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Abstract

Innovation is an important driver for the promotion of manufacturing, and governmental intervention could significantly affect the development of innovation and manufacturing in China. The purpose of this article was to reveal different modes of innovation regarding the development of manufacturing in China. A theoretical model was used to analyze the effects of incremental innovation and disruptive innovation on the sustainable development of the manufacturing industry from the aspect of the industry’s ability to survive under governmental intervention; a corresponding empirical analysis was performed from perspectives of the whole nation and various regions using a panel data model. The results demonstrated that the promotion of the incremental innovation ability was not beneficial to enhance the ability to survive and that the effect of disruptive innovation was the opposite from the aspect of national level. The influencing directions of various innovation modes in the developed region and the underdeveloped region coincided generally with those at the national level but achieved larger effects individually, with stronger effects occurring in the underdeveloped region compared with those in the developed region, while opposite effects occurring in the region with medium development.

Keywords

incremental innovation disruptive innovation manufacturing governmental intervention sustainable development

The Question Proposed

China introduced the New Normal of Economic Development in 2014 and introduced “Intelligent Manufacturing in China 2025” in 2015 to demonstrate that it is difficult to improve the manufacturing industry rapidly. In addition, the real economy experienced a comparative hollowing out phenomenon due to the continuously increasing price of real estate in 2016, which caused the manufacturing industry in China to be in a state of comparative difficulty; thus, the manufacturing industry faces serious challenges for its survival. The advantages of the manufacturing industry in China gradually disappeared due to the aging of the industry, the high labor cost resulting from a shortage of labor, and the comparatively high tax rate, which prompted some enterprises to remove of China. For example, Yaohui Glass Group’s spending of US$1 billion to invest in the United States in December 2016 caused a national discussion and self-examination in China. An innovation strategy was main strategy proposed for overcoming the New Normal Development, and the government could significantly influence innovation and the survival of manufacturing. Therefore, it was necessary to detect the various modes of the effects of innovation on the development of the manufacturing industry under the condition of governmental intervention.

Incremental innovation is a strategy with low risk and low earnings, and a disruptive strategy is a strategy with high risk and high earnings (Gobble, 2016; Laursen & Salter, 2006). Therefore, the local government should care about the economic reality when it chooses the mode of innovation. There existed obvious differences between the above two modes of innovation, mainly including the technology (Schmidt & Druehl, 2008; Vecchiato, 2017), R&D activity (Rovert & Veryzer, 1998), capacity and capital (Corso & Pellefrini, 2007; Suseno, 2018), knowledge management (Roy & Sarkar, 2016; Tiwana, 2002), organizational learning (Bessant, 2005; Sheng & Chien, 2016), organizational culture (Naranjo-Valencia, Jimenez-Jimenez, & Sanz-Valle, 2017), market competition intensity (Ghosh, Kato, & Morita, 2017), and innovation magnitude (Schuelke-Leech, 2018). When one region implements the strategy of incremental innovation, the mature enterprise might have difficulty achieving disruptive innovation (Christensen & Bower, 1996; Pellegrino, 2018), and this might be an obstacle to implementing advanced innovation because the government’s path is locked-in. There are many factors which would influence incremental innovation and disruptive innovation, such as the market characteristics, institutional development, consumption behavior (González, Macho-Stadler, & Pérez-Castrillo, 2016; Iyer, LaPlaca, & Sharma, 2006), R&D cooperation and absorption ability (Ritala & Hurmelinna-Laukkanen, 2013), diversity of strategic alliances (Oerlemans, Knoben, & Pretorius, 2013), market-oriented strategy (Chang, Franke, Butler, Musgrove, & Ellinger, 2014; Ettlie, Bridges, & O’Keefe, 1984), knowledge accumulation capability (Forés & Camisón, 2016; Rupietta & Backes-Gellner, 2019), and market entrance (Davis & Tomoda, 2018). In addition, Chen, Zhu and Zhang (2017) divided disruptive innovation into high-end and low-end categories, which could be regarded as disruptive innovation and incremental innovation to some extent, and studied the factors influencing innovation in Chinese small- to medium-sized enterprises (SMEs). The common opinion was that incremental innovation and disruptive innovation were not separate, and there was a possibility of interaction when some conditions were met (Hacklin, Raurich, & Marxt, 2004; Karlsson & Tavassoli, 2016).

China was considered to be in the middle-income trap (Lin, 2017; Otsuka, Higuchi, & Sonobe, 2017; Wong, 2016; Woo, 2012), and the gradual transformation and upgrading of the manufacturing industry were the main challenges faced by the country (Huang, 2016). At the same time, innovation was an important method to solve these challenges (Chin & Liu, 2017; Lewin, Kenney, & Murmann, 2016). China should change the mode of economic development to avoid the middle-income trap, whereas overcoming the manufacturing development dilemma is an important aspect, as is efficiently escaping the comparative advantage trap. China’s manufacturing capacity is comparatively limited and is especially comparatively weak in terms of equipping manufacturing capacity for integration (Wang, 2013); the rapid decrease in comparative advantages and increased competitiveness is elevating the place of the manufacturing industry, so it is necessary to promote development by reconstructing the manufacturing industry. Viewed from the micro aspect, the obstacles restricting the promotion of the technology progress and technology levels were important driving forces for China to overcome the upgrading trap in the manufacturing industry.

As per the influence of governmental intervention on innovation, the main focus of scholars was from the aspect of the national innovation system. For example, Freeman (2002) discussed the relevance between the innovation system and economic growth rates, Kim and Lee (2011) analyzed the effect of government financial support on business innovation in South Korea, and Filippetti and Archibugi (2011) detected the structural characteristics of national systems that had an effect on the firms’ persistency in terms of innovation investment. Tung (2013) detected the differences in the national innovation system and policy implications for entrepreneurship between Taiwan and Japan. Wang and Liu (2015) discovered the government’s rationale regarding the national innovation system when intervening on innovation. Resende and Torres (2016) studied governmental intervention on the national innovation system and its external constraint on economic growth. Qu, Wei, Jiang and Zheng (2017) investigated the role of the R&D strategy, the national innovation system, and foreign direct investment in firm performance. Wang (2018) detected the governmental intervention’s influence on innovation performance by examining the cases of Singapore and Hong Kong. Considering the above literature, scholars focused on innovation as a whole and paid attention to the manufacturing industry generally but did not divide it into different aspects or provide further analysis of its effects on the manufacturing industry.

Innovation, especially disruptive innovation, with the characteristic of comparatively high risk, determined that various parties should play an important role when carrying out the innovation strategy (Green & Newman, 2017; Pinkse, Bohnsack, & Kolk, 2014; Wan, Williamson, & Yin, 2015), either for the government or for the enterprise. As per the impact of incremental innovation and disruptive innovation on the manufacturing development in China, scholars performed the corresponding analyses mainly from the aspects of absorption capacity (Zhang, Yi, Luo, Liu, & Rozelle, 2013) and SMEs (Chen et al., 2017). The literature regarding the influence of governmental intervention on incremental innovation and disruptive innovation is lacking; after checking literature carefully, only cases focusing on the electric bike (Wells & Lin, 2015; Yi, Chang, & Yan, 2014), the electric vehicle (Li, Zhan, Jong, & Lukszo, 2016), and E-business microcredit (Zhang, Daim, & Zhang, 2018; Zhang & Zhang, 2017) were found.

There is no doubt that China’s economy is in a state of stagnation, and the manufacturing industry is the main point to overcome this situation. In fact, the current comparative dilemma of the manufacturing industry in China is the compound effect of inadequate innovation and governmental intervention, and this is the focus of this article. Many studies examined innovation and the relationship of innovation on the manufacturing industry from many aspects, such as the influencing factors, transformation and upgrading of the manufacturing industry, national innovation system, and impact of innovation on economic development, but little research has focused on the relationship between governmental intervention and various modes of innovation in China. Meanwhile, the above literature hinted at the problem. The aim of this article was to examine the manufacturing industry in different regions that might need different modes of innovation under the background of governmental intervention in China, which might be a good method for disrupting the New Normal of Economic Development. To achieve the above target, we first established a theoretical model to analyze the influence of incremental innovation and disruptive innovation on the sustainable development of the manufacturing industry from the perspective of governmental intervention; then, we used China’s data to detect the regional differences influencing the performance of the above two modes of innovation using a panel data model; and, finally, the corresponding discussion is presented in reference to the econometric results.

Theoretical Model

The striving for the survival of the manufacturing industry is a global challenge (Cucculelli & Peruzzi, 2018; Ortiz-Villajos & Sotoca, 2018), and there has been much research investigating it in China (Arrighetti, 1994; Audretsch, Guo, Hepfer, Menendez, & Xiao, 2016; Sharif & Huang, 2012; Zhang, Zhou, Yang, & Shao, 2017); innovation is usually considered to be important for the survival of manufacturing in China (Dai, Sun, & Liu, 2018; Zhang & Mohnen, 2013). Under the background of the New Normal of Economic Development and the comparatively obvious trend of the economy being in a downward trend in China, the sustainable development of the manufacturing industry is a substantial problem. Here, we took the survival as the index measuring degree of sustainable development. The survival of the manufacturing industry is influenced not only by its internal ability but also by other aspects, especially the direct relationship with the industrial development trend guided by the government. There were many factors that influence the ability of the manufacturing industry to survive, such as increasing labor costs and changes in the international economic and political environment, which could be demonstrated from the perspective of the internal ability to survive, implying that the enterprise engages in the corresponding interaction in response to relevant change in the above factors. In addition, we regarded the government’s effort to eliminate the comparative dilemma of the manufacturing industry as the external ability to survive. From the mentioned literature, we could also learn that the government might implement administrative or financial interventions in the development of the manufacturing industry. Based on the above analysis, the ability of the industry to survive could be divided into two parts, that is, internal and external. The local government implemented a method for improving the ability of the manufacturing industry to survive, which would strengthen the target if the developing trend of the regional manufacturing industry coincided with the aims of governmental operation; it would have no effect if the developing trend of regional manufacturing had no relationship with that of the government, and it would weaken the target if the developing trend of regional manufacturing was opposite to that of the government. In fact, many areas in China issued oriented development directories for the manufacturing industries, among which many industries were to be supported and enhanced by the local government, and this might strengthen the surviving abilities of these industries from many perspectives. Based on the above analysis, we set a theoretical model for evaluating the manufacturing industry’s ability to survive:

T = X \cdot (Y Z)

(1)

Here, the external ability to survive (abbreviated as $T$ ) and the internal ability to survive (here, abbreviated as $X$ ) were represented; the external ability to survive was demonstrated considering the influence of the local government, which included the direct aspect (here, abbreviated as $Y$ ) and the indirect aspect (here, abbreviated as $Z$ ). The above model implied that the ability to survive was the compound effect of the internal ability to survive and the effect of the local government. The reason for developing the model was that, by putting incremental innovation and disruptive innovation into the aspect of the internal ability to survive, various countermeasures brought by the local government to support innovation might affect the above two modes of innovation.

The Internal Ability to Survive

The manufacturing industry in China is in the state of a development dilemma, which mainly includes transformation pressure and upgrading pressure. Restricted by the increase in the level of economic development and improvement of the environmental standard and by needing to adjust to changes in the domestic and international markets, there existed certain difference regarding the development dilemma among the different development levels. Generally, a developed region would pay more attention to harmonious and coordinated development; because of the many restrictions usually implemented, such as land resource restrictions and environmental standards, many polluting industries were not permitted to develop, which implied that the green economy and the land-management economy were the main reasons for economic development. Viewed from the perspective of the industrial chain, the developed region should achieve progress from the relative bottom to a relatively advanced level; therefore, it could be considered to be mainly an upgrading dilemma. Under the background of industrial transfer, receiving external industries might be the necessary path for achieving economic elevation in the underdeveloped region, which requires maintaining a feasible transformation to overcome the development dilemma as per the former industries at this region. However, either for the developed region or for the underdeveloped region, the transformation dilemma and the upgrading dilemma might exist simultaneously. In fact, transformation and upgrading were the main methods for China’s manufacturing to achieve steadily sustainable development and overcome development traps (Li & Wang, 2015; Pan & Song, 2017), including global value chain trap (Kuang, Zhao, Hao, & Liu, 2018; Rasiah, Kong, & Vinanchiarachi, 2011), and bottom lock-in (Lai, Wu, & Wong, 2013; Lockström & Liu, 2013). At the same time, transformation and upgrading could reflect the internal pressure of the manufacturing industry, which is a compound effect of factors influencing its operation, and this would affect its survival (here, these factors did not include those affected by the government). For convenience, we supposed that the internal ability to survive is only affected by innovation, transformation, and upgrading pressure, as follows:

X = A K

(2)

Here, $A$ and $K$ are innovation and the transformation and upgrading pressure of the manufacturing industry, respectively. We divided the transformation and upgrading pressure of manufacturing into the transformation pressure (here, abbreviated as $T R$ ) and the upgrading pressure (here, abbreviated as $U P$ ) and assumed that the above two types of pressure are obedient to the Cobb–Douglas function, which implied the following:

K = B T R^{α} U P^{β}

(3)

The influence of innovation on the development dilemma of the manufacturing industry could be detected from $A$ (here, it was the technology spillover effect). To meet the needs of our research, the influence was divided into incremental innovation (here, abbreviated as $J J$ ) and disruptive innovation (here, abbreviated as $D F$ ). Here, we did not care about other aspects, such as the forward reaction effect or backward reaction effect. Hence, $A$ could be rewritten as follows:

A = C \cdot J J^{χ} \cdot D F^{δ}

(4)

Here, the function demonstrating the internal ability to survive could be rewritten as follows:

X = B \cdot C \cdot J J^{χ} \cdot D F^{δ} \cdot T R^{α} \cdot U P^{β}

(5)

Combined with the above assumption, the following could be obtained. The developed region would care more about disruptive innovation when measures were taken to overcome the developmental challenges of the manufacturing industry (Schmidt & Druehl, 2008) because this mode of innovation had a comparatively strong pulling effect on the promotion of economic development and competitiveness, so disruptive innovation had a stronger influence on the elevation of the manufacturing industry. The underdeveloped region would care more about incremental innovation when measures were taken to eliminate development difficulties (Chang et al., 2014), and this might have a direct relationship with the manufacturing foundation being comparatively lacking in the region, as the gradual promotion and adjustment of its mainstream products are necessary; thus, the transformation development of the manufacturing industry might be more suitable. For convenience in performing the analysis, we supposed that the developed region cared only about upgrading development, whereas the underdeveloped region cared only about transformation development. As per the manufacturing industry, incremental innovation and disruptive innovation might occur at the same region, and these two modes of innovation could be switched without much difficulty (Hacklin et al., 2004; Ji & Gunasekaran, 2014). The manufacturing industry is the most important aspect of the economic development; therefore, the local government should actively implement corresponding countermeasures to eliminate the development trap. Regarding the regional economic division in China, the divisions were usually determined to be as the eastern region, the middle region, and the western region, which originated from the Seventh Five-Year National Plan adopted at the Fourth Session of the Sixth National People’s Congress of China in 1986. Chongqing, the municipality directly under the central government, was included in the western region, and Inner Mongolia and Guangxi were included in the range of the favorable region for implementing western development strategy. According to the above illustration, the eastern region, the middle region, and the western region include 11, 8, and 12 provincial administrative areas, respectively. The eastern region includes Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan; the middle region includes Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, and Hunan; and the western region includes Sichuan, Chongqing, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang, Guangxi, and Inner Mongolia. According to the regional per capita GDP (gross domestic product) in 2015, only Hainan was lower than the national average level in the eastern region; only Inner Mongolia, Hubei, and Jilin were higher than the national average level in the middle region; and only Chongqing was higher than the national average level in the middle region, which indicated obvious regional differences as per the level of development.

In fact, China’s economic transformation and upgrading phenomenon could be explained mainly by economic adjustment (Zhang, Zhang, Liu, & Nie, 2017), especially as the adjustment of the manufacturing industries in the eastern region, the middle region, and the western region undertook industrial transfers for promoting economic development requiring the achievement of transformation development of manufacturing in these regions. The differences in regional economic development determined that different regions would carry out different measures to smooth out the comparative development dilemma of the manufacturing industry, which implied that the eastern region would mainly enhance the economic position by implementing industrial upgrading; the middle and western regions might undertake industrial transfer to accelerate the regional industrial adjustment. Hereto, different regions would take different approaches toward different modes of innovation. For example, the developing region might have no courage to eliminate industrial foundation. Therefore, incremental innovation might be better for the development of the manufacturing industry. Therefore, we propose the following hypothesis: The developed region would utilize a strategy of disruptive innovation, and the developing region would implement a strategy of incremental innovation. In fact, the manufacturing industry’s survival would be greatly influenced by the government, and the analysis was performed from the aspect of the external ability to survive.

The External Ability to Survive

There were many studies on the effect of governmental intervention on manufacturing development in China, and it is usually considered to be affected deeply (Chen, Sun, Tang, & Wu, 2011; Colombo, Cumming, & Vismara, 2016; Zhang, Shi, He, & Wen, 2017). According to the above analysis, $Y$ and $Z$ could be regarded as results of the government’s impact on the external production capacity of the manufacturing industry, which implies a direct influence and an indirect influence, respectively. For convenience in performing the analysis, we defined the direct influence as various expenditures supported by the government to incentivize manufacturing development, which includes many aspects (Guan & Yam, 2015; Wei & Liu, 2015). However, due to limitations of the data available in China, we included only the following factors: the fiscal input for innovation (here, abbreviated as $F I$ ), tax deductions and exemptions for innovation (here, abbreviated as $T A$ ), and subsidized loans for innovation (here, abbreviated as $D S$ ), and we did not consider other factors directly influencing innovation.

The hierarchy of the indirect impacts might be more complex, such as whether the innovation mode of the manufacturing industry is in accordance with the requirements of the local government directly or not, which was the kernel factor for the local government choosing to support a relevant innovation or not. Science and technology were the first productive forces, and indirect support for an innovation made by the government was mainly demonstrated through education inputs (Chi & Qian, 2010). Generally, some colleges and universities’ partial education funds were supported by relevant ministries or departments, but a majority of them were funded by the local government in China (Jiang, 2015). Therefore, we focused on the model of innovation (here, abbreviated as $I D$ ) and the research and education fees paid to the institutions and colleges (here, abbreviated as $J Y$ ) when performing the analysis of indirect impacts. In addition, the style of the innovation had veto power for the corresponding supporting policies on innovation, which implied that the local government might resist when the mode of innovation did not meet its need. In fact, the local government perhaps would not follow contrastive countermeasures for innovative development of the manufacturing industry, either for incremental innovation or for disruptive innovation. As to the local government, the feasible approach for innovation might be laissez-faire if it was not able to reach its expectation, which appeared to be neutral in regard to its implementation. However, if the trend of the manufacturing innovation met the need, the government would undoubtedly support it.

It was difficult to analyze the direct impact and indirect impact separately when performing the relevant analysis; therefore, we combined them. Taking $W$ as the compound effect of $Y$ and $Z$ , $W$ could be summarized by the following formula based on the above illustration:

W = K \cdot F I^{λ} \cdot T A^{ω} \cdot D S^{ε} \cdot J Y^{φ}

(6)

In formula (6), $K$ represents the fitness degree between the mode of innovation and the oriented innovation made by the local government. We assumed that the value of $K$ was only one or zero, for convenience of dealing with the above model. A value of zero indicated that the innovation trend of the manufacturing industry was not completely fit for the oriented innovation made by the local government, whereas a value of one implied that the innovation trend of the manufacturing industry was a perfect fit. Here, $F I$ , $T A$ , and $D S$ could be regarded as $Y$ , which implied a direct effect of governmental intervention; $J Y$ could be regarded as $Z$ , which implied an indirect effect of governmental intervention.

We supposed that the innovation trend of the manufacturing industry was perfectly fit for the oriented innovation made by the local government. Combined with the above corresponding analysis, the model demonstrating that the ability to survive could be rewritten as the following formula:

T = B \cdot C \cdot J J^{χ} \cdot D F^{δ} \cdot T R^{α} \cdot U P^{β} \cdot F I^{λ} \cdot T A^{ω} \cdot D S^{ε} \cdot J Y^{φ}

(7)

Let $B \cdot C$ be $H$ , and formula (7) could be rewritten as follows:

T = H \cdot J J^{χ} \cdot D F^{δ} \cdot T R^{α} \cdot U P^{β} \cdot F I^{λ} \cdot T A^{ω} \cdot D S^{ε} \cdot J Y^{φ}

(8)

Let formula (8) take the logarithm, we could achieve the following:

\begin{matrix} \ln T = χ \cdot \ln J J + δ \cdot \ln D F + α \cdot \ln T R + β \cdot \ln U P + λ \cdot \\ \ln F I + ω \cdot \ln T A + ε \cdot \ln D S + φ \cdot \ln J Y \end{matrix}

(9)

Because the relevant data of $T A$ and $D S$ were not effectively achieved, we replaced them with the index of deduction and exemption for innovation (here, abbreviated as $Y H$ ) when we performed the detailed econometric analysis; therefore, formula (9) could be rewritten as follows:

\begin{matrix} \ln T = c + χ \cdot \ln J J + δ \cdot \ln D F + α \cdot \ln T R + β \cdot \ln U P \\ + λ \cdot \ln F I + η \cdot \ln Y H + φ \cdot \ln J Y \end{matrix}

(10)

Formula (10) was the basic model for evaluating the various modes of the effect of innovation on the ability to survive of the manufacturing industry under the condition of governmental intervention. The ability to survive included internal and external aspects, and the incremental and disruptive innovation factors relating to the internal aspect, so it was necessary to control the external factors by making them the control variables for performing the econometric analysis. Furthermore, if a control variable did not pass the significance test, it was necessary to perform further analysis. Later, we used a panel data model to perform the economic analysis.

Data Resource, Index Implication, and Data Description

Data Resources

The data needed were obtained from the Industrial Enterprise Science and Technology Activities Statistical Yearbook, but we could not obtain the 2016 Yearbook on December 28, 2017. Therefore, we took the relevant data from 2000 to 2014 to perform the analysis. Considering the change of the statistical method in the Yearbook in 2010, it was necessary to make estimations regarding some relevant indexes. Due to cases of partial data and some data being comparatively small in Tibet and Hainan, we eliminated the above two provincial areas; thus, we collected relevant data for the other 29 provincial areas to ensure the credibility of the study, and the pooled data of the 29 provincial regions served as the national data. In addition, we used the weighted average method to deal with some missing data. For example, if some data in 2013 in some area were lost, the method for calculating the corresponding index would be as follows: the value of that variable in 2012 would be added to the value of that variable in 2014 and divided by two.

Viewed from the reality of economic development in China, there was not much meaning when the regions are divided into the above three regions, especially regarding the aspect of innovation, because the three regions could not reflect the status quo of regional innovation. The innovations reflected the development level in some areas, but not the administrative regional division (Niu & Liu, 2016). In fact, the level of economic development had comparatively large impacts on incremental innovation and disruptive innovation, so it was necessary to reclassify the 29 provincial areas. Taking the per capita GDP in 2015 as the standard, we regarded the top 10, 11 to 19, and 20 to 29 as the developed region, the medium developed region, and the underdeveloped region, respectively. Combined with the above definition, the developed region includes Beijing, Tianjin, Shanghai, Jiangsu, Zhejiang, Inner Mongolia, Fujian, Guangdong, Shandong, and Liaoning; the medium developed region includes Chongqing, Jilin, Hubei, Shaanxi, Ningxia, Qinghai, Hebei, Hunan, and Heilongjiang; and the underdeveloped region includes Henan, Jiangxi, Anhui, Sichuan, Yunnan, Guangxi, Shanxi, Guizhou, Gansu, and Xinjiang.

All data were relevant to the manufacturing industry, and each index in formula (10) needed to be calculated. Therefore, we proposed using the following original variables: the product sales income, the prime operating revenue, the total profit, the quantity of new product development projects, the quantity of patents, the R&D expenditure on new products, the sales income of new products, the exporting value of new products, the fiscal fund for innovation, the total expenditure on science and technology activities, the internal expenditure of R&D funds, the external expenditure of R&D funds, the tax deduction and exemption for technology exploration derived from various governments, the research and exploration expenditure except for tax deduction and exemption, the input to institutes and colleges, the expenditure on domestic institutions, and the expenditure on domestic colleges. The above original variables were measured in units of 10,000 RMB, except for that of the quantity of new product development projects and the quantity of patents, which were measured in units of pieces.

Index Implication

Now, we will provide a detailed description of indexes needed for the econometric analysis:

The surviving index $(T)$ , which was represented by 100 times the total profit divided by the product sales income in 2000 to 2008 and 100 times the total profit divided by the prime operating revenue in 2009 to 2014. The larger the index, the stronger the ability to survive would be.

The incremental innovation and disruptive innovation were relative in various areas in China, that is, the exploration of new products demonstrated incremental innovation to some extent, and patents obviously showed disruptive innovation. Therefore, the incremental innovation at one area $(J J)$ could be represented by 100 times the quantity of new product development projects in this area divided by the total quantity of new product development project in China; the disruptive innovation at one area $(D F)$ was represented by 100 times the quantity of patents in this area divided by the total quantity of patents in China. The larger the two indexes were, the stronger the innovation ability would be.

The transformation performance and the upgrading performance could be revealed by exploration and sales of new products, and the significant signal of succession of upgrading should be detected by stronger competition in the global market. Therefore, the transformation performance index $(T R)$ could be represented by 100 times the R&D expenditures on new products divided by the sales income of new products, and the upgrading performance index $(U P)$ could be represented by 100 times the exporting value of new products divided by the sales income of new products. The larger the two indexes, the larger the performance would be.

The fiscal input of innovation revealed the influence of the direct input of the government. The fiscal supporting index $(F I)$ could be represented by 100 times the fiscal fund for innovation divided by the total expenditure on science and technology activities in 2000 to 2008 and 100 times the fiscal fund for innovation divided by the sum of the internal expenditure of R&D funds and the external expenditure of R&D funds in 2009 to 2014.

The favored deduction and exemption index $(Y H)$ could be represented by 100 times the tax deduction and exemption for technology exploration derived from various governments divided by the total expenditure on science and technology activities in 2000 to 2008 and 100 times the sum of the tax deduction and exemption for technology exploration derived from various governments and the research and exploration expenditure, except for the tax deduction and exemption, divided by the sum of the internal expenditure of R&D funds and the external expenditure of R&D funds in 2009 to 2014.

The input of the institution and the college index $(J Y)$ could be represented by 100 times the input of institutes and colleges divided by the total expenditure on science and technology activities in 2000 to 2008 and 100 times the sum of the expenditure on domestic institutions and the expenditure on domestic colleges divided by the sum of the internal expenditure of R&D funds and the external expenditure of R&D funds in 2009 to 2014.

Data Description

We set the time and the various variables to be the independent variable and the dependent variables, respectively, and set the equation be $y = a + b x$ for detecting the development trend. When the value of $b$ was greater than zero, it indicated an increasing trend; otherwise, it would be decreasing. The aggregated values of $J J$ or $D F$ in the 29 provincial areas were considered to be 100%, which would cause the average value to be 3.4483 each year. Therefore, it was necessary to detect the trend of $J J$ and $D F$ by taking the average value at various regions. Tables 1 and 2 show the average means for the whole nation and the average mean of $J J$ and $D F$ in various regions during the period of 2000 to 2014.

Table 1.

The Average Mean at the Whole Nation at Period of 2000 to 2014.

	T	TR	UP	FI	YH	JY
2000	5.7379	6.5215	12.3301	6.8048	2.2470	4.8735
2001	5.6264	6.5663	11.6892	5.6010	1.9580	4.1875
2002	5.4674	6.1658	10.9033	6.7055	1.4903	3.9802
2003	6.6082	5.7253	14.0222	5.2075	1.7580	4.1939
2004	7.5083	5.3078	14.4246	4.4040	3.9464	3.2528
2005	7.7540	7.5603	14.1452	4.2724	2.6359	3.7649
2006	7.7669	6.3145	15.3034	4.1626	3.6991	3.7276
2007	9.3158	6.2461	17.0830	4.7106	2.2613	4.0100
2008	7.1503	6.5546	17.4400	4.7336	2.1995	4.0092
2009	7.0725	9.2710	13.4185	5.0573	10.7295	5.9574
2010	8.7536	7.7604	12.6014	5.9903	11.0766	4.7367
2011	8.0838	8.7796	12.1341	5.1989	11.6408	4.9559
2012	7.1910	10.2232	12.2149	5.2657	16.7991	5.3808
2013	6.8025	10.2756	11.5537	5.9762	9.7960	3.3490
2014	6.1907	11.7820	11.6554	5.5320	9.6764	4.1838
b value	0.0947	0.3664	–0.0404	–0.0250	0.8971	0.0333

Table 2.

The Average Mean of JJ and DF at Various Regions at Period of 2000 to 2014.

	The developed region		The medium developed region			The underdeveloped region
	JJ	DF	JJ	DF	JJ	DF
2000	6.0559	6.8100	2.0388	1.9950	2.1092	1.3981
2001	5.8813	7.3871	2.1186	1.7241	2.2120	1.0612
2002	5.8100	7.9819	1.9610	1.2331	2.4251	0.9083
2003	5.9192	8.0626	1.8368	0.9373	2.4277	1.0938
2004	5.8128	8.1656	2.4128	1.0527	2.0157	0.8870
2005	5.9634	8.3032	1.9988	1.0019	2.2376	0.7951
2006	6.3132	8.2973	1.9954	0.9288	1.8909	0.8667
2007	6.3326	8.3700	1.8609	0.8053	1.9926	0.9053
2008	6.3135	8.1880	2.0552	0.9027	1.8368	0.9996
2009	6.4996	7.7993	1.9575	1.3584	1.7386	0.9782
2010	6.6724	7.7079	1.8086	1.2779	1.6999	1.1420
2011	6.8919	7.6409	1.6661	1.3236	1.6085	1.1679
2012	7.0297	7.4803	1.5361	1.3370	1.5878	1.3164
2013	6.9323	7.2937	1.5955	1.3630	1.6317	1.4796
2014	7.0016	7.1652	1.5373	1.2900	1.6149	1.6737
b value	0.0949	–0.0170	–0.0403	–0.0131	–0.0586	0.0287

From Table 1, we could observe the following: (a) The survival ability appeared to have a trend of being relatively strong, which could be detected by the value of $b$ , which is 0.0947. Meanwhile, the average mean of $T$ fluctuated significantly, with the feature rising and falling, for example, the largest value was achieved in 2007. (b) The changes in the pressure of transformation and the pressure of upgrading showed the opposite direction, with the values of $b$ being 0.3664 and −0.0404, respectively, which showed that China’s manufacturing industry was in a state of obvious transformation pressure and had comparatively weak or no upgrading pressure, which might be the reason for the comparatively low developing level of the manufacturing industry causing a focus on transformation development. (c) The average mean of $F I$ showed a decreasing trend in general, with the value of $b$ being −0.0250, but the average mean increased obviously after it reached its minimum in 2006. (d) The average mean of $Y H$ increased significantly, with the value of $b$ reaching 0.8971, for example, it was 1.4903 in 2002 and approximately or more than 10 after 2009. (e) The average mean of $J Y$ was comparatively stable, with an increasing trend in general, for example, the value of $b$ was 0.0333.

Regarding the abilities of incremental innovation and disruptive innovation, those in the developed region were larger than those in the medium developed region and in the underdeveloped region, and those in the medium developed region were similar to those in the underdeveloped region, for example, the average means of $J J$ and $D F$ in the developed region were more than 5 in each year and were almost less than 2 in the medium developed region or the underdeveloped region. The value of $b$ was positive for incremental innovation in the developed region and was negative for the other five values; this could mean that the innovation ability of the manufacturing industry was in a comparatively weak state. Meanwhile, this developing trend might not be beneficial for China to overcome the bottom trap (Chen et al., 2017).

Empirical Analysis and Discussion

The panel data model included three basic models: the fixed effect model, the random effect model, and the pooled model. Using the Hausman test, we learned that the fixed effect model was better for performing the econometric analysis. Later, we performed the relevant econometric analysis from the aspects of the whole nation and three regions as defined above using the EViews 5.0 and the ordinary least squares method, and the detailed results are shown in Tables 3 and 4. Here, Table 3 shows the econometric results without the control variables, and Table 4 shows the econometric results with the control variables.

Table 3.

The Econometric Results Without the Control Variables.

	Whole nation	The developed region	The medium developed region	The underdeveloped region
c	1.8836	1.6848	1.7886	2.1585
χ	–0.0555	–0.1704	0.3302	–0.3509
δ	0.0594	0.0915	–0.2069	0.2547
α	0.0625	0.0615	0.1726	–0.1943
β	–0.0674	0.0547	–0.2066	0.0714
Adjusted R²	0.0236	0.0640	0.2150	0.0698
p value	.0000	.0000	.0000	.0000

The econometric result passing 1 significance test.

Table 4.

The Econometric Results With the Control Variables.

	Whole nation	The developed region		The medium developed region			The underdeveloped region
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8
c	2.0492	2.3171	2.3223	1.6534	1.6711	1.6728	2.5813	2.5946
χ	–0.0411	–0.2069	–0.2096	0.2976	0.2974	0.2981	–0.2541	–0.2400
δ	0.0173*	0.0359	0.0334	–0.1937	–0.1941	–0.1928	0.0999	0.0912
α	0.0238*	0.0600	0.0631	0.0681	0.0683	0.0721	–0.2389	–0.2315
β	–0.0804	–0.0278*	–0.0283*	–0.2154	–0.2174	–0.2168	0.0200**
λ	–0.0572	–0.3375	–0.3296	0.2048	0.2064	0.2057	–0.1742	–0.1766
η	0.0808	0.0872	0.0873	0.0062**	0.0081**		0.1721	0.1750
φ	–0.0571	0.0043**		0.0157**			–0.1424	–0.1384
Adjusted R²	0.0571	0.3791	0.3781	0.2684	0.2689	0.2692	0.1737	0.1738
p value	.0000	.0000	.0000	.0000	.0000	.0000	.0000	.0000

Passing 5% but not 1% significance test.

Not passing 5% significance test.

The other passing 1% significance test.

Regarding Table 3, all variables passed the 1% significance test. A summary of the significance of the results found in Table 4 is as follows: All variables in Model 1 passed the 5% significance test; some variables in Model 2 did not pass the 5% significance test; all variables in Model 3 passed the 1% significance test; some variables in Model 4 or Model 5 did not pass the 5% significance test; all variables in Model 6 passed the 1% significance test; some variables in Model 7 did not pass the 5% significance test, but all variables in Model 8 passed the 1% significance test. In addition, Models 1, 3, 6, and 8 did not change the influencing direction of incremental innovation and disruptive innovation as per the relevant region demonstrated in Table 3, which indicated the choice of the control variables to be correct. Therefore, the above four models should be appropriate models to perform the econometric analysis at the level of the whole nation, the developed region, the medium developed region, and the underdeveloped region. Meanwhile, each model’s adjusted coefficient was not large enough, for example, all the adjusted coefficients in Table 3 were approximately or less than 0.2. However, the correlation coefficient was not the most important aspect for judging the fitness of the panel data model, and the value of $p$ or the value of $t$ of each variable should be the focus. Therefore, the correlation coefficient being high or low was not the criterion used for evaluating the overall effect of the model.

Econometric Results at the National Level

As for the whole nation, all indexes in the model passed the 5% significance test, which implied that they could be used to analyze the economic meaning represented. Many developing bottlenecks exist in China, for example, the economy being in the New Normal was some proof. However, the manufacturing in China experienced an obvious de-stocking phase, for which the local government usually implemented a corresponding countermeasure to solve; this might therefore cause the parameter to be comparatively small because of the transformation pressure being comparatively larger compared with the upgrading pressure. However, because the relative index was adopted, the manufacturing industry in China gave priority to transformation development and not to upgrading development, which lead to the coefficient of the transformation performance index being positive and the upgrading performance index being negative. The econometric result coincided with the above analysis, indicating that α and β were 0.0238 and −0.0804, respectively. The relative increase in the fiscal input and the institution and college input did not have a great effect on improving the ability to survive, which was shown to have negative effect generally. However, the favored deduction and exemption index had a strong influence on promoting the ability to survive, with the coefficient being 0.0808.

As seen from the perspective of innovation, the elevation of capacity of incremental innovation was not beneficial to improving the ability to survive, and disruptive innovation achieved the opposite effect, with the coefficients being −0.0411 and 0.0173, respectively. In fact, incremental innovation was gradual innovation with many hierarchical levels and was mainly achieved by product improvement and high-leveled innovation, which could be considered as some comparatively low-leveled innovation; disruptive innovation affected the whole manufacturing process by taking technology and product innovation, for example, implementing the patent, which could be considered as a comparatively high-leveled innovation. China was in the state of the post-period of industrialization. Meanwhile, from this stage, it was very easy to enter into the middle-income trap (Wan & Morgan, 2017). Therefore, innovation being upward (disruptive innovation) or downward (incremental innovation) is essential for China to succeed or fail at escaping the middle-income trap effectively. As the above hypothesis mentioned, one nation or region with a higher development level would implement a strategy of disruptive innovation, which implied that the survival of the manufacturing industry relied greatly on disruptive innovation. From this point, we could judge that China’s manufacturing industry was in state of disruptive innovation to some extent.

Seen from the reality in China, the comparatively low-leveled innovation in the manufacturing industry made it increasingly difficult to enhance competitiveness and presented a relatively weakening trend; thus, advanced innovation should be inevitable, which implies that the influencing performance of disruptive innovation was positive. In addition, it should be pointed out that China’s regional competition was fierce (Chen, Boarnet, & Partridge, 2014; Xia & Liu, 2017). The above-mentioned incremental innovation and disruptive innovation were comparative indexes as per various areas in China. For example, if the ratio of one area’s quantity of patents to the whole nation’s quantity of patents was comparatively high, this indicated that the disruptive innovation ability was comparatively stronger. Meanwhile, the adjusted correlation coefficient was comparatively small as per the degree of fitting precision.

Econometric Results at the Regional Level

The econometric results indicated that the input of the institution and the college index in the developed region, the favored deduction and exemption index, and the input of the institution and the college index in the medium developed region did not pass the 5% significance test, and the three indexes demonstrating the influence of governmental intervention passed the 5% significance test, thus revealing that the reaction to various aspects of governmental intervention in the underdeveloped region was larger than the other two regions. Meanwhile, the input of the institution and the college index in the developed region and the medium developed region did not pass the 10% significance test, and the coefficient of that index at the underdeveloped region reached −0.1374. This result implies that this type of input did not have much of a relationship with the elevation of the ability to survive or with a high level of occupation of resources, which this might induce decreased manufacturing input and decrease the promotion of the ability to survive. The favored deduction and exemption index in the developed region and the underdeveloped region had significant effects on the promotion of the ability to survive, especially in the underdeveloped region. However, another problem was that a comparative increase in the fiscal input was not beneficial to promoting the ability to survive in the developed region and the underdeveloped region, whereas the effect was the opposite in the medium developed region. The influencing directions of the transformation performance index and the upgrading performance index in the three regions were similar to those of the whole nation, but the correlation coefficient in the developed region was closer to the average effect in the whole nation.

Analyzing innovation is the main aim of this article, and now we return to this point. Regarding incremental innovation or disruptive innovation, the influencing direction of the relevant indexes in the developed region and the underdeveloped region was the same as those of the whole nation but with stronger effects. For example, the coefficients of the incremental innovation index in the underdeveloped region and the developed area were −0.2400 and −0.2060, respectively, whereas that of the whole nation was −0.0411; the coefficients of the disruptive innovation index in the underdeveloped region and the developed region were 0.0912 and 0.0352, respectively, whereas that of the whole nation was 0.0173. From the above, we could learn that a comparatively strong pulling effect of disruptive innovation on the ability to survive of the developed region and the underdeveloped region existed, as did a comparatively strong restriction effect of incremental innovation in the above two regions. For the medium developed region, the effect of innovation on the ability to survive was the opposite to that of the whole nation, both for incremental innovation and disruptive innovation, which implied a comparatively strong positive effect of incremental innovation (here, the correlation coefficient was 0.2981) and a comparatively strong negative effect of disruptive innovation (here, the correlation coefficient was –0.1928). Comparing the three regions, the innovation performance effect on the ability to survive in the medium developed region was larger than that in the underdeveloped region, and it was least in the developed region. In addition, the econometric result showed that various modes of the effect of innovation on various regions had large differences from those of the whole nation, which implied difference of the effect of the influencing direction on the ability to survive. In general, the developed region supported the hypothesis, whereas the underdeveloped region showed the opposite. Later, we analyzed the incremental innovation and disruptive innovation in various regions.

The developed region might own much excess capacity that is difficult to eliminate efficiently, and the region may explore new capacity of these industries at the same time. It accelerated and quickly developed new products for these excess industries, which would cause sales difficulty, occupation of much capital, and enlargement of other costs, and this might produce a negative effect on the elevation of the ability to survive considering the negative value of χ. The underdeveloped region had much backward production capacity with features that are difficult to manage and digest in a short time, and the manufacturing enterprise would try to maintain these capacities according to the market mechanism, which would require investing more resources on product exploration of these industries. However, these industries would be eliminated by the market gradually; meanwhile, this might not be beneficial to the ability to survive in the underdeveloped region. The status quo of the industrial chain in the medium developed region might be between that of the developed region and of the underdeveloped region, and the comparative insufficiency of factors and resources in the developed region would force some industries to transfer to the medium developed region (Zhang, Hinger, Audretsch, & Song, 2015). In addition, the products made by the medium developed region owned much marketing space in the underdeveloped region, and this might accelerate the industrial transformation by undertaking industrial transfer from the developed region, which might promote the elevation of the ability to survive steadily as per incremental innovation.

The developed region and the underdeveloped region faced the dilemmas of maintaining excess capacity and maintaining backward capacity, respectively. Thus, these regions might experience obviously biased resources and factors as per incremental innovation; therefore, the survival of the manufacturing industry might be comparatively difficult. The excess capacity was backward capacity in the developed region to some extent, and the capacity was comparatively high grade compared with that in the medium developed region and the underdeveloped region. Hence, to a certain extent, excess capacity has the same definition as backward capacity. However, disruptive innovation, such as the patents, might achieve comparatively good promotion for the manufacturing industry in the above two regions, indicating the significant effect of disruptive innovation on the ability to survive, and this was much more obvious in the underdeveloped region. The reason might be that the backward production capacity in the underdeveloped region was significant, and it urgently needed advanced and high productivity capacity to enhance the vitality of economic development. The medium developed region achieved many benefits from undertaking an industrial transfer, such as those obtained from the catfish and ratchet effects, and a comparatively beneficial market guaranteed the promotion of the ability to survive. Therefore, the medium developed region did not have an obvious intention to engage in disruptive innovation due to the characteristic of needing many input factors, which demonstrated that disruptive innovation was negative to the ability to survive.

Conclusion

Innovation is an important driver for China’s overcoming of the New Normal of Economic Development, and governmental intervention plays an essential role in achieving innovation. At the same time, different modes of innovation might have different functions. Therefore, it is necessary to analyze various modes of the effect of innovation on the sustainable development of the manufacturing industry from the aspect of the ability to survive under governmental intervention. Based on the theoretical model established, this article performed the empirical analysis using a panel data model to test the relevant hypothesis.

The ability to survive of the manufacturing industry was the combination of two aspects, that is, internal and external. Here, the function of innovation could be divided into incremental innovation and disruptive innovation. The internal aspect mainly focused on the transformation pressure and the upgrading pressure of the manufacturing industry. The external aspect attached importance to factors demonstrating governmental intervention, such as the fiscal input for innovation, tax deduction and exemption for innovation, and research fees paid to the institutions and colleges.

The empirical analysis was performed from the perspectives of the whole nation and regions by obtaining available data for the 2000 to 2014 and taking the external aspect as the control variables. As per the regional analysis, China was divided into the developed region, the medium developed region, and the underdeveloped region by taking the per capita GDP in 2015 as the criterion, which included 10, 9, and 10 provincial administrative areas, respectively. Here, the 29 areas mentioned above did not include Tibet and Hainan because of missing data. The econometric result at the region level showed that there were large differences among the different regions, and some of these differences supported the hypothesis. Generally, the effects of incremental innovation and disruptive innovation on the manufacturing industry’s survival were positive in the developed region but those in the underdeveloped region were not. The influencing directions of incremental innovation and disruptive innovation in the developed region were the same as those in the developing region, but the opposite results were found in the medium developed region.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by National Planning Office of Philosophy and Social Science (grant number 18AJY023).

ORCID iD

Bingqiang Li

Author Biographies

Bingqiang Li (PhD in Economics) is vice-president of Jiangxi Vocational Education and Industrial Development Research Institute of Jiangxi Science & Technology Normal University and assistant professor. His research focuses on innovation, manufacturing transformation, and upgrading.

Lei Huang (PhD in Economics) is a lecturer of Jiangxi Vocational Education and Industrial Development Research Institute of Jiangxi Science & Technology Normal University. His research focuses on innovation, manufacturing transformation, and upgrading.

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The Effect of Incremental Innovation and Disruptive Innovation on the Sustainable Development of Manufacturing in China

Abstract

Keywords

The Question Proposed

Theoretical Model

The Internal Ability to Survive

The External Ability to Survive

Data Resource, Index Implication, and Data Description

Data Resources

Index Implication

Data Description

Empirical Analysis and Discussion

Econometric Results at the National Level

Econometric Results at the Regional Level

Conclusion

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iD

Author Biographies

References