Abstract
The strengthening scientific and reasonable quantitative evaluation of innovation performance of low-carbon technology breakthrough innovation network in manufacturing industry under the background of economic globalization is of important significance, which can enrich relevant theoretical system of low-carbon technology breakthrough and improve the core competitiveness of Chinese manufacturing industries. In this paper, the scientific and reasonable quantitative evaluation of the innovation performance of low-carbon technology breakthrough innovation network is firstly determined as a dynamic (time-varying) evaluation problem, which involves multi-source influencing factors. Note that many evaluation objects in the innovation performance of an innovation network are gray, ambiguous and dynamic variables, which are difficult to be quantified and the collected industrial data has certain discreteness and fluctuation. Hence, a fuzzy clustering analysis on influencing variables of innovation performance of low-carbon technology innovation network is carried out via the sampling data from 28 manufacturing industries during 2011–2016. Moreover, an innovation performance evaluation model for low-carbon technology innovation network is constructed through analytic hierarchy process (AHP), grey theory and fuzzy clustering analysis. Based on this model, the qualitative and quantitative evaluations of innovation performances of the innovation network before and after low-carbon technology breakthrough are realized. The results show that the low-carbon technology breakthrough innovation has a positive effect on improving the development level of manufacturing industry. In addition, the construction performance evaluation model not only decreases the influences of subjective factors by combining qualitative analysis with quantitative analysis, but also applies the grey theory innovatively to determine the membership matrix. It realizes accurate, systematic and scientific evaluation of the innovation performance of the low-carbon technology breakthrough innovation network in the manufacturing industries. Finally, the research conclusions provide the theoretical and practical references to the strategic arrangement of the low-carbon technology breakthrough innovation in Chinese manufacturing industries.
Highlights
With the continuous transition of global technological innovative ability, the innovation-driven development has become a core strategy of China’s economic development.The associate editor coordinating the review of this manuscript and approving it for publication was Miltiadis Lytras .The innovation-driven development is the key factor to improve the overall innovation level of China, and is an effective guarantee to improve the innovative efficiency
An innovation performance evaluation model for low-carbon technology innovation network is constructed through analytic hierarchy process (AHP), grey theory and fuzzy clustering analysis
The results show that the low-carbon technology breakthrough innovation has a positive effect on improving the development level of manufacturing industry
Summary
With the continuous transition of global technological innovative ability, the innovation-driven development has become a core strategy of China’s economic development. Based on complementation of knowledge, capability and profession through integration, low-carbon breakthrough innovation changes associations among different industries and drive rapid development of relevant industries, and promote industrial upgrading and optimization toward rationality and a higher advanced level. Since there are many dynamic and static factors that can influence the innovation performance evaluation of low-carbon technology innovation network, this study determined four level-1 indexes, namely, Technology Spillover, Social Capital & Technological Innovation, Technology Assimilation and Others. Based on the constructed quantitative evaluation index system for innovation performance of low-carbon technology innovation network in manufacturing industries, relative importance among different indexes is further determined by expert scoring and order of magnitudes and priority of elements in the same layer. We have rij rij rij12 · · · rij1n rij rij rij22 · · · rij2n rij
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