Comparative Study of Knowledge Absorptive Capacity in Regional Innovation System

EASTERN ACADEMIC FORUM
Comparative Study of Knowledge Absorptive Capacity in Regional
Innovation System
WANG Qingxi, ZHANG Zhuyi
School of Management and Economics, Zhejiang University of Technology, P.R.China, 310023
[email protected]
Abstract: Regional knowledge absorptive capacity is a critical factor for effects of interregional
knowledge spillovers. It has a great impact on regional innovation and economic development. In this
article, we use data from 31 provinces through years 2009-2011 and establish measurement system of
knowledge absorptive capacity in regional innovation system by identifying the dimensions of potential
and realized knowledge absorptive capacity. Then we employ factor analysis to evaluate and compare
knowledge absorptive capacity among various provinces in China. The empirical results indicate that
knowledge absorptive capacity of Chinese provinces have a big difference and that this trend is further
expanding. On this account we put forward the corresponding conclusions and suggestions.
Keywords: Innovation, Absorptive capacity, Factor analysis
1 Introduction
In the knowledge economy era, technological progress and innovation have become an important
support for sustainable economic development, which can be effectively improved by external
knowledge spillovers. Knowledge absorptive capacity affects a region’s ability of acquiring, absorbing
and creating new knowledge on some level. From the recipient of knowledge spillovers, the stronger a
recipient’s knowledge absorptive capacity, the faster it can acquire and apply new knowledge. Especially
for economically backward areas, due to poor infrastructure and knowledge application ability which
can rapidly acquire and assimilate external knowledge from economically developed areas to promote
technical progress and economic development.
Cohen and Levinthal (1989) proposed the concept of knowledge absorptive capacity on the level of
enterprise and defined as, “The ability of recognizing the value of external information, assimilating and
applying to commercial application”. Then Zabra and George (2002) classified knowledge absorptive
capacity as potential and realized absorptive capacity. With the rising of new economic geography
theory and rapidly development of spatial metrology, the perspective of studying knowledge absorptive
capacity transfers from enterprise to space. The concept of regional knowledge absorptive capacity was
born on the discussion between knowledge spillovers and technical innovation. Mainstream view holds
that regional knowledge absorptive capacity determines the effect of knowledge spillovers on innovation
output on some level. Compared to the level of enterprise, the study of knowledge absorptive capacity in
regional innovation system is not only related to a region’s firms, universities and research institution
but also involved a region's macroeconomic environment, economic structure and policy. So it is
increasingly becoming a new study direction.
2 Index System Establishment
From the concept of regional knowledge absorptive capacity, which is a qualitative index and can’t be
measured. So we have to select appropriate dimensions and proxy indexes to build evaluation system.
This paper refers to Ning Dongling (2009) and divides regional knowledge absorptive capacity into
potential and realized absorptive capacity. The former includes prior knowledge, human capital and
government support for innovation. Prior knowledge determines a region’s stock of knowledge, human
capital reflects a region's ability of absorbing and assimilating external knowledge, government provides
innovative environment and financial support. The latter involves the ability of exchanging and applying
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EASTERN ACADEMIC FORUM
new knowledge. The former can reduce time cost and effectively increase and update the stock of
knowledge. The latter reflects a region’s knowledge application effectiveness.
Table 1 Measurements of regional knowledge absorptive capability
Technical contract turnover (X1)
Prior knowledge
Potential
absorptive
capacity
Realized
absorptive
capacity
R&D expenditures (X2)
R&D personnel full-time equivalent (X3)
Human capital
The number of junior-college or higher educational
background among over six-years-old population(X4)
Government support
Government R&D expenditures (X5)
Exchange ability
The number of Internet users (X6)
The number of patents (X7)
High-tech enterprise output (X8)
Application capacity
New product sales revenue in above-scale enterprises (X9)
3 Measurement on Knowledge Absorptive Capacity
3.1 Data sources and processing
In this paper, all of the original data are from "China Statistical Yearbook", "China Statistical Yearbook
on Science and Technology" and "Chinese Statistical Yearbook on High Technology Industry" in 2010
and 2012. Since the dimensions of original indexes are not exactly same, therefore need to be
standardized to make it comparable. In this paper, indexes of X3, X4, X6 and X7 are standardized by
regional population, indexes of X1, X2, X8 and X9 are standardized by regional GDP, index of X5 is
standardized by government financial expenditures.
3.2 Factor score analysis
SPSSl6.0 is used to make KMO and Bartlett test, KMO values respectively are 0.797 and 0.864.
Accompanied probability of Bartlett test is 0. 000, less than 1% of significance level. Indicating that the
correlation matrix is suitable for factor analysis. In accordance with the principle of eigenvalues greater
than 1, SPSSl6.0 is extracted two common factors to replace the original 9 indexes, the cumulative
variance contribution rate are 87.962% and 89.451%, more than 85% of the general requirement.
information loss are only 13.038% and 10.549% which includes most of information of original
variables and Indicates this method is reasonable and feasible.
Table 2 shows the meaning of two common factors. The first common factor namely potential
absorptive factor (PAC), it have a greater load on X1, X2, X3, X4 and X5 which reflects a region’s
ability of acquiring and assimilating new knowledge. The second common factor namely realized
absorption factor (RAC), it have a greater load on X6, X7, X8 and X9 which reflects a region’s ability of
exchanging and applying knowledge.
Years
Factor
X1
X5
X4
X2
X3
X8
Table 2 Factor load matrix after rotation
2009
1
2
0.966
0.171
0.956
0.152
0.810
0.469
0.853
0.460
0.806
0.579
0.191
0.878
28
2011
1
0.981
0.961
0.858
0.840
0.779
0.127
2
0.157
0.387
0.503
0.610
0.906
EASTERN ACADEMIC FORUM
X7
X9
X6
0.394
0.153
0.517
0.840
0.819
0.766
0.151
0.398
0.529
0.906
0.834
0.705
AC
2.7242
Rank
Beijing
Table 3 PAC, RAC and AC scores in 2011
PAC
Rank
RAC
Rank
5.0989
-0.1512
1
13
Tianjing
0.5086
4
1.3877
5
0.9062
3
District
1
Heibei
-0.3576
21
-0.5334
19
-0.4371
24
Shanxi
-0.1902
13
-0.5287
18
-0.3433
18
Inner Mongolia
-0.0717
11
-0.7901
27
-0.3966
22
Liaoning
0.1827
5
-0.0572
11
0.0742
9
Jilin
-0.3975
24
0.0901
9
-0.1769
13
Heilongjiang
0.0205
7
-0.7268
25
-0.3176
17
0.6613
3
2.4031
1
1.4491
2
Jiangsu
-0.5118
30
2.3754
2
0.7941
4
Zhejiang
-0.2183
14
1.6534
4
0.6283
6
Anhui
-0.3656
23
-0.0352
10
-0.2162
15
Fujiang
-0.2734
16
0.7066
6
0.1699
7
Shanghai
Jiangxi
-0.4313
28
-0.4674
17
-0.4476
27
Shandong
-0.3054
17
0.5231
8
0.0694
10
Henan
-0.3388
19
-0.4535
16
-0.3907
20
Hubei
0.0947
6
-0.1513
14
-0.0166
12
Hunan
-0.4180
26
-0.1032
12
-0.2756
16
Guangdong
-0.6070
31
2.1652
3
0.6470
5
Guangxi
-0.3604
22
-0.5336
20
-0.4387
25
Hainan
-0.3530
20
-0.6174
21
-0.4726
28
Chongqing
-0.4223
27
0.5340
7
0.0102
11
Sichuan
-0.0919
12
-0.3028
15
-0.1873
14
Guizhou
-0.4109
25
-0.6618
23
-0.5244
29
Yunan
-0.3255
18
-0.9034
29
-0.5869
30
Tibet
-0.4713
29
-0.9791
31
-0.7010
31
Shaanxi
0.6977
2
-0.6535
22
0.0865
8
Gansu
-0.0692
10
-0.7906
28
-0.3955
21
Qinghai
-0.0105
8
-0.9306
30
-0.4267
23
Ningxia
-0.2205
15
-0.7044
24
-0.4394
26
Xinjiang
-0.0422
9
-0.7635
26
-0.3684
19
Table 3 shows factor score in 2011. From PAC score, Beijing, Shaanxi and Shanghai are top three,
scores are: 5.098 9, 0.697 7 and 0.661 3. It is obvious that Beijing’s score is much higher than other
regions. Because its average technical contracts turnover, R&D expenditures and government R&D
expenditures are significantly ahead of other regions. Guangdong, Jiangsu and Tibet are last three,
scores are: -0.607 0, -0.511 8 and -0.471 3. From RAC score, there are obvious geographical
characteristics that high scores are concentrated in the eastern coastal areas. Shanghai, Jiangsu and
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EASTERN ACADEMIC FORUM
Guangdong are top three, scores are: 2.403 1, 2.375 4 and 2.165 2. Yunnan, Tibet and Qinghai are last
three, scores are: -0.903 4, -0.979 1 and -0.903 6. All of those are economically backward areas.
At the same time, we find that Shaanxi, Hubei and Heilongjiang have a higher PAC (ranked 2, 6 and 7)
while RAC are relatively backward (ranked 22, 14 and 25). The situation of Guangdong, Jiangsu and
Zhejiang is opposite, PAC is lower (ranked 31, 30 and 14) while RAC are relatively high (ranked 3, 2
and 4). So we can draw the conclusion that Shaanxi, Hubei and Heilongjiang have a stronger ability of
acquiring knowledge while their ability to apply knowledge is relatively poor. Compared to the former,
Guangdong, Jiangsu and Zhejiang obviously have a higher efficiency of knowledge application.
From total AC score, Beijing, Shanghai and Tianjin are top three while Tibet, Yunnan and Guizhou, are
last three. Only Beijing, Shanghai, Tianjin, Jiangsu, Guangdong, Zhejiang, Fujian, Shaanxi, Liaoning,
Shandong and Chongqing those 11 regions’ total score are positive while the rest of areas are below the
national average level. We can find that inter-provincial knowledge absorptive capacity has a greater
difference in China. The reason is that the eastern coastal areas have a higher level of economic
development, basic education, knowledge innovation and information transformation than other areas.
The central and western areas are due to geographical remoteness and lack of innovation expenditures so
that limit the circulation and dissemination of new knowledge and constrain regional economic
development. This situation is mainly the same as the current level of economic development in those
three areas.
3.3 Trend of AC score comparison
Figure 1 shows the general rank of AC scores are not vary greatly, Beijing, Shanghai and Tianjin are the
top three, Tibet, Yunnan and Guizhou are the last three. Shandong and Chongqing’s score are from
negative to positive, 13 areas’ difference are positive in which Jiangsu, Guangdong, Chongqing, Fujian
and Zhejiang have increased considerably. At the same time, the remaining 18 areas’ differences are
negative in which Shanghai, Jilin, Shanxi, Qinghai and Heilongjiang have a larger decline.
Figure 2 shows the average score of 11 eastern areas is 0. 595 7 higher than the average score (-0. 273 1)
of 8 central areas and 12 western areas (-0. 364 0). 9 in 11 eastern areas’ score are positive in which only
Shaanxi and Chongqing belong to the central and western areas. Compared with 2009, the average score
of eastern areas has increased while the average score of the central and western areas has declined.
Thus, we can draw the conclusion that interregional knowledge absorptive capacity have a strong
imbalance and that this trend is further expanding.
Figure 1 Variation of regional total score
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EASTERN ACADEMIC FORUM
Figure 2 Average Score in three regions
4 Conclusion
This paper establishes the regional knowledge absorptive capacity evaluation index system from two
dimensions: potential and realized absorptive capacity. Then taking 31 provincial data in 2009 and 2011
as samples, we obtain evaluation results and conclude basing on these results. The empirical results
show that knowledge absorptive capacity among China's regions have a greater imbalance and that this
trend is further expanding which is consistent with the current situation of regional economic
development. According to the results of this study, we propose two following suggestions:
For the situation of knowledge absorptive capacity of the central and western areas are far behind the
eastern area and that this trend is further expanding. Yunnan, Qinghai, Guizhou and other economically
underdeveloped are should increase investments on innovative research and government support. At the
same time, they should focus on infrastructure construction and improve the ability of knowledge’s
circulation and application. The central and western areas should effort to improve regional knowledge
accumulation and innovation capacity and accelerate the pace on building regional innovation capacity.
For those areas have quite difference between potential and realized absorptive capacity should establish
focused innovative policy. For example, Shaanxi, Hubei and Liaoning and other areas that the potential
absorptive capacity is stronger than realized one. They should pay more attention to improve
knowledge’s circulation and application. Jiangsu, Guangdong and Fujian and other areas are opposite to
the former case. It is necessary to increase R&D investment and focus on the accumulation of the stock
of knowledge.
Acknowledgment:
Supported by the National Natural Science Foundation of China (Grant No. 71103160).
References
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Academy of Management Review, 2002, 27 (2): 185-203
[4]. Ning Dongling. Evaluation of Knowledge Absorptive Capacity on Regional Innovation System.
Science Technology and Industry, 2009 (5): 30-32 (in Chinese)
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