022-07-22 04:41:28 |

Video Upload Options

Do you have a full video?

Confirm

Are you sure to Delete?
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Wang, Q.;  Chen, Y.;  Guan, H.;  Lyulyov, O.;  Pimonenko, T. Technological Innovation Efficiency in China. Encyclopedia. Available online: https://encyclopedia.pub/entry/25393 (accessed on 14 June 2024).
Wang Q,  Chen Y,  Guan H,  Lyulyov O,  Pimonenko T. Technological Innovation Efficiency in China. Encyclopedia. Available at: https://encyclopedia.pub/entry/25393. Accessed June 14, 2024.
Wang, Qian, Yang Chen, Heshan Guan, Oleksii Lyulyov, Tetyana Pimonenko. "Technological Innovation Efficiency in China" Encyclopedia, https://encyclopedia.pub/entry/25393 (accessed June 14, 2024).
Wang, Q.,  Chen, Y.,  Guan, H.,  Lyulyov, O., & Pimonenko, T. (2022, July 21). Technological Innovation Efficiency in China. In Encyclopedia. https://encyclopedia.pub/entry/25393
Wang, Qian, et al. "Technological Innovation Efficiency in China." Encyclopedia. Web. 21 July, 2022.
Technological Innovation Efficiency in China
Edit

Innovation is the engine and accelerator that drives high-quality economic and enterprise development. In recent years, the output of scientific and technological innovation in China has been high, but the phenomenon of low efficiency and low quality of innovation occurs frequently. Under the high-intensity systemic investment in innovation, China’s overall innovation capability continues to rise.According to the National Bureau of Statistics of China, China ranks first in research and experimental development (R&D) activities in the world’s major economies. From the perspective of innovation output, China is at the forefront of the world regarding the scale of patent authorisation and the number of international papers published. However, it is puzzling that China’s national innovation index has always been outside the top 10 in the world (in 2021, it ranked 12th). With the increasingly fierce scientific and technological competition between China and the United States of America (USA), the negative list of some core technologies from the USA has highlighted the problem of “sticking neck” in China’s key technologies. It reflects the fact that although China has a large amount of innovations, many are low-quality innovations. There are core technologies still controlled by others. The surging output of innovation in China has not been accompanied by the improvement of innovation quality, which also shows that China’s technological innovation is facing the dilemma of innovation inefficiency caused by the input–output mismatch. Technological innovation efficiency (TIE) is a key indicator to measure the output level of innovation input factors per unit time. Compared with other developed countries, China’s innovation efficiency is still far away in terms of TIE.

technological innovation efficiency SBM-Tobit model kernel density estimation

1. Introduction

Under the multiple and complex backgrounds of global economics, environmental protection and the difficulties of industrial transformation, the sixth plenary session of the 19th Central Committee of the Chinese Communist Party once again emphasised the core position of innovation in the overall situation of China’s modernisation drive. To make innovation become the first driving force to “achieve high-quality development”, China should adhere to the innovation-driven development strategy and strengthen the national strategic, scientific and technological strength [1]. As a key element in accelerating the transformation of kinetic energy between the new and the old in the new era, promoting regional innovation is the essential intention of the five major development concepts, and it has great significance to the realisation of coordinated development and common prosperity [2][3][4][5].
Under the high-intensity systemic investment in innovation, China’s overall innovation capability continues to rise. According to the National Bureau of Statistics of China [6], China ranks first in research and experimental development (R&D) activities in the world’s major economies. Furthermore, China is the leader in both the proportion of government funds and the number of people engaged in R&D. As the main forces engaged in R&D activities, enterprises were invested directly with 76.4% of China’s R&D funds to carry out innovation activities, which is only exceeded by South Korea (80.3%) and Japan (79.4%) in the world. In addition, the intensity of R&D investment in China increased from 0.65% to 2.24% of growth domestic product (GDP) from 1998 to 2019, even higher than the average of 2.12% in the European Union (EU). In the same period, the number of patent applications granted increased from 68,000 to 2.457 million, rapidly growing nearly 35-fold. From the perspective of innovation output, China is at the forefront of the world regarding the scale of patent authorisation and the number of international papers published. However, it is puzzling that China’s national innovation index has always been outside the top 10 in the world (in 2021, it ranked 12th). With the increasingly fierce scientific and technological competition between China and the United States of America (USA), the negative list of some core technologies from the USA has highlighted the problem of “sticking neck” in China’s key technologies. It reflects the fact that although China has a large amount of innovations, many are low-quality innovations. There are core technologies still controlled by others. The surging output of innovation in China has not been accompanied by the improvement of innovation quality, which also shows that China’s technological innovation is facing the dilemma of innovation inefficiency caused by the input–output mismatch. Technological innovation efficiency (TIE) is a key indicator to measure the output level of innovation input factors per unit time. Compared with other developed countries, China’s innovation efficiency is still far away in terms of TIE.

2. Technological Innovation Efficiency

In general, efficiency is the ratio between outputs and the costs required to produce them [7]. The study [8] applied a DEA model to analyse the technological innovation efficiency of China’s high-tech industries. The scientists working on [8] confirmed that required resourses (capital, labour, knowledge, etc.) for innovations are less than the generated output when comparing similar technologies in the sector. Furthermore, in the papers [9][10], the innovative activity of companies was analysed as a goal-directed process. Such an approach allows simultaneous estimating of initial, intermediate and output data during the entire period of industrial production. The paper [11] defined the TIE as the capability to maximize the results from innovations compared to innovation cost. The study [12] confirmed that industry technological innovation efficiency was the core driver of sustainable development of the mining industry in China. The authors developed a DEA model to confirm that technological innovation efficiency impacted sustainable development of the mining industry. Thus, within the investigation, the TIE was analysed as the proportion changes between input and output of the production process.
In recent years, a large number of low-efficiency or even ineffective innovation behaviours (such as dormant patents and innovation bubbles) have emerged under the guidance of existing innovation policies [13]. Promoting technological innovation being more efficient and high-quality has become the fundamental way to solve the lack of stamina of current economic and social development [14] as well as curb the unchecked spread of innovation bubbles. However, before improving TIE, increasing the level of innovation efficiency has become the first concern that should be addressed. From the perspective of method, the non-parametric DEA method is generally used to separate the technical efficiency from the production efficiency. The Solow residual method is also used to analyse its regression residual to characterise technological progress [15]. The super-efficiency SBM-DEA model can better consider the unexpected output. It allows comparison of an effective decision-making unit (DMU) whose efficiency is not less than 1, which solves the problem that the previous DEA model may cause deviation in the radial selection and angle selection, and has become a reliable method to measure efficiency.
From the selection of indicators, the existing research mainly uses a single index to measure enterprises’ innovation capability or performance, such as R&D investment, the number of patent applications or patents authorised and the number of science and technology employees [16][17]. Patents, especially invention patents, have become common indicators when measuring micro-subject innovation output. Generally, the number of invention patents is used to measure the number of innovations, and the citation of invention patents is used to measure the quality of innovation [16]. Although patent data can more accurately measure the output of innovation activities rather than input, only using patent citation to measure patent quality is not accurate enough. Worse, some enterprises often misquote or over-quote patents to better cater to the examination of patent examination institutions. Therefore, there are still many drawbacks to only taking the number of patent citations as patent quality and then regarding it as the innovation output.
After completing the efficiency measurement, some scholars use the two-stage Tobit model to explore the factors that affect the efficiency of TIE and finally give targeted countermeasures and suggestions. For example, the study [18] used the non-radial and non-angle DEA model, including unexpected output, to measure the ecological efficiency of urban agglomerations in the Yangtze River Economic Belt from 2005 to 2015, and it empirically analysed the impact of green-technology innovation on ecological efficiency through the Tobit model. The paper [19] analysed the effect of material and energy consumption reduction on innovation efficiency, considering both innovation inputs and outputs, and then utilised data of 388 manufacturing enterprises in Korea and performed DEA and Tobit regression analysis. The study [20] used the two-stage network DEA model to measure the green innovation efficiency of China’s local high-tech manufacturing industry and established a Tobit regression model to analyse the role of different technology transfer modes in improving green innovation efficiency. The study [21] used the Bootstrap-modified DEA–Tobit model to evaluate the green-technology innovation efficiency of 21 biomass power generation enterprises in 2018 and analysed the influencing factors.

3. Conclusions

3.1. For Government

China should increase the government support for technological innovation and endowment of regional innovation resources. This could be realised by providing financial subsidies and taxation, which was also proposed by [22]. The government should become the main body of the top-level design in the innovation system.
As the leaders in innovation, high-tech enterprises play an essential role. The government should develop policy for high-tech enterprises’ development. The number of high-tech enterprises will help invigorate regional resources and improve the allocation efficiency of input resources, as well as the efficiency of regional technological innovation.
Economic globalisation is an irreversible trend of this era, and General Secretary Xi Jinping has stressed that China should persist in opening its doors for construction. The higher the degree of economic opening to the outside world, the higher TIE might be. Some studies have shown that opening wider to the outside world is more conducive to the improvement of China’s industrial environmental performance rather than deterioration [20], which also means that further opening to the outside world is a reliable way to promote sustainable economic and social development in China’s backward areas.

3.2. For Practice

Some regions should build a sharing cooperation mechanism and scientific and technological innovation platform. They could enhance the regional original innovation capability and promote the diffusion and transformation of innovation achievements. It is necessary to improve the construction of information infrastructure and provide a good hardware environment for cultivating innovation. As the material basis and information guarantee of technological innovation, information infrastructure has become a significant force in promoting regional innovation. The innovation spillover effect led by the Internet should be taken seriously. At present, society has entered the era of the digital economy. As the primary carrier of knowledge and information, the Internet and other infrastructure is of great significance to speed up the construction of modern information networks and further promote technological innovation.

3.3. For Society

Increasing of TIE allowed achieving direct and indirect effects. Thus, the penetrating of effective innovations among society lead to improving quality of life, accessibility to the knowledge, providing well-being, etc. Furthermore, technological innovation reduces destructive impacts on the environment [23], which subsequently reduces morbidity and mortality. Notably, the ability of society to implement innovations determines the level of social, cultural and economic development of a country.
Despite the actual findings pertaining to the drivers of TIE, there are several limitations. In further investigation, it would be necessary to consider the cointegration analysis between all variables that impact TIE. Furthermore, the number of drivers should be extended (social, digital, ecological, etc). This allows for identifying the relevant and significant drivers for boosting countries’ innovative development.

References

  1. Chen, Q.; Lin, S.; Zhang, X. The effect of China’s incentive policies for technological innovation: Incentivising quantity or quality. China Ind. Econ. 2020, 4, 79–96.
  2. Trzeciak, M.; Kopec, T.P.; Kwilinski, A. Constructs of Project Programme Management Supporting Open Innovation at the Strategic Level of the Organisation. J. Open Innov. Technol. Mark. Complex. 2022, 8, 58.
  3. Shkarlet, S.; Kholiavko, N.; Dubyna, M. Information economy: Management of educational, innovation, and research determinants. Mark. Manag. Innov. 2019, 3, 126–141.
  4. Fiľa, M.; Levicky, M.; Mura, L.; Maros, M.; Korenkova, M. Innovations for Business Management: Motivation and Barriers. Mark. Manag. Innov. 2020, 4, 266–278.
  5. Vasylieva, T.A.; Kasyanenko, V.O. Integral assessment of innovation potential of ukraine’s national economy: A scientific methodical approach and practical calculations. Actual Probl. Econ. 2013, 144, 50–59.
  6. National Bureau of Statistics of China. 2022. Available online: http://www.stats.gov.cn/english/ (accessed on 1 March 2022).
  7. Chygryn, O.; Lyulyov, O.; Pimonenko, T.; Mlaabdal, S. Efficiency of oil-production: The role of institutional factors. Eng. Manag. Prod. Serv. 2020, 12, 92–104.
  8. Wang, Y.; Pan, J.-F.; Pei, R.-M.; Yi, B.-W.; Yang, G.-L. Assessing the technological innovation efficiency of China’s high-tech industries with a two-stage network DEA approach. Socio-Econ. Plan. Sci. 2020, 71, 100810.
  9. Bi, K.; Huang, P.; Wang, X. Innovation performance and influencing factors of low-carbon technological innovation under the global value chain: A case of Chinese manufacturing industry. Technol. Forecast. Soc. Change 2016, 111, 275–284.
  10. Li, H.; Zhang, J.; Wang, C.; Wang, Y.; Coffey, V. An evaluation of the impact of environmental regulation on the efficiency of technology innovation using the combined DEA model: A case study of Xi’an, China. Sustain. Cities Soc. 2018, 42, 355–369.
  11. Cruz-Cázares, C.; Bayona-Sáez, C.; Garcίa-Marco, T. You can’t manage right what you can’t measure well: Technological innovation efficiency. Res. Policy 2013, 42, 1239–1250.
  12. Zuo, Z.; Guo, H.; Li, Y.; Cheng, J. A two-stage DEA evaluation of Chinese mining industry technological innovation efficiency and eco-efficiency. Environ. Impact Assess. Rev. 2022, 94, 106762.
  13. Albort-Morant, G.; Leal-Millán, A.; Cepeda-Carrión, G. The antecedents of green innovation performance: A model of learning and capabilities. J. Bus. Res. 2016, 69, 4912–4917.
  14. Aghion, P.; Askenazy, P.; Berman, N.; Cette, G.; Eymard, L. Credit constraints and the cyclicality of r&d investment: Evidence from france. J. Eur. Econ. Assoc. 2012, 10, 1001–1024.
  15. Dong, Z.; Wang, H. Local-neighborhood effect of green technology of environmental regulation. China Ind. Econ. 2019, 1, 100–118.
  16. Sharma, S.; Thomas, V.J. Inter-country R&D efficiency analysis: An application of data envelopment analysis. Scientometrics 2008, 76, 483–501.
  17. Qian, L.; Wang, W.; Xiao, R. Analysis on the Regional Differences and Loss Sources of Green Innovation Efficiency of Chinese Enterprises Under Technology Heterogeneity. Sci. Res. Manag. 2021, 11, 1–16.
  18. Liu, Y.Q.; Quan, Q.; Zhu, J.; Wang, F. Green technology innovation, industrial agglomeration and ecological efficiency: A case study of urban agglomerations on Yangtze River Economic Belt. Resour. Environ. Yangtze Basin 2018, 27, 2395–2406.
  19. Shin, J.; Kim, C.; Yang, H. Does Reduction of Material and Energy Consumption Affect to Innovation Efficiency? The Case of Manufacturing Industry in South Korea. Energies 2019, 12, 1178.
  20. Zhou, S.H.; Deng, Q. Impact of Technology Transfer on Green Innovation Efficiency in High-tech Manufacturing Industry. Sci. Technol. Prog. Countermeas. 2021, 38, 46–52.
  21. Wang, H.; Yang, T. Research on Green Technology Innovation Efficiency and Its Influencing Factors of Biomass Power Generation Enterprises: Test Based on Bootstrap-DEA Method. Sci. Technol. Manag. Res. 2021, 41, 191–198.
  22. Jianzhong, X.; Yanan, Z. Research on the Efficiency of Regional Low Carbon Innovation Network Based on J-SBM Three-stage DEA Model. Manag. Rev. 2021, 33, 97.
  23. Chygryn, O.; Krasniak, V. Theoretical and applied aspects of the development of environmental investment in Ukraine. Mark. Manag. Innov. 2015, 3, 226–234.
More
Information
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register : , , , ,
View Times: 274
Revisions: 2 times (View History)
Update Date: 22 Jul 2022
1000/1000
Video Production Service