[논문]Comparative Study on the Efficiency of Technological Innovation in Industrial Enterprises in the Yangtze River Economic Belt of China

Chen Luo; Sangwook Kim

doi:10.9708/jksci.2023.28.06.181

Comparative Study on the Efficiency of Technological Innovation in Industrial Enterprises in the Yangtze River Economic Belt of China 원문보기

韓國컴퓨터情報學會論文誌 = Journal of the Korea Society of Computer and Information, v.28 no.6, 2023년, pp.181 - 190

Chen Luo (Dept. of East-Asia Studies, Paichai University) , Sangwook Kim (Dept. of China Trade and Commerce, Paichai University)

초록
AI-Helper

본 연구는 중국에서 혁신자원이 가장 풍부하고 혁신을 선도하는 양쯔강경제벨트를 대상으로 공업의 기술혁신 효율성을 분석하고 있다. 분석기간은 2011년부터 2020년까지로 설정하고 있으며 분석에는 양쯔강경제벨트를 구성하는 11개 성 및 직할시를 이용하고 있다. 기술혁신의 효율성에는 정태적인 DEA 방법과 동태적인 Malmquist 지수를 이용한다. 투입변수는 R&D 투입인원과 R&D 경비지출을 이용한다. 산출변수는 R&D 프로젝트 수, 발명특허인정 수, 기술계약금액을 이용한다. 정태적인 DEA 분석결과에 의하면 2011-2020년 동안 양쯔강경제벨트의 전반적인 기술 효율성 수준은 상대적으로 낮게 나타나고 있다. 그러나 동태적인 Malmquist 지수는 2011년부터 2020년까지 전반적으로 상승 추세를 보이고 있다. 지역별로 보면 상류 지역의 효율성 변화가 가장 낮고, 중류 지역이 가장 높으며, 하류 지역이 가장 낮게 나타나고 있다.

Abstract ▼ AI-Helper

The Yangtze River Economic Belt has abundant innovation resources and is a leader in innovation in China. In order to explore the efficiency of technological innovation of its industrial enterprises, this paper measures and analyzes the efficiency of technological innovation of industrial enterprises in the Yangtze River Economic Belt using DEA-Malmquist index method based on the panel data of industrial enterprises in 11 provinces and cities in the Yangtze River Economic Belt from 2011 to 2020. The results show that (1) according to the static DEA analysis, the overall level of technical efficiency of technological innovation of industrial enterprises in the Yangtze River Economic Belt is relatively low during 2011-2020; there are significant differences among the three regions in the upper, middle and lower reaches of the Yangtze River. (2) The dynamic Malmquist index shows that the overall Malmquist index of technological innovation of industrial enterprises in the Yangtze River economic zone during 2011-2020 shows an improving trend, and the main source of its improvement is technological change rather than the efficiency of the utilization of technological innovation factors.

주제어

표/그림 (3)

표 Table 1. DEA analysis Results
표 Table 2. Malmquist Index by Annuals
표 Table 3. Malmquist Index Results by regions and provinces and municipalities

AI 본문요약
AI-Helper

제안 방법

From the static viewpoint, the DEA analysis of the technological innovation efficiency of industrial enterprises in the Yangtze River Economic Belt during the period of 2011-2020 are shown in table 1(due to space limitations, only the results for the years 2011, 2015, and 2020 are presented). The static DEA analysis of technological innovation efficiency is conducted at three levels, includes overall, regional, and provinces and municipalities levels, using three indicators, technical efficiency(TE), pure technical efficiency(PE), and scale efficiency(SE). At the overall level, the average of the mean values for the technological innovation efficiency of industrial enterprises in the Yangtze River Economic Belt during the period of 2011-2020 is 0.

대상 데이터

The above literature mainly adopts the DEA model or DEA-Malmquist index as research methods, which have been proven to be mature and reliable. The research focuses mainly on medium and large-sized enterprises, as well as industrial enterprises in the Yangtze River Economic Belt. Although there have been studies on the efficiency of technological innovation in industrial enterprises in the Yangtze River Economic Belt, the results have shown significant differences due to variations in the data used, and a consensus has not yet been reached.

성능/효과

According to the data in table 3, the Malmquist index of technological innovation efficiency for industrial enterprises in the Yangtze River Economic Belt during 2011-2020 is greater than 1, and the overall average of each decomposition index is also greater than 1. This indicates that the technological innovation efficiency of industrial enterprises in the Yangtze River Economic Belt has generally improved based on the Malmquist index.
However, none of these values reach the level of DEA efficiency, indicating that the technological innovation efficiency of industrial enterprises in the Yangtze River Economic Belt is relatively low. Additionally, the technical efficiency is influenced by both pure technical efficiency and scale efficiency, but the influence of scale efficiency is slightly greater.
According to the data in table 2, the Malmquist index of technological innovation efficiency for industrial enterprises in the Yangtze River Economic Belt showed improvement during the periods of 2011-2020, except of 2014-2015. Among them, the largest increase in Malmquist index was observed in the period of 2018-2019 (12.5%). The period of 2012-2013 had the smallest increase in Malmquist index (3.
990), with scale efficiency decreasing for two years. Anhui province had four years of DEA-efficient technical efficiency, influenced by both pure technical efficiency (0.969) and scale efficiency (0.988), with pure technical efficiency having a slightly greater impact. However, scale efficiency decreased for six years.
As mentioned in the introduction of this paper, after the reform and opening-up, the Chinese economy relied primarily on tangible factors of production such as capital stock, land, and labor forces to drive rapid economic growth. However, starting from 2011, China's annual GDP growth rate showed a declining trend, staying below 10% each year.
The static DEA analysis of technological innovation efficiency is conducted at three levels, includes overall, regional, and provinces and municipalities levels, using three indicators, technical efficiency(TE), pure technical efficiency(PE), and scale efficiency(SE). At the overall level, the average of the mean values for the technological innovation efficiency of industrial enterprises in the Yangtze River Economic Belt during the period of 2011-2020 is 0.911. The average of the mean values for pure technical efficiency is 0.
At the provinces and municipalities level, the results show that Sichuan province has achieved DEA efficiency levels in technical efficiency for the entire period of 2011-2020, with scale efficiency remaining constant. Chongqing municipality had four years of DEA-efficient technical efficiency during the same period.
Currently, the DEA model consists of two fundamental models, the constant returns to scale (CCR) model and the variable returns to scale (BCC) model. Considering that this study focuses on the technological innovation of industrial enterprises, which cannot remain in an optimal and constant steady state in the long term, the BCC model based on inputs is chosen to assess the efficiency of technological innovation in the Yangtze River Economic Belt.
Conversely, if SEC<1, it implies that in period t+1 relative to period t, the efficiency is further away from the constant returns to scale or the optimal scale in the long run.
950. However, none of these values reach the level of DEA efficiency, indicating that the technological innovation efficiency of industrial enterprises in the Yangtze River Economic Belt is relatively low. Additionally, the technical efficiency is influenced by both pure technical efficiency and scale efficiency, but the influence of scale efficiency is slightly greater.
978), with pure technical efficiency having a slightly greater impact. However, scale efficiency decreased for four years. Hunan province had one year of DEA-efficient technical efficiency, influenced by both pure technical efficiency (0.
978), with pure technical efficiency having a slightly greater impact. However, scale efficiency decreased for four years. Hunan province had one year of DEA-efficient technical efficiency, influenced by both pure technical efficiency (0.
988), with pure technical efficiency having a slightly greater impact. However, scale efficiency decreased for six years.
949), with pure technical efficiency having a slightly greater impact. However, scale efficiency decreased for two years. Hubei province had four years of DEA-efficient technical efficiency, influenced by both pure technical efficiency (0.
972), but the impact of pure technical efficiency is slightly greater. However, scale efficiency decreased in one year. Guizhou province did not achieve DEA-efficient technical efficiency throughout the period.
The technical efficiency of each region is influenced by both pure technical efficiency and scale efficiency. However, the technical efficiency in the up stream and down stream regions is slightly more affected by scale efficiency, while in the middle stream region, it is slightly more influenced by pure technical efficiency.
However, scale efficiency decreased for two years. Hubei province had four years of DEA-efficient technical efficiency, influenced by both pure technical efficiency (0.928) and scale efficiency (0.978), with pure technical efficiency having a slightly greater impact. However, scale efficiency decreased for four years.
However, scale efficiency decreased for four years. Hunan province had one year of DEA-efficient technical efficiency, influenced by both pure technical efficiency (0.825) and scale efficiency (0.987), with pure technical efficiency having a slightly greater impact. However, scale efficiency decreased for four years.
In addition, the total amount of technology contract transactions is used in this study to represent the economic outcomes of technological innovation, rather than using the commonly adopted metric of new product sales revenue. This is because the concept of new products is broad, and using new product sales revenue to measure economic outcomes may overstate the achievements.
4%, respectively. In the down stream region, Shanghai municipality, Jiangsu, Zhejiang, and Anhui provinces all showed improvements in various indicators, although the magnitude of improvement was not very large.
6% in both efficiency change and scale efficiency change. In the middle stream region, Hunan province experienced decreases of 1.3% in efficiency change, 1% in pure technical efficiency change, and 0.3% in scale efficiency change, but it had a significant increase of 5% in technological change. Jiangxi province and Hubei province saw increases of 4.
It can be seen that the technological innovation of industrial enterprises in the Yangtze River Economic Belt is crucial for the implementation of China's innovation-driven development strategy
Guizhou province did not achieve DEA-efficient technical efficiency throughout the period. Its technical efficiency is influenced by scale efficiency (0.885), which increased over time. Yunnan province had five years of DEA-efficient technical efficiency, with scale efficiency (0.
Shanghai municipality achieved DEA-efficient technical efficiency throughout the period, with scale efficiency remaining constant. Jiangsu province did not achieve DEA-efficient technical efficiency throughout the period, and its technical efficiency is influenced by scale efficiency (0.736), which decreased over time. Zhejiang province had eight years of DEA-efficient technical efficiency, influenced by scale efficiency (0.
3% in scale efficiency change, but it had a significant increase of 5% in technological change. Jiangxi province and Hubei province saw increases of 4.9% and 4.8%, respectively, in efficiency change during the period of 2011-2020, and their technological changes increased by 3.4% and 9.4%, respectively. In the down stream region, Shanghai municipality, Jiangsu, Zhejiang, and Anhui provinces all showed improvements in various indicators, although the magnitude of improvement was not very large.
Jiangxi province had one year of DEA-efficient technical efficiency, influenced by both pure technical efficiency (0.877) and scale efficiency (0.949), with pure technical efficiency having a slightly greater impact. However, scale efficiency decreased for two years.
Looking at the decomposition indicators, the changes in technological efficiency have shown decreases of 0.8% in 2011-2012, 1.8% in 2014-2015, 1% in 2015-2016, and 2.9% in 2019-2020. However, improvements were observed in other years.
Looking specifically at the provincial and municipality level, in the up stream region of the Yangtze River, Yunnan province had a decrease of 1.6% in both efficiency change and scale efficiency change. In the middle stream region, Hunan province experienced decreases of 1.
The Malmquist index shows an overall upward trend, with technological change being the main source of improvement. Moreover, significant differences exist among the upstream, middle, and downstream regions of the Yangtze River Economic Belt, as well as among different provinces and cities.
This suggests that the growth of technological innovation efficiency in the middle and up stream regions is unstable, indicating that it is still in the early stage of growth. On the other hand, the growth of technological innovation efficiency in the down stream region is relatively stable, indicating that it has entered a more mature stage of growth.
5% in 2014-2015, but improvements were seen in other years. Pure technical efficiency change decreased by 0.6% in 2011-2012, 1.1% in 2014-2015, 1.2% in 2016-2017, and 1.5% in 2019-2020, while improvements were observed in other years. Scale efficiency change decreased by 0.
5% in 2019-2020, while improvements were observed in other years. Scale efficiency change decreased by 0.2% in 2011-2012, 0.7% in 2014-2015, 1.5% in 2015-2016, 0.7% in 2017-2019, and 1.5% in 2019-2020, with improvements in other years. This indicates that the number of years with decreases in technological change is the lowest, and all years after 2015 showed improvements.
Shanghai municipality achieved DEA-efficient technical efficiency throughout the period, with scale efficiency remaining constant. Jiangsu province did not achieve DEA-efficient technical efficiency throughout the period, and its technical efficiency is influenced by scale efficiency (0.
However, improvements were observed in other years. Technological change experienced decreases of 3.1% in 2012-2013 and 0.5% in 2014-2015, but improvements were seen in other years. Pure technical efficiency change decreased by 0.
911. The average of the mean values for pure technical efficiency is 0.960, and the average of the mean values for scale efficiency is 0.950. However, none of these values reach the level of DEA efficiency, indicating that the technological innovation efficiency of industrial enterprises in the Yangtze River Economic Belt is relatively low.
The main reasons for this were the severe overcapacity in China's lower-end manufacturing sector and the disorderly expansion of the real estate industry, which became major obstacles to sustained economic development
3% in the Malmquist index during the period of 2014-2015. The overall mean data shows that there have been improvements in the changes of technological efficiency, technological change, pure technical efficiency change, and scale efficiency change. Among them, the largest increase is observed in technological change (5%).
5%). The period of 2012-2013 had the smallest increase in Malmquist index (3.1%). However, there was a decrease of 2.
The potential academic contributions of this study are as follows: (1) This study, considering the background of China's macroeconomy, adopts relevant data from 2011 to 2020, which has not been used in previous studies; (2) When selecting output variables, this study takes into account the uncertainty of the cycle for patent application and approval and estimates the number of authorized invention patents for the current year, making the calculation results more realistic and reliable.
Chongqing municipality had four years of DEA-efficient technical efficiency during the same period. The technical efficiency in Chongqing is influenced by both pure technical efficiency (0.962) and scale efficiency (0.972), but the impact of pure technical efficiency is slightly greater. However, scale efficiency decreased in one year.
The total amount of technology contract transactions better reflects the actual economic outcomes of industrial enterprises' innovation efforts and also indicates their application capabilities for innovative achievements.
In addition, the overall trend of efficiency change follows a "low-high-middle" spatial distribution. The up stream region has the lowest change (decreased by 0.2%), the middle region has the highest change (increased by 2.8%), and the down stream region is in the middle(increased by 0.5%).
As for technological change, it also exhibits a "low-high-middle" spatial distribution. The up stream region has the lowest change (increased by 4.9%), the middle stream region has the highest change(increased by 5.9%), and the down stream region is in the middle (increased by 4.3%).
In terms of regional comparison, the Malmquist index exhibits a "low-high-middle" spatial distribution. The up stream region has the lowest index (improved by 4.6%), the middle stream region has the highest index (improved by 9%), and the down stream region is in the middle (improved by 4.8%). It is also observed that the main source of improvement in the Malmquist index for all three regions is technological change.
The average values of scale efficiency display a "low-high-medium" spatial distribution. The up stream region has the lowest value (0.929), the middle stream region has the highest (0.971), and the down stream region is in the middle (0.955).
To eliminate the influence of heterogeneity among provinces and municipalities, the sum of annual patent approval rates for each provinces and municipalities is divided by the total number of years (10 years from 2011 to 2020) to obtain the average approval rate. Then, the annual number of patent applications for each provinces and municipalities is multiplied by the average approval rate to estimate the number of granted invention patents for that year, thus improving the accuracy of the measurement. R&D projects are the basic organizational form for conducting R&D activities.
Among them, the largest increase is observed in technological change (5%). Therefore, it can be inferred that technological change is the main driver behind the improvement in the Malmquist index of technological innovation efficiency for industrial enterprises in the Yangtze River Economic Belt during the period of 2011-2020, rather than the efficiency of utilizing technological innovation factors.
These results indicate significant spatial differences in technical efficiency among the industrial enterprises in the 11 provinces and municipalities within the Yangtze River Economic Belt. This suggests that these regions' industrial enterprises exhibit significant variations in the intensive utilization of various technological innovation factors.
This article calculates and analyzes the efficiency of technological innovation in industrial enterprises in the Yangtze River Economic Belt and finds that the overall level of technological efficiency in this area during the period of 2011-2020 is relatively low. The Malmquist index shows an overall upward trend, with technological change being the main source of improvement.
5% in 2019-2020, with improvements in other years. This indicates that the number of years with decreases in technological change is the lowest, and all years after 2015 showed improvements. It suggests that industrial enterprises in the Yangtze River Economic Belt increased their investment in technological innovation factors after 2015.
According to the data in table 3, the Malmquist index of technological innovation efficiency for industrial enterprises in the Yangtze River Economic Belt during 2011-2020 is greater than 1, and the overall average of each decomposition index is also greater than 1. This indicates that the technological innovation efficiency of industrial enterprises in the Yangtze River Economic Belt has generally improved based on the Malmquist index. In terms of regional comparison, the Malmquist index exhibits a "low-high-middle" spatial distribution.
885), which increased over time. Yunnan province had five years of DEA-efficient technical efficiency, with scale efficiency (0.962) influencing its technical efficiency and showing an increasing or constant trend.
736), which decreased over time. Zhejiang province had eight years of DEA-efficient technical efficiency, influenced by scale efficiency (0.990), with scale efficiency decreasing for two years. Anhui province had four years of DEA-efficient technical efficiency, influenced by both pure technical efficiency (0.

후속연구

However, due to the limited depth of this article, future research can be optimized in terms of research methods or indicator selection. Further exploration can be conducted to investigate the causes, evolutionary patterns, and spatial relationships underlying the efficiency differences in technological innovation among industrial enterprises in different regions of the Yangtze River Economic Belt. This will enable a more comprehensive assessment of the efficiency of technological innovation among industrial enterprises in the Chinese Yangtze River Economic Belt from 2011 to 2020.
The study on the efficiency of technological innovation among industrial enterprises in the Yangtze River Economic Belt provides us with important references to deeply understand and respond to these challenges. However, due to the limited depth of this article, future research can be optimized in terms of research methods or indicator selection. Further exploration can be conducted to investigate the causes, evolutionary patterns, and spatial relationships underlying the efficiency differences in technological innovation among industrial enterprises in different regions of the Yangtze River Economic Belt.
Therefore, based on the existing research findings, this study cautiously selects relevant data on technological innovation in industrial enterprises in the Yangtze River Economic Belt from 2011 to 2020. It constructs an evaluation index system for the efficiency of technological innovation in industrial enterprises in the Yangtze River Economic Belt and applies the DEA-Malmquist index method to calculate and analyze it, aiming to enrich the research on the efficiency of technological innovation in industrial enterprises in the Yangtze River Economic Belt.
Therefore, based on considering the overall macroeconomic environment of China, this study selects relevant data on technological innovation in industrial enterprises in the Yangtze River Economic Belt from 2011 to 2020. The aim is to objectively reflect the actual situation of technological innovation during this period and provide decision-making basis for improving the efficiency of technological innovation in industrial enterprises in the Yangtze River Economic Belt.
However, the academic community's attention to the Yangtze River Economic Belt's technological innovation has not yet reached the required level. Therefore, based on considering the overall macroeconomic environment of China, this study selects relevant data on technological innovation in industrial enterprises in the Yangtze River Economic Belt from 2011 to 2020. The aim is to objectively reflect the actual situation of technological innovation during this period and provide decision-making basis for improving the efficiency of technological innovation in industrial enterprises in the Yangtze River Economic Belt.
Therefore, based on the existing research findings, this study cautiously selects relevant data on technological innovation in industrial enterprises in the Yangtze River Economic Belt from 2011 to 2020. It constructs an evaluation index system for the efficiency of technological innovation in industrial enterprises in the Yangtze River Economic Belt and applies the DEA-Malmquist index method to calculate and analyze it, aiming to enrich the research on the efficiency of technological innovation in industrial enterprises in the Yangtze River Economic Belt.
Further exploration can be conducted to investigate the causes, evolutionary patterns, and spatial relationships underlying the efficiency differences in technological innovation among industrial enterprises in different regions of the Yangtze River Economic Belt. This will enable a more comprehensive assessment of the efficiency of technological innovation among industrial enterprises in the Chinese Yangtze River Economic Belt from 2011 to 2020.

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