research articles on e commerce

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How E-Commerce Fits into Retail’s Post-Pandemic Future

• Kathy Gramling,
• Jeff Orschell,
• Joshua Chernoff

New data from Ernst & Young suggests it will be an important part of the consumer experience — but not everything.

The pandemic has changed consumer behavior in big and small ways — and retailers are responding in kind. Since the early days of the pandemic Ernst & Young has been tracking these shifting trends using the EY Future Consumer Index and EY embryonic platform, which show a significant and widespread industry shift toward e-commerce. In this article, the authors suggest that while e-commerce will continue to be an essential element of retail strategy, the future success of retailers will ultimately depend on creating a cohesive customer experience, both online and in stores.

If we have learned one thing from the past year, it’s that things can change in an instant — changes we thought we had years to prepare for, behaviors we assumed we’d stick to forever, expectations we have of ourselves and our organizations. This is true of the way we live, the way we work, and the way we shop and buy as consumers.

• KG Kathy Gramling is EY Americas Consumer Industry Markets Leader
• JO Jeff Orschell is EY Americas Consumer Retail Leader
• JC Joshua Chernoff is EY-Parthenon Americas Managing Director

Partner Center

ORIGINAL RESEARCH article

Research on e-commerce data standard system in the era of digital economy from the perspective of organizational psychology.

• Henan University School of Law/Intellectual Property School, Institute of Civil and Commercial Law of Henan University, Kaifeng, China

With the rapid development of technology and the economy, the expansion of the network has had a huge impact on the rapid expansion of the industrial agglomeration e-commerce industry, as well as ensuring the shopping experience of consumers. The rapid expansion of industrial cluster e-commerce has avoided precisely the limitations of logistical bottlenecks. Current networks and modern information technologies can provide good support and maintain a huge growth potential. In addition, digital technologies such as multimedia are becoming increasingly important in industry cluster marketing, and the concept of industry cluster e-commerce models is gaining more and more attention from companies. However, virtual e-commerce systems under industrial clusters have not been well researched in the existing studies. In this paper, through extensive research, literature reading and website browsing statistics, the virtual e-commerce models of different industrial agglomerations are studied. Firstly, the concept of big data and the processing of big data are given. Secondly, the concept of industrial agglomeration and the relationship between industrial agglomeration and e-commerce are analyzed. The basic number of domestic Internet users in the last 10 years is also counted, proving that the expansion of the Internet has led to a substantial growth of Internet users in the country and that e-commerce plays a significant role in the future of business activities. Finally the study concludes that different e-commerce models have different performance and roles in industrial agglomeration e-commerce and cannot be generalized. Instead, it is not good and can only develop different industrial agglomeration e-commerce models according to different environments.

Introduction

In the long history of mankind, when people explore and discover the law of unknowns, they rely mainly on reasoning methods such as experience, theory, and assumptions, which are largely influenced by personal prejudice. Later, people invented mathematical tools such as statistics, sampling, and probability. Through careful design and extraction methods, a small number of data samples were obtained to infer the whole picture of things. Therefore, there are often deviations and distortions in understanding things. According to Victor Meyer, thanks to advances in technology, people can access all the data of a research object and understand things from different angles. Analyze the different dimensions of all data from an incomprehensible perspective. With the rapid expansion of electronic signal technology, e-commerce ( Anam et al., 2017 ; Irene, 2018 ) has changed an inevitable outcome of the expansion of the times and is also a form of transaction that adapts to market demand. The expansion of e-commerce is very gratifying. After more than 10 years of expansion, B2C ( Gui et al., 2019 ) and C2C ( Navarro-Méndez et al., 2017 ) have become the main mode of e-commerce in China. The model has the vitality of information transparency, flexible trading, high efficiency and price advantage. With the rapid propagate of the Net, by the end of 2018, the number of Internet users in China reached 1.08 billion. A great deal of Internet users has established a good customer base for the expansion of e-commerce. In addition, the continuous improvement of relevant laws and regulations and the maturity of information technology have laid the foundation for the expansion of e-commerce. By combining big data with e-commerce, e-commerce based on big data will become the main research direction of the future society ( Nik et al., 2017 ).

Mega data (big data) ( Wang et al., 2017 ; Zhou et al., 2017 ) is what we often call big data, also known as massive data. Giant data is actually a data repository. In this era, it can be used as an asset. After professional analysis, the efficiency is higher, the amount of data is larger, the data is diverse, and the sources are different, most of which are instantaneous. The communication information generated during the sales process is also generated instantaneously. For example, customer basic data, website clicks, network data, etc., are all counted in big data, some are part of customer information, and some are not counted. In the 1980s, some scholars predicted big data and believed that big data will surely ignite the new wave of the third technological revolution. Since 2009, “Big Data” has made great progress with the rapid expansion of e-commerce and cloud computing ( Liu et al., 2018 ) and is gradually becoming well known to the public. As can be seen from the latest data, the growth of data on the Internet and mobile Internet has gradually approached Moore’s Law, and global data and information have been created “over doubling every 18 months” over the years. The application of big data in industrial agglomeration ( Xuan, 2017 ; Nádudvari et al., 2018 ) e-commerce is also getting more and more wide-ranging.

Industrial clusters ( Cao et al., 2017 ; Wang and Yu, 2017 ) have a long history as well-functioning organizations. At the end of the 19th century, Marshall creatively defined the concept of “industry zone,” that is, industrial clusters. He defines “industrial zone” as the agglomeration of certain industrial zones, which is determined by two factors: history and natural resources. There are many companies of different sizes in the area. There is a close relationship between cooperation and competition, which gradually affects the integration of industrial clusters and society. According to Marshall, the reason for the emergence of “industry zones” in the region is a combination of inside and outside factors. Later, Weber believed that the phenomenon of industrial clusters was the result of regional and geographic influences. Companies with regional and geographic advantages have established close partnerships through partnerships with other related companies. Establish complex and close internal network relationships, achieve the aggregation of enterprises in a specific region, and then develop into industrial clusters. In recent decades, academia and industry have been highly involved in the expansion of synergies between industrial clusters and supply chains. They actively used industrial clusters and supply chains in corporate management ( Heiner and Marc, 2018 ) and achieved remarkable results. Clusters and supply chains can provide a competitive advantage for businesses. However, with the rapid expansion of e-commerce, industrial clusters are faced with the dilemma of optimizing transformation and upgrading. The traditional approach to supply chain management is far from meeting the needs of users. Therefore, it is a major problem to study how e-commerce uses the first-mover advantage to promote synergy between industrial clusters and supply chains.

For the core enterprises in the industrial agglomeration, because of their own advantages in terms of capital and technology, as well as a number of strong manufacturers and suppliers, so that the online market established by the enterprise has a large number of members and good prospects for development, and attracts some new members to join, once the establishment of close cooperation in this online market, its members want to move to other online market will be very expensive, so that the core enterprises in the online market to consolidate their existing position ( Yang et al., 2022 ; Han et al., 2021 ; Setiawan et al., 2022 ; Suska, 2022 ; Yu et al., 2022 ). Therefore, e-commerce has developed into a new opportunity to enhance the synergy of China’s supply chain and enhance its competitive advantage. In the end, this paper starts from the business reality of big data-based industry agglomeration e-commerce, fully considers the dependence of industrial agglomeration area on e-commerce in the era of big data, and studies the relationship between the concept of industrial agglomeration and the relationship between e-commerce and industrial agglomeration. Therefore, with the support of big data, this paper analyses the number of netizens, the level of economic expansion, etc., and compares the impact of e-commerce yield and industrial agglomeration e-commerce investment and big data and e-commerce on industrial agglomeration. The merits and demerits of e-commerce in the type of industrial agglomeration, and the expectation is to provide a summary and reference for the industry to gather e-commerce enterprises to obtain competitive advantages in the market competition.

Big Data and E-Commerce Related Definitions

Big data overview.

With the popularity of the Internet and the rapid expansion of information technology, the signal age is making a subtle transition to the big data era. The network has turned into an integral part of people’s production and life. While enjoying the convenience brought by the information network, people also continuously feedback and input information to the network. Some information involves individual privacy, and network information security has become one of the hot topics of research. At present, the social network information security problem is becoming more and more obvious, the conventional information security software has been unable to deal with the endless information security problem, the network society urgently needs a new information technology to protect the increasingly huge information assets, and the big data technology has stronger insight, more scientific decision-making power and more accurate process optimization ability compared with the conventional software. Must be able to play a positive effect.

Professor Victor is known as the “Big Data Prophet.” Big data also called huge amount of data, refers to the amount of data involved is so large that it cannot be captured, managed, processed and collated in a reasonable time through the human brain or even mainstream software tools to help enterprises to make more positive decisions. By analyzing big data, we can draw conclusions that cannot be obtained in the case of small data. The big data we usually talk about is more about getting valuable information in a short time by quickly analyzing a large amount of data.

Big Data Analysis Process and Features

In general, there are many methods for analyzing big data, and in theory it is still in the exploration stage, but no matter what kind of big data analysis method follows the basic process, the flow chart is shown in Figure 1 .

Figure 1. Big data processing flow.

The first process of big data analysis is acquisition. Big data sampling ( Bivand and Krivoruchko, 2018 ; Cohen et al., 2018 ) means collect information collection platforms to collect users or other data. In the process of big data collection, the main problem is that the amount of data is huge, the amount of collection is large, and the data collection point is large. A large amount of data needs to be collected at the same time. Therefore, in the process of collecting big data, it is necessary to establish a larger database and how to further design the reasonable use and distribution of the database.

The second step is import and beneficiate. This mainly means that invalid information, redundant information and low-value information are excluded after the first information collection is completed, so it is necessary to execute the data before processing. Effective screening and brief analysis, and then import the resulting preliminary filtering information into another large database, this step is mainly to pre-process the big data.

The third step in big data processing is to perform statistics and analysis. This process is a process of further refinement of big data, analyzing and screening valid data, and performing statistical processing to obtain effective information.

The fourth step in big data is to deal with the mining ( Rezaei-Hachesu et al., 2017 ; Fan et al., 2018 ; Svefors et al., 2019 ) process. Unlike the above process, there is no clear path or statistical analysis method for big data information mining. It is mainly used for databases that collect large amounts of data and use various algorithms for calculations, so it is complex data. Try to get predictions or get other valid conclusions. The statistical analysis and mining process of big data is considered to be a key process for transforming data from data into value space and value sources in the process of big data information processing.

The final step is using information obtained from big data. In particular, it can be used for business decision behavior predictions, while sales companies can provide accuracy. Marketing, achieving service conversion, etc. The application prospect of big data is very broad, and it has a good application prospect in transportation, sales management, economic research and forecasting.

At present, there is no authoritative unified standard. At present, the “4V” function of big data has been widely recognized.

First, the data size is huge. In 2012, the world produced about 2.7 billion GB of data per day, the amount of data per day equals the sum of all stored data in the world before 2000. Baidu must process more than 70,000 GB of search data per minute, and Alipay generates an average of 73,000 transactions per minute. Traffic flow monitoring systems and video capture systems can generate large amounts of video data at any time. Temperature sensors in greenhouses and various detectors in the factory are also big data manufacturers. It can be said that the amount of data we generate per minute is unimaginable. Now, the scale of data that big data needs to process continues to grow, reaching orders of magnitude unimaginable in small data.

Second, there is a wide variety of data (Variety). In big data, in addition to the ever-increasing data size, the types of data that people need to deal with are beginning to emerge. The various data types are very numerous and very strange, and only a few can be handled using traditional techniques. Some are unstructured data that traditional technologies cannot handle, and this trend will be long-term, with unstructured data accounting for 90% of all data over the next decade. For example, Tudou’s video library, photos on social networking sites, records, etc., even include RFID status, mobile operator call history, video surveillance video, Weibo and status posted on WeChat. The size, format, and type of data from various sources may vary. Existing data processing techniques are useless and can cause significant difficulties when performing large amounts of processing.

Third, value is difficult to mine. The first two features show that the amount of data and data types in big data are amazing. Faced with a large amount of data, in order to mine hidden “treasures,” the analysis and processing of powerful cloud computing systems is only one aspect, not even the main one. How to analyze big data from the perspective of innovation according to needs, what to use big data ideas to examine big data to explore unimaginable economic and social values. In other words, only the combination of technology and innovation can unlock the value of big data. Otherwise, no amount of data will be useful.

Fourth, the processing speed is high (Velocity). This is the most significant feature of the big data era, unlike the era of small data and the era of probability and statistics. In traditional economic censuses, censuses and other areas, data can be tolerated for days or even a year, as the data obtained at this time still makes sense. Moreover, due to technical limitations, the collected data has been lagging behind, and the structure of statistical analysis is lagging behind, but it must be accepted. Data generation and collection is very fast, and the amount of data is growing all the time. With advanced technology, people can collect data in real time. But in most cases, if you don’t process the data in time, the advanced collection and sorting methods will be meaningless and you won’t need big data. For example, IBM proposed the concept of “big data-level stream computing,” which is designed for real-time analysis of data and results to increase practical value. Therefore, timely and fast processing of data and results is the most important feature of big data.

This is the most significant feature of the big data, unlike the era of small data and the era of probability and statistics. Due to technical limitations, the collected data is backward, and the structure of statistical analysis is also backward, but it must be accepted. Data generation and collection is very fast, and the amount of data has been growing. With advanced technology, people can collect data in real time. But in most cases, if you don’t process the data in time, the advanced collection and sorting methods will be meaningless. For example, IBM proposed the concept of “big data-level stream computing,” which aims to analyze data and results in real time to increase practical value. Therefore, timely and fast processing of data and results is the most significant feature of big data.

E-Commerce Concept

E-commerce generally refers to Internet technology, based on browser/server applications, through the Internet platform, buyers and sellers through various trade activities to achieve consumer online shopping, online payment and new business activities of various business activities and other models. The expansion history of e-commerce has a close relationship with the progress of computer network technology. E-commerce includes many models, such as B2B ( Ning et al., 2018 ) (Business to Business), B2C (Business to Consumer), C2C (Consumer to Consumer), and O2O (Online to Offline). The main centralized e-commerce model is shown in Figure 2 .

Figure 2. Main e-commerce model.

This article focuses on C2C ( Sukrat and Papasratorn, 2018 ) e-commerce. C2C e-commerce refers to a network service provider that uses computer and network technology to provide e-commerce platforms and transaction processes to users in a paid or non-paid manner. Allow both parties to conduct online transactions on their platform. The two sides of the transaction are mainly individual users, and the trading method is based on bidding and bargaining. Like B2B and B2C, C2C is also a basic e-commerce transaction model. In real life, it is similar to the “small commodity wholesale market.” There are many self-employed people in a website, and the website’s role in e-commerce is equivalent to the “market manager” in actual market transactions. At the same time, in order to promote smooth transactions between buyers and sellers, C2C e-commerce provides a series of support services for both parties. For example, in cooperation with market information collection, credit evaluation systems and various payment methods have been established. Due to the rapid expansion of e-commerce, industrial agglomeration has become more impressive. The most prominent performance of industrial agglomeration is the industrial concentration of “Internet + traditional industries” such as “Taobao Village.” C2C is the mainstream of this e-commerce business model. C2C e-commerce “Taobao Village” is a product based on urban and rural expansion in China. It has Chinese characteristics and is a “Chinese product.” This is both a theoretical issue and a very real social phenomenon. The Chinese government has put forward the “Internet+” proposal. With the expansion of China’s strategic emerging industries, “Internet + traditional industries” will become a shortcut for China’s backward regions to seek expansion, which can shorten the time required for expansion, making C2C e-commerce a “hometown of Taobao.” Therefore, in order for the industry to complete transactions, an e-commerce platform and online and offline resources and services are needed. It can be said that the C2C model is an e-commerce model that is very suitable for industrial agglomeration. The biggest advantage of the C2C e-commerce model is that it can produce and deliver enterprise products or services on demand, so that enterprises can quickly develop into large enterprises, and the C2C e-commerce model provides consumers with cheap and affordable purchases. Product and service platforms enable businesses and consumers to achieve a win-win situation.

In traditional market transactions, the delivery of goods from producers to stores requires warehouse storage, vehicle transportation, etc., which increases inventory costs and transportation costs, resulting in increased transaction costs. Unlike real-world trading, since e-commerce joins the virtual network, both buyers and sellers trade through the e-commerce platform, so there is no need for face-to-face communication. This form saves the seller’s transaction costs, including physical store and merchandise inventory and transportation costs. At the same time, buyers can also shop without going out, and can quickly compare products of different merchants through the network, which allows buyers to get more information, more efficient and lower cost. C2C e-commerce uses Internet communication channels based on open standards. Compared with traditional communication methods (such as mail, fax, newspaper, radio, and television), communication costs are greatly reduced.

Industry Agglomeration Virtual E-Commerce

Industrial cluster concept.

Industrial clusters attract the attention of many scholars by attracting resources, economies of scale, knowledge learning and innovation, saving transaction costs, and improving cooperation efficiency. Many mathematicians have studied the composition, characteristic mechanism, and identification criteria of industrial clusters through theoretical derivation, model construction, structural equations, and case studies, and elaborated and summarized the concept of industrial clusters. The definition of industrial agglomeration is that in a relatively limited space of a certain area, geographically adjacent or different geographical entities closely related to relevant institutions and government agencies spontaneously gather together, called industrial clusters. The division of labor between entities and continuous cooperation and innovation have formed a complex cluster network, providing environmental and technical support. The difference is that industrial clusters can adapt to economic expansion, and further transformation and upgrading will form a new industrial cluster model. At the same time, mutual trust, mutual decision-making, and close cooperation have created the greatest value and benefits for the industry. Finally, for the measurement and acquisition of industrial clusters, combined with the practical significance of empirical research, the measurement of industrial clusters is unified by the concentration of specific industries, that is, specific industries. A measure of the spontaneous aggregation of related entities or institutions in a particular industry in the region. If the total quantity or total capacity reaches the previous unified level, it indicates that there is an industrial cluster in the area.

The Relationship Between E-Commerce and Industrial Clusters

With the rise and prosperity of e-commerce, the new business organization system breaks the regional and spatial barriers, promotes the use of e-commerce and partners, establishes synergy and sharing mechanisms, and continuously meets the needs of users. Proactively improve user experience and satisfaction. In addition, e-commerce platforms and logistics platforms are increasingly used in new business models. Although these platforms are very different, the role of the company cannot be underestimated. The platform typically includes several key functional modules such as trading markets, logistics platforms, enterprise services, cluster information, and corporate communities. Cluster companies can conduct informal technology and information exchange on the platform. Through the construction of an e-commerce platform, industrial cluster enterprises can share market conditions, the latest industry technologies, and related industry information in real-time and quickly, creating greater economic benefits for enterprises. This close partnership helps industry clusters increase trust and mutual benefit. In short, e-commerce applications can help industrial clusters effectively integrate regional resources, meet market demands promptly, expand clusters, and increase the level of collaboration and competitiveness of enterprises within the cluster. Currently, the introduction of e-commerce applications has further promoted the expansion of supply chain coordination. As an effective spatial organization model, industrial clusters play an increasingly important role in improving the overall economic level of the region and optimizing the allocation of industrial resources. The rapid expansion of industrial clusters provides natural conditions for the expansion of enterprises, between enterprises and between supply chain members. Similar companies continue to gather, and upstream and downstream companies in the supply chain are also gathered to promote the use of e-commerce. A deeper impact on the synergy of the supply chain. Therefore, for the sake of strengthening the application of e-commerce. Based on continuous research by many scholars, it is further proved that the rapid expansion of industrial clusters promotes the coordinated management of supply chains.

Since 1980, the economy and the world have continued to develop. The Internet and information technology are constantly innovating. In addition to constantly affecting people’s daily lives in various aspects, it also leads the transformation of modern new production organizations. Figure 3 shows the statistics of Chinese netizens in the past decade. As can be seen from the above data, since the popularity of smartphones in 2013, mobile network users have occupied almost the entire network in the past 7 years. In the future expansion, mobile network users will develop more rapidly, making the popularity of mobile Internet and smart phones break the expansion of PC networks. At anytime, anywhere, and on the Internet, the online concept of the PC era has been broken, and immediacy has become a unique personality in the age of network information. A large amount of information, rapid response and scale effect are the main features of the e-commerce. The rapid spread of mobile phone business applications shows that the use of mobile phone networks by netizens has changed from basic communication entertainment to life entertainment. Since 2013, thanks to the expansion of domestic smart phone technology, the Internet access method based on mobile Internet has opened a new period of e-commerce and access to the Internet anytime and anywhere, so that more buyers and sellers can conduct transactions through the network, and Each transaction is based on online trading of various trading tools, and the trading platform and trading model have been rapidly developed. E-commerce has become a grassland, which has an impact on the production value chain, profit model and marketing methods of traditional industries. It can be seen that the growth of the network has boosted the expansion of e-commerce. The growth of e-commerce has promoted industrial agglomeration, and industrial agglomeration has formed economic globalization.

Figure 3. Number and proportion of mobile phone users.

Industrial Agglomeration Virtual E-Commerce Analysis

China’s e-commerce transaction scale.

In the e-commerce environment, China’s e-commerce has undergone earth-shaking changes, especially in the past 30 years, the rapid growth of signal technology and technological innovation have made all aspects related to e-commerce stand out. The cost of online transactions has been greatly reduced, network communication is extremely convenient, and e-commerce is everywhere. On the basis of China’s national conditions, the application and expansion of e-commerce in China is different from that of other countries, but its expansion is in full swing. The expansion of China’s e-commerce is a signification part of accelerating the informationization of the national economy. At the same time, the application of e-commerce has also changed the production organization of enterprises to a large extent. Enterprises and users can interact directly with e-commerce related R&D, technology expansion, production, procurement, marketing and product operations. Other services and links can fully introduce user engagement and control market demand trends in real time. Table 1 shows the scope of China’s e-commerce transactions collected from the China E-Commerce Research Center.

Table 1. Scope of China’s e-commerce transactions.

Figure 4 shows the scope of China’s e-commerce market transactions from 2013 to 2017. It can be seen that as of 2018, China’s e-commerce still maintains a rapid growth trend. With the continuous encouragement and support of the government, all relevant systems are in a stage of continuous improvement. Under the impetus of e-commerce, enterprises and users, constantly proposing new consumer demand will help the rapid expansion of the upstream and downstream industry chains of traditional enterprises and provide new impetus for China’s economic expansion.

Figure 4. Trends in the scale of China’s e-commerce transactions.

It can be seen from Figure 5 that from 2001 to 2008, industrial agglomeration e-commerce investment and fixed asset investment are all levels of sustained growth, which proves that e-commerce expansion is relatively rapid during this period. In the future, industrial agglomeration investment profits can be Add a lot. From 2008 to 2015, the level of China’s economy was in a period of slow growth, and the investment level during this period was almost stable. After 2015, due to the saturation of the economy, the investment level remained at a certain level and the economic expansion region was stable.

Figure 5. Changes in national fixed asset investment and industrial agglomeration e-commerce investment from 2001 to 2018.

The Impact of the Level of Big Data Expansion on E-Commerce in Industrial Agglomeration

With the increasing popularity and expansion of the Internet, e-commerce has become an important aspect of Internet applications. In addition to the old e-commerce companies, traditional stores also opened their own online shopping malls. Consumers are also increasingly enjoying this convenient and fast way to shop. According to CNNIC’s statistical report, as of last year, the number of Internet users in China has reached 1.008 billion, and the proportion of online shopping among netizens has increased to 55.7%. In addition to online shopping, many service industries or national administrative departments have also increased the construction of online platforms, such as online car rental, travel route booking, room service, online transaction management fees, etc., further expanding the application field. E-commerce has created more business growth points. The expansion of business types and the explosive growth of business volume have brought a lot of data information. The old e-commerce companies Amazon, Alibaba and so on are all beneficiaries of big data. It can be said that without the support of big data technology, there is no e-commerce enterprise today.

The expansion of big data makes practitioners more competitive in e-commerce. From the perspective of the number of competitors, China’s e-commerce industry is currently in a highly concentrated stage. Although a large number of e-commerce companies have emerged, in the field of online retail, Taobao, Tmall, Jingdong, No. 1 store, Amazon and many others occupy most of the market. The emergence of big data has further increased barriers to entry, so the number of competitors in the online retail industry will change less. From the perspective of foreign competitors, it will undoubtedly increase the intensity of market competition. For example, the way Amazon enters the Chinese market is to acquire Joyo. From the perspective of switching costs, the e-commerce industry has typical low-cost conversion characteristics for consumers. On-site e-commerce companies often use large subsidies, promotions and free shipping to retain old users and win new users, which makes the market competitive. The pressure is constantly increasing. Pursuit of economies of scale. Most industries have significant economies of scale. E-commerce operators are pursuing economies of scale and blindly expanding, resulting in overcapacity, which ultimately led to fierce competition in the industry.

Figure 6 shows the expansion index for big data and e-commerce. As can be seen from the above data, in the future expansion process, big data is indispensable as a tool to support e-commerce and industrial agglomeration, and e-commerce is expanding very rapidly. The expansion of industrial agglomeration plays a very significant role. In the future, the expansion of e-commerce in all walks of life cannot be ignored. The future world is the electronic world and the data world. As an effective management mode of enterprise manufacturing and industrial organization, industrial cluster and supply chain management have become the inevitable requirements and strategic measures for enterprises to survive and develop in various fields. The coupled industrial cluster supply chain provides a new expansion trend for resource coordination and industrial upgrading, enabling cluster enterprises to improve traditional production methods, respond quickly to user needs, and consciously work closely together to grasp rapid changes more quickly and accurately. In order to deal with this problem, it is necessary to improve the operational efficiency of the enterprise through the information charge platform and modern management tools. Through the information platform, this work-use management becomes more complex, professional and standardized, thus freeing up enough energy to respond to industry changes.

Figure 6. Impact of big data and e-commerce on industrial agglomeration expansion.

In industrial agglomeration, the pioneering role of core enterprises should not be overlooked. If the pioneering enterprises can be cultivated effectively, through it to other enterprises and supporting enterprises to enter the industry to play a direct demonstration and produce cohesion, so that the formation of industrial agglomeration has a driving effect. At present, some of the core enterprises in the industry have already established a relatively complete e-commerce system. If we can combine the needs of SMEs in the industry, open up some of the functions of the system to a certain extent, and realize the sharing of information and knowledge with enterprises in the industry, this is very beneficial to enhancing the enthusiasm of SMEs to participate in industrial division of labor and cooperation, and at the same time lowering the this is very beneficial to increase the enthusiasm of SMEs to participate in industrial division of labor and cooperation, and at the same time reduces the threshold for SMEs to participate in e-commerce. A well-developed social network based on marketization or externalization is the basis for the formation and development of industrial clusters. To this end, the construction of information service organizations and networks within industrial clusters should be supported and encouraged to provide a variety of information services to enterprises, reducing the wasted costs and incomplete information caused by enterprises collecting information alone. At the same time, the construction of public institutions and means of communication that facilitate interaction between producers and the market should be strengthened, cooperation between enterprises and universities or research institutes should be encouraged, and the establishment of local public institutions that provide technical training, technical support and market information to producers should be supported. In addition, the construction of information advisory services should be accelerated and a multi-level public information platform should be established. In this regard, government departments or professional information service providers can intervene to provide a full range of information service approaches and dovetail with government public data platforms to achieve low-cost information services and knowledge provision within the industry.

Analysis of Advantages and Disadvantages of Different Business Models in Industrial Agglomeration

As shown in Figure 7 , for the industrial agglomeration of the B2C e-commerce model, all goods and services of the enterprise are carried out through the network, including online shopping, online payment, logistics and after-sales. They are all done over the Internet and won’t be traded face to face. This model puts forward higher requirements for industrial agglomeration enterprises. Compared with the C2C and O2O models, the selection of the B2C e-commerce model requires that the industrial agglomeration area has a good organizational management level and complete information construction, because all activities are carried out online. Among the three e-commerce models, the B2C model has the highest information security requirements and requires more financial support and sufficient strength to ensure smooth transactions. For the C2C model, the needs of enterprises are much lower than those of B2C. For industries with insufficient funds, low level of enterprise informatization and low management level, C2C e-commerce model can be selected. The industrial cluster area builds an e-commerce trading platform through website construction. Consumers can find the trading objects and negotiate the transaction through the platform. Industrial agglomeration enterprises only need to optimize platform management, maintain transaction order, formulate transaction specifications, and improve trust mechanisms. Therefore, the C2C e-commerce model has lower requirements for the company’s capital, information and management level than the B2C model. For the O2O model, the network becomes the platform for offline transactions. For industrial clusters, the function of the C2C e-commerce model is to undertake the browsing work of consumers, let consumers understand the information through the platform, and then conduct transactions online. Therefore, it is necessary to reduce the investment cost of the C2C e-commerce model, and its management level and informatization level are lower than the B2C and O2O e-commerce models. Most industrial clusters can conduct business activities through the C2C platform.

Figure 7. Advantages and disadvantages of different e-commerce in industrial agglomeration.

As the rising of Internet industry and other technologies, on the basis of the rapid expansion of e-commerce, the coordination problem of e-commerce has gradually emerged, affecting the organizational environment. At present, the research related to e-commerce and supply chain collaboration is getting more and more attention. As a new impetus for economic expansion, e-commerce has brought new impetus to the supply chain. In the process of supply chain coordination, e-commerce means making the required information more convenient and accurate, thus further enhancing the trust of the supply chain enterprises and the internal and external trust, and bringing economic benefits, the company has further expanded. In this paper, through the different applications of virtual e-commerce in industrial agglomeration, different e-commerce types highlight different characteristics in big data. Therefore, this paper analyses industrial agglomeration and electronics through literature comparison and data survey. The relationship between business, and through the investigation, we can see that the industrial agglomeration investment has been continuously expanded with the expansion of e-commerce and big data, which also proves that the future expansion of e-commerce is promising. Finally, the application of three different e-commerce models in industrial agglomeration is compared. The results show that different e-commerce models are determined by their own different, so we must choose the correct e-commerce model to adapt to the expansion of society through the actual situation.

Industrial agglomeration is an important way to enhance regional economic development, while e-commerce promotes the integration of enterprises into the world market. The author intends to analyse the problems of enterprise e-commerce in this context from the perspective of industrial agglomeration, and propose how to better realize the interaction between e-commerce and industrial agglomeration, so as to achieve the improvement of the competitiveness of enterprises in the industry.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author Contributions

HY was responsible for designing the framework of the entire manuscript from topic selection to solution to experimental verification.

Research on the Path and Countermeasures of Cultivating and Expanding Rural Collective Economy in Kaifeng City.

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Anam, F., Asad, A., Wan, M., and Ahmad, N. Z. (2017). Analyzing the academic research trends by using university digital resources: a bibliometric study of electronic commerce in China. Univ. J. Educ. Res. 5, 1606–1613. doi: 10.13189/ujer.2017.050918

CrossRef Full Text | Google Scholar

Bivand, R., and Krivoruchko, K. (2018). Big data sampling and spatial analysis: “which of the two ladles, of fig-wood or gold, is appropriate to the soup and the pot? Stat. Probab. Lett. 136, 87–91. doi: 10.1016/j.spl.2018.02.012

Cao, Y., Xia, Y., Cheng, J., Huade, Z., and Yuancai, C. (2017). A novel visible-light-driven In-based MOF/graphene oxide composite photocatalyst with enhanced photocatalytic activity toward the degradation of amoxicillin. Appl. Catal. B Environ. 200, 673–680. doi: 10.1016/j.apcatb.2016.07.057

Cohen, M. C., Lobel, R., and Perakis, G. (2018). Dynamic pricing through data sampling. Prod. Oper. Manag. 27, 1074–1088. doi: 10.1111/poms.12854

Fan, C., Xiao, F., and Yan, C. (2018). Research and applications of data mining techniques for improving building operational performance. Curr. Sustain. Renew. Energy Rep. 5, 181–188. doi: 10.1007/s40518-018-0112-x

Gui, Y.-M., Wu, Z., and Gong, B.-G. (2019). Value-added service investment decision of B2C platform in competition. Kongzhi Juece Control Decis. 34, 395–405.

Google Scholar

Han, Y., Shao, X. F., Tsai, S. B., Fan, D., and Liu, W. (2021). E-government and foreign direct investment: evidence from Chinese cities. J. Glob. Inf. Manag. (JGIM) 29, 1–17. doi: 10.4018/jgim.20211101.oa42

Heiner, E., and Marc, G. (2018). Looking forward, looking back: British journal of management 2000-2015: looking forward, looking back. Br. J. Manag. 29, 3–9. doi: 10.1111/1467-8551.12257

Irene, N. (2018). Review: electronic commerce. ITNOW 42, 31–31.

Liu, X.-F., Zhan, Z.-H., Deng, J. D., Li, Y., Gu, T., and Zhang, J. (2018). An energy efficient ant colony system for virtual machine placement in cloud computing. IEEE Trans. Evol. Comput. 22, 113–128. doi: 10.1109/tevc.2016.2623803

Nádudvari, Á, Fabiańska, M. J., Marynowski, L., Kozielska, B., Konieczyński, J., Smołka-Danielowska, D., et al. (2018). Distribution of coal and coal combustion related organic pollutants in the environment of the upper silesian industrial region. Sci. Total Environ. 62, 1462–1488. doi: 10.1016/j.scitotenv.2018.02.092

PubMed Abstract | CrossRef Full Text | Google Scholar

Navarro-Méndez, D. V., Carrera-Suárez, L. F., Sánchez-Escuderos, D., Cabedo-Fabres, M., Baquero-Escudero, M., Gallo, M., et al. (2017). Wideband double monopole for mobile, WLAN, and C2C services in vehicular applications. IEEE Antennas Wirel. Propag. Lett. 16, 16–19. doi: 10.1109/lawp.2016.2552398

Nik, B., Mark, W., Ranjit, S., and Michael, T. (2017). The use of microsoft excel as an electronic database for handover and coordination of patients with trauma in a district general Hospital. BMJ Innov. 3:130. doi: 10.1136/bmjinnov-2016-000182

Ning, J., Babich, V., Handley, J., and Keppo, J. (2018). Risk-aversion and B2B contracting under asymmetric information: evidence from managed print services. Soc. Sci. Electron. Publ. 66, 392–408. doi: 10.1287/opre.2017.1673

Rezaei-Hachesu, P., Oliyaee, A., Safaie, N., and Ferdousi, R. (2017). Comparison of coronary artery disease guidelines with extracted knowledge from data mining. J. Cardiovasc. Thorac. Res. 9, 95–101. doi: 10.15171/jcvtr.2017.16

Setiawan, A. B., Dunan, A., and Mudjianto, B. (2022). “Policies and innovations of financial technology business models in the digital economy era on the E-business ecosystem in indonesia,” in Handbook of Research on Green, Circular, and Digital Economies as Tools for Recovery and Sustainability , eds de Pablos, O. Patricia, Z. Xi, and A. Mohammad Nabil (Pennsylvania. PA: IGI Global), 22–42. doi: 10.4018/978-1-7998-9664-7.ch002

Sukrat, S., and Papasratorn, B. (2018). An architectural framework for developing a recommendation system to enhance vendors’ capability in C2C social commerce. Soc. Netw. Anal. Min. 8:22.

Suska, M. (2022). “E-commerce: the pillar of the digital economy,” in The European Union Digital Single Market , eds L. D. Dabrowski and M. Suska (Abingdon: Routledge), 63–91.

Svefors, P., Sysoev, O., Ekstrom, E. C., Persson, L. A., Arifeen, S. E., Naved, R. T., et al. (2019). Relative importance of prenatal and postnatal determinants of stunting: data mining approaches to the MINIMat cohort, Bangladesh. BMJ Open 9:e025154. doi: 10.1136/bmjopen-2018-025154

Wang, K., Xu, C., Yan, Z., Guo, S., and Zomaya, A. (2017). Robust big data analytics for electricity price forecasting in the smart grid. IEEE Trans. Big Data 5, 34–45. doi: 10.1109/tbdata.2017.2723563

Wang, X.-S., and Yu, C.-Y. (2017). Impact of spatial agglomeration on industrial pollution emissions intensity in China. Zhongguo Huan. Kexue China Environ. Sci. 37, 1562–1570.

Xuan, S. U. N. (2017). Multi-indicator evaluation and analysis of coordinated industrial expansion of urban agglomerations. Urban Environ. Stud. 05:1750006. doi: 10.1142/s2345748117500063

Yang, L., Zheng, Y. Y., Wu, C. H., Dong, S. Z., Shao, X. F., and Liu, W. (2022). Deciding online and offline sales strategies when service industry customers express fairness concerns. Enterp. Inf. Syst. 16, 427–444.

Yu, P., Gong, R., and Sampat, M. (2022). “Blockchain technology in china’s digital economy: balancing regulation and innovation,” in Regulatory Aspects of Artificial Intelligence on Blockchain , ed. T. Pardis Moslemzadeh (Pennsylvania. PA: IGI Global), 132–157. doi: 10.4018/978-1-7998-7927-5.ch007

Zhou, Y.-Z., Li, P.-X., Wang, S.-G., Xiao, F., Li, J. Z., and Gao, L. (2017). Research progress on big data and intelligent modelling of mineral deposits. Bull. Miner. Petrol. Geochem. 36, 327–331.

Keywords : virtual e-commerce, industrial agglomeration expansion, big data, e-commerce model, standard system

Citation: Yue H (2022) Research on E-Commerce Data Standard System in the Era of Digital Economy From the Perspective of Organizational Psychology. Front. Psychol. 13:900698. doi: 10.3389/fpsyg.2022.900698

Received: 21 March 2022; Accepted: 14 April 2022; Published: 04 May 2022.

Reviewed by:

Copyright © 2022 Yue. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Hongqiang Yue, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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• Published: 04 May 2024

E-commerce and foreign direct investment: pioneering a new era of trade strategies

• Yugang He   ORCID: orcid.org/0000-0001-5758-069X 1  

Humanities and Social Sciences Communications volume  11 , Article number:  566 ( 2024 ) Cite this article

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This study explores the dynamic interplay between foreign direct investment, e-commerce, and China’s export growth from 2005 to 2022 against the backdrop of the rapidly evolving global economy. Utilizing advanced analytical models that combine province- and year-fixed effects with fully modified ordinary least squares and dynamic ordinary least-squares methodologies, we delve into how foreign direct investment and e-commerce collectively boost China’s export capabilities. Our findings highlight a significant alignment between China’s export expansion and the global sustainable development agenda. We observe that China’s export growth transcends mere international investment and digital market engagement, incorporating sustainable practices such as effective utilization of local labor resources and an emphasis on technological advancements. This study also uncovers how knowledge capital and educational attainment positively impact export figures. A notable regional disparity is observed, with the eastern regions of China being more responsive to foreign direct investment and e-commerce influences on export trade compared to their western counterparts. This disparity underscores the need for region-specific policy approaches and sustainable strategies to evenly distribute the benefits of foreign direct investment and e-commerce. The study concludes that while foreign direct investment and e-commerce are crucial for China’s export growth, the underlying theme is sustainable development, with technological innovation and human capital being key to ongoing export success. The findings advocate for policies that balance economic drivers with sustainable development goals, ensuring both economic prosperity and environmental sustainability.

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Introduction.

In its ascent towards global economic preeminence, China has undergone transformative alterations in its provincial export trade architecture, metamorphosed by the intricate orchestration of economic vectors and technological advents within the globally interconnected milieu. Central to this paradigm shift is the synthesis of foreign direct investment, the burgeoning trajectory of e-commerce, the proper deployment of indigenous labor resources, and tactically channeled technological capital. An adept comprehension of these intricate dynamics becomes essential for informed forecasts pertaining to China’s export evolution and its symbiotic relationship with sustainable developmental objectives. The exponential proliferation of China’s export vertical can be attributed to its accurate incorporation of foreign direct investment, pivotal in catalyzing technological assimilations, fortifying workforce competencies, and forging novel market corridors. In tandem, the surge in e-commerce has revolutionized market penetration modalities, enabling Chinese offerings to seamlessly infiltrate global commerce arenas. Moreover, China’s abundant labor capital, juxtaposed with deliberate technological ventures, has accentuated its competitive foothold in global trade arenas. Yet, the velocity of this expansive trajectory beckons a meticulous assessment through a prism of sustainability, addressing facets of resource optimization, laboral integrity, and ecological prudence.

In the current academic landscape, a significant emphasis has been placed on dissecting the myriad ramifications of foreign direct investment on export enhancement, with studies underscoring its cardinal role in technological integration and amplifying operational efficacy. The integration of e-commerce facets, as delineated by Hao et al. ( 2023 ), offered a refined perspective, spotlighting the instrumental role of digital conduits in transcending conventional trade barriers. The interrelation of labor capital, as articulated by Zhang et al. ( 2016 ), in concert with technological advancements, as expounded by Autor et al. ( 2015 ), underscored the salience of indigenous assets and frontier innovations in the export dialog. However, despite the expansive literature, a comprehensive appraisal amalgamating these aspects, especially within the framework of China’s regional disparities, is palpably lacking. From a methodological standpoint, diverse econometric paradigms have been employed in antecedent research, yet province- and year-fixed effect models are increasingly lauded for their analytical precision. The eastern corridors, advantaged by their littoral proximity, have conventionally steered the export zeitgeist. Academic contributions, such as those by Duan et al. ( 2020 ), underscored this region’s proficiency in harnessing foreign direct investment and e-commerce potentialities. In juxtaposition, the central and western sectors, albeit resource-rich and labor-abundant, evince a marked lag in technological embrace and foreign direct investment influx. This regional polarization, as theorized by Zhong et al. ( 2022 ), accentuates the necessity for tailored policy interventions to promulgate balanced and sustainable growth vectors. In this context, our scholarly pursuit seeks to redress the prevailing knowledge chasm. The intricate interplay of foreign direct investment, e-commerce, labor dynamics, and technological innovation in molding China’s export tapestry is indubitable. Yet, exhaustive scrutiny, particularly one sensitive to regional grades, stands as an academic imperative. Grounded in methodological robustness and echoing sustainability principles, this study aims to demystify this intricate interconnection, catalyze informed policy deliberations, and buttress China’s odyssey towards a sustainable export paradigm.

Drawing upon the aforementioned analytical discourse, this research delves into the complex relationship between foreign direct investment, e-commerce, and the growth of exports in China from 2005 to 2022, situated within the context of a rapidly changing global economic landscape. Using advanced statistical methods, such as province- and year-fixed effects analysis along with fully modified ordinary least squares and dynamic ordinary least-squares methods, the study gives a more complete picture of how foreign direct investment and e-commerce work together to make China’s exports stronger. A key aspect of this study is its alignment with the global sustainable development agenda, examining how China’s export growth extends beyond basic international investment and digital commerce. It integrates sustainable practices, such as the effective use of local labor and a focus on technological advancement, offering insights into the role of knowledge capital and educational attainment in boosting export figures. Our analysis reveals a pronounced regional variation in the impact of foreign direct investment and e-commerce on export trade, with Eastern China showing greater responsiveness compared to the Western regions. This finding highlights the necessity for region-specific policies and sustainable strategies to ensure a balanced distribution of foreign direct investment and e-commerce benefits across the country. The study’s methodology stands out in the existing literature for its comprehensive approach, combining advanced econometric techniques to dissect the multifaceted influences on China’s export sector. It addresses a gap in previous research by providing a clearer picture of the interplay between foreign direct investment, e-commerce, and export growth within the unique context of China’s evolving economy. The research emphasizes the need for a nuanced understanding of China’s position in the global economy, exploring the relationship between foreign direct investment and e-commerce in a way that prior empirical studies have not fully captured. By doing so, the study offers valuable insights for policymakers and stakeholders, advocating for strategies that not only foster economic growth but also align with sustainable development objectives, ensuring the long-term prosperity and environmental sustainability of China’s economy.

This study presents several significant contributions to the current academic understanding of China’s export sector, particularly focusing on sustainable development. First, our analysis synthesizes the roles of foreign direct investment and e-commerce, offering fresh insights into their collective influence on China’s exports. This aspect builds upon the work of Fidrmuc and Korhonen ( 2010 ), who underscored the impacts of global capital and digital advancements on emerging economies. Our study extends this perspective by explicitly linking these factors to export growth in the Chinese context. Second, we introduce a nuanced approach by examining regional variations in export performance, moving beyond the limitations of previous studies that often treated China’s economy as a uniform entity. Grübler et al. ( 2007 ), who emphasized the value of regional analysis in producing more thorough economic insights than national overviews, served as an inspiration for this strategy. Third, our research highlights the role of local labor resources as a key component of sustainable export strategies. This aligns with Sun’s ( 2022 ) assertion of human capital as a critical driver of economic growth, positioning it as a sustainable asset in China’s export framework. Fourth, the study delves into the impact of technological investment on sustainable export growth, expanding upon Qian et al. ( 2021 ) thesis that technology is fundamental to achieving green growth. We explore how technological advancements contribute specifically to the sustainability of China’s export sector. Lastly, the research advocates for a balanced approach to economic growth and environmental sustainability, echoing Wright’s ( 2019 ) argument for the necessity of balancing economic development with ecological preservation. Our study furthers this dialog by illustrating how such a balance can be achieved within the context of China’s export dynamics. Together, these facets of our research offer new perspectives on the complex relationship between economic activities, technological innovation, and sustainable development in the context of China’s growing role in the global market. These insights are particularly relevant for policymakers and business leaders looking to navigate the challenges and opportunities presented by China’s evolving export landscape.

The subsequent sections of this article are structured as follows: section “Literature review” delves into a comprehensive review of extant literature, shedding light on prior research in this domain. In the section “Variables and model”, we elucidate the methodological approach, detailing the variables employed and the underlying model. Section “Empirical results” offers a synthesis of the empirical results, coupled with a discussion of the implications. Lastly, the section “Conclusions” culminates with conclusions, policy recommendations, and avenues for future research in this field.

Literature review

In today’s global trade environment, the interplay between foreign direct investment and e-commerce has become a critical factor influencing export trends. Current research highlights foreign direct investment’s pivotal role in driving technology transfer and expanding markets. Concurrently, e-commerce platforms have revolutionized trading patterns, facilitating instantaneous market connections and broadening international reach. Additionally, elements like labor resource allocation and technological progress intertwine with these primary factors, creating a multifaceted framework that reveals the complexities of modern export strategies.

In contemporary academic discourse, the impact of foreign direct investment on China’s export trade has received significant attention. Yet, the complex relationship with e-commerce remains insufficiently explored. The prevailing literature, as seen in the works of Li et al. ( 2019 ), Wang et al. ( 2020 ), and Jin and Huang ( 2023 ), mainly focuses on the direct effects of foreign direct investment on export efficiency through capital infusion and technological transfers. These studies, however, tend to overlook the burgeoning dimension of digital commerce. Addressing this gap, Fu et al. ( 2016 ), Chen et al. ( 2023 ), and Lei and Xie ( 2023 ) provide a more nuanced perspective by acknowledging the role of digital transformation in global trade. They underscore e-commerce’s potential to complement foreign direct investment, particularly in enhancing market access for Chinese exports. Expanding on this viewpoint, Qi et al. ( 2020 ), Klimenko and Qu ( 2023 ), and Yan et al. ( 2023 ) examine how e-commerce platforms democratize export opportunities, even for smaller entities, thus amplifying foreign direct investment’s impact. The insights of Zhang and Yang ( 2022 ), Mahalik et al. ( 2023 ), and Cordes and Marinova ( 2023 ) served as inspiration for this research’s more integrative approach. It goes beyond the traditional analysis of foreign direct investment’s influence on exports to include the transformative role of e-commerce. This methodological advancement builds upon and extends the analyses of Götz ( 2020 ), Auboin et al. ( 2021 ), and Ha ( 2022 ), who, despite their thoroughness, did not fully address the synergistic relationship between foreign direct investment and digital trade channels. Aligned with the analytical frameworks of Agarwal and Wu ( 2015 ), He et al. ( 2021 ), and Shanmugalingam et al. ( 2023 ), this study emphasizes a thorough understanding of trade dynamics in the digital era. By incorporating e-commerce as a key variable alongside foreign direct investment, it fills a critical gap in the literature. This approach resonates with the findings of Zhang and Zeng ( 2023 ), Xiao and Abula ( 2023 ), and Sun et al. ( 2024 ) on the growing influence of digital platforms on trade and extends their work by empirically quantifying this impact within the context of China’s export landscape. In conclusion, this research contributes significantly to the existing body of literature by integrating the crucial role of e-commerce. It provides a more comprehensive view of the dynamics shaping China’s export trade, thereby addressing a vital need in the ongoing academic conversation.

The existing literature recognizes the impact of e-commerce on China’s export trade but lacks a thorough exploration of its synergistic effects with foreign direct investment and traditional trade mechanisms. Previous studies, such as those by Giuffrida et al. ( 2017 ) and Li et al. ( 2019 ), have primarily focused on the direct impact of e-commerce on market expansion and customer engagement, emphasizing its role in broadening the global reach of Chinese products. However, these studies often treat e-commerce as an isolated factor, not integrating it with broader economic elements like foreign direct investment. A more nuanced perspective is emerging from research such as Blanchard, Jean-Marc ( 2019 ), Villegas-Mateos ( 2022 ), and Singh and Singh ( 2022 ), which begin to address the interaction between e-commerce and foreign direct investment but do not provide a comprehensive analysis. These studies show how e-commerce platforms can enhance export efficiency in conjunction with foreign direct investment, yet they stop short of examining how e-commerce is transforming traditional export models. This research addresses this gap by adopting an integrative methodology, drawing on the approaches of Wang et al. ( 2021 ) and Yin and Choi ( 2023 ). This methodology extends beyond evaluating the direct effects of e-commerce on exports to also consider its interplay with foreign direct investment. Such an approach expands upon the frameworks used in studies by Zhang ( 2019 ) and Phang et al. ( 2019 ), which, while insightful, did not fully capture e-commerce’s complex dynamics within China’s integrated market economy. Additionally, this study aligns with the emerging literature, such as the works of Gao ( 2018 ) and Li et al. ( 2020 ), advocating for a holistic view of digital trade’s role in economic growth. By incorporating a comprehensive array of variables, including technological advancement and digital infrastructure quality, this research provides a more robust analysis than previous studies like those by Katz and Callorda ( 2018 ), Sinha et al. ( 2020 ), and Wei and Ullah ( 2022 ). In conclusion, this study overcomes previous shortcomings in academic research by offering a detailed empirical examination of how e-commerce, in conjunction with foreign direct investment and traditional trade mechanisms, shapes China’s export landscape. It contributes significantly to academic discourse by presenting a more complete understanding of e-commerce’s role in the modern economy, thus fulfilling a critical need in the ongoing narrative on global trade and digital economics.

In analyzing China’s export sector, the influence of labor resource allocation, technological advancements, knowledge capital, and educational attainment, particularly in relation to e-commerce, warrants a deeper exploration. Initial research efforts, exemplified by Bhaumik et al. ( 2016 ), Song and Wang ( 2018 ), and Liu and Xie ( 2020 ), have individually evaluated the impacts of labor and technology on export performance, underscoring their roles in bolstering China’s position in international markets. Yet, these studies typically overlooked the integration of e-commerce into their analytical models. Recent scholarly works, including those by Kwak et al. ( 2019 ), Elia et al. ( 2021 ), and Tang and Li ( 2023 ), have started to recognize the combined effect of technological prowess and labor skills within the framework of e-commerce. However, these investigations fall short of comprehensively examining how knowledge capital and education intersect with e-commerce to affect export trends. The methodologies of Lin et al. ( 2020 ), Hanelt et al. ( 2021 ), and Abdul-Rahim et al. ( 2022 ) served as the foundation for this study’s holistic approach to closing this research gap. Our approach is comprehensive, assessing not just the direct impacts of labor, technology, education, and knowledge on exports but also situating these impacts in the context of the growing e-commerce domain. This method expands upon the analytical scope of previous studies like Wei et al. ( 2020 ) and Li et al. ( 2023 ), which, despite their thoroughness, did not fully delve into the complex relationship between e-commerce and China’s export dynamics. Furthermore, our study aligns with the evolving scholarly narrative, as seen in the works of Banalieva and Dhanaraj ( 2019 ) and Huang et al. ( 2023 ), advocating for an integrated view of digital commerce’s interaction with traditional economic variables. By including an extensive analysis of factors such as digital infrastructure and market development in e-commerce, this research offers a more detailed examination than earlier studies by Gorla et al. ( 2017 ) and Wang et al. ( 2024 ). In summary, this research fills existing gaps in the literature by thoroughly investigating how labor resources, technological investments, knowledge capital, and education, in conjunction with e-commerce, shape the export sector in China. It provides a comprehensive perspective on the synergy between traditional economic elements and digital trade, addressing a critical need in the ongoing discussion of global trade and economic progression.

Variables and model

Numerous studies have explored the significant impact of foreign direct investment on a nation’s export trends, highlighting foreign direct investment’s critical role in reshaping export strategies. Researchers like Choong ( 2012 ) and Otchere et al. ( 2016 ) have pointed out that foreign direct investment not only provides essential capital but also facilitates technological transfer, thereby boosting efficiency and productivity in host countries. Moreover, the aspect of sustainability is increasingly becoming interlinked with foreign direct investment, often bringing eco-friendly technologies and sustainable methodologies to the forefront, enhancing a nation’s prospects for long-term export stability, as noted by Perrini and Tencati ( 2006 ). Simultaneously, the influence of the digital revolution, particularly the rise of e-commerce, has significantly transformed the nature of exports. Studies by Wang ( 2010 ) and Teng et al. ( 2022 ) highlight that in China’s expanding digital landscape, e-commerce platforms have leveled the playing field, allowing even smaller businesses to access the global market. According to Rita and Ramos ( 2022 ), Amornkitvikai et al. ( 2022 ), and He et al. ( 2021 ), e-commerce is also in line with the global trend towards sustainable trading due to its traceable and transparent nature. Considering these complex interactions, export trade volume becomes an appropriate variable to study, representing the combined and sustainable effects of foreign direct investment and e-commerce. This research, therefore, focuses on the export trade volumes of China’s provinces, incorporating foreign direct investment inflows and e-commerce transaction data as independent variables. This approach aims to shed light on their hypothesized influence on provincial export patterns.

To fully grasp the complex factors affecting export trade, it’s crucial to look beyond conventional indicators like foreign direct investment and e-commerce. A deeper exploration into academic literature and fundamental economic theories uncovers the critical role of labor resource allocation and technological advancements in shaping export patterns. The foundational Heckscher-Ohlin theorem, supported by research from Castilho et al. ( 2012 ) and Antràs et al. ( 2017 ), underscores the vital impact of labor resources on global trade trends. Darku ( 2021 ) extends this perspective, emphasizing the sustainability aspects and suggesting that effectively managed labor resources can contribute to more equitable and environmentally responsible trading practices. Additionally, examining the role of technology provides insights into the nuances of export competitiveness. Rooted in Romer’s theory of endogenous growth and backed by findings from Jones ( 2019 ) and Anzoategui et al. ( 2019 ), there is a consensus that deliberate technology investments boost productivity and support sustainable growth through cleaner, more efficient production methods. Zhou et al. ( 2021 ) further clarify this idea, showing how sustainable technologies and competitive exports are interlinked. Recognizing the importance of these two factors, this study incorporates labor resource allocation and technological inputs as key control variables. To empirically anchor these theoretical concepts, we use urban employment data from various provinces as indicators of labor resource allocation and local government spending on technology as a reflection of technological investments. Building on the work of Mansion and Bausch ( 2020 ), Lyu et al. ( 2022 ), and Mohammad Shafiee et al. ( 2023 ), this paper also introduces knowledge capital quantified by the number of patent licenses. Following Atkin ( 2016 ), Ahmed et al. ( 2020 ), and Blanchard and Olney ( 2017 ), the paper incorporates education level, measured by the average number of schooling years.

Due to data availability, this paper selects balanced provincial-level data from 2005 to 2022. Since 2005, e-commerce across various Chinese provinces has seen rapid development, making this period particularly relevant to the study’s context. The unavailability of data from Tibet necessitates the inclusion of 30 other provinces and municipalities in China, namely Beijing, Tianjin, Shanxi, Inner Mongolia, Liaoning, Jilin, Heilongjiang, Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Shandong, Henan, Hubei, Hunan, Guangdong, Guangxi, Hainan, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang. The data used in this study is sourced from three official databases, each providing specific insights into our variables of interest. The Bureau of Statistics of China supplies data on export trade volume, knowledge capital, education level, labor resource endowment, and technological investment. Information on e-commerce is obtained from the China E-commerce Report, while data on foreign direct investment is sourced from the Statistical Bulletin of China’s Outward Foreign Direct Investment.

In examining the interplay between foreign direct investment and e-commerce on export trade using China’s province data, it is imperative to adopt a robust econometric technique that effectively captures both time-invariant and entity-specific heterogeneities. The two-way fixed effects regression model, as elaborated upon by Wooldridge ( 2010 ) and advocated by Baltagi ( 2021 ), is particularly adept at mitigating potential omitted variable biases in panel data, making it especially suitable for our study’s empirical context. By incorporating both entity and time-fixed effects, this approach controls for unobserved province-specific factors that may influence export trade (such as local policies or geographical advantages) and time-specific shocks (like global economic trends or national regulatory shifts) that uniformly affect all provinces. By accounting for these dual dimensions of variability, the model ensures that the estimated effects of foreign direct investment and e-commerce are purged of confounding influences, thus bolstering the credibility of causal inferences drawn from the results. Given the dynamism of China’s economic landscape, combined with the evolving trajectories of foreign direct investment and e-commerce, leveraging the two-way fixed effects regression offers a rigorous and robust approach to discerning their impact on export trade. The model is shown as follows:

In Eq. ( 1 ), the subscript ‘ i ’ represents individual provinces, while t delineates the temporal dimension, capturing the yearly variations. Within this model, ex symbolizes the export trade volume, serving as our dependent variable. On the explanatory side, ec corresponds to e-commerce metrics, ‘fdi’ quantifies foreign direct investment inflows, ‘lab’ encapsulates labor resource endowment, and ‘tec’ signifies the magnitude of technological investment. ‘kn’ indicates knowledge capital. ‘ed’ stands for education level. The term a 0 denotes the intercept, providing a baseline measure for our regression. The vector [ a 1 , a 6 ] comprises the coefficients estimated for each explanatory variable, reflecting their respective strengths and directions of influence on export trade. To control for potential unobserved heterogeneities, η embodies province-specific fixed effects, while δ accounts for year-specific fixed effects, ensuring that time-invariant provincial attributes and common temporal shocks are appropriately adjusted for. The error term, ϵ , is presumed to follow a white noise process, indicating randomness and the absence of serial correlation. The empirical focal points of this study are the coefficients a 1 and a 2 . In our analytical framework, the ‘+’ symbol is strategically used to represent the expected positive effect of various independent variables—including e-commerce, foreign direct investment, labor resource endowment, technological investment, knowledge capital, and education level—on the export volumes of Chinese provinces. This symbolism is central to our hypothesis, positing that these variables play a beneficial role in shaping export trends across different provinces. The underlying premise of this hypothesis is that variables like foreign direct investment, enhanced e-commerce capabilities, and other pertinent factors positively stimulate export activities. In essence, the '+' sign indicates a probable correlation where increases or improvements in these independent variables are likely to correspond with a rise in export volumes from the provinces. Such a correlation is instrumental in dissecting how various economic elements and technological progressions, specific to China’s varied regional landscapes, can bolster the nation’s export capacity. This exploration is particularly salient for understanding China’s export mechanics. It provides a nuanced view of how strategic investments in technology, human capital development, and leveraging local resources can collectively uplift the export sector, reinforcing China’s position in the global economy. The ‘+‘ sign, therefore, not only signifies a positive correlation but also serves as a gateway to understanding the multifaceted drivers that enhance export efficiency in the context of China’s evolving economic landscape.

Robustness test

Considering the relatively modest scale of our sample in this study, there exists a plausible risk of heteroskedasticity and autocorrelation in the outcomes estimated through the use of annual and provincial fixed effects models. These statistical issues could potentially introduce biases into our analytical results, thereby affecting the reliability of our conclusions. To mitigate this challenge, our research strategically implements two advanced econometric methodologies: fully modified ordinary least squares and dynamic ordinary least squares. The fully modified ordinary least-squares technique, a refined version of the ordinary least-squares methodology, is particularly adept at addressing complexities arising from heteroskedasticity and autocorrelation. The efficacy of this approach in handling such statistical nuances is well documented in the works of scholars such as Pedroni ( 2001 ), Christou and Pittis ( 2002 ), Trapani ( 2015 ), Li et al. ( 2020 ), Kripfganz and Sarafidis ( 2021 ), Norkutė et al. ( 2021 ), and Kheifets and Phillips ( 2023 ). These studies validate the use of fully modified ordinary least squares as a robust tool for enhancing the accuracy of econometric estimations, especially in scenarios similar to those in our study. Similarly, the dynamic ordinary least squares method offers a comprehensive solution for addressing the challenges of endogeneity and serial correlation, which are common in time-series data. Research by Chudik and Pesaran ( 2015 ), Moon and Weidner ( 2017 ), Liu et al. ( 2020 ), Ahn and Thomas ( 2023 ), Hartono et al. ( 2023 ), and Fingleton ( 2023 ) underscore the effectiveness of dynamic ordinary least squares in ensuring more precise and reliable results in econometric analysis. This technique, by adjusting for both the lead and lag dynamics of the variables, enhances the accuracy of regression coefficients, thereby providing a more nuanced understanding of the underlying data patterns. Both fully modified ordinary least squares and dynamic ordinary least squares are sophisticated enhancements of the traditional least-squares approach. These methods have been specifically adapted to address the intricate statistical issues inherent in panel data analysis, like the one employed in our study. By incorporating these advanced techniques, we aim to mitigate potential biases arising from heteroskedasticity, autocorrelation, and endogeneity, thereby enhancing the credibility and robustness of our findings. Equation ( 2 ) in our study meticulously outlines the application of these methods, demonstrating their integration into our analytical framework to yield more reliable and insightful results.

This outcome is directly derived from the differential regression, as shown in Eq. ( 3 ).

Let’s consider \(\tilde{\Theta }\) and \(\tilde{\Psi }\) to represent the long-term covariance matrix calculated using the residuals denoted by \([\tilde{{\uptau }_{{\rm{t}}}}=(\tilde{{\uptau }_{1{\rm{t}}}},\tilde{{\uptau }_{2{\rm{t}}{^\prime} }}){^\prime} ]\) . Based on this assumption, we are able to represent the modified data as depicted in Eq. ( 4 ). This representation considers the complex interdependencies reflected in the covariance matrix, laying the groundwork for subsequent examination and understanding of the data within the framework of our chosen model.

In our study, the term for bias correction, crucial for refining our model, is detailed in Eq. ( 5 ). This component is essential for enhancing the accuracy and dependability of our results, as it compensates for possible biases encountered during the estimation phase.

Therefore, the formulation of the fully modified ordinary least-squares estimator, pivotal to our analysis, is encapsulated in Eq. ( 6 ). This estimator is integral to refining our estimations, as it addresses potential issues of serial correlation and endogeneity within our regression models. By employing the fully modified ordinary least-squares method, we gain a more accurate and insightful comprehension of the relationships present in our dataset.

In Eq. ( 6 ), \({\rm{Z}}_{\rm{t}}=({\rm{y}}_{\rm{t}}^{{\prime} }{\rm{D}}_{\rm{t}}^{{\prime} })\) . Developing estimators for the long-term covariance matrix, a critical component in the implementation of fully modified ordinary least squares, is highlighted in studies by Atil et al. ( 2023 ), Wagner ( 2023 ), Phillips and Kheifets ( 2024 ), and Pelagatti and Sbrana ( 2024 ). This process is essential for the precision and efficacy of the fully modified ordinary least-squares approach. It entails refining the OLS regression by including both preceding and subsequent factors, ensuring that the error component in Eq. ( 1 ) remains uncorrelated with the entire historical sequence of random regressor variations. This method, as detailed in the research by Mark and Sul ( 2003 ), Panopoulou and Pittis ( 2004 ), Bruns et al. ( 2021 ), and Wang et al. ( 2024 ), is efficiently captured in Eq. ( 7 ).

By incorporating q lags and r leads of the differenced regressors, the persistent correlation between variables τ 1 t and τ 2 t is effectively neutralized. This adjustment allows the estimation of φ  = ( β ′, γ ′)’ through the least-squares estimator to align with the asymptotic distribution achieved via the fully modified ordinary least-squares method. These methods are notably effective, as emphasized by Bai et al. ( 2021 ), Chebrolu et al. ( 2021 ), De Menezes et al. ( 2021 ), Zhao et al. ( 2022 ), and Bollen et al. ( 2022 ), in overcoming challenges like endogeneity, serial correlation, and biases that are typically prevalent in studies with smaller sample sizes.

Empirical results

Descriptive statistical analysis.

For the purpose of this study, data extraction was conducted, harnessing information from 31 distinct provincial datasets covering the temporal bracket of 2005–2022. This compilation was sourced directly from the authoritative National Bureau of Statistics of China, ensuring data authenticity and integrity. An initial stage of rigorous analytical procedures was executed, encompassing both qualitative descriptive statistical evaluations and quantitative correlation analyses. This served to provide a holistic view of the data landscape, enabling the identification of patterns and inter-variable relationships. The culminating findings from this analytical phase are methodically tabulated in Table 1 . For clarity and comprehensive representation, the results are segmented into two distinct panels: Panel A elucidates the statistical analysis of variable description, while Panel B delineates the correlation matrices.

Within Panel A of Table 2 , an examination of the data yields insights into provincial economic dynamics. The export trade registers an average value of 2.226, complemented by a notably narrow standard deviation of 0.085. This suggests a trend of ascent in export trade across the majority of provinces. Conversely, the foreign direct investment landscape, with a mean of 0.241 and a slightly more dispersed standard deviation of 0.117, indicates a predominant trajectory of foreign direct investment enhancement among provinces, albeit with some variability. E-commerce, represented by a mean of 2.276, portrays a positive trend; however, its relatively expansive standard deviation of 0.572 implies a diverse range of advancements and perhaps volatility within this sector. This is emblematic of the rapidly evolving and heterogeneous landscape of e-commerce in China, a reflection that aligns with empirical observations on the nation’s digital commerce forefront. The labor resource endowment is quantified with a mean of 2.791 and a standard deviation of 0.315, providing insights into a generally favorable labor capital across provinces. The metrics for technological investment, with an average of 0.997 and a standard deviation of 0.441, underline the ongoing endeavors in technological innovation but also hint at disparities in the extent and pace of such investments across the provinces. Finally, the metric for knowledge capital is calculated with an average value of 4.012 and a standard deviation of 1.506. Meanwhile, the education level is measured, showing an average value of 0.907 and a standard deviation of 0.217.

In the wake of conducting a correlation analysis, the subsequent findings are articulated in Panel B of Table 1 . An inaugural examination of the data reveals a discernible positive relationship between foreign direct investment and e-commerce relative to the scope of China’s provincial export trade. Parallel to this, a deeper analytical traverse into the data underscores a tangible connection between labor resource endowment and technological forays as pivotal determinants of export trajectories. This interrelationship accentuates the premise that provinces emphasizing sustainable labor methodologies and avant-garde technological endeavors are not solely shaping a resilient economic structure but are concurrently enhancing their export trade capacities. This synergy between sustainability-oriented strategies and burgeoning trade volumes fortifies the argument that sustainability stands as a potent stimulant, accentuating both foreign direct investment and e-commerce outcomes. Furthermore, the analysis essentially establishes a positive correlation between knowledge capital, education level, and the export trade of China’s provinces.

In this investigation, a quintet of econometric techniques is deployed to discern the nuanced impacts of foreign direct investment and e-commerce on export trade. These methodologies encompass pooled ordinary least squares (Model 1), panel ordinary least squares (Model 2), province-specific fixed effects (Model 3), year-fixed effects (Model 4), and a provincial and year-fixed-effects approach integrating both provincial and annual dimensions (Model 5). The outcomes of these estimations are documented in Table 2 . Upon evaluating the data through the prism of the Chow test, we discerned a clear rejection of the null hypothesis, indicating the inadequacy of pooled ordinary least squares for this dataset. Subsequent to this, the Hausman test was executed, which further rejected the null hypothesis, rendering the province-fixed effect model suboptimal. The decision to employ Model 5—integrating both province- and year-fixed effects—is grounded in several advanced econometric postulations. Kropko and Kubinec ( 2020 ), Hill et al. ( 2020 ), and Fernández-Val and Weidner ( 2018 ) posited that in the presence of unobserved heterogeneity—factors that remained constant over time but vary across entities or vice versa—implementing province- and year-fixed effects can yield unbiased and consistent estimators. This became particularly salient when considering phenomena such as global economic oscillations or overarching regulatory changes, which exerted a consistent impact across all provinces. By accounting for these twin axes of variability, Model 5 ensures the extrication of extraneous influences from the core relationship between foreign direct investment, e-commerce, and export trade. This approach enhances the robustness of the analysis, fortifying the validity of causal extrapolations drawn from the empirical results.

In Table 2 , our primary focus is on the insights garnered from Model 5. However, it is crucial to recognize the crucial role that the outcomes of the additional four models played. These models act as a robustness check, lending further credibility to our main findings. Model 5’s empirical data highlights a robust and statistically significant link between the surge in foreign direct investment and the increase in export trade within Chinese provinces. Specifically, a 1% increase in foreign direct investment inflows is associated with a 0.209% rise in provincial export trade volume. Shifting our analysis to the impact of e-commerce on the export landscape of Chinese provinces, we observe a compelling dynamic. E-commerce is identified as a significant driver of export growth. Quantitatively speaking, a 1% growth in e-commerce activities results in a 0.405% increase in provincial export volumes. Moreover, our research identifies critical factors influencing export patterns in Chinese provinces, notably labor resources and technological investments. The study reveals that a 1% elevation in labor resource availability correlates with a 0.715% increment in export volumes at the provincial level. In the same vein, a 1% rise in technological investments is linked to a 0.304% boost in exports. Additionally, the study brings to light the constructive effects of knowledge capital and education levels on provincial export trade. An increase of 1% in these variables is found to enhance export volumes by 0.083% and 0.101%, respectively.

The positive correlation between foreign direct investment inflows and increased export trade can be understood through various theoretical frameworks and empirical studies. Drawing on the research of Adikari et al. ( 2021 ), Rehman et al. ( 2023 ), and Zhang and Chen ( 2020 ), the eclectic paradigm suggests that foreign direct investment promotes export trade by transferring advanced technologies, managerial expertise, and marketing skills to the host country. These spillover effects enhance the competitiveness of domestic firms, boosting their export potential. Additionally, foreign direct investment helps to establish export-oriented industries within host economies, as seen in China’s Special Economic Zones, which act as production and export hubs (Chiang and De Micheaux, 2022 ; Ngoc et al., 2022 ; Huang et al., 2023 ; Vukmirović et al., 2021 ). This influx of capital, technology, and knowledge through foreign direct investment acts as a catalyst, creating a trade-friendly environment and aligning provinces with a more globally integrated economic path. Several factors support e-commerce’s positive impact on provincial export volumes. Firstly, e-commerce reduces informational disparities, fostering a transparent market conducive to robust exports. Additionally, as e-commerce platforms grow, their value proposition to users strengthens, encouraging an environment ripe for increasing transactions, including exports. Thirdly, e-commerce inherently reduces transactional friction, enabling businesses to engage more effectively in international trade. The theories and results of researchers like Onjewu et al. ( 2022 ), who contend that e-commerce lowers traditional trade barriers and enables even small businesses to participate in global markets, support this viewpoint. Lipton et al. ( 2018 ) and Fritz et al. ( 2004 ) show that online platforms allow businesses to overcome geographic limitations, thus expanding their export reach. Tolstoy et al. ( 2021 ) and Zhong et al. ( 2022 ) discuss how e-commerce’s digital footprint lessens the constraints of geographical distance, creating a more fluid international trade environment. Khan and Khan ( 2021 ) and Watson et al. ( 2018 ) illustrate how digital trade avenues boost export growth by adapting to market changes and consumer preferences. Additionally, Xi et al. ( 2023 ) and Deng et al. ( 2023 ) highlight the relationship between digital infrastructures and export portfolio diversification, with e-commerce spurring product innovation and differentiation. In conclusion, the integration of these theoretical insights and empirical evidence underlines the significant role of e-commerce as a key driver in enhancing the scale of export trade in Chinese provinces.

Labor resources and technological investments have been identified as key factors positively influencing the scale of export trade in Chinese provinces. This result is consistent with the Heckscher–Ohlin theorem, which states that regions typically export goods that effectively use their most abundant resources, according to research from Kunroo and Ahmad ( 2023 ) and Akther et al. ( 2022 ). Given China’s substantial labor force, provinces endowed with richer labor resources are naturally capable of higher production, thereby supporting larger export volumes. Conversely, the relationship between technological investments and the strength of exports is anchored in contemporary economic growth theories, particularly those emphasizing the role of technology in economic development. Aghion et al. ( 1998 ) reinforce this notion, demonstrating that technological investment in regions not only enhances productivity but also provides a competitive advantage in international markets, thus boosting export capacity. Moreover, the study finds that both knowledge capital and education levels positively impact the scale of export trade in Chinese provinces. This underscores the importance of intellectual resources and educational attainment as drivers of export dynamics in a rapidly evolving economy like China’s. The correlation with knowledge capital reflects China’s strategic emphasis on innovation and intellectual property. Liu et al. ( 2017 ) emphasize that investments in research and development, especially in technology and sciences, have significantly enhanced China’s export capabilities, leading to an increase in patents and technological breakthroughs. Due to these advancements, Chinese products now have a competitive advantage in the global market with higher value and higher quality. Similarly, the significance of education in boosting export trade is notable. Yang ( 2012 ) points out that China’s focus on higher education and vocational training has equipped its workforce with the necessary skills for export-oriented industries, facilitating the production of more sophisticated, high-value products. Chen et al. ( 2022 ) further discuss how the synergy between technological advancement and educational development contributes to a more dynamic and diversified export sector. This interplay is vital for China’s ability to adapt to global economic changes and more effectively participate in international trade. In conclusion, the increase in exports due to heightened knowledge capital and education levels signifies China’s strategic transition towards a knowledge-based economy. This shift is reshaping the structure of its domestic industries and redefining China’s role and competitiveness in the global market.

In this study, meticulous measures were taken to guarantee both the accuracy and reliability of the results, especially those obtained from the analysis using the province and year-fixed effect models. To ensure the dependability of our findings, an extensive robustness check was conducted on the outcomes of the province and year-fixed effect model. This involved the use of two econometric techniques: fully modified ordinary least squares and dynamic ordinary least squares. The implementation of fully modified ordinary least squares and dynamic ordinary least squares was critical in substantiating the integrity of the inferences drawn from the province and year-fixed effect model. The employment of these methods not only bolsters the solidity of our results but also reflects a commitment to the best standards of empirical rigor and methodological thoroughness. This approach to data verification underlies the credibility and trustworthiness of our conclusions. The specifics of these findings are systematically outlined in Table 3 .

Table 3 presents a detailed evaluation of the estimated parameters, focusing on both their magnitude and statistical significance. Remarkably, the findings obtained through the application of fully modified ordinary least squares and dynamic ordinary least squares align closely with those from the initial province and year-fixed effect model. This alignment between fully modified ordinary least squares and dynamic ordinary least squares, in comparison to the province and year-fixed effect model, robustly confirms the accuracy of the original model. The consistency observed across these varied econometric methods not only strengthens the trustworthiness of the province and year-fixed effect model but also substantiates the reliability of the study’s overall findings. The convergence of results across these methodologies indicates that the initial province and year-fixed effect model was meticulously crafted and successfully captured the essential dynamics of the variables under examination. The adoption of this comprehensive cross-validation process, which incorporates multiple analytical techniques, reinforces the solidity and validity of the research’s conclusions. This multi-faceted approach to analysis assures a high level of confidence in the integrity and reliability of the study’s results.

Regional heterogeneity analysis

Spanning a considerable geographical expanse, China is officially categorized into three distinct regional demarcations: eastern, central, and western. The eastern precinct is widely acknowledged as the epitome of China’s developmental zenith, encapsulating its most economically advanced locales. Conversely, the central sector is recognized for its intermediary developmental status, while the western swathes are often delineated by developmental lacunae. These territories, though unified under a single nationhood, manifest disparate attributes ranging from their economic growth trajectories, state-directed policy nuances, and infrastructural development gradients to their inherent geographical peculiarities. To delve into the multifaceted influence of foreign direct investment and e-commerce on regional export dynamics, our empirical approach disaggregated the core dataset, structuring it into three region-specific sub-samples. This strategic bifurcation aimed at discerning the variable intensities of foreign direct investment and e-commerce influences across these heterogeneous regions. The analytical outcomes derived from this region-centric examination are detailed in Table 4 .

Reflected in Table 4 , the repercussions of foreign direct investment on export trade reveal intricate regional gradations within China’s geographical tapestry. Concretely, a marginal ascent of 1% in foreign direct investment is associated with a 0.278% enhancement in the export dynamism of the eastern provinces. This increment tapers to 0.179% for central provinces and further diminishes to 0.161% for their western counterparts. The scholarly discourses of Contractor et al. ( 2020 ), Dang and Zhao ( 2020 ), and Batschauer da Cruz et al. ( 2022 ) elucidated that the synergy between foreign direct investment and export growth hinged upon a triad of factors: intrinsic firm capabilities, locational attributes, and the operational modus operandi. The eastern provinces, historically recognized as China’s economic epicenter, are imbued with a robust infrastructural matrix, streamlined trade corridors, and a business milieu that gravitates towards global market integration. These intrinsic locational advantages, complemented by the spatial competition theory proposed by Proost and Thisse ( 2019 ), Redding and Rossi-Hansberg ( 2017 ), and Goerzen et al. ( 2013 ), amplify the efficacy of foreign direct investment in spurring export trade. On the contrary, the central belt, despite its ascending economic trajectory, is intermittently stymied by transitional economic impediments, occasionally attenuating the foreign direct investment-export nexus. The western provinces, albeit burgeoning, still navigate developmental constraints, resonating with Wang and Zhao ( 2015 ) and Jiang et al. ( 2016 )‘s backwash effects, wherein peripheral regions grapple to harness the complete spectrum of foreign direct investment benefits. From a sustainability lens, echoing the tenets of Milne and Gray’s ( 2013 ) Triple Bottom Line framework, the magnitude and mode of foreign direct investment’s assimilation should be judiciously balanced to ensure economic, social, and environmental equanimity. The immediate economic impetus observed, particularly in the eastern provinces, warrants an integrated approach wherein foreign direct investment infusion aligns with sustainable practices, ensuring that regional development dovetails with ecological stewardship and socio-cultural inclusivity. Such a harmonized trajectory ensures that the fruits of foreign direct investment are not ephemeral but perennial, fostering a resilient and sustainable export landscape across all regions.

Referring to the results presented in Table 4 , an augmentation of 1% in e-commerce transaction volume is observed to lead to a differentiated impact on the export trade across China’s tripartite regional structure: specifically, a surge of 0.397% in the eastern provinces, an enhancement of 0.365% in the central provinces, and a growth of 0.325% in the western provinces. This regional heterogeneity in the influence of e-commerce on export trade can be supported by a confluence of academic perspectives and established theoretical underpinnings. Drawing insights from Porter, Michael’s ( 2011 ) Competitive advantage theory, the eastern provinces, having established themselves as economic powerhouses, have already harnessed advanced infrastructural frameworks and digital ecosystems. This enables them to efficiently leverage the capabilities of e-commerce, thereby reflecting a more pronounced augmentation in their export trade. The central provinces, as highlighted by North and Douglass’s ( 1989 ) theory of institutional change, are navigating through evolving institutional landscapes, mediating between traditional trade mechanisms and burgeoning digital frontiers. While they have made significant strides, the transformational gaps that exist temper the full realization of e-commerce benefits in the domain of exports. The western provinces, on the other hand, are still grappling with foundational challenges. Drawing from Sachs and Warner’s ( 2001 ) resource curse hypothesis, these provinces, abundant in natural resources, might have historically focused more on primary sectors, leading to a lag in the adoption and integration of e-commerce into their economic tapestry. This could partially elucidate the relatively muted growth in export trade from e-commerce advancements. Incorporating the sustainability ethos, as expounded in the triple bottom line approach by Elkington ( 1998 ), the expansion of e-commerce should not merely serve economic objectives. It should be orchestrated in a manner that respects ecological boundaries and promotes social inclusivity. Especially in regions like the western provinces, where development is paramount, it is critical to ensure that the surge in e-commerce-driven exports is not at the expense of environmental degradation or social disparities, thereby upholding a balanced, sustainable developmental trajectory.

Conclusions

Amidst the fast-paced evolution of the global economy, key factors such as foreign direct investment and e-commerce have become instrumental in reshaping China’s export sector between 2005 and 2022. Our analytical models, which utilize a combination of province- and year-fixed effects analysis along with fully modified ordinary least squares and dynamic ordinary least-squares methodologies, shed light on how foreign direct investment and e-commerce synergistically enhance China’s export capabilities. Significantly, this expansion in China’s exports aligns with the global agenda for sustainable development. It’s encouraging to see that China’s growth in exports extends beyond the realms of international investment and digital marketplaces, intertwining sustainable practices like optimizing local labor resources and prioritizing technological advancements. These approaches contribute to a more sustainable export environment. Our findings further reveal that variables such as knowledge capital and educational levels positively influence China’s export figures. Additionally, our analysis of regional disparities provides a deeper understanding. The eastern regions of China show greater responsiveness to foreign direct investment and e-commerce in driving export trade, whereas the western regions respond more modestly. This variation highlights the need for tailored policies and sustainability strategies to ensure a fair distribution of the benefits from foreign direct investment and e-commerce across all regions. In conclusion, while foreign direct investment and e-commerce are key drivers of China’s export growth, the broader story is one of sustainable development. Technological innovation and human capital development are pivotal to China’s continued success in exports. Moving forward, it is essential for policymakers to maintain a careful equilibrium between these economic drivers and sustainable development goals, fostering a balance between economic growth and environmental sustainability.

Drawing upon the insights derived from this study, we elucidate several policy recommendations along with practical solutions. First, for policymakers and business leaders, investing in technology and education is identified as a crucial strategy. The significant impact of technological innovation and a well-educated workforce on export growth underscores the necessity for ongoing investment in these domains. For academia, this opens avenues for further research into specific types of educational programs and technological innovations that most effectively enhance export capabilities. Businesses, especially in the export sector, should prioritize employee training and the adoption of cutting-edge technologies to maintain competitiveness. Second, considering the varied responsiveness to foreign direct investment and e-commerce between China’s eastern and western regions, it’s imperative for regional authorities and business managers to customize their policies and strategies to suit the unique needs and strengths of their regions. This could involve targeted investments in infrastructure and digital capabilities in the eastern regions while simultaneously focusing on cultivating other competitive advantages in the western regions. Academia can play a role by conducting region-specific research to identify the most effective strategies for each area. Third, the intersection of export growth with sustainable development goals necessitates a comprehensive approach to policymaking. Managers in the export sector are encouraged to integrate sustainable practices into their business models, such as the utilization of environmentally friendly technologies and adherence to fair labor practices. This area also presents an opportunity for academic research into the effective implementation of sustainable practices in the export sector, aiming to balance profitability and competitiveness. Finally, our findings suggest that although foreign direct investment and e-commerce are significant drivers of export growth, their benefits are not uniformly experienced across all regions. This indicates the need for balanced development strategies that ensure equitable benefits from foreign direct investment and e-commerce across various regions. Strategies might include enhancing e-commerce infrastructure in less-developed areas or offering incentives for foreign investment in regions currently less engaged with these investments. For academics, this highlights the necessity of researching ways to optimize the impact of foreign direct investment and e-commerce across diverse regions, promoting equitable economic growth. These policy implications offer a strategic roadmap for leveraging key drivers of export growth in China, highlighting the importance of regional customization, sustainable development, and balanced economic strategies.

In the course of this research, certain limitations emerged that warrant acknowledgment. Firstly, the study’s timeframe, spanning from 2005 to 2022, may not fully capture the evolving dynamics of foreign direct investment and e-commerce in the context of China’s longer-term economic history. A more expansive temporal analysis could provide deeper historical insights. Secondly, while the fully modified ordinary least squares and dynamic ordinary least-squares methodologies and fixed effect models offer robustness, they may not encompass all the nuanced intricacies of the interactions between the chosen variables. Future research could employ mixed-method approaches, blending quantitative and qualitative inquiries to attain a richer understanding. Thirdly, our focus on regional heterogeneity, while pivotal, may overlook intra-regional variances that can significantly influence export trends. Subsequent studies might delve deeper into micro-level analyses, probing district- or city-level data. Fourthly, the emphasis on sustainability, though aligned with global imperatives, is predominantly viewed through the lenses of labor and technology. Incorporating other sustainability metrics, such as environmental or social indicators, could render a holistic view. Lastly, the external validity of our findings, primarily centered on China, might be limited in their generalizability to other nations. Comparative studies juxtaposing China’s experiences with those of other global players could bridge this gap. Addressing these limitations would not only refine the existing body of knowledge but also ensure a more comprehensive alignment of economic strategies with sustainable development goals.

Data availability

All data generated or analyzed during this study are included in this published article.

Abdul-Rahim R, Bohari SA, Aman A, Awang Z (2022) Benefit–risk perceptions of FinTech adoption for sustainability from bank consumers’ perspective: the moderating role of fear of COVID-19. Sustainability 14(14):8357, https://www.mdpi.com/2071-1050/14/14/8357

Article   CAS   Google Scholar  

Adikari AMP, Liu H, Marasinghe MMSA (2021) Inward foreign direct investment-induced technological innovation in Sri Lanka? Empirical evidence using ARDL approach. Sustainability 13(13):7334, https://www.mdpi.com/2071-1050/13/13/7334

Article   Google Scholar  

Agarwal J, Wu T (2015) Factors influencing growth potential of E-commerce in emerging economies: an institution-based N-OLI framework and research propositions. Thunderbird Int Bus Rev 57(3):197–215. https://doi.org/10.1002/tie.21694

Aghion P, Howitt P, Brant-Collett M, García-Peñalosa C (1998) Endogenous growth theory. MIT Press

Ahmed Z, Zafar MW, Ali S (2020) Linking urbanization, human capital, and the ecological footprint in G7 countries: an empirical analysis. Sustain Cities Soc 55:102064, https://www.sciencedirect.com/science/article/pii/S2210670720300512

Ahn SC, Thomas GM (2023) Likelihood-based inference for dynamic panel data models. Empir Econ 64(6):2859–2909. https://doi.org/10.1007/s00181-023-02375-0

Akther T, Voumik LC, Hasanur Rahman M (2022) The pattern of international trade between Bangladesh and USA: Heckscher–Ohlin and Rybczynski analysis. Mod Supply Chain Res Appl 4(3):162–176. https://doi.org/10.1108/MSCRA-03-2022-0011

Amornkitvikai Y, Tham SY, Harvie C, Writthym Buachoom W (2022) Barriers and factors affecting the e-commerce sustainability of Thai micro-, small-and medium-sized enterprises (MSMEs). Sustainability 14(14):8476, https://www.mdpi.com/2071-1050/14/14/8476

Antràs P, Alonso De Gortari, Itskhoki O (2017) Globalization, inequality and welfare. J Int Econ 108:387–412. https://www.sciencedirect.com/science/article/pii/S0022199617300958

Anzoategui D, Comin D, Gertler M, Martinez J (2019) Endogenous technology adoption and R&D as sources of business cycle persistence. Am Econ J 11(3):67–110. https://doi.org/10.1257/mac.20170269

Atil L, Fellag H, Sipols AE, Santos-Martín MT, De Blas CS (2023) Non-linear cointegration test, based on record counting statistic. Comput Econ https://doi.org/10.1007/s10614-023-10520-1

Atkin D (2016) Endogenous skill acquisition and export manufacturing in Mexico. Am Econ Rev 106(8):2046–2085. https://www.aeaweb.org/articles?id=10.1257/aer.20120901

Auboin M, Koopman R, Xu A (2021) Trade and innovation policies: coexistence and spillovers. J Policy Model 43(4):844–872. https://www.sciencedirect.com/science/article/pii/S0161893821000375

Autor DH, Dorn D, Hanson GH (2015) Untangling trade and technology: evidence from local labour markets. Econ J 125(584):621–646. https://academic.oup.com/ej/article-abstract/125/584/621/5077881

Bai J, Choi SH, Liao Y (2021) Feasible generalized least squares for panel data with cross-sectional and serial correlations. Empir Econ 60(1):309–326. https://doi.org/10.1007/s00181-020-01977-2

Baltagi BH (2021) Econometric analysis of panel data. Springer texts in business and economics. Springer International Publishing, Cham

Banalieva ER, Dhanaraj C (2019) Internalization theory for the digital economy. J Int Bus Stud 50(8):1372–1387. https://doi.org/10.1057/s41267-019-00243-7

Batschauer da Cruz, Beatriz C, Eliete Floriani D, Amal M (2022) The OLI paradigm as a comprehensive model of FDI determinants: a sub-national approach. Int J Emerg Mark 17(1):145–176. https://www.emerald.com/insight/content/doi/10.1108/IJOEM-07-2019-0517/full/html

Bhaumik, Kumar S, Driffield N, Zhou Y (2016) Country specific advantage, firm specific advantage and multinationality—sources of competitive advantage in emerging markets: evidence from the electronics industry in China. Int Bus Rev 25(1):165–176. https://www.sciencedirect.com/science/article/pii/S0969593115000050

Blanchard EJ, Olney WW (2017) Globalization and human capital investment: export composition drives educational attainment. J Int Econ 106:165–183. https://www.sciencedirect.com/science/article/pii/S0022199617300302

Blanchard, Jean-Marc F (2019) Brazil’s Samba with China: economics brought them closer, but failed to ensure their Tango. J Chin Political Sci 24(4):583–603. https://doi.org/10.1007/s11366-018-09586-8

Bollen KA, Fisher ZF, Giordano ML, Lilly AG, Luo L, Ye A (2022) An introduction to model implied instrumental variables using two stage least squares (MIIV-2SLS) in structural equation models (SEMs). Psychol Methods 27(5):752, https://psycnet.apa.org/record/2021-70427-001

Article   PubMed   Google Scholar  

Bruns SB, Moneta A, Stern DI (2021) Estimating the economy-wide rebound effect using empirically identified structural vector autoregressions. Energy Econ 97:105158, https://www.sciencedirect.com/science/article/pii/S0140988321000633

Castilho M, Menéndez M, Sztulman A (2012) Trade liberalization, inequality, and poverty in Brazilian states. World Dev 40(4):821–835. https://www.sciencedirect.com/science/article/pii/S0305750X11002403

Chebrolu N, Läbe T, Vysotska O, Behley J, Stachniss C (2021) Adaptive robust kernels for non-linear least squares problems. IEEE Robot Autom Lett 6(2):2240–2247. https://ieeexplore.ieee.org/abstract/document/9361339/

Chen H, Yan B, Fei R, Bao S (2023) Assessing the impact of trade policy uncertainty on pollution emissions: an analysis of Chinese firms’ green transformation. Environ Sci Pollut Res 30(47):104577–104591. https://doi.org/10.1007/s11356-023-29778-x

Chen J, Hu X, Huang J, Lin R (2022) Market integration and green economic growth—recent evidence of China’s city-level data from 2004–2018. Environ Sci Pollut Res 29(29):44461–44478. https://doi.org/10.1007/s11356-022-19070-9

Chiang M‐H, De Micheaux EL (2022) China’s outward foreign direct investment in Southeast Asia: analyzing the Chinese state’s strategies and potential influence. Thunderbird Int Bus Rev 64(6):581–593. https://doi.org/10.1002/tie.22311

Choong C-K (2012) Does domestic financial development enhance the linkages between foreign direct investment and economic growth? Empir Econ 42(3):819–834. https://doi.org/10.1007/s00181-011-0455-2

Christou C, Pittis N (2002) Kernel and bandwidth selection, prewhitening, and the performance of the fully modified least squares estimation method. Econom Theory 18(4):948–961. https://www.cambridge.org/core/journals/econometric-theory/article/kernel-and-bandwidth-selection-prewhitening-and-the-performance-of-the-fully-modified-least-squares-estimation-method/9F28178B61EF30D406AC2ABDC0AECBC0

Article   MathSciNet   Google Scholar  

Chudik A, Pesaran MH (2015) Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors. J Econ 188(2):393–420. https://www.sciencedirect.com/science/article/pii/S0304407615000767

Contractor FJ, Dangol R, Nuruzzaman N, Raghunath S (2020) How do country regulations and business environment impact foreign direct investment (FDI) inflows? Int Bus Rev 29(2):101640, https://www.sciencedirect.com/science/article/pii/S0969593118305997

Cordes DL, Marinova D (2023) Systematic literature review of the role of E-commerce in providing pathways to sustainability for poverty alleviation in sub-Saharan Africa. Discov Sustain 4(1):7. https://doi.org/10.1007/s43621-022-00109-3

Article   ADS   Google Scholar  

Dang L, Zhao J (2020) Cultural risk and management strategy for Chinese enterprises’ overseas investment. China Econ Rev 61:101433, https://www.sciencedirect.com/science/article/pii/S1043951X20300304

Darku AB (2021) International trade and income convergence: sorting out the nature of bilateral trade. Int J Financ Econ 26(4):5337–5348. https://doi.org/10.1002/ijfe.2068

De Menezes DQF, Prata DM, Secchi AR, Pinto JC (2021) A review on robust M-estimators for regression analysis. Comput Chem Eng 147:107254, https://www.sciencedirect.com/science/article/pii/S0098135421000326

Deng T, Khan MA, Uddin M, Haider A (2023) Connecting fiscal decentralization with climate change mitigation in China: directions for carbon capturing systems. Processes 11(3):712, https://www.mdpi.com/2227-9717/11/3/712

Duan W, Zhu S, Lai M (2020) The impact of COVID-19 on China’s trade and outward FDI and related countermeasures. J Chin Econ Bus Stud 18(4):355–364. https://doi.org/10.1080/14765284.2020.1855395

Elia S, Giuffrida M, Mariani MM, Bresciani S (2021) Resources and digital export: an RBV perspective on the role of digital technologies and capabilities in cross-border e-commerce. J Bus Res 132:158–169. https://www.sciencedirect.com/science/article/pii/S0148296321002484

Elkington J (1998) Partnerships from cannibals with forks: the triple bottom line of 21st-century business. Environ Qual Manag 8(1):37–51. https://doi.org/10.1002/tqem.3310080106

Fernández-Val I, Weidner M (2018) Fixed effects estimation of large- T panel data models. Annu Rev Econ 10(1):109–138. https://doi.org/10.1146/annurev-economics-080217-053542

Fidrmuc J, Korhonen I (2010) The impact of the global financial crisis on business cycles in Asian emerging economies. J Asian Econ 21(3):293–303. https://www.sciencedirect.com/science/article/pii/S1049007809000657

Fingleton B (2023) Estimating dynamic spatial panel data models with endogenous regressors using synthetic instruments. J Geogr Syst 25(1):121–152. https://doi.org/10.1007/s10109-022-00397-3

Fritz M, Hausen T, Schiefer G (2004) Developments and development directions of electronic trade platforms in US and European agri-food markets: impact on sector organization. Int food Agribus Manag Rev 7(1030-2016–82569):1–21. https://ageconsearch.umn.edu/record/8146/

Google Scholar  

Fu X, Woo WT, Hou J (2016) Technological innovation policy in China: the lessons, and the necessary changes ahead. Econ Change Restruct 49(2–3):139–157. https://doi.org/10.1007/s10644-016-9186-x

Gao H (2018) Digital or trade? The contrasting approaches of China and US to digital trade. J Int Econ Law 21(2):297–321. https://academic.oup.com/jiel/article-abstract/21/2/297/4996884

Giuffrida M, Mangiaracina R, Perego A, Tumino A (2017) Cross-border B2C e-commerce to Greater China and the role of logistics: a literature review. Int J Phys Distrib Logist Manag 47(9):772–795. https://www.emerald.com/insight/content/doi/10.1108/IJPDLM-08-2016-0241/full/html

Goerzen A, Asmussen CG, Nielsen BB (2013) Global cities and multinational enterprise location strategy. J Int Bus Stud 44(5):427–450. https://doi.org/10.1057/jibs.2013.11

Gorla N, Chiravuri A, Chinta R (2017) Business-to-business e-commerce adoption: an empirical investigation of business factors. Inf Syst Front 19(3):645–667. https://doi.org/10.1007/s10796-015-9616-8

Götz M (2020) Attracting foreign direct investment in the era of digitally reshaped international production. The primer on the role of the investment policy and clusters—the case of Poland. J East-West Bus 26(2):131–160. https://doi.org/10.1080/10669868.2019.1692985

Grübler A, O’Neill B, Riahi K, Chirkov V, Goujon A, Kolp P, Prommer I, Scherbov S, Slentoe E (2007) Regional, national, and spatially explicit scenarios of demographic and economic change based on SRES. Technol Forecast Soc Change 74(7):980–1029. https://www.sciencedirect.com/science/article/pii/S0040162506001454

Ha LT (2022) Are digital business and digital public services a driver for better energy security? Evidence from a European sample. Environ Sci Pollut Res 29(18):27232–27256. https://doi.org/10.1007/s11356-021-17843-2

Hanelt A, Bohnsack R, Marz D, Antunes Marante C (2021) A systematic review of the literature on digital transformation: insights and implications for strategy and organizational change. J Manag Stud 58(5):1159–1197. https://doi.org/10.1111/joms.12639

Hao S, Chen Z, Wang C-C, Hung C-Y (2023) Impact of digital service trade barriers and cross-border digital service inputs on economic growth. Sustainability 15(19):14547, https://www.mdpi.com/2071-1050/15/19/14547

Hartono, Gian P, Robiyanto Robiyanto (2023) Factors affecting the inconsistency of dividend policy using dynamic panel data model. SN Bus Econ 3(2):53. https://doi.org/10.1007/s43546-023-00431-6

He Y, Wu R, Choi Y-J (2021) International logistics and cross-border E-commerce trade: who matters whom? Sustainability 13(4):1745, https://www.mdpi.com/2071-1050/13/4/1745

Hill TD, Davis AP, Roos JM, French MT (2020) Limitations of fixed-effects models for panel data. Sociol Perspect 63(3):357–369. https://doi.org/10.1177/0731121419863785

Huang Y, Muhammad S, Raza F, Usman M (2023) Asymmetric role of natural resources dependence, industrialization, and foreign direct investment in China’s economic growth. Resour Policy 85:103932, https://www.sciencedirect.com/science/article/pii/S0301420723006438

Jiang X, Zhang L, Xiong C, Wang R (2016) Transportation and regional economic development: analysis of spatial spillovers in China provincial regions. Netw Spat Econ 16(3):769–790. https://doi.org/10.1007/s11067-015-9298-2

Jin G, Huang Z (2023) Asymmetric influence of China’s outward FDI and exports on trade-adjusted resources footprint in belt and road node countries: moderating role of governance. Resour Policy 82:103558, https://www.sciencedirect.com/science/article/pii/S0301420723002696

Jones CI (2019) Paul Romer: ideas, nonrivalry, and endogenous growth. Scand J Econ 121(3):859–883. https://doi.org/10.1111/sjoe.12370

Katz R, Callorda F (2018) Accelerating the development of Latin American digital ecosystem and implications for broadband policy. Telecommun Policy 42(9):661–681. https://www.sciencedirect.com/science/article/pii/S0308596117302914

Khan H, Khan Z (2021) The efficacy of marketing skills and market responsiveness in marketing performance of emerging market exporting firms in advanced markets: the moderating role of competitive intensity. Int Bus Rev 30(6):101860, https://www.sciencedirect.com/science/article/pii/S0969593121000676

Kheifets IL, Phillips PCB (2023) Fully modified least squares cointegrating parameter estimation in multicointegrated systems. J Econ 232(2):300–319. https://www.sciencedirect.com/science/article/pii/S030440762100186X

Klimenko M, Qu J (2023) Global Digital Platforms, technology transfer and foreign direct investment policies in two‐sided markets. Econ Inq 61(3):584–604. https://doi.org/10.1111/ecin.13132

Kripfganz S, Sarafidis V (2021) Instrumental-variable estimation of large- T panel-data models with common factors. Stata J 21(3):659–686. https://doi.org/10.1177/1536867X211045558

Kropko J, Kubinec R (2020) Interpretation and identification of within-unit and cross-sectional variation in panel data models. PLoS ONE 15(4):e0231349, https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0231349

Article   CAS   PubMed   PubMed Central   Google Scholar  

Kunroo MH, Ahmad I (2023) Heckscher–Ohlin theory or the modern trade theory: how the overall trade characterizes at the global level? J Quant Econ 21(1):151–174. https://doi.org/10.1007/s40953-022-00330-x

Kwak J, Zhang Y, Yu J (2019) Legitimacy building and e-commerce platform development in China: the experience of Alibaba. Technol Forecast Soc Change 139:115–124. https://www.sciencedirect.com/science/article/pii/S0040162518309636

Lei T, Xie P (2023) Fostering enterprise innovation: the impact of China’s pilot free trade zones. J Knowl Econ https://doi.org/10.1007/s13132-023-01501-8

Li F, Frederick S, Gereffi G (2019) E-Commerce and industrial upgrading in the Chinese apparel value chain. J Contemp Asia 49(1):24–53. https://doi.org/10.1080/00472336.2018.1481220

Li K, Kim DJ, Lang KR, Kauffman RJ, Naldi M (2020) How should we understand the digital economy in Asia? Critical assessment and research agenda. Electron Commer Res Appl 44:101004, https://www.sciencedirect.com/science/article/pii/S1567422320300818

Article   PubMed   PubMed Central   Google Scholar  

Li K, Cui G, Lu L (2020) Efficient estimation of heterogeneous coefficients in panel data models with common shocks. J Econ 216(2):327–353. https://www.sciencedirect.com/science/article/pii/S0304407619302222

Li R, Liu Y, Bustinza OF (2019) FDI, service intensity, and international marketing agility: the case of export quality of Chinese enterprises. Int Mark Rev 36(2):213–238. https://www.emerald.com/insight/content/doi/10.1108/IMR-01-2018-0031/full/html

Li Y, He Y, Wu R (2023) Traversing the macroeconomic terrain: an exploration of South Korea’s economic responsiveness to cross-border e-commerce production technology alterations in the global arena. Sustainability 15(15):11719, https://www.mdpi.com/2071-1050/15/15/11719

Lin J, Luo Z, Luo X (2020) Understanding the roles of institutional pressures and organizational innovativeness in contextualized transformation toward e-business: evidence from agricultural firms. Int J Inf Manag 51:102025, https://www.sciencedirect.com/science/article/pii/S0268401219301896

Lipton A, Hardjono T, Pentland A (2018) Digital Trade Coin: towards a more stable digital currency. R Soc Open Sci 5(7):180155. https://doi.org/10.1098/rsos.180155

Article   ADS   PubMed   PubMed Central   Google Scholar  

Liu J, Xie J (2020) Environmental regulation, technological innovation, and export competitiveness: an empirical study based on China’s manufacturing industry. Int J Environ Res Public Health 17(4):1427, https://www.mdpi.com/1660-4601/17/4/1427

Liu L, Moon HR, Schorfheide F (2020) Forecasting with dynamic panel data models. Econometrica 88(1):171–201. https://doi.org/10.3982/ECTA14952

Liu X, Schwaag Serger S, Tagscherer U, Chang AY (2017) Beyond catch-up—can a new innovation policy help China overcome the middle income trap? Sci Public Policy 44(5):656–669. https://academic.oup.com/spp/article-pdf/doi/10.1093/scipol/scw092/20917267/scw092.pdf

Lyu C, Peng C, Yang H, Li H, Gu X (2022) Social capital and innovation performance of digital firms: serial mediation effect of cross-border knowledge search and absorptive capacity. J Innov Knowl 7(2):100187, https://www.sciencedirect.com/science/article/pii/S2444569X22000270

Mahalik MK, Pal S, Le T-H, Mishra S (2023) Does environmental policy stringency improve nature’s health in BRICS economies? Implications for sustainable development. Environ Sci Pollut Res 31(1):509–528. https://doi.org/10.1007/s11356-023-31134-y

Mansion SE, Bausch A (2020) Intangible assets and SMEs’ export behavior: a meta-analytical perspective. Small Bus Econ 55(3):727–760. https://doi.org/10.1007/s11187-019-00182-5

Mark NC, Sul D (2003) Cointegration vector estimation by panel DOLS and long‐run money demand*. Oxf Bull Econ Stat 65(5):655–680. https://doi.org/10.1111/j.1468-0084.2003.00066.x

Milne MJ, Gray R (2013) W(h)Ither ecology? The triple bottom line, the global reporting initiative, and corporate sustainability reporting. J Bus Ethics 118(1):13–29. https://doi.org/10.1007/s10551-012-1543-8

Mohammad Shafiee M, Warkentin M, Motamed S (2023) Do human capital and relational capital influence knowledge-intensive firm competitiveness? The roles of export orientation and marketing knowledge capability. J Knowl Manag https://www.emerald.com/insight/content/doi/10.1108/JKM-11-2022-0921/full/html

Moon HR, Weidner M (2017) Dynamic linear panel regression models with interactive fixed effects. Econom Theory 33(1):158–195. https://www.cambridge.org/core/journals/econometric-theory/article/dynamic-linear-panel-regression-models-with-interactive-fixed-effects/CE84629C05BB652892D7B7659A1D5CD5

Ngoc PTB, Vu HQ, Long PD (2022) Domestic total factor productivity with trade and heterogenous foreign direct investment in developing countries: a case of Vietnamese manufacturing. Int J Emerg Mark. https://www.emerald.com/insight/content/doi/10.1108/IJOEM-08-2021-1333/full/html

Norkutė M, Sarafidis V, Yamagata T, Cui G (2021) Instrumental variable estimation of dynamic linear panel data models with defactored regressors and a multifactor error structure. J Econ 220(2):416–446. https://www.sciencedirect.com/science/article/pii/S0304407620301275

North, Douglass C (1989) Final remarks-institutional change and economic history. J Inst Theor Econ/Z Gesamt Staatswiss 238:45, https://www.jstor.org/stable/40751187

Onjewu A-KE, Hussain S, Haddoud MY (2022) The interplay of e-commerce, resilience and exports in the context of COVID-19. Inf Syst Front 24(4):1209–1221. https://doi.org/10.1007/s10796-022-10342-w

Otchere I, Soumaré I, Yourougou P (2016) FDI and financial market development in Africa. World Econ 39(5):651–678. https://doi.org/10.1111/twec.12277

Panopoulou E, Pittis N (2004) A comparison of autoregressive distributed lag and dynamic OLS cointegration estimators in the case of a serially correlated cointegration error. Econom J 7(2):585–617. https://academic.oup.com/ectj/article-abstract/7/2/585/5073337

Pedroni P (2001) Fully modified OLS for heterogeneous cointegrated panels. In: Baltagi BH, Fomby TB, Carter Hill R (eds) Nonstationary panels, panel cointegration, and dynamic panels. Emerald Group Publishing Limited, pp. 93–130

Pelagatti M, Sbrana G (2024) Estimating correlations among elliptically distributed random variables under any form of heteroskedasticity. Quant Finance 1–14. https://doi.org/10.1080/14697688.2023.2278502

Perrini F, Tencati A (2006) Sustainability and stakeholder management: the need for new corporate performance evaluation and reporting systems. Bus Strategy Environ 15(5):296–308. https://doi.org/10.1002/bse.538

Phang DCW, Wang K, Wang Q, Kauffman RJ, Naldi M (2019) How to derive causal insights for digital commerce in China? A research commentary on computational social science methods. Electron Commer Res Appl 35:100837, https://www.sciencedirect.com/science/article/pii/S1567422319300146

Phillips PCB, Kheifets IL (2024) High-dimensional IV cointegration estimation and inference. J Econ 238(2):105622, https://www.sciencedirect.com/science/article/pii/S030440762300338X

Porter ME (2011) Competitive advantage of nations: creating and sustaining superior performance. Simon and Schuster

Proost S, Thisse J-F (2019) What can be learned from spatial economics? J Econ Lit 57(3):575–643. https://doi.org/10.1257/jel.20181414

Qi X, Chan JH, Hu J, Li Y (2020) Motivations for selecting cross-border e-commerce as a foreign market entry mode. Ind Mark Manag 89:50–60. https://www.sciencedirect.com/science/article/pii/S0019850118308071

Qian Y, Liu J, Cheng Z, Forrest JY-L (2021) Does the smart city policy promote the green growth of the urban economy? Evidence from China. Environ Sci Pollut Res 28(47):66709–66723. https://doi.org/10.1007/s11356-021-15120-w

Redding SJ, Rossi-Hansberg E (2017) Quantitative spatial economics. Annu Rev Econ 9(1):21–58. https://doi.org/10.1146/annurev-economics-063016-103713

Rehman A, Radulescu M, Ahmad F, Kamran Khan M, Iacob SE, Cismas LM (2023) Investigating the asymmetrical influence of foreign direct investment, remittances, reserves, and information and communication technology on Pakistan’s economic development. Econ Res-Ekonom Istraž 36(2):2131591. https://doi.org/10.1080/1331677X.2022.2131591

Rita P, Ramos RF (2022) Global research trends in consumer behavior and sustainability in E-Commerce: a bibliometric analysis of the knowledge structure. Sustainability 14(15):9455, https://www.mdpi.com/2071-1050/14/15/9455

Sachs JD, Warner AM (2001) The curse of natural resources. Eur Econ Rev 45(4–6):827–838. https://www.sciencedirect.com/science/article/pii/S0014292101001258

Shanmugalingam P, Shanmuganeshan A, Manorajan A, Kugathasan M, Pathirana GY (2023) Does e-commerce really matter on international trade of Asian countries: evidence from panel data. PLoS ONE 18(4):e0284503, https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0284503

Singh S, Singh R (2022) Revisiting the debate on import-led substitution and export-led industrialization: where is India heading under self-reliant India? J World Trade 56(1). https://kluwerlawonline.com/journalarticle/Journal+of+World+Trade/56.5/TRAD2022005

Sinha A, Sengupta T, Alvarado R (2020) Interplay between technological innovation and environmental quality: formulating the SDG policies for next 11 economies. J Clean Prod 242:118549, https://www.sciencedirect.com/science/article/pii/S0959652619334195

Song M, Wang S (2018) Market competition, green technology progress and comparative advantages in China. Manag Decis 56(1):188–203. https://www.emerald.com/insight/content/doi/10.1108/MD-04-2017-0375/full/html

Stojanovic V, He S, Zhang B (2020) State and parameter joint estimation of linear stochastic systems in presence of faults and n on‐Gaussian noises. Int J Robust Nonlinear Control 30(16):6683–6700. https://doi.org/10.1002/rnc.5131

Sun L, Li,M X, Wang Y (2024) Digital trade growth and mineral resources In developing countries: implications for green recovery. Resour Policy 88:104338, https://www.sciencedirect.com/science/article/pii/S0301420723010498

Sun X (2022) Human capital, radical product innovation, and product proliferation: evidence from China. Emerg Mark Financ Trade 58(7):1938–1950. https://doi.org/10.1080/1540496X.2021.1945437

Tang W, Li G (2023) Enhancing competitiveness in cross-border e-commerce through knowledge-based consumer perception theory: an exploration of translation ability. J Knowl Econ https://doi.org/10.1007/s13132-023-01673-3

Teng Z, He Y, Wu R (2022) E-commerce: does sustainable logistics development matter? Sustainability 15(1):579, https://www.mdpi.com/2071-1050/15/1/579

Tolstoy D, Nordman ER, Hånell SM, Özbek N (2021) The development of international e-commerce in retail SMEs: an effectuation perspective. J World Bus 56(3):101165, https://www.sciencedirect.com/science/article/pii/S1090951620300936

Trapani L (2015) Testing for exogeneity in cointegrated panels. Oxf Bull Econ Stat 77(4):475–494. https://doi.org/10.1111/obes.12072

Villegas-Mateos A (2022) Toward a sustainable entrepreneurial ecosystem in Qatar. Sustainability 15(1):127, https://www.mdpi.com/2071-1050/15/1/127

Vukmirović V, Kostić-Stanković M, Pavlović D, Ateljević J, Bjelica D, Radonić M, Sekulić D (2021) Foreign direct investments’ impact on economic growth in Serbia. J Balk East Stud 23(1):122–143. https://doi.org/10.1080/19448953.2020.1818028

Wagner M (2023) Fully modified least squares estimation and inference for systems of cointegrating polynomial regressions. Econ Lett 228:111186, https://www.sciencedirect.com/science/article/pii/S0165176523002112

Wang C, Liu T, Wen D, Li D, Vladislav G, Zhu Y (2021) The impact of international electronic commerce on export trade: evidence from China. J Theor Appl Electron Commer Res. 16(7):2579–2593. https://www.mdpi.com/0718-1876/16/7/142

Wang D, Zheng Y, Li G (2024) High-dimensional low-rank tensor autoregressive time series modeling. J Econ 238(1):105544, https://www.sciencedirect.com/science/article/pii/S0304407623002609

Wang J-F (2010) E-commerce communities as knowledge bases for firms. Electron Commer Res Appl 9(4):335–345. https://www.sciencedirect.com/science/article/pii/S1567422309000842

Wang Y, Zhao T (2015) Impacts of energy-related CO 2 emissions: evidence from underdeveloped, developing and highly developed regions in China. Ecol Indic 50:186–195. https://www.sciencedirect.com/science/article/pii/S1470160X14005330

Wang Y, Gao H, Wang H (2024) The digital silk road and trade growth—a quasi-natural experiment based on silk road E-commerce. Res Int Bus Financ 67:102140, https://www.sciencedirect.com/science/article/pii/S0275531923002660

Wang Y, Jia F, Schoenherr T, Gong Y, Chen L (2020) Cross-border e-commerce firms as supply chain integrators: the management of three flows. Ind Mark Manag 89:72–88. https://www.sciencedirect.com/science/article/pii/S0019850118308642

Watson GF, Weaven S, Perkins H, Sardana D, Palmatier RW (2018) International market entry strategies: relational, digital, and hybrid approaches. J Int Mark 26(1):30–60. https://doi.org/10.1509/jim.17.0034

Wei L, Ullah S (2022) International tourism, digital infrastructure, and CO 2 emissions: fresh evidence from panel quantile regression approach. Environ Sci Pollut Res 29(24):36273–36280. https://doi.org/10.1007/s11356-021-18138-2

Wei YD, Lin J, Zhang L (2020) E-commerce, Taobao Villages and regional development in China * . Geogr Rev 110(3):380–405. https://doi.org/10.1111/gere.12367

Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE (2014) Permutation inference for the general linear model. Neuroimage 92:381–397. https://www.sciencedirect.com/science/article/pii/S1053811914000913

Wooldridge JM (2010) Econometric analysis of cross section and panel data. MIT Press

Wright DK (2019) Long-term dynamics of pastoral ecology in northern Kenya: an old model for new resilience. J Anthropol Archaeol 55:101068, https://www.sciencedirect.com/science/article/pii/S0278416518302216

Xi X, Wei M, Teo BS-X (2023) The construction and evolution path of cross-border e-commerce ecosystem. Cent Eur Manag J 31(2):378–387. http://journals.kozminski.cem-j.org/index.php/pl_cemj/article/view/710

Xiao Y, Abula B (2023) Examining the impact of digital economy on agricultural trade efficiency in RCEP region: a perspective based on spatial spillover effects. J Knowl Econ https://doi.org/10.1007/s13132-023-01484-6

Yan Z, Lu X, Chen Y, Wang K (2023) Institutional distance, internationalization speed and cross-border e-commerce platform utilization. Manag Decis 61(1):176–200. https://www.emerald.com/insight/content/doi/10.1108/MD-09-2021-1172/full/html

Yang C (2012) Restructuring the export-oriented industrialization in the Pearl River Delta, China: institutional evolution and emerging tension. Appl Geogr 32(1):143–157. https://www.sciencedirect.com/science/article/pii/S0143622810001463

Yin ZH, Choi CH (2023) The effects of China’s cross-border e-commerce on its exports: a comparative analysis of goods and services trade. Electron Commer Res 23(1):443–474. https://doi.org/10.1007/s10660-021-09483-y

Zhang C, Zeng W (2023) Evaluating the construction of a digital supervision platform for digital trade systems: a multilateral perspective. J Knowl Econ 十一月. https://doi.org/10.1007/s13132-023-01597-y

Zhang C-H, Zhang SS (2014) Confidence intervals for low dimensional parameters in high dimensional linear models. J R Stat Soc Ser B: Stat Methodol 76(1):217–242. https://academic.oup.com/jrsssb/article-abstract/76/1/217/7075942

Zhang M, Yang R (2022) FDI and spillovers: new evidence from Malaysia’s manufacturing sector. Rev Dev Econ 26(2):847–877. https://doi.org/10.1111/rode.12857

Zhang Q, Sun Z, Wu F, Deng X (2016) Understanding rural restructuring in China: the impact of changes in labor and capital productivity on domestic agricultural production and trade. J Rural Stud 47:552–562. https://www.sciencedirect.com/science/article/pii/S0743016716300638

Zhang S, Chen C (2020) Does outward foreign direct investment facilitate China’s export upgrading? China World Econ 28(5):64–89. https://doi.org/10.1111/cwe.12328

Zhang X (2019) Investigation of E‐commerce in China in a geographical perspective. Growth Change 50(3):1062–1084. https://doi.org/10.1111/grow.12307

Zhao Y, He X, Ma L, Liu H (2022) Unbiasedness-constrained least squares state estimation for time-varying systems with missing measurements under round-robin protocol. Int J Syst Sci 53(9):1925–1941. https://doi.org/10.1080/00207721.2022.2031338

Article   ADS   MathSciNet   Google Scholar  

Zhong M, Wang Z, Ge X (2022) Does cross-border E-commerce promote economic growth? Empirical research on China’s pilot zones. Sustainability 14(17):11032, https://www.mdpi.com/2071-1050/14/17/11032

Zhou X, Cai Z, Tan KH, Zhang L, Du J, Song M (2021) Technological innovation and structural change for economic development in China as an emerging market. Technol Forecast Soc Change 167:120671, https://www.sciencedirect.com/science/article/pii/S0040162521001037

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20 years of Electronic Commerce Research

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2021 marks the 20th anniversary of the founding of Electronic Commerce Research ( ECR ). The journal has changed substantially over its life, reflecting the wider changes in the tools and commercial focus of electronic commerce. ECR ’s early focus was telecommunications and electronic commerce. After reorganization and new editorship in 2014, that focus expanded to embrace emerging tools, business models, and applications in electronic commerce, with an emphasis on the innovations and the vibrant growth of electronic commerce in Asia. Over this time, ECR ’s impact and volume of publications have grown rapidly, and ECR is considered one of the premier journals in its discipline. This invited research summarizes the evolution of ECR ’s research focus over its history.

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1 Introduction

The year 2021 marks the 20th anniversary of the founding of Electronic Commerce Research ( ECR ). The journal has changed substantially over its life, reflecting the wider changes in the tools and commercial focus of electronic commerce. ECR ’s early focus was on telecommunications and electronic commerce. After reorganization and new editorship in 2014, that focus expanded to embrace emerging tools, business models, and applications in electronic commerce, with an emphasis on emerging technologies and the vibrant growth of electronic commerce in Asia. Over these years, ECR has steadily improved its stature and impact, as evidenced through various quantitative (e.g., citations, impact factors) and qualitative (e.g., peer-informed journal ranks) measures. According to Clarivate Analytics, ECR ’s impact factor in 2019 was 2.507, Footnote 1 which means that articles published in ECR between 2017 and 2018 received an average of 2.507 citations from journals indexed in Web of Science in 2019. The five-year impact factor of ECR was 2.643, 1 which indicates that articles published in ECR between 2014 and 2018 received an average of 2.643 citations from Web of Science-indexed journals in 2019. According to Scopus, ECR ’s CiteScore was 4.3, Footnote 2 which implies that articles published in ECR between 2016 and 2019 received an average of 4.3 citations from journals indexed in Scopus in 2019. The source normalized impact per paper (SNIP) of ECR was 1.962, which suggests that the average citations received by articles in the journal is 1.962 times the average citations received by articles in the same subject area of Scopus-indexed journals in 2019. Apart from these quantitative measures, ECR has also been rated highly by peers in the field, as seen through journal quality lists. For example, ECR has been consistently ranked as an “A” journal by the Excellence in Research for Australia (ERA 2010) and the Australian Business Deans Council (ABDC 2013, 2016, 2019) journal ranking lists.

This research presents a 20-year retrospective bibliometric analysis of the evolution of context and focus of ECR ’s articles [ 1 , 2 , 3 , 4 , 5 ]. To curate a rich bibliometric overview of ECR ’s scientific achievements, this study explores seven research questions (RQ) which are commonly asked by both authors and our Editorial Board members:

RQ1. What is the trend of publication and citation in ECR ?

RQ2. Who are the most prolific contributors (authors, institutions, and countries) in ECR ?

RQ3. What are the most influential publications in ECR ?

RQ4. Where have ECR publications been cited the most?

RQ5. What is the trend of collaboration in ECR ?

RQ6. Who are the most important constituents of the collaboration network in ECR ?

RQ7. What are the major research themes in ECR ?

A bibliometric analysis can offer a broad, systematic overview of the literature to delineate the evolution of electronic commerce technologies, and point the direction to trending topics and methodologies [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. Our research is organized as follows. Section  2 outlines our bibliometric methodology. Section  3 goes on to performance analysis to uncover contributor and journal performance trends (RQ1–RQ4), the co-authorship analysis performed to unpack collaboration and constituent characteristics (RQ5–RQ6), and the bibliometric coupling and keyword analyses used to reveal the major themes and trends within the ECR corpus (RQ7). Section  4 applies graph theoretic analysis. Section  5 applies cluster analysis. Section 6 applies thematic analysis. Finally, we conclude the study with key takeaways from this retrospective.

2 Methodology

Bibliometric methodologies apply graph theoretic and statistical tools for analysis of bibliographic data [ 15 ] and include performance analysis and science mapping [ 16 ]. To answer research question 1 to research question 4, this study uses performance analysis to measure the output of authors’ productivity and impact, with productivity measured using publications per year, and impact measured using citations per year. We begin by measuring the productivity and impact of ECR , and then the productivity and impact of authors, institutions, and countries using both publications and citations per year metrics on top of ancillary measures such as citations per publication and h -index. Finally, we measure the impact of ECR articles using citations and shed light on prominent publication outlets citing ECR articles.

To answer research question 5 to research question 7, this study uses co-authorship, bibliographic coupling, and keyword analyses. We begin by conducting a co-authorship analysis, which is a network-based analysis that scrutinizes the relationships among journal contributors [ 17 ]. Next, we perform bibliographic coupling to obtain the major themes within the ECR corpus. The assumption of bibliographic coupling connotes that two documents would be similar in content if they share similar references [ 18 , 19 ]. Using article references, a network was created, wherein shared references were assigned with edge weights and documents were denoted with nodes. The documents were divided into thematic clusters using the Newman and Girvan [ 20 ] algorithm. Finally, we track the development of themes throughout different time periods using a temporal keyword analysis. The assumption of this analysis suggest that keywords are representative of the author’s intent [ 21 ] and thus important for understanding the prominence of themes pursued by authors across different time periods. Indeed, we found that these bibliometric methods complement each other relatively well, as bibliographic coupling was useful to locate general themes while keywords were useful to understand specific topics.

To acquire bibliographic data of ECR articles for the bibliometric analyses mentioned above, this study uses the Scopus database, which is one of the largest academic database that is almost 60% larger than the Web of Science [ 21 ]. Past research has also indicated that the citations presented within the Scopus database correlate more with expert judgement as compared to Google Scholar and Web of Science [ 22 ]. We begin by conducting a source search for “ Electronic Commerce Research ,” which resulted in 927 articles, and after filtering out non- ECR articles, we obtain a list of 516 ECR articles (see Fig.  1 ). However, ECR only gained Scopus indexation in 2005, and thus, only 443 ECR articles (2005–2020) contained full bibliometric data, whereas the remaining 73 ECR articles (2001–2004) contained only partial bibliometric data (e.g., no affiliation, abstract, and keyword entry). All 516 ECR articles were fetched and included in the performance analysis as partial bibliometric data was sufficient, but only 443 ECR articles were included in science mapping (e.g., co-authorship, bibliographic coupling, and keyword analyses using VOSviewer [ 23 ] and Gephi [ 24 ]) as full bibliometric data was required. This collection of articles met the minimum sample size of 200 articles for bibliometric analysis recommended by Rogers, Szomszor, and Adams [ 25 ].

Research design. Note Bibliometric analysis was conducted for only 443 (primary) documents as 73 (secondary) documents lack full data (affiliation, abstract and keywords)

3 Performance analysis: productivity and impact

The publication and citation trends of ECR between 2001 and 2020 are presented in Fig.  2 (RQ1). In terms of publication, the number of articles published in ECR has grown from 20 articles per year in 2001 to 81 articles per year in 2020, with an average annual growth rate of 7.64%. In terms of citations, the number of citations that ECR articles received has grown from three citations in 2001 to 1219 citations in 2020, with an average annual growth rate of 37.19%. These statistics suggest that ECR ’s publications and citations have seen exponential growth since its inception, and that the journal’s citations have grown at a much faster rate than its publication, which is very positive.

Annual publication and citation structure of ECR

3.2 Authors

The most prolific authors in ECR between 2001 and 2020 are presented in Table 1 (RQ2). The most prolific author is Jian Mou, who has published six articles in ECR , which have garnered a total of 95 citations. This is followed by Yan-Ping Liu and Liyi Zhang, who have published three articles each in ECR , which have received a total of 46 and 42 citations, respectively. Among the top 20 contributors, the author with the highest citation average per publication is Katina Michael (TC/TP and TC/TCP = 59 citations), who is followed closely by Yue Guo (TC/TP and TC/TCP = 51 citations); they are the only two authors who have an average citation greater than 50 for their ECR articles.

3.3 Institutions

The most prolific institutions for ECR between 2001 and 2020 are presented in Table 2 (RQ2). IBM, with 14 articles and 371 citations, emerges as the highest contributing institution to ECR . It is surprising yet encouraging to see a high number of contributions coming from practice, which reflects the ECR ’s receptiveness to publish industry-relevant research. Nonetheless, it is worth mentioning that this contribution is derived from the collective effort of IBM’s research labs around the world (e.g., Delhi, Haifa, and New York)—a unique advantage that most higher education institutions do not enjoy unless they have full-fledged research-active international branch campuses around the world. The second and third most contributing institutions are Nanjing University and Xi’an Jiaotong University, with 11 and 10 articles that have been cited 116 and 29 times, respectively. This is yet another interesting observation, as the contributions by Chinese institutions suggest that ECR is a truly international journal despite its origins and operations stemming in the United States. Finally, the University of California (TC/TP and TC/TCP = 34.86 citations) emerges as the institution that averages the most citations per publication, followed by IBM (TC/TP and TC/TCP = 26.50 citations) and Texas Tech University (TC/TP and TC/TCP = 26.20 citations).

3.4 Countries

The most prolific countries in ECR between 2001 and 2020 are presented in Table 3 (RQ2). China emerges as the most prolific contributor, with 152 articles and 1066 citations. This is followed by the United States, which has contributed 143 articles and 2813 citations. No country other than China and the United States has contributed more than 50 articles to ECR . Nevertheless, it is important to note that ECR also receives contributions from many countries around the world, as the remaining ± 50% of contributions in the top 20 list comes from 18 different countries across Asia, Europe, and Oceania.

3.5 Articles

The most cited articles in ECR between 2001 and 2020 are presented in Table 4 (RQ3). The most cited article published in ECR during this period is Füller et al.’s [ 26 ] article on the role of virtual communities in new product development (TC = 270). This is followed by Sotiriadis and van Zyl’s [ 27 ] article on electronic word of mouth and its effects on the tourism industry (TC = 188), Nonnecke et al.’s [ 28 ] article on the phenomena of ‘lurking’ in online communities (TC = 185), Lehdonvirta’s [ 29 ] article on the factors that drive virtual product purchases (TC = 170), and Bae and Lee’s [ 30 ] article on the effect of gender on consumer perception of online reviews (TC = 125). The diversity of topics in the most cited articles indicate that electronic commerce is indeed a multi-faceted subject, which we will explore in detail in the later sections.

3.6 Publication outlets

The publication outlets that have cited ECR articles the most between 2001 and 2020 are presented in Table 5 (RQ4). The list includes many prestigious journals such as International Journal of Information Management (ABDC = A*, IF = 8.210), Information and Management (ABDC = A*, IF = 5.155), and Decision Support Systems (ABDC = A*, IF = 4.721), among others. The presence of such reputed journals reflects ECR ’s own reputation of high standing among its peers. Apart from ECR , the publication outlets that have highly cited ECR include Lecture Notes in Computer Science including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics (TC = 218), Computers in Human Behavior (TC = 95), and ACM International Conference Proceeding Series (TC = 88), which reflect the diversity in publication outlets that ECR is making an impact (e.g., book, conference, journal).

4 Co-authorship analysis: scientific network

4.1 co-authorship.

The co-authorships in ECR between 2005 and 2020 are presented in Table 6 (RQ5). On the one hand, the co-authorship analysis shows that the share of articles written by a single author has gone down over the years from 10.94% (2005–2008) to 8.61% (2017–2020). The small and decreasing share of single-authored articles do not come as a surprise given the importance and proliferation of collaboration to address increasing thematic and methodological complexity in research [ 31 ]. On the other hand, the co-authorship analysis shows that multi-authored articles have increased their share in ECR , especially articles with three authors or more. In particular, the share of articles with three and five or more authors have increased from 31.25% and 4.69% between 2005 and 2008 to 34.45% and 14.35% between 2017 and 2020, respectively. These statistics suggests that collaboration is growing in prominence, which is consistent with recent observations reported by other premier journals in business [ 32 , 33 , 34 ], and that ECR is a good home for collaborative research.

4.2 Network centrality

The most important authors, institutions, and countries across different measures of centrality are presented in Table 7 (RQ6). In this study, we employ four measures of centrality: degree of centrality, betweenness centrality, closeness centrality, and eigen centrality.

In essence, degree of centrality refers to the number of relational ties a node has in a network. In contrast, betweenness centrality refers to a node’s ability to connect otherwise unconnected groups of nodes, wherein nodes act as a gateway for the flow of information. Whereas, closeness centrality refers to a node’s closeness to every other node in the network, whereby nodes that reflect a greater number of shortest paths than others in a network indicates the ability of those nodes to transmit information and knowledge across the network with relative ease. Finally, eigen centrality refers to a node’s relative importance in a network, whereby nodes that are connected to other highly connected nodes are crucial to information transfer.

In terms of authors, Jian Mou emerged as the most important author for degree of centrality and betweenness centrality, whereas Xin Luo and Jian-xin Wang were flagged as the most important authors for closeness centrality and eigen centrality, respectively. In terms of institutions, Renmin University emerged as the most important institution for degree centrality and betweenness centrality, whereas the University of Ottawa was rated as the most important institution for closeness centrality and eigen centrality. In terms of countries, China emerged as the most important country for betweenness centrality, whereas the United States emerged as the most important country for the other three measures of centrality. Collectively, these findings indicate the most important constituents for degree of centrality, betweenness centrality, closeness centrality, and eigen centrality in terms of authors, institutions, and countries.

4.3 Collaboration network

The author collaboration network in Fig.  3 indicates that authors groups in ECR are fairly separated from each other, especially among highly connected authors (more than five links in the network). This suggests that most authors in ECR chose to work in a single team rather than across multiple teams. The institution collaboration network in Fig.  4 reaffirms our earlier finding that Renmin University is indeed the most important constituent of the network, especially among highly connected institutions (more than five links in the network). The institution collaboration network also appears to be more complex than the author collaboration network, wherein institutions appear to be far more connected to each other, indicating a good degree of collaboration across institutional lines. The country network in Fig.  5 presents a similar network scenario, where countries appear to be fairly well connected, with the United States being at the center of the country-level collaboration network. These findings suggest that ECR authors collaborate more actively across institutions and countries than teams.

Author co-authorship network. Note Threshold for inclusion is five or more links in the network

Institution co-authorship network. Note Threshold for inclusion is five or more links in the network

Country co-authorship network. Note Threshold for inclusion is five or more links in the network

5 Bibliographic coupling: thematic clusters

Bibliographic coupling is applied to unpack the major clusters (themes) within the ECR corpus. The method is predicated on the assumption that documents that share the same references are similar in content [ 18 , 35 ]. The application of bibliographic coupling on 443 ECR articles resulted in the formation of 30 clusters, wherein 11 major clusters were identified. The 11 major clusters, which contained 401 (or 90.5%) ECR articles, were ordered based on number of publications and average publication years, with more recent clusters ordered before older clusters in the case of clusters sharing the same number of publications. The summary of the 11 major clusters, which take center stage in this study, is presented in Table 8 .

5.1 Cluster #1: online privacy and security

Cluster #1 contains 74 articles that have been cited 963 times with an average publication year of 2013.09. The most cited article in this cluster is Zarmpou et al.’s [ 36 ] article on the adoption of mobile services. This is followed by Chaudhry et al.’s [ 37 ] article on user encryption schemes for e-payment systems, and Antoniou and Batten’s [ 38 ] article on purchaser’s privacy and trust in online transactions. Other articles in this cluster have considered topics such as e-commerce trust models [ 39 ], consumer privacy [ 40 ], cybercrime and cybersecurity issues [ 41 ], gender differences [ 42 ], and the development and implementation of various authentication systems [ 43 , 44 ]. Thus, ECR articles in this cluster appear to be centered on online privacy and security issues , including equivalent solutions for improved authentication and encryption to improve trust in electronic commerce.

5.2 Cluster #2: online channels and optimization

Cluster #2 contains 49 articles that have been cited 415 times with an average publication year of 2016.67. The most cited article in this cluster is Jeffrey and Hodge’s [ 45 ] article on impulse purchases in online shopping. This is followed by Biller et al.’s [ 46 ] article on dynamic pricing for online retailing in the automotive industry, and Yan’s [ 47 ] article on profit sharing and firm performance in manufacturer-retailer dual-channel supply chains. Other articles in this cluster have examined online channels such as peer-to-peer networks and social commerce [ 48 , 49 ] and optimal supply chain configuration [ 50 , 51 ]. Thus, ECR articles in this cluster appear to be concentrated on online channels and optimization , particularly in terms of the channel characteristics and price and supply chain optimization in electronic commerce.

5.3 Cluster #3: online engagement and preferences

Cluster #3 contains 49 articles that have been cited 982 times with an average publication year of 2013.98. The most cited article in this cluster is Nonnecke et al.’s [ 28 ] article on online community participation. This is followed by Sila’s [ 52 ] article on business-to-business electronic commerce technologies, and Ozok and Wei’s [ 53 ] article on consumer preferences of using mobile and stationary devices. Other articles in this cluster have explored topics such as online community participation and social impact across countries [ 54 ], online opinions across regions and its impact on consumer preferences [ 55 , 56 ], content and context factors [ 57 ], data mining techniques [ 58 ], and recommender systems and their application in online environments [ 59 , 60 ]. Thus, ECR articles in this cluster appear to be focused on online engagement and preferences , including the adoption and usage of technology (e.g., data mining, recommender systems) to curate engagement and shape preferences among target customers in electronic commerce.

5.4 Cluster #4: online market sentiments and analyses

Cluster #4 contains 41 articles that have been cited 198 times. This cluster has the highest average publication year among the 11 major clusters (2018.56), which indicates that most articles in this cluster are fairly recent. The most cited article in this cluster is Zhou’s [ 61 ] article on multi-layer affective modeling of emotions in the online environment. This is followed by Suki’s [ 62 ] article on online consumer shopping insights, and Chen et al.’s [ 63 ] article on information markets. Other articles in this cluster have investigated topics such as Internet queries and marketplace prediction [ 64 ], cross-border electronic commerce using the information systems success model [ 65 ], and electronic [ 66 ] and social [ 67 ] commerce using big data. Thus, ECR articles in this cluster appear to be centered on online market sentiments and analyses , with the use of advanced modeling techniques to unpack fresh insights on electronic commerce being relatively prominent.

5.5 Cluster #5: online reviews and ratings

Cluster #5 contains 40 articles that have been cited 611 times with an average publication year of 2017.28. The most cited article in this cluster is Bae and Lee’s [ 30 ] article on online consumer reviews across gender. This is followed by Flanagin et al.’s [ 68 ] article on user-generated online ratings, and Fairlie’s [ 69 ] on the digital divide in online access, which speaks to the technological infrastructure required to post and respond to online reviews and ratings. Other articles in this cluster have examined quantitative and qualitative feedback in online environments [ 70 ], electronic word of mouth platforms and persuasiveness [ 71 ], online reviews and product innovation [ 72 ] , recommender systems and product ranking [ 73 ], and online rating determinants [ 74 ]. Thus, ECR articles in this cluster appear to be concentrated on online reviews and ratings , including its potential differences among consumers coming from different demographic backgrounds.

5.6 Cluster #6: online exchanges and transactions

Cluster #6 contains 34 articles that have been cited 320 times with an average publication year of 2011.29. The most cited article in this cluster is Narayanasamy et al.’s [ 75 ] article on the adoption and concerns of e-finance. This is followed by Dumas et al.’s [ 76 ] article on bidding agents in e-auction, and Marinč’s [ 77 ] article on the impact of information technology on the banking industry. Other articles in this cluster have explored topics such as game theoretic aspects of search auctions [ 78 ], auction mechanism for ad space among advertisers [ 79 ], trust analysis in online procurement [ 80 ], efficiency of reverse auctions [ 81 ], and effect of hedonic and utilitarian behaviors on the e-auction behavior [ 82 ]. Thus, ECR articles in this cluster appear to be focused on online exchanges and transactions , particularly in terms of auction mechanisms and banking-related services.

5.7 Cluster #7: online media and platforms

Cluster #7 contains 30 articles that have been cited 668 times with an average publication year of 2016.23. The most cited article in this cluster is Sotiriadis and van Zyl’s [ 27 ] article on social media in the form of Twitter. This is followed by Huang and Liao’s [ 83 ] article on augmented reality interactive technology, and Hsieh et al.’s [ 84 ] article on online video persuasion in electronic commerce. Other articles in this cluster have investigated topics such as the role of social media in disseminating product information [ 85 ], the effect of video formats on person-to-person streaming [ 86 ], interpersonal relationship building using social media [ 87 ], and microblog usage [ 88 ]. Thus, ECR articles in this cluster appear to be centered on online media and platforms , particularly in terms of its variation, use, and impact in shaping consumer behavior in electronic commerce.

5.8 Cluster #8: online technology acceptance and continuance

Cluster #8 contains 26 articles that have been cited 244 times with an average publication year of 2016.37. The most cited article in this cluster is Zhou’s [ 89 ] article on the adoption of location-based services. This is followed by Chen et al.’s [ 90 ] article on the adoption of electronic customer relationship management, and Royo and Yetano’s [ 91 ] article on crowdsourcing usage in local governments. Other articles in this cluster have examined topics such as gender discrimination in online peer-to-peer lending [ 92 ], continued usage of e-auction services [ 93 ], and investor trust in peer-to-peer lending platforms [ 94 ]. Thus, ECR articles in this cluster appear to be concentrated on online technology acceptance and continuance , including determinants and discriminants that explain online technology-mediated behavior across different forms of electronic commerce such as e-auction, e-lending, e-government, and e-customer relationship management.

5.9 Cluster #9: online communities and commercialization in the virtual world

Cluster #9 contains 22 articles that have been cited 771 times with an average publication year of 2012.23. The most cited article in this cluster is Füller et al.’s [ 26 ] article on the role of virtual communities in new product development. This is followed by Lehdonvirta’s [ 29 ] article on the revenue model of virtual products, and Guo and Barnes’s [ 95 ] article on the purchase behavior of virtual products. Other articles in this cluster have investigated topics such as metaverse retailing [ 96 ], issues faced by developers of virtual worlds [ 97 ], the impact of virtual world on e-business models [ 98 ], e-commerce transactions in virtual environments [ 99 ], and customer value co-creation in virtual environments [ 26 ]. Thus, ECR articles in this cluster appear to be focused on the online communities and commercialization in the virtual world , particularly in virtual environments such as online gaming.

5.10 Cluster #10: online customer expectations, satisfaction, and loyalty

Cluster #10 contains 18 articles that have been cited 291 times with an average publication year of 2016.11. The most cited article in this cluster is Hanafizadeh and Khedmatgozar’s [ 100 ] article on consumer expectations of risk in online banking. This is followed by Valvi and Fragkos’s [ 101 ] article on purchase-centered e-loyalty, and Aloudat and Michael’s [ 102 ] article on regulatory expectations of ubiquitous mobile government. Other articles in this cluster have examined topics such as continued usage of e-services [ 103 ], determinants of e-loyalty [ 104 ] , risk expectations of e-services [ 105 ], and e-service quality implications for customer satisfaction and loyalty [ 106 ]. Thus, ECR articles in this cluster appear to be centered on online customer expectations, satisfaction, and loyalty , particularly in e-service settings such as online banking.

5.11 Cluster #11: online purchase intention

Cluster #11 contains 18 articles that have been cited 671 times with an average publication year of 2014.00. The most cited article in this cluster is Kim’s [ 107 ] article on online purchase intention using trust theory and technology acceptance model. This is followed by Gregg and Walczak’s [ 108 ] article on the effects of website quality on online purchase intention, and Taylor et al.’s [ 109 ] article on the effects of privacy concerns on online purchase intention. Other articles in this cluster have explored topics that either reaffirm the findings of the highly cited articles in this cluster, such as privacy concerns and personalization [ 109 , 110 ], or that extend the breadth of cluster coverage, such as store image [ 111 ], risk, and trust [ 112 ] as determinants of online purchase intention. Thus, ECR articles in this cluster appear to be concentrated on online purchase intentions , particularly in terms of its multi-faceted determinants that avail or transpire in electronic commerce.

6 Temporal keyword analysis: thematic evolution

Building on the thematic clusters uncovered using bibliographic coupling (see Fig.  6 ), this study performs a temporal keyword analysis to unpack the development of themes and its evolutionary trajectory in ECR over time.

Period wise publication trend in major clusters. Note Cluster #1 = online privacy and security. Cluster #2 = online channels and optimization. Cluster #3 = online engagement and preferences. Cluster #4 = online market sentiments and analyses. Cluster #5 = online reviews and ratings. Cluster #6 = online exchanges and transactions. Cluster #7 = online media and platforms. Cluster #8 = online technology acceptance and continuance. Cluster #9 = online communities and commercialization in the virtual world. Cluster #10 = online customer expectations, satisfaction, and loyalty. Cluster #11 = online purchase intention

6.1 Thematic development from 2005 to 2008

Most ECR articles between 2005 and 2008 appear in Clusters #1, #3, and #6 (see Fig.  6 ), which indicate research concentration on online privacy and security, online engagement and preferences, and online exchanges and transactions. The keyword network in Fig.  7 confirms this observation. Apart from general keywords such as “e-commerce,” keywords such as “cryptography,” “privacy,” and “security” relate directly to the theme of Cluster #1, which is about online privacy and security. The prominence of the word “cryptography” indicates the popularity and importance of the topic during this period. Other keywords such as “auctions,” “online auctions,” and “bidding strategies” relate to the theme of Cluster #6, which is about online exchanges and transactions, with particular focus on online auction and banking. Other keywords such as “collaborative filtering,” “online communities,” and “mobile commerce” relate to the theme of Cluster #3, which is about online engagement and preferences. The bigger and bolder keywords observed in Clusters #1 and #3 suggest that the direct benefits and costs of electronic commerce were most pertinent in the early stages of ECR , with the augmented aspects of electronic commerce in Cluster #6 emerging closely behind the two leading clusters in this period.

Keyword network between 2005 and 2008. Note Threshold for inclusion is a minimum of two occurrences

6.2 Thematic development from 2009 to 2012

Most ECR articles between 2009 and 2012 are located in Cluster #1 (see Fig.  6 ), which reveal the continued pertinence of research concentrating on online privacy and security during this period. Nonetheless, ECR experienced a substantial growth in research focusing on online media and platforms, online communities and commercialization in the virtual world, online customer expectations, satisfaction, and loyalty, and online purchase intention, as seen through ECR articles in Clusters #7, #9, #10, and #11 during this period. The keyword network in Fig.  8 adds to this observation. In particular, keywords such as “security,” “payment protocol,” and “trust management” relate to the theme of Cluster #1 on online privacy and security, whereas keywords such as “metaverses,” “second life,” “virtual reality,” and “virtual world” speak to the emergence of online communities and commercialization in the virtual world characterizing Cluster #9. Similarly, keywords such as “reputation” and “trust” are important to online customer expectations, satisfaction, and loyalty (Cluster #10) and their online purchase intention (Cluster #11). Interestingly, though Cluster #7 emerged during this period, we did not observe any unique or specific keywords relating to this cluster, which may be attributed to online media and platform research early focus on its “adoption,” a keyword that we felt resonates more with Cluster #8.

Keyword network between 2009 and 2012. Note Threshold for inclusion is a minimum of two occurrences

6.3 Thematic development from 2013 to 2016

Most ECR articles between 2013 and 2016 continue to be situated in Cluster #1 (see Fig.  6 ), which suggest the continued pertinence of research concentrating on online privacy and security during this period. Nonetheless, there are a number of clusters that saw noteworthy growth, such as Clusters #2, #5, #7, #8, and #10, which indicate that research attention has also been invested in topics related to online channels and optimization, online reviews and ratings, online media and platforms, online technology acceptance and continuance, and online customer expectations, satisfaction, and loyalty. The keyword network in Fig.  9 supports this observation. More specifically, keywords such as “personal information” and “privacy” indicate continued research in Cluster #1, though it appears that the focus has shifted from authentication and security mechanisms to privacy matters, which may be attributed to the rise of personalized and targeted online marketing activities (e.g., tracking of user activity for personalized advertisements). Whereas, keywords such as “B2C e-commerce” and “e-government” denote emerging interest in online channels and optimization (Cluster #2), “electronic word of mouth” indicates growing interest in online reviews and ratings (Cluster #5), “cloud computing,” “IPTV,” and “social media” reveal increasing interest in online media and platforms (Cluster #7), “information technology,” “technology adoption,” and “technology acceptance model” speak to research on online technology acceptance and continuance (Cluster #8), and “product type,” “quality of service,” and “user satisfaction” resonate with research on online customer expectations, satisfaction, and loyalty (Cluster #10).

Keyword network between 2013 and 2016. Note Threshold for inclusion is a minimum of two occurrences

6.4 Thematic development from 2017 to 2020

Most ECR articles between 2017 and 2020 are located in Cluster #4 (see Fig.  6 ), which reflect the noteworthy emergence and shift of research concentration from online privacy and security to online market sentiments and analyses. Other thematic clusters such as Clusters #2, #3, and #5 have also witnessed a massive increase in publications during this period. This implies that ECR has become relatively diverse in the research that it publishes, which also explains the rise in the number of papers that the journal publishes during this period. The keyword network in Fig.  10 sheds further light on this observation. In particular, many keywords in the network illustrate a strong research concentration on online market sentiments and analyses, such as “big data,” “data mining,” machine learning,” “sentiment analysis,” and “social network analysis” (Cluster #4). Similarly, keywords such as “dual channel supply chain,” “supply chain coordination,” and “social commerce” indicate the type of research focusing on online channels and optimization (Cluster #2), “social influence,” “social media,” and “social media marketing” reflect research in the area of online engagement and preferences (Cluster #3), and “consumer reviews,” “online reviews,” “reputation,” and “word of mouth” speak to research on online reviews and ratings (Cluster #5).

Keyword network between 2017 and 2020. Note Threshold for inclusion is a minimum of two occurrences

7 Conclusion

This study presents a 20-year retrospective of ECR since its inception in 2001. Several research questions were proposed and pursued using a bibliometric methodology consisting of performance analysis and science mapping (e.g., co-authorship analysis, bibliographic coupling, and temporal keyword analysis).

Our first four research questions—i.e., research question 1 to research question 4—concentrated on the publication and citation trends of ECR . Through performance analysis, we found that ECR has grown exponentially in terms of its publications and citations. Most contributors of ECR come from China and the United States, which reflect (1) China’s standing as the world’s largest e-commerce market with 50 percent of the world’s online transactions occurring in this country, and (2) the United States’ standing as the world’s pioneer of e-commerce (e.g., Amazon) and her expectation for e-commerce to reach 50% of total retail sales in the country in 10 years [ 113 ]. Interestingly, IBM, a non-academic institution, emerged as the highest contributing institution to the journal, which is unsurprising given that IBM is the largest industrial research organization in the world with 12 research labs across six continents [ 114 ]. More importantly, ECR was found to be well received among its peers, with many of its citations coming from prestigious journals in the field of information systems and management. Nevertheless, we observed that ECR receives very little contribution from Africa and several parts of Asia, particularly South Asia and South East Asia. Though electronic commerce may not have been very prominent in these regions in the past, we believe that the coronavirus pandemic that has taken the world by storm in 2020 has accelerated the proliferation and adoption of electronic commerce in these regions, and thus, we would encourage authors from these regions to submit their best papers to ECR in the near future. Thus, we raise two future research questions (FRQs) for exploration:

FRQ1: What are the e-commerce innovations that avail in underexplored regions (e.g., Africa, South Asia, and South East Asia) and how do such innovations fare in terms of similarities and differences in manifestations and impact against their more richly explored counterparts (e.g., China, United States)?

FRQ2: How can global pandemics such as COVID-19 change or impact e-commerce around the world (e.g., can the pandemic accelerate e-commerce adoption across all layers of society; can the pandemic lead to new innovations; can e-commerce contribute to positive and/or negative economic and social impact during the pandemic—and if yes, what and how, and if no, why)?

Our next two research questions—i.e., research question 5 and research question 6—focused on the collaboration trends in and the important constituents of ECR in the co-authorship network. Using co-authorship analysis, we found that the collaboration culture in ECR has grown with the passage of time, as evidenced through the decreasing share of single-authored articles and the increasing share of multi-authored publications, especially in the five or more authors category. We also observed that the share of multi-authored articles has always been dominant in the journal, with such publications forming nearly 90% of the corpus at any given point in time. Indeed, these observations reflect the increasing emphasis that universities place on multi-author and inter-/multi-/trans-disciplinary collaborations in promotion and tenure practices and policies [ 115 ]. In terms of important constituents in the co-authorship network, Jian Mou emerged as the most important author across two measures of centrality, whereas Renmin University and University of Ottawa emerged as the most important institutions at the institution level, and the United States emerged as the most important constituent at the country level. Nonetheless, we noted that authors who collaborate in ECR do not work much across diverse teams, but they do, however, work a lot across institutions and countries. Future scholars could rely on the centrality networks that we have curated herein this study for potential collaboration with authors from varying institutions and countries who have a good publication record and a research interest to publish with ECR .

Our final research question—i.e., research question 7—was dedicated to unpacking the major themes in ECR . Through bibliographic coupling, our study found 11 major clusters that reflected the major themes underpinning research published in ECR : (1) online privacy and security, (2) online channels and optimization, (3) online engagement and preferences, (4) online market sentiments and analyses, (5) online reviews and ratings, (6) online exchanges and transactions, (7) online media and platforms, (8) online technology acceptance and continuance, (9) online communities and commercialization in the virtual world, (10) online customer expectations, satisfaction, and loyalty, and (11) online purchase intention. Through temporal keyword analysis, our study observed that the topics published in ECR has become more diverse over time, with a noteworthy shift from an early concentration on online privacy and security to a contemporary focus on newer, industry-informed topics, such as online market sentiments and analyses, which we reckon coincides with the emergence of the unique peculiarities of the fourth industrial revolution (IR 4.0), such as big data and machine learning, in recent years [ 116 , 117 ]. Thus, to extend the line of research that concentrates on unpacking the contemporary realities of e-commerce, we propose another two future research questions (FRQs) for exploration:

FRQ3: How can emergent technologies (e.g., artificial intelligence, big data analytics, blockchain, machine learning) be applied to improve forecasting (e.g., cybercrime, social network), optimize functions (e.g., advertising, sales), and protect stakeholders (e.g., privacy, security) in e-commerce?

FRQ4: How can e-commerce operators leverage on emergent technologies to acquire competitive advantages (e.g., how to build trust and good relationships with customers [e.g., digital natives, digital migrants], and how to respond to changes in customer demands and marketplace trends with agility), and whether these competitive advantages that they acquired are sustainable or transient (and if transient, then what can they do to curate, maintain, or replenish their competitive advantages in the long run)?

Though thorough in its approach, this study does suffer from certain limitations. First, this study relies on the Scopus for bibliometric data. Though the database has its merits, as laid out in the methodology section, the bibliographic data is not created for the purpose of bibliometric analysis. This may lead to errors in the data source. Through data cleaning, we have attempted to minimize errors, but any remaining error in the source data, which we might have missed, could have an impact on the final analysis, though we believe that the margin for such errors would be relatively small, if not, negligible. Second, ECR has been around for 20 years, but the dataset available on Scopus, which we used, is only complete for 16 years (2005–2020). Due to this limitation, the science mapping part of the study—i.e., co-authorship, bibliographic coupling, and temporal keyword analysis—had to be restricted to this period only. We do not discount the possibility that the complete set of earlier data (2001–2004) may become available on Scopus in the future, and thus, we would encourage future research aiming to conduct a bibliometric review for ECR , perhaps in the next milestone (e.g., 30, 40, or 50 years), to check on such data availability, and if available, to take advantage and conduct a full-fledged science mapping for the journal. Finally, the scientific insights that could be uncovered through a bibliometric methodology, though rich, remain limited. In particular, bibliometric reviews such as ours do not delve into expert information, such as the theories, contexts, and methods employed to create new knowledge on electronic commerce in the ECR corpus. This, in turn, makes it difficult for bibliometric reviews to put forth a comprehensive set of data-informed proposals for future research. Nonetheless, we opine that bibliometric reviews do provide a good starting point of data-informed insights that future research can rely on to understand the trajectory of the extant discussion of electronic commerce in the journal. In particular, we believe that such insights would be useful, not only for future empirical research (e.g., potential collaboration networks, research themes of interest), but also for future reviews on thematic domains in ECR (e.g., systematic reviews on online market sentiments), which can be done in a number of ways, such a critical review [ 118 , 119 , 120 ], a thematic review [ 121 , 122 ], a theory-driven review [ 123 ], a method-driven review [ 124 , 125 ], or a framework-based review [ 126 ].

Web of Science single-year and five-year impact factors for ECR : https://www.springer.com/journal/10660 .

Scopus CiteScore and SNIP for ECR : https://www.scopus.com/sourceid/145669 .

Schwert, G. W. (1993). The Journal of Financial Economics . A retrospective evaluation (1974–1991). Journal of Financial Economics, 33 (3), 369–424. https://doi.org/10.1016/0304-405x(93)90012-z .

Article   Google Scholar  

Baker, H. K., Kumar, S., & Pandey, N. (2021). Thirty years of the Global Finance Journal : A bibliometric analysis. Global Finance Journal , 47 , 100492. https://doi.org/10.1016/j.gfj.2019.100492 .

Merigó, J. M., Pedrycz, W., Weber, R., & de la Sotta, C. (2018). Fifty years of Information Sciences : A bibliometric overview. Information Sciences, 432, 245–268. https://doi.org/10.1016/j.ins.2017.11.054 .

Kumar, S., Marrone, M., Liu, Q., & Pandey, N. (2020). Twenty years of the International Journal of Accounting Information Systems : A bibliometric analysis. International Journal of Accounting Information Systems, 39, 100488. https://doi.org/10.1016/j.accinf.2020.100488 .

Donthu, N., Kumar, S., & Pattnaik, D. (2020). Forty-five years of Journal of Business Research : A bibliometric analysis. Journal of Business Research, 109 (1), 1–14. https://doi.org/10.1016/j.jbusres.2019.10.039 .

Ellegaard, O., & Wallin, J. A. (2015). The bibliometric analysis of scholarly production: How great is the impact? Scientometrics, 105 (3), 1809–1831. https://doi.org/10.1007/s11192-015-1645-z .

Ramos-Rodrígue, A. R., & Ruíz-Navarro, J. (2004). Changes in the intellectual structure of strategic management research: A bibliometric study of the Strategic Management Journal , 1980–2000. Strategic Management Journal, 25 (10), 981–1004. https://doi.org/10.1002/smj.397 .

Valenzuela, L. M., Merigó, J. M., Johnston, W. J., Nicolas, C., & Jaramillo, J. F. (2017). Thirty years of the Journal of Business and Industrial Marketing : A bibliometric. Journal of Business and Industrial Marketing, 31 (1), 1–17. https://doi.org/10.1108/JBIM-04-2016-0079 .

Amiguet, L., Gil-lafuente, A. M., Kydland, F. E., & Merigó, J. M. (2017). One hundred twenty-five years of the Journal of Political Economy  : a bibliometric overview. Journal of Political Economy , 1–41. Retrieved from https://www.journals.uchicago.edu/pb-assets/docs/journals/jpe-bib-125.pdf

Mulet-Forteza, C., Martorell-Cunill, O., Merigó, J. M., Genovart-Balaguer, J., & Mauleon-Mendez, E. (2018). Twenty five years of the Journal of Travel and Tourism Marketing : A bibliometric ranking. Journal of Travel and Tourism Marketing, 35 (9), 1201–1221. https://doi.org/10.1080/10548408.2018.1487368 .

Baker, H. K., Kumar, S., & Pandey, N. (2020). A bibliometric analysis of managerial finance: A retrospective. Managerial Finance, 46 (11), 1495–1517. https://doi.org/10.1108/MF-06-2019-0277 .

Donthu, N., Kumar, S., & Pandey, N. (2020). A retrospective evaluation of Marketing Intelligence and Planning : 1983–2019. Marketing Intelligence and Planning , 39 (1), 48–73. https://doi.org/10.1108/MIP-02-2020-0066 .

Kumar, S., Spais, G. S., Kumar, D., & Sureka, R. (2020). A bibliometric history of the Journal of Promotion Management (1992–2019). Journal of Promotion Management, 26 (1), 97–120. https://doi.org/10.1080/10496491.2019.1685622 .

Kumar, S., Pandey, N., & Haldar, A. (2020). Twenty years of Public Management Review (PMR): A bibliometric overview. Public Management Review, 22 (12), 1876–1896. https://doi.org/10.1080/14719037.2020.1721122 .

Broadus, R. N. (1987). Toward a definition of “bibliometrics.” Scientometrics, 12 (5–6), 373–379. https://doi.org/10.1007/BF02016680 .

Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of Informetrics, 5 (1), 146–166. https://doi.org/10.1016/j.joi.2010.10.002 .

Crane, D. (1969). Social structure in a group of scientists: A test of the invisible college. American Sociological Review, 34 (3), 335–352.

Weinberg, B. H. (1974). Bibliographic coupling: A review. Information Storage and Retrieval, 10 (5–6), 189–196. https://doi.org/10.1016/0020-0271(74)90058-8 .

Baker, H. K., Kumar, S., & Pandey, N. (2020). A bibliometric analysis of European Financial Management ʼs first 25 years. European Financial Management, 26 (5), 1224–1260. https://doi.org/10.1111/eufm.12286 .

Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69 (2), 26113. https://doi.org/10.1103/PhysRevE.69.026113 .

Comerio, N., & Strozzi, F. (2019). Tourism and its economic impact: A literature review using bibliometric tools. Tourism Economics, 25 (1), 109–131. https://doi.org/10.1177/1354816618793762 .

Li, J., Sanderson, M., Willett, P., Norris, M., & Oppenheim, C. (2010). Ranking of library and information science researchers: Comparison of data sources for correlating citation data, and expert judgments. Journal of Informetrics, 4 (4), 554–563. https://doi.org/10.1016/j.joi.2010.06.005 .

van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84 (2), 523–538. https://doi.org/10.1007/s11192-009-0146-3 .

Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: An open source software for exploring and manipulating networks. In Proceedings of the Third International ICWSM Conference (2009) (pp. 361–362). https://doi.org/10.1136/qshc.2004.010033 .

Rogers, G., Szomszor, M., & Adams, J. (2020). Sample size in bibliometric analysis. Scientometrics, 125, 777–794. https://doi.org/10.1007/s11192-020-03647-7 .

Füller, J., Bartl, M., Ernst, H., & Mühlbacher, H. (2006). Community based innovation: How to integrate members of virtual communities into new product development. Electronic Commerce Research, 6 (1), 57–73. https://doi.org/10.1007/s10660-006-5988-7 .

Sotiriadis, M. D., & van Zyl, C. (2013). Electronic word-of-mouth and online reviews in tourism services: The use of twitter by tourists. Electronic Commerce Research, 13 (1), 103–124. https://doi.org/10.1007/s10660-013-9108-1 .

Nonnecke, B., Andrews, D., & Preece, J. (2006). Non-public and public online community participation: Needs, attitudes and behavior. Electronic Commerce Research, 6 (1), 7–20. https://doi.org/10.1007/s10660-006-5985-x .

Lehdonvirta, V. (2009). Virtual item sales as a revenue model: Identifying attributes that drive purchase decisions. Electronic Commerce Research, 9 (1–2), 97–113. https://doi.org/10.1007/s10660-009-9028-2 .

Bae, S., & Lee, T. (2011). Gender differences in consumers’ perception of online consumer reviews. Electronic Commerce Research, 11 (2), 201–214. https://doi.org/10.1007/s10660-010-9072-y .

Acedo, F. J., Barroso, C., Casanueva, C., & Galan, J. L. (2006). Co-authorship in management and organizational studies: An empirical and network analysis. Journal of Management Studies, 43 (5), 957–983. https://doi.org/10.1111/j.1467-6486.2006.00625.x .

Baker, H. K., Kumar, S., & Pandey, N. (2020). Thirty years of Small Business Economics : A bibliometric overview. Small Business Economics , 56 , 487–517. https://doi.org/10.1007/s11187-020-00342-y .

Burton, B., Kumar, S., & Pandey, N. (2020). Twenty-five years of The European Journal of Finance (EJF): A retrospective analysis. The European Journal of Finance, 26 (1), 1817–1841. https://doi.org/10.1080/1351847X.2020.1754873 .

Andrikopoulos, A., Samitas, A., & Kostaris, K. (2016). Four decades of the Journal of Econometrics : Coauthorship patterns and networks. Journal of Econometrics, 195 (1), 23–32. https://doi.org/10.1016/j.jeconom.2016.04.018 .

Kessler, M. M. (1963). Bibliographic coupling between scientific articles. American Documentation, 14 (1), 123–131.

Zarmpou, T., Saprikis, V., Markos, A., & Vlachopoulou, M. (2012). Modeling users’ acceptance of mobile services. Electronic Commerce Research, 12 (2), 225–248. https://doi.org/10.1007/s10660-012-9092-x .

Chaudhry, S. A., Farash, M. S., Naqvi, H., & Sher, M. (2016). A secure and efficient authenticated encryption for electronic payment systems using elliptic curve cryptography. Electronic Commerce Research, 16 (1), 113–139. https://doi.org/10.1007/s10660-015-9192-5 .

Antoniou, G., & Batten, L. (2011). E-commerce: Protecting purchaser privacy to enforce trust. Electronic Commerce Research, 11 (4), 421–456. https://doi.org/10.1007/s10660-011-9083-3 .

Morid, M. A., & Shajari, M. (2012). An enhanced e-commerce trust model for community based centralized systems. Electronic Commerce Research, 12 (4), 409–427. https://doi.org/10.1007/s10660-012-9099-3 .

Smith, R., & Shao, J. (2007). Privacy and e-commerce: A consumer-centric perspective. Electronic Commerce Research, 7 (2), 89–116. https://doi.org/10.1007/s10660-007-9002-9 .

Kshetri, N. (2013). Cybercrime and cyber-security issues associated with China: Some economic and institutional considerations. Electronic Commerce Research, 13 (1), 41–69. https://doi.org/10.1007/s10660-013-9105-4 .

Goh, T. T., & Sun, S. (2014). Exploring gender differences in Islamic mobile banking acceptance. Electronic Commerce Research, 14 (4), 435–458. https://doi.org/10.1007/s10660-014-9150-7 .

Chen, Y., & Chou, J. S. (2015). ECC-based untraceable authentication for large-scale active-tag RFID systems. Electronic Commerce Research, 15 (1), 97–120. https://doi.org/10.1007/s10660-014-9165-0 .

Isaac, J. T., Zeadally, S., & Cámara, J. S. (2012). A lightweight secure mobile payment protocol for vehicular ad-hoc networks (VANETs). Electronic Commerce Research, 12 (1), 97–123. https://doi.org/10.1007/s10660-011-9086-0 .

Jeffrey, S. A., & Hodge, R. (2007). Factors influencing impulse buying during an online purchase. Electronic Commerce Research, 7 (3–4), 367–379. https://doi.org/10.1007/s10660-007-9011-8 .

Biller, S., Chan, L. M. A., Simchi-Levi, D., & Swann, J. (2005). Dynamic pricing and the direct-to-customer model in the automotive industry. Electronic Commerce Research, 5 (2), 309–334. https://doi.org/10.1007/s10660-005-6161-4 .

Yan, R. (2008). Profit sharing and firm performance in the manufacturer-retailer dual-channel supply chain. Electronic Commerce Research, 8 (3), 155. https://doi.org/10.1007/s10660-008-9020-2 .

Cui, Y., Mou, J., & Liu, Y. (2018). Knowledge mapping of social commerce research: A visual analysis using CiteSpace. Electronic Commerce Research, 18 (4), 837–868. https://doi.org/10.1007/s10660-018-9288-9 .

Li, S., & Sun, W. (2016). A mechanism for resource pricing and fairness in peer-to-peer networks. Electronic Commerce Research, 16 (4), 425–451. https://doi.org/10.1007/s10660-016-9211-1 .

Wang, Z. B., Wang, Y. Y., & Wang, J. C. (2016). Optimal distribution channel strategy for new and remanufactured products. Electronic Commerce Research, 16 (2), 269–295. https://doi.org/10.1007/s10660-016-9225-8 .

Rofin, T. M., & Mahanty, B. (2018). Optimal dual-channel supply chain configuration for product categories with different customer preference of online channel. Electronic Commerce Research, 18 (3), 507–536. https://doi.org/10.1007/s10660-017-9269-4 .

Sila, I. (2013). Factors affecting the adoption of B2B e-commerce technologies. Electronic Commerce Research, 13 (2), 199–236. https://doi.org/10.1007/s10660-013-9110-7 .

Ozok, A. A., & Wei, J. (2010). An empirical comparison of consumer usability preferences in online shopping using stationary and mobile devices: Results from a college student population. Electronic Commerce Research, 10 (2), 111–137. https://doi.org/10.1007/s10660-010-9048-y .

Grace-Farfaglia, P., Dekkers, A., Sundararajan, B., Peters, L., & Park, S. H. (2006). Multinational web uses and gratifications: measuring the social impact of online community participation across national boundaries. Electronic Commerce Research, 6 (1), 75–101. https://doi.org/10.1007/s10660-006-5989-6 .

Chen, K., Jin, J., Zhao, Z., & Ji, P. (2020). Understanding customer regional differences from online opinions: A hierarchical Bayesian approach. Electronic Commerce Research (in press) . https://doi.org/10.1007/s10660-020-09420-5 .

Ma, Y., Chen, G., & Wei, Q. (2017). Finding users preferences from large-scale online reviews for personalized recommendation. Electronic Commerce Research, 17 (1), 3–29. https://doi.org/10.1007/s10660-016-9240-9 .

Yoon, S. H., & Kim, H. W. (2019). What content and context factors lead to selection of a video clip? The heuristic route perspective. Electronic Commerce Research, 19 (3), 603–627. https://doi.org/10.1007/s10660-019-09355-6 .

Pan, H., & Zhou, H. (2020). Study on convolutional neural network and its application in data mining and sales forecasting for e-commerce. Electronic Commerce Research, 20 (2), 297–320. https://doi.org/10.1007/s10660-020-09409-0 .

Pu, P., Chen, L., & Kumar, P. (2008). Evaluating product search and recommender systems for e-commerce environments. Electronic Commerce Research, 8 (1–2), 1–27. https://doi.org/10.1007/s10660-008-9015-z .

Panniello, U., & Gorgoglione, M. (2012). Incorporating context into recommender systems: An empirical comparison of context-based approaches. Electronic Commerce Research, 12 (1), 1–30. https://doi.org/10.1007/s10660-012-9087-7 .

Zhou, Q. (2018). Multi-layer affective computing model based on emotional psychology. Electronic Commerce Research, 18 (1), 109–124. https://doi.org/10.1007/s10660-017-9265-8 .

Suki, N. M. (2013). Consumer shopping behaviour on the Internet: Insights from Malaysia. Electronic Commerce Research, 13 (4), 477–491. https://doi.org/10.1007/s10660-013-9131-2 .

Chen, Y., Mullen, T., & Chu, C. H. (2006). An in-depth analysis of information markets with aggregate uncertainty. Electronic Commerce Research, 6 (2), 201–221. https://doi.org/10.1007/s10660-006-6958-9 .

Kang, H. G., Bae, K., Shin, J. A., & Jeon, S. (2020). Will data on internet queries predict the performance in the marketplace: An empirical study on online searches and IPO stock returns. Electronic Commerce Research (in press) . https://doi.org/10.1007/s10660-020-09417-0 .

Cui, Y., Mou, J., Cohen, J., & Liu, Y. (2019). Understanding information system success model and valence framework in sellers’ acceptance of cross-border e-commerce: A sequential multi-method approach. Electronic Commerce Research, 19 (4), 885–914. https://doi.org/10.1007/s10660-019-09331-0 .

Liu, S., Shao, B., Gao, Y., Hu, S., Li, Y., & Zhou, W. (2018). Game theoretic approach of a novel decision policy for customers based on big data. Electronic Commerce Research, 18 (2), 225–240. https://doi.org/10.1007/s10660-017-9259-6 .

Tian, X., & Liu, L. (2017). Does big data mean big knowledge? Integration of big data analysis and conceptual model for social commerce research. Electronic Commerce Research, 17 (1), 169–183. https://doi.org/10.1007/s10660-016-9242-7 .

Flanagin, A. J., Metzger, M. J., Pure, R., Markov, A., & Hartsell, E. (2014). Mitigating risk in ecommerce transactions: perceptions of information credibility and the role of user-generated ratings in product quality and purchase intention. Electronic Commerce Research, 14 (1), 1–23. https://doi.org/10.1007/s10660-014-9139-2 .

Fairlie, R. W. (2007). Explaining differences in access to home computers and the Internet: A comparison of Latino groups to other ethnic and racial groups. Electronic Commerce Research, 7 (3–4), 265–291. https://doi.org/10.1007/s10660-007-9006-5 .

Gerdes, J., Stringam, B. B., & Brookshire, R. G. (2008). An integrative approach to assess qualitative and quantitative consumer feedback. Electronic Commerce Research, 8 (4), 217–234. https://doi.org/10.1007/s10660-008-9022-0 .

Tsao, W. C., & Hsieh, M. T. (2015). eWOM persuasiveness: Do eWOM platforms and product type matter? Electronic Commerce Research, 15 (4), 509–541. https://doi.org/10.1007/s10660-015-9198-z .

Zhang, H., Rao, H., & Feng, J. (2018). Product innovation based on online review data mining: A case study of Huawei phones. Electronic Commerce Research, 18 (1), 3–22. https://doi.org/10.1007/s10660-017-9279-2 .

Liu, Q., Zhang, X., Zhang, L., & Zhao, Y. (2019). The interaction effects of information cascades, word of mouth and recommendation systems on online reading behavior: An empirical investigation. Electronic Commerce Research, 19 (3), 521–547. https://doi.org/10.1007/s10660-018-9312-0 .

Chen, R., & Xu, W. (2017). The determinants of online customer ratings: a combined domain ontology and topic text analytics approach. Electronic Commerce Research, 17 (1), 31–50. https://doi.org/10.1007/s10660-016-9243-6 .

Narayanasamy, K., Rasiah, D., & Tan, T. M. (2011). The adoption and concerns of e-finance in Malaysia. Electronic Commerce Research, 11 (4), 383–400. https://doi.org/10.1007/s10660-011-9081-5 .

Dumas, M., Aldred, L., Governatori, G., & Ter Hofstede, A. H. M. (2005). Probabilistic automated bidding in multiple auctions. Electronic Commerce Research, 5 (1), 25–49. https://doi.org/10.1023/B:ELEC.0000045972.75533.90 .

Marinč, M. (2013). Banks and information technology: Marketability vs. relationships. Electronic Commerce Research, 13 (1), 71–101. https://doi.org/10.1007/s10660-013-9107-2 .

Maillé, P., Markakis, E., Naldi, M., Stamoulis, G. D., & Tuffin, B. (2012). Sponsored search auctions: An overview of research with emphasis on game theoretic aspects. Electronic Commerce Research, 12 (3), 265–300. https://doi.org/10.1007/s10660-012-9094-8 .

Maillé, P., & Tuffin, B. (2018). Auctions for online ad space among advertisers sensitive to both views and clicks. Electronic Commerce Research, 18 (3), 485–506. https://doi.org/10.1007/s10660-017-9267-6 .

Håvaldsrud, T. V., Ligaarden, O. S., Myrseth, P., Refsdal, A., Stølen, K., & Ølnes, J. (2010). Experiences from using a UML-based method for trust analysis in an industrial project on electronic procurement. Electronic Commerce Research, 10 (3–4), 441–467. https://doi.org/10.1007/s10660-010-9063-z .

Dráb, R., Štofa, T., & Delina, R. (2020). Analysis of the efficiency of electronic reverse auction settings: Big data evidence. Electronic Commerce Research (in press) . https://doi.org/10.1007/s10660-020-09433-0 .

Bei, L. T., & Chen, M. Y. (2015). The effects of hedonic and utilitarian bidding values on e-auction behavior. Electronic Commerce Research, 15 (4), 483–507. https://doi.org/10.1007/s10660-015-9197-0 .

Huang, T. L., & Liao, S. (2015). A model of acceptance of augmented-reality interactive technology: The moderating role of cognitive innovativeness. Electronic Commerce Research, 15 (2), 269–295. https://doi.org/10.1007/s10660-014-9163-2 .

Hsieh, J. K., Hsieh, Y. C., & Tang, Y. C. (2012). Exploring the disseminating behaviors of eWOM marketing: Persuasion in online video. Electronic Commerce Research, 12 (2), 201–224. https://doi.org/10.1007/s10660-012-9091-y .

Zhang, L., Luo, M., & Boncella, R. J. (2020). Product information diffusion in a social network. Electronic Commerce Research, 20 (1), 3–19. https://doi.org/10.1007/s10660-018-9316-9 .

Zhanikeev, M. (2015). How variable bitrate video formats can help P2P streaming boost its reliability and scale. Electronic Commerce Research, 15 (1), 25–47. https://doi.org/10.1007/s10660-015-9175-6 .

Cheng, X., Gu, Y., & Mou, J. (2020). Interpersonal relationship building in social commerce communities: Considering both swift guanxi and relationship commitment. Electronic Commerce Research, 20 (1), 53–80. https://doi.org/10.1007/s10660-019-09375-2 .

Chu, X., Liu, Y., Chen, X., Ding, Z., & Tao, S. (2020). What motivates consumer to engage in microblogs? The roles of brand post characteristics and brand prestige. Electronic Commerce Research (in press). https://doi.org/10.1007/s10660-020-09435-y .

Zhou, T. (2013). An empirical examination of user adoption of location-based services. Electronic Commerce Research, 13 (1), 25–39. https://doi.org/10.1007/s10660-013-9106-3 .

Chen, Q., Chen, H. M., & Kazman, R. (2007). Investigating antecedents of technology acceptance of initial ECRM users beyond generation X and the role of self-construal. Electronic Commerce Research, 7 (3–4), 315–339. https://doi.org/10.1007/s10660-007-9009-2 .

Royo, S., & Yetano, A. (2015). “Crowdsourcing” as a tool for e-participation: Two experiences regarding CO 2 emissions at municipal level. Electronic Commerce Research, 15 (3), 323–348. https://doi.org/10.1007/s10660-015-9183-6 .

Chen, D., Li, X., & Lai, F. (2017). Gender discrimination in online peer-to-peer credit lending: Evidence from a lending platform in China. Electronic Commerce Research, 17 (4), 553–583. https://doi.org/10.1007/s10660-016-9247-2 .

Yu, C. H., & Lin, S. J. (2013). Fuzzy rule optimization for online auction frauds detection based on genetic algorithm. Electronic Commerce Research, 13 (2), 169–182. https://doi.org/10.1007/s10660-013-9113-4 .

Yan, Y., Lv, Z., & Hu, B. (2018). Building investor trust in the P2P lending platform with a focus on Chinese P2P lending platforms. Electronic Commerce Research, 18 (2), 203–224. https://doi.org/10.1007/s10660-017-9255-x .

Guo, Y., & Barnes, S. (2009). Virtual item purchase behavior in virtual worlds: An exploratory investigation. Electronic Commerce Research, 9 (1–2), 77–96. https://doi.org/10.1007/s10660-009-9032-6 .

Bourlakis, M., Papagiannidis, S., & Li, F. (2009). Retail spatial evolution: Paving the way from traditional to metaverse retailing. Electronic Commerce Research, 9 (1–2), 135–148. https://doi.org/10.1007/s10660-009-9030-8 .

MacInnes, I. (2006). Property rights, legal issues, and business models in virtual world communities. Electronic Commerce Research, 6 (1), 39–56. https://doi.org/10.1007/s10660-006-5987-8 .

Cagnina, M. R., & Poian, M. (2009). Beyond e-business models: The road to virtual worlds. Electronic Commerce Research, 9 (1–2), 49–75. https://doi.org/10.1007/s10660-009-9027-3 .

Scarle, S., Arnab, S., Dunwell, I., Petridis, P., Protopsaltis, A., & de Freitas, S. (2012). E-commerce transactions in a virtual environment: Virtual transactions. Electronic Commerce Research, 12 (3), 379–407. https://doi.org/10.1007/s10660-012-9098-4 .

Hanafizadeh, P., & Khedmatgozar, H. R. (2012). The mediating role of the dimensions of the perceived risk in the effect of customers’ awareness on the adoption of Internet banking in Iran. Electronic Commerce Research, 12 (2), 151–175. https://doi.org/10.1007/s10660-012-9090-z .

Valvi, A. C., & Fragkos, K. C. (2012). Critical review of the e-loyalty literature: A purchase-centred framework. Electronic Commerce Research, 12 (3), 331–378. https://doi.org/10.1007/s10660-012-9097-5 .

Aloudat, A., & Michael, K. (2011). Toward the regulation of ubiquitous mobile government: A case study on location-based emergency services in Australia. Electronic Commerce Research, 11 (1), 31–74. https://doi.org/10.1007/s10660-010-9070-0 .

Tsai, H. T., Chien, J. L., & Tsai, M. T. (2014). The influences of system usability and user satisfaction on continued Internet banking services usage intention: Empirical evidence from Taiwan. Electronic Commerce Research, 14 (2), 137–169. https://doi.org/10.1007/s10660-014-9136-5 .

Li, H., Aham-Anyanwu, N., Tevrizci, C., & Luo, X. (2015). The interplay between value and service quality experience: E-loyalty development process through the eTailQ scale and value perception. Electronic Commerce Research, 15 (4), 585–615. https://doi.org/10.1007/s10660-015-9202-7 .

Khedmatgozar, H. R., & Shahnazi, A. (2018). The role of dimensions of perceived risk in adoption of corporate internet banking by customers in Iran. Electronic Commerce Research, 18 (2), 389–412. https://doi.org/10.1007/s10660-017-9253-z .

Zhou, R., Wang, X., Shi, Y., Zhang, R., Zhang, L., & Guo, H. (2019). Measuring e-service quality and its importance to customer satisfaction and loyalty: an empirical study in a telecom setting. Electronic Commerce Research, 19 (3), 477–499. https://doi.org/10.1007/s10660-018-9301-3 .

Kim, J. B. (2012). An empirical study on consumer first purchase intention in online shopping: Integrating initial trust and TAM. Electronic Commerce Research, 12 (2), 125–150. https://doi.org/10.1007/s10660-012-9089-5 .

Gregg, D. G., & Walczak, S. (2010). The relationship between website quality, trust and price premiums at online auctions. Electronic Commerce Research, 10 (1), 1–25. https://doi.org/10.1007/s10660-010-9044-2 .

Taylor, D. G., Davis, D. F., & Jillapalli, R. (2009). Privacy concern and online personalization: The moderating effects of information control and compensation. Electronic Commerce Research, 9 (3), 203–223. https://doi.org/10.1007/s10660-009-9036-2 .

Castañeda, J. A., & Montoro, F. J. (2007). The effect of Internet general privacy concern on customer behavior. Electronic Commerce Research, 7 (2), 117–141. https://doi.org/10.1007/s10660-007-9000-y .

Chen, M. Y., & Teng, C. I. (2013). A comprehensive model of the effects of online store image on purchase intention in an e-commerce environment. Electronic Commerce Research, 13 (1), 1–23. https://doi.org/10.1007/s10660-013-9104-5 .

Goel, L., & Prokopec, S. (2009). If you build it will they come? An empirical investigation of consumer perceptions and strategy in virtual worlds. Electronic Commerce Research, 9 (1–2), 115–134. https://doi.org/10.1007/s10660-009-9031-7112 .

Ninia, J. (2020). The impact of e-commerce: China versus the United States . Ithaca, New York: Cornell University.

Google Scholar  

IBM Research. (2020). Our labs. IBM . Available at https://www.research.ibm.com/labs/

Klein, J. T., & Falk-Krzesinski, H. J. (2017). Interdisciplinary and collaborative work: Framing promotion and tenure practices and policies. Research Policy, 46 (6), 1055–1061. https://doi.org/10.1016/j.respol.2017.03.001 .

Lim, W. M. (2019). To what degree is the fourth industrial revolution an opportunity or a threat for the ASEAN community and region? Modern Applied Science, 13 (9), 105–106. https://doi.org/10.5539/mas.v13n9p105 .

Wijaya, D. R., Paramita, N. L. P. S. P., Uluwiyah, A., Rheza, M., Zahara, A., & Puspita, D. R. (2021). Estimating city-level poverty rate based on e-commerce data with machine learning. Electronic Commerce Research . https://doi.org/10.1007/s10660-020-09424- .

Lim, W. M. (2020). Challenger marketing. Industrial Marketing Management, 84, 342–345. https://doi.org/10.1016/j.indmarman.2019.08.009 .

Lim, W. M. (2020). In defense of challenger marketing. Journal of Business-to-Business Marketing, 27 (4), 397–406. https://doi.org/10.1080/1051712X.2020.1831214 .

Lim, W. M. (2020). The sharing economy: A marketing perspective. Australasian Marketing Journal, 28 (3), 4–13. https://doi.org/10.1016/j.ausmj.2020.06.007 .

Lim, W. M. (2016). Understanding the selfie phenomenon: Current insights and future research directions. European Journal of Marketing, 50 (9/10), 1773–1788. https://doi.org/10.1108/EJM-07-2015-0484 .

Lim, W. M. (2018). Demystifying neuromarketing. Journal of Business Research, 91, 205–220. https://doi.org/10.1016/j.jbusres.2018.05.036 .

Lim, W. M. (2018). Dialectic antidotes to critics of the technology acceptance model: Conceptual, methodological, and replication treatments for behavioural modelling in technology-mediated environments. Australasian Journal of Information Systems . https://doi.org/10.3127/ajis.v22i0.1651 .

Lim, W. M. (2018). What will business-to-business marketers learn from neuro-marketing? Insights for business marketing practice. Journal of Business-to-Business Marketing, 25 (3), 251–259. https://doi.org/10.1080/1051712X.2018.1488915 .

Lim, W. M., Ahmed, P. K., & Ali, M. Y. (2019). Data and resource maximization in business-to-business marketing experiments: Methodological insights from data partitioning. Industrial Marketing Management, 76, 136–143. https://doi.org/10.1016/j.indmarman.2018.08.007 .

Lim, W. M., Yap, S. F., & Makkar, M. (2021). Home sharing in marketing and tourism at a tipping point: What do we know, how do we know, and where should we be heading? Journal of Business Research, 122, 534–566. https://doi.org/10.1108/EJM-07-2015-0484 .

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Kumar, S., Lim, W.M., Pandey, N. et al. 20 years of Electronic Commerce Research . Electron Commer Res 21 , 1–40 (2021). https://doi.org/10.1007/s10660-021-09464-1

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Research Article

The impact of rural e-commerce participation on farmers’ entrepreneurial behavior: Evidence based on CFPS data in China

Roles Formal analysis, Writing – original draft, Writing – review & editing

Affiliation Inner Mongolia University of Finance and Economics, Hohhot, Inner Mongolia, China

Roles Conceptualization, Data curation

Roles Resources, Software, Visualization

Roles Data curation, Formal analysis, Methodology

Affiliation Inner Mongolia Open University, Hohhot, Inner Mongolia, China

Roles Writing – review & editing

Affiliations Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America, Inner Mongolia Honder College of arts and Science, Hohhot, Inner Mongolia, China

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Affiliation School of Economics, Capital University of Economics and Business, Hohhot, China

Roles Project administration, Supervision

* E-mail: [email protected]

• Haiying Lin, 
• Huayuan Wu, 
• Haihua Lin, 
• Tianqi Zhu, 
• Muhammad Umer Arshad, 
• Haonan Chen, 

• Published: May 9, 2024
• https://doi.org/10.1371/journal.pone.0300418
• Peer Review
• Reader Comments

The "Three Rural Issues", encompass challenges related to agriculture, farmer, and rural area, which hold significant importance in driving comprehensive rural revitalization efforts in China. Farmer entrepreneurship, as a crucial means to enhance productivity, create job opportunities, and increase residents’ income, has gradually become a key driving force in promoting rural revitalization in the new stage of development in China. With the rapid development of rural e-commerce, farmer entrepreneurship has encountered new opportunities. This study utilizes the 2020 China Family Panel Studies (CFPS) data and employs a structural equation model (SEM) to analyze the direct impact of rural e-commerce participation on farmer entrepreneurial behavior, considering factors such as human capital, social capital, and network infrastructure. This study further explores the indirect effects and mechanisms of e-commerce participation as a mediating variable and analyzes the impact and mechanisms on agricultural entrepreneurship behavior. The findings are as follows: (1) E-commerce participation significantly promotes farmer entrepreneurial behavior; (2) E-commerce participation as a mediating variable has a positive indirect effect on the relationship between social trust, network infrastructure, human capital, and farmer entrepreneurial behavior; (3) E-commerce participation has a significant positive influence on farmer entrepreneurship in the agricultural sector, and farmers with higher levels of network infrastructure and human capital have a higher probability of choosing agricultural entrepreneurship under the influence of e-commerce participation. Finally, this study provides policy recommendations in terms of infrastructure construction, entrepreneurial policy environment, and education level, aiming to optimize the situation of farmer entrepreneurship and contribute to the comprehensive promotion of rural revitalization.Overall, the research in this paper effectively combines theory and empirical evidence to outline the direct and indirect impact mechanisms of rural e-commerce participation on farmers’ entrepreneurial behavior and agriculture-related entrepreneurial behavior and to test the effects of their impacts. First, most of the existing literature deals with farmers in individual sample areas, while the sample selected in this paper is farmers in the whole country, which is relatively more generalizable; second, most of the previous studies explore the level of e-commerce in the inter-provincial or county areas, while this paper expands the empirical study of rural e-commerce on the entrepreneurial behavior of farmers and the micro-period of agricultural entrepreneurial behavior, and focuses on the impacts of the e-commerce activities of farmers on their entrepreneurial behavior.

Citation: Lin H, Wu H, Lin H, Zhu T, Arshad MU, Chen H, et al. (2024) The impact of rural e-commerce participation on farmers’ entrepreneurial behavior: Evidence based on CFPS data in China. PLoS ONE 19(5): e0300418. https://doi.org/10.1371/journal.pone.0300418

Editor: Ioana Gutu, Alexandru Ioan Cuza University of Iasi, Faculty of Philosophy and Social-Political Sciences, ROMANIA

Received: November 30, 2023; Accepted: February 26, 2024; Published: May 9, 2024

Copyright: © 2024 Lin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data underlying the results presented in the study are available from the 2020 China Family Panel Studies database. It is a free and public Chinese database, everyone can download data vie the following link: http://www.isss.pku.edu.cn/cfps/download .

Funding: This work was supported by the National Key Research and Development Program of China - Science & Technology Cooperation Project of the Chinese and Russian Governments, entitled "Sustainable Transboundary Nature Management and Green Development Modes in the Context of Emerging Economic Corridors and Biodiversity Conservation Priorities in the South of the Russian Far East and Northeast China" (No. 2023YFE0111300); the National Social Science Fund of China (grant number 23BGL204); the Natural Science Foundation of Inner Mongolia (grant numbers 2021MS07011, 2022MS04001); the Program for Young Talents of Science and Technology in Universities of the Inner Mongolia Autonomous Region (grant numbers NJYT22113, NJYT20B31); and the Inner Mongolia University Direct Scientific Research Business Fee Project (grant numbers NCYWR22020, NCYWR22021).

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

In recent years, the rapid advancement of e-commerce has brought about significant transformations in various sectors of the economy, including the agricultural industry [ 1 ]. As digital platforms continue to penetrate rural areas, farmers are increasingly engaging in e-commerce activities as a means to enhance their entrepreneurial endeavors [ 2 ]. This paradigm shift has led to a growing interest in understanding the impact of rural e-commerce participation on farmers’ entrepreneurial behavior [ 3 ]. While several studies have examined the relationship between e-commerce and entrepreneurship [ 4 , 5 ], there is a paucity of research specifically focusing on the agricultural context, particularly in the Chinese setting.

The 20th National Congress of the Communist Party of China pointed out the need to comprehensively promote rural revitalization and ensure stable and increased agricultural production as well as steady income growth for farmers. The No.1 Central Document for the year 2023 emphasizes the utmost importance of resolving the "Three Rural Issues" as a top priority for the Communist Party of China (CPC) (reference). It aims to promote increased income and employment opportunities for farmers while actively fostering rural entrepreneurial leaders. Therefore, farmer entrepreneurship has gradually become a key means of enhancing productivity, creating employment opportunities, and increasing farmers’ income in China’s new development stage, serving as an important catalyst for revitalizing rural areas, promoting sustainable agricultural development, and advancing comprehensive rural revitalization [ 6 ].

In 2015, the State Council issued the "Opinions on Supporting Rural Migrant Workers and Others to Start Businesses in Their Hometowns," which explicitly stated the need to support the entrepreneurship of rural migrant workers, college graduates, and retired soldiers, and to promote widespread entrepreneurship and innovation to revitalize various industries in rural areas. Subsequently, the government introduced various agricultural subsidies, insurance policies, and financial support policies to encourage rural entrepreneurship. In April 2022, the Director of the Development Planning Department in Ministry of Agriculture and Rural Affairs of the People’s Republic of China, Zeng Yande, mentioned that there were over 11 million people engaged in returning to their hometowns and starting businesses across the country, and according to comprehensive calculations, an average entrepreneurial project can provide stable employment for 6 to 7 farmers and flexible employment for 17 individuals [ 7 ]. The phenomenon of "one person starting a business and lifting others out of poverty" is emerging, with numerous representative farmer entrepreneurial projects and examples appearing, and the scale of farmer entrepreneurship continues to expand. With the rapid popularization of the internet, rural entrepreneurship has also encountered new opportunities [ 8 ].

As of December 2022, the internet penetration rate in China has reached 74.4% high, and the rural internet penetration rate has exceeded 60% [ 9 ]. E-commerce has gradually become an important driving force for sustainable economic development in rural areas. The widespread adoption of rural e-commerce plays a crucial and positive role in enhancing the confidence and probability of farmers’ entrepreneurship [ 10 – 12 ]. Rural e-commerce not only helps address the issues of lengthy agricultural product distribution channels and information asymmetry between farmers and consumers, but also improves farmers’ profit margins and agricultural product circulation efficiency. It can also reduce farmers’ daily living costs [ 13 – 15 ], enhance their human capital, accumulate social capital [ 16 ], and promote employment and entrepreneurship among farmers [ 17 ]. Since the 18th National Congress of the Communist Party of China, the Central Committee of the Communist Party of China, the State Council, and various ministries have successively launched more than 170 policies [ 18 ] and documents to promote the development of rural e-commerce. The implementation of the "Promoting Agricultural Development through Digital Commerce" project has actively promoted the penetration of e-commerce into rural areas and accelerated the construction of e-commerce infrastructure. Particularly, after the Ministry of Commerce launched the “e-commerce in rural areas” initiative, rural e-commerce in China has experienced rapid development. The total rural online retail sales increased from 180 billion yuan in 2014 to 21.7 trillion yuan in 2022, with over 16.32 million rural online businesses and shops, and the number of Taobao villages surpassing 7,000. A large proportion of farmers have found employment and entrepreneurial opportunities through rural e-commerce, leading to increased income levels [ 19 ].

In terms of existing literature, since entrepreneurship has become an important driving force for economic growth in various countries, academics have maintained a passion for research on entrepreneurship (Fitz-Koch et al, 2018; Kuratko et al, 2015) [ 20 , 21 ], which has so far involved a number of disciplinary fields such as psychology, economics, management, law, and so on. Among them, studies related to farmers’ entrepreneurship are getting richer and richer, and the research perspectives are endless, mainly including entrepreneurial behavior, entrepreneurial willingness, entrepreneurial field, entrepreneurial performance, entrepreneurial environment and so on. From the view of existing literature, the research on the influencing factors of farmers’ entrepreneurial behavior mainly focuses on the following levels: first, the human capital level, such as the level of education [ 22 ], the experience of migrant labor, etc. [ 23 , 24 ]; second, the level of social capital, which mainly includes the social network and social trust, and scholars have shown that empirical studies show that social network and social trust can help to increase the probability of farmers’ entrepreneurial activities [ 25 , 26 ]; Third, the Internet level, exploring the impact of network base on farmers’ entrepreneurial behavior is rich in literature, and many studies show that the Internet has a significant and positive impact on farmers’ entrepreneurial behavior [ 27 , 28 ]. Some scholars have also studied the impact of macro-social environmental factors such as financial support, land transfer and other policy support factors on farmers’ entrepreneurial behavior [ 29 – 31 ]. With the gradual sinking of information technology in rural areas, the opportunities brought by rural e-commerce for farmers’ entrepreneurship have begun to trigger discussions among scholars, and the current research on the impact of rural e-commerce on farmers’ entrepreneurial behavior is mainly from two perspectives, one is to study the direct or indirect impact of rural e-commerce level on farmers’ entrepreneurship, and the majority of scholars have shown that the level of rural e-commerce has a significant positive impact [ 32 , 33 ]. The second is to analyze farmers’ e-commerce entrepreneurial behavior, i.e., the entrepreneurial behavior of economic and trade activities through electronic means, such as Taobao stores, microbusinesses, and live streaming with goods, etc. E-commerce is conducive to breaking the differences in entrepreneurial opportunities caused by the differences in social capital, and to breaking the limitations imposed by the traditional social capital on farmers’ entrepreneurship, which indirectly improves the confidence of farmers’ entrepreneurship, and promotes farmers’ entrepreneurship [ 34 ]; some scholars have also carried out different regional farmers’ e-commerce entrepreneurial behavior has been studied in depth, such as Jiangxi Province [ 8 ], Guizhou Province and Chongqing Municipality [ 35 ], Jiangsu Province and so on [ 36 , 37 ]. Rural e-commerce participation is a core branch of rural e-commerce, which is also known as farmers’ e-commerce adoption, farmers’ participation in e-commerce, and so on. For rural e-commerce participation there is no clearer definition, and the existing literature on the connotation of rural e-commerce participation mainly involves the following dimensions: first, farmers’ online shopping behavior in the common sense, Chinese scholar Han Feiyan pointed out through empirical research that online shopping improves farmers’ knowledge of e-commerce, stimulates their consumption vitality, and breaks down the market information barriers [ 38 ]; the second concept is in the further development on the basis of the first, i.e., farmers enter the e-commerce market as sellers, participate in market sales, and utilize rural e-commerce to improve their income.Ma Jingtao defines rural e-commerce as a business activity centered on the trading of agricultural products under the influence of the Internet environment and with the help of various mobile communication devices. The rural e-commerce participation in this paper focuses on the first concept. Currently, there are still few studies in this area, and the existing literature focuses mainly on the willingness to participate in rural e-commerce and its influencing factors, and the impact of rural e-commerce participation on farmers’ income. However, considering the rapid development of rural e-commerce and its important role in farmers’ entrepreneurship and rural economic development, there is still a lot of room for expansion of research in this area. First, Hao Jinlei and Xing Xiangyang (2016) studied farmers’ willingness to participate in e-commerce at the levels of literacy, government support, and commercial bank coverage, and further pointed out the importance of this behavior for the development of rural e-commerce [ 39 ]; Guo Jinlong et al. (2023) empirically analyzed the influencing factors of farmers’ e-commerce participation behavior from the perspective of social interactions, arguing that social interactions positively influences farmers’ e-commerce participation behavior by changing farmers’ information cognitive norms and social norms [ 40 ]; Lin Haiying et al. (2020) conducted an empirical study on the e-commerce participation willingness of farmers and herdsmen in some impoverished areas of Inner Mongolia and the factors influencing them, and found that factors such as infrastructure, personal traits, social networks, and resource endowment significantly affect farmers’ and herdsmen’s e-commerce participation willingness [ 13 ]. Secondly, Cao et al. (2021) explored the impact of e-commerce participation on rural gender income gap based on the theory of household specialization division of labor and gender comparative advantage theory [ 41 ]. Yu Hao et al. (2021) focused on the research data of 303 farming households in Shaanxi Province and applied the Fields income decomposition method to explore the direction and effect of e-commerce participation on the income gap of farming households [ 42 ]. Furthermore, the role of agriculture and farmers is particularly important in the context of the current global food security situation. It has been pointed out that, in addition to economic aspects, the impact of agricultural production on the environment, landscape, and land use is more prominent than that of other economic sectors (Britz et al., 2012) [ 43 ], and it is crucial to understand how farmer-agricultural entrepreneurs acquire entrepreneurial capabilities (Pindado et al, 2017; Seuneke et al, 2013) [ 44 , 45 ]. Agricultural entrepreneurial behavior, on the other hand, is an important tool to improve farmers’ income and promote agricultural diversification, and the current international research literature on agricultural entrepreneurial behavior mainly includes the role of agricultural entrepreneurial behavior, and the influencing factors of agricultural entrepreneurial behavior.Some scholars have studied farmers’ entrepreneurial behavior in agriculture-related tourism, pointing out that farmers’ entrepreneurial behavior in agriculture-related tourism is an effective way to strengthen the construction of rural spiritual civilization and alleviate the pressure of urban and rural employment, and encouraging more farmers to carry out entrepreneurial activities related to agriculture [ 46 ]. Some scholars have deeply explored the impact of farmers’ entrepreneurship on the ecological environment of farmland and pastureland, pointing out that agricultural entrepreneurship provides opportunities for environmentally sound use of resources, which can serve as an important remedy for environmental challenges, and is mainly affected by market opportunities, infrastructure, especially network facilities, educational level, social capital, etc. [ 47 , 48 ].George Saridakis et al. (2021) through the method of empirical analysis pointed out that agricultural entrepreneurship as a business activity can be effective in improving economic well-being [ 49 ].While China’s research in related fields started late, the earliest one to conduct a more in-depth exploration of agricultural entrepreneurial behavior was Yu Ning (2013) in China, whose dissection of the importance of agricultural entrepreneurial behavior and the related influence mechanism laid a solid foundation for the research in the field of agricultural entrepreneurial behavior in China [ 50 ]. In recent years, some scholars in China have begun to pay attention to how to cultivate high-quality talents for agricultural entrepreneurship in the new era, and some scholars focus on agriculture-related colleges and universities and college students returning to their hometowns to engage in agricultural entrepreneurship (Hao Zhenping et al., 2023; Zhao Fangfang et al., 2023) [ 51 , 52 ], and most of them use theoretical analysis and lack of empirical data to support it; there are also individual scholars who have used the method of empirical analysis of a certain region’s farmers’ agricultural entrepreneurial behavior has been explored [ 53 ]. However, in general, there is a lack of articles in the new development stage that use empirical methods to conduct an in-depth exploration of farmers’ agricultural entrepreneurial behavior and the influencing factors behind it on a national scale.

In summary, the current research on farmers’ entrepreneurial behavior has been relatively rich, first, scholars have analyzed the influence factors of rural e-commerce on farmers’ entrepreneurial behavior from different levels and regions, but most of the scholars have only studied the influence of a single level on farmers’ entrepreneurial behavior, such as social capital or network base, and very few literatures have explored these factors in a comprehensive way; second, there is a lot of literature exploring the influence of rural Secondly, there is also a lot of literature exploring the impact of rural e-commerce on farmers’ entrepreneurial behavior, but most of the existing studies use probit model, binary logistic model and other methods to study the impact of rural e-commerce on farmers’ entrepreneurial behavior from the perspective of e-commerce level of the provinces and counties, and there is rarely any literature that analyzes the impact of individual farmer’s participation in e-commerce on his or her entrepreneurial behavior by using structural equation modeling. Again, there are fewer studies on rural e-commerce participation, and most of them are based on empirical studies in a specific region, which lacks a certain universality;In addition, although existing studies mention the impact of rural e-commerce participation on farmers’ income, there is a lack of empirical studies related to the impact of rural e-commerce participation on farmers’ entrepreneurial behavior and agricultural entrepreneurial behavior. Overall, at the level of theoretical contribution, this paper may make some contributions to the research field of farmers’ entrepreneurship and rural e-commerce in the following aspects: (1) Most of the existing literature involves farmers in individual sample areas, whereas this paper selects a sample object of farmers in the whole country, which is relatively more generalized. (2) Most of the previous studies explore the level of e-commerce in inter-provincial or county areas, while this paper expands the empirical study of rural e-commerce on farmers’ entrepreneurial behavior and agricultural entrepreneurial behavior from a micro point of view, and the study focuses on the impact of individual farmers’ e-commerce activities on farmers’ entrepreneurship. This study focuses on the impact of individual farmers’ e-commerce activities on farmers’ entrepreneurship, combines the existing authoritative literature and the real-world background, analyzes the theoretical mechanism of the impact of rural e-commerce participation on farmers’ entrepreneurial behavior and agricultural entrepreneurial behavior under the role of multiple factors, and explores the key influencing factors, which is conducive to supplementing and perfecting the theoretical system of farmers’ entrepreneurial behavior, especially agricultural entrepreneurial behavior. (3) The importance of rural e-commerce participation in agricultural entrepreneurial behavior is rarely mentioned in the existing literature, especially in developing countries such as China, which has a large number of mountainous areas where mechanization of agricultural production is difficult. E-commerce is a bridge between Chinese farmers and external producers, rural e-commerce participation in farmers’ agricultural entrepreneurial behavior is the most direct impact is that it can make the farmers of agricultural products on the network sales of low-cost, low-threshold, and the possibility of understanding the market mechanism is greater, for the long term to maintain the livelihood of the farmers in agricultural production, rural e-commerce is the importance of agricultural entrepreneurship is not negligible, but the literature does not have an in-depth discussion of this phenomenon. There is no literature to explore this phenomenon in depth, and this paper enriches the theoretical research in this field to a certain extent. On the practical level, this study utilizes research data to provide an in-depth discussion on the importance of farmers’ participation in e-commerce and entrepreneurial activities, and tests the intrinsic factors affecting farmers’ entrepreneurship in the empirical analysis, such as human capital and social capital, which can provide theoretical references for the farmers who hope to achieve income growth and improve their living standards through entrepreneurship, so that they can have a certain way of thinking about the preparation for their entrepreneurship, and improve the farmers’ It can provide theoretical references for farmers who wish to increase income and improve living standards through entrepreneurship, so that they can have certain ideas to prepare for entrepreneurship and improve their motivation and self-confidence. In addition, the research results of this paper can help the relevant government departments to formulate targeted policies to create a favorable entrepreneurial environment for the promotion of farmers’ entrepreneurship and agricultural development, and provide a certain micro reference basis for improving the level of grassroots governance.

Therefore, this paper aims to address several key questions. Firstly, we utilized the 2020 CFPS data, supplemented by data from 2018, and employs a SEM model to analyze the significant impact of farmers’ participation in rural e-commerce on their entrepreneurial behavior and involvement in agricultural entrepreneurship, taking into account various factors such as social capital, network infrastructure, and human capital. Compared to general regression analysis models, the SEM research method allows for the consideration of the correlation between multiple variables and accommodates the presence of multiple dependent variables and measurement errors. It enables the simultaneous observation of both the direct and indirect effects of e-commerce participation on farmers’ entrepreneurial behavior and involvement in agricultural entrepreneurship. The CFPS database covers individual survey data from 25 provinces nationwide, providing a broader sample representation and thus enhancing the generalizability of the empirical results. Secondly, we explored the indirect effects and mechanisms of e-commerce participation as a mediating variable. Thirdly, based on the empirical analysis, we have proposed some policy recommendations. The aim is to provide effective means for promoting farmer entrepreneurship through rural e-commerce and to offer micro-level reference for advancing high-quality development of the agricultural economy and facilitating comprehensive rural revitalization in China.

2. Theoretical analysis and research hypotheses

2.1 direct mechanisms of rural e-commerce impact on farmers’ entrepreneurial behavior.

For farmers, rural e-commerce participation is not only an emerging way of shopping, but also a means of obtaining information, accumulating entrepreneurial experience, and mobilizing entrepreneurial capital. First, rural e-commerce participation can help farmers more easily understand market conditions, explore business opportunities, and acquire entrepreneurial knowledge and skills. In the process, they can improve their ability to collect and utilize information, thus laying a solid foundation for entrepreneurship [ 13 ]. Second, rural e-commerce participation allows farmers to be exposed to a broader market and observe and learn more business models and business strategies. For example, they can accumulate business experience by comparing the price and quality of goods on different e-commerce platforms. Third, rural e-commerce participation can provide more diversified and affordable goods, effectively reducing farmers’ cost of living, helping them save money and accumulate start-up capital.Particularly in some less developed rural areas, there are fewer physical stores and a limited choice of goods due to geographical and economic constraints. Rural e-commerce participation, on the other hand, breaks this limitation and enables farmers to enjoy the same shopping experience and services as urban residents. Not only that, rural e-commerce participation is also conducive to increasing farmers’ awareness of market participation. In the traditional agricultural business model, farmers are often only producers, their understanding of the market and participation is limited. However, rural e-commerce participation breaks this limitation, so that farmers can directly contact the market and understand consumer demand and behavior, thus improving their market sensitivity and participation awareness.In addition, e-commerce online transactions are based on credit evaluation and systematic payments, and farmers’ understanding of such mechanisms can promote a more trusting attitude towards market mechanisms, enable a wider range of economic transactions, and contribute to farmers’ better participation in market transactions, reduce their perceived entrepreneurial risk, and increase their entrepreneurial likelihood [ 32 ]. Finally, numerous studies related to farmers’ entrepreneurial behavior point out that prior experience has an important impact on entrepreneurial behavior [ 54 ], i.e., farmers’ entrepreneurial behavior is influenced by their acquired social experience. E-commerce participation is also a type of prior experience that can make it more likely for farmers to understand the low cost, low threshold, and market mechanism of selling agricultural products online, and to accumulate prior experience in e-commerce entrepreneurship, which can contribute to their entrepreneurial behavior.

Based on this, the following hypothesis is proposed.

• H1: E-commerce participation promotes farmers’ entrepreneurial behavior by enhancing their information gathering ability, accumulating entrepreneurial capital, and acquiring prior experience in e-commerce entrepreneurship.

2.2 E-commerce as a mediator for farmers’ entrepreneurial behavior

Literature review shows that there are several important factors influencing farmers’ entrepreneurial behavior. This study focuses on three factors that are related to both e-commerce participation and farmers’ entrepreneurial behavior: human capital, social capital, and network infrastructure. The study argues that e-commerce participation, as a mediating variable, can have an indirect impact on the relationship between these factors and farmers’ entrepreneurial behavior.

2.2.1 Rural e-commerce’s impact on human capital -farmers’ entrepreneurial behavior.

In this study, farmers’ human capital primarily includes their level of education and work experience. The level of education has a significant positive impact on their ability to identify entrepreneurial opportunities [ 55 ]. Farmers with higher levels of education are more likely to utilize various platforms to gather and understand entrepreneurial information and skills [ 35 ]. Work experience mainly refers to farmers’ experience in working outside their hometown. The accumulated work experience and social exposure gained from working outside the hometown contribute to the development of farmers’ individual capabilities and promote entrepreneurship [ 36 ]. Firstly, farmers with higher levels of education not only possess higher levels of human capital but also have a stronger ability to accept and learn new things. They are more likely to have a positive risk attitude [ 56 ] and are more capable of acquiring the skills required for e-commerce participation, leading them to engage in e-commerce activities different from traditional shopping methods. Secondly, farmers with extensive work experience also exhibit a greater acceptance of new things and are more likely to participate in e-commerce activities. Additionally, under China’s dual rural-urban household registration system, discrimination exists against individuals who have no household registration permit in terms of wage determination, social security, and contract guarantees in most urban employment settings [ 57 ]. Such unfavorable employment conditions greatly discourage farmers from seeking employment opportunities in urban areas. For entrepreneurial farmers, education accumulates their human capital, while e-commerce participation accumulates their prior experience. Combined with the negative impact of limited employment opportunities in urban areas, they are more likely to identify entrepreneurial opportunities and choose entrepreneurship instead of seeking employment elsewhere [ 58 , 59 ]. Therefore, this study suggests that farmers with higher levels of education and more extensive work experience are more inclined to choose entrepreneurship under the influence of e-commerce participation.

Accordingly, we proposed Hypothesis H2: E-commerce participation mediates the positive relationship between human capital and farmers’ entrepreneurial behavior.

2.2.2 E-commerce’s Impact on social capital-farmers’ entrepreneurial behavior.

Social capital refers to the social resources that farmers possess to sustain their livelihoods, pursue their own development, and cope with risky shocks, and social networks and social trust are two of the core elements for measuring social capital [ 60 , 61 ]. Social capital can accelerate the accumulation of entrepreneurial resources and provide some psychological support for entrepreneurship, which can have a significant impact on an individual’s decision to engage in entrepreneurship [ 62 , 63 ]. Among them, social network support helps to increase the probability of farmers engaging in entrepreneurial activities [ 60 ]. In rural areas of China, where there is often a strong sense of interpersonal relationships, farmers who already have entrepreneurial intentions are more likely to seek entrepreneurial funding from relatives and friends in their relational networks [ 48 ]. Social trust, in turn, helps to fill the gaps in the formal system, reduces transaction costs and entrepreneurial risks, and facilitates the collection of entrepreneurial information and knowledge, thereby increasing the probability of farmers’ entrepreneurship [ 64 ]. E-commerce transactions are based on credit assessment and secure payment systems. Farmers who have some knowledge of this mechanism are more likely to trust strangers and thus develop a more trusting attitude towards market mechanisms. This wider range of economic transactions, especially beyond the boundaries of kinship and geographical trust, gradually builds a higher level of broad social trust. This, in turn, helps farmers to better participate in market transactions, reduces their perceived entrepreneurial risk, and increases their likelihood of starting a business. Higher levels of social trust can trigger the "herd effect", whereby farmers’ behavioral decisions are influenced by the behavior of the surrounding groups, such as when farmers consider whether to engage in e-commerce participation, they tend to pay close attention to the experiences and feelings of those who have already adopted them [ 40 ], which in turn promotes their e-commerce participation behavior, and further facilitates entrepreneurial information collection and knowledge acquisition, thus increasing the probability of farmers’ entrepreneurship.In this paper, it is argued that e-commerce participation mainly affects the relationship between social trust in social capital and farmers’ entrepreneurial behavior, but social networks are also included as one of the latent variables in consideration of the rigor and rationality of the model design.

Based on this, we have proposed following hypotheses:

• H3: E-commerce participation mediates the positive relationship between social trust and farmers’ entrepreneurial behavior.

2.2.3 E-commerce’s impact on network infrastructure-farmers’ entrepreneurial behavior.

The Internet has a significant and positive impact on farmers’ entrepreneurship [ 65 – 67 ]. And farmers with more experience in Internet use are more likely to engage in promoting their entrepreneurial behavior through e-commerce. First, farmers with correspondingly better familiarity with and trust in online platforms are better able to understand and use relevant e-commerce platforms, such as e-commerce platforms like Pinduoduo, Jingdong, Taobao, and social platforms like WeChat and Jieyin. These platforms can provide space for farmers to display, sell and market their products. At the same time, farmers who are experienced in Internet use have more trust in online platforms, and they are more willing to trade and sell on the Internet, which undoubtedly provides possibilities for their entrepreneurial behavior. Second, farmers who are more skilled in Internet use are relatively more likely to use the Internet to learn about the advantages of e-commerce participation and keenly identify entrepreneurial opportunities in online sales. The Internet provides farmers with a wealth of information resources, and they can learn about the advantages of e-commerce, such as open markets, convenient transactions, and flexible hours, through query searches, browsing web pages, and watching videos. At the same time, they can also use social networks, forums and other platforms to identify and grasp entrepreneurial opportunities in online sales. All these advantages and opportunities may stimulate farmers’ entrepreneurial desire and prompt them to take entrepreneurial actions. Finally, the Internet provides rich learning resources. Farmers can learn the basics and skills of online sales, such as e-commerce operations and product promotion, through online courses and video tutorials. Not only that, the Internet connects global information, so farmers can get in touch with the latest sales concepts and methods, compare and learn online sales skills through the Internet, thus ultimately motivating their entrepreneurial behavior.

Accordingly, we have proposed the fourth hypothesis:

• H4: E-commerce participation mediates the positive relationship between network infrastructure and farmers’ entrepreneurial behavior.

3. Data and model

The data used in this study is obtained from the CFPS database. CFPS is a nationwide longitudinal survey conducted by the Institute of Social Science Survey at Peking University. It covers individual-level survey data from 25 provinces in China and provides a representative sample that reflects the development and changes in Chinese society and rural economy. For the empirical analysis in this study, the 2020 CFPS data [ 68 ] is primarily used. Considering the stability of social networks and the availability of data, referring to Zhou and his colleagues’ method [ 69 ] the 2018 survey data is used for the selection of social network observation variables, and the individual-level data from the 2020 CFPS is matched with the household economic data from the 2018 CFPS using the unique personal identifier "pid" in the CFPS database [ 70 ]. Missing values for key variables are excluded, resulting in a final sample size of 4057 valid observations.

3.2 Methodology

Given that our study relies on analyzing secondary data and doesn’t involve direct interaction with human participants, we didn’t seek institutional review board (IRB) approval, as it isn’t applicable to our research. Based on the research hypotheses, a conceptual model is constructed to examine the relationships among six latent variables: entrepreneurial behavior, e-commerce participation, social networks, social trust, human capital, and network infrastructure. The model is depicted in Fig 1 .

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https://doi.org/10.1371/journal.pone.0300418.g001

3.3 Variable selection and statistic descriptions

This study focuses on rural farmers and comprehensively analyzes the direct impact of rural e-commerce participation on entrepreneurial behavior, taking into account factors such as human capital, social capital, and network infrastructure. It also explores the indirect effects and mechanisms of e-commerce participation as a mediating variable, further examining the effects and mechanisms on agricultural entrepreneurship. The sample of farmers is selected based on the "rural/urban attribute of permanent residence" indicator from the 2020 and 2018 CFPS databases. Taking into account practical considerations and referencing previous literature [ 54 , 71 – 75 ], a total of 10 observed variables are selected from the individual survey questionnaire to reflect the five latent variables of entrepreneurial behavior, e-commerce participation, human capital, network infrastructure, and social networks.

3.3.1 Entrepreneurial behavior.

Constructed as a binary variable to measure the entrepreneurial behavior of farmers based on the CFPS 2020 questionnaire item "What is your current main job/most recent job type?". If the response is "own agricultural production and operation/private enterprise/individual business/self-employment", the variable is assigned a value of 1; otherwise, it is assigned a value of 0.

3.3.2 E-commerce participation.

Constructed based on the CFPS 2020 question "Have you made online purchases in the past week?" to measure the e-commerce participation of farmers. If the response is "yes", the variable is assigned a value of 1; otherwise, it is assigned a value of 0.

3.3.3 Social networks.

In rural China, maintaining social connections through gift-giving is an important tradition. The variable is constructed based on the CFPS 2018 question "Including goods and cash, how much did your household spend on gifts in the past 12 months?" using the reported value of "gift expenditures (in yuan/year)".

3.3.4 Social trust.

Given that social trust cannot be directly observed and has rich connotations, this latent variable is measured using three observed variables. Based on the CFPS 2020 questionnaire, the item "In general, do you think most people can be trusted or is it better to be cautious when dealing with others?" is used. If the response is "most people can be trusted", the observed variable "liking to trust or doubt others" is assigned a value of 1; if the response is "better to be cautious when dealing with others", it is assigned a value of 0. Additionally, the variable "trust in strangers" is constructed based on the respondent’s rating on a scale from 1 to 10, indicating the level of trust in strangers. Similarly, the variable "trust in local government" is constructed based on the respondent’s rating on a scale from 1 to 10, indicating the level of trust in the local government.

3.3.5 Network infrastructure.

Mainly refers to the respondent’s internet usage. The observed variable "importance of the internet as an information channel" is constructed based on the CFPS 2020 question "Rate the importance of the internet as an information channel" on a scale from 0 to 5. The observed variable "daily duration of mobile internet usage" is constructed based on the CFPS 2020 question "How many minutes do you spend on average using the internet on your mobile device?"

3.3.6 Human capital.

Mainly considers the respondent’s education level and work experience. The observed variable "education level" is constructed based on the CFPS 2020 question "What is the highest level of education you have completed (graduated from)?" with values assigned as follows: "0 = illiterate/semi-literate, 3 = primary school, 4 = junior high school, 5 = high school/vocational school/technical school, 6 = college, 7 = bachelor’s degree, 8 = master’s degree, 9 = doctorate". The observed variable "full-time work experience" is constructed based on the CFPS 2020 question "Have you ever had full-time work experience?" with a value of 1 assigned for "yes" and 0 for "no".

Based on the initial conceptual model diagram ( Fig 1 ), a SEM diagram is constructed ( Fig 2 ), and descriptive statistics are performed. The results are presented in Table 1 .

https://doi.org/10.1371/journal.pone.0300418.g002

https://doi.org/10.1371/journal.pone.0300418.t001

Table 1 shows among the surveyed individuals, approximately 39% of farmers have engaged in entrepreneurship, and around 57% of farmers have a high level of e-commerce participation. The observable variable "gift expenditures" under the category of "social networks" has a mean value of 4092.96 and a standard deviation of 5317.874, indicating significant variation in gift expenditures among farmers. Among the observed variables in the "social trust" category, the mean value for "preference for trusting or doubting others" is 0.59, indicating that the level of social trust among surveyed farmers is generally moderate to slightly higher. The mean value of "trust in strangers" is 2.49, with a mode of 0, suggesting that most farmers have a low level of trust in strangers. However, the mean value of "trust in local government" is 5.66, with a standard deviation of 2.523 and a mode of 5, indicating that farmers generally hold a moderate to slightly higher level of trust in the local government. Regarding "network infrastructure," the mean value for "daily duration of mobile internet usage" is 158.24, with a mode of 120 minutes and a standard deviation of 156.151. This suggests that the daily internet usage duration of farmers varies significantly, with the majority spending around two hours online. The mean value for the importance of the internet as an information channel is 4.11, with a standard deviation of 1.067 and a mode of 5. This indicates that the internet is considered an important source of information for the majority of surveyed farmers. For the "human capital" category, the mean value of "education level" is 4.19, with a mode of 4, suggesting that the education level of most sampled farmers is at the junior high school level. Overall, the education level is relatively low. The mean value for "full-time work experience" is 0.53, indicating that 53% of sampled farmers have had full-time work experience.

4. Empirical analysis

4.1 reliability and validity testing.

To ensure the reliability and validity of the data, reliability and validity testing was conducted. To standardize the data and unify the measurement scale, the data was normalized using Z-Score before conducting reliability and validity testing, which improved the comparability of the data. The Cronbach’s alpha value for the sample is 0.642, and the Cronbach’s alpha values for each observed variable are all above 0.6, indicating acceptable questionnaire reliability. The overall KMO (Kaiser-Meyer-Olkin) test coefficient for the sample population is 0.797, and the Bartlett’s sphericity test is significant (P = 0.000). The factor loadings of the six latent variables on their respective measurement items are significant, consistent with the proposed hypotheses of the model, indicating that the questionnaire data is suitable for factor analysis and has good validity.

4.2 Fitness testing

SEM model is a statistical method based on the covariance matrix of variables to analyze relationships between variables. It can specify a latent variable model to estimate the relationships between latent structures and observed variables, as well as analyze the relationships between multiple independent variables and multiple dependent variables. In this study, a structural equation model was constructed to examine the influence of rural e-commerce participation on farmers’ entrepreneurial behavior, incorporating six latent variables: entrepreneurial behavior, e-commerce participation, social network, social trust, network infrastructure, and human capital, along with their corresponding observed variables. The fitness of the structural equation model need to be assessed through a series of fit indices, including absolute fit indices: AGFI, GFI, RMSEA; incremental fit indices: CFI, NFI, IFI; and parsimonious fit index: χ 2 / df . AGFI is expected to be greater than 0.85, while GFI, CFI, NFI, and IFI should be greater than 0.9, with values closer to 1 indicating better fit. A smaller RMSEA value indicates better fit, typically below 0.08 for larger samples and below 0.05 for excellent fit. A smaller χ 2 / df value indicates better fit, with values between 2 and 5 considered good fit. The data, which passed reliability and validity testing, were imported into AMOS 24.0 software for model fit testing, and the fit indices are presented in the following table. From Table 2 , it can be observed that the overall fit of the hypothesized model is acceptable, as χ 2 / df , AGFI, GFI, and RMSEA have met the requirements for model fit indices. However, CFI, NFI, and IFI did not meet the standards, and the values of χ 2 / df and RMSEA are relatively high, indicating that further modifications are needed to improve the model.

https://doi.org/10.1371/journal.pone.0300418.t002

4.3 Model modification

To improve the model fitness and achieve better results, it was necessary to make adjustments to the initial model. This study primarily employed model modification methods by observing the modification indices (MI) in the model estimation results and following the principle of releasing one parameter at a time for model modification. Based on the results from AMOS 24.0 software ( Table 3 ), the model was modified.

https://doi.org/10.1371/journal.pone.0300418.t003

At first step we run the model and result showed the highest MI value between the residual items of "duration of mobile internet usage" and "educational level," which reached 188.899. Considering the questionnaire data and relevant literature, the educational level influences farmers’ learning and cognitive abilities. Farmers with higher education levels are more likely to benefit from internet usage. Therefore, a correlation path was added between the residual items e8 and e14 to improve the model fit. After re-estimating the model, it was observed that the residual items e8 and e13 between "educational level" and "importance of the internet as an information channel" had the highest MI value of 105.430. Similarly, farmers with higher education levels are more likely to benefit from the information dividends brought by the internet. Thus, the correlation paths between e8 and e13 were added.

Further sequential estimation of the model revealed that the residual items with the highest MI value were between "trust in strangers" and "educational level." Literature suggests that educational level is related to the allocation of educational resources, and whether educational resources are fairly and effectively distributed can influence farmers’ trust in the general trust network of strangers. Higher education levels are associated with higher levels of general trust [ 76 ]. Therefore, a correlation path was added between the residual items e5 and e8 to improve the model fit. Upon re-estimating the model, it was observed that the highest MI value was between the residual items of "duration of mobile internet usage" and "full-time work experience," reaching 93.081. Considering practical perspectives, many job postings are published on the internet, and farmers actively utilize the internet to find employment information. Hence, correlation paths were added between the residual items e14 and e9. Another significant MI value was observed between the residual items of "importance of the internet as an information channel" and "trust in local government," indicating that the widespread use of the internet expands the traditional limitations of information acquisition. It allows farmers to receive more timely policy information from the government, broadening channels for expressing opinions and influencing farmers’ trust in the government [ 77 ]. Thus, a correlation path was added between the residual items e13 and e6.

At second step we run the model again and results showed a relatively high MI value between the residual items of "educational level" and "trust or suspicion of others," reaching 74.023. Numerous studies have indicated that individuals with more resources are more likely to trust others because resource conditions affect their "disaster line" and vulnerability. Education can improve farmers’ resource holding status and promote individual trust [ 78 ]. Therefore, a correlation path was added between the residual items e8 and e4. The next significant MI values were observed between the residual items of "trust in strangers" and "full-time work experience" and between "full-time work experience" and "importance of the internet as an information channel," with values of 56.852 and 47.024, respectively. Considering the questionnaire data and actual situations, trust in strangers and previous full-time work experience may be related. Higher trust in strangers among farmers when seeking employment opportunities can lead to lower contract costs and higher job efficiency. Similarly, job quality can also influence farmers’ trust in strangers [ 79 ]. Therefore, a correlation path was added between the residual items e5 and e9 to reduce the chi-square value. The internet can effectively expand farmers’ channels for obtaining employment information, allowing them to find suitable jobs more efficiently. Thus, a correlation path was constructed between the residual items e9 and e13 [ 80 ]. The specific results of the model modifications based on the MI values are shown in Table 3 .

4.4 Model fitness results

After the model modification, the fitness of the model has been significantly improved. Moreover, the weight coefficients of each observed variable on its corresponding latent variable have passed the significance level test at 1%, indicating that the selected observed variables in this study effectively reflect the corresponding latent variables. The final fitness results are shown in Table 4 , and Fig 3 presents the standardized path coefficients of the modified model.

https://doi.org/10.1371/journal.pone.0300418.g003

https://doi.org/10.1371/journal.pone.0300418.t004

4.5 Path analysis

4.5.1 direct effects testing..

According to Table 5 , the standardized path coefficient of e-commerce participation on entrepreneurial behavior is positively significant. Thus, hypothesis H1 is supported. The standardized path coefficient is 0.282, indicating that e-commerce participation has a direct effect of 0.282 on farmers’ entrepreneurial behavior. Holding other conditions constant, a one-unit increase in e-commerce participation can increase the probability of farmers’ entrepreneurial choice by 28.2%. In other words, e-commerce participation contributes to an increased likelihood of farmer entrepreneurship to a certain extent. On the other hand, the standardized path coefficient of social networks on entrepreneurial behavior is 0.005, but it is not significant, indicating that social networks do not have a direct impact on farmers’ entrepreneurial behavior. The standardized path coefficient of social trust on entrepreneurial behavior is -0.055, indicating that social trust has a direct and significant negative impact on farmers’ entrepreneurial behavior. Considering relevant literature and questionnaire data, social trust can be divided into "special trust" based on kinship and geographical proximity, such as trust in parents, and "general trust" supported by non-kinship relationships, such as trust in strangers and government institutions [ 70 ]. People with higher levels of special trust tend to have higher risk aversion, thus reducing their probability of engaging in high-risk business activities, and vice versa [ 81 ]. Entrepreneurship is generally considered a high-risk activity. Looking at the selected observed variables under the latent variable "social trust," one of them is "liking or trusting others." This variable does not specifically refer to trust in parents or strangers. In practice, some respondents are likely to subconsciously answer based on trust in parents, relatives, and friends, i.e., "special trust." Holding other conditions constant, a higher level of special trust may inhibit farmers’ entrepreneurial intentions and reduce their probability of entrepreneurship. Lastly, the standardized path coefficients of network infrastructure and human capital on entrepreneurial behavior are -0.509 and -0.691, respectively. This indicates that network infrastructure and human capital have significant direct negative effects on entrepreneurial behavior. The possibility of this outcome is that farmers with higher education levels and work experience possess higher human capital but may not necessarily have the necessary funds and other resources for entrepreneurship. In the absence of other necessary resources for entrepreneurship, education level alone cannot directly determine farmers’ entrepreneurial behavior. Empirical studies show that farmers’ experience of working outside their hometown inhibits their social evaluation and personal relationships locally, hindering their access to entrepreneurial resources through relatives and friends, and suppressing their entrepreneurial behavior [ 82 ].

https://doi.org/10.1371/journal.pone.0300418.t005

4.5.2 Indirect effect testing of e-commerce participation.

To further analyze the indirect effect of e-commerce participation as a mediating variable on farmers’ entrepreneurial behavior, this study utilizes the Bootstrapping sampling method to examine the indirect effect generated by e-commerce participation as a mediating variable in the model [ 44 ]. The study employs AMOS software to perform Bootstrapping with 5000 repeated samples at a 95% probability level, in order to test the indirect effect of e-commerce participation as a mediating variable in the model. Fig 3 presents the standardized path coefficients of the modified model, and the results are shown in Table 6 .

https://doi.org/10.1371/journal.pone.0300418.t006

Table 6 represents that, at a 95% probability level, none of the three confidence intervals include zero. This means that, except for social networks, the empirical results indicate that e-commerce participation as a mediating variable for social trust, network infrastructure, and human capital has a significant effect on entrepreneurial behavior. Firstly, the indirect effect coefficient of e-commerce participation on the relationship between social trust and farmers’ entrepreneurial behavior is 0.003. This implies that social trust has a significant positive impact on entrepreneurial behavior through e-commerce participation. In other words, holding other conditions constant, an increase of one unit in farmers’ level of social trust would lead to a 0.3% increase in the probability of choosing entrepreneurship after engaging in e-commerce. This validates hypothesis H3. Secondly, e-commerce participation has significant positive effects on network infrastructure and human capital in relation to entrepreneurial behavior, with indirect effect coefficients of 0.124 and 0.082 respectively. This validates hypotheses H4 and H5. Holding all other conditions constant, an increase of one unit in network infrastructure and human capital would lead to a 12.4% and 8.2% increase, respectively, in the probability of farmers choosing entrepreneurial behavior after participating in e-commerce.

4.6 Discussion

First of all, the above empirical results show that rural e-commerce participation has a significant positive direct impact on farmers’ entrepreneurial behavior, most of the previous studies pointed out that the level of development of rural e-commerce in the macro sense of the positive impact on the entrepreneurial behavior of farmers [ 55 , 56 , 83 ], while the study in this paper confirms the positive impact of individual farmers’ e-commerce participation on farmers’ entrepreneurial behavior, the conclusion for the government to formulate relevant policies to promote farmers’ entrepreneurship provides new ideas. Entrepreneurship provides a new way of thinking, the existing literature on rural e-commerce to promote farmers’ entrepreneurship policy recommendations are often to encourage villages or counties to actively apply for "e-commerce demonstration villages", e-commerce sales training for farmers, etc. [ 57 ], this kind of practice is indeed conducive to the promotion of rural e-commerce to a certain extent, the widespread dissemination of e-commerce, but the results are Not necessarily the best, such as scholars through case studies found that the government has developed Taobao users for e-commerce training, the effect is far greater than the farmers who have not been involved in e-commerce, this part of the farmers tend to be more interested in e-commerce training and entrepreneurship, and for the mastery of the relevant skills faster [ 58 ], revealing that we should pay more attention to the importance of rural e-commerce participation. Secondly, e-commerce participation as a mediating variable has a significant positive effect on the relationship between human capital, social trust, network base and other variables respectively and farmers’ entrepreneurial behavior, that is, the theoretical hypotheses of this paper are supported by the empirical results. As can be seen from Fig 3 , the direct factors that are more affected are: "prefer to trust or doubt others" in the latent variable of social trust, "the length of time spent on the Internet on mobile devices" in the latent variable of network infrastructure, "full-time work experience" in the latent variable of human capital, and "the length of time spent on the Internet on mobile devices" in the latent variable of human capital. "full-time work experience", these factors are more likely to have a positive impact on farmers’ entrepreneurial behavior under the effect of e-commerce participation, and this finding is to some extent conducive to providing preparatory ideas for farmers who have the intention to carry out entrepreneurial activities, and to improve the motivation and self-confidence of farmers’ entrepreneurship.

Moreover, given that China was the world’s largest developing country, the agricultural sector in China was dominated by small-scale family farmers and the country had a large number of mountainous regions, which made it difficult to mechanize agricultural production as well. E-commerce, however, bridges the gap between Chinese farmers and external producers, promotes the development of the local agricultural economy, provides farmers with diversified distribution channels, effectively solves the information asymmetry between farmers and final consumers faced in the process of farmers’ entrepreneurship, and improves the profit margins of farmers as well as the efficiency of agricultural product distribution [ 61 , 75 , 84 ]. The most direct impact of rural e-commerce participation on farmers’ agricultural entrepreneurial behavior is that it can make the farmers of agricultural products network sales of low-cost, low-threshold, and the possibility of understanding the market mechanism is greater. The traditional sales channels for agricultural products rely on local markets, but rural e-commerce participation helps farmers to directly reach a broader market. In addition, rural e-commerce participation is conducive to improving the efficiency of farmers’ agricultural production, such as more efficiently purchasing good and inexpensive agricultural production materials and agricultural machinery, etc., improving productivity and accumulating start-up capital. Not only that, rural e-commerce participation promotes the modernization and refined management of agriculture with its unique advantages. Through the network e-commerce platform, farmers can obtain the latest information on agricultural technology and management methods, carry out refined management of agricultural production, and promote the modernization of agriculture. In this study, to analysis the impact of e-commerce participation on agricultural entrepreneurship, we have chosen a sample of 4,057 farmers, 1,700 samples with entrepreneurial behavior. The concept of "agricultural entrepreneurship behavior" was used to measure the extent to which farmers engage in entrepreneurial activities in the agricultural field. A value of 1 was assigned if the entrepreneurial activity was related to agriculture, and 0 if it was unrelated to agriculture. It was found that 1,270 farmers (74.71% of the sample) were engaged in agricultural entrepreneurship behavior. Using AMOS software, a revised conceptual model was employed, and the entrepreneurial behavior variable was replaced with the agricultural entrepreneurship behavior variable for analysis. The standardized path coefficients are shown in Fig 4 , and the results are presented in Table 7 .

https://doi.org/10.1371/journal.pone.0300418.g004

https://doi.org/10.1371/journal.pone.0300418.t007

Table 7 indicates that several key findings emerge. Firstly, the standardized path coefficient of e-commerce participation on agricultural entrepreneurship behavior is 0.296, and the direct effect is significant. This indicates that e-commerce participation has a significant positive impact on agricultural entrepreneurship. Holding other factors constant, an increase of one unit in e-commerce participation level leads to a 29.6% increase in the probability of farmers engaging in agricultural entrepreneurship. Secondly, the indirect effects of e-commerce participation on social networks and social trust in relation to agricultural entrepreneurship are not significant. This implies that e-commerce participation does not have an indirect effect through these mechanisms. Lastly, e-commerce participation has significant and positive indirect effects on the relationships between network infrastructure and human capital with agricultural entrepreneurship. Notably, the empirical results show that these two variables have a significant negative direct effect on agricultural entrepreneurship. In other words, under unchanged conditions, higher levels of network infrastructure and human capital tend to decrease the probability of farmers engaging in agricultural entrepreneurship. However, when e-commerce participation is introduced as a mediating variable, it increases the likelihood of farmers with higher levels of network infrastructure and human capital choosing agricultural entrepreneurship. Considering the empirical results and the real-world perspective, this situation is likely due to the fact that many farmers initially engage in agricultural activities. The reason for this situation may be that farmers with higher levels of human capital have more employment options, and due to the influence of traditional Chinese culture, most farmers have a stronger sense of risk aversion, and some of them are more inclined to choose less risky and stable jobs without having been exposed to e-commerce, which is a low-cost entrepreneurial activity [ 62 ]. Farmers with higher levels of human capital and network infrastructure possess the ability to gather and process information. With e-commerce participation, these farmers gain a better understanding of market mechanisms. Additionally, considering their existing skills and resources, they are more likely to choose agricultural entrepreneurship activities that involve selling agricultural products through online platforms. These types of activities are relatively easier to start and have lower costs, increasing the probability of engagement in agricultural entrepreneurship.

5. Conclusions and policy recommendations

This study utilized the 2020 CFPS data and employed SEM model to comprehensively analyze the direct impact of e-commerce participation on entrepreneurial behavior among farmers under the influence of various factors such as social networks, social trust, internet infrastructure, and human capital. The study also explored the indirect effects and mechanisms of e-commerce participation as a mediating variable, further analyzing the impact and mechanisms of e-commerce participation on agricultural entrepreneurial behavior. The research reveals that: first, e-commerce participation significantly promotes entrepreneurial behavior among farmers; second, social trust, internet infrastructure, and human capital, under the mediating role of e-commerce participation, facilitate entrepreneurial behavior among farmers; third, e-commerce participation has a positive and significant impact on agricultural entrepreneurial behavior. Farmers with higher levels of internet infrastructure and human capital are more likely to choose agricultural entrepreneurship under the influence of e-commerce participation. Given these revelations, the following policy recommendations are proposed.

First, government or authorities should continue to improve and accelerate the construction of cold chain logistics and internet infrastructure for rural e-commerce. Due to factors such as rural economic development and convenience, many people are unwilling to stay in rural areas. The rapid development of cold chain logistics is conducive to improving the entrepreneurial environment in rural areas and promoting farmers’ choice of e-commerce entrepreneurship. According to questionnaire data, the largest proportion of entrepreneurial activities among farmers is in their own agricultural production and operation, followed by individual businesses and private enterprises. Among the farmers who engage in entrepreneurial activities, approximately 74.71% are engaged in agricultural entrepreneurship. Particularly with the development of rural e-commerce, the sale and operation of fresh agricultural products have become an important source of income for farmers’ individual businesses or private enterprises. Fresh agricultural products are perishable and susceptible to damage, and cold chain logistics directly affect the speed and quality of their transportation. Therefore, it is necessary to accelerate the construction of rural logistics infrastructure, especially cold chain transportation, to enhance the entrepreneurial confidence of farmers choosing agricultural entrepreneurship. At the same time, the government should increase efforts to popularize the internet in rural areas and improve internet coverage, laying the foundation for creating a favorable e-commerce environment. The internet can help farmers quickly obtain information about e-commerce entrepreneurship from a vast amount of information. Many farmers are skeptical and cautious about new things, and their fundamental reason for not participating in e-commerce activities lies in their lack of understanding. The internet can help farmers acquire various knowledge about e-commerce and understand the basic transaction rules, enabling them to establish a good market concept, strengthen their connections with the outside world, enhance their market integration capabilities, and increase their confidence in choosing entrepreneurship. Furthermore, the internet can help farmers establish a richer social network, enhance their social capital, and promote their entrepreneurial choices.

Second, local governments should actively promote policies and projects related to e-commerce in rural areas, creating a favorable environment for e-commerce participation among farmers and enhancing their trust in local governments. As indicated by the research conclusions, farmers with higher levels of trust in the government are more likely to choose entrepreneurship through e-commerce participation. Combined with the questionnaire data, approximately 30% of the sampled farmers have a moderate level of trust in the government, and around 40% of farmers rate their trust in the government above 5 points. This indicates that most of the sampled farmers have a relatively high level of trust in the government, reflecting the good achievements of grassroots governance in China and the superiority of the basic political system. However, there are still some farmers who do not have a high level of trust in the government. Therefore, it is necessary to continue to improve governance levels and promote government trust by creating a favorable environment for e-commerce participation. Firstly, various media platforms should vigorously promote the benefits that e-commerce participation can bring to farmers, such as the affordability of online products. Secondly, young people should be encouraged to lead the participation of older people in e-commerce, bridging the generational gap in e-commerce participation. Thirdly, e-commerce rights protection institutions should be established locally to help farmers effectively address technical difficulties they may encounter in e-commerce participation and protect their legitimate rights and interests, alleviating their concerns about security risks in e-commerce participation. As a result, farmers will be more willing to participate in e-commerce activities, accumulate prior experience in e-commerce participation, and promote entrepreneurship among farmers.

Finally, governments should continue to improve the education level in rural areas and enhance the entrepreneurial environment for farmers engaged in e-commerce. The research conclusions indicate that farmers with higher education levels and more full-time work experience are more likely to choose entrepreneurship under the influence of e-commerce participation. Observing the sample data, 30% of the interviewed farmers have primary school education or below, 40% have junior high school education, and only around 10% have college education or above. It shows that the education level of most farmers is relatively low. A low level of education will result in a lower level of awareness and inadequate skills related to e-commerce participation, which will affect farmers’ access to prior experience in the e-commerce industry. To some extent, it will restrict farmers’ ability to obtain relevant entrepreneurial information and hinder their ability to apply the entrepreneurial knowledge they have learned. In some rural areas, especially underdeveloped and remote mountainous regions, there are still many shortcomings in education. Therefore, it is necessary to continuously improve the education level in rural areas through adult education and vocational training. In underdeveloped counties, townships, and villages, the construction of high schools and vocational schools should be newly established or expanded. Efforts should be made to strengthen the construction of agricultural colleges, agricultural vocational colleges, and agricultural disciplines. At the same time, it is important to pay attention to the employment and entrepreneurship environment for farmers engaged in e-commerce. The government should increase efforts to popularize knowledge about e-commerce entrepreneurship, national policies, and local support policies. It should also provide relevant skills and vocational training based on the needs of farmers’ e-commerce employment and entrepreneurship. Simplifying the procedures for farmers’ e-commerce entrepreneurship qualification reviews, increasing financial support for e-commerce entrepreneurship, regulating relevant laws and regulations, guiding local farmers to legally utilize e-commerce for employment and entrepreneurship are also crucial. Meanwhile, relevant employment policies should be introduced to strongly support the development of local e-commerce and logistics enterprises, drive rural employment and entrepreneurship, help farmers accumulate industry experience and entrepreneurial capital.

5.1 Limitation and further research direction

It is important to acknowledge that the this study has certain limitations. Due to limitations in data availability, the selection of indicators for assessing rural e-commerce participation is constrained, leading to a narrow focus on a single dimension. Currently, research predominantly examines the influence of rural e-commerce participation on entrepreneurial behavior and agricultural entrepreneurship. However, it is essential to extend these investigations to encompass a broader perspective, including an exploration of the impact of rural e-commerce participation on the rural entrepreneurial process and entrepreneurial performance. In addition, understanding the influence of rural e-commerce participation on the rural entrepreneurial process represents a crucial research area. In-depth analyses of how rural e-commerce participation inspires, influences decision-making, and shapes the execution of entrepreneurial activities among farmers can provide valuable insights into the challenges and opportunities they encounter throughout their entrepreneurial Process. Such investigations facilitate an examination of the mechanisms through which rural e-commerce fosters entrepreneurial engagement among farmers, thereby shedding light on the decision-making and actions taken by farmers during the entrepreneurial process. Moreover, investigating the impact of rural e-commerce participation on farmers’ entrepreneurial performance is of significant scholarly interest. This research direction involves an examination of the effects of rural e-commerce participation on various aspects of entrepreneurial ventures, such as market performance, profitability, and sustainable development. Through comprehensive evaluations of the impact of rural e-commerce on farmers’ entrepreneurial projects, researchers can gain a deeper understanding of their market performance, profitability, and long-term viability. By examining the multifaceted effects of rural e-commerce participation, scholars can provide comprehensive insights into the dynamics between rural e-commerce and farmers’ entrepreneurial activities, thereby informing policy decisions and facilitating the development of effective strategies to promote rural entrepreneurship.

Supporting information

https://doi.org/10.1371/journal.pone.0300418.s001

• View Article
• Google Scholar
• PubMed/NCBI
• 7. Ministry of Agriculture and Rural Affairs, http://www.moa.gov.cn/
• 9. China Internet Network Information Center, http://www.cnnic.net.cn/
• 19. China.com News Center, http://news.china.com.cn/
• 68. CFPS database link, http://www.isss.pku.edu.cn/cfps/download
• 74. Development Research Center of the State Council, Ministry of Agriculture and Rural Affairs Project Team, Ye X., Xu X. (2014). From Dualism to Integration of Urban and Rural: Prominent Contradictions and Future Trends of China’s Urban and Rural Dual System. China Development Press, 2014, 14–18.

Research Article

Research on Consumer Behavior Analysis and Recommendations for Cross-border E-commerce based on Big Data

• @INPROCEEDINGS{10.4108/eai.15-12-2023.2345387, author={Shasha Wu}, title={Research on Consumer Behavior Analysis and Recommendations for Cross-border E-commerce based on Big Data}, proceedings={Proceedings of the 3rd International Conference on Public Management and Big Data Analysis, PMBDA 2023, December 15--17, 2023, Nanjing, China}, publisher={EAI}, proceedings_a={PMBDA}, year={2024}, month={5}, keywords={cross-border e-commerce; consumer behavior analysis; recommendations; neural network}, doi={10.4108/eai.15-12-2023.2345387} }
• Shasha Wu Year: 2024 Research on Consumer Behavior Analysis and Recommendations for Cross-border E-commerce based on Big Data PMBDA EAI DOI: 10.4108/eai.15-12-2023.2345387
• 1: Wuhan Qingchuan University

At present, cross-border e-commerce is becoming an important transaction method for international trading. Therefore, the reasonable analysis for purchasing behaviors and precise recommendations are essential for the trading platforms. However, existing recommendation system relies on the collaborative filtering from users and ignore the extraordinary characteristic information from users including gender, hometown, and historical preferences. In this work, we initially determine the review scoring in consumer behavior, the influence of external factors such as time and geography on consumer behavior is analyzed, and the influence of time, space and other factors on the quality of merchants is eliminated. Subsequently, a training neural network is established to obtain the recommendations results for different consumers from the previous analysis module. Finally, we estimate our model in real-world commerce data and compare with existing recommendation algorithms. From our simulation results, we can observe that our proposed model can achieve the precise recommendations with reasonable time complexity.

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E-commerce and artificial intelligence, research and article written to investigate the role of ai in e-commerce.

Businesses are always looking for new and creative methods to improve customer experience, streamline processes, and stimulate growth in the ever-changing world of e-commerce. Artificial intelligence (AI) has become an influential player in the e-commerce industry, providing several tools and platforms that  completely change  how e-commerce  is done . In addition to offering individualized client experiences, these AI-powered solutions also give developers and business owners  useful  insights and automation capabilities. Customer service stands out as a crucial element that can make or break a business in the dynamic world of e-commerce. As the appeal of online shopping continues to soar, offering exceptional customer service has never been  more difficult  or important. To overcome this challenge, many e-commerce companies use artificial intelligence (AI) solutions to improve their customer service processes .  We will look at three AI platforms and tools that are changing the e-commerce scene in this  article

     1. Personalized Suggestions for Products: AI-powered product recommendation engines are  yet  another effective tool that optimizes the online shopping experience. These search engines examine user data, including browsing preferences, purchasing patterns, and demographics, to generate targeted product recommendations.  Recommendation engines that use machine learning algorithms can anticipate user preferences and present relevant products , hence raising the probability of a purchase.  Enhancing the shopping experience for clients is one of the main advantages of personalized product recommendations.  Businesses may provide a more personalized and engaging shopping experience  for customers  by presenting products that match their interests and preferences.   This  will increase customer satisfaction and conversion rates.  Furthermore, the cross-selling potential increases when clients find new products through personalized recommendations that they might not have otherwise considered.  

A crucial  component of e-commerce  is customer service, which frequently acts as the first point of contact for client inquiries, feedback, and support. However, managing these interactions across several channels might  take a lot of  effort and resources. Here's where AI-powered solutions like Gorgias come into play, enhancing customer and business experiences by optimizing customer care processes.

• Benefits for Customers: Gorgias uses AI to develop chatbots that respond to common questions from clients.  This  improves customers' shopping experience by offering them prompt assistance day or night. For instance, a consumer can rapidly obtain the information they require without having to wait on hold or navigate complicated menus when they have a query about a product or the status of their purchase. Additionally, Gorgias' chatbots may assist clients with troubleshooting procedures and offer  basic  query solutions. Customers will be more satisfied with their time saved and assurance that they will obtain consistent and correct information.
• Benefits for Developers: Gorgias has an intuitive user interface with drag-and-drop features that make it simple for developers to build unique processes and automated responses.  This  allows developers to customize the AI assistance system to the unique demands and specifications of the online business. Developers can, for instance, design automatic answers to often-requested queries or configure workflows to handle particular types of requests.  Furthermore, Gorgias  gives developers the freedom  to combine the AI support system with other programs and platforms,  hence  expanding its functionalities.   This  enables developers to design an integrated and seamless customer support experience that fits the particular requirements of their online store.
• Advantages for Business Owners: Gorgias provides several  important  advantages for business owners. It primarily  allows  companies to offer  effective  and affordable customer assistance. Gorgias allows personnel to work on  more difficult  and valuable activities, including addressing escalating concerns or offering individualized support, by automating frequently asked questions. Moreover, Gorgias incorporates sales information, providing customer service representatives  with  a comprehensive understanding of the customer's past and brand interactions.  This  makes it possible for support staff to offer more individualized assistance, such as making product recommendations based on previous purchases or resolving particular issues brought up by clients.  In the end , this results in increased client happiness and loyalty, which encourages recurring business and favorable word-of-mouth recommendations.

In conclusion, Artificial intelligence (AI)-driven tools and platforms are revolutionizing the e-commerce space by providing creative ways to improve consumer satisfaction, optimize processes, and spur expansion. Artificial intelligence (AI)  is transforming  how businesses interact with customers and perform e-commerce. Examples  of this  include chatbots for customer assistance, personalized product recommendations, and visual search technologies. By utilizing these technologies, companies may maintain a competitive edge and provide outstanding shopping experiences that foster client loyalty and expansion. Businesses and customers alike can gain from  a variety of  advantages  provided  by Gorgias' AI-powered customer support system. Gorgias assists e-commerce companies in growing their customer base and enhancing the overall shopping experience by optimizing consumer inquiries, expediting response times, and offering more individualized service. 

Great article! AI's impact on e-commerce is indeed fascinating.

Personalized product suggestions and visual search are game-changers.

Your insights on Gorgias' AI-driven customer service are also spot-on. It's exciting to see how AI continues to revolutionize the online shopping experience. Keep up the great work!

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Chinese E-Commerce Giants Face Delicate Balance Between Discounts, Profit

FILE PHOTO: People ride on a scooter past a JD.com's advertisement promoting Singles Day shopping festival, in Beijing, China October 26, 2023. REUTERS/Tingshu Wang/File Photo

By Casey Hall

SHANGHAI (Reuters) - Quarterly earnings reports from Chinese e-commerce giants Alibaba and JD.com this week will be closely watched as barometers for the mood of consumers in the world's second-largest economy.

Both firms, which combined account for about 69% of China's e-commerce market revenue, according to DBS estimates, have faced increasing competition in recent years from low-cost platforms, such as PDD Holding's Pinduoduo and ByteDance-owned Douyin.

Chinese consumers are seeking discounts and lower-cost shopping because of their cautious attitude toward spending after the COVID-19 pandemic amid lower economic growth and the slowdown in the property sector. Alibaba and JD.com have responded to this trend but they risk lower margins by doing so.

This low-cost battleground presents a challenge for Alibaba's Tmall and JD.com. Both have traditionally sought to move up the consumer value chain by selling increasingly premium products, such as Apple iPhones, Estee Lauder skincare and Tiffany & Co jewellery, but are now forced to defend that space while also offering a wider array of cheap products to stem market share leakage.

"As long as consumers remain highly cost-conscious such policies are likely to further slow revenue growth and erode profit margins," said S&P Global analyst Cathy Lai, adding that both Alibaba and JD.com are moving more into the unbranded goods territory that has been Pinduoduo's stronghold.

Alibaba "cannot ignore PDD, but nor can it quell the competitive threat by wholly adopting PDD's strategy. JD.com is in a similar position," she said.

“Under its user first strategy, Taobao and Tmall Group proactively and aggressively invested in product supply, competitive pricing and quality service to meet all tiers of consumer demands," Alibaba's Taobao and Tmall Group said in statement responding to Reuters request for comment.

JD.com did not respond to a request for comment.

Last year Alibaba's platforms, as well as JD.com pledged billions of yuan to subsidise discounts and coupons across regular sales events.

That effort resulted in mixed returns. In the September to December quarter last year, which included the year's biggest sales festival of Singles Day, revenue at Alibaba's Taobao and Tmall Group increased only 2% year-on-year while JD.com rose only 3.6%.

For the March quarter this year, analysts expect overall revenue at Alibaba, 65% of which is generated by its domestic e-commerce arm, to grow 5.3% year-on-year while JD.com will rise by about 6%, according to LSEG data. That is roughly in line with growth trends in recent quarters.

In contrast, PDD Holdings revenue grew 123% in the December quarter, though this figure includes its fast-growing international platform, Temu, as well as domestic platform Pinduoduo, which generates the vast majority of PDD's revenue. Douyin, which does not regularly disclose sales data, was tipped to grow 60% for 2023, according to research firm eMarketer's estimates.

China's e-commerce companies are again entering a major discounting period, with weeks-long sales for major mid-year event 618, named for the date of JD.com's founding on June 18, to begin at the end of May.

Adding to the current competitive environment facing Alibaba and JD.com, brands are spending more on live-streaming on sites such as Douyin and away from sites such as Tmall, said Jacques Roizen, managing director of China consulting at Digital Luxury Group.

The impact of the continuous discounts will "kill" the profits of brands such as cosmetics makers L'Oreal and Estee Lauder, which garner as much as 30%-40% of their China sales from e-commerce, Roizen said.

"At some point the brands are going to realize that they're not making any money (on low-price platforms)," he said.

"But instead of taking the opportunity to counteract as a more premium, elevated, trustworthy platform, (Alibaba) decided to double down on discounts and promotion and guaranteeing the best price and all that stuff. To me, it's a race to the bottom."

Alibaba will report earnings for the quarter ending in March on Tuesday and JD.com on Thursday.

(Reporting by Casey Hall, Additional reporting by Sophie Yu; Editing by Christian Schmollinger)

Copyright 2024 Thomson Reuters .

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