Industrial Innovation Efficiency Research Articles

Study on the Evaluation of Technological Innovation Efficiency of Industrial Enterprises in Central and Western China: Based on DEA approach

Based on the importance of industrial innovation efficiency, this paper conducts dynamic and static analyses of the technological innovation efficiency of industrial enterprises in the central and western areas of China from years 2010 to 2021 using the DEA-BCC model and Malmquist index based on relevant data. The results indicate that the efficiency of technological innovation in China has steadily increased, with the innovation efficiency in the central and western areas stabilizing at a relatively high level. Additionally, the study identifies that the prosperity of local industries significantly influences industrial scientific and technological innovation efficiency. Consequently, the article provides more specific recommendations for formulating industrial policies that impact industrial prosperity.

Evaluating the impact of the innovation efficiency of high-tech industry on carbon emissions: a case study of the manufacturing industry in China.

Amid China's economic shift to high-quality development, addressing environmental challenges like greenhouse gas emissions and manufacturing pollution, there is a crucial demand for sustainable and eco-friendly development strategies. This study aims to investigate the impact of innovation efficiency in the high-tech industry on carbon emissions. It seeks to explore regional differences, mechanisms, and the influence of energy consumption structures in achieving sustainable development goals. Utilizing data from 30 provinces spanning 2009 to 2020, the study employs the DEA-Malmquist index model, spatial and temporal classification evaluation, and a panel measurement model to assess the efficiency of innovation and development in high-tech industries and their relationship with carbon emissions. The results indicate several key findings: (1) The overall operational efficiency of high-tech industry innovation and development in China is steadily increasing. However, there are distinct characteristics observed among provinces and cities, reflecting diverse input and output types. (2) High-tech industry innovation efficiency significantly contributes to carbon emission reduction, and there is regional heterogeneity in this impact. The central and western regions exhibit greater effects compared to other provinces and cities. (3) The optimization of the energy structure is identified as a mechanism through which high-tech industry innovation efficiency reduces carbon emissions. Moreover, different intervals of high-tech industry innovation efficiency yield varying effects on carbon emissions. This research underscores the importance of fostering high-tech industry innovation efficiency as a means to reduce carbon emissions. It also identifies key areas for future policy development and resource allocation, emphasizing the support needed for low-carbon technology research and development.

The impact of government support and market competition on China's high-tech industry innovation efficiency as an emerging market

With the building of an innovative country in an emerging market, the role of government support (GS) and market competition (MC) in the innovation efficiency of China's high-tech industry (HTI) has become increasingly prominent. Considering innovation value chain (IVC) theory, based on 2004–2016 panel data covering 30 regional HTIs in mainland China, the spatial autoregressive (SAR) Tobit model, stepwise regression method with spatial factors, and threshold panel model are applied to examine the main effects, moderating effects, mediating effects, and nonlinear effects of GS and MC on the innovation efficiency of China's HTI. GS significantly inhibits the industry's technological development efficiency but promotes its innovation achievement transformation efficiency. However, the impact of MC on the industry's technological innovation efficiency has stage homogeneity, significantly promoting both stages of innovation efficiency. Economic policy uncertainty (EPU) exerts a significant moderating effect on GS and technological development efficiency. The transmission mechanism test reveals that technology market development (TMD) has a partial mediating effect between GS and technological development efficiency, while a masking effect exists between MC and technological development efficiency. A complete mediating effect exists between GS and innovation achievement transformation efficiency, while a partial mediating effect exists between MC and innovation achievement transformation efficiency. Regional heterogeneity is apparent. Of note, the impact of MC on the industry's technological development and innovation achievement transformation efficiency has a nonlinear effect based on GS.

Evaluation of Green Industry Innovation Efficiency Based on Three-Stage DEA Model: A Case Study of Chinese Information Technology Industry

The information technology industry as a new engine driving China’s economy has made more and more contributions to Chinese sustainable development. At present, it has overtaken real estate as the new cradle of Chinese billionaires. The information technology industry not only has its own characteristics of high economic, social benefits, and small impact on the ecological environment, but also can enable the green development of the economy and society. So it is the core industry to support the realization of the “double carbon” goal. This paper evaluated the innovation efficiency of 80 enterprises in the software and information technology service from 2017 to 2018 by constructing a three-stage DEA model. It puts forward countermeasures, which points out the direction for the development of environmental protection and green low-carbon industry. Empirical results show that environmental variables have different effects on innovation efficiency. After excluding the influence of environmental and random factors, the increase in innovation efficiency, while generally significant, is not high. Low innovation efficiency is caused by both pure technical efficiency and scale efficiency, especially pure technical efficiency. Enterprises’ adjusted scale returns are mostly increasing; the innovation investment scale is not optimal. Regional differences of enterprise innovation exist; the East and Midwest have obvious polarization both in quantity and quality. These results quantify the effect of the factors affecting enterprise innovation efficiency and put forward policies and suggestions for promoting the development of China’s information technology industry accordingly.

Research on Industrial Innovation Efficiency and the Influencing Factors of the Old Industrial Base Based on the Lock-In Effect, a Case Study of Jilin Province, China

In the context of the increasingly intensified innovation competition, improving industrial innovation efficiency is the key to achieving the sustainable development of the old industrial base. This paper adopts the thinking of regional research to study the laws of industrial innovation in the old industrial base and takes the lock-in effect as the connection point between the industrial evolution history and industrial innovation efficiency. Based on the perspective of the lock-in effect, the three-stage industrial innovation model, the lock-in effect identification method, and the extended Porter model are creatively constructed. This paper chooses Jilin Province in Northeast China as a case, dissects the evolution history of industrial innovation in detail, and uses super-efficiency DEA, the Granger test, geographical detectors, and the panel regression method for quantitative analysis. The results show the following: (1) The lock-in effect faced by the industrial innovation of the old industrial base is significant, which has an impact on industrial innovation through industrial structure, enterprise composition, management system, and degree of marketization. (2) The lock-in effect causes the old industrial base to fall into an unhealthy circle in which it is difficult for industrial enterprises to obtain sufficient benefits through industrial innovation and the ability of industrial enterprises to absorb regional innovation resources is weakened. (3) The impact mechanism of industrial innovation in the old industrial base is very complex and the lock-in effect factors are not all negative. The improvement of industrial innovation in the old industrial base needs to increase the role of market forces, reform large traditional enterprises, and increase foreign economic ties. However, it also needs policy support, and it should avoid overly radical industrial transformation and enterprise strategies.

The Impact of Government Support and Market Competition on China's High-Tech Industry Innovation Efficiency as an Emerging Market

The Impact of Government Support and Market Competition on China's High-Tech Industry Innovation Efficiency as an Emerging Market

Innovation efficiency and technology heterogeneity within China's new energy vehicle industry: A two-stage NSBM approach embedded in a three-hierarchy meta-frontier framework

Exploring innovation efficiency (IE) and technology heterogeneity within the new energy vehicle (NEV) industry is significant for advising industrial growth and healthy development. This paper embeds a two-stage network slacks-based measure (NSBM) approach into a three-hierarchy meta-frontier framework to evaluate the value chain's IE and the technology heterogeneity under the industrial chain structure. The observations were as follows. The industrial IE was low, particularly in the product creation stage, and the downstream IE ranked at the top, followed by the midstream and upstream. Furthermore, technological heterogeneities existed within the industry; the upstream technology level aligned with that of the industry, whereas the gaps between midstream, downstream, and industry were significant. Analogously, the battery enterprises' gap was minor upstream; nevertheless, the gaps between electric engine, electric control enterprises, and upstream were more pronounced. Additionally, management inefficiency contributed more to the industrial innovation inefficiency than the technology gaps inefficiency. Finally, industrial productivity declined sharply due to the technology gaps' change, except in battery enterprises. Our work will provide valuable insights for industrial policies' evaluation and improvement.

Does China's new energy vehicle industry innovate efficiently? A three-stage dynamic network slacks-based measure approach

Innovation ability is vital to the new energy vehicle(NEV) industry in China. To bridge the previous studies’ gaps, a dynamic network slacks-based measure(DNSBM) model, coupled with the three-stage innovation framework, is employed to evaluate the industrial innovation efficiency. Overall, dynamic efficiencies and the divisional efficiencies of the R&D, manufacturing, and marketing process are present, drawing on 17 listed companies’ data from 2012 to 2019. Additionally, the Malmquist index measures industrial productivity and its decomposition, including the Frontier-shift and Catch-up effect. The firm-level efficiencies’ analysis ends up following the types of enterprises and divisional performance. The findings reveal that the overall innovation efficiency was low, and marketing and manufacturing efficiency were higher than those of the R&D division. Then, the annual overall and divisional productivities were low, whereas the cumulative productivities were high. In the end, all enterprises’ performance differentiated, and commercial-vehicle firms’ efficiencies were higher than those of the passenger-vehicle firms except for the marketing division. Meanwhile, the state-owned companies’ efficiencies were higher than those of private companies’ except for the marketing process. The theoretical and practical implications are available for the policymakers, managers, and academic researchers from this study.

Research on the spatial association network structure for innovation efficiency of China's new energy vehicle industry and its influencing factors.

It is of great significance to study the spatial network of the new energy vehicle (NEV) industry innovation efficiency and its factors to promote the rational allocation of innovative resources and the coordinated development of Chinese NEV industry. First, the Super Efficiency Data Envelope Analysis model is used to measure innovation efficiency in the NEV industry in Chinese provinces, and based on the results, the improved gravity model is applied to construct a spatial correlation network. Then, by applying social network analysis (SNA) to study NEV industry development node spatial correlations, we conclude that there is no overall hierarchical structure. The SNA are applied to examine spatial correlations with respect to NEV industry innovation efficiency in each province, and to analyze the role and position of each province in the spatial correlation network. Finally, the influencing factors of spatial correlation of the innovation efficiency of China’s NEV industry has been discussed. The result shows that the difference in spatial distance and R&D investment has a significant impact on the spatial correlation of the NEV industry.

Research on the Influence of High-tech Industry Specialization Agglomeration on Innovation Efficiency

In the new economic era with innovation as the first driving factor, high-tech industry, as an important industry support for innovation, its innovation efficiency will greatly affect the regional innovation level. In order to improve the innovation efficiency of the high-tech industry, the practice of accelerating the professional agglomeration of the industry has been widely adopted. What is the impact of high-tech industry specialization agglomeration on innovation level? Based on this, this paper constructs a theoretical analysis framework between industrial specialization agglomeration and innovation efficiency, and empirically analyzes it. The research results are as follows: there is an inverted U-shaped relationship between high-tech industry specialization agglomeration and innovation efficiency, that is, there is a specialized agglomeration scale, which makes industrial innovation efficiency reach the highest level. According to the empirical results, this paper provides a data level support for the proposal of industrial agglomeration policy.

A New Internet DEA Structure: Measurementof Chinese R&D Innovation Efficiency in High Technology Industry

Studying from the characteristics of the two stagesin the high-tech industry, combining with the practice anddeficiency of the actual measurement, wesummarize China's high-tech industry innovation efficiency calculationmethodsand explore the one of DEA methodin depth to construct a new index system, on which a new internetDEA model (a methodological innovation) is constructed. This new model can be applied to get overallefficiency and the efficiency of the two phases in high-tech industry R&D activities. In the end, the articleshows the application of this model: the ability to analyze China's high-tech industrial innovation efficiency andthe efficiency of high-tech industry's R&D activity in different regions and will have implications for thegovernmental policy-making.