Frontiers in Business, Economics and Management 
ISSN: 2766-824X | Vol. 11, No. 3, 2023 

 

207 

The	Impact	of	Digital	Transformation	on	Enterprise	
Innovation:	Based	on	The	Empirical	Analysis	of	Listed	
Companies	
Linghao Xu1, * 

1 East China University of Political Science and Law, Shanghai 201600, China 
* Corresponding author: xulh1998@163.com 

 

Abstract: Based on the 2011-2020 Digital Finance Index and the data of Shanghai and Shenzhen A-share listed companies, 
the panel fixed utility model is used to investigate how the digital transformation of companies affects the innovation ability of 
enterprises. The research finds that the digital transformation of enterprises can have a significant positive effect on the 
improvement of the innovation and innovation ability of enterprises. The digital transformation of enterprises can promote the 
improvement of the innovation ability of enterprises by affecting the financing constraints, and then enable the innovation of 
enterprises；further research finds that enterprise digital transformation has a more significant positive effect on the innovation 
ability of private enterprises. 

Keywords: Digital Economy; Financing constraint; Enterprise innovation. 
 

1. Introduction 
As the most active field in China's economic development, 

the digital economy has continuously expanded the breadth 
and depth of integration with various fields of economy and 
society, and plays an important role in stimulating 
consumption, stimulating investment and creating 
employment. From a domestic perspective, the relevant data 
of the China Academy of Information and Communications 
shows that the scale of China's digital economy was 22.4 
trillion yuan in 2016 and 35.8 trillion yuan in 2019, 
accounting for 36.2% of GDP, and a nominal growth of 15.6% 
compared with the same period last year, much higher than 
the GDP growth rate. From an international perspective, the 
2019 Digital Economy Report released by the United Nations 
Conference on Trade and Development points out that the 
United States and China are becoming the main leaders in this 
field, accounting for 90 percent of the market capitalization 
of the world's 70 largest digital platforms, with China's 
Tencent and Alibaba among seven of these "super platforms." 
China's digital economy has gradually become an important 
component and growth driver of the national economy under 
the rapid growth of scale.China is emerging as the dominant 
leader in this space, accounting for 90 percent of the market 
capitalization of the world's 70 largest digital platforms, with 
China's Tencent and Alibaba among seven of these "super 
platforms." China's digital economy has gradually become an 
important component and growth driver of the national 
economy under the rapid growth of scale. 

Since the "13th Five-Year Plan" period, China's digital 
economy has entered the fast track of development, and 
digital transformation has gradually become a new driving 
force for national economic growth. In this context, the deep 
integration of digital technologies such as the Internet, big 
data and artificial intelligence with the real economy will 
empower the transformation and upgrading of traditional 
industries and improve enterprise productivity [1][2] .In the 
face of ever-changing markets and increasingly fierce 
competition, can Chinese enterprises explore the 

development path combining digital transformation and 
enterprise innovation and promote high-quality economic 
development? It has become a hot topic in academic circles. 

By combing through previous studies, we can find that 
digital transformation has a positive effect on enterprise 
innovation. Some scholars believe that the development of the 
Internet can directly and indirectly promote the efficiency and 
level of regional innovation[3]. At the same time, digital 
economy mainly affects enterprise innovation performance 
through technology diversification channels [4]. Digital 
transformation can effectively improve the performance of 
enterprises, help enterprises reduce costs and increase 
efficiency, and enhance the impetus for innovation. However, 
existing studies generally focus on the role of digital 
transformation at the macro level, and few start from the 
perspective of micro digital transformation of enterprises and 
focus on enterprise innovation itself. In the long run, 
innovation will bring more competitive advantages, more 
super profits and higher market value to enterprises[5]. 
However, due to the high failure rate, large capital demand 
and long cycle of innovation projects[6], financing constraints 
are large and the willingness of enterprises to innovate is often 
not strong. This paper believes that the digital transformation 
path based on the integration of artificial intelligence, 
blockchain, cloud computing, big data and other technologies 
with the real economy is likely to become the thrust of 
enterprise innovation. 

The follow-up arrangement of this paper is as follows: The 
second part reviews and summarizes the existing literature; 
The third part analyzes the relationship between digital 
transformation and enterprise innovation and puts forward the 
hypothesis of this paper. The fourth part introduces the data 
source, describes the related variables, and establishes the 
econometric analysis model. The fifth part shows the 
benchmark regression results and analyzes the transmission 
mechanism of digital transformation on enterprise innovation. 
The sixth part puts forward the conclusion and enlightenment 
of this study. 



 

208 

2. Theoretical Analysis and Research 
Hypotheses 

Digital transformation is not the simple combination and 
application of digital technologies, but the elevation of data 
to a new production factor and innovation output driver as 
important as people, capital and land. From the perspective of 
China's current situation, the country regards digital 
transformation as an important strategic deployment, and has 
issued documents for many times to vigorously cultivate new 
digital formats and seek the organic combination of digital 
economy and real economy. Combing through the previous 
literature, the role of digital transformation in the real 
economy mainly has the following three points: First, the 
digital transformation of enterprises can help enterprises 
reduce costs and increase efficiency, and improve production 
and operation efficiency. In the face of massive and non-
standard data, digital technology can be used as an effective 
means of data processing to help enterprises use information 
efficiently and cheaply, achieve internal production and 
external market demand matching, and then optimize the 
production process. Second, the digital transformation of 
enterprises can significantly reduce the degree of information 
asymmetry. The reason is that companies can not only process 
internal information through digital transformation, but also 
communicate information to market participants, enabling 
information to be open, connected and shared. Third, 
enterprise digital transformation can effectively enhance 
enterprise value and financing capacity. As an important part 
of the national macro development blueprint, digital 
transformation is consistent with the current development 
trend of the digital economy. Based on this, policy-oriented 
digital enterprises will be more recognized by the capital 
market, with higher valuations and stronger financing 
capabilities. 

Digital transformation has gradually become endogenous 
and integrated into the daily operation and decision-making 
process of enterprises. From an internal perspective, it is clear 
that the initiative, ability and results of digital transformation 
will also affect the degree of digital transformation. Before 
the implementation of digital transformation, the information 
of enterprises in production, logistics, sales and other aspects 
can not form a system, and therefore can not provide decision-
making advice for enterprises. digital transformation 
integrates scattered information and resources of enterprises, 
optimizes the connection between supply and demand, and 
fully improves the future operation efficiency of 
enterprises[7], so that enterprises can achieve higher marginal 
innovation output. At the same time, the combination of 
digital transformation and real economy can give full play to 
its "multiplier" creation effect[8], activate idle resources, 
achieve the deep integration of cross-border resources, and 
empower enterprises to innovate. Based on this, the 
discussion of enterprise digital transformation and what 
channels will affect enterprise innovation has important 
theoretical significance for in-depth understanding of 
enterprises' motivation and benefits of digital transformation 

Specifically, enterprise innovation will be subject to 
financing constraints[9], management incentives[10], 
innovation failure tolerance[11],innovation attention and 
other factors. With the characteristics of high failure rate, low 
certainty, large capital demand and long cycle of innovation 
projects, enterprises now begin to rely on the enabling effect 
of digital transformation on enterprise innovation. The 

disclosure of the concept and orientation of digital 
transformation by enterprises has the following effects on 
enterprise innovation: it can release positive signals of their 
own transformation and upgrading to external information 
users such as financial institutions and government agencies, 
effectively alleviate the information asymmetry and financing 
constraints between enterprises and market participants, and 
empower enterprises to innovate; To sum up, digital 
transformation mainly enhances enterprises' innovation 
willingness and promotes enterprise innovation through 
financing constraints. 

Based on this, this paper proposes hypothesis 1: Digital 
transformation can help improve the level of enterprise 
innovation. 

Financing constraint is an important factor affecting the 
innovation activities of enterprises. Innovation investments 
are different from material investments, which makes them 
more vulnerable to financial constraints. Due to its large scale 
of investment, high uncertainty and long payback period, it is 
often difficult to obtain stable and sufficient internal funds 
from the enterprise itself. In terms of external financing, 
because enterprises are reluctant to provide accurate 
information about their innovation projects to financial 
institutions, it is difficult to guarantee the quality of the 
projects, and innovation investment cannot be pledged by 
tangible assets, resulting in information asymmetry and 
adverse selection, resulting in credit rationing or the increase 
of financing costs. Especially for developing countries, the 
constraints on innovation investment are more significant due 
to the imperfect financial market. 

From the perspective of financing constraint theory, when 
the project with positive net present value needs investment 
but the internal financing is insufficient, the enterprise will 
obtain external funds to promote the project. When 
enterprises have limited access to internal and external funds, 
enterprises lacking available funds will be more inclined to 
invest in short-term profitable projects, and reduce the 
willingness to invest in high-risk, high-investment, long-term 
innovation projects with uncertain returns[12,13]. Therefore, 
one way to promote innovation is to ease the financing 
constraints that companies face. 

When evaluating the innovation activities of enterprises, 
the traditional financial industry often reduces or refuses to 
lend due to the limitations and uncertainties of information 
(uncertain income, uncertain cycle, etc.), resulting in 
insufficient supply of gold for innovative enterprises. But in 
the context of digital transformation, first, digital finance, due 
to the combination of finance and digital technology, can 
cover all regions to the greatest extent and benefit all 
enterprises. The digital transformation of enterprises sends 
positive signals. As the main body of information production, 
the digital transformation information disclosed by 
enterprises not only means that enterprises will have greater 
investment in this aspect, but also indicates the high-quality 
development of enterprises in the future. Second, based on the 
financial technology supported by the digital economy, it can 
effectively broaden the service boundaries of financial 
enterprises. External fund providers can better identify and 
reduce risks in the credit process through enterprise disclosure, 
improve the efficiency and effect of credit resource 
allocation.[14] Digital finance overcomes the shortcomings 
of financial institutions such as banks, and its network 
advantages and digital technology reduce the problems 
existing in the old financial system such as banks, speed up 



 

209 

the approval speed and simplify the approval procedure. The 
communication cost between banks and enterprises is reduced, 
the consumption of relevant resources is relatively low, and it 
is easier to stimulate the innovation vitality of enterprises. 
Second, the traditional financial system, dominated by banks, 
requires a large amount of data to record procedures, and there 
is the possibility of errors in manual processing. While 
solving such problems, digital finance allows enterprises to 
participate in the process through the Internet platform, 
improving the efficiency of enterprises in using financial 
redundant resources, and thus realizing the "throttling" in the 
process of development of enterprises. Third, there is often a 
high information asymmetry between the traditional financial 
system represented by banks and enterprises. State-owned 
enterprises can better obtain the support of traditional 
financial institutions by relying on the government 
background, while private enterprises have big problems in 
obtaining the support of the traditional financial system. At 
the same time, it is difficult for institutions under the 
traditional financial system to have sufficient impetus to 
provide more support for the digital innovation of private 
enterprises. However, digital finance draws the spatial and 
temporal distance between financial institutions and 
enterprises, improves the communication efficiency of 
financial institutions when assessing corporate borrowing 
risks, reduces information asymmetry, and at the same time, 
resources can also play a role in the areas where their utility 
can be maximized. Companies will also become more willing 
to go green. Fourth, digital finance enables individual 
investors to understand corporate information in more detail, 
enabling them to participate more actively in the capital 
market. In recent years, as the state attaches great importance 
to environmental protection, individual investors will have 
higher expectations for the future development prospects of 
innovative enterprises, and are more inclined to invest in such 
enterprises, thus easing the financing constraints faced by 
enterprises, increasing the financial redundancy of enterprises, 
and promoting enterprises to innovate. Therefore, the 
application and development of digital technology greatly 
reduces the degree of information asymmetry between 
external capital providers and enterprises, optimizes the 
capital structure of enterprises, and reduces the capital cost of 
enterprises. Therefore, digital transformation can ease the 
financing constraints of enterprises and empower enterprises 
to innovate 

Based on this, this paper proposes hypothesis 2: financing 
constraints play an intermediary effect between digital 
transformation and enterprise innovation level. 

Due to differences in factors such as the nature of equity 
ownership, state-owned enterprises can better obtain the 
support of traditional financial institutions by relying on the 
government background, while private enterprises have big 
problems in obtaining the support of traditional financial 
system. Meanwhile, it is difficult for institutions under the 
traditional financial system to have sufficient impetus to 
provide more support for the digital innovation of private 
enterprises. Therefore, the degree of digital transformation 
has different effects on the innovation performance of 
enterprises. Due to the existence of these objective differences, 
the impact of digital transformation degree on enterprise 
innovation should not be simply regarded as incentive effect, 
so it is necessary to explore the impact of digital 
transformation degree on innovation output under different 
property rights background. Based on this, hypothesis 3 is 

proposed. 
Hypothesis 3: The degree of digital transformation has 

heterogeneity on the enterprise innovation performance of 
state-owned enterprises and private enterprises. 

3. Research Design 

3.1. Sample source 
This paper takes Chinese listed companies from 2011 to 

2020 as research samples. The number of patent applications 
of enterprises came from the CNRDS database, and the data 
on the degree of digital transformation of enterprises, the 
identification data of high-tech enterprises and other financial 
variables were all from the CSMAR database. Finally, a total 
of 25702 observations were obtained in this paper. All kinds 
of data in this paper were collected from the CSMAR 
database, and in order to eliminate the influence of outliers, 
the first and last 1% tail processing was carried out on the data. 
The following data reports were based on the data results after 
processing, and the empirical analysis was carried out with 
stata16 software. 

3.2. Primary variable 
Primary variable: For the first time, Wu Fei [15]et al. used 

the word frequency of "digital transformation" related words 
in the annual report of listed companies as a measure of the 
degree of digital transformation of enterprises. They believe 
that previous studies mainly use virtual variables of whether 
an enterprise has carried out digital transformation as proxy 
variables, which can not well measure the degree of enterprise 
digital transformation. As a summary and oriented external 
report, the annual report of an enterprise is the carrier of 
information transmission to external information users. The 
willingness and results of an enterprise to actively carry out 
digital transformation are likely to be reflected in the annual 
report of an enterprise. Therefore, it is reasonable to use the 
word frequency of "enterprise digital transformation" in the 
annual report to measure the degree of digital transformation. 
Specifically, the CSMAR database cited in this paper covers 
terms in five categories when calculating the degree of 
enterprise digital transformation: artificial intelligence, 
blockchain, cloud computing, big data, and digital technology 
applications, which is consistent with previous research.The 
names of specific subdivisions are shown in Table 1. 

Based on this, this paper sums up the word frequencies of 
five categories to measure the degree of enterprise digital 
transformation (dig). Since the data has the feature of "right-
bias", this paper carries out logarithmic processing on this 
basis, and the specific calculation formula is as follows: 

 
 𝐿𝑛𝑑𝑖𝑔 , 𝐿𝑛 AI+BC+CC+BD+DTA+1)  (1) 

3.3. Enterprise innovation 
Patent application reflects the results of innovation projects 

invested by enterprises, and can measure the innovation 
ability of enterprises. At the same time, the number of patent 
applications is closer to the actual time of innovation output 
than the number of patents granted, which can more truly 
reflect the level of innovation. Therefore, this paper refers to 
the practice of Li Chuntao et al[16]., Li Wenjing and Zheng 
Manni[17], and takes the number of patent applications as the 
proxy variable of enterprise innovation. Patent data also has 
the characteristics of "right bias", this paper takes the natural 
logarithm of the number of patent applications after adding 1. 



 

210 

In order to alleviate the possible reverse causation problem, 
the patent application is also dealt with in the future phase. 

The specific calculation formula is as follows: 
 

 
Table 1. Digital transformation segmentation indicators 

Index class Segmentation index name 

Artificial Intelligence 
Technology (AI) 

Artificial intelligence, Business intelligence, image understanding, investment decision aid 
systems, intelligent data analysis, intelligent robotics, machine learning, deep learning, semantic 

search, biometrics, face recognition, speech recognition, authentication, autonomous driving, 
natural language processing 

 
Blockchain Technology 

(BC) 
Digital currency, smart contracts, distributed computing, decentralization, Bitcoin, alliance 

chain, differential privacy technology, consensus mechanism 

Cloud Computing 
Technology (CC) 

Memory computing, cloud computing, stream computing, graph computing, Internet of Things, 
multi-party secure computing, brain-like computing, green computing, cognitive computing, 
Fusion architecture, 100 million level concurrency, EB level storage, information physical 

systems 
Big Data Technology 

(BD) 
Big data, data mining, text mining, data visualization, heterogeneous data, credit information, 

augmented reality, mixed reality, virtual reality 

 
Digital Technology 
Applications (DTA) 

Mobile Internet, Industrial Internet, mobile Internet, Internet medical, e-commerce, mobile 
payment, third-party payment, NFC Payment, B2B, B2C, C2B, C2C, O2O, Internet connection, 

smart wear, smart agriculture, smart transportation, smart medical, smart customer service, 
smart home, smart investment, smart travel, smart environmental protection, smart grid, smart 
energy, smart marketing, digital marketing, unmanned retail, Internet finance, digital finance, 

Fintech, financial technology, Quantitative finance, open banking 
 

 𝑖𝑛𝑛 , 𝐿𝑛 PatentApplei，t+1+1)       (2) 

3.4. Other variables 
This article refers to the existing literature, In the model, 

the company Size (Size) is expressed as the natural logarithm 
of the business income of the enterprise, the asset-liability 
ratio (Lev) is the total liabilities of the enterprise divided by 
the total assets, the proportion of fixed assets (PPE), the fixed 
assets of the enterprise divided by the total assets, the return 
on total assets (ROA) is equal to the net profit divided by the 
total assets, and the net cash from operating activities divided 
by the total assets (CR). SOE is 1 if it is a state-owned 
enterprise, or 0 if it is not, and TobinQ is equal to the total 
shareholders' equity divided by the company's market value 
as the control variable. In addition, the year fixed effect and 
individual fixed effect are added. 

3.5. Research model 
To test hypothesis 1, the following model is set up: 
 
𝑖𝑛𝑛 𝛽 𝛽 𝐷𝑖𝑔 𝛽 𝐶𝑜𝑡𝑟𝑜𝑙𝑠 𝜇 𝜆 𝜀   (3) 
 
Where, 𝑖𝑛𝑛  represents the innovation capability of the 

enterprise, 𝐷𝑖𝑔   represents the degree of digital 
transformation of the enterprise, 𝐶𝑜𝑡𝑟𝑜𝑙𝑠   including the 
control variables mentioned above, 𝜇   represents the fixed 
effect of the year, 𝜆   represents the fixed effect of the 
enterprise. The main coefficient concerned in this paper is 
𝛽  .If hypothesis 1 is true, that is, enterprise digital 
transformation has a significant promoting effect on 
enterprise innovation, then β   should be significantly 
positive. 

In order to ensure the robustness of the results, the 
following treatments are carried out in this paper: First, 
considering that the transmission of digital transformation to 
enterprise innovation takes some time, and there may be 
reverse causality problem, the natural logarithm of the 
number of patent applications in the future period plus 1 is 

selected as the explained variable; Second, in order to avoid 
the influence of extreme values, all continuous variables were 
tail-tailed at 1% and 99% quantiles. Third, year and individual 
fixed effects were added to all regression models at the same 
time, and standard errors were clustered at the enterprise level 
to minimize errors caused by model factors. 

To test hypothesis 2, with reference to Wen Zhonglin and 
Ye Baojuan's practice[18], a causal stepwise regression model 
was constructed to test the intermediary effect: 

 
𝐾𝑍 γ γ Dig γ𝐶𝑜𝑡𝑟𝑜𝑙𝑠 μ λ ε    (4) 

 
𝑖𝑛𝑛 α α Dig α 𝐾𝑍 α𝐶𝑜𝑡𝑟𝑜𝑙𝑠 μ λ ε  (5) 

4. Empirical Results and Analysis 

4.1. Descriptive statistics 
Table 2 shows the descriptive statistics of the main 

variables. The results show that the difference of enterprise 
innovation (Inn) is obvious. The degree of enterprise digital 
Transformation (dig) showed similar characteristics, with a 
minimum of 0 and a median of 2.7 DIGs and an average of 
2.77 digs, meaning that more than half of the sample had 
undertaken digital transformation. 

4.2. Reference regression 
Table 3 shows the results without adding control variables 

and fixed effects, without adding control variables but adding 
fixed effects, and adding control variables and fixed effects at 
the same time. The regression coefficient of dig is significant 
at the level of 1% and above, indicating that digital 
transformation has a significant promoting effect on 
enterprise innovation. Hypothesis 1 is valid. From the point 
of view of the fitting degree of the model, with the 
introduction of control variables and fixed effects, the 
adjusted R2 of the model increased from 0.085 to 0.580, 
indicating that the introduction of control variables and fixed 
effects strengthened the model's interpretation of the 
explained variables. From an economic point of view, an 



 

211 

increase of one standard deviation (1.211) in the degree of 
digital transformation of an enterprise will increase its 

innovation capacity by about 0.219(0.173×1.273). 

 
Table 2. Descriptive Statistics of Variables 

variable N mean p50 sd min max 
inn 25702 2.538 2.708 1.796 0 9.607 
dig 25702 2.776 2.708 1.273 0 6.909 
size 25702 21.31 21.20 1.512 17.52 25.44 
Lev 25702 0.432 0.425 0.208 0.0547 0.908 
PPE 25702 0.208 0.174 0.161 0.00164 0.698 
ROA 25702 0.0354 0.0379 0.0755 -0.371 0.211 
CR 25702 0.169 0.129 0.136 0.00732 0.660 
Soe 25702 0.347 0 0.476 0 1 

TobinQ 25702 0.307 0.283 0.164 0.0103 0.782 
KZ 25702 0.754 0 2.078 -11.34 13.66 

 
Table 3. Regression Results 

 (1) (2) (3) 
 inn inn inn 

dig 0.410*** 0.349*** 0.173*** 
 (0.008) (0.009) (0.008) 

size   0.547*** 
   (0.007) 

lev   -0.591*** 
   (0.065) 

PPE   -0.155** 
   (0.066) 

ROA   0.165 
   (0.129) 

CR   -0.404*** 
   (0.073) 

Soe   0.131*** 
   (0.020) 

TobinQ   -0.265*** 
   (0.065) 

_cons 1.404*** 0.498*** -10.493*** 
 (0.024) (0.142) (0.194) 

year no Yes Yes 
industry no Yes Yes 
province no Yes Yes 

N 25702.000 25702.000 25702.000 
r2 0.085 0.443 0.587 
ar2 0.085 0.434 0.580 

Standard errors in parentheses 
* p < 0.1, ** p < 0.05, *** p < 0.01 

 

4.3. Robustness test 
1. Considering that digital transformation and enterprise 

innovation may have the endogenous problem of mutual 
causation, this paper will explain the variable lag one stage to 
investigate the causal relationship between enterprise digital 
transformation and enterprise innovation ability. The specific 
test results are shown in the first column of Table 3. The 
results of one-stage lag are generally consistent with the 
aforementioned main results in terms of coefficient symbol 
and significance level, which partly verifies the robustness of 

the results in this paper. 
2. Replacement of explained variables In order to eliminate 

the influence of the selection of explained variable indicators 
on the regression results, this paper increases the number of 
invention patents by 1 and takes the natural logarithm for 
another test. The test results are shown in column 2 of Table 
3. It can be seen that after changing the explained variables, 
the digital transformation of enterprises is significantly 
positive at the 1% level, which verifies the robustness of the 
results in this paper. 

 
 
 
 
 
 



 

212 

Table 4. Robustness Results 
 (1) (2) 
 inn linn 

ldig 0.058***  
 (0.003)  

size 3.564*** 12.172*** 
 (0.064) (0.170) 

lev -0.191*** -0.575*** 
 (0.025) (0.066) 

PPE -0.071*** -0.150** 
 (0.025) (0.066) 

ROA 0.121** 0.153 
 (0.048) (0.130) 

CR -0.181*** -0.382*** 
 (0.028) (0.074) 

Soe 0.027*** 0.138*** 
 (0.007) (0.020) 

TobinQ -0.009 -0.290*** 
 (0.025) (0.065) 

dig  0.175*** 
  (0.008) 

_cons -10.376*** -36.611*** 
 (0.196) (0.523) 

year Yes Yes 
industry Yes Yes 
province Yes Yes 

N 23899.000 25702.000 
r2 0.554 0.584 

r2_a 0.545 0.576 
Standard errors in parentheses 

* p < 0.1, ** p < 0.05, *** p < 0.01 

 

4.4. The impact mechanism of digital 
transformation on enterprise innovation: 
easing financing constraints 

Because technological innovation needs a lot of technology 
and talents, enterprises have an increasing demand for capital 
in the process of technological innovation. [17]Li Wenjing 
and Zhu Xi 'an found that financing constraints seriously 
hindered the improvement of enterprises' technological 
innovation ability by studying enterprises in strategic 
emerging industries with relatively greater capital demand. 
However, facing serious financing constraints, enterprises 
will improve the efficiency of capital utilization and resource 
allocation, so they will choose the path of technological 
innovation more carefully to reduce the risk of technological 
innovation. 

Due to the unsound capital market system and imperfect 
market environment, the main reason why enterprises face 
financing constraints is information asymmetry. Based on the 
signal transmission theory, the increase of R&D intensity and 
technological innovation results can convey the signal of 
good operation of enterprises to the media, shareholders and 
potential investors, attract resources and value investors for 
enterprises, and effectively alleviate the financing constraints 
of enterprises and solve the capital problem. 

Therefore, with reference to the practice of Wen Zhonglin 
and Ye Baojuan[18], this paper adopts the three-step 
regression method to investigate the relationship among 
financing constraints, digital transformation and enterprise 
innovation ability. At the same time, some literatures also use 
KZ index to measure the financing constraints faced by 

enterprises, so this paper uses KZ index to test the 
intermediary path and mechanism between enterprises' digital 
transformation and enterprises' innovation ability. The larger 
the kz index is, the greater the financing constraint of 
enterprises. The specific test results are shown in Table 5. 

As can be seen from Table 5, the degree of digital 
transformation, financing constraints and innovation ability 
are significantly correlated at 1% level, and the degree of 
broken digital transformation and financing constraints are 
significantly negatively correlated at 5% level. Combined 
with the main regression, it can be seen that financing 
constraints play a partial intermediary effect between the 
degree of digital transformation and the innovation ability of 
enterprises. 

4.5. Heterogeneity analysis of enterprise digital 
transformation degree 

Enterprises with different ownership types in China have 
differences in digital transformation, which will have a non-
negligible heterogeneous incentive effect on the innovation 
performance of enterprises with different ownership. In Table 
6, Columns 1 and 2 show the impact of enterprises' digital 
transformation on the innovation ability of state-owned 
enterprises and private enterprises respectively. It can be seen 
that enterprise digital transformation has a significant 
incentive effect on the innovation performance of both private 
and public enterprises, and the positive impact of enterprise 
digital transformation on the innovation ability of private 
enterprises is relatively greater, indicating that the impact of 
enterprise digital transformation on enterprises is 
heterogeneous. Therefore, hypothesis 3 is confirmed.  



 

213 

 
Table 5. Descriptive Statistics of Variables 

 (1) (2) 
 KZ inn 

dig -0.023** 0.159*** 
 (0.011) (0.010) 

size -0.330*** 0.552*** 
 (0.010) (0.010) 

lev 3.656*** -0.419*** 
 (0.086) (0.084) 

PPE -0.956*** -0.149* 
 (0.084) (0.079) 

ROA -9.925*** 0.163 
 (0.184) (0.186) 

CR -8.180*** -0.433*** 
 (0.093) (0.105) 

Soe 0.325*** 0.120*** 
 (0.026) (0.024) 

TobinQ -3.381*** -0.292*** 
 (0.087) (0.084) 

KZ  -0.015** 
  (0.007) 

_cons 11.050*** -10.641*** 
 (0.248) (0.244) 

year Yes Yes 
industry Yes Yes 
province Yes Yes 

N 17763.000 17752.000 
r2 0.749 0.577 

r2_a 0.742 0.566 
Standard errors in parentheses 

* p < 0.1, ** p < 0.05, *** p < 0.01 
 

Table 6. Heterogeneity Results 
 (1) (2) 
 inn inn 

dig 0.170*** 0.160*** 
 (0.010) (0.015) 

size 0.501*** 0.601*** 
 (0.010) (0.012) 

lev -0.441*** -0.744*** 
 (0.078) (0.120) 

PPE 0.100 -0.259** 
 (0.089) (0.106) 

ROA 0.278* -0.053 
 (0.143) (0.280) 

CR -0.333*** -0.913*** 
 (0.085) (0.142) 

Soe 0.000 0.000 
 (.) (.) 

TobinQ -0.243*** -0.125 
 (0.076) (0.125) 

_cons -9.339*** -11.789*** 
 (0.254) (0.316) 

year Yes Yes 
industry Yes Yes 
province Yes Yes 

N 16181.000 9521.000 
r2 0.548 0.688 

r2_a 0.536 0.677 
Standard errors in parentheses 

* p < 0.1, ** p < 0.05, *** p < 0.01 
 
 



 

214 

 

5. Conclusion 
Based on the 2011-2020 Digital Finance Index and the data 

of Shanghai and Shenzhen A-share listed companies, this 
paper uses A panel fixed utility model to examine how a 
company's digital transformation affects its innovation ability. 
The research results show that enterprise digital 
transformation can have a significant positive effect on the 
improvement of enterprise innovation and innovation ability. 
Enterprise digital transformation can promote the 
improvement of enterprise innovation ability by influencing 
financing constraints, and then enable enterprise innovation. 

Digital transformation is closely related to enterprise 
innovation, and the improvement of enterprise digital 
transformation can enhance the R&D and innovation ability 
of enterprises. Second, since technological innovation is 
closely related to financing constraints, reducing the 
financing constraints of enterprises can promote the 
improvement of enterprises' innovation ability, and at the 
same time has positive significance for high-quality economic 
development. Third, digital transformation can solve the 
financing difficulties faced by enterprises to a certain extent, 
thus promoting enterprise innovation. Therefore, from the 
perspective of enterprises, they should not only actively carry 
out digital transformation, give full play to their supervision 
and management functions within the organization, and 
improve the incentive for enterprise innovation activities, but 
also improve the information transparency of innovation 
projects, reduce the information asymmetry with banks, 
governments and other capital providers, and effectively 
promote enterprise innovation and improve enterprise value 
from the perspective of easing financing constraints. 

As a "big data strategy" vigorously developed by the 
country, the national and local governments continue to 
strengthen their efforts to promote digital development. 
Therefore, based on this, it is taken as the main research object 
of this paper, which has positive inspiration and reference 
value for policy makers to improve and optimize policies 
supporting enterprise innovation. On the one hand, we should 
improve the laws and regulations on internal control and 
accounting quality, strengthen information supervision, and 
improve the quality and level of information disclosure. On 
the other hand, we should further improve the financial 
market through digital technology, break information barriers, 
create a fair and open financing environment for enterprises, 
and promote the implementation of China's digital 
transformation policy. 

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