Search

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]

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]

Improving technological innovation efficiency and eco-efficiency is the key to achieving a high-quality, sustainable mining industry in China. In order to explore the internal process of technological innovation to promote economic and environmental benefits and to clarify the optimization path to improve the overall efficiency of the mining industry. A two-stage DEA model is used to construct indicators to measure Chinese 30 provinces mining technological innovation efficiency (MTIE), mining eco-efficiency (MEE), and mining comprehensive efficiency (MCE). Because of the lag in innovation, the study period was divided into nine periods from 2008 to 2018 into three-year time slices, and the dynamic evolution of time and space was analyzed. It was found that: (i) MCE and MTIE maintained the same changing trends, with the MCE being generally higher in areas with higher levels of mining economic development; (ii) the MTIE and MEE in the western area were higher than in the other regions, of which Qinghai was the only province that had efficiencies of more than 1 in both stages; (iii) most provinces belonged to type II regions with high MEE-low MTIE or type III regions with low MEE-low MTIE. The scientific achievements output in the first stage was generally insufficient, and the inputs and outputs in the second stage were redundant. There are three paths to optimize efficiency: unilateral optimization, gradual optimization, and jumping optimization. Finally, based on the above findings, policy recommendations are given from three aspects; reducing excess capacity, increasing investment, and building a platform, all of which can provide optimized plans and references for realizing the high-quality development of the Chinese mining industry. • A two-stage DEA model for measuring Chinese MTIE, MEE, and MCE from 2008 to 2018. • The MTIE and MEE in the western area were higher than in the other regions. • Most provinces belonged to type II or type III. • Three optimization paths are proposed to improve MCE. [ABSTRACT FROM AUTHOR]

This study firstly adopts Data Envelopment Analysis (DEA) window analysis with an ideal window width to dynamically investigate the technological innovation efficiency of China's high-tech industry during 2009–2016, simultaneously from provincial, regional and industrial perspective. The ideal window widths in the high-tech industry and its five sub-industries are all 4. The findings indicate that the efficiency of high-tech industry is low and presents a wave-shaped trend, as well as presents large inter-provincial and inter-regional differences. The efficiency in eastern region is always the highest, while the efficiency in northeastern region is the lowest. Moreover, the efficiencies in eastern region and western region both presented wave-shaped decrease trends, while the efficiencies in central region and northeastern region both presented wave-shaped increase trends. There are significant inter-regional and inter-provincial differences in efficiency of each sub-industry. The distributions of efficiencies of various provinces in five sub-industries are different. No province has always been on the innovation frontier for the entire evaluation period. The province with larger number of years on the frontier generally has the higher efficiency score, although there are some exceptions. Among the provinces on the frontier in various industries, the eastern provinces account for a large proportion. • The efficiency of high-tech industry in China is relatively lower. • It presents inter-provincial, regional and industrial differences. • No province has always been on the innovation frontier. [ABSTRACT FROM AUTHOR]

As significant strategic players in China's economy, high-tech industries need to evaluate and analyze the technological innovation activities from a system point of view to understand and improve their technological innovation efficiency and, thereby, promote their development. Different high-tech industries have different characteristics and thus benefit from different industrial development policies. However, few studies to date have discussed this issue from a systematic perspective. In this study, technological innovation activities are divided into a research and development (R&D) stage and a commercialization stage. A high-tech industrial evaluation framework of technological innovation efficiency based on two-stage network data envelopment analysis (DEA) is constructed with shared inputs, additional intermediate inputs, and free intermediate outputs. Our empirical results indicate that the overall efficiency of most industries is relatively low and the differences between the five high-tech industries (i.e., sub-sectors) we examined are large. The Spearman correlation shows that overall efficiency and R&D efficiency are more correlated than overall efficiency and commercialization efficiency. Additionally, R&D has better average efficiency. The sub-sector with the highest average efficiency is computers and office equipment, and the one with the lowest average efficiency is medicines. These findings indicate the inadequacy but potential for breakthroughs in the evolution of high-tech industries in China. The analysis proves that it is necessary to create different industrial policies to encourage effective progress in certain high-tech industries, and some guidelines for doing so are provided. • Assessment framework of the technological innovation efficiency of China's high-tech industries is developed. • A two-stage network data envelopment analysis approach is used in this paper. • Significant findings can be obtained from empirical evidence. • Several policy suggestions are proposed for China's high-tech industries. [ABSTRACT FROM AUTHOR]