Can Industrial Convergence Improve The Innovation Efficiency Of Traditional Enterprises

The power of China's conventional industries to drive the development of economy has gradually declined since 21 century.Many conventional industries have stepped into the sunset period.China's economy is facing a complicated international environment,especially after the 2008 financial crisis.It is urgent to optimize the industrial structure and realize industrial transformation and upgrading so as to foster a new engine for economic growth.There are many conventional industries in other countries around the world facing high energy consumption and low productivity,such as the United States,Germany and the European Union,which have proposed relevant policies and measures to develop the emerging industries and encourage innovation.China has also carried out a series of policies to promote the healthy development of industries.The Strategic emerging industries was proposed in 2009 for the first time,which specified the direction of China's industrial development.In response,many traditional enterprises begin to have emerging industries' business to realize industrial transformation.However,whether the transformation can improve the innovative ability is the key issue that needs to be paid attention to.Traditional enterprises are the units of conventional industries.It relates to the development of industries whether the transformation will improve the innovation efficiency.The paper has a certain significance to enrich the theory of industrial convergence,and provides empirical support for the innovation effect of industrial convergence.Taking the traditional enterprises as the object of empirical research has the certain reference value to the transformation of conventional industry.By combing the existing literature,it is not difficult to find that the theory of industrial convergence is not perfect,and scholars have different views on the innovative effects of industrial convergence.Innovation efficiency is an important index to evaluate the ability of innovation,but there is not much research on that.Whether industrial convergence can improve the efficiency of traditional enterprise innovation is an important criterion to judge whether the new economic growth point has been cultivated.Based on the industrial integration theory,the paper is combined with theory and empirical research.It is more comprehensive to face the manufacturing industry situation analysis in the integration of macro industry and use panel data to establish PSM-DID model for quantitative analysis in the micro enterprise level.The research content of this paper is divided into six parts.First of all,this paper expounds the background and significance of this study,and accordingly determines the research question,namely whether industrial convergence can improve the efficiency of traditional enterprise innovation.Second,it makes a summary of the industrial convergence,innovation efficiency and the relationship between them,finding that scholars have different ideas on the relationship and use less methods of empirical analysis.Third,based on the theory of industry convergence,the hypothesis that industrial convergence influences innovation efficiency is put forward,which provides a theoretical basis for empirical research.Fourth,this paper analyzes the convergence of China's manufacturing industry and other three major industries from the industry and region point of view.Fifth,this part uses the panel data of 136 enterprises in 9 years to establish a panel data model for the propensity score matching 20 traditional enterprises which have the transformation to the matching group,and then establishes a double difference model for studying the effect of industrial convergence on the innovation efficiency of traditional enterprises and analyzes other factors that may affect the innovation efficiency.Sixth,according to the results of the fourth and fifth parts,draw the corresponding conclusions and make policy recommendations.The main conclusions are the following three points.First,the performance of industry convergence between the same industry and different industries is different.Second,the effect of industry convergence on improving the innovation efficiency of traditional enterprises' performance is not obvious,the enterprise ownership is not a factor hindering the effect,and regional differences may affect the convergence of conventional industries.Third,we find that R&D investment has a negative effect on innovation efficiency,and government subsidies do not have a significant impact on innovation efficiency.For different ownership and regions of the enterprise,the financial characteristics that affect innovation efficiency are also different.But R&D investment will inhibit the improvement of innovation efficiency,and government subsidies can't play a role in promoting innovation efficiency.This paper may have the following innovative points.First,there is not much research on the effect of industry convergence through empirical research in the past.There may be some innovations in research methods.Second,unlike many previous studies using industry level data,this paper uses enterprise level data for empirical research and makes some innovations in data usage.Third,most literatures follow the hot spots to study the strategic emerging industries,but this paper distinguishes the impact of the traditional industry foundation on its innovation efficiency.Fourth,the PSM-DID model is used to study industry convergence and innovation,and there may be some innovations in the research perspective.The inadequacies of this article are as follows.Firstly,the enterprises selected in this paper are listed companies,and the unlisted companies are not in the scope of research,which may lead to bias in the results of the study.Second,the research time of enterprise integration is more than the time before fusion,and the time cross section is not long.Third,the definitions of the enterprises based on traditional industries are obtained by hand searching and sorting,and there may be errors.