Frontiers in Business, Economics and Management 
ISSN: 2766-824X | Vol. 6, No. 3, 2022 

 

81 

The	Empirical	Research	on	Impact	of	Digital	Finance	
Development	on	the	Performance	of	Small	and	
Medium‐sized	Company	in	Manufacture	Industry	in	
China	
Yuyi Zhang1, * 

1Faculty of Business Administration, University of Macau, Taipa, Macau, 999078, China 
*Corresponding author’s e-mail: bb90602@connect.um.edu.mo 

 

Abstract: Based on Digital Financial Inclusion Index of China (DIFI) established by research group of the Digital Finance 
Research Center of Peking University, this paper chose the panel data of samples from 2011-2020 Chinese manufacture industry 
from CSMAR gem data, analysis the digital finance development impact on the performance of Chinese SMEs in manufacture 
industry, Test the impact of enterprise performance, and the heterogeneity analysis of different companies in different regions in 
China, and conduct the necessary endogeneity analysis and robustness test. The empirical research results show that: digital 
finance development improve the performance of Chinese manufacture industry SMEs, and there is reginal heterogeneity, the 
promotion effect is more effective in western region of China. Also there exist significant lag effect on performance improvement. 

Keywords: Digital Inclusion Finance, Manufacture Industry, Corporation Performance. 
 

1. Introduction 
At the end of 2015, The State Council issued the 

Development Plan for Promoting Inclusive Finance (2016-
2020), which formally defined inclusive finance. The 2016 
G20 summit established a detailed plan for the development 
of digital financial inclusion. Digital services enable groups 
to access corresponding financial services through mobile 
electronic devices. At the same time, the online storage of data 
ensures the convenience of data acquisition and accuracy of 
storage, reduce the threshold of borrowing loan. To achieve 
high-quality economic development, the development of 
SMEs is an indispensable component, which is conducive to 
the construction of a complete and active market economy 
system. Although the SMEs developed rapidly, there still are 
some problems that need to be solved the quickly the better. 
Due to the small scale and weak ability of risk resistance, the 
SMEs face difficult finance problem which makes it difficult 
to conduct digital innovation and transformation. The digital 
innovation and transformation are the key factors for entities 
to improve the produce efficiency and corporation 
performance.  

2. Theory Analysis and Research 
Assumption 

According to previous research, it is more easily for the 
corporation who disclosed its performance information 
strictly comply with specific standards to obtain commercial 
credit and loan [1]. The SMEs face more retrains when obtain 
finance support due to lack of high value collateral and 
reliability of information disclosed. For SMEs in manufacture 
industry, constantly updated technology is necessary for the 
survival. They need enough capital to implement technical 
renovation. However, due to the characteristics of long 
recovery waiting period of the research and development cost, 
high risk of return and huge initial investment, the SMEs lack 
capability of internal finance, they have great level of reliance 

on external finance.  
The development of digital finance makes use of many IT 

technology to achieve high level of information sharing, 
which increase the integrity and credibility of information, 
reduce the cost of information transform and exchange [2]. 
On the other hand, it helps more transparent governance of 
management. In addition to the function of reducing finance 
constrain and helping maintain innovation of SMEs, digital 
finance can also increase the sufficiency of production. The 
first assumption is that:  

H1: Digital finance development promote the performance 
of China SMEs in manufacture industry. 

The aggregate development of economy and technology of 
east, center and west of China exist big difference. The 
development of economy and digital finance become 
weakens from east to west. The advantage of gathering of 
high talented people and rapid increase rate of regional 
overall economy level, make the factors affecting 
performance of enterprises in the eastern region more 
complicated. Comparing with western region, the percentage 
of influence of digital finance among all factors is smaller 
than in eastern region. It is reasonable that the influence of 
increase level of digital finance has bigger influence on 
western company than those in east region. Second 
assumption: 

H2: the impact of promotion function of digital finance to 
the performance of small and medium company in 
manufacture industry is more obvious in west than east. 

The main purpose of SMEs finance behavior is to get 
enough capital to support research and development activities 
and reform manufacture technology. The inherent features of 
research and development cost are that it needs huge amounts 
of initial investment, the developing period is long, and there 
is risk of success [3], which indicate that the investment may 
end up as loss. Even though the development is extremely 
successful, before make use of the new technology, the cost 
of retraining employees and the refrain strategy would bother 
the company as well [4]. Therefore, it takes a long period for 



 

82 

the influence of digital finance reflect on the corporation 
performance. The third assumption is: 

H3: the impact of digital finance to corporation 
performance exists lag effect. 

3. Design of Research 

3.1. Sample selection and data sources 
The enterprise data in the paper comes from the Cathay 

Pacific Security Database (CSMAR), chose information of 
listed SMEs in manufacturing industry from 2011 to 2020 as 
empirical research sample. Data of the development level of 
digital financial inclusion is compiled by the Digital Finance 
Research Center of Peking University (DIFI) [5]. Process the 
data as follows: the sample data of ST listed companies is 
removed; the samples of listed companies which have 
incomplete key data are removed. 

3.2. Variable Design 
Explained variables. corporation operating performance. 

The paper use return on total assets (ROA= Net profit/total 
assets) to measure the business performance of enterprises. 

Explanatory variables. The paper selects the digital 
financial inclusion index which is compiled by the Digital 
Finance Research Center of Peking University to measure the 

development level of digital financial inclusion. To solve the 
problem of too large value related to the explained variables, 
divided the digital financial inclusion index by 500.  

Control variables. This article chose following control 
variables: total assets (TA= Natural logarithm of total assets), 
financial leverage (Lev= Total liability / total assets), the 
proportion of cash and cash equivalents to total asset (Cash= 
Cash and cash equivalents /total assets), the turnover rate of 
current assets (Liq= Main operating benefit / current asset), 
and the ownership of the largest shareholder (TOP1) as 
control variables. 

Model design. The model is designed as follows: 
ROAit = β0 + βDIFIit + β2Xit + εit 
Where: i is enterprise; t is the year; ROAitis the operating 

performance of i enterprises in year t; DIFIit is the digital 
financial inclusion index in the province of enterprises I in 
year t; Xit is the collection of control variables，εit is random 
perturbation term, 

4. Empirical Analysis 

4.1. Descriptive statistics 
The descriptive statistics of all variables are in table 1. 

 
Table 1. Descriptive statistics 

Variables observations mean Standard deviation minimum maximum 
ROA 1,936 0.0620 0.0412 -0.126 0.379 
DIFI 1,936 0.0504 0.0187 0.00377 0.0864 
TA 1,936 9.261 0.363 8.217 10.90 

cash 1,936 0.218 0.164 0.00382 0.883 
lev 1,936 1.143 1.611 -5.758 60.08 
liq 1,936 0.825 0.399 0.0778 3.879 

TOP1 1,936 0.321 0.132 0.0559 0.898 
 

4.2. Correlation analysis  
The correlation analysis results are in table 2. 

 
Table 2. Correlation analysis 

 ROA DIFI TA lev cash TOP1 liq 
DIFI -0.070*** 1      
TA -0.165*** 0.456*** 1     
lev -0.127*** 0.105*** 0.091*** 1    

cash 0.187*** -0.487*** -0.361*** -0.127*** 1   
TOP1 0.091*** -0.214*** -0.110*** 0.00400 0.096*** 1  

liq 0.089*** 0.164*** 0.119*** 0.078*** -0.442*** -0.0320 1 
 

4.3. Basic regression 
To make the test results more credible, carry out regression 

with control variables.  
Before the benchmark regression, the variance inflation 

factor (VIF) was calculated, and the VIF value was much less 
than 10, indicating that there was no multicollinearity 
problem in the model.  

The regression results are shown in table 3(1). When a 

series of control variables are considered, the coefficient of 
digital financial inclusion development level is significantly 
positive. Digital inclusive finance has a significant role in 
promoting the performance of SMEs in manufacturing 
industry. The influence of total assets on return on assets is 
significant and negative, which supports that the decline of 
ROA is due to the excessive growth of total assets. In addition, 
the more concentrated the ownership, the better the company 
performance.  

 
 
 
 
 
 



 

83 

Table 3. Basic regression and regional heterogeneity analysis 
 (1) (3) (3) (4) 

VARIABLES ROA West Mid East 
DIFI 0.248*** 0.585** 0.385*** 0.244*** 

 (3.79) (2.59) (3.13) (2.80) 
TA -0.014*** 0.019 -0.026*** -0.015*** 

 (-4.41) (1.11) (-5.36) (-3.78) 
lev -0.003** -0.012 -0.013*** -0.002* 

 (-2.03) (-1.23) (-2.79) (-1.79) 
cash 0.068*** 0.097*** 0.071*** 0.070*** 

 (9.15) (3.12) (5.08) (7.25) 
TOP1 0.026*** 0.049 0.052*** 0.014 

 (3.48) (1.58) (4.01) (1.43) 
liq 0.022*** 0.062*** 0.022*** 0.019*** 

 (8.14) (4.21) (5.38) (5.09) 
Constant 0.143*** -0.208 0.242*** 0.160*** 

 (4.90) (-1.26) (5.81) (4.34) 
Observations 1,936 130 326 1,233 

R-squared 0.104 0.234 0.249 0.097 
Robust t-statistics in parentheses 
*** p<0.01, ** p<0.05, * p<0.1 

 

4.4. Regional heterogeneity analysis 
The impact of digital finance development on corporation 

performance varies with the province of the companies. The 
regional heterogeneity analysis results show in table 3 (2) (3) 
and (4). The impact of digital inclusive finance on the 
performance of enterprises in the west of China is more 
obvious.  

4.5. Problem of endogeneity 
To alleviate the potential endogeneity problem, this paper 

adopts the method of re-regression of the core explanatory 
variables with one-period lag and two-period treatment 
respectively to analyze the endogeneity of the results of the 
benchmark regression. The regression results of the core 
variables with a lag of one and two periods are shown in table 
5 (1) and (2). The coefficient of the core explanatory variable 
in the lag one period is larger than that in the original 
regression, and the coefficient of the core explanatory 
variable of lag two period is more positively correlated than 
that lag one period, indicating that digital financial inclusion 
has a lag effect on the development of SMEs.  

 
Table 5. Endogeneity results and robustness test results 

 (1) (2) (3) (4) (5) 
VARIABLES Lag one period Lag two periods ROE Lag one period 

(Using ROE) 
Lag two period 
(Using ROE) 

L.DIFI 0.317*** 0.323*** 0.191** 0.308*** 0.316*** 
 (4.63) (4.40) (2.25) (3.52) (3.43) 

TA -0.008*** -0.006* 0.007 0.018*** 0.023*** 
 (-2.61) (-1.80) (1.43) (3.81) (4.56) 

lev -0.003** -0.003** -0.004** -0.004** -0.004** 
 (-2.08) (-2.04) (-2.16) (-2.20) (-2.15) 

cash 0.073*** 0.073*** 0.056*** 0.067*** 0.068*** 
 (8.53) (7.26) (5.58) (5.95) (5.32) 

TOP1 0.017** 0.018** 0.046*** 0.031*** 0.032*** 
 (2.26) (2.11) (4.26) (2.92) (2.71) 

liq 0.020*** 0.020*** 0.039*** 0.036*** 0.037*** 
 (7.30) (7.09) (9.24) (8.40) (8.30) 

Constant 0.087*** 0.067** -0.041 -0.146*** -0.194*** 
 (3.06) (2.17) (-0.94) (-3.34) (-4.10) 

Observations 1,687 1,464 1,936   
R-squared 0.089 0.087 0.086 1,687 1,464 

    0.092 0.108 
Robust t-statistics in parentheses 
*** p<0.01, ** p<0.05, * p<0.1 

 

4.6. Robustness Test 
To make results more credible, this paper adopts replacing 

explanatory variables to carry out the robustness test. Use 
return on equity (ROE) to replace return on total assets (ROA) 
to re-regression, lag the core explanatory variables to test 

whether there still exist lag effect. As shown in table 5 (3) (4) 
(5), the impact of digital finance on corporation performance 
is still significantly positive, and there still exist an obvious 
lag effect. The results are still robust.  



 

84 

5. Conclusion 

5.1. Research results 
This paper chose the information of SMEs in 

manufacturing industry from 2011 to 2020 as empirical 
research samples to explore the influence of digital finance 
development on the performance of SMEs in manufacturing 
industry. The results of the empirical research are follows: 

(1) The development of digital finance has positive 
influence on performance of SMEs in manufacturing industry. 
The development of digital finance reduces information 
asymmetry and increase finance speed and quality. Digital 
finance also maintains technical innovation and improve 
manufacture efficiency and reduce produce cost. 
Consequently, the operation performance can increase.  

(2) The impact of promotion function of digital finance to 
the performance of SMEs in manufacture industry is more 
obvious in west than east. Heterogeneity analysis showed that 
the rapid development of digital finance has more obvious 
effect on the company in western region. 

(3) The impact of digital finance to corporation 
performance exists lag effect. The coefficient of the core 
explanatory variable in the lag one period is larger than that 
in original regression, and coefficient of the core explanatory 
variable of lag two period is more positively correlated than 
that lag one period. 

5.2. Recommendation 
It is important to combine digital finance and inclusive 

finance, government should draw up different plan and 
strategies for the entities of various size in different province. 
Government can introduce relevant incentive policies, such as 
reducing taxes or increasing government subsidies or grants, 
to promote continuous innovation of SMEs in manufacturing 
industry.  

Government should maintain stable policies to create a 
stable and favorable developing environment for the 

corporations [6]. As it takes time for the facilitation of digital 
financial inclusion obviously reflect on business performance, 
the frequently changing and fluctuating political or economic 
environment can do great harm to companies.  

It worth noting that SMEs should pay high attention to the 
coordination between digital technology and original 
resources when keeping up with technological development 
and innovation. Adopting digital finance and other new 
technologies does not mean to overthrow the current structure 
of production because the cost of giving up luxury exiting 
fixed asset is numerous. So, it is important to achieve 
harmony between new technology and original resources.  

References 
[1] Tao, Z., Yahui, B. (2016) Research on influence of quality of 

SMEs information disclosure to constrain of debt finance. 
Journal of Shandong University of Finance and Economics, 
28(1): 109-117. 

[2] Huan, T., Peng, T. (2021) Empirical analysis of digital 
financial inclusion alleviating financing constraints for SMEs: 
Based on data from 2011 to 2018. Investment and 
Entrepreneurship, 9: 1-5. 

[3] Zhengping, Z. (2021) The impact of digital finance on the 
operational efficiency of small and medium-sized enterprises: 
An empirical study based on the data of GEM in Shenzhen. 
China's circulation economy, 35: 30-39. 

[4] Yudong, Q., Chengwei, C. (2020) Research on the multiple 
effects of digitization on manufacturing enterprise performance 
and its mechanism. Learning and Exploration, 07: 108-119. 

[5] Feng, G., Jingyi, W., Fang, W., et al. (2020) Measuring China’s 
Digital Financial Inclusion: Index Compilation and Spatial 
Characteristics. China Economic Quarterly, 19(4): 1401-1418. 

[6] Lulu, C., Jingjia, X., You, L. (2021) The impact of digital 
financial inclusion on business performance- Empirical 
research based on A-share listed companies. Technology and 
Innovation management, 42: 674-680.