# Research on the Contribution of Electronic Commerce to Economic Development: An Empirical Analysis Based on Artificial Neural Network

Authors: Zhitan Feng
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

Data collinearity, nonlinearity, and even the data itself helped us solve regression of blind areas throughout this study. When it comes to China’s economic change in the new normal era, high-quality development will be an inescapable trend. By capitalizing on the industrial advantages of speed, flexibility, and convenience, e-commerce has given new momentum to the change of industrial structure and created prospects for the times’ high-quality economic growth. Based on a comparison of the changes in provincial panel data from 2013 to 2020, the following conclusion may be drawn: following per capita education and per capita online sales weight in ANN analysis of economic growth is e-contribution commerce’s to economic growth followed by e-commerce volume, express volume, and domain name; other factors are less important.


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## Body

## 1. Introduction

As e-commerce continues to expand at a fast pace, it is becoming an increasingly essential factor in economic growth. E-commerce has had a significant influence on the consumption patterns and structures of people, as well as on the economic transformation and upgrading that has taken place. Populations’ consumption patterns have altered as a result of e-commerce, with rural residents seeing an increase in their consumption (Ma [1]). E-commerce is a method of doing business through the Internet, but it is anticipated to have an impact on the conventional forms of economic activity in the near future [2]. E-commerce minimizes the distance between producers and consumers, lowers transaction costs, boosts market transparency, ignites the excitement of all transaction participants, and, as a result, raises the total amount of transactions between these two parties ([3–5]; Peng [6]; Zhu [7]; Hu [8]).With the use of the Internet to convey information, there is tremendous potential for e-commerce. [9]. Due to the rapid growth of online marketing transactions such as sales and purchases, E-commerce provides customers with a tool to comment and comment on products or services at the time of purchase [10]. As a result of the integration of different e-commerce forms with the real economy, a new model of market growth has emerged, which has the potential to minimize marketing expenses. Within the context of the new economic normal, it is also an essential method for promoting consumption, increasing income, and speeding up economic transition (Wang [11]). As consumers, we benefit from a greater selection of products at a more reasonable price [12]. E-commerce improves transaction and service efficiency [13]. E-commerce also opens up new possibilities for international trade between companies and consumers, with the volume of international trade over the internet growing at an incredible rate [14]. Chinese e-commerce sales are expected to reach 37.21 trillion yuan in 2020, with cross-border e-commerce imports and exports totaling 1.69 trillion yuan.There are many new types of e-commerce, such as live streaming of commodities, online schooling, and online office space. Many Internet application situations have gotten more complex as a result of the pandemic. E-commerce and other new forms of digital economy have had a significant impact on the growth of the global economy. There has been a 16.6 percent average annual growth rate in China’s digital economy production value, which now accounts for more than half of the country’s GDP, up from 30.3% in 2016 to 36.2% in 2019. More than 19 trillion yuan (1914.47 billion yuan) was added to China’s digital economy in 2020, accounting for nearly 18.8 percent of the country’s gross domestic product (GDP). During COVID-19 pandemic time, the digital economy has successfully stabilized and sustained the orderly functioning of economic and social systems, generating over 70% of economic development (Wang [15]).


## 2. Literature Review

In 2000, Goldmanis et al. [4] used the MULTIMOD model (multiregion econometrics model) to estimate the macroeconomic impact of B2B (Business to Business) on America, Japan, the US, Germany, Britain, and France [16]. Cisco System (Inc.) Research Report (http://www.netimpactstudy.com/) in November 2002 that in the period 1995-2010, e-popularity commerce’s raised U.S. productivity from 1.2% to 2.1%, at which point it was predicted to expand by 48%. From 2000 to 2010, e-commerce increased EU productivity from an average annual growth rate of 1.3% to an average annual growth rate of 1.7%, and e-commerce improved productivity by 1.3 percent. In today’s world, it is apparent that e-commerce has a significant impact on societal progress ([17, 18]; Ge [19]). It is via an in-depth investigation of three countries—Singapore, Finland, and Egypt—that Robert et al. [20] build a theoretical framework for ICT-based education and the growth of the economy and society as a whole [20]. E-commerce significantly promotes economic growth (Yang Jianet al., 2011; Fan [21]; Zhou [22]; Tang [23]; Wang [11]); Internet promotes green economic growth (Huang [24]); digital economic growth is cyclical fluctuations. Revenue and employment gains from online retailing are more likely to come from integrating the digital economy and industries (Yao [25]). Liu and Chen [26] utilized the panel data from 2007-2015 in 31 provinces and cities to study the relationship between the Internet and regional economic growth level and regional economic growth structure. A rise in economic growth of 0.742% and a decrease in tertiary industry growth of 0.067% were found in the empirical data (Liu [26]). Zhang [27] was the first to conduct a theoretical investigation into the impact of the Internet on economic development and the evolution of the industrial sector (Ye Xinsheng, 2018). Li [28] based on micro (enterprise efficiency), Midwest (structural adjustment), and macro (overall economic growth) (Li [28]). Some academics have contrasted the influence of e-commerce and other businesses on economic development (Zhou [29]; Lu [30, 31]).China’s economic growth in the eastern, central, and western areas of the country is greatly influenced by the development of e-commerce in those regions (Yan [32]; Zhang [33]). Comparative analysis was done by several researchers in both industrialized and developing countries. E-positive commerce’s impact on economic development extends beyond provinces. E-impact commerce’s on the backward sections of the economy is greater than that on the developed regions (Lin [34]; Guo [35]). The project of “E-commerce into Rural Comprehensive Demonstration” benefits the county economy by promoting economic development via policy effects, employment effects, industry agglomeration effects, and human capital enhancement effects. The policy influence of demonstration projects on county economic growth is very regionally heterogeneous. The center and western regions have a stronger impact on county economic growth than the eastern coastline regions do (Ge [19]). E-commerce fosters social innovation by encouraging villages to sell their wares to buyers around the nation and even the globe, therefore increasing revenue and promoting local economic growth. (Cui, 2016). Zhang [27] conducted a thorough assessment of the state of e-commerce development in different areas and discovered that there are regional variances in the state of e-commerce in China (Zhang [27]). The standard is the degree of development of the digital economy. China’s regional economic development may be classified into three tiers, with significant variances between them (Wang [15]).On a macroeconomic level, the economic usefulness of e-commerce may be seen as a kind of technical innovation, as it enables enterprises and industries to enhance their economic efficiency, hence, increasing the overall supply level of the whole community (Yang [36]). Electricity and economic developments are inextricably linked, with studies focusing on the role of electricity in promoting economic progress (He [37]). Instead of utilizing separate measurement techniques to assess regional variations in e-commerce development, this study employs multidimensional indicators to produce an e-commerce development index, a quality evaluation system for economic growth, and a provincial-based index system.


## 3. Research Model Development

### 3.1. E-Commerce Principles and Mechanisms for Economic Development

Economic growth may be aided by increasing the amount of e-commerce development. The rise and expansion of e-commerce have made resource allocation more efficient. Making it easier for people to share information Consumers’ willingness to shop will rise as a result of more pricing transparency. According to Garicano and Kaolar (2000), the merger of conventional retail with e-commerce may enhance the optimization and integration of businesses and lower costs (Lin [38]; Liu Yu, 2022). An examination of transaction costs was developed by Coos, and it substantially broadened the application field for Economic Growth Theory; describe system dynamics and their effect on economic growth [39]. Five dimensions of transaction costs may be reduced by the e-commerce sector, according to Kos theory. Cost-cutting measures specifically include identifying transaction objects, decreasing price search costs, reducing the research and negotiating costs, as well as cutting costs associated with keeping order in the market. Economic development may be boosted by reducing transaction costs, which in turn improves efficiency (Yang [40]). As opposed to traditional retail, online shopping fosters personal growth. E-commerce boosts the number of people employed and the number of businesses, as well as the efficiency of manufacturing. According to Adam Smith, the key to promoting economic growth is improving labor productivity and efficiency. On the other hand, Lucas proposed the human capital spillover model, which suggests that spillover effect (spillover effect) of human capital causes externalities. Finally, improving the human capital level of individual businesses can help the social economy develop sustainably. At the macro level, the digital economy has the potential to significantly increase the sources of factors, enhance the efficiency of factor allocation, and achieve the capital deepening effect, while also promoting economic development via economic growth as the medium of expression (Ding [41]). Third, by encouraging industrial agglomeration, e-commerce may contribute to long-term economic development. That is exactly why e-commerce has the potential to substantially enhance the matching efficiency of customers and sellers and dramatically lower transaction costs. Since many businesses are attracted to e-commerce in established locations, it will create an industrial agglomeration effect, as well (Yan [32]). This agglomeration effect encourages regional economic progress, but it also produces regional disparities.


### 3.2. Index Selection

Level of economic progress using GDP per capita as a representative level (Hu [42]), GDP is a useful reference for gauging the comprehensive strength of the national economic economy and is often employed as an essential measurement variable of economic development in domestic and international research. E-influence commerce’s on economic development is being studied in terms of both magnitude and speed by certain researchers, who use GDP totals and GDP growth as proxy variables to gauge the health of an economy (Dong [36]). Macroeconomics may be better understood and grasped with the use of per capita GDP. Using the cointegration approach, Huang looked at the influence of online shopping on GDP. His findings showed a positive correlation between the number of e-commerce transactions and GDP (Huang [43]). This study measures economic growth using per capita GDP (yuan), which takes into account the aforementioned factors. To assess the development level of e-commerce, six indicators have been identified: the number of domain names (10,000), the per capita express volume (pieces), the transaction volume of e-commerce (100 million yuan), the number of enterprises (individuals) with e-commerce transaction activities, and the per capita online retail sales (yuan). These indicators are used in conjunction to assess the development level of e-commerce. The participation of enterprises in e-commerce activities to carry out transactions can reflect the level of e-commerce in a region, and overall, the number of enterprises participating in e-commerce activities accounts for an upward trend in the total number of enterprises. The development level of transportation reflected by the per capita express delivery volume index will affect the cost of sales and procurement of goods. If the development level of transportation in a region is relatively backward, it will not be able to attract relevant enterprises to settle down, and thus will not be able to form interindustry and industrial within the agglomeration effect. Economic growth will be limited. E-commerce transaction volume is the sum of e-commerce sales and e-commerce purchases. E-commerce sales refer to the total amount of goods and services (including VAT) sold by enterprises (units) through online orders during the reporting period. Online orders refer to accepting orders through the network, and payment and delivery may not rely on the network. E-commerce purchases refer to the total amount of goods and services (including VAT) purchased by enterprises (units) through online orders during the reporting period. Online orders refer to sending orders through the network, and payment and delivery may not rely on the network. This indicator reflects the level of e-commerce transactions in a region.Three variables are chosen as control variables in this paper: the level of opening up (total import and export volume, US $10,000) and per capita education level (per capita years of education, year). The index system is shown in Table1.Table 1
Index system.
Target systemIndex interpretationReference sourceDevelopment level of e-commerceE-commerce transaction volume (ECJY)Reflects the regional e-commerce transaction scaleWang and She [15]; Dong et al. [36]Number of domain (YMS)Reflect the development level of enterprise and individual networksWang [11]Express volume per capita (KDL)The development level of transportation can reflect the level of e-commerce to improve the efficiency of resource allocation, increase the employment rate, and promote industrial agglomerationYan [32]; Han and Zhang [44]The number of enterprises with e-commerce trading activities (ECQY)Reflect the development current situation and development potential of e-commerceWang and She [15]Per capita online retail sales (WSLS)Reflects the consumers’ online shopping situationZhang [27]External opening level (KFSP)Reflects the regional import and export levelYan [32]Education level per capita (JYSP)Reflect the degree of education within the regionYan [32]Economic growthPer capita GDP (GDPP)Reflects the level of economic developmentHu [42]


### 3.3. Methods

#### 3.3.1. Impact Analysis Based on the BP Artificial Neural Network

Some scholars conduct simulation analysis through simulation algorithms, such as Khalaf et al. [45] based on simulation technology to achieve nonlinear image expansion (Osamah Ibrahim [45]). In the application of neural network algorithms, modeling and innovation in the fields of mechanical engineering and technology, numerical solutions of multiorder fractional differential equations, or natural language clustering phenomena have been carried out (Naveed Ahmad [46]; Khan, N.A.; [30, 47]). Analysis of e-effect commerce’s on economic growth using BP artificial neural network presents an entirely new way of looking at it, and it is possible to gain an idea of how different elements affect economic growth by analyzing the BP artificial neural network model training. The BP neural network model is an essential foundational model in artificial intelligence that can imitate the human brain’s neural network and conduct distributed and parallel information processing in a distributed manner [48]. One or more hidden layers may be included in the BP artificial neural network, which normally comprises of three layers: an input layer, an implying layer, and an output layer that feeds back information. Network weights are constantly adjusted in order to reduce the network’s overall error or to achieve a set number of training sessions. There are many benefits of use a BP neural network to examine the influence of e-commerce on economic growth: first, due to the fact that the weight of the BP artificial neural network is bigger than 0, it successfully solves the regression blind spot problem, which occurs in multiple regression. If certain known critical variables fail to pass the statistical test, or even the variable sign and common sense, the conclusion is that their performance is poor, but they cannot be eliminated. Its contribution must be calculated, but the conventional regression-based approach is incapable of resolving this issue, which is known as the regression blind spot (Yu [49]). Any variable that is greater than zero has an influence on this dependent variable, as long as the artificial neural network learns successfully. The total of all variables or influencing factors is 100 percent, though. If the regression coefficient fails the statistical test, the former indicates that the variable does not work, which does not correlate with the objective reality in the regression analysis. While its effect may not be great, the variable should nonetheless play a role in the equation. In this case, the BP artificial neural network handles this issue, and the weight of a variable determines its effect. Second, the BP artificial neural network’s primary benefit is its capacity to mimic complicated nonlinear relationships between variables, and the influence of e-commerce on economic development is a textbook example of a nonlinear connection ([50]; Tang [23]; Yan [32]; Zhou [51]; Wang [15]).


#### 3.3.2. Test of Robustness

Additional robustness testing is necessary for the outcomes of the BP artificial neural network’s impact factor analysis. Due to the fact that there are often two independent variables and several dependent and control variables, the estimates may be checked for robustness using panel-ridge regression, which can be classified into fixed-effect and random-effect models. Their fundamental distinction is whether the intercept component (intercept) of individual heterogeneity is connected to constant individual characteristics or to factors that fluctuate over time. Because the fixed-effect model implies that the individual impact is not random, it may be seen as a component of the explanatory variable. Individual effects are treated as random in the random effect model and are included in the interference term. By comparison, the fixed-effect models’ assumptions are relaxed and realistic, while the random-effect models’ assumptions are stringent and unrealistic. As a result of the theoretical assumptions about fixed and random effects, the model is estimated using panel fixed effects ridge regression. Because this regression technique is more effective in overcoming the issue of multicollinearity. The robustness test is used to determine if the rank of the input variable regression coefficients is consistent with the rank of the BP neural network weights. The following are the test steps: to begin, the regression coefficients of the dependent and control variables, as well as economic development, were analyzed using ridge regression. Second, if the weighting of the elements in the BP ANN is consistent, a comparison study of the weighting will be performed. It may be determined that the BP neural network is dependable; but, if it is inconsistent, additional study of the causes for the inconsistency is required.


## 3.1. E-Commerce Principles and Mechanisms for Economic Development

Economic growth may be aided by increasing the amount of e-commerce development. The rise and expansion of e-commerce have made resource allocation more efficient. Making it easier for people to share information Consumers’ willingness to shop will rise as a result of more pricing transparency. According to Garicano and Kaolar (2000), the merger of conventional retail with e-commerce may enhance the optimization and integration of businesses and lower costs (Lin [38]; Liu Yu, 2022). An examination of transaction costs was developed by Coos, and it substantially broadened the application field for Economic Growth Theory; describe system dynamics and their effect on economic growth [39]. Five dimensions of transaction costs may be reduced by the e-commerce sector, according to Kos theory. Cost-cutting measures specifically include identifying transaction objects, decreasing price search costs, reducing the research and negotiating costs, as well as cutting costs associated with keeping order in the market. Economic development may be boosted by reducing transaction costs, which in turn improves efficiency (Yang [40]). As opposed to traditional retail, online shopping fosters personal growth. E-commerce boosts the number of people employed and the number of businesses, as well as the efficiency of manufacturing. According to Adam Smith, the key to promoting economic growth is improving labor productivity and efficiency. On the other hand, Lucas proposed the human capital spillover model, which suggests that spillover effect (spillover effect) of human capital causes externalities. Finally, improving the human capital level of individual businesses can help the social economy develop sustainably. At the macro level, the digital economy has the potential to significantly increase the sources of factors, enhance the efficiency of factor allocation, and achieve the capital deepening effect, while also promoting economic development via economic growth as the medium of expression (Ding [41]). Third, by encouraging industrial agglomeration, e-commerce may contribute to long-term economic development. That is exactly why e-commerce has the potential to substantially enhance the matching efficiency of customers and sellers and dramatically lower transaction costs. Since many businesses are attracted to e-commerce in established locations, it will create an industrial agglomeration effect, as well (Yan [32]). This agglomeration effect encourages regional economic progress, but it also produces regional disparities.


## 3.2. Index Selection

Level of economic progress using GDP per capita as a representative level (Hu [42]), GDP is a useful reference for gauging the comprehensive strength of the national economic economy and is often employed as an essential measurement variable of economic development in domestic and international research. E-influence commerce’s on economic development is being studied in terms of both magnitude and speed by certain researchers, who use GDP totals and GDP growth as proxy variables to gauge the health of an economy (Dong [36]). Macroeconomics may be better understood and grasped with the use of per capita GDP. Using the cointegration approach, Huang looked at the influence of online shopping on GDP. His findings showed a positive correlation between the number of e-commerce transactions and GDP (Huang [43]). This study measures economic growth using per capita GDP (yuan), which takes into account the aforementioned factors. To assess the development level of e-commerce, six indicators have been identified: the number of domain names (10,000), the per capita express volume (pieces), the transaction volume of e-commerce (100 million yuan), the number of enterprises (individuals) with e-commerce transaction activities, and the per capita online retail sales (yuan). These indicators are used in conjunction to assess the development level of e-commerce. The participation of enterprises in e-commerce activities to carry out transactions can reflect the level of e-commerce in a region, and overall, the number of enterprises participating in e-commerce activities accounts for an upward trend in the total number of enterprises. The development level of transportation reflected by the per capita express delivery volume index will affect the cost of sales and procurement of goods. If the development level of transportation in a region is relatively backward, it will not be able to attract relevant enterprises to settle down, and thus will not be able to form interindustry and industrial within the agglomeration effect. Economic growth will be limited. E-commerce transaction volume is the sum of e-commerce sales and e-commerce purchases. E-commerce sales refer to the total amount of goods and services (including VAT) sold by enterprises (units) through online orders during the reporting period. Online orders refer to accepting orders through the network, and payment and delivery may not rely on the network. E-commerce purchases refer to the total amount of goods and services (including VAT) purchased by enterprises (units) through online orders during the reporting period. Online orders refer to sending orders through the network, and payment and delivery may not rely on the network. This indicator reflects the level of e-commerce transactions in a region.Three variables are chosen as control variables in this paper: the level of opening up (total import and export volume, US $10,000) and per capita education level (per capita years of education, year). The index system is shown in Table1.Table 1
Index system.
Target systemIndex interpretationReference sourceDevelopment level of e-commerceE-commerce transaction volume (ECJY)Reflects the regional e-commerce transaction scaleWang and She [15]; Dong et al. [36]Number of domain (YMS)Reflect the development level of enterprise and individual networksWang [11]Express volume per capita (KDL)The development level of transportation can reflect the level of e-commerce to improve the efficiency of resource allocation, increase the employment rate, and promote industrial agglomerationYan [32]; Han and Zhang [44]The number of enterprises with e-commerce trading activities (ECQY)Reflect the development current situation and development potential of e-commerceWang and She [15]Per capita online retail sales (WSLS)Reflects the consumers’ online shopping situationZhang [27]External opening level (KFSP)Reflects the regional import and export levelYan [32]Education level per capita (JYSP)Reflect the degree of education within the regionYan [32]Economic growthPer capita GDP (GDPP)Reflects the level of economic developmentHu [42]


## 3.3. Methods

### 3.3.1. Impact Analysis Based on the BP Artificial Neural Network

Some scholars conduct simulation analysis through simulation algorithms, such as Khalaf et al. [45] based on simulation technology to achieve nonlinear image expansion (Osamah Ibrahim [45]). In the application of neural network algorithms, modeling and innovation in the fields of mechanical engineering and technology, numerical solutions of multiorder fractional differential equations, or natural language clustering phenomena have been carried out (Naveed Ahmad [46]; Khan, N.A.; [30, 47]). Analysis of e-effect commerce’s on economic growth using BP artificial neural network presents an entirely new way of looking at it, and it is possible to gain an idea of how different elements affect economic growth by analyzing the BP artificial neural network model training. The BP neural network model is an essential foundational model in artificial intelligence that can imitate the human brain’s neural network and conduct distributed and parallel information processing in a distributed manner [48]. One or more hidden layers may be included in the BP artificial neural network, which normally comprises of three layers: an input layer, an implying layer, and an output layer that feeds back information. Network weights are constantly adjusted in order to reduce the network’s overall error or to achieve a set number of training sessions. There are many benefits of use a BP neural network to examine the influence of e-commerce on economic growth: first, due to the fact that the weight of the BP artificial neural network is bigger than 0, it successfully solves the regression blind spot problem, which occurs in multiple regression. If certain known critical variables fail to pass the statistical test, or even the variable sign and common sense, the conclusion is that their performance is poor, but they cannot be eliminated. Its contribution must be calculated, but the conventional regression-based approach is incapable of resolving this issue, which is known as the regression blind spot (Yu [49]). Any variable that is greater than zero has an influence on this dependent variable, as long as the artificial neural network learns successfully. The total of all variables or influencing factors is 100 percent, though. If the regression coefficient fails the statistical test, the former indicates that the variable does not work, which does not correlate with the objective reality in the regression analysis. While its effect may not be great, the variable should nonetheless play a role in the equation. In this case, the BP artificial neural network handles this issue, and the weight of a variable determines its effect. Second, the BP artificial neural network’s primary benefit is its capacity to mimic complicated nonlinear relationships between variables, and the influence of e-commerce on economic development is a textbook example of a nonlinear connection ([50]; Tang [23]; Yan [32]; Zhou [51]; Wang [15]).


### 3.3.2. Test of Robustness

Additional robustness testing is necessary for the outcomes of the BP artificial neural network’s impact factor analysis. Due to the fact that there are often two independent variables and several dependent and control variables, the estimates may be checked for robustness using panel-ridge regression, which can be classified into fixed-effect and random-effect models. Their fundamental distinction is whether the intercept component (intercept) of individual heterogeneity is connected to constant individual characteristics or to factors that fluctuate over time. Because the fixed-effect model implies that the individual impact is not random, it may be seen as a component of the explanatory variable. Individual effects are treated as random in the random effect model and are included in the interference term. By comparison, the fixed-effect models’ assumptions are relaxed and realistic, while the random-effect models’ assumptions are stringent and unrealistic. As a result of the theoretical assumptions about fixed and random effects, the model is estimated using panel fixed effects ridge regression. Because this regression technique is more effective in overcoming the issue of multicollinearity. The robustness test is used to determine if the rank of the input variable regression coefficients is consistent with the rank of the BP neural network weights. The following are the test steps: to begin, the regression coefficients of the dependent and control variables, as well as economic development, were analyzed using ridge regression. Second, if the weighting of the elements in the BP ANN is consistent, a comparison study of the weighting will be performed. It may be determined that the BP neural network is dependable; but, if it is inconsistent, additional study of the causes for the inconsistency is required.


## 3.3.1. Impact Analysis Based on the BP Artificial Neural Network

Some scholars conduct simulation analysis through simulation algorithms, such as Khalaf et al. [45] based on simulation technology to achieve nonlinear image expansion (Osamah Ibrahim [45]). In the application of neural network algorithms, modeling and innovation in the fields of mechanical engineering and technology, numerical solutions of multiorder fractional differential equations, or natural language clustering phenomena have been carried out (Naveed Ahmad [46]; Khan, N.A.; [30, 47]). Analysis of e-effect commerce’s on economic growth using BP artificial neural network presents an entirely new way of looking at it, and it is possible to gain an idea of how different elements affect economic growth by analyzing the BP artificial neural network model training. The BP neural network model is an essential foundational model in artificial intelligence that can imitate the human brain’s neural network and conduct distributed and parallel information processing in a distributed manner [48]. One or more hidden layers may be included in the BP artificial neural network, which normally comprises of three layers: an input layer, an implying layer, and an output layer that feeds back information. Network weights are constantly adjusted in order to reduce the network’s overall error or to achieve a set number of training sessions. There are many benefits of use a BP neural network to examine the influence of e-commerce on economic growth: first, due to the fact that the weight of the BP artificial neural network is bigger than 0, it successfully solves the regression blind spot problem, which occurs in multiple regression. If certain known critical variables fail to pass the statistical test, or even the variable sign and common sense, the conclusion is that their performance is poor, but they cannot be eliminated. Its contribution must be calculated, but the conventional regression-based approach is incapable of resolving this issue, which is known as the regression blind spot (Yu [49]). Any variable that is greater than zero has an influence on this dependent variable, as long as the artificial neural network learns successfully. The total of all variables or influencing factors is 100 percent, though. If the regression coefficient fails the statistical test, the former indicates that the variable does not work, which does not correlate with the objective reality in the regression analysis. While its effect may not be great, the variable should nonetheless play a role in the equation. In this case, the BP artificial neural network handles this issue, and the weight of a variable determines its effect. Second, the BP artificial neural network’s primary benefit is its capacity to mimic complicated nonlinear relationships between variables, and the influence of e-commerce on economic development is a textbook example of a nonlinear connection ([50]; Tang [23]; Yan [32]; Zhou [51]; Wang [15]).


## 3.3.2. Test of Robustness

Additional robustness testing is necessary for the outcomes of the BP artificial neural network’s impact factor analysis. Due to the fact that there are often two independent variables and several dependent and control variables, the estimates may be checked for robustness using panel-ridge regression, which can be classified into fixed-effect and random-effect models. Their fundamental distinction is whether the intercept component (intercept) of individual heterogeneity is connected to constant individual characteristics or to factors that fluctuate over time. Because the fixed-effect model implies that the individual impact is not random, it may be seen as a component of the explanatory variable. Individual effects are treated as random in the random effect model and are included in the interference term. By comparison, the fixed-effect models’ assumptions are relaxed and realistic, while the random-effect models’ assumptions are stringent and unrealistic. As a result of the theoretical assumptions about fixed and random effects, the model is estimated using panel fixed effects ridge regression. Because this regression technique is more effective in overcoming the issue of multicollinearity. The robustness test is used to determine if the rank of the input variable regression coefficients is consistent with the rank of the BP neural network weights. The following are the test steps: to begin, the regression coefficients of the dependent and control variables, as well as economic development, were analyzed using ridge regression. Second, if the weighting of the elements in the BP ANN is consistent, a comparison study of the weighting will be performed. It may be determined that the BP neural network is dependable; but, if it is inconsistent, additional study of the causes for the inconsistency is required.


## 4. Empirical Analysis

### 4.1. Data Sources

All statistics in this article are from the China Statistical Yearbook (2013-2020) and the Statistical Yearbook of various provinces and cities (2013-2020). Due to the fact that e-commerce growth is influenced by a variety of circumstances, it is impossible to accurately explain the reality of e-commerce development using a single variable. As a result, this research intends to use multiple regression and neural network techniques to ascertain the influence of e-commerce on China’s economic development. Table2 contains descriptive data for each variable.Table 2
Descriptive statistics.
Variable nameNMinimumMaximumMeanStd. deviationYMS248.000.50882.49106.06145.41KDL248.000.70277.4622.9437.45ECQY248.0018.0016936.002921.243274.96ECJY248.0038.7052413.106239.989033.01WSLS248.000.00481.9944.5079.93JYSP248.007.4612.949.760.78KFSP248.0031053.24109158143.7013734876.9522247921.91GDPP248.0022825.00164889.4758363.996929232.66622ValidN (listwise)248.00


### 4.2. Stability Test

Stability in e-commerce and economic development is a necessary condition for understanding the evolution of the two relationships, and it is also a necessary condition for ensuring the effectiveness of the future structure. To minimize the impacts of endogenicity, heterovariasticity, and collinearity, all panel data utilized in this work are transformed logarithmically and tested using the ADF technique. Table3 summarizes the findings of the specific tests.Table 3
Results of single-unit root inspections (ADF).
Variable namePConclusionTest for unit root inLog(YMS)0.0000Stability1st differenceLog(KDL)0.0000Stability2st differenceLog(ECQY)0.0007StabilityLevelLog(ECJY)0.0000StabilityLevelLog(WSLS)0.0153Stability2st differenceLog(JYSP)0.0000Stability1st differenceLog(KFSP)0.0028StabilityLevelLog(GDPP)0.0996Stability2st difference


### 4.3. Multiple Regression Analysis

As shown in Table3, e-commerce index variables and control variables, GDP under the original sequence condition, e-commerce transactions, e-commerce trade activities (a), and open to the rest of the world ($) are all included. Three variables remained stable, while the remaining three failed the stability test: first-order difference, domain number, and per capita education; two variables remained stable: per capita delivery, per capita online sales, and GDP; and the remaining three variables remained unstable after the second-order difference. E-commerce transactions were statistically significant at the 5% level, per capita GDP was statistically significant at the 10% level, and the other test findings were all statistically significant at the 1% level.The panel data model is capable of reducing some of the heterovariance caused by individual variations, which is critical when dealing with linear relationships between variables. As a result, this article studies the economic contribution impact of e-commerce using a panel fixed-effect model to experimentally evaluate the two relationships’ linear changes.Here, formula (1) encapsulates the essence of e-commerce:
(1)logGDPPit=α1logYMSit+α2logKDLit+α3logECQYit+α4logECJYit+α5logWSLSit+β1logJYSPit+β2logKFSPit+eit+μit.There are two variables in the formula (1): GDPP, which stands for the level of economic growth, and GDP, which stands for the GDP per person. E-commerce transaction volume and domain name number, as well as the number of businesses that do business in e-commerce and the number of people who open businesses, are all variables that have an explanatory variable coefficient, control variable coefficient, e, constant, and residual. The other variables are ECJY, KDL, ECQ, WSLS, KFSP, and JYSP. The panel data collinearity diagnostic revealed that the VIF of per capita online retail sales was 8.082, the number of e-commerce transactions was 7.10 around 935, and there were some collinearity issues in other variables, as well. Table 4 shows the findings of the Hausman test and the fixed-effect model for panel data, where the Hausman test value of 9 and the P value of 0 are unacceptable null hypotheses for panel data. However, e-commerce transaction volume and the number of companies engaging in e-commerce transactions failed the significance test, although e-commerce transaction volume and activity have a significant influence on economic growth, as shown by the findings of the regression study (Wang [11]). Even if a critical variable has bad performance, no statistical test reveals that there is a regression blind spot issue with that variable [52]. Through the influence of the statistical test data, the biggest impact is the per capita education degree (coefficient of 1.0434, positive correlation), followed by the per capita online sales volume (coefficient of 0.1060, positive correlation).Table 4
Panel data estimation results.
Variable nameVariable meaningFixed effectt-statisticCollinear diagnosis (VIF value)eConstant term6.88110.9330Log(YMS)Number of domain names0.02021.94862.187Log(KDL)Express volume per capita0.05553.20935.863Log(ECQY)The number of businesses with e-commerce trading activities0.02601.41116.106Log(ECJY)E-commerce transaction volume0.01941.15357.935Log(WSLS)Per capita online retail sales0.10607.39758.082Log(JYSP)Education level per capita1.04343.91272.129Log(KFSP)External opening level0.09854.63554.903P0.0000R2Goodness of fit0.9779


### 4.4. Artificial Neural Network Analysis

To better understand the impact of e-commerce on economic growth, a BP artificial neural network model was built using e-commerce transactions, domain names, express per capita, mobile phone users, e-commerce trading activities, per capita online retail sales, and industry level, opening level, and education variables as input layers, and GDP per capita is used as the output layer to train the model, which is then used to predict economic growth. Neural network training diagram is shown in Figure1. In BP, there are a number of aspects that affect the outcomes of the artificial neural network’s learning process, such as the network topology, model type, model training durations, and training parameters. Training was halted when the overall error MSE minimal principle’s criterion of less than 0.0000001 was reached, and the average of each weight was used as the final training result to increase the study’s robustness. It was determined that 70.2% of the data was trained, and only 29.8% of the data was evaluated in a hidden layer of three neurons. According to Table 5, in terms of the impact of e-commerce on economic growth, the highest weights are per capita education level and per capita online sales, at 26.4% and 24.1%, respectively, followed by e-commerce transaction volume, express delivery volume, and domain name count, respectively, with the influence of other variables being relatively weak. Unlike traditional panel data regression analysis, which is incapable of analyzing the impact of e-commerce transactions and the number of enterprises engaged in e-commerce transaction activities on economic growth, the BP artificial neural network can further estimate the magnitude of its economic impact, thereby resolving the problem of returning to the blind spot.Figure 1
Neural network training diagram.Table 5
Variable importance independent variable importance.
Variable nameVariable meaningImportanceNormalized importanceLog(YMS)Number of domain names0.11945.2%Log(KDL)Express volume per capita0.12145.7%Log(ECQY)The number of businesses with e-commerce trading activities0.03613.7%Log(ECJY)E-commerce transaction volume0.13751.9%Log(WSLS)Per capita online retail sales0.24191.2%Log(JYSP)Education level per capita0.264100.0%Log(KFSP)Open to the outside world0.08231.2%


### 4.5. Robustness Test of Ridge Regression

When dealing with collinear data, a partial regression method such as ridge regression can be more effective than traditional least squares because it does not sacrifice any information in order to obtain a more accurate regression coefficient. This makes it an excellent choice for pathological data analysis because it is more precise than traditional least squares. Figure2 depicts the ridge plot of the ridge regression, where the model becomes stable when the standard coefficient is approximately 0.8, and the regression findings are as follows:
(2)logGDPPit=0.0756YMSit+0.0702logKDLit++0.0144logECQYit+0.0253logECJYit+0.0633logWSLSit+1.1657logJYSPit+0.0198logKFSPit+7.2728R2=77.94.Figure 2
Ridge plot.A good goodness-of-fit score of 0.7794 was obtained from the ridge regression findings. The elastic coefficients for per capita education and per capita online retail sales were 1.1657 and 0.0633, respectively, which were larger relative to other variables, consistent with the BP ANN results, indicating that the results were robust.Due to the fact that the BP artificial neural network is influenced by a variety of elements, including data, network outcomes, and network parameters, the weight assigned to each learning result varies. It is worth noting that BP’s artificial neural networks are not infallible. Additionally, we must take note of some applicable circumstances in order to resolve the regression blind spot. To begin, the neural network’s goodness of fit should not be too low. Generally, be greater than 0.50, as a model with a low goodness-of-fit explains the data poorly; second, the sample size should not be too small, as learning is frequently poor in this case; third, the robustness test must pass, which means using at least one of partial least squares or ridge regression. Additionally, the goodness of fit of the robustness test should not be too low; third, the weight values should be broadly compatible with economic theory.


## 4.1. Data Sources

All statistics in this article are from the China Statistical Yearbook (2013-2020) and the Statistical Yearbook of various provinces and cities (2013-2020). Due to the fact that e-commerce growth is influenced by a variety of circumstances, it is impossible to accurately explain the reality of e-commerce development using a single variable. As a result, this research intends to use multiple regression and neural network techniques to ascertain the influence of e-commerce on China’s economic development. Table2 contains descriptive data for each variable.Table 2
Descriptive statistics.
Variable nameNMinimumMaximumMeanStd. deviationYMS248.000.50882.49106.06145.41KDL248.000.70277.4622.9437.45ECQY248.0018.0016936.002921.243274.96ECJY248.0038.7052413.106239.989033.01WSLS248.000.00481.9944.5079.93JYSP248.007.4612.949.760.78KFSP248.0031053.24109158143.7013734876.9522247921.91GDPP248.0022825.00164889.4758363.996929232.66622ValidN (listwise)248.00


## 4.2. Stability Test

Stability in e-commerce and economic development is a necessary condition for understanding the evolution of the two relationships, and it is also a necessary condition for ensuring the effectiveness of the future structure. To minimize the impacts of endogenicity, heterovariasticity, and collinearity, all panel data utilized in this work are transformed logarithmically and tested using the ADF technique. Table3 summarizes the findings of the specific tests.Table 3
Results of single-unit root inspections (ADF).
Variable namePConclusionTest for unit root inLog(YMS)0.0000Stability1st differenceLog(KDL)0.0000Stability2st differenceLog(ECQY)0.0007StabilityLevelLog(ECJY)0.0000StabilityLevelLog(WSLS)0.0153Stability2st differenceLog(JYSP)0.0000Stability1st differenceLog(KFSP)0.0028StabilityLevelLog(GDPP)0.0996Stability2st difference


## 4.3. Multiple Regression Analysis

As shown in Table3, e-commerce index variables and control variables, GDP under the original sequence condition, e-commerce transactions, e-commerce trade activities (a), and open to the rest of the world ($) are all included. Three variables remained stable, while the remaining three failed the stability test: first-order difference, domain number, and per capita education; two variables remained stable: per capita delivery, per capita online sales, and GDP; and the remaining three variables remained unstable after the second-order difference. E-commerce transactions were statistically significant at the 5% level, per capita GDP was statistically significant at the 10% level, and the other test findings were all statistically significant at the 1% level.The panel data model is capable of reducing some of the heterovariance caused by individual variations, which is critical when dealing with linear relationships between variables. As a result, this article studies the economic contribution impact of e-commerce using a panel fixed-effect model to experimentally evaluate the two relationships’ linear changes.Here, formula (1) encapsulates the essence of e-commerce:
(1)logGDPPit=α1logYMSit+α2logKDLit+α3logECQYit+α4logECJYit+α5logWSLSit+β1logJYSPit+β2logKFSPit+eit+μit.There are two variables in the formula (1): GDPP, which stands for the level of economic growth, and GDP, which stands for the GDP per person. E-commerce transaction volume and domain name number, as well as the number of businesses that do business in e-commerce and the number of people who open businesses, are all variables that have an explanatory variable coefficient, control variable coefficient, e, constant, and residual. The other variables are ECJY, KDL, ECQ, WSLS, KFSP, and JYSP. The panel data collinearity diagnostic revealed that the VIF of per capita online retail sales was 8.082, the number of e-commerce transactions was 7.10 around 935, and there were some collinearity issues in other variables, as well. Table 4 shows the findings of the Hausman test and the fixed-effect model for panel data, where the Hausman test value of 9 and the P value of 0 are unacceptable null hypotheses for panel data. However, e-commerce transaction volume and the number of companies engaging in e-commerce transactions failed the significance test, although e-commerce transaction volume and activity have a significant influence on economic growth, as shown by the findings of the regression study (Wang [11]). Even if a critical variable has bad performance, no statistical test reveals that there is a regression blind spot issue with that variable [52]. Through the influence of the statistical test data, the biggest impact is the per capita education degree (coefficient of 1.0434, positive correlation), followed by the per capita online sales volume (coefficient of 0.1060, positive correlation).Table 4
Panel data estimation results.
Variable nameVariable meaningFixed effectt-statisticCollinear diagnosis (VIF value)eConstant term6.88110.9330Log(YMS)Number of domain names0.02021.94862.187Log(KDL)Express volume per capita0.05553.20935.863Log(ECQY)The number of businesses with e-commerce trading activities0.02601.41116.106Log(ECJY)E-commerce transaction volume0.01941.15357.935Log(WSLS)Per capita online retail sales0.10607.39758.082Log(JYSP)Education level per capita1.04343.91272.129Log(KFSP)External opening level0.09854.63554.903P0.0000R2Goodness of fit0.9779


## 4.4. Artificial Neural Network Analysis

To better understand the impact of e-commerce on economic growth, a BP artificial neural network model was built using e-commerce transactions, domain names, express per capita, mobile phone users, e-commerce trading activities, per capita online retail sales, and industry level, opening level, and education variables as input layers, and GDP per capita is used as the output layer to train the model, which is then used to predict economic growth. Neural network training diagram is shown in Figure1. In BP, there are a number of aspects that affect the outcomes of the artificial neural network’s learning process, such as the network topology, model type, model training durations, and training parameters. Training was halted when the overall error MSE minimal principle’s criterion of less than 0.0000001 was reached, and the average of each weight was used as the final training result to increase the study’s robustness. It was determined that 70.2% of the data was trained, and only 29.8% of the data was evaluated in a hidden layer of three neurons. According to Table 5, in terms of the impact of e-commerce on economic growth, the highest weights are per capita education level and per capita online sales, at 26.4% and 24.1%, respectively, followed by e-commerce transaction volume, express delivery volume, and domain name count, respectively, with the influence of other variables being relatively weak. Unlike traditional panel data regression analysis, which is incapable of analyzing the impact of e-commerce transactions and the number of enterprises engaged in e-commerce transaction activities on economic growth, the BP artificial neural network can further estimate the magnitude of its economic impact, thereby resolving the problem of returning to the blind spot.Figure 1
Neural network training diagram.Table 5
Variable importance independent variable importance.
Variable nameVariable meaningImportanceNormalized importanceLog(YMS)Number of domain names0.11945.2%Log(KDL)Express volume per capita0.12145.7%Log(ECQY)The number of businesses with e-commerce trading activities0.03613.7%Log(ECJY)E-commerce transaction volume0.13751.9%Log(WSLS)Per capita online retail sales0.24191.2%Log(JYSP)Education level per capita0.264100.0%Log(KFSP)Open to the outside world0.08231.2%


## 4.5. Robustness Test of Ridge Regression

When dealing with collinear data, a partial regression method such as ridge regression can be more effective than traditional least squares because it does not sacrifice any information in order to obtain a more accurate regression coefficient. This makes it an excellent choice for pathological data analysis because it is more precise than traditional least squares. Figure2 depicts the ridge plot of the ridge regression, where the model becomes stable when the standard coefficient is approximately 0.8, and the regression findings are as follows:
(2)logGDPPit=0.0756YMSit+0.0702logKDLit++0.0144logECQYit+0.0253logECJYit+0.0633logWSLSit+1.1657logJYSPit+0.0198logKFSPit+7.2728R2=77.94.Figure 2
Ridge plot.A good goodness-of-fit score of 0.7794 was obtained from the ridge regression findings. The elastic coefficients for per capita education and per capita online retail sales were 1.1657 and 0.0633, respectively, which were larger relative to other variables, consistent with the BP ANN results, indicating that the results were robust.Due to the fact that the BP artificial neural network is influenced by a variety of elements, including data, network outcomes, and network parameters, the weight assigned to each learning result varies. It is worth noting that BP’s artificial neural networks are not infallible. Additionally, we must take note of some applicable circumstances in order to resolve the regression blind spot. To begin, the neural network’s goodness of fit should not be too low. Generally, be greater than 0.50, as a model with a low goodness-of-fit explains the data poorly; second, the sample size should not be too small, as learning is frequently poor in this case; third, the robustness test must pass, which means using at least one of partial least squares or ridge regression. Additionally, the goodness of fit of the robustness test should not be too low; third, the weight values should be broadly compatible with economic theory.


## 5. Research Conclusion

It is hoped that this study will help researchers better understand the effect of numerous aspects in e-commerce on economic growth, as well as help them to tackle the regression blind spot problem caused by data collinearity, nonlinearity, and data itself. During my country’s economic reorganization in the new normal period, high-quality development is an unavoidable tendency. Using industry advantages such as speed, flexibility, and convenience as a foundation, e-commerce has given a new impetus to the restructuring of the industrial structure, while also creating prospects for the high-quality economic growth of the modern era. The paper does an empirical examination of the economic consequences of e-commerce using province panel data from 2013 to 2020. The following conclusions are made from a comparison of the changes in the two in different regions: e-commerce makes a substantial contribution to the expansion of the global economy. One of the most essential aspects of the process of e-commerce is the role played by human capital in terms of stimulating economic development. As a result, it is vital to raise the overall quality of human education as well as the training system for e-commerce experts. E-commerce development and economic growth are linked, according to empirical study. The elasticity coefficient of e-commerce is positive, indicating that e-commerce has a significant role in promoting economic growth.First, human capital plays an important role in the process of e-commerce promoting economic growth. Therefore, it is necessary to improve the level of human education and improve the training system for e-commerce professionals. As a result, as e-commerce grows in popularity, a set of experts who are well-versed in e-commerce technologies will need to emerge (Wang [11]).As a second example, per capita online retail sales are linked to economic development. There has been a steady rise in online retail sales. It is the rise of online transactions that has primarily fueled the expansion of e-commerce (Zhang [27]). Aside from raising economic development, the growth of e-commerce has also increased income and consumption for individuals worldwide. E-commerce will play a vital part in China’s new dual-circulation mechanism of internal and external circulation. What happened before the rise of online shopping is radically different now. Consumer demand, consumption scene, circulation mode, and other facets of the panorama must be integrated to build an integrated development thinking, regardless of whether they are part of the main body of commercial and trade circulation or the construction of regional commercial and trade circulation systems that break traditional single thinking (Lin [38]).Third, economic development and the amount of express delivery per person are linked. The degree of transportation has a considerable influence on the link between the development of e-commerce and regional economic growth, as shown by the per capita express volume (Yan [32]). It is critical to maximize the significance of e-commerce in fostering regional economic development. For this, the level of transportation is critical.Fourth, the volume of transactions and the number of e-commerce businesses are also significant elements impacting economic development. Promoting the comprehensive development of e-commerce is critical for the sustained expansion of my country’s economy and strengthening its worldwide competitiveness (Dong [36]). Laws governing e-commerce should be strengthened further. Regulations are increasing support for e-commerce, guiding traditional enterprises to actively use e-commerce tools for sales and communication, preserving the scale and market position of e-commerce development, focusing on cultivating e-commerce projects with growth potential, and promoting the e-commerce industry’s health and well-being. There is a positive association between the number of e-commerce businesses and economic growth, which indicates that as the economy continues to expand and e-commerce levels continue to improve, the number of e-commerce businesses and user involvement will continue to rise. As a result, the construction of a sound network is critical. The security and social credit systems are critical for the growth of e-commerce, and the establishment of a strong social credit system is beneficial to constricting companies’ and users’ credit ratings (Wang [11]).Fifth, the degree to which an economy is accessible to the outside world has an effect on economic development, and the most typical example of an e-commerce sector that is open to the outside world is cross-border e-commerce. Cross-border e-commerce is a vital aspect of fostering economic progress, thus its development is essential. Due to the benefits of globalization and transparency, cross-border e-commerce may facilitate the free flow of production components across the market and eliminate the development imbalance created by disparities in regional resource endowment [30]. The government must increase the efficiency of customs clearance and enhance the overall quality of customs clearance. To facilitate cross-border e-commerce trade and to boost the development of import and export while also improving the overall economic level, the logistics system and the supply of governmental support are the most significant things to consider.Sixth, establishing an e-commerce infrastructure lays the groundwork for advancing the network economy’s growth. There must be a financial and governmental commitment to informatization infrastructure building. The government and industry organizations should prudently manage the development trend and direction of my country’s e-commerce, further build and improve high-speed information transmission backbone networks and broadband Internet technologies, boost Internet access speed, and lower network costs (Wang [11]). Infrastructural development lays the groundwork for e-commerce ventures as well (Zhang [27]). In line with government macrodirection, local circumstances, thorough planning, and practical development of e-commerce businesses, steps are taken according to local situations (Yao [53]).



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*Source: 6271717-2022-05-19.xml*