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© 2019 by the authors; licensee Eastern Centre of Science and Education, USA 

 

Asian Business Research Journal 
Vol. 4, 10-16, 2019 
ISSN(E) : 2576-6759 
DOI: 10.20448/journal.518.2019.41.10.16 
© 2019 by the authors; licensee Eastern Centre of Science and Education, USA 

 
 

 
 
A Quantile Regression Analysis of Contributing Factors Influencing Agribusiness 
Growth and Entrepreneurship Development: Evidence from Rural China 
 
 
Owusu Samuel Mensah1 
Chen Jianlin2 
Ji You Jun3 

 

 
( Corresponding Author) 

 

 

1,2,3School of Business, Jinggangshan University, Qingyuan District, Ji'an City, Jiangxi Province, China 

 

 
Abstract 

The rapid development of some sectors in the Chinese economy has crippled the growth of 
agribusiness. Agribusiness sector, which consists of various business activities, plays a vital role in 
socio-economic development in the areas of job creation, food production and rural development. 
However, the environmental issues that are more prevalent to the agro-industries thwart the 
growth and development of the sector. This study employs the quantile regression approach to 
investigate the relationship between government policies, agribusiness growth and 
agripreneurship development. Pearson‟s product-moment correlation was also employed to 
investigate the degree of the linear relationship between the variables. The results of the study 
revealed a significant positive correlation between agribusiness development, rural education, 
Research and Development (R&D), legalities, development of family households and intellectual 
properties. The quantile regression results also disclosed a positive relationship between 
investments in the rural areas and agribusiness growth and entrepreneurship development.  The 
quantile plots measured the deviations in the asymmetric quantile coefficients, and the results 
revealed that the coefficients seem to depart slightly at the various quantile points, from the OLS 
mean effect estimates. 

 
Keywords: Agribusiness growth, Entrepreneurship development, Government policies, Quantile regression and rural China. 

JEL Classification: H51; 128; J43; K49; L26; Q10; Q19. 
 
1. Introduction 

The role of agriculture in sustainable development and poverty reduction in most developing countries has 
become a major issue under discussion. The growth in the agricultural sector greatly contributes to Sustainable 
Development Goal of ending extreme poverty by 2030 and provides means to feed the expected nine billion people 
in the world by 2050  (Christiaensen et al., 2011). The future of agriculture has been intrinsically linked to the need 
for better stewardship of natural resources. In China, the agricultural sector serves as an important source of 
livelihood among many people in rural areas. For a sustainable and continual supply of food in China, the business 
ethics approach needs to be further encouraged in the agricultural production system among the rural dwellers. 
China, the country with the largest population worldwide and with arable land of 7 to 10 percent feeds the largest 
number of mouths in the world. However, the rapid population growth has called for an increase in the production 
and supply of food to meet the current and future food demand. Therefore, increasing food production means 
providing enabling environment to support the activities of the agricultural entrepreneurs and the agribusiness 
sector. 

According to Edwards and Shultz “agribusiness is a dynamic and systemic endeavor that serves consumers 
globally and locally through innovations and management of multiple value chains that deliver valued goods and 
services derived from the sustainable orchestration of foods, fiber and natural resources”(Cristian and Felzensztein, 
2013). The Oxford dictionary explains businesses as buying and selling or trade or commercial work. According to 
Acharya (2007) the word trade or commerce can be an exchange of goods as a means of livelihood or profit. That is 
agro-processing, production of agro-chemicals and farm machinery, and trade (wholesaling and retailing) are 
considered as parts of manufacturing (industrial) or service (tertiary) sector. With the structural transformation of 
the Chinese economy, there has been a decline in the share of agricultural production (farming), whilst the other 
sectors of the economy such as the processing, distribution and trade are increasingly developing.  

 Therefore, to address this issue, the agribusiness sector in China is adopting a more sustainable approach to 
ensure an increase in food production, effective and efficient food processing and distribution of agricultural 
products to make the sector more sustainable. Sustainable agriculture is perceived to be a philosophy and a system 

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of farming because it is rooted in a set of values that reflects the awareness of both ecological and social realities   
(Altieri, 2018). Studies recognize agricultural systems as human systems, so that „what is sustainable‟ will also be 
value-laden. The term agribusiness is generic and takes into consideration the involvement of the businesses in 
food production. According to Acharya (2007) agribusiness is about producing and adding value to the farm 
produce to meet the needs of customers. Agribusiness sector can be explained in four distinct sub-sectors, which 
are agricultural inputs; agricultural production; agro-processing; and marketing and trade (Acharya, 2007).  
Therefore, the concept “agribusiness” involves  science and practice of activities, with backward and forward 
linkages, related to production, processing, marketing, trade, and distribution of raw and processed food, feed and 
fiber, including supply of inputs and services for these activities by the agri-food industries (Acharya, 2007).  

The agri-food industry sector is a large, multifaceted industry sector that exists worldwide, and involves a 
range of businesses that create industry-specific. With increasingly competitive and quality conscious global 
marketplaces for food products, governments and agro-industry chain members are creating an enabling 
environment to promote food production. However, integrating the growing environmental and social issues of the 
changing agribusiness sector with prevailing economic imperatives is progressively becoming more difficult, 
however finding a lasting solution to these problems needs the involvement of agro-industries in the development 
of sustainable agricultural systems through the adoption of appropriate strategies. This will enhance a 
comprehensive conceptual framework and identify the most critical supportive policies, programs, and regulations 
needed to promote the agribusiness development. According to Kharaishvili et al. (2015) unfavorable business 
environment coupled with the introduction and adaptation of modern agricultural technology by agricultural 
entrepreneurs has been major obstacles facing the sector. 

Agribusiness sector in China faces several challenges due to the market globalization, increased customer 
quality requirements in food products, changes in the market environment and the introduction and adoption of 
new technologies to enhance food production and supply. The adoption of the open market economy by the 
government has further resulted in growing competitive pressures among the agricultural clusters to improve farm 
revenue streams, development of new consumer market niches and creation of an enabling environment that 
supports the agri-food industries. According to  Mintzberg (1996) the degree of stability or dynamism, simplicity 
or complexity, homogeneity or diversity, and munificence or hostility in the business environment play a 
significant role in developing the sector.  

The external environment of the agro-industries is characterized by its narrow and broader senses, which focus 
on all the external objects and unequivocally affects the agro-industry performance. Hornsby et al. (2002) maintain 
that the interactions between forces in the external environment and the organization challenge firms to effectively 
sustain the business through appropriate business strategies. The concept of business environment talks about 
every variable, powers and the various institutions that have an immediate impact on the business activities.  

The rest of the paper is structured as follows: Section 2 contains data and descriptive statistics; Section 3 
explains the methods used in the analyses; Section 4 presents the results; Section 5 offers a discussion of the study‟s 
results, and Section 6 presents a brief conclusion. 
 

2. Materials and Methods 
2.1. Measure  

In this paper, we adopted time series data and quantile regression model to investigate the contributing factors 
influencing agribusiness growth and agripreneurship development. The quantile regression approach helps to 
construct confident intervals for the fitted dependent variables. According to literature, common regression 
techniques focus on the mean effects, which may lead to either under-estimating or over-estimating the relevant 
coefficient or even failing to detect important relationships (Binder and Coad, 2011).  

The study employed data from the China Statistical Yearbook, 2017. According to the study, the factors 
contributing to agribusiness development were measured using Household systems in the rural China (X 1), 
government expenditure on the rural education (X 2), patent grants (X 3), research and development (X4), tax 
incentives (X5), and the government expenditure in providing conducive legal environment to the entrepreneurs 
(X6). 

The agribusiness performance (Y1) and the development of the agricultural entrepreneurs (Y2) were measured 
by and the total profit of agro-food industries and the number of agro-food industries from the period of 1978 to 
2017, respectively. 

 
2.2. Model Estimation 

The study, however, adopted a quantile regression approach introduced by Koenker and Bassett (1978) to find 
out the complex relationship between the dependent and independent variables. According to Uematsu et al. (2013) 
quantile regression is more robust to the non-normal error term and outliers in the model and takes into 
consideration the major effects of the covariates on the distribution of the dependent variable holistically and not 
only the conditional mean. 

For a random variable Y with probability distribution function      PrG y ob Y y  ,                                                                                

the th  quartile of Y* is d the inverse function,     inf :y F y                                                                                                       

Where 0 1  . In particular, the median is  1/ 2Q . The random sample  1,..., zy y of Y, means that the sample 

median minimizes the sum of absolute deviations
1

min
M

i
R

i

y







 . Likewise, the general th sample quantile    , 

which is analogue of    is represented as  
1

min
M

i
R

i

y


 




 , where     0 ,0 1k k I k       . Thus, 



Asian Business Research Journal, 2019, 4: 10-16 

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 .I represents the indicator function. However, Chen (2005)further states that likewise the sample mean, which 

tries to minimize the sum of the squared residuals  
2

1

1

ˆ arg min
M

i

R y 



  .   Can be extended to linear 

conditional mean function as  E Y X x x   by solving   
2

1

ˆ arg min
M

H

i i

i

R y x  



   the linear 

conditional quantile function modeled as     ,X x x     is also estimated by solving  

   
1

ˆ arg min
M

H

i i

i

R y x   



    for any quantiles  0,1  .The, quantity  ̂  represents the th

quantile regression. However, the 0.5  , relate to the median regression called the 1L regression (Chen, 2005). 

 

3. Results and Discussion  
It is important to study the impact of Chinese government expenditure on the growth of the agribusiness 

sector and entrepreneurial development. The goodness-of-fit statistics for the quantile regression models are 
pseudo-R2 coefficients (based on the change in the deviance statistic).  The study further runs a Pearson's product-
moment correlation to assess the relationship between the agribusiness performance and government expenditure 
in education, patent, research institutions, tax incentives, legalities, and the family household system Table 1.  

Table 2 shows the correlation between the agribusiness performance and government expenditure in 
education, patent, research institutions, tax incentives, legalities and the family household system. From Table 2, 
there is a significant positive relationship between numbers of households in the rural areas and agribusiness 
growth in rural China, r= (22) =0.996, p=0.000. Moreover, the study revealed a significant positive relationship 
between investment in rural education and agribusiness growth. r= (24) =0.971, p=0.000.  The findings further 
revealed a significant positive relationship between government expenditure in protecting the intellectual 
properties of the entrepreneurs r= (24) =0.822, p=0.000. The correlation between the government expenditure in 
promoting agricultural research and development and agribusiness growth was a significant positive relationship, 
r= (24) =0.671, p=0.000. The legal environment is also positively correlated with the agribusiness development at 
0.001 significant level, r= (24) =0.849, p=0.001. However, the tax incentives given to the agro-industries in the 
rural was positively correlated with the industrial growth in the rural areas but not significant, r= (24) =0.239, 
p=0.236.   
 

Table-1.Variables Definition and Summary Statistics. 

Variable Mean Std. Dev Min. Max. 

Dependent Variables     
Y1 10.826 10.189 10.055 12.551 
Y2 11.124 10.351 10.298 11.481 

Contributing Factors     
X1 10.134 10.016 9.110 10.431 
X2 9.085 11.232 6.123 7.324 
X3 10.365 11.088 7.251 10.972 
X4 8.085 10.214 8.037 9.137 
X5 9.749 11.320 9.321 11.974 
X6 11.578 10.799 9.131 12.431 

         Source: Authors‟ calculations based on data compiled from China Rural Statistical Yearbook (1978-2017). 
 
Table 3 and Table 4 report the standard errors of each regression coefficients estimated in the equation as well 

as the estimated results of the OLS method. According to the results from Table 3 and Table 4, most of the 
regression coefficients estimates are distinctly unequal to zero. Finally, the study estimated all the quantile 
regression estimated in the study. The estimated coefficients for the selected sample quantiles (20th, 40th, 60th and 
80th), the standard errors, and confidence intervals for the quantile regression coefficient estimates are presented in 
Table 3 and Table 4.  

In addition, apart from the quantile regression results at the various quantile points being different from the 
OLS estimation, the 20th, 40th, 60th and 80th quantiles estimations have a different significant effect on the 
conditional mean regression model. According to Karami and Mansoorabadi (2008) while the OLS regression 
describes the central tendency of the data, the regression quantile results give the exact picture of the importance 
of the explanatory variables for the different quantiles. This study, however, reports the statistical comparison 
coefficient, that is, testing the coefficients at the various quantile points and OLS estimate in Table 3 and Table 4. 
According to the study, the effect of the household system in rural China is lower in the OLS estimate and 80th 
quantile distribution. The mean effect of the family system is negatively significant according to the OLS estimate, 
but the quantile regression results show that the disparity is higher in the 20th, 40th, and 60th quantiles of the 
distribution. From Table 4, the mean effect of the family system is negative according to the OLS estimates and 
negatively significant in the 20th quantile point, but the quantile regression shows that the disparity is much 
higher in the upper quantiles of the distribution, such as the 40th, 60th and 80th quantiles at 0.05 significant levels. 
This implies that the family system in most of the rural areas in China is operating beyond subsistence farming by 
engaging in large scale farming, which generates income for the family. However, the results from Table 3 imply 
that the income from the sales of the farm produce is not enough to meet the needs of the farmers in most of the 
rural areas. 

 
 



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Table-2.Pearson Product Moment Correlation. 

Variables 1 2 3 4 5 6 7 

1.  Y1 1 0.996** 0.971** 0.822** 0.671** 0.239 0.849** 
  0.000 0.000 0.000 0.000 0.261 0.001 

2. X1 

 
 1 0.967** 

0.000 
0.831** 
0.000 

0.683** 
0.000 

0.213 
0.317 

0.854** 
0.000 

3. X2 
 

  1 0.930** 
0.000 

0.817** 
0.000 

0.015 
0.945 

0.939** 
0.000 

4. X3 
 

5. X4 

   1 
 

0.930** 
0.00 

1 

0.286 
0.186 

-0.545** 

0.985** 
0.00 

0.930** 
      0.006 0.000 

6. X5      1 -0.255 
       0.229 

7. X6       1 
**. Correlation is significant at the 0.01 level (2-tailed). 
*. Correlation is significant at the 0.05 level (2-tailed). 

 
The study further looked at the influence of government expenditure on rural education on agribusiness 

growth and entrepreneurial development. From Table 3, the government commitment to equip the rural 
entrepreneurs with the requisite knowledge and skills recorded negatively on the agribusiness growth at the OLS 
estimate, 20th and 40th quantile points. However, across all the quantile points the effects were highly significant 
at 5% and 10% level and the effect of rural education on agribusiness growth in the upper tail of the distribution 
was positive. According to Table 4, the impact of public investment in rural education on the empowerment of 
entrepreneurship among the rural farmers was positive in the OLS estimate and positively significant in the 20th 
and 80th quantile points.   
 

Table-3.OLS and quantileregression coefficients for covariates of agribusiness performance (Y1). 

Variables(No.) OLS 0.20 0.40 0.60 0.80 

X1 -1.2006** 
(0.203) 

3.879** 
(0.103) 

2.108*** 
(0.331) 

1.043* 
(0.187) 

-0.576 
(0.059) 

X2 
 

-0.0651 
(0.268) 

-1.581** 
(0.036) 

-0.325* 
(0.197) 

0.257** 
(0.137) 

0.159* 
(0.212) 

X3 
 

0.0143** 
(0.043) 

0.043* 
(1.137) 

0.061 
(0.158) 

0.154*** 
(0.191) 

0.412** 
(0.267) 

X4 
 

0.0142 
(0.006) 

-1.424 
(0.198) 

0.195** 
(0.110) 

0.0597* 
(0.290) 

0.019 
(0.099) 

X5 
 

0.219 
(0.002) 

-0.024 
(0.225) 

0.002** 
(0.197) 

0.161 
(0.145) 

-0.061** 
(0.083) 

X6 0.294** 
(0.003) 

0.2795* 
(0.225) 

-0.006 
(0.198) 

-1.417* 
(0.277) 

-1.025** 
(0.330) 

      
R2 

Pseudo 
0.9925 

 
 

0.9335 
 

0.8923 
 

0.9406 
 

0.930 
                 Notes: t-statistics in parentheses. *significant at the 10% level;** significant at the 5% level; 
                  * Significant at the 1% level. 

 
Getting government support in the area of patent application plays a major role in protecting investment and 

promoting innovative ideas among entrepreneurs. Therefore, the study investigated how to patent right leads to 
agribusiness growth and entrepreneurial development in rural China.  

The results from Table 3 show that the effect of government support in granting and protecting the patent 
right is superior to that of agricultural entrepreneurs in rural areas. The mean effect of the patent is about 0.01 
according to the OLS estimate. However, the quantile regression reveals that the disparity is much smaller in the 
lower quantiles of the distribution and higher in the upper quantiles of the distribution at 0.01, 0.05 and 0.1 
significant level. 

The study further examined the contribution of investment in research and development (R&D) in improving 
agribusiness performance and entrepreneurial development. From Table 3, the effect of the research and 
development (R&D) on agribusiness development is positively significant for the 40th and 80th tails of the 
distribution and recorded positively insignificant at the 60th quantile.  

However, at the lowest (20th) quantile, the results showed that the coefficient of the R&D negatively affected 
the agribusiness growth during the study period. From Table 4, the contribution of R&D in developing the farmer 
entrepreneur reaches 5% and 10% significant levels at 0.40 and 0.60 quantile regression models. This revealed that 
R&D does not completely contribute to entrepreneurial development in most of the rural areas in China. This 
result matches the result from Table 4, which revealed that patent application and patent right failed to contribute 
positively and significantly to entrepreneurial development at the 40th and 80th quantile distributions. 
 

 

 

 

 



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Table-4.OLS and quantile regression coefficients for covariates of Entrepreneurship Development (Y2). 

Variables(profit) OLS 0.20 0.40 0.60 0.80 

X1 -0.246 
(0.022) 

-0.139* 
(0.244) 

0.0443** 
(0.385) 

0.910** 
(0.381) 

1.492*** 
(0.680) 

X2 
 

0.307 
(0.159) 

1.176* 
(0.729) 

-0.0948 
(0.755) 

-0.1016** 
(0.529) 

0.1022* 
(0.855) 

X3 
 

0.2972** 
(0.002) 

0.625* 
(0.085) 

-0.1016 
(0.563) 

0.0122** 
(0.944) 

-0.0540 
(0.785) 

X4 
 

0.0231 
(0.052) 

-0.1120 
(0.138) 

0.0482** 
(0.164) 

0.0803* 
(0.075) 

0.397 
(0.183) 

X5 
 

0.009* 
(0.126) 

-0.0944 
(0.692) 

-0.0232** 
(0.836) 

-0.1084 
(0.414) 

0.005* 
(0.840) 

X6 0.054** 
(0.321) 

0.054* 
(0.122) 

0.0387** 
(0.005) 

0.0201* 
(0.053) 

0.1040* 
(0.227) 

R2 
Pseudo 

0.9807 0.9260 0.9064 0.9013 0.9016 

Notes: t-statistics in parentheses. * Significant at the 10% level; ** significant at the 5% level;  
* significant at the 1% level. 

 
In the case of tax incentives, the OLS estimate indicated a positive relationship between tax incentives given to 

agro-industries and agribusiness growth; however the quantile regression showed negatively significant effect only 
at the 80th quantile. From Table 4, the effect of the tax incentives given to the agricultural entrepreneurs is 
positively significant at both the OLS estimates and the higher (80th) quantile distribution. However, at the lower 
and middle quantiles (20th, 40th and 60th) of the distribution, the coefficients showed a negative impact on the 
entrepreneurial development. This may suggest that government grants to farmers and agro-industries in the rural 
areas are not appropriately and evenly distributed, therefore, it is advisable for the central government to increase 
the incentives given to the small businesses and redefine "small business", more especially in the rural areas.  

Providing an effective legal environment for businesses to operate according to the study promote SMEs‟ 
growth and development. The legal support given to the agro-industries by the central government in most of the 
rural areas is statistically significant at the OLS estimate and the lower quantile point, however, the coefficients at 
the higher (60th and 80th) quantiles of the distribution was statistically insignificant. Contrary to the results from 
Table 3, the legal support was found to be statistically significant in developing the agricultural entrepreneurs in 
rural areas across the OLS estimate and the quantile points, Table 4. The study, therefore, suggests the 
introduction of Small Business Legal assistance programs to provide legal information to small business owners 
and prospective agricultural entrepreneurs who operate in low-income communities.  

The graphical representations of the estimates for all the quantiles are given in Figure 1 and Figure 2. The 
shaded area gives confidence band of coefficients estimated across different quantiles. From Figure 1, the effect of 
the family system on the agribusiness development recorded positively at the lower tails of the distribution as 
compared to the higher quantiles of the distribution. From Figure 2, the effect on entrepreneurial development was 
positive at the higher quantile point and by examining the pattern of the plot presented in Figure 1 and Figure 2 at 
the higher quantile (80th). Moreover, a similar pattern is observed for the variable research and development. 
According to the results, the coefficient changes over the range of about -1.4 to approximately 0.1 for the quantile 
varies between 0.2 and 0.8., Figure 1.  As reported in Figure 1, the influence of tax incentive is negative at the 
lower quantile point but different at the upper quantile of the distribution. From Figure 2, the influence of the tax 
incentive on the entrepreneurship development is higher at the 80th quantile of the distribution. In addition, the 
influence of the legal environment on agribusiness growth above the higher quantile is stronger as compared to 
entrepreneurship development.  The quantile plots revealed deviations in the asymmetric quantile coefficients and 
departs slightly at the various quantile points and from the OLS estimates.  Figure 1 and Figure 2show a series of 
plots whereby the OLS mean effect and the 80 percent confidence interval is compared with the regression quantile 
effects and the 80 percent confidence intervals. The plots clearly reveal how the OLS estimates represent the 
conditional distribution of agribusiness performance, quantitatively and qualitatively. The graphs further revealed 
striking differences between OLS and quantile results.  
 

 
Figure-1.Graphical representation of the regression estimates (Agribusiness Growth). 

 



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Figure-2. Graphical representation of the regression estimates (Entrepreneurship Development). 

 

4. Conclusion  
The paper primarily sought to determine the factors promoting agribusiness growth and entrepreneurial 

development in rural China. Times series data from China Statistical Yearbook from 1978 to 2017 were selected for 
the study, and further employed quantile regression approach, which  is more robust to the non-normal error term 
and outliers in the model and takes into consideration the major effects of the covariates on the distribution of the 
dependent variable holistically and not only the conditional mean.  

The results of the study revealed a positive effect of the family system on agribusiness performance at the 
quantile distributions. This implies that favorable institutional policies by the central government have some level 
of impact on the growth of the agribusiness sector and development of agricultural entrepreneurs in rural China. 
Moreover, investment in education, which equips the human capital with knowledge and skills, plays a major role 
in promoting agribusiness sector.  

Therefore, investment in rural education, which is a major human capital tool, is necessary for the development 
of agricultural entrepreneurs. The contributing effect of patents rights and patent application to agribusiness are 
significantly positive on agribusiness development.  

The results from the study imply that the ability of firms to cope with technological changes play a critical role 
in developing the agricultural entrepreneurs and the agribusiness sector. Concerning legal issues, the results 
indicate that building vibrant and favorable legal business environment for businesses to operate boost the 
confidence level of the local entrepreneurs, which has a consequential effect on business performance. However, the 
effect of the policy environment on the firm‟s performance differs slightly, despite the strong effect on agro-
industry performance at various quantile points. The coefficient of legal support in the 20th quantile is almost the 
same as that compare to the OLS estimate in Table 3.  

The study, however, suggests that for development of agribusiness in rural areas, most of the rural policies 
should be focusing on equipping the rural farmers to develop entrepreneurial skills and development to see their 
farms as a business. 
 

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Citation | Owusu Samuel Mensah; Chen Jianlin; Ji You Jun (2019). 
A Quantile Regression Analysis of Contributing Factors Influencing 
Agribusiness Growth and Entrepreneurship Development: Evidence 
from Rural China. Asian Business Research Journal, 4: 10-16. 
History:  
Received: 10 January 2019 
Revised: 18 February 2019 
Accepted: 22 March 2019 
Published: 15 May 2019 
Licensed: This work is licensed under a Creative Commons 

Attribution 3.0 License  
Publisher:  Eastern Centre of Science and Education 
 

Acknowledgement: All authors contributed to the conception and design of 
the study. 
Funding: This study is funded by National Self-finance Fund Project “Based 
on the Dual Network Embedding of Small and Micro Enterprises' Life Cycle 
Trap Breakthrough Mechanism and Path Research” with Item Number: 
7156302. 
Competing Interests: The authors declare that they have no conflict of 
interests. 
Transparency: The authors confirm that the manuscript is an honest, 
accurate, and transparent account of the study was reported; that no vital 
features of the study have been omitted; and that any discrepancies from the 
study as planned have been explained. 
Ethical: This study follows all ethical practices during writing.   

 
Eastern Centre of Science and Education is not responsible or answerable for any loss, damage or liability, etc. caused in relation to/arising out of the use 
of the content. Any queries should be directed to the corresponding author of the article. 

 

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http://creativecommons.org/licenses/by/3.0/

