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An effective evaluation of the factors affecting technological innovation efficiency (TIE) at the system level is an important focus in promoting high-quality economic development. Previous studies are extensively concerned with the relationship between fiscal decentralisation and TIE in recent years, but little attention has been paid to whether and how fiscal decentralisation affects TIE systematically from the perspective of efficiency decomposition. Using China's panel data at the province level from 1998 to 2018, we explore this question by decomposing the TIE into scale efficiency and pure technical efficiency. Findings demonstrate that fiscal decentralisation has a positive and significant influence on TIE and scale efficiency, but has no significant influence on pure technical efficiency. The influences of fiscal decentralisation on TIE and its decomposition efficiency have evident regional heterogeneity. Fiscal decentralisation can improve TIE and scale efficiency by increasing fiscal expenditure on science and technology. These findings may aid policymakers in reasonably designing future innovation policies and the next-stage reform package. [ABSTRACT FROM AUTHOR]

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This study aims to fill a gap in our knowledge by analyzing how digital financing has affected technological innovation in China's firms across different growth phases. This article investigates the relationship between technological innovation efficiency and digital finance in China's A-share listed corporations between 2012 and 2022 using basic logistic modelling. Its main goal is to comprehend how digital finance affects these businesses' technological innovation efficiency from various angles. The study concludes that digital finance significantly improves the efficiency of technological innovation; more importantly, the Digital Finance Adoption Rate (DFAR) has a more significant influence than the Digital Finance Level (DFL). According to the Digital Transformation Maturity (DTM) scale, digital finance helps businesses transform digitally, increasing the effectiveness of technological innovation. State-owned businesses are more affected by the influence of digital finance on innovation efficiency than non-state-owned businesses. Other factors, including enterprise lifecycle, innovation investment, digital leadership, revenue strategies, and market rivalry, also shape the relationship between digital finance and technical innovation efficiency. To increase innovation efficiency, particularly in state-owned businesses, the study suggests expanding access to and adoption of digital finance. It also means leveraging elements such as enterprise lifecycle, innovation investment, digital leadership, revenue strategies, and market competition to spur more technological innovation. The impact of digital finance on technological innovation at different phases of organizational growth in China highlights the need for customized methods to harness its potential.

With the implementation of "carbon peaking and carbon neutrality" in China, new energy enterprises, as the vanguard in this strategy, have entered a new era of innovation-driven development. However, enterprises at different lifecycle stages will face different internal and external conditions, and there are differences in their internal mechanisms and business performance. In this case, whether technological innovation efficiency can have an obviously positive effect on their growth and what different effects it can have for enterprises at different lifecycle stages have become issues of great concern to company management, investors, governments, and other stakeholders. This research takes 81 new Chinese energy enterprises as the research objects. First, they are divided into growing, mature, and declining enterprises based on the cash flow combination method. Then, their technological innovation efficiencies from 2016 to 2021 are calculated based on the stochastic frontier method and their growth evaluations are performed by taking both financial and non-financial indicators into consideration. Finally, by taking mediating effects into consideration, the heterogeneity effects of technological innovation efficiency on their growth are studied from the perspective of enterprise lifecycles based on the fixed-effect model. The research results indicate that the technological innovation efficiency of new Chinese energy enterprises has fluctuated around 0.90 in recent years, and is generally at a high level. The efficiency ranking of enterprises at different lifecycle stages is mature period > growing period > declining period. Technological innovation efficiency has a positive impact on their growth, and market share plays a mediating role in this process. The effects of technological innovation efficiency on enterprises at different stages are different, with growing and mature enterprises showing a positive impact. Growing enterprises are more affected by technological innovation efficiency due to their demand for innovation-driven development, while declining enterprises often face difficulties such as unstable operating conditions and outdated equipment, and unreasonable technological innovations may actually accelerate their decline. [ABSTRACT FROM AUTHOR]

Green, low-carbon circular economy and the development model of energy-saving and environmental protection are emerging worldwide, and the world's economies have taken the energy-saving and environmental protection industry as a strategic emerging industry to cultivate and develop. Coordinating finance and technological innovation is crucial to promoting the healthy development of Energy-Saving and Environmental Protection Enterprises (ESEPEs). According to industry classification, this paper divides 121 ESEPEs into 12 categories as research objects. Firstly, the DEA-SBM model is used to calculate financing efficiency (FE) and technological innovation efficiency (TIE). Subsequently, the changes in the two efficiencies are analyzed from the perspectives of time series, industry, and annual mean value, and the changes of the two efficiencies are analyzed from the perspectives of time series, industry, and annual average values. Secondly, through the coupling coordination degree (CCD) model, the development status of the coupling coordination between the two efficiencies is innovated expounded. Finally, the Tobit regression model is used to discuss the factors influencing CCD from the whole and industry perspectives. The results show that: (1) TIE is generally lower than FE, and FE shows a downward trend while TIE shows an upward trend. (2) CCD between the two efficiencies is Near dysfunction, and TIE restricts the development of CCD between the two efficiencies to a certain extent. (3) R&D intensity, enterprise size, and government subsidy intensity have a significant positive impact on CCD between FE and TIE of ESEPEs, while the impact of other factors is not significant.

Technological innovation efficiency is an important index to measure the development level of financial enterprises, and scientific and reasonable assessment of technological innovation efficiency is of great significance to play the role of financial enterprises in economic development. The article takes the upstream and downstream financial enterprises in province A as the research object. It proposes the super-efficiency DEA model on the basis of the data envelopment analysis method to measure the technological innovation efficiency of the sample enterprises. On this basis, the fuzzy set qualitative comparative analysis method is utilized to compare group paths in five aspects: digital economy, government subsidies, enterprise scale, profitability, and operational capacity. The results of the study show that the overall consistency of the conditional grouping scheme (H1: 0PER*GOV*DEC*PROF, scheme H2: DEC*SIZE*0PER*GOV, scheme H3: DEC*0PER*GOV) that promotes the technological innovation efficiency of the upstream manufacturing enterprises reaches 0.78, and the total coverage rate reaches 0.51. The conditional grouping scheme (scheme H2: DEC*SIZE*0PER*GOV, scheme H3: DEC*0PER*GOV) that promotes the technological innovation efficiency of the downstream manufacturing enterprises reaches 0.51. The overall consistency of the conditional configuration scheme (Scheme L1: DEC*~SIZE*0PER, Scheme L2: ~SIZE*PROF*~GOV, Scheme L3: DEC*PROF) reaches 0.91, and the total coverage rate reaches 0.45. The research results reveal the differences between the current upstream and downstream technological innovation efficiency and driving paths in the financial industry and have some guiding significance for the choice of paths of the financial industry innovation drive. It has a certain relevance that is guiding.

Despite the rapid advancement of intelligent process, few studies investigate the impact of intelligence on the efficiency of technological innovation, and none focus on examining the mechanism underlying this impact or investigating whether this effect exhibits heterogeneity. Accordingly, this paper uses panel data from 30 regions in China from 2005 to 2016 to test the impact of intelligence on technological innovation efficiency and the mechanism underlying this impact. The paper further investigates the heterogeneity of this impact given different levels of government attention, different levels of R&D intensity, and different geographical locations. The results show that first, on the whole, intelligence can significantly improve the efficiency of regional technological innovation. This conclusion remains valid after robustness testing and instrumental variable regression. Second, the results concerning the mechanism by which intelligence impacts the efficiency of technological innovation show that intelligence improves the efficiency of regional technological innovation through human capital accumulation, knowledge spillover, and optimization of the allocation of innovation factors. Third, further analysis shows that the beneficial effect of intelligence on regional technological innovation efficiency is obviously heterogeneous. In areas featuring high levels of government attention, R&D intensity, and geographical advantages, the beneficial effect of intelligence on technological innovation efficiency is more obvious. [ABSTRACT FROM AUTHOR]

In recent years, China's construction companies have placed increasing emphasis on technological innovation and have invested significant resources in it. How the input-output performance of these resources needs to be clarified. The purpose of this study is to understand the differences and variations in the input-output performance of China's listed construction companies in terms of technological innovation by measuring their technological innovation efficiency (TIE), which can inform their technological innovation decisions. A data envelopment analysis (DEA) based Malmquist productivity index (MPI) model was constructed to measure the TIE of 48 China's listed construction companies from 2015 to 2020. The study has the following findings. Firstly, the overall TIE of listed construction companies decreased by 0.4% per year on average during the study period, which needs further improvement. Secondly, the changes of TIE in different sub-sectors were different. The TIE of companies in the sectors of building decoration, infrastructure construction and mining, metallurgical, and petrochemical engineering increased by 10.7%, 3.7%, and 9.5% annually, respectively. Thirdly, the TIE varies among different companies with 23 companies increasing and 25 companies declining. For most companies, the management level was the main factor restricting TIE's improvement. [ABSTRACT FROM AUTHOR]

Under the constraint of “double carbon” goal, both environmental regulation and resource endowment are important factors affecting the technological innovation efficiency. Based on the panel data of Chinese renewable energy enterprises from 2010 to 2019, this paper uses fixed effect and sys-GMM model to investigate the moderating effect of resource endowment between environmental regulation and technological innovation efficiency. The results show that the economical and supervised environment regulation are positively correlated with technological innovation efficiency, while the legal environmental regulation is the opposite. Moreover, resource endowment is positively related to technological innovation efficiency, especially for non-state-owned renewable energy enterprises, which reflects the existence of a certain degree of “resource curse” in state-owned renewable energy enterprises. In addition, resource endowment has a significant negative moderating effect in the relationship between environmental regulation and technological innovation efficiency, which shows that with the increase of resource endowment, the impact of economical and supervised environmental regulation on technological innovation efficiency will weaken, while the impact of legal environmental regulation on technological innovation efficiency will strengthen. It is helpful for the government to formulate more accurate resource and environmental regulation policies to improve the technological innovation efficiency.

Previous studies on the relationship between environmental regulation (ER) and technological innovation efficiency of defence industry (TIE-DI) mainly focus on variability and complexity, while few empirical studies have incorporated environmental flexibility into models, and most of them are based on questionnaires. Therefore, this paper takes environmental regulation and environmental policy complexity (EPC) as the entry point, so as to discuss the feasibility of improving the technological innovation efficiency of China's defence industry enterprises (CDI), which aims to empirically test the mechanism of ER and EPC on improving the efficiency of applied technological innovation of China's defence industry enterprises. The research conclusion provides sufficient theoretical basis and empirical support for strengthening the technical innovation efficiency support, standardising the market order and the market leading, establishing the information disclosure mechanism and improving the internal control of industrial enterprises. [ABSTRACT FROM AUTHOR]

Innovation is the main driving force to promote national technological progress. It is of great significance to explore the optimal path to improve innovation efficiency by using the qualitative method and neural network prediction model to promote the high-quality development of the national economy. This study focuses on high-tech industries in the eastern, central and western regions of China; a factor-dependent research framework for innovation efficiency improvement in high-tech industries is constructed in China. The fuzzy-set qualitative comparative analysis method (QCA) is used to explore multiple paths to enhance the innovation efficiency of China's high-tech industries. Then, a GA-PSO-BP neural network is used to construct an optimization model for the enhancement path of technological innovation efficiency, which clarifies the optimal path for the enhancement of innovation efficiency of high-tech industries in the eastern, central and western regions of China. Finally, innovation management strategies for high-tech industries are presented with regional features. The study finds that none of the individual conditions are necessary to promote the innovation efficiency of China's high-tech industries, and only the linkage effect of the factors can achieve the goal of improving the innovation efficiency level of China's high-tech industries. There are four configuration paths to improve the innovation efficiency of China's high-tech industries, which are: "Multinational company (MNC) innovation—economic development—government support"; "MNC innovation—government support"; "economic development—government support"; and "economic development". The characteristics of regional heterogeneity make differences in the optimal paths of innovation efficiency improvement in high-tech industries in eastern, central and western regions of China. [ABSTRACT FROM AUTHOR]

The allocation of innovation-related elements is influenced by intangible elements such as technological information, which affects knowledge capital, human capital, and material element investment, resulting in the stochastic change of technological innovation efficiency. The endogenous change in technological information in element investment reduces friction, lowers costs, improves efficiency, and gradually reduces the deviation between knowledge capital investment and technological innovation efficiency. In terms of small knowledge capital investment characterized by the low demand for technological information and a single source, it is easier to make more-accurate predictions, so the stochastic change in technological innovation efficiency tends to be gentle. The endogenous change in technological information continuously increases the proportion of replacing other elements with knowledge capital investment, and the efficiency of technological innovation improves steadily. Under the condition of unchanged investment in other elements, there is a great difference in the duration of technological information between the simultaneous selection and the successive selection of knowledge capital investment. When one-time instantaneous information and continuous complete information are respectively acquired, the stochastic variation of technological innovation efficiency is obvious. On the basis of the technological innovation data of 287 listed companies in eight industries, this paper compares and analyzes the measurement results of GMM and OLS to verify the above findings. [ABSTRACT FROM AUTHOR]

In recent years, agribusiness firms have increasingly diversified their product portfolio and expanded into new business areas through internal growth or acquisition, which can lead to the reallocation of innovation resources. This study examines the impact of agribusiness diversification (AD) on technological innovation efficiency (TIE) in China. Based on an 8‐year longitudinal dataset of 101 Chinese listed agribusiness firms from 2012 to 2019, this study applies the Tobit regression technique to reveal the underlying mechanism between AD and TIE. The empirical results show that: (1) the AD‐TIE relationship follows a U‐shaped pattern, that is, at low levels of AD, the AD‐TIE relationship is negative; at high levels of AD, the AD‐TIE relationship is positive. (2) government subsidies (GS) negatively moderate the U‐shaped relationship between AD and TIE, such that GS flattens the U‐shaped curve. Our findings have important implications for agribusiness managers and public policymakers. [EconLit citations: H25, L25, Q16]. [ABSTRACT FROM AUTHOR]

As a typical technology-intensive industry, the renewable energy industry is a standard sample for scholars to study the R&D innovation of enterprises. At present, the industry is strongly supported by the government subsidy policy in China and has developed rapidly in recent years. Its own financing constraints and lack of innovation power are also the main problems for the development of renewable energy enterprises. Taking A-share renewable energy enterprises from 2016 to 2020 as the research object, this paper constructs a panel model to empirically study the relationship between government subsidies and enterprise innovation efficiency. The findings are as follows. First, government subsidies can significantly promote the innovation efficiency of enterprises; second, the government subsidy has an obvious double threshold effect on the innovation efficiency of enterprises, and when the government subsidy is in a certain range, the subsidy effect is the best; third, the government subsidy effect of non-state-owned renewable energy enterprises is better than that of state-owned enterprises; the effect of government subsidies for renewable energy enterprises in the central and western regions is better than that in the eastern region. The threshold effect of government subsidies on enterprise innovation efficiency is also quite different. [ABSTRACT FROM AUTHOR]

Determining what factors influence firm performance constitutes an essential issue in both the management and the family firm research fields. This article, building on the resource-based view perspective, develops a mediation model that involves a unique intervening mechanism, namely, technological innovation efficiency (TI efficiency), with the potential to explain the inconsistencies found in prior work on the ways through which family involvement in management affects performance outcomes. Regression analyses utilizing a longitudinal sample of 1,118 Spanish private firms largely support the hypothesized mediating relationship, revealing that TI efficiency leads to richer firm performance in family firms with active family involvement in management. Overall, our findings help elucidate the black box of performance outcomes within family firms and make several contributions to theory and practice. JEL CLASSIFICATION L25; M12; O32 [ABSTRACT FROM AUTHOR]

Reasonable spatial system distribution is the prerequisite for the optimization of resource and spatial allocation and the coordinated development of the regional economy. Therefore, correctly identifying the impact of different types of spatial structures on scientific and technological innovation is an important way to promote the rational layout of regional spatial structures and enhance the strength of regional scientific and technological innovation. Based on the theory of regional spatial structure and panel data of 26 provinces and autonomous regions in China from 2005 to 2019, this paper tested the impact of regional spatial structure on innovation efficiency and gap by constructing the regional spatial structure index, the innovation efficiency index, and the innovation gap index. The research results show that: First, the agglomeration effect produced by a single-center spatial structure is conducive to improving the efficiency of scientific and technological innovation, whereas the spillover effect generated by a multi-center spatial structure is more favorable for narrowing the gap in scientific and technological innovation. Second, the single-center spatial structure is more suitable for provinces and cities in the western region with relatively low levels of economic development, whereas the multi-center spatial structure is more beneficial to the achievement of innovative and high-quality development in the eastern region. Third, the moderating effect analysis shows that with an increase in cultural diversity and inter-city distance, the partial effects of the single-center spatial structure on innovation efficiency present an "N" shape and an "inverted-U" shape, respectively, whereas the partial effects of the multi-center spatial structure on the innovation gap exhibit "inverted-U" shapes. This research not only provides theoretical support for the impact of regional spatial structure on innovation efficiency and gap but also offers empirical evidence for future regional development path choices. [ABSTRACT FROM AUTHOR]

Digital financial inclusion is a new product of traditional finance through digitalization and technologization. This paper uses the data of China's A-share listed agricultural companies from 2015 to 2020 to explore the impact of digital financial inclusion on the technological innovation efficiency of agricultural enterprises and answer the question of whether digital financial inclusion can solve the financing problems of agricultural enterprises and provide them the necessary support to stimulate their technological innovation effectively. Firstly, the data envelopment analysis (DEA) is used to measure the technological innovation efficiency of agricultural enterprises, and the Tobit model is adopted to analyze the impact of digital financial inclusion on the technological innovation efficiency of agricultural enterprises. Secondly, the influence mechanism of digital financial inclusion is explored. Thirdly, the heterogeneity test is conducted for enterprises with different characteristics. Finally, we discuss how the marginal effect of digital financial inclusion changes. The empirical results show that: first, digital financial inclusion has a significant promoting effect on the technological innovation efficiency of agricultural enterprises, and the impact is prominent in a wide range; second, digital financial inclusion can promote technological innovation through the mechanism of enterprise digitization, financing constraints and market efficiency; third, non-state-owned enterprises with high financing level are more suitable to encourage innovation through digital financial inclusion; fourth, the promoting effect of digital financial inclusion has structural characteristics, and it shows an increasing trend with the improvement of enterprise innovation level.

Copyright of Journal of Changzhou University (Social Science Edition) is the property of Editorial Department of Journal of Changzhou University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Science Technology & Industry / Keji he Chanye is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Science Technology & Industry / Keji he Chanye is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. In this study, first, technological innovation efficiency (TIE) was measured. Then, a dynamic evaluation and analysis of spatial-temporal characteristics of efficiency were performed. Lastly, the driving factors of innovation efficiency were explored. TIE was calculated dynamically in 30 provinces of China from 2011 to 2019 based on the improved super-efficiency SBM-DEA model. Then, the kernel density estimation method was adopted to analyse the spatial-temporal differentiation characteristics and dynamic evolution process of provincial efficiency. The findings confirm that from 2011 to 2019, the top five provinces for TIE in China were Beijing (1.0), Shanghai (0.96), Hainan (0.96), Jilin (0.94) and Tianjin (0.91). The provinces with lowest average efficiency were Qinghai (0.77), Ningxia (0.73) and Inner Mongolia (0.73). The significant differences in the level of technological innovation in different regions were caused by the long-term and in-depth implementation of the government's strategy of revitalising science and driving innovation in parts of areas. The findings of kernel function confirm that the TIE in most parts of China was gradually polarised. Furthermore, the results show that for every 1 unit of government R&D funding support, the average marginal utility of the expected TIE will reach 0.192, which is more significant in the central and western regions. On this basis, combined with environmental factors of innovation market, infrastructure, financing and enterprise innovation potential, the article also extracts the driving factors that affect the differences in provincial efficiency. The findings provide a reference for guiding provinces to carry out innovation activities independently and improve innovation quality and efficiency. [ABSTRACT FROM AUTHOR]

Innovation is the main driving force to promote national technological progress. It is of great significance to explore the optimal path to improve innovation efficiency by using the qualitative method and neural network prediction model to promote the high-quality development of the national economy. This study focuses on high-tech industries in the eastern, central and western regions of China; a factor-dependent research framework for innovation efficiency improvement in high-tech industries is constructed in China. The fuzzy-set qualitative comparative analysis method (QCA) is used to explore multiple paths to enhance the innovation efficiency of China’s high-tech industries. Then, a GA-PSO-BP neural network is used to construct an optimization model for the enhancement path of technological innovation efficiency, which clarifies the optimal path for the enhancement of innovation efficiency of high-tech industries in the eastern, central and western regions of China. Finally, innovation management strategies for high-tech industries are presented with regional features. The study finds that none of the individual conditions are necessary to promote the innovation efficiency of China’s high-tech industries, and only the linkage effect of the factors can achieve the goal of improving the innovation efficiency level of China’s high-tech industries. There are four configuration paths to improve the innovation efficiency of China’s high-tech industries, which are: “Multinational company (MNC) innovation—economic development—government support”; “MNC innovation—government support”; “economic development—government support”; and “economic development”. The characteristics of regional heterogeneity make differences in the optimal paths of innovation efficiency improvement in high-tech industries in eastern, central and western regions of China.

Copyright of Science Technology & Industry / Keji he Chanye is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aims to fill a gap in our knowledge by analyzing how digital financing has affected technological innovation in China's firms across different growth phases. This article investigates the relationship between technological innovation efficiency and digital finance in China's A-share listed corporations between 2012 and 2022 using basic logistic modelling. Its main goal is to comprehend how digital finance affects these businesses' technological innovation efficiency from various angles. The study concludes that digital finance significantly improves the efficiency of technological innovation; more importantly, the Digital Finance Adoption Rate (DFAR) has a more significant influence than the Digital Finance Level (DFL). According to the Digital Transformation Maturity (DTM) scale, digital finance helps businesses transform digitally, increasing the effectiveness of technological innovation. State-owned businesses are more affected by the influence of digital finance on innovation efficiency than non-state-owned businesses. Other factors, including enterprise lifecycle, innovation investment, digital leadership, revenue strategies, and market rivalry, also shape the relationship between digital finance and technical innovation efficiency. To increase innovation efficiency, particularly in state-owned businesses, the study suggests expanding access to and adoption of digital finance. It also means leveraging elements such as enterprise lifecycle, innovation investment, digital leadership, revenue strategies, and market competition to spur more technological innovation. The impact of digital finance on technological innovation at different phases of organizational growth in China highlights the need for customized methods to harness its potential., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author.)

Green, low-carbon circular economy and the development model of energy-saving and environmental protection are emerging worldwide, and the world's economies have taken the energy-saving and environmental protection industry as a strategic emerging industry to cultivate and develop. Coordinating finance and technological innovation is crucial to promoting the healthy development of Energy-Saving and Environmental Protection Enterprises (ESEPEs). According to industry classification, this paper divides 121 ESEPEs into 12 categories as research objects. Firstly, the DEA-SBM model is used to calculate financing efficiency (FE) and technological innovation efficiency (TIE). Subsequently, the changes in the two efficiencies are analyzed from the perspectives of time series, industry, and annual mean value, and the changes of the two efficiencies are analyzed from the perspectives of time series, industry, and annual average values. Secondly, through the coupling coordination degree (CCD) model, the development status of the coupling coordination between the two efficiencies is innovated expounded. Finally, the Tobit regression model is used to discuss the factors influencing CCD from the whole and industry perspectives. The results show that: (1) TIE is generally lower than FE, and FE shows a downward trend while TIE shows an upward trend. (2) CCD between the two efficiencies is Near dysfunction, and TIE restricts the development of CCD between the two efficiencies to a certain extent. (3) R&D intensity, enterprise size, and government subsidy intensity have a significant positive impact on CCD between FE and TIE of ESEPEs, while the impact of other factors is not significant., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Facing increasingly serious environmental problems, technological innovation has become the key for industrial enterprises to coordinate energy conservation and emission reduction constraints and achieve steady growth of the industrial economy. Considering the impact of energy consumption and environmental pollution on the technological innovation efficiency of industrial enterprises, this paper incorporates industrial energy consumption, pollution control, and wastewater and exhaust emissions into the technical inefficiency equation. Based on the panel data of industrial enterprises in 30 provinces and autonomous regions in China from 2009 to 2016, the stochastic frontier analysis (SFA) model is used to study the effect of energy consumption and environmental pollution on technological innovation efficiency of industrial enterprises. The research results show that reducing energy consumption and increasing pollution treatment investment both have a significant driving effect on the improvement of industrial enterprises' technological innovation efficiency. Industrial wastewater and exhaust emissions have the opposite effect; unreasonable input mode of pollution control and personnel allocation have hindered the improvement of industrial enterprises' technological innovation efficiency. The average annual trend of technological innovation efficiency in industrial enterprises shows a curve of first rising, then falling, and rising again. The average values of Chongqing, Zhejiang, and Hunan rank in the top three, and the average values of Qinghai, Heilongjiang, and Inner Mongolia rank the bottom three. The average values of other provinces are higher than 0.9, and the difference is small. A suitable incentive mechanism should be established for industrial enterprises to save energy and reduce emissions and strengthen pollution control, improve the training program for environmental protection technical personnel, and provide important support for improving the green competitiveness of industrial enterprises. [ABSTRACT FROM AUTHOR]

Discussing the technological innovation efficiency of big data enterprises and carrying out in-depth research on big data enterprises will help to accurately evaluate the development status of big data industry and guide the development of big data industry. This paper uses DEA-BCC model to evaluate the technological innovation efficiency of 21 big data enterprises. It is found that the technological innovation efficiency of big data enterprises is better. The average values of technological efficiency, pure technological efficiency and scale efficiency are 0.587, 0.772 and 0.750, respectively. The technological efficiency of big data enterprises is lower, and the individual differences are larger. At the same time, the research shows that there are 15 big data enterprises need to further expand the scale, and then improve production efficiency.

The study was designed to evaluate the impact of differentiated technological innovation efficiencies of industrial enterprises on the local emissions of environmental pollutants in Anhui province, China, during the period of 2012–2016. The econometric models of DEA and SEM-PLS and Malmquist index are used to explore the potential impacts of differentiated technological innovation efficiencies of industrial enterprises on the local emissions of environmental pollutants in Anhui province. After an initial analysis of SEM-PLS model, the models of DEA and Malmquist index are used to evaluate the differentiated degrees and dynamic development levels of local technological innovation efficiencies of industrial enterprises in different regions of Anhui province. With these analyses, the study presents three main results as follows. There is a positive correlation between the technological innovation efficiencies of industrial enterprises and the technological performance levels of environmental disposal. Meanwhile, there is a large gap among the environmental disposing performances of industrial enterprises in different regions of Anhui province. There is also a large gap between the expected and actual technological performances of industrial enterprises' environmental disposal, according to the results of SEM-PLS analysis. Furthermore, there are several obvious characteristics of geographical distribution in the impact of differentiated technological innovation efficiencies of industrial enterprises on local environmental pollutant emissions observed from the results of the DEA and Malmquist index models. However, it is not consistent with the overall provincial development trend and regional distribution pattern of industrial economics in Anhui province over the period of 2012–2016. Under the rapid development of social economics and modern technological advance, there is a weak impact of differentiated technological innovation efficiency on the technological performance of industrial environmental disposal in different regions of Anhui province. Meanwhile, the environmental disposal capacity of enterprises' technological innovation become declining too. Finally, some countermeasures and policy suggestions are put forward based on the investigation and comprehensive analyses of the DEA and SEM-PLS and Malmquist index models. [ABSTRACT FROM AUTHOR]

Aiming at the evaluation problem of technological innovation evaluation in equipment manufacturing companies, this paper proposes a comprehensive evaluation method based on entropy method and improved aggressive cross-efficiency model (EIA). First of all, this paper builds an evaluation index system for technological innovation of equipment manufacturing companies based on the existing research results of technological innovation evaluation and the Delphi method and obtains relevant indexes data by field investigation. Secondly, the basic DEA method is used to analyze the two aspects of the basic efficiency and effectiveness. Next, this paper prove the superiority of the EIA mode by comparing the basic DEA model and the EIA model. Finally, the efficiency of technological innovation of 200 equipment manufacturing companies in Liaoning Province is calculated and ranked by using [ABSTRACT FROM AUTHOR]

Abstract Considering the interrelationships between periods and the influences of non-operational factors, a new framework based generalized three-stage DEA model, grey relational analysis theory and disparity disassembly model is proposed in this paper. Then, we measure the technical efficiency, scale efficiency, and pure technical efficiency of innovation in China's semiconductor industry between 2009 and 2014. In addition, we conducted projection analysis of the inputs to innovation and the disparities analysis in innovation efficiency across the industrial chain and within each segment. The results of our analyses reveal four key findings. The overall innovation efficiency of China's semiconductor industry is increasing; however, each segment of the industrial chain had different trends and different levels of innovation efficiency. All segments show a consistently upward trend except for package testing, which dipped in 2012 due to the time lag between the inputs and outputs associated with major technological advancements. The most efficient innovation is occurring in design and package testing, followed by manufacturing, materials, and equipment, in that order. Low levels of innovation efficiency were found to be the most significant factor restricting further improvement in the design, manufacturing, and equipment segments of the industrial chain. But the opposite is true for the package testing segment, where pure technical efficiency is the main factor. A range of redundancies in input were found across the industrial chain, mostly in manufacturing and equipment. These two segments are capital-intensive and characterized by a high level of technical complexity coupled with a long research cycle. The disparities in innovation efficiency in and between the segments decreased over the period. However, interestingly, the main disparities were found among the enterprises within each segment, which we attribute to the Chinese government's concerted efforts to support particular companies. Package testing and manufacturing had the highest levels of disparity due to relatively high agglomeration of these two segments. The materials segment had the lowest disparity, with equipment and design falling somewhere in-between. Highlights • We proposed a generalized three-stage DEA method to measure the innovation efficiency of China's semiconductor industry. • Projection analysis of innovation inputs and disparities analysis in innovation efficiency are further conducted. • The innovation efficiency of industry is increasing and each segment of the industrial chain has different trends. • A range of redundancies in input were found across the industrial chain, mostly in manufacturing and equipment. • The main disparities in innovation efficiency were found among the enterprises within each segment. [ABSTRACT FROM AUTHOR]

This article tries to adopt data envelopment analysis window analysis to evaluate the technological innovation efficiencies of seven types of China's industrial enterprises during 2006–15. However, different window widths will lead to very different results. We designate the ideal window width through the scientific method rather than subjective judgments and obtain the efficiencies closer to reality. We also measure the efficiencies under the worst width opposite to the ideal window width to highlight differences. The results indicate that the efficiencies under the ideal width are more reasonable. During 2006–15, the efficiency of medium-sized enterprises was higher than that of large enterprises, that of private enterprises was higher than that of state-owned and state-holding enterprises, and that of foreign-funded enterprises was higher than that of enterprises with funds from Hong Kong, Macao, and Taiwan and that of domestic-funded enterprises. Moreover, the efficiencies of various types of enterprises all maintained upward trends. [ABSTRACT FROM AUTHOR]

China’s traditional industrial model is characterized by high energy consumption and high pollution, which results in many environmental problems that cannot be ignored. To achieve sustainable development, the Chinese government has proposed five development concepts of “innovation, coordination, green, openness, and sharing”. This initiative highlights the urgency of China’s efforts to strengthen environmental regulation. Based on the panel data of industrial enterprises in China from 2006 to 2015, this study not only investigates the spatial features of technological innovation efficiency, but also examines the relationship between technology innovation efficiency and environmental regulations from a spatial perspective. The results indicate that first, China’s provincial-level technological innovation efficiencies are uneven in space. Second, voluntary regulation positively affects the technological innovation efficiency of industrial enterprises at the provincial level, while mandatory regulation has no significant impact. Third, there is a spatial spillover effect in voluntary regulation at the provincial level. One highlight implication is that the government should promulgate environmental regulations based on each province’s technological innovation potential, due to the spatial differences in technological innovation activities. [ABSTRACT FROM AUTHOR]

Highlights • Exploring the impact of environmental regulation on the efficiency of technological innovation in an innovative city. • Proposing a model to analyze the efficiency’s spatial-temporal evolution further. • Using the panel data of Xi’an from China to analyze the technical efficiency with the 3-stage and Network DEA model. • Environmental regulation has no significant influence on the technological innovation efficiency of Xi’an. • Xi’an has four kinds of technological innovation mode, and differences exist between the different innovation system and its subsystems. Abstract The objective of this paper is to explore the impact of environmental regulation on the efficiency of technological innovation and its temporal and spatial evolution in an innovative city with a combined Data Envelopment Analysis (DEA) model. DEA is a common non-parametric frontier model for studying multi-input and multi-output systems. The DEA method uses an input-output index data to mine information such as technology frontier and efficiency evaluation relative to reference technology. Using environmental factors, the efficiency of technological innovation in Xi’an from 2006 to 2015 is measured. The research results show that low scale efficiency is the main factor that restricts the efficiency of technological innovation in innovative city Xi’an. Environmental regulations have no influence on the measurement of technological innovation efficiency in Xi’an. The “Porter hypothesis” is not tenable. There are some differences in the pure technical and scale efficiencies of the two subsystems (R&D and technology transformation) of the network DEA model. The technology transformation process has a more important objective position in the innovation system of Xi'an. The results provide a valuable reference for the measurement of technological innovation efficiency and the formulation and implementation of environmental regulation policies in the innovative cities of developing counties. [ABSTRACT FROM AUTHOR]

China's medical manufacturing industry is in a critical stage of transition to independent innovation. The government should create a business environment that is conducive to the development of the industry, and firms need to accurately identify obstacles to their innovation governance. However, the relationship between corporate governance (CG) and innovation efficiency remains unclear. Based on the CG configuration research framework, this study analyzes, using a three-stage data envelopment analysis (DEA) model and panel data fuzzy-set qualitative comparative analysis (fsQCA), the relationship between the internal governance and technological innovation efficiency (TIE) of China's medical manufacturing listed firms (CMMLFs) with the consideration of the impact of environmental parameters on innovation performance. The results show that (1) government support and medical spending among environmental factors create innovation input redundancy, and education investment and industrial competition promote innovation efficiency; and (2) the high TIE of firms is the outcome of multiple internal mechanisms, and there are five possible paths conducive to improving TIE and two mainstream, stable configurations for high technical efficiency. Our study systematically analyzes the external and internal organizational factors as two situational factors for CG and provides a reference for decision-making to further promote the efficient innovation of medical manufacturing firms. • 32 Chinese medical manufacturing listed firms (CMMLFs) are taken as the study subjects. • Using a hybrid approach involving three-stage DEA and panel data fsQCA. • The true TIE of CMMLFs are evaluated after elimination of the influence of the external environment. • The path to improve TIE in CMMLFs are obtained through the combined allocation of firms' internal governance mechanisms. [ABSTRACT FROM AUTHOR]

Technological innovation and low-carbon economy are significant for the high-quality development of China's industrial sectors. However, few scholars combine the two stages closely and discuss their coordinated development. This paper establishes an evaluation index system of technological innovation and low-carbon economy in China's industrial sectors. The technological innovation efficiency, low-carbon economy efficiency, and comprehensive efficiency of technological innovation and low-carbon economy are dynamically investigated by the two-stage data envelopment analysis (DEA) model and the DEA window analysis with 35 subsectors panel data during 1996–2018. The inter-industrial differences in the technological innovation efficiency and low-carbon economy efficiency are considered, and the influencing factors of the comprehensive efficiency of technological innovation and low-carbon economy are studied by the bootstrap truncation regression. The results show that: (1) The development of the technological innovation and low-carbon economy is uncoordinated, and the low-carbon economy efficiency needs improvement; (2) There is heterogeneous of the technological innovation efficiency and low-carbon economy efficiency in the 35 subsectors; (3) The density of science and technology institutions, and the average enterprises scale are positive to the comprehensive efficiency of technological innovation and low-carbon economy, while excessive reliance on technology introduction has a negative impaction. The corresponding suggestions are provided for promoting technological innovation efficiency and low-carbon economy efficiency of industrial sectors. • Establishing an evaluation index system of technological innovation and low-carbon economy in China's industrial sectors. • The coordinated development of technological innovation and low-carbon economy is noticeable. • The affecting factors of comprehensive efficiency of technological innovation and low-carbon economy are studied. • Exploring the win-win path of industrial growth and energy conservation and emission reduction. [ABSTRACT FROM AUTHOR]

As an important leading industry in China's economy, the high-tech industry needs to systematically analyze technological innovation activities, thereby measuring the technological innovation efficiency and further improving sustainable development. The high-tech industry in different provinces benefits from individual policies due to their geographical locations. However, since the intensity of policy implementation in the technological innovation activities of the provincial high-tech industry is qualitative information, which is difficult to be expressed by quantitative data and rarely considered in the efficiency assessment. Therefore, this study adopts interval type-2 fuzzy sets to characterize the uncertain qualitative information and constructs an interval type-2 fuzzy evaluation method to measure the technological innovation efficiency of China's high-tech industry. More specifically, considering the handling of undesirable outputs and the ranking of multiple efficient decision-making units, the super-efficiency slack-based measure (SBM)-Data envelopment analysis (DEA) method has been selected as the basic evaluation model. Then, the super-efficiency SBM-DEA model has been extended into an interval type-2 evaluation approach by considering the uncertain variables. Moreover, the α -cuts and best-and-worst methods are introduced to conduct the decomposition of the proposed evaluation model and aggregate the ultimate efficiency. Finally, a comparative analysis of technological innovation efficiency in the regional high-tech industry has been performed to validate the applicability of the proposed model. [ABSTRACT FROM AUTHOR]