Analysis of the Correlation between Cross-Border E-Commerce and Economic Growth Based on Hierarchical Multilevel Gray Evaluation Model

Abstract

Cross-border e-commerce is a new form of global trade development under the background of “Internet+,” and it is also a new engine driving economic development. Cross-border e-commerce is growing rapidly and has broad development prospects. As a pioneering pilot zone for comprehensive cross-border e-commerce, it has played a leading role in the development of the country’s cross-border e-commerce industry. This paper takes China’s 35 cross-border e-commerce comprehensive pilot areas as the research object, selects the annual data of 31 provinces across the country, and conducts an empirical research based on the gray-related Internet development level and economic growth. It analyzes the influencing factors of cross-border e-commerce development, constructs a cross-border e-commerce development influencing factor model, and applies gray theory to conduct an empirical analysis of the correlation between cross-border e-commerce development influencing factors and cross-border e-commerce. The research results show that foreign direct investment has the greatest correlation with the number of websites and webpages; the number of patent applications has the greatest correlation with the number of domains and websites; the total fixed assets have the greatest correlation with the number of Internet users and the number of mobile phones at the end of the year; the total amount of foreign investment enterprises in and out. It has the greatest correlation with the number of URLs and Webpages; GDP has the greatest correlation with the number of Internet users and the number of mobile phones at the end of the year. The Internet infrastructure and popularity have a close relationship with economic growth, and the relationship between foreign investment and patent applications and the level of Internet development is more significant.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

In the recent years, the inherent relationship between the Internet and economic growth has also become one of the academic research hotspots. Compared with traditional trade distributors, the richness and optional categories have increased significantly, and the product update speed has been faster. Thanks to the aid of cross-border e-commerce platforms, more high-quality brands can be sold globally, forming new profit growth points and lowering trade barriers. This is undoubtedly important for the internationalization of brands, the sustainable development of enterprises, and the integration of industries into the global value chain [1–6].

Cross-border e-commerce is a new model of “Internet” + “manufacturing” + “brand” + “digitalization” + “trade,” bringing new opportunities to manufacturing companies. It allows manufacturing companies to directly face foreign retailers and consumers. It has the characteristics of diversification, small batches, and less capital. It avoids the problems of large scale of traditional export trade, long transaction cycles, large capital, and information asymmetry. Cross-border e-commerce allows manufacturing companies with mature processing technology to go from behind the scenes to the world stage. Driven by information technology, it enables companies to overcome geographic and organizational boundaries and become more open, easier to collaborate, and more convenient to develop networks. Innovation, through the development of data technology, accumulation of data resources, application of data analysis, mining cross-cultural value, to meet the individual needs of consumers, with an international perspective, build a brand international operation mechanism on an international scale, and integrate domestic and foreign parties resources, promote multiparty collaboration to create brand value. Therefore, it is necessary to in-depth research, quantitatively analyze the correlation between factors, clarify the driving force and bottleneck of cross-border e-commerce development, and propose effective development countermeasures [7–9]. The e-commerce model of export activities mainly includes product selection, cross-border payment, settlement, logistics, and data transmission. The cross-border e-commerce ecosystem is composed of multiple modules such as buyers and sellers, public service agencies, professional service agencies, and the government [10–15]. It also can be shown as Figure 1.

[figure omitted; refer to PDF]

Informatization is produced on the basis of advanced technologies such as computer science technology, communication engineering technology, and bioengineering technology, and represents the most advanced productivity in today’s society. Informatization is an important support for promoting economic development, and it has a huge boosting effect on the economic development of a region. Informatization can promote the overall progress of the regional economy. Informatization can speed up the accumulation of knowledge in the region and create room for innovation in economic development [16–19].

China has a large land and a large population, and is affected by resources and national development policies, and its economic regional characteristics are relatively obvious. In recent years, the central government has also paid special attention to the construction of informatization, so many domestic experts and scholars have conducted in-depth discussions and research on this. Teng Li made a quantitative estimation and comprehensive evaluation of the informatization level of various provinces in China. Through data analysis, Meng Yuan concluded that the deepening of informatization has had a positive impact on economic growth. Gaoxincai analyzed the contribution rate of information elements and found that there is a significant positive correlation between the level of regional informatization and the level of economic development. Meilin used the gray correlation method and linear regression model to analyze the regional economic growth level of a region to a large extent determined by the level of informatization construction. Li Ling used the unary linear regression model to conclude that the level of informatization development plays an indelible role in promoting the regional economy. It can be seen that domestic scholars pay more attention to the influence of informatization on economic growth and affirm the important role of informatization in economic and social development [20–25].

Zhang Yue et al. established an evaluation model of the influence of the Internet development level on economic development by collecting relevant data on the economic and Internet development level of various regions. Tang Lingchao and others combined with time series data on economic growth, conducted an empirical analysis of the relationship between Internet development and GDP. More scholars pay attention to the macro analysis of Internet development and economic growth, while there is less research on the relationship between the two specific indicators. This paper uses the gray correlation model to quantitatively study the degree of correlation between the Internet development level and economic growth indicators, and calculate and analyze the degree of correlation between the indicators.

In 1982, Professor Deng Julong founded the Gray Theory, a new type of decision-making discipline and divided the system into three categories: white, black, and gray. The white system represents a system with completely transparent information, the black system is a system with completely unknown information, and the gray system is in the middle zone. Gray system theory is precisely through detailed analysis and in-depth mining of “known information” to predict and estimate “unknown information.” It uses a small sample of “limited” and “poor information” to eliminate uncertainty.

On the contrary, the smaller the degree of relevance. The gray correlation degree is essentially a quantitative analysis method to describe and compare the dynamic development of the system [26–29]. The neural networks are shown in Figure 2. Cross-border e-commerce is achieved by buyers and sellers located in different zones, through the Internet information platform to achieve the information exchange and transaction of various goods or services, and through the provision of offline logistics services.

[figure omitted; refer to PDF]

The development of cross-border e-commerce requires necessary regulation, support, and training. Xu Mingxia and others believe that the factors that determine the development of trade include not only the hardware environment such as geographic location, natural resources, transportation routes, and infrastructure, but also the software environment such as market maturity, industrial development, institutional advantages, and policy dividends. Zhou Fang believes that industrial input and output, technological innovation, technological environment, development policies, support, and incubation have a greater impact. Xu Jinbo has constructed a cross-border e-commerce development evaluation system that includes five dimensions: network marketing, international logistics, electronic payment, electronic customs clearance, and government policies. Yang Jianzheng and others believe that online marketing, international payments, cross-border e-commerce logistics, cargo clearance, legal protection, and so on will have a huge impact on cross-border e-commerce.

Hao Binkai summarized the four major factors, including production factors, demand conditions, and corporate behavior. Chen Nan conducted research based on the perspective of complex networks and found that government support policies can increase the enthusiasm of enterprises to participate in cross-border e-commerce, but it will show short-term characteristics. Brand managers should focus on making products traceable, empower consumers, and strengthen coordination with local authorities to control counterfeiting. Zhang Xiaodong took e-commerce logistics, economic development, social consumption, talent environment, and technological innovation. The degree of coupling and coordination of the system.

Cross-border e-commerce is a complex industry that drives a wide range of industries, involves many industries, and has many regions and countries. It is related to all levels of economic development. First, the most important influence system for the development of cross-border e-commerce is the foundation of e-commerce, including trade, e-commerce platforms, and logistics and distribution; among them, the trade dimension includes trade basic factors and trade radiation factors, and the e-commerce dimension includes e-commerce development factors, e-commerce scale factor and e-commerce penetration factor. The logistics dimension includes logistics income factor and logistics business volume factor. Second, the development of export cross-border e-commerce needs the support of the local industrial economy. A large number of cross-border e-commerce industries can only provide products suitable for the international market and can highlight the scale effect and improve industrial efficiency; economic dimensions include economic scale factors and economic structure factor. The third is consumption power, which is directly related to the development of imported cross-border e-commerce. Only with certain consumption power can consumers have the economic basis for choosing and consuming high-quality products from all over the world; consumption dimensions include consumption income factors and consumption expenditures factor. The fourth is the driving force for the sustainable development of cross-border e-commerce, including talents and innovation. These two factors are ultimately the core competitiveness of cross-border e-commerce to enter the international market; the talent dimension includes talent training factors and talent attraction factors. The innovation dimension includes innovation input factor, input intensity factor, and input effect factor. The constructed cross-border e-commerce development impact factor model is shown in Figure 4. Combining existing research and the specific development of China’s cross-border e-commerce industry, construct a cross-border e-commerce development impact factor analysis model.

[figure omitted; refer to PDF]

Based on the gray correlation analysis model, the calculated absolute correlation, relative correlation, and comprehensive correlation. Sorted by absolute gray correlation, the top four are innovation input, logistics revenue, economic scale, and e-commerce scale, indicating that these four factors are closely related to the absolute increase in cross-border e-commerce transaction volume. According to the gray relative degree, the top four are the talent attraction of logistics business volume, logistics income, and e-commerce scale, indicating that these four factors match the absolute growth rate of cross-border e-commerce transaction volume. From a comprehensive perspective, logistics revenue, innovation investment, logistics business volume, e-commerce scale, and cross-border e-commerce development are the closest, as shown in Figure 7.

[figure omitted; refer to PDF]

The number of year-end mobile phones X2, the number of domain names X3, the number of URLs X4, the number of Webpages X5, the mobile phone penetration rate X6, the Internet penetration rate X7, and the telephone penetration rate X8. The economic growth indicators are foreign direct investment Y1, the number of patent applications Y2, the total fixed assets Y3, the total entry and exit of foreign-invested enterprises Y4, and GDP Y5. Empirical analysis has been done.

According to the aforementioned analysis steps of the gray correlation model, the gray correlation degree between the Internet development level and economic growth indicators is calculated. According to the calculation results, it can be seen that (1) from the perspective of the development level of the Internet, the correlation between the number of Internet users (X1) and economic growth indicators in descending order is GDP (Y5) 0.894, total fixed assets (Y3) 0.823, the number of patent applications (Y2) 0.777, foreign direct investment (Y1) 0.728, and total foreign investment enterprises (Y4) 0.726. Similarly, the correlation between X2 ∼ X8 and economic growth indicators can be obtained. Among them, the number of mobile phones at the end of the year (X2), the number of domain names (X3), the number of URLs (X4), the number of Webpages (X5), the mobile phone penetration rate (X6), the Internet penetration rate (X7), and the telephone penetration rate (X8) are important to the economy. The maximum correlation degree of the growth indicators corresponds to GDP (Y5) 0.887, the number of patent applications (Y2) 0.855, foreign direct investment (Y1) 0.865, foreign direct investment (Y1) 0.880, and total fixed assets (Y3) 0.618, the number of patent applications (Y2) 0.687, and the total in and out of foreign-invested enterprises (Y4) 0.760. The average correlation between the number of Internet users (X1), the number of domain names (X3), and the number of URLs (X4), and the five economic growth indicators are 0.790, 0.792, and 0.794, respectively, which is a relatively high correlation among the eight indicators. For the three notable ones, it can be considered that the Internet infrastructure and popularity have the closest relationship with economic growth. The weights are compared in Figure 8.

[figure omitted; refer to PDF]

(2) From the perspective of economic growth, the correlation between foreign direct investment (Y1) and the eight indicators of the Internet development level, in descending order, is the number of Webpages (X5) 0.880, the number of URLs (X4) 0.865, and the number of domain names (X3) 0.843, telephone penetration rate (X8) 0.759, number of people online (X1) 0.728, year-end number of mobile phones (X2) 0.725, Internet penetration rate (X7) 0.680, and mobile phone penetration rate (X6). Foreign direct investment has the largest correlation with the number of Webpages, and the smallest correlation with mobile phone penetration. The number of patent applications (Y2), total fixed assets (Y3), total import and export volume of foreign-invested enterprises (Y4), and GDP (Y5) correspond to the number of domain names (X3) 0.855 and the number of Internet users (X1) 0.823, the number of Webpages (X5) 0.879, the number of people surfing the Internet (X1) 0.894, the minimum relevance index is the mobile phone penetration rate (X6), and the relevance values are 0.479, 0.618, and 0, respectively. From the two average correlation values being 0.743, 0.745, and 0.743, it can be considered that foreign investment and patent applications have the closest relationship with the development level of the Internet. The value versus time is shown in Figures 9 and 10.

[figure omitted; refer to PDF][figure omitted; refer to PDF]

5. Conclusion

Selecting the annual data of 31 provinces in China in 2015, empirical analysis of the degree of correlation between the level of Internet development and economic growth indicators. According to the empirical results, the following conclusions can be drawn: foreign direct investment has the greatest correlation with the number of websites and Webpages, and the smallest correlation with the penetration rate of mobile phones and the Internet.

(1) The number of patent applications has the largest correlation with the number of domain names and URLs, and the smallest correlation with the penetration rate of mobile phones and the Internet. The total fixed assets have the greatest correlation with the number of Internet users and the number of mobile phones at the end of the year, and the smallest correlation with the number of Webpages and the penetration rate of mobile phones.

(2) The total volume has the largest correlation with the number of websites and Webpages, and the smallest correlation with the penetration rate of mobile phones and the Internet. GDP has the largest correlation with the number of people surfing the Internet and the number of mobile phones at the end of the year, and the smallest correlation with the number of Webpages and the penetration rate of mobile phones.

(3) Internet infrastructure and popularity have the closest relationship with economic growth, and the relationship between foreign investment and patent applications and the level of Internet development is the most significant. Cross-border e-commerce supply chain greatly affects consumer experience and corporate core capabilities.

(4) A good supply chain can not only reduce operating costs but also deliver products as quickly as possible. Speed reaches the hands of consumers, so it is necessary to vigorously connect domestic and foreign logistics, unblock international logistics channels, and vigorously develop overseas warehouse business; second, enhance the R & D and design innovation capabilities of cross-border e-commerce companies and conduct in-depth research and analysis of market environment and consumer habits, and so on.

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