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The theory of innovation factor thinks that regional research and development (R&D) investment directly affects the new product development (NPD) performance of the enterprise, while the theory of innovation efficiency considers that regional R&D investment affects the NPD performance of the enterprise by affecting the enterprise internal R&D efficiency. To test the two-theoretical hypothesis, we match up Chinese provincial-level data with the enterprise data from China industrial enterprise database. The empirical results show that, the theory of innovation factor and the theory of innovation efficiency exist simultaneously. The regional R&D investment can affect NPD performance of enterprises. However, R&D investment of other enterprises in the same region has a beneficial impact on the enterprise internal R&D efficiency. And the regional R&D investment in human resources has a positive effect on the enterprise internal R&D efficiency. According to the conclusions, this paper constructs three policy advices, which are increasing regional R&D investment, expanding and consolidating the enterprise as the core of the status of R&D and increasing regional R&D investment in human resources.

In a market economy, the competitive advantages of enterprises come from the new products [

While enterprises urgently need to increase the performance of NPD, another phenomenon has drawn our attention, namely, the ever-increasing investment in R&D. To obtain the ability of sustainable development and enhance the market competitiveness of enterprises, the government and enterprises have continuously increased their investment in R&D activities. The R&D expenditure of China increased from 304 billion yuan in 2007 to 15,677 billion yuan in 2016, an increase of 4 times in 10 years. Among them, government investment increased from 91 billion yuan to 314 billion yuan, an increase of 2.4 times in 10 years.

Under the background of increasing R&D investment and the urgent need for NPD, a large amount of research focuses on the “black box” relationship between R&D investment and enterprise NPD. However, there is no literature to directly study the relationship between regional R&D investment and NPD performance of enterprises. Among the many factors related to the NPD performance of the enterprise, whether regional R&D investment as an external factor affects the performance of NPD of the enterprise depends on the production and operation of the enterprise. This is of great practical significance. Many studies have shown that different R&D investment in enterprises will lead to different performance of NPD [

Enterprises as the main subject of NPD belong to micro-level and individual level. The region as R&D environment belongs to macro-level and overall level. From the point of view of NPD, the enterprise as a microscopic subject is always nested in a specific regional environment, and they form a hierarchical relationship [

Knowledge-based view (KBV) considers that new product development is the process of knowledge activities of enterprises, which is divided into knowledge acquisition, integration and creation. In addition, knowledge creation is a dynamic process of knowledge acquisition and integration, and that is the process of expanding and internalizing internal knowledge [

This article takes KBV as the theoretical background of the whole framework. The knowledge activities of external R&D institutions, other enterprises and colleges are collectively called the external knowledge activities. Through the external knowledge activities influencing the internal knowledge activities, we connect enterprise and environment and build a theory model that regional R&D investment influence the NPD performance of enterprises, which shown in

KBV considers that the enterprise knowledge has two dimensions: depth and breadth [

acquisition is an extension of these two dimensions. Knowledge can be reused to deepen the understanding of knowledge in a certain area. Individuals may gain more important and complicated knowledge through the epiphany to solve the problems appearing in the process of NPD creatively [

Enterprise knowledge is divided into explicit knowledge and tacit knowledge according to the degree of knowledge transfer and the way of delivery [

Therefore, the efficiency of internal R&D can be understood as the efficiency of R&D investment to promote the spiral transformation of knowledge. Knowledge space agglomeration theory holds that powerful knowledge activities outside the enterprise will form a knowledge gathering space. In this space, knowledge exchange and collision often occur. It forms a gathering space for knowledge transformation, integration and creation, which changes knowledge spiral transformation by alter unidimensional, closed and linear. All these will help to improve the efficiency of spiral transformation of knowledge. Therefore, from the appearance point of view, enterprises in the same region can make use of the advantages in space, which makes enterprise easier to obtain new knowledge through cooperation. This will help enterprises to deepen the knowledge depth and broaden the knowledge breadth so that enterprises can provide solutions to handle the problem in NPD and improve NPD performance. In addition to the new knowledge, the fruit of the R&D investment in the region also has innovative knowledge talents. The greater the R&D investment in a region, the greater intensity of corresponding knowledge activities and the more trained personnel, which helps enterprises to recruit the right people, get more tacit knowledge, cultivate innovative talents, and improve the performance of NPD.

Therefore, this paper proposes the following assumption:

H1: Regional R&D investment has both direct and moderate effects on the NPD performance of enterprises, which means the theory of innovation factor and the theory of innovation efficiency are both established.

At the macro level, this paper uses the internal expenditures data of regional research funds in the 2007 China Science and Technology Statistics Year book and selects 31 provinces except Taiwan, Hong Kong and Macau with provinces as the standard of regional division. At the micro level, this paper chooses the enterprise samples that meet the requirements in the 2007 China Industrial Enterprise Database. Since the latest official statistics in 2007, we can only select data in 2007. But due to the large amount of data, the nature of the data will not change much. Therefore, we do not think old data will affect the empirical results of this article seriously.

First, we exclude the enterprise samples of which total industrial output value, fixed assets, total assets, salary, business status and enterprise code are missing or abnormal or duplicate with reference to Cai and Qianli Xie [

The variables of this model are divided into dependent variable, independent variable and control variable, as shown in

Types of variables | Index | Symbol | Describe |
---|---|---|---|

Dependent variable | NPD performance | NPD | Output of new product/gross output |

Microscopic independent variables | R&D investment of enterprise | RD | R&D expenditure of enterprise/major business income |

regional R&D investment | area_rd | Regional R&D expenditure/GDP | |

R&D investment in R&D institutions | lab_rd | All institutions R&D expenditure/GDP | |

R&D investment in all enterprises | firm_rd | All enterprises R&D expenditure/GDP | |

R&D investment in universities | college_rd | All universities R&D expenditure/GDP | |

R&D investment in human resource | wage_rd | R&D expenditure in human resources/GDP | |

R&D investment in material resource | inf_rd | R&D expenditure in material resources/GDP | |

Control variables | salary and welfare income per capita | wage | The logarithmic form of per capita wage and welfare |

education costs per capita | edu | The logarithm of the education cost per person | |

government subsidy | subsidy | 0 is no subsidy, 1 is subsidized | |

enterprise scale | size | The logarithmic form of total assets | |

enterprise ownership | ownership | 0 is the private enterprise, 1 is the public−owned enterprise |

・ Dependent Variable: The dependent variable in this paper is the performance of NPD, which expressed as the ratio of the output value of new products to the total industrial output value. The ratio is used to make the index value of different enterprises comparable and to exclude other factors such as enterprise size.

・ Independent Variables: According to multiple linear model set, the independent variables can be divided into microscopic independent variables and macroscopic independent variables. And the microscopic independent variables are enterprise spending on R&D, which expressed as the ratio of R&D costs to primary business income. Macroscopic independent variable is the regional R&D investment, which is expressed as the ratio of the expenditure of regional R&D to the regional GDP. According to the knowledge activity of different subjects, we divided regional R&D investment in to enterprise, R&D institutions and university, which expressed as the ratio of their corresponding R&D expenditure to the regional GDP. According to the different usage, we divided regional R&D investment into human resources investment and material resources investment, which expressed as the ratio of the incorresponding R&D expenditure to the regional GDP.

・ Control Variables: The control variables focus on the enterprise level. The contribution of enterprises to human resources of workers will also exert an influence on the innovation of enterprises. There are two parts in human resources investment: wages and educational expenditure. They are expressed as the logarithm of the average salary per worker and the average educational expenditure per worker. The government tends to subsidize innovative industries or innovative enterprises. This article uses government subsidies to control this factor. In addition, enterprises of varied sizes and ownership systems will have different capability of NDP. This article uses the total amount of assets and enterprise ownership to control the impact of these factors on the results.

The traditional linear regression model can only analyze data that involves only one layer and cannot reflect the relationship between variables in different layers [

1) Zero Model

The first and second layer of model does not contain independent variables, only the intercept, that is, for analysis of variance component. It can be used to illustrate the differences between regions can explain the degree of variance of NPD performance of enterprises. Whether there is a significant difference in the NPD performance of enterprise in different regions decides the rationality of using a hierarchical linear modeling.

Level 1: NPD i j = β 0 j + ε i j

Level 2: β 0 j = γ 00 + μ 0 j

Mixed: NPD i j = γ 00 + μ 0 j + ε i j

2) Covariance Model

Covariance model only have the first layer that contains the independent variables, and the second layer does not contain independent variables. In this paper, we join the enterprise R&D investment and other control variables in the first layer of the model. At this point, the first layer of the intercept and enterprise R&D investment slope of the regression coefficient is set to the second layer of the dependent variable. The intercept term is the random effect, and the slope term is the fixed effect.

Level 1: NPD i j = β 0 j + β 1 j ∗ RD + β p j ∗ control variables + ε i j

Level 2: β 0 j = γ 00 + μ 0 j β 1 j = γ 10

Mixed: NPD i j = γ 00 + μ 0 j + γ 10 ∗ RD + β p j ∗ control variables + ε i j

3) Random Coefficient Model

The basic set-up is the same as the covariance model except that the slope of the R&D investment in the second layer is replaced by the random effect. By comparing with the result of the covariance model, we can determine whether there is a random effect in the dependent variable of the second level of the model.

Level 1: NPD i j = β 0 j + β 1 j ∗ RD + β p j ∗ control variables + ε i j

Level 2: β 0 j = γ 00 + μ 0 j

β 1 j = γ 10 + μ 1 j

Mixed: NPD i j = γ 00 + μ 0 j + γ 10 ∗ RD + μ 1 j ∗ RD + β p j ∗ control variables + ε i j

4) Situational Model

The first layer is a complete model including enterprise R&D investment and all control variables. The first layer intercept term is the second layer dependent variable, and then selecting the macro-independent variables as the first layer intercept item’s independent variables. According to whether there is a random effect of slope of the model in the second layer, the situational model is divided into a fixed situational model and a random situational model.

Level 1: NPD i j = β 0 j + β 1 j ∗ RD + β p j ∗ control variables + ε i j

Level 2: β 0 j = γ 00 + γ 0 k ∗ macro-independentvariables + μ 0 j

β 1 j = γ 10 (fixed situational model) or β 1 j = γ 10 + μ 1 j (random situational model)

Mixed: NPD i j = γ 00 + μ 0 j + γ 0 k ∗ macro-independentvariables + γ 10 ∗ RD + β p j ∗ control variables + ε i j ( fixedsituationalmodel )

NPD i j = γ 00 + μ 0 j + γ 0 k ∗ macro-independentvariables + γ 10 ∗ RD + μ 1 j ∗ RD + β p j ∗ control variables + ε i j ( randomsituationalmodel )

5) Complete Model

The first layer includes R&D investment and control variables. Then the first layer of intercept and the R&D input slope of the enterprise are the dependent variable of the second layer. The second layer contains the macro-independent variables and error terms. So, there is a random effect in this model.

Level 1: NPD i j = β 0 j + β 1 j ∗ RD + β p j ∗ control variables + ε i j

Level 2: β 0 j = γ 00 + γ 0 k ∗ macro-independentvariables + μ 0 j

β 1 j = γ 10 + γ 1 k ∗ macro-independentvariables + μ 1 j

Mixed： NPD i j = γ 00 + μ 0 j + γ 0 k ∗ macro-independentvariables + γ 10 ∗ RD + μ 1 j ∗ RD + γ 1 k ∗ macro-independentvariables ∗ RD + β p j ∗ control variables + ε i j

Prior to the model calculation, we centralize the selected index data to avoid the problem of multiple collinearity caused by interaction items. And then we calculate the correlation coefficient between each independent variable in the complete model, which shown in

RD | subsidy | ownership | size | wage | edu | area_rd | RD*area_rd | |
---|---|---|---|---|---|---|---|---|

RD | 1.0000 | |||||||

subsidy | 0.0839 | 1.0000 | ||||||

ownership | 0.0373 | 0.0696 | 1.0000 | |||||

size | 0.0902 | 0.2360 | 0.2438 | 1.0000 | ||||

wage | 0.1004 | 0.0612 | 0.1163 | 0.3294 | 1.0000 | |||

edu | 0.0579 | 0.0015 | 0.0789 | 0.0249 | 0.1964 | 1.0000 | ||

area_rd | 0.1155 | 0.0389 | 0.0214 | 0.0228 | 0.2149 | 0.0248 | 1.0000 | |

RD*area_rd | 0.4460 | 0.0077 | 0.0302 | 0.0177 | 0.0645 | 0.0247 | 0.2252 | 1.0000 |

As can be seen from

We take the corresponding index data into the zero model and calculate the zero model results. The results are shown in

As can be seen from

The results of the covariance model, the random coefficient model, the situational model and the complete model are shown in

It can be seen from

Variables | VIF | 1/VIF |
---|---|---|

RD*area_rd | 1.30 | 0.7886 |

RD | 1.27 | 0.7867 |

size | 1.25 | 0.7995 |

wage | 1.23 | 0.8123 |

area_rd | 1.11 | 0.9045 |

ownership | 1.07 | 0.9340 |

subsidy | 1.07 | 0.9375 |

edu | 1.05 | 0.9529 |

Mean VIF | 1.17 |

Dependent variable | Intercept term | Variance among regional | Variance among enterprises | LR |
---|---|---|---|---|

NPD | 0.0339*** (4.89) | 0.0015 | 0.0240 | 14114.50 *** |

a. *, **, ***, at 10%, 5%, 1% significant level.

Independent variables | Covariance Model | Random Coefficient Model | Situational Model | Complete Model |
---|---|---|---|---|

RD | 1.6210*** (63.78) | 1.4740*** (7.23) | 1.4942*** (7.30) | 1.5309*** (7.67) |

subsidy | 0.0190*** (14.62) | 0.1719*** (13.28) | 0.0172*** (13.29) | 0.0172*** (13.29) |

ownership | −0.0042*** (−3.14) | −0.0036*** (−2.73) | −0.0036*** (−2.74) | −0.0036*** (−2.74) |

size | 0.0158*** (44.84) | 0.0157*** (44.69) | 0.0157*** (44.68) | 0.0157*** (44.69) |

wage | 0.0062*** (6.14) | 0.0057*** (5.67) | 0.0056*** (5.59) | 0.0056*** (5.59) |

edu | 0.0018*** (5.01) | 0.0016*** (4.47) | 0.0016*** (4.48) | 0.0016*** (4.48) |

area_rd | 2.9725*** (6.43) | 3.1657*** (6.56) | ||

RD*area_rd | 28.5358 (1.44) | |||

Interceptterm | −0.1406*** (−16.34) | −0.1378*** (−16.77) | −0.1318*** (−21.37) | −0.1314*** (−21.33) |

Log likelihood | 44192.598 | 45187.478 | 45200.177 | 45201.175 |

a. *, **, ***, at 10%, 5%, 1% significant level.

is less than that of private-owned enterprises. Private-owned enterprises have stronger innovation ability than public-owned enterprises. Enterprises in innovative industries are generally more likely to get government subsidies. Therefore, government-subsidized enterprises have significantly higher performance in developing new products than other enterprises.

Regional R&D investment coefficient was significantly positive, and it indicates that R&D investment in the region has a positive direct effect on the performance of NPD, which meets the hypothesis of innovative factors. However, the interaction coefficient between regional R&D investment and enterprise R&D investment is not significant, and the complete model cannot pass the likelihood ratio test, which shows that regional R&D investment cannot explain the difference in coefficient of enterprise R&D investment. The moderate effect of the relationship between enterprise R&D investment and NPD performance is not significant. Therefore, the hypothesis of innovation efficiency is not valid here.

According to the main subject of knowledge activity, the R&D investment in the region is divided into three parts. The following are the research results of effect R&D investment in R&D institutions, R&D investment in the other enterprises and R&D investment in universities have on the NPD performance of enterprise. The complete model contains the most information to facilitate the judgment of the relationship between the variables, so this paper chose to use the complete model to study the above problems. The calculation results are shown in

As can be seen from

Independent variables | R&D institutions | All enterprises | Universities |
---|---|---|---|

RD | 1.4588*** (7.07) | 1.7540*** (7.62) | 1.4610*** (7.25) |

subsidy | 0.0172*** (13.28) | 0.0172*** (13.29) | 0.0172*** (13.30) |

ownership | −0.0036*** (−2.77) | −0.0036*** (−2.71) | −0.0037*** (−2.79) |

size | 0.0157*** (44.67) | 0.0157*** (44.69) | 0.0157*** (44.68) |

wage | 0.0057*** (5.65) | 0.0057*** (5.65) | 0.0056*** (5.62) |

edu | 0.0016*** (4.47) | 0.0016*** (4.48) | 0.0016*** (4.48) |

labor_rd | 5.1454*** (4.28) | ||

RD*labor_rd | 18.6967 (0.45) | ||

firm_rd | 4.6658** (2.32) | ||

RD*firm_rd | 120.1524** (2.08) | ||

college_rd | 31.7014*** (7.67) | ||

RD*college_rd | 214.8706 (1.13) | ||

Intercept term | −0.1411*** (−20.22) | −0.1262*** (−13.72) | −0.1399*** (−24.20) |

a. *, **, ***, at 10%, 5%, 1% significant level.

that is significantly positive at 5% level. This shows that R&D investment of other enterprises in the region positively moderate relationship between R&D investment and NPD performance in the enterprise. R&D investment of other enterprises in the region has a significant positive impact on R&D efficiency within the enterprise, which proves the same holds true for the hypothesis of innovation efficiency. However, the moderate effects of R&D investment of R&D institutions and universities are not significant, although the mutual coefficients of R&D investment of R&D institutions, R&D investment of universities and R&D investment of enterprise are positive, and the coefficients are not significant. Therefore, the impact of R&D investment of R&D institutions and universities on R&D efficiency of enterprises is also not significant. The results show that the interaction among knowledge-based activities among regional enterprises is more prominent and relevant, while the knowledge activities correlation between R&D institutions, universities and enterprise is relatively weak.

To verify the robustness and reliability of the model results, this paper also extracted the data of science and technology expenditure in “China Science and Technology Statistics Yearbook”. Since science and technology expenditure and R&D investment are all essentially promotion of knowledge activities and creation. These data can also well reflect the situation of knowledge activities in the region. And the theoretical framework of this article is still in place. The original regional R&D expenditure is replaced by science and technology expenditure. Using a complete hierarchical linear model, we explore the impact of different science and technology expenditure in the region on the internal NPD performance. The results are shown in

As the Statistical Yearbook further divides the regional science and technology input into labor costs to measure human resources investment and fixed assets construction cost to measure the investment in material resources, this paper can also continue to study the impact of R&D investment in two different resources on the NPD performance of enterprise. Continue to use the above models and methods, and the complete model results shown in

Independent variables | R&D institutions | All enterprises | Universities |
---|---|---|---|

RD | 1.4566*** (7.05) | 1.6524*** (7.70) | 1.4603*** (7.20) |

subsidy | 0.0172*** (13.28) | 0.0172*** (13.28) | 0.0172*** (13.29) |

ownership | −0.0036*** (−2.78) | −0.0036*** (−2.72) | −0.0036*** (−2.79) |

size | 0.0157*** (44.67) | 0.0157*** (44.69) | 0.0157*** (44.69) |

wage | 0.0057*** (5.65) | 0.0057*** (5.66) | 0.0056*** (5.63) |

edu | 0.0016*** (4.39) | 0.0016*** (4.47) | 0.0016*** (4.48) |

labor_rd | 3.7476*** (4.28) | ||

RD*labor_rd | 13.0953 (0.44) | ||

firm_rd | 2.2330** (2.01) | ||

RD*firm_rd | 59.6200* (1.84) | ||

college_rd | 17.2223*** (5.39) | ||

RD*college_rd | 120.4807 (1.00) | ||

Intercept term | −0.1415*** (−20.39) | −0.1306*** (−15.13) | −0.1400*** (−21.46) |

a. *, **, ***, at 10%, 5%, 1% significant level.

investment in material resources on R&D efficiency within the enterprise is not significant. Human resources investment and enterprise internal knowledge activities, R&D activities are more closely linked. Regional R&D investment in human resources gave birth to talent, will enhance the efficiency of enterprise knowledge acquisition, knowledge integration. However, the investment and purchase of material assets in the region as an innovative element that have a direct impact on NPD performance of the enterprise by providing R&D infrastructure.

The empirical results show that regional R&D investment as a regional innovation impacts the performance of NPD directly and significantly, which meets the

Independent variables | R&D investment in human resources | R&D investment in material resources |
---|---|---|

RD | 1.5484*** (7.80) | 1.5434*** (7.704) |

subsidy | 0.0172*** (13.29) | 0.0172*** (13.29) |

ownership | −0.0036*** (−2.75) | −0.0036*** (−2.72) |

size | 0.0157*** (44.69) | 0.0157*** (44.69) |

wage | 0.0056*** (5.59) | 0.0056*** (5.63) |

edu | 0.0016*** (4.49) | 0.0016*** (4.47) |

wage_rd | 7.9421*** (6.07) | |

RD*wage_rd | 86.2877* (1.71) | |

inf_rd | 7.1944*** (4.87) | |

RD*inf_rd | 82.6873 (1.55) | |

Intercept term | −0.1308*** (−20.60) | −0.1316*** (−19.30) |

a. *, **, ***, at 10%, 5%, 1% significant level.

hypothesis of innovation factor. R&D investment in other enterprises in the region, regional R&D investment in human resources will have a significant impact on the enterprise internal R&D efficiency. Then they can change the NPD performance of enterprise, which meets the innovation efficiency hypothesis. From the perspective of KBV, it shows that there is a stronger correlation of knowledge-based activities among enterprises compared with R&D institutions and universities. In addition, talents generated from R&D investment in human resources will improve the efficiency of enterprise internal knowledge acquisition and integration.

The possible contributions of this paper are mainly reflected in the following two aspects.

・ Regional R&D investment as a driving force for external knowledge activities and a part of the regional environment can promote NPD performance of enterprises. Most of the empirical research on this subject has been proved from the side, for example, the existence of regional research alliances will significantly affect the innovation of enterprises. However, the direct empirical research does not exist yet. This article makes up for the blank in this respect.

・ Theoretical circles on the micro-mechanism of regional environmental impact on enterprise innovation are still debating. There are two main theories. First, the regional environment will directly affect the enterprise innovation, and second, the regional environment will affect the efficiency of use of innovative resources. That is to adjust the relationship between R&D investment and enterprise innovation. In this paper, two kinds of microscopic mechanism of action are tested by empirical way, which adds a new evidence for the research in this field.

The policy significance of the conclusions is that the mechanisms of different regional R&D investment affecting the NPD performance of enterprises are different. Both the hypothesis of innovation factor and the hypothesis of innovation efficiency have been established. Corresponding policies should be formulated according to the specific condition and objective.

・ We should increase regional R&D investment. The innovation factor theory considers that the development of new product is the result of R&D factor investment. As a basic factor in NPD, regional R&D investment can exert a direct effect on the performance of NPD. Regional R&D investment improves NPD performance of enterprises by providing various key resources needed for the development of new products, such as human resources, key knowledge and problem-solving methods.

・ We should expand and consolidate the status of enterprises as the core of R&D. The theory of innovation efficiency shows that there is a stronger correlation between knowledge activities among enterprises in the region and the stronger spillover effect of R&D investment among enterprises in the region. To increase the effect of social R&D resources, the society should invest more resources in enterprises and subsidize R&D activities of enterprises. And government should make enterprises play a decisive role in the allocation of resources for R&D, which is conducive to the transformation of the fruits of knowledge activities among different enterprises.

・ We should increase regional R&D investment in human resources. In the mechanism of R&D investment spillover, the subject of human resources has always been the focus of domestic research. This paper verifies the role of R&D investment in human resources in the spillover effect from the side. So we should increase the R&D investment in human resources and expand the spillover benefit of R&D investment.

This article contains at least the following deficiencies.

・ Limited by data access, older data may have an impact on the empirical results. Once new data is available in the future, test hypotheses can be tested again.

This article lacks corresponding mathematical analysis in part 2. We can make further theoretical proof.

Xu, H. (2018) Regional R&D Investment and New Product Development Performance of Enterprises under the Background of Knowledge Activities. Open Journal of Social Sciences, 6, 183-199. https://doi.org/10.4236/jss.2018.63013