Intelligent analysis on the factors influencing the innovation and development performance of high-tech industry in China,based on two-stages model

Abstract

Using the panel data of China’s top five high-tech industries from 1995–2015, and adopting two-stage model, we intelligent analysis the influence that the innovation outlay of non-R&D has on the innovation efficiency of the high-tech industry. It has been concluded that the performance of our country’s high-tech industries vary from one to another, among which the highest one is computer and office equipment manufacturing and the lowest one is the manufacturing of the aerospace vehicles and its equipment. The average number of the former is 0.909, while the average number of the latter is 0.125. The mean number of the former is three times higher than the latter. In the innovation outlay, the expense on the technical reform inhibited the performance of the high-tech industry, the expense on the technology introduction and absorption has a positive effect on the high-tech industry, and there is no obvious connection between the domestic purchase expense and the performance of the high-tech industry.

Keywords

Performance of high-tech industry the random frontier analysis intelligent analysis the outlay of the non-R&D

1 Introduction

As the most active industry in the technical innovation of the current knowledge-driven economy, the high-tech industry is the new growth point in the economic development. Considered as the booster of our country’s economic development, its influence is more and more obvious day by day. And it has great effect on the upgrading of industry structure and transformation of economic development model. As a result, it has promoted the transformation from the extensive economy to the intensive economy in China. It makes sense to analyze the historical performance of China’s high-tech industry in order to guide its strategic development. According to the National “twelve-five” Science and Technology Development Plan, the importance should be attached to the transformation of the economy development model, and technological innovation should be strengthened and the development of high-tech industry must be put in the first place.

In practice, apart from the expense used on its own research activities, technology expenses of high-tech industry are used on many non-R&D aspects, including technical reform, technology introduction, adsorption and purchase of the domestic technology. The expenditure of non-R&D innovation increased from 11.796 billion in 1995 to 46.903 billion in 2015. Although the proportion (the total amount of non-R&D and R&D) of non-R&D innovation expenditure was decreasing constantly, it still accounted for 17.69% in 2015. Therefore, it is necessary to do research on the effects that non-R&D innovation expenditure has on the performance of high-tech industry.

Some studies have focused on the relationship between the enterprise performance and non-R&D innovation capital allocation, such as external technology acquisition. The research by Xie Ziyuan and Huang Wenjun [1] shows that non-R&D innovation expenditure, such as external technology acquisition and technical reform, not only has “promotional effects” but also “substitution effects” on the high-tech industry. The study by Fan Luqing and Liu Wenwen [2] indicates that from the perspective of dualism, when acquiring technology, the enterprise needs to weigh the internal technology acquisition against its external acquisition. Yan Yan and Chi Renyong [3] didi some research on the regulating role which research model and the external technology acquisition method have played between the enterprise’s R&D input and its innovation performance, and have found that, based on the research data of High-tech Enterprises in Zhejiang Province, the enterprise’s R&D input is obviously related with the innovation performance. They have also suggested that if enterprise regards cooperation research as the main technology resource and considers the introduction of foreign technology, like the equipment or sample purchase, and foreign technical personnel employment, as the main way, there will be a reverse regulation between the enterprise’s R&D input and its innovation performance. Zhao Wenhong and Liang Qiaozhuan [4] did some research on the impact the external technology acquisition model and internal technology acquisition have on the enterprise performance, based on the research data of Shaanxi Province. And they have found that both of them has an obvious positive effect on the enterprise performance. Using the research data of Suzhou Enterprise, Song Baoxiang, Peng Jisheng and Wang Wei [5] did some studies on how to make the most of the external technology resource to promote the enterprise’s technological ability. As a result, it is repoorted that generally speaking, the promotional function of the external technology acquisition is very limited. Bi Kexin, Yang Zhaojun and Ai Mingye [6] using the panel data from China’s 29 manufacturing industries in 2005–2010, did research on the influence which the external technology acquisition has on China’s manufacturing technology innovation and have found that foreign technology introduction and domestic technology purchase have a positive effect on China’s manufacturing innovation output.

When it comes to the high-tech industry, the current researches mainly focus on some main problems, including its innovation efficiency [7], the effect that the R&D input has on the enterprise performance [8], and the relationship between innovation input and its output [9]. At the same time, the research on how the non-R&D innovation capital allocation, like the external technology acquisition and technical reform, affects the high-tech innovation performance is relatively less. Chesbrough [10] believes that in the open innovation environment, the enterprise should combine and balance the external and internal innovation resources in order to develop its performance. Non-R&D innovation expenditure and the innovation performance of the high-tech industry are closely connected. What effects does the non-R&D innovation expenditure have on the innovation performance of the high-tech industry? Does it promote the innovation performance? The questions mentioned above is the main focuses of many high-tech industries or enterprises, which are also the questions that this research tries to answer.

2 Theoretical model

Innovation efficiency is a powerful index to measure industrial performance [11, 12], which refers to the ratio of input and output. The rise in innovation efficiency means that the same input will yield more output or the same output needs less input. Technical efficiency can usually be measured by data envelopment Analysis (DEA) [13] or Stochastic Frontier Analysis (SFA). The data Envelopment analysis uses the linear programming method to construct the non-parametric frontier of observation data, without setting up the specific production function. However the random factors should not be considered. Stochastic Frontier Analysis assumes that there is a given function form in the relationship between input and output, and the error term is divided into two parts, including the random error term and the technology inefficiency term. Because there may be some random factors in the efficiency of the high-tech industry [14], this essay adopts SFA to measure the performance of hi-tech industry. ${LnY}_{it} = Lnf (X_{it}, β) + v_{it} - u_{it}$ (1)

In this formula, X_it is the innovation output of the observation unit i in the time t, f (X_it, β) refers to the definite frontier innovation output (theoretical maximum innovation output) in the possible boundary, X_it is the input of the observation unit i in the time t, and β is the parameter vector to be evaluated. $v_{it} \sim N (0, δ_{v}^{2})$ is the random interference term obeying the normal distribution hypothesis, including the uncontrollable factors in production. $u_{it} \sim N^{+} (u, δ_{u}^{2})$ is the management error term or non-efficiency term, which obeys the non-negative half-tailed normal distribution and reflects the loss of the efficiency in production. And it is also the distance between the actual output and the theoretical maximum output. $uit = ui exp [- η (t - T)]$ (2)

In the formula mentioned above, η indicates the effects the time factor has on the technology non-efficiency items. It is by setting up u_it = 0 that we can get the theoretical maximum output ${\hat{Y}}_{it}$ . And then the technology efficiency of the observation unit i can be deduced as:

$\begin{matrix} TEit = Yit / \overset{\land}{Y} it \\ = \frac{E [f (Xit, β) exp (vit - uit)]}{E [f (Xit, β) exp (vit) | uit = 0]} = exp (- uit) \end{matrix}$ (3)

Based on what have been analyzed, the stochastic frontier model of C-D production function form is ${LnY}_{it} = β_{0} + β_{1} {LnK}_{it} + β_{2} {LnL}_{it} + v_{it} - u_{it}$ (4)

In this formula, i refers to the high-tech technology industry, t refers to time, K_it refers to the innovation input of the industry in the year t, and L_it refers to the innovation labor input of industry i in the year t, in addition, time variation tendency can be introduced to production function to explore the effects of the technological development. Its function form is as follows: ${LnY}_{it} = β_{0} + β_{1} {LnK}_{it} + β_{2} {LnL}_{it} + ρ t + v_{it} - u_{it}$ (5)

ρ indicates the time variation tendency. As for the random frontier production function, besides Cobb-Douglas function, the transcendental logarithm production function is also often used. This kind function takes the substitution effect and the interaction among the input factors into consideration. Its form is more flexible. The stochastic frontier model of transcendental logarithm production function is as follows:

$\begin{matrix} {LnY}_{it} = β_{0} + β_{1} {LnK}_{it} + β_{2} {LnL}_{it} + β_{11} {({LnK}_{it})}^{2} \\ + β_{12} {LnK}_{it} {LnL}_{it} + β_{22} {({LnL}_{it})}^{2} + v_{it} - u_{it} \end{matrix}$ (6)

This essay uses likelihood ratio statistic to test and determine the most suitable form of stochastic frontier production function. Variance parameters $γ = σ_{u}^{2} / (σ_{u}^{2} + σ_{v}^{2})$ is used to test the proportion of non-efficiency in the compound distractor. In this formula, γ ∈ [0, 1]. If γ = 0, it means the distance between actual innovation output and the frontier innovation output comes from uncontrollable random factors. Therefore, there is no need to use the Stochastic Frontier analysis method. The OLS method can be directly used. The closer γ gets to 1, the more suitable the Stochastic Frontier analysis method is, because the actual output deviation is more likely to come from the technology non-efficiency terms.

What’s more, in order to explore the impact which the non-R&D innovation input has on the industry performance, measuring model can be constructed as follows:

$\begin{matrix} {TE}_{it} = δ_{0} + δ_{1} {LnGZ}_{it} + δ_{2} {LnYJ}_{it} + δ_{3} {LnXS}_{it} \\ + δ_{4} {LnGM}_{it} + μ_{i} + ɛ_{it} \end{matrix}$ (7)

In this formula, NRD refers to the non-R&D innovation expenditure, GZ refers to the technical reform expenditure, YJ is the introduction expenditure, XS is the absorption expenditure, GM is the domestic purchase expenditure, δ_i (i = 0, 1, 2, 3, 4) is the variable parameters to be evaluated, and is the individual effect. In order to eliminate the variance, the variables have been.

3 Empirical test

3.1 Index measurement

If the stochastic frontier Analysis is adopted to measure the innovation performance of our country’s high-tech industry, the innovation input variable and its output variable should be determined.

The new product sales income is selected as the output variable in this research. As for the selection of the innovation input, as is often the case, the internal R&D expenditure and its personnel are selected to indicate the innovation input in many researches. The R&D internal expenditure reflects the actual research capital input in the annual Observation unit of the report, which is obviously a flow indicator. Wu Yanbin [15] believes the R&D internal expenditure not only has effects on the innovation at present, but also has a far-reaching impact on the future knowledge production. If we use PIM method to calculate the innovation capital stock, the research capital stock (K_i0) in the base period and the research capital stock (K_it) in period can be expressed as follows. $K_{i 0} = I_{i 0} / (g_{i} + δ_{i})$ (8) $K_{it} = K_{it - 1} (1 - δ_{i}) + I_{it}$ (9)

K_i0 refers to the stocking amount of the R&D internal expenditure, g_i is the average annual growth rate of actual R&D expenditure, and the is the depreciation rate of R&D capital stocking amount, which is 15%. As for the construction of R&D price index, it is because the “labor cost” and “instrument and equipment fees” in the internal expenditure of the research activities accounts for the same proportion every year that this essay uses the research results by Wang Lin and Szirma [16] and the R&D price index is set as the mean value of the permanent assets investment price index and consuming price index. When it comes to the R&D personnel, which is the other input indicator in the innovation activities, there are two options in many other researches. One is using the number of the labor in the innovation activities to represent it, while the other one is expressed in terms of the full-time equivalent which reflects the actual workload of the R&D personnel. It is obvious that as an input indicator, the actual full-time equivalent of the personnel is more appropriate.

3.2 Data sources and empirical results analysis

The data used in this paper is derived from the Yearbook of China High-tech industry statistics and the Yearbook of Chinese Statistics. All data are sorted out and gained from the EPS database, and the time range of the data is from 1995 to 2015.

Forinter 4.1 software package is adopted, and the SPA is employed to measure the performance of our country’s high-tech industry. According to LR, the appropriate production function has been tested, the results of which is shown in Table 1. From the Table 1, the function form is the CD production function without time tendency. The calculation results in Table 2. From Table 2, the stochastic frontier analysis is applicable because it is close to 1 and is very obvious under 1% confidence level. In Table 3, the performance of high-tech industries in China is measured.

Table 1
Test of production function in stochastic frontier analysis

Original Assumption LR statistics Freedom 5% Threshold Value 1% Threshold Value Result

CD production function is not applied 235.3 3 7.04 10.5 objection

CD production function with time tendency is not applied 0.58 3 7.04 10.5 acceptance

The Transcendental logarithm production function is not applied 0.42 3 7.04 10.5 acceptance

Original Assumption	LR statistics	Freedom	5% Threshold Value	1% Threshold Value	Result
CD production function is not applied	235.3	3	7.04	10.5	objection
CD production function with time tendency is not applied	0.58	3	7.04	10.5	acceptance
The Transcendental logarithm production function is not applied	0.42	3	7.04	10.5	acceptance

Note: according to the results of this research, LR test agrees with the mixed chi-square distribution.

Table 2

Calculation results

Variable	Coefficient	Freedom	T value	5% Threshold Value	1% Threshold Value
Constant	6.3	3	16.4	3.18	5.8
LnL	0.151	3	2.13	3.18	5.8
LnK	0.626	3	12.02	3.18	5.8
γ	0.930	3	10.7	3.18	5.8

Note: this is gained by sorting out.

Table 3

The performance level of the top-five high-tech industries in China

Time	Medicine Manufacturing	Aerospace, Spacecraft and Its Equipment Manufacturing	Electronics and Communication Equipment Manufacturing	Computer and Office Equipment Manufacturing	Medical Equipment and Instrument Manufacturing
1995	0.184191	0.066157	0.639102	0.885555	0.145139
1996	0.192194	0.070833	0.646315	0.888234	0.152356
1997	0.200331	0.075710	0.653426	0.890855	0.159737
1998	0.208594	0.080787	0.660435	0.893419	0.167277
1999	0.216978	0.086064	0.667342	0.895927	0.174969
2000	0.225476	0.091540	0.674145	0.89838	0.182809
2001	0.234080	0.097214	0.680845	0.900779	0.19079
2002	0.242784	0.103083	0.687441	0.903126	0.198905
2003	0.251581	0.109146	0.693935	0.90542	0.207149
2004	0.260464	0.115400	0.700324	0.907664	0.215514
2005	0.269426	0.121841	0.706611	0.909858	0.223994
2006	0.278459	0.128466	0.712795	0.912002	0.232583
2007	0.287556	0.135271	0.718876	0.914099	0.241272
2008	0.296711	0.142252	0.724855	0.916148	0.250055
2009	0.305917	0.149404	0.730732	0.918151	0.258926
2010	0.315166	0.156722	0.736509	0.920109	0.267876
2011	0.324451	0.164201	0.742184	0.922023	0.276899
2012	0.333767	0.171835	0.747759	0.923893	0.285987
2013	0.343106	0.179619	0.753235	0.92572	0.295134
2014	0.352461	0.187547	0.758613	0.927506	0.304333
2015	0.361827	0.195612	0.763893	0.929251	0.313577
Mean Value	0.270739	0.125176	0.704732	0.908958	0.225966

Note: this is gained by sorting out.

From Table 3, there is great differences among our country’s high-tech industries in knowledge production. The highest mean value, which is 0.909, belongs to computer and office equipment manufacturing. The lowest mean value, which is 0.125, belongs to the aerospace, spacecraft and its equipment manufacturing. And the highest one is 7.3 times as much as the lowest one. That is the reason why we should do researches on the factors which influence the industry performance.

This essay adopts the panel data to do research on the factors. Due to the small data sample, there is no stability test. In view of the fact that there are two influencing model, including the fixed influence and random influence, we uses Hausman to test and determine the influencing model and construct the random effect model. $T E_{i t} = α + β_{i} X_{i t} + μ_{i} + ε_{i t}$ (10)

The original hypothesis is applicable to the random effect model. Because the p-value is 0.000 [17], the original hypothesis is rejected and the fixed influence model is more suitable. Having determined the influencing model, we can identify the model form.

Form 1: Variable Coefficient Model $T E_{i t} = α_{i} + β_{i} X_{i t} + μ_{i} + ε_{i t}$ (11)

Form 2: Fixed Influencing Model $T E_{i t} = α + β_{i} X_{i t} + μ_{i} + ε_{i t}$ (12)

Form 3: Invariant parameter model $T E_{i t} = α + β X_{i t} + μ_{i} + ε_{i t}$ (13)

According to F, the original hypothesis can be tested as follows.

H1: β₁ = β₂ = … = β_N

H2: α₁ = α₂ = … = α_N, β₁ = β₂ = … = β_N

If we accept the hypothesis H2, then the model will be form 3. If the hypothesis H2 is rejected, the hypothesis H1 should be further tested. And at this time, if the assumption H1 is accepted, the model will be form 2, while if assumption H1 is rejected, the model will be form 1. Three models is constructed respectively to calculate the residual square and S₁, S₂, S₃, with the freedom level taken into consideration. F statistics is calculated as follows:

$\begin{matrix} F_{1} = \frac{(S_{2} - S_{1}) / (N - 1) k}{S_{1} / [NT - N (k + 1)]} \sim \\ F [(N - 1) k, N (T - k - 1)] \end{matrix}$ (14) $\begin{matrix} F_{2} = \frac{(S_{3} - S_{1}) / (N - 1) (k + 1)}{S_{1} / [NT - N (k + 1)]} \sim \\ F [(N - 1) (k + 1), N (T - k - 1)] \end{matrix}$ (15)

In this essay, N = 5, K = 4, T = 21, S₁= 3.317, S₂= 4.914, S₃= 7.049. After calculation, the results can be expressed as follows:

$\begin{matrix} F_{1} = \frac{(S_{2} - S_{1}) / 16}{S_{1} / 80} = 1 . 61, \\ F_{2} = \frac{(S_{3} - S_{1}) / 20}{S_{1} / 80} = 3 . 59 \end{matrix}$ (16)

In 5% confidence level, F threshold value is $F_{α_{1}} (16, 80) = 1 . 772, F_{α_{2}} (20, 80) = 1.703$ (17) Because of F₂ > 1.703, H2 is rejected. And owing to F₁ < 1.703, H1 is accepted. Therefore, the fixed influencing model is more suitable. The test results are as follows:

$\begin{matrix} {TE}_{it} = \underset{(0.00)}{1.5} - \underset{(0.00)}{0.25} {LnGZ}_{it} + \underset{(0.00)}{0.158} {LnYJ}_{it} \\ + \underset{(0.09)}{0.03} {LnXS}_{it} + \underset{(0.89)}{0.004} {LnGM}_{it} \end{matrix}$ (18)

Value P is in the brackets. From what have been calculated, in 1% confidence level [18], technical reform expenditure is −0.25, which indicates the technical reform inhibited the industry performance to some degree. If it increases by 1%, the industry performance will decrease by 0.25%. That is to say, it is necessary to decrease the technical reform expenditure. In 1% confidence level, technology introduction expenditure is 0.158, which means technology introduction can benefit our country’s high-tech industry performance. If the technology introduction increases by 1%, the industry performance will also increase by 0.158%. Therefore, the technology introduction should be strengthened, which also suggests that our country is still in the stage of “introduction–innovation”. In 10% confidence level, adsorption expenditure is obviously positive, which means the absorption expenditure promotes our country’s high-tech industry performance to some extent. Although the coefficient of domestic purchase expenditure is positive, it doesn’t pass the test, which shows the domestic purchase expenditure has no significant impact on the performance of high-tech industry in China.

4 Conclusions

In this essay, the two-stage model is used comprehensively, and the innovation efficiency of our country’s top-five high-tech industries from 1995 to 2015 has been measured. The different effects, which the non-R&D innovation expenditure intended for different purposes has on the high-tech industry performance, has been mainly analyzed.

Technical reform focuses on the gradual improvement of the existing technical conditions rather than fundamentally changing the existing technology. Therefore, its effects can be directly seen from the quality improvement of the products, the increasing of the product colors and varieties, and the products upgrading. And finally, it can lead to the expansion of the number of the new products and the increasing of the sales income. However, it inhibits the patent output level of the high-tech industry. And the reason is that the technical reform can extended the life span of the existing technique and slows down the enterprises’ desire for the new technology, which naturally decreases the enterprises’ technology innovation impetus and the industry performance level.

Technology introduction expenditure includes the expenditure covering foreign patent purchase, design, blueprint, formulation, and the key equipment. The proportion which the technology introduction expenditure has in the whole technology expenditure is the present common evaluation indicator in the analysis relevant to the external dependent degree of the technology. The calculation of Guo Tiecheng and Zhang Chidong [19] shows that the technology external dependent degree of China is 40.1% in 2010, which is obviously higher than that in USA, Japan, and South Korea and so on. The technology external dependent degree of those countries are below 30%. It can be concluded in this essay that the technology introduction expenditure has a positive effect on the performance of high technology industry in China, which also shows that the development of our country’s high-tech industry still relies on the foreign technology, and our country’s independent innovation ability remains to be improved.

The absorption expenditure and the domestic purchase expenditure has a positive effect on the high-tech industry performance, but it is not obvious. So we need to deepen the technological management system reform of high-tech industry. The innovation performance of China’s high-tech industries has been improving gradually in the inspecting period, but varies greatly from one industry to another. The technology management system needs to be improved in China’s high-tech industries, which can balance the technology development management and the speed or needs of the technology development. In the meantime, it the micro-enterprise level, we should learn from international advanced management experience, strengthen market management, optimize the allocation of innovative resources, improve the scale effect and reduce the waste of the innovation factors. And we also improve the system of proprietary intellectual property rights. The improvement of the innovation performance and independent innovation ability need the scientific management of the technology resources, increasing investment of the research resources, the improvement of the innovation model, as well as the improvement of the system of proprietary intellectual property rights, which is of great importance in the era of knowledge-driven economy.

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