1
2
1 Department of Technology Innovation, University of Zhejiang Sci-ech, Hangzhou, China
2 Department of Technology Innovation, University of Zhejiang Sci-ech, Hangzhou, China
(ddz1217@163.com)
Abstract – Based on literature search, and the data from -se [1] . Hsieh, Smishra and Gobeli(2003), based on
307 high-tech enterprises in Zhejiang Province (2008-2010), the paper constructs a model of enterprise investment in
American pharmaceutical and chemical enterprises in the science and technology, enterprises competitiveness and performance, and proceeds empirical analysis. The results
1975-1996, found that the gain from R&D expenditure was greater than capital assets [2] . A.G.Hu and
G.H.Jefferson(2004), based on large and medium-sized shows as follow: the S&T (science and technology) input of high-tech enterprises in Zhejiang province is significant industrial enterprises in Beijing, China, found that R&D expenditure has significant influence on enterprise positive correlated with performance. Enterprises competitiveness has part intermediary effect between R&D performance both in large and medium-sizes industrial expenditure and enterprise sales revenue. The number of
R&D staff is not significant with performance. Enterprises performance has lag effect to S&T input. The effect of R&D enterprises, but the degree and effect size was different in different industries and the effect will gradually become smaller as time goes on [3] [4] . expenditure to performance is better when the interactive role is considered, which returns to scale turns into increasing from decreasing.

There are not a consistent conclusion between R&D expenditure and performance due to different samples from different industries or regions. Some found that
Keywords - enterprise investment in S&T, enterprises competitiveness, intermediary effect, performance enterprise R&D expenditure is correlation with performance (Laixin Liang, 2006 [5] ; Yuepin Du, 2011 [6] ),
I. INTRODUCTION some found R&D expenditure is irrelevant with performance (Lunrui, 2004 [7] ; Laixin Liang, 2005 [8] ), the
The relationship between performance and enterprise others found R&D expenditure has lag effect on performance (Hewei, 2003 [9] ; Zhouyan, 2011 [10] ). investment in S&T (science and technology) has always
Most of the study focused on the two variables: R&D been the focus of research. Based on the data collected expenditure and performance, seldom on the interactive from 307 high-tech enterprises in Zhejiang Province, the effect of capital input and other variables such as human paper puts research on enterprise competitiveness, which capital input. as a bridge in connecting the input of science and
Based on literature review, and data from 307 technology in high-tech enterprises and performance, high-tech enterprises in Zhejiang Province, the paper where lag and interactive effects are considered. explored the enterprises competitiveness as a bridge in connecting between input of science and technology in
II. LITERATURE REVIEW
The S&T input is mainly about the input used to support scientific and technological activities, including the R&D expenditure and the R&D staff.
Enterprise performance means the evaluation of enterprise profit capability, the internal and external resources allocation and the efficiency of resources utilization, including enterprise sales revenue, net profit etc.
Bart Los and Bart Verspagen (2000) found that the spillover effect of R&D expenditure has positive effect on enterprise productivity in a different level of influence degree in the high, middle and low technological enterpri high-tech enterprises and performance, and lag and interactive effects was introduced.
III. MODEL CONSTRUCTION
A. Variable Selection
Based on literature review, the competitiveness of enterprises is divided into three dimensions, with the characterization of nine indicators. Since 2008 to 2010 the environment of innovation has little change, each indicators chooses three years average. Enterprise investment in science and technology included R&D expenditure and numbers of R&D staff. In order to study

The research was granted by national nature and science fund (70973114), Minister of education social science project (10YJA630078), Zhejiang nature and science fund
(Y6110055), Zhejiang new-shoot talents project.
the lag effect, independent variables are selected in 2008.
In order to avoid the autocorrelation, DW value is used to identification. The sales revenue is the dependent variables on the behalf of enterprise performance, current
(2008), lag 1(2009) and lag 2(2010). See TABLE I

TABLE I
Variable Selection
Level 3 index Level 1 index Level 2 index Unit
S&T input
The competitiveness of enterprises
Performance
Capital investment
Human capital investment
R&D intensity
Innovation ability
HR elements
Manufacture technology level
Direct performance
R&D expenditure
The number of R&D staff
Proportion of R&D expenditure in Sales revenue
Number of authorized patents for invention by thousand R&D personnel
The number of patent application
Sales revenue of new products
Proportion of new products’ sales revenue in sales revenue
The number of employees
Proportion of the educated above junior college in total staff
Proportion of senior technical labor and technician in total staff
Annual added value
The overall labor productivity
Sales revenue
Tenthousandyuan
%
Each
Each
Tenthousandyuan
%
Each
%
%
Ten thousand yuan
%
Ten thousand yuan
(Note: 1. Enterprises competitiveness index’s’ selection is according to Xiaozhi, Songkuang Ran(2002), Jinpei(2003), Laixin Liang, Yongbang
Zhang(2005). 2. R&D intensity is correlation with R&D expenditure, so it will not be included in equation as an independent variable, but it will be imported in interactive analysis).
B. Model Construction
Theory Model
Competitivenes s
S&T input
Equation Construction
Performance
Y = c
1
X1 + c
2
X2 + e
1
(1)
M = a
1
X1 + a
2
X2 + e
2
(2)
Y = c
1
X1 + c
2
X2 + bM + e
3
(3)
Y = d
1
X1 + d
2
X2 + d
3
△
1
+ d
4
△
2
+ d
5
△
3
+ e
4
(4)
X1=R&D expenditure, X2=the number of R&D staff,
M=competitiveness, Y=Sales revenue,
△
1
=R&D input*R&D intensity,
△
2
=R&D expenditure*the number of R&D staff,
△
3
=R&D intensity*the number of R&D staff.
IV. EMPIRICAL ANALYSIS
A. Sample Selection
Based on the database of Zhejiang innovation enterprises construction platform, this paper collected the data of 307 high-tech enterprises in Zhejiang Province from 2008 to 2010. The sample involves 12 regions, including: Hangzhou, Shaoxing, Jiaxing, Ningbo, Jinhua,
Wenzhou, Taizhou, Quzhou, Huzhou, Lishui, Yiwu and
Zhoushan, and mainly involved in 11 industry, including: equipment manufacturing industry, automobile industry, building materials industry, electronic information industry, medicine industry, petrochemical industry, light industrial food industry, nonferrous metal industry, PV and other new energy industry, textile industry and steel industry.
B. Factor Analysis (based on the sample after standardization)
Firstly, this paper will extract factors from intermediary variable M by PCA (principal component analysis). KMO=0.634>0.6, and pass the Bartlett’test of sphericity. Each variables’communality is bigger than 0.5 basically, so the variables can be quite comprehensive to explain the competitiveness of enterprises. With the characteristic value>1 and the rotated component matrix, picking out four main factors. Regarding four main factors’variance contribution as explain intensity to calculate the competitiveness of enterprises’total score F.
See TABLE II
F=0.29232*F1+0.13668*F2+0.1298*F3+0.11802*F4
C. Intermediary Effect Analysis
Current: using stepwise regression, the paper finds that the number of R&D staff is not significant and is removed. Because the DW value is 1.842, there is no autocorrelation between intermediary variable and each independent variable. Using stepwise regression, the paper finds that R&D expenditure passes the test and the coefficient to intermediary variable is 0.736 (note: the result of Equation 2 is all the same in the three periods, so the paper will no longer marked it in the table below).
Using stepwise regression, the paper finds that the competitiveness of enterprises has part intermediary
Component Total
TABLE II
Total Variance Explained
% of
Variance
Cumulative %
1
2
3
4
2.631
1.23
1.168
1.062
29.232
13.668
12.98
11.802
29.232
42.901
55.881
67.683

Sales revenue
Current
Lag1
Lag2
Index
R&D expenditure
M
R
2 adjR
2
R&D expenditure
M
R 2 adjR 2
R&D expenditure
M
R 2 adjR 2
0.892
0.795
0.794
0.914
0.835
0.835
0.899
0.809
0.808
TABLE III
The Result of Stepwise Regression Analysis
Coefficient
Equation1
T
34.375***
0.798
0.796
39.339***
0.846
0.845
35.926***
0.813
0.812
Sig
0
0
0
(* significance in 10% level, ** significance in 5% level, *** significance in 1% level)
Coefficient
0.835
0.077
0.8
0.154
0.829
0.095 effect between R&D expenditure and the performance, and the coefficient is 0.077*0.736<<0.835(the direct effect).
Lag1: The number of R&D staff is not significant and is removed. The competitiveness of enterprises has part intermediary effect between R&D expenditure and the performance, the coefficient is 0.154*0.736<<0.8(the direct effect). Lag2: all the same, and the coefficient is
0.095*0.736<<0.829(the direct effect). See TABLE III
D. Interaction Analysis
Current: Based on the result of above, Equation 4 was modified to Equation 4’: Y = d
1
X1 + d
3
△
1
+ d
4
△
2
+ d
5
△
3
+ e
4
. Using stepwise regression, this paper finds that the interactive effect of R&D intensity and numbers of R&D staff significantly promotes the increasing of sales revenue, while the interactive effect of R&D expenditure and R&D intensity has significantly negative effect on sales revenue, so as the interactive effect of
R&D expenditure and numbers of R&D staff.
Lag1: paper finds that the interactive effect of R&D intensity and numbers of R&D staff significantly promotes the increasing of sales revenue, there is a complementary relationship between R&D intensity and numbers of R&D staff, while the interactive effect of
R&D expenditure and R&D intensity has significantly negative effect on sales revenue. Paper gets the same result in Lag 2. Refer to TABLE IV
Equation3
T
21.892***
2.031**
24.092***
4.646***
22.629***
2.605***
Sig
0
0.043
0
0
0
0.01
V. CONCLUSION
A. Conclusion
On the whole, the S&T input of high-tech enterprises in Zhejiang province is significant positive correlated with its performance. The competitiveness of enterprises has part intermediary effect between the R&D expenditure and enterprise sales revenue. The effect of
R&D expenditure on the sales revenue presents inverted
U type in the three periods, which means that the S&T input not only affects the present sales revenue, but also the sales revenue in the future.
The R&D expenditure affects enterprise sales revenue in two ways, first, it had direct effect, second, it affects by its intermediary role---the competitiveness of enterprises, which is smaller than the direct effect. The number of
R&D staff is not related to the enterprise sales revenue.
According to the study, R&D expenditure has still been the main input in high-tech enterprises in Zhejiang province recently, and human capital investment doesn’t play an important role.
The interactive effect of R&D intensity and numbers of R&D staff significantly promotes the increasing of sales revenue in the three periods, while the interactive effect of R&D expenditure and R&D intensity has significantly negative effect on sales revenue in the three periods, and the interactive effect of R&D expenditure and numbers of R&D staff also has negative effect on present sales revenue.
Sales revenue Current
TABLE IV
The Result of Interaction Analysis
Lag1 Lag2
Index
R&D expenditure
△
1
Coefficient
1.406
-0.445
T
29.08***
-8.185***
Sig
0
0
Coefficient
1.095
-0.418
T
39.905***
-7.849***
Sig
0
0
Coefficient
1.093
-0.457
T
36.631***
-7.895***
Sig
0
0
△
2
-0.36 -7.959*** 0
△
3
0.191 4.063*** 0 0.181 3.931*** 0 0.213 4.256*** 0
R 2 /adjR 2 0.873/0.871 0.878/0.877
(* significance in 10% level, ** significance in 5% level, *** significance in 1% level)
0.856/0.854

The essence of R&D expenditure and R&D intensity is representative of quality and quantity of input. The study shows that even though the quantity of R&D expenditure input has been increasing recently, there is a certain lack of coordination between the number of R&D staff and R&D intensity, not only in the current period, but also in lag 1 or lag 2 periods. After importing the interactive items, the R&D expenditure has greater effect on sales revenue than before, which returns to scale turns into increasing from decreasing in three periods. The result demonstrates that, under the interactive effect of internal factor of S&T input, the effect of S&T input is better than in single factor, and has greater contribute on enterprise performance.
B. Shortage
Due to the data of availability, the paper only covers lag effects in two periods, the long-term effects by input of science and technology should be observed. Upon the case data of enterprises in Zhejiang Province, the implication of the research in nationwide remains further study.
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