Frontiers in Business, Economics and Management 
ISSN: 2766-824X | Vol. 4, No. 2, 2022 

 

19 

Research	on	the	Impact	of	Digital	Economy	on	Regional	
Innovation	Capability	
Huiling Wang, Yue Ling 

Lanzhou University & School of Economics, Lanzhou, China 

 

Abstract: In this paper, an evaluation system of digital economy development level is constructed based on China's provincial 
panel data from 2011 to 2019, and ridge regression method is used to test whether digital economy development promotes 
innovative knowledge condensation and market transformation. The results show that the development of digital economy 
significantly promotes the knowledge condensation of innovation, but has no significant impact on the market transformation of 
innovation. Besides, the development of digital economy has significant heterogeneity in the innovation knowledge condensation 
and market transformation of the three regions. The suggestions are that precise measures should be taken in the three regions, 
labor and capital investment should be increased in basic and applied research and experimental development research. 

Keywords: Regional innovation, Digital economy, Knowledge condensation, Market transformation, Ridge regression. 
 

1. Introduction 
Since reform and opening up, China's innovation level has 

been greatly improved.  According to the 2021 Global 
Innovation Index Report released by the World Intellectual 
Property Organization (WIPO), China ranks 25th in 
innovation input and 7th in innovation output, showing a 
strong momentum of growth in its comprehensive innovation 
capacity. However, there are still problems such as "Valley of 
death" and "Darwin Sea". In order to achieve high-quality 
regional economic development, China should focus on 
commercializing and industrializing a large number of 
scientific and technological achievements in the direction of 
innovation-driven development.  

At present, new generation of information technologies 
gradually have a close connection with social and economic 
fields. The development of digital economy undoubtedly 
provides a new channel for the transformation of China's 
scientific and technological achievements from samples to 
products to commodities. Based on this, this paper intends to 
use the research of Liu Shufeng et al(2019) to divide China's 
innovation process into two stages: knowledge condensation 
stage and market transformation stage.  

2. Literature Review 
Related to this paper is the role of digital economy on 

regional innovation. This kind of literature can be sorted out 
from both theoretical and empirical aspects. In terms of theory, 
Zhang Xinwei(2019) pointed out that under the condition of 
digital economy, the technological paradigm change will 
realize the evolution of innovation mode by promoting the 
transformation of industrial organization mode and 
accelerating the evolution of innovation organization mode. 
In terms of empirical analysis, Wen Jun et al.(2020) verified 
that digital economy can indeed improve regional innovation 
capability by analyzing data of 287 prefecture-level cities in 
China in 2015 and by means of ordinary least square method 
and robust standard error method. Hansen(2007) believed that 
the innovation process actually includes the generation, 
transformation and dissemination of innovation. Based on 
this, this paper intends to decompose the innovation process 

and explore the impact of digital economy on the innovation 
process in detail.  

3. Theoretical Analysis 

3.1. Digital Economy and Knowledge 
Condensation Stage 

First, the development of the digital economy has extended 
the breadth of innovation at the stage of knowledge 
condensation. The wide application of the new generation of 
information technology will alleviate the knowledge overlap 
among innovation subjects caused by poor communication, 
thus promoting the knowledge production and transfer of 
innovation subjects. Second, the development of digital 
economy deepens the innovation depth of knowledge 
condensation stage. Consumer demand for products is 
diversified, which forces each innovation subject to reduce 
the output of homogeneous innovation results in the 
knowledge condensation stage. Third, the development of 
digital economy can accelerate the speed of innovation and 
strengthen the precision of innovation. The development of 
digital economy can reduce the time to search for market 
demand, speed up the flow of innovation elements in the 
region, avoid the homogeneous output and ultimately 
improve the innovation output’s quality at this stage.  

3.2. Digital Economy and Market 
Transformation Stage 

Shi Zhenquan(2020)believed that the main body of 
transformation of innovation results can form various 
information data including user data, transaction data, 
interactive communication data and task data through the 
business process of transformation of innovation results. 
Zhang Xinwei(2019) pointed out that the application of big 
data makes it possible to "accurately predict" consumer 
preferences. With new-generation information technologies, 
the problem of information asymmetry between innovation 
subjects and market demand is alleviated, and the precise 
matching between innovation achievements and market 
demand is also achieved, and the transfer and transformation 
accuracy of innovation achievements in the market 
transformation stage is finally improved.  



 

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4. Research Design 

4.1. Cobb-douglas Production Function 
The Cobb-Douglas production function is mainly used to 

measure the contribution rate of technological progress, 
capital and labor growth to output growth. Based on this, this 
paper constructs a model of the impact of digital economy 
development level on innovation from the dual perspectives 
of knowledge condensation and market transformation.  

 

   ( t ) L K                1, 2
i i

i i iY A i
        (1) 

 

     ( 1 ) ( 1 )(1 )   i t i t i tK E d K           (2) 
 
Referring to the relevant research of Wu Yanbing(2008), it 

will be set in the following form:  
 

 

( , , , , , , )
( )

f D E M A R P G D P I S H R U R B F T D
tA A e

  (3) 
 

1 2 3

4 5 6 7

( , , , , , , )f D E M A R P G D P I S H R U R B F T D L N D E L N M A R L N P G D P

L N I S L N H R L N U R B L N F T D

  
   

  

    (4) 
 

5 6 7

i 1 2 3 4

i i i i

L N Y = D + L N D E + L N M A R + L N P G D P + L N IS

L N H R L N U R B L N F T D L N L L N K

   
           1, 2i   (5) 

 

4.2. Data Sources and Descriptive Statistics 
The paper selects panel data of 30 provinces, municipalities 

and autonomous regions in China (excluding Hong Kong, 
Macao, Taiwan and Tibet) from 2011 to 2019. Data from 
China Statistical Yearbook of Science and Technology, China 
Statistical Yearbook, Statistical Bulletin of national Economic 
and Social Development of each province, the Research 
Center of Digital Inclusive Finance of Peking University, the 
Report of China's Provincial Marketization Index. The 
descriptive statistics of each variable are shown in Table 1.  

 
Table 1. Descriptive statistics of variables 

Variable types Variable symbol Sample size Mean Standard deviation
The 

minimum 
The 

maximum 
Explained 
Variable 

PN 270 10.006 1.429 6.219 13.176 
TC 270 13.828 1.761 8.642 17.858 

Core Explanatory 
Variable 

DE 270 -1.443 1.129 -4.605 0.010 
BC 270 14.284 1.181 11.456 17.184 
BL 270 9.705 0.931 7.315 11.958 
EC 270 15.838 1.349 12.523 18.380 
EL 270 10.847 1.285 7.729 13.479 

Control Variables 

MAR 270 1.854 0.317 0.864 2.408 
PGDP 270 10.757 0.442 9.691 12.011 

IS 270 0.167 0.380 -0.640 1.655 
HR 270 -2.074 0.400 -2.925 -0.683 

URB 270 4.046 0.202 3.556 4.495 
FTD 270 2.784 0.966 0.128 4.982 

 
Table 2. Ridge regression results in knowledge condensation stage 

 LNPN

 
All 
0.10k   

(1) 

East 
0.01k   

(2) 

Center 
0.19k   

(3) 

West 
0.01k   

(4) 

LNDE  
0.142** 
(5.670) 

0.092* 
(2.478) 

0.121** 
(2.732) 

0.053 
(0.965) 

LNBL  
0.475** 
(14.529) 

0.739** 
(6.576) 

0.413** 
(4.115) 

0.070 
(0.508) 

LNBC  
0.281** 
(10.698) 

0.131 
(1.297) 

0.032 
(0.518) 

0.615** 
(6.619) 

Control Yes Yes Yes Yes 

Constant 
-8.328** 
(-7.702) 

-0.952 
(-0.317) 

-4.320 
(-1.403) 

-5.221* 
(-2.137) 

Obs 270 99 72 99 
Adjusted R ² 0.897 0.942 0.682 0.921 

F 261.954** 178.529** 17.914** 128.594** 
Note: * P <0.05 ** P <0.01 are t values in parentheses. 

 

5. Results Analysis 

5.1. In Knowledge Condensation Stage 
Ridge regression results of knowledge condensation stage 

are shown in Table 2. As can be seen from the ridge regression 
results of column (1), the development level of digital 

economy has a significant positive impact on innovation in 
this stage, and the investment of basic and applied research 
capital and personnel also shows a significant positive impact. 
This indicates that the level of development of digital 
economy and the investment of personnel and capital in basic 
and applied research are conducive to the formation of 



 

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knowledge results. Columns (2) to (4) in Table 2 are the 
results of regional heterogeneity analysis. (This article refers 
to the standard of the National Bureau of Statistics to divide 
the eastern and western regions) The results show that 
regional heterogeneity exists and is prominent in the 
knowledge condensation stage. 

5.2. In Market Transformation Stage 
Ridge regression results in the market transformation stage 

are shown in Table 3. As can be seen from the ridge regression 
results in column (1), the development level of digital 

economy has a positive but insignificant impact on innovation 
in this stage, which indicates that the development of digital 
economy currently mainly affects innovation output in the 
stage of knowledge condensation, and has no significant 
effect on the promotion of innovation in the stage of market 
transformation. As can be seen from the regression results of 
column (1), the input of R&D researchers and R&D capital 
has a significant positive impact on the commercialization 
and industrialization of innovation outputs. As can be seen 
from Columns (2) to (4), regional heterogeneity also exists 
and is prominent in the market transformation stage.  

 
Table 3. Ridge regression results in market transformation stage 

 LNTC

 
ALL 

0.46k   
(1) 

East 
0.01k   

(2) 

Center 
0.11k   

(3) 

West 
0.99k   

(4) 

LNDE  
0.055 

(1.532) 
0.001 

(0.036) 
-0.146* 
(-2.453) 

0.114* 
(2.231) 

LNEL  
0.328** 
(15.479) 

0.192 
(1.804) 

-0.092 
(-0.983) 

0.365** 
(9.082) 

LNEC  
0.416** 
(19.074) 

1.160** 
(13.222) 

0.548** 
(4.884) 

0.388** 
(9.573) 

Control Yes Yes Yes Yes 

Constant 
0.142 

(0.133) 
-16.686** 
(-5.301) 

-17.768** 
(-4.259) 

2.924 
(1.288) 

Obs 270 99 72 99 
Adjusted R ² 0.694 0.964 0.773 0.449 

F 68.628** 289.105** 27.928** 9.889** 
Note: * P <0.05 ** P <0.01 are t values in parentheses. 

 

6. Conclusions and Suggestions 
Based on the above theoretical and empirical analysis, the 

development of digital economy not only has different 
impacts on the knowledge condensation stage and the market 
transformation stage of China's innovation at the national 
level, but also has obvious regional heterogeneity. Based on 
the above conclusions, the paper puts forward the following 
policy suggestions: first, comprehensively promote the 
development of digital economy, promote digital inclusion, 
and bridge the regional difference in innovation capacity 
caused by the digital divide. Second, further increase labor 
and capital input in basic and applied research and 
experimental development research to ensure adequate 
intellectual support and capital supply in the knowledge 
condensation stage and market transformation stage of 
innovation. 

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