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Humanities and Social Sciences Communications volume 11, Article number: 789 (2024)
267
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In the era of the digital economy, rural e-commerce has emerged as a compelling catalyst for driving the modernization of agriculture and rural areas. It has given rise to notable exemplars such as “Taobao villages” and “Taobao towns”, paving the way for scalable pathways toward rural revitalization. The transition from Taobao villages to Taobao towns signifies the evolution of rural e-commerce from scattered nodes to interconnected networks, reflecting a significant development trend. While existing studies have primarily focused on the spatiotemporal patterns of Taobao villages, limited research has been conducted on the spatiotemporal characteristics and the underlying evolution mechanism of Taobao towns. To fill the research gap, this study investigates the spatial-temporal evolution of Taobao towns employing geospatial methods and identifies key contributing factors using GeoDetector in China. The results show that: (1) The coverage of Taobao towns expands year by year in all provinces and cities, showing a “T-shaped and three-center” spatial pattern with a decreasing gradient from southeast to northwest. (2) Taobao towns in the central area are developing rapidly and are becoming an emerging development center. (3) The development of Taobao towns is the result of a combination of multiple factors, including regional economic base, social environment, market atmosphere, transportation conditions, infrastructure construction, etc. The study can provide Chinese experience for rural development and transformation.
The electronic commerce (e-commerce) industry, with its low threshold, large number of employees, and freedom from time and space constraints, plays an important role in the rural revitalization of China (Molla & Heeks, 2007; Baller et al., 2016; Xiong et al., 2017). In the newly announced “14th Five-Year Plan for the Development of E-commerce” (General Office of the State Council, 2021), it is pointed out that e-commerce is an important engine for bolstering supply-side structural reform, promoting related employment, accelerating the digitalization process of the industrial chain, and linking domestic and international markets. As an emerging economic form, the rural e-commerce industry is inclusive and innovative (World Bank, 2016: 10), and plays a great role in promoting rural economic transformation and upgrading, realizing rural revitalization, and narrowing the gap between urban and rural areas (Peng et al., 2021). Therefore, in the context of the new economic norm in China and the new development pattern of dual circulation of domestic and foreign markets, e-commerce can serve as the adhesive of the digital divide between urban and rural areas, and “Taobao villages” and “Taobao towns” are created under the dual drive of realistic demand and policy.
Taobao is an e-commerce platform invested in and founded by Alibaba Group on May 10, 2003. As of September 2021, the size of China’s Internet users reached 1.032 billion (CNNIC, 2022), while the number of annual active customers (AAC) of Taobao and Tmall has exceeded 900 million (Alibaba Group, 2021), making it a representative integrated retail circle in China, covering multiple businesses such as customer to customer (C2C), group buying, distribution, and auctions. In 2009, Taobao started to get involved in the field of rural revitalization, prompting a wave of e-commerce-driven rural digitization and urban-rural integration to begin spreading across the country (Kwak et al., 2019). Taobao villages and Taobao towns are new terms that have both industrial attributes and administrative features under the Taobao platform, and due to the “Internet +” project and a series of policy support in the digital economy era, the number of distributions is showing exponential growth, which is the most typical forms of rural business gathering at present (Liu et al., 2020). According to the Ali Research Institute, Alibaba Group’s research arm, a village with an annual gross merchandise volume (GMV) of 10 million RMB (approximately US$1.38 million), and an e-commerce scale greater than or equal to 100 online stores, or 10% of the number of households in the villages with active outlets, is a Taobao village (Allizila, 2016). In addition, the Ali Research Institute started to identify Taobao towns in 2014 and defined them as towns with Taobao villages greater than or equal to three, or on Alibaba’s e-commerce platform (including Taobao, Tmall, Alibaba, Ali International, etc.), where the annual GMV of the entire township’s e-commerce reaches 30 million RMB (approximately US$4.13 million), and the number of active outlets is greater than or equal to 300 (Ali Research, 2019). In short, Taobao villages and Taobao towns are highly dependent on e-commerce platforms and the digital economy, relying on the Alibaba ecosystem, and are rapidly forming industrial clusters with the advantages of quantity and quality (Lan et al., 2022).
At present, academic research on Taobao villages is still emerging, and the early studies tended to summarize the formation mechanism and evolution process of Taobao villages from case studies (Qi et al., 2019; Zeng et al., 2019), and explore the dynamic mechanism of the flourishing rural e-commerce industry (Martindale, 2021; Wang & Zhang, 2019; Xie & He, 2020; Leong et al., 2016) to form a replicable practice path (Hagberg et al., 2016; Long & Wang, 2018). Specifically, the development of Taobao villages relies on government support (Cui et al., 2019; Miao & Phelps, 2019), talent introduction and training (Lin, 2019), a sound logistics network (Sarkis et al., 2020), and upgrading of supporting facilities (Li et al., 2021; Zhou et al., 2021). With the proliferation of Taobao villages, scholars have gradually begun to pay attention to the countervailing effects of the e-commerce industry concentration on the issues relating to agriculture, rural areas, and farmers, including the adjustment of rural industrial structure (Zhou et al., 2021; Jing & Jie, 2021), the increase or decrease of income benefits (Peng et al., 2021; Su et al., 2021; Zeng et al., 2018), the change of urban spatial form (Sarkis et al., 2020), innovation and entrepreneurship (Huang et al., 2021; Wu et al., 2020; Mei et al., 2020; Liang et al., 2017) and the change of social environment (Lin et al., 2016). In addition, the analysis of the spatial and temporal distribution characteristics of Taobao villages at macro and micro scales is a key area of research (Lin et al., 2021; Liu et al., 2020). Benefit from multi-source big data, the quantitative approach based on spatial analysis tools and econometric model prediction, and the qualitative approach based on field research and case summaries, the distribution characteristics are generally summarized as a gradient decreasing pattern from coastal to inland (Zeng et al., 2020). Notably, Peng & Ding (2021) found that the central and western areas face a high growth and disappearance dilemma in their study of the distribution pattern of Taobao villages, raising a new question: how to break through the traditional model and help rural revitalization with the transformation and upgrading of e-commerce units?
On the contrary, Taobao towns, due to the formation of the early stage only focus on the upgrading of their spatial attributes but ignore the advantages of their industrial agglomeration and scale efficiency. Therefore, not much attention has been paid to Taobao towns, which are more often described as a supplementary analytical perspective to Taobao villages (Ma et al., 2017; Qian et al., 2017), but the research on its development mode and formation mechanism is still in its initial stage, and there are still few studies conducted from a geographic spatial perspective. In recent years, with the continuous evolution of the rural Taobao platform, although Taobao villages and Taobao towns are still two types of commercial spatial aggregation forms with undertaking relationships, villages and towns, as two levels of e-commerce carriers, have gradually moved away from the development law of overlapping trajectories in terms of spatial distribution, and have derived two independent development systems, each with its applicable development stage and regional scale. Specifically: in terms of spatial distribution, although Taobao villages outperform Taobao towns in terms of total volume, the proportion of their administrative characteristics is much lower than that of the latter, and the internal lack of network structure makes them relatively less stable (Ma et al., 2017); in terms of transaction scale, due to the limitation of geographical isolation, each administrative village develops independently, there is industrial convergence and homogeneous competition. Given this, Taobao villages have a lower development ceiling, and the degree of industrial clustering and transaction scale is second to Taobao towns (Chen & Zhang, 2018). It can be said that from Taobao villages to Taobao towns, the internal nature and external form are upgraded. A Taobao town is undoubtedly the advanced form of a Taobao village, and also the basic unit of the future rural e-commerce development pattern. Relevant academic research is of great significance.
Based on the literature analysis above, it is evident that there has already been a certain amount of research on Taobao Towns. These studies hold significant academic value and provide crucial support for the research presented in this paper. However, the investigation into Taobao Town is still in its preliminary stages. The spatiotemporal evolution patterns of Taobao Town are not yet clear, lacking systematic study and requiring further exploration. Several questions have not been effectively answered: What growth stages did the transition from Taobao Village to Taobao Town undergo? What are the global and local spatial characteristics of Taobao Town’s distribution pattern? What are the main factors influencing the development of Taobao Town, and What is its mechanism of action?
Taking Taobao towns as the research objects, this research uses spatial autocorrelation analysis and standard deviation ellipse analysis to explore the temporal and spatial evolution law of Taobao towns in China and analyzes its spatial characteristics of dispersion and aggregation. It also studies the influencing factors in the development of rural e-commerce based on the geographic detector method to provide new ideas for the transformation and development of small and medium-sized towns and industrial upgrading and provide the Chinese experience for the rural development of other developing countries.
The examination of the spatial distribution of Taobao towns contributes to understanding the developmental trajectory of China’s rural e-commerce industry across various regions through the lens of economic geography. By scrutinizing the patterns of geographic agglomeration and dispersion, this study aims to uncover the underlying mechanisms by which factors such as geographic economic disparities and industrial clustering effects influence the evolution of e-commerce. Furthermore, it seeks to provide a more thorough and insightful comprehension of the dynamics characterizing the development of Taobao towns during distinct periods. Such an analysis holds significant importance for accurately identifying the geographical distribution patterns of emblematic e-commerce industries and forecasting their future progression.
This study comprehensively utilizes various spatial analysis methods to analyze the growth stages, distribution characteristics, and influencing factors of Taobao Towns, exploring the development patterns of rural e-commerce. The study first summarizes and inducts the growth stages of Taobao Towns based on empirical evidence, and then applies a global spatial autocorrelation model to determine the spatial association level of Taobao Towns. It further uses a local spatial autocorrelation model, identifying local spatial patterns in Taobao Town’s distribution by creating LISA cluster maps. Following this, the standard deviational ellipse method is used to identify and predict the evolutionary direction and development trends of Taobao Towns. Finally, the geodetector model is utilized to explore the factors influencing the spatial distribution of Taobao Towns. The reason for choosing the Geodetector is that it is a statistical method used to detect the sources of spatial heterogeneity and the impact of different factors on a phenomenon by analyzing the similarity in spatial distribution patterns among variables. The Geodetector can not only detect the impact of a single factor on the target variable but also analyze the interactive effects of multiple factors on the target variable. In contrast, traditional regression analysis methods are sensitive to the linear assumptions of data, which may lead to errors if the actual data do not match the model assumptions. Moreover, regression analysis seldom directly considers the characteristics of spatial data, such as spatial autocorrelation.
Spatial autocorrelation model is used to represent an attribute’s spatial dependence and determine whether and to what extent an attribute is spatially correlated (Anselin et al., 1993). This method mainly includes global spatial autocorrelation and local spatial autocorrelation analysis.
Global spatial autocorrelation model is used to measure the average spatial correlation of a certain attribute value over a total spatial extent, and the most commonly used correlation index is the Global Moran’s I. The calculation formula is shown in (1).
The General G index (Getis-Ord General G) is an inferential statistical algorithm used to further determine whether spatial aggregation is high-value or low-value (Getis & Ord, 1992). If the Z score is positive, the observed General G index is greater than the expected General G index, indicating that the data are clustered in the high-value area; conversely, the data are clustered in the low-value area. The calculation formula is shown in (2).
In addition, since the spatial distribution of Taobao towns is not balanced, local spatial autocorrelation analysis is used to explore the spatial locations and specific types of agglomerations or anomalies occurring to more accurately visualize the variability of local areas (Anselin, 2010). This method divides the x and y axes into four quadrants: H–H (high-value agglomerates), L–H (high-value surrounded by low-value anomalies), L–L (low-value agglomerates), and H–L (low-value surrounded by high-value anomalies) by comparing with domain data with standardized observations and spatially lagged values (Cui et al., 2021). The calculation formula is shown in (3).
where I is the degree of spatial association; (,{x}_{i}) and ({x}_{j}) are the distribution attribute of Taobao towns in the study area; (bar{x}) is the mean value of areas; ({{rm{w}}}_{{ij}}) is the spatial weight between areas i and j; S2 is the variance of the attribute values.
The standard deviation ellipse model is a statistical method used to characterize the spatial distribution of a research object, including its central location, evolutionary direction, dispersion trend, etc. (Peng et al., 2016; Lefever, 1926). In this research, the mean center of each Taobao town is used as the starting point, and the standard deviation of the X/Y coordinate direction is used as the axis to generate a multi-year ellipse containing most of the data using ArcGIS to describe the spatial distribution trend and evolutionary characteristics of Taobao towns. Among them, the center point of the ellipse indicates the central location of Taobao town, the long axis indicates the distribution direction, and the short axis indicates the distribution range. The shorter the short axis, the stronger the centripetal force presented by the data, and vice versa; the greater the difference between the long and short axis, the greater the elliptical flatness, and the more obvious the directionality of the distribution in Taobao towns. The calculation formula is shown in (4) and (5).
Where ({{SDE}}_{x}) and ({{SDE}}_{y}) are the central coordinates of the ellipse; ({x}_{{rm{i}}}) and ({x}_{{rm{j}}}) are the spatial location coordinates of each Taobao town; (bar{x}) and (bar{y}) are the arithmetic mean center of each Taobao town.
where (theta) is the azimuth of the ellipse; (left(widetilde{{x}_{i}},widetilde{{y}_{i}}right)) is the deviation of each Taobao town from the center of the ellipse.
GeoDetector is a statistical method to reveal the driving forces behind spatial data, proposed by Wang & Xu (2017), mainly contains four parts: risk detection, factor detection, ecological detection, and interaction detection. Among them, factor detection is mainly used to analyze how much the detection factor explains the spatial variation of attributes. The calculation formula is shown in (6).
Where q is each influence factor (i.e., factor) on the spatial heterogeneity of Taobao towns (i.e., attributes) with explanatory power ranging from [0, 1]; ({partial }_{i}^{2}) and ({partial }^{2}) are the variance of the explanatory power of the influence factors for i layer and the whole area, respectively; ({n}_{i}) and n are the number of cells in layer i and the whole area, respectively; i is 1, 2, … ; l is the stratification of the attribute and the factor.
Interaction detection is mainly used to evaluate whether the explanatory power of the dependent variable will be increased or decreased when the factors and factors act together. The types of interactions are shown in Table 1.
The data used mainly include text data and map data. The basic information on the number and distribution of Taobao towns are obtained from the text files of each province and city in China from 2014 to 2021 published by Ali Research Institute (http://www.aliresearch.com/cn/index), and the text data are converted into point data according to the Amap open source platform (https://lbs.amap.com/) to capture and identify the geographic coordinate information of each Taobao town one by one and transform the text data into point data; the descriptions and sources of socio-economic data of the provinces where Taobao towns are located are shown in Table 2.
As shown in Fig. 1, with 2009 as the cut-off point, the Taobao platform focused on urban development before then, while it gradually began to focus on the development of rural e-commerce thereafter. Since 2009, when three Taobao villages were identified, the scale of Taobao villages’ development has gradually stabilized, and the operation mechanism has been improved, laying a solid foundation for the formation and development of Taobao towns and providing a development path to be learned from. With 2017, and 2019 as the time node, the development history of Taobao towns can be roughly divided into the following three stages.
The development of Taobao towns has gone through stages of inception, rapid growth, steady growth, and now into the current phase of lean development.
The first is the explosive growth stage from 2014 to 2016: these two years were the stage when the country took rural e-commerce as a strategic industry, providing full-factor support in various fields such as rural inclusive finance, hometown innovation and entrepreneurship, e-commerce talent training, logistics park planning, mobile hardware facilities, and standardized management of market rules, which provided a fertile ground for the development of rural e-commerce. Numerically, the relatively low certification threshold at the early stage of development has led to the rapid growth of Taobao towns and the concentration of distribution areas, which has played a good leading role in the subsequent development of rural e-commerce.
The second is the steady growth stage from 2017 to 2018, this stage is “from quantity to quality” of the starting stage. In the agricultural supply-side reform at the peak, Alibaba’s platform to create several high-quality industries, the promotion of the “Internet plus agriculture” model, improve the e-commerce ecosystem, according to local conditions to innovate special industries, rooted in local reality to create high-quality products, to overcome homogeneous competition, emphasizing the “quality” and weaken the “quantity”, so the development of Taobao towns at this stage is moving forward smoothly.
The third is the lean development stage from 2019 to 2021. In 2019, the Alibaba platform updated the identification criteria of Taobao towns, from only focusing on the number of Taobao villages’ clusters to focusing on the entire town’s e-commerce transaction scale, i.e., in the original requirement of the number of Taobao villages in the township or street is greater than or equal to three, GMV and the number of active outlets are also included in the identification system. With more comprehensive and scientific policy changes, the Alibaba platform brings new opportunities and chances for the development of Taobao towns and also provides the possibility to promote the upgrading of rural e-commerce forms. Rural e-commerce is a “marginal revolution” initiated by “marginal people” in “marginal location” and “marginal products”. The triple marginal attributes make the rise and prosperity of Taobao villages full of tension (Ali Research, 2019: 18). With the accumulation of early development experience, several mature industrial chains and development models have been formed, with the increment expanding and the stock upgrading, forming the current rural economic situation. At present, it is in a critical stage of transformation and development. It is the key issue to give full play to its economic driving role and serve the development of all areas of the country.
The overview of the study area is shown in Fig. 2. Referring to the Ali Research Institute’s classification of Taobao villages’ clusters, the number of Taobao towns in the province reaches or exceeds 10, which is called “Taobao town clusters”; the number reaches or exceeds 30, which is called “large Taobao town cluster”; the number reaches or exceeds 100, which is called “super large Taobao town cluster”.
Displays the spatial location of the study area.
The spatial distribution map of Taobao towns from 2014 to 2021 is shown in Fig. 3. From the overall spatial characteristics, the regional differences in the distribution of Taobao towns in China are obvious, with more in the east and less in the west, less in the north and more in the south, gradually forming a “T-shaped and three-centers” spatial pattern with gradient decline from southeast to northwest. That is to say, it interlinks the coastal, central, and western regions in a contiguous “T-shaped” framework; the Pearl River Delta, the Yangtze River Delta, and the Bohai Sea city cluster are the “three centers”. This spatial pattern overlaps with the key areas of China’s coastal development, the rise of central China, and western development policies, and is conducive to exploring a new mechanism of regional linkage and coordinated development (Fang, 2020). Among them, it is worth noting that the central area is developing rapidly, including the Chengdu-Chongqing economic circle and the central plains economic area (CPER), both in terms of the distribution and quantity of Taobao towns have an obvious growth trend, gradually narrowing the gap with the eastern area. At the same time, this also indicates that the future development direction of rural e-commerce will be the reverse extension from the coast to the interior. With the improvement of the transportation network and the digital economy’s universal scope and depth of vertical and horizontal extension, the central and western inland areas will become the new growth pole, breaking the traditional geographical and natural constraints, playing their advantages, and expanding the spatial distribution of Taobao towns.
The Taobao towns in China exhibit a “T-shaped and three-center” spatial pattern, with a decreasing gradient from southeast to northwest.
In the examination of the size structure of Taobao towns across China, a discernible pyramid-type distribution model emerges. This model is characterized by provinces harboring between 0 and 10 Taobao towns forming the broad base of the pyramid, while those boasting more than 100 Taobao towns are positioned at its apex, where town density is markedly sparse. Such a distribution paradigm is reminiscent of nascent stages in the evolution of emergent phenomena or industries, signaling geographic disparities alongside immense prospects for subsequent expansion.
In the early stage of Taobao town development, those provinces at the bottom of the pyramid will actively learn and absorb the successful experiences and business models of those fast-growing Taobao towns, and apply them to their own Taobao towns to cultivate and develop them according to local conditions. At the same time, they have grasped the strong support of national policies for rural e-commerce and new urbanization and effectively enhanced the overall scale and competitiveness of Taobao towns in the region by continuously improving infrastructure, optimizing resource allocation, and improving network coverage and service capacity. From 2014 to 2017, with the popularisation and deepening of e-commerce, the number of Taobao towns showed a steady upward trend and gradually expanded from the initially developed eastern coastal regions to the central and western regions and the inland less-developed regions, which fully reflected the strong vitality and wide adaptability of e-commerce as a new type of industry. After entering 2018, the number of Taobao towns has seen a significant jump in growth, not only the total amount of surge but also the rapid expansion of the geographical distribution, almost all over the country. The rise of Taobao towns, both in terms of the overall development history and within individual provincial administrative units, has followed this law of from few to many, from points to networks, forming a positive cycle of continuous accumulation and reinforcement.
In particular, it is worth mentioning that regions such as Tibet Autonomous Region, Inner Mongolia Autonomous Region, and Hainan Province, despite the challenges they faced in the development of Taobao towns due to natural geographical conditions, lagging development of traditional markets and low-level of rural informatization, were able to find an alternative way to make use of the rich local ethnic minority cultures and unique product resources, breaking down geographical barriers through the Internet and e-commerce platforms, and bringing the original Taobao towns to a new level of development. In 2020, these regions finally achieved a historic breakthrough in the construction of Taobao towns from scratch, followed by a rapid growth rate in 2021, showing a strong momentum of development. Anticipating the future trajectory, these regions are poised to ascend as high-growth Taobao towns in the ensuing phase, leveraging their distinctive resource endowments and latecomer advantages. Such advancements are primed to catalyze the continued expansion of rural e-commerce development in China, concurrently bolstering the execution of the rural revitalization strategy.
To determine the precise degree of aggregation, the data of Taobao towns from 2014 to 2021 are projected and transformed in ArcGIS, and the spatial autocorrelation analysis is carried out. Constraints posed by the limited spatial extent of county units and the finite number of Taobao towns hinder the visually intuitive representation of their distribution characteristics in graphical form. Therefore, we summarize the point data of Taobao towns in the city and count the quantity distribution in all cities nationwide for observation.
As shown in Table 3, the p-value of the Global Moran’s I index in 2021 is less than 0.01, i.e., the confidence level of this data is higher than 99%, indicating that the number of Taobao towns is positively correlated with their spatial aggregation. At the same time, the General G observation is also much higher than the expected value, indicating that China’s Taobao towns in 2021 show high-value clustering characteristics in space. It can be seen that Taobao towns are at the early stage of development with strong momentum and initial prototypes of industrial agglomeration.
In addition, to explore the spatial heterogeneity of Taobao town distribution, ArcGIS is used to conduct LISA analysis on the spatial distribution in 2021. As shown in Fig. 4, Low-High outliers, i.e., low-value surrounded by high-value areas, which usually lag behind the surrounding areas in development, should be classified as a typical heterogeneous area. From the accelerated growth rate of the number of Taobao towns in 2018, the scope of the Low-High outliers has also gradually increased, with a patchy distribution in the central and northeastern areas. This is because of the limitation of geographical location, which makes the development speed of coastal areas faster than that of inland areas. As a result, the inland areas are dotted with Low-Low clusters. In terms of large-scale, standardized, and specialized production, there is still much room for development compared with the “cradle of e-commerce” areas like Zhejiang Province, which have certain first-mover advantages. The existence of Low-High outliers indicates that Taobao towns’ development has a hollow imbalance, high-level areas gradually strong combination, and low-level areas slow development or even disappear, how to overcome the siphoning effect while expanding the scope of the diffusion effect, to achieve the common prosperity between Taobao towns, this problem needs to be solved.
It demonstrates the local spatial agglomeration characteristics of Taobao towns in China, with the main types including L–H and H–H.
For the analysis of the diffusion trend of Taobao towns, the spatial data of Taobao towns in 2015, 2018, and 2021 are selected for the standard deviation ellipse analysis, and the parameter “1_STANDARD_DEVIATION” is chosen for the analysis because all the data had a spatial normal distribution with an obvious aggregation trend (See Fig. 5). The results show that the coordinates of the center of the standard deviation ellipse change from 118.53°E, 30.09°N to 117.58°E, 29.56°N until it shifts to 116.50°E, 31.23°N in 2021. The long semi-axis and short semi-axis increase year by year, the area and perimeter of the generated ellipse increase year by year, and the rotation angle θ changes from −170.29° to 4.04°. The conclusions are as follows: ①The spatial distribution pattern of Taobao towns from 2014 to 2021 shows an obvious north-south direction and a significant trend of belt-like distribution. ②The difference between the long and short semi-axes of the three standard deviation ellipses gradually decreases, indicating that the ellipse flatness decreases and the directionality of the data distribution becomes more and more ambiguous. It indicates that the standard deviation ellipse fitted in the three stages gradually increases, especially the significant change in the coverage of Taobao towns in 2021 when it is in the period of the fastest growth rate of Taobao towns, and its diffusion trend overlaps with the change of the standard deviation ellipse. ③The center of the ellipse gradually shifted to the southwest after 2018, and the center shifted from Hangzhou, Zhejiang Province to Huangshan and Lu’an, Anhui Province year by year. This indicates to some extent that the distribution of Taobao towns in China has gradually expanded and is no longer limited only to the southeast coast, and the development center of gravity has gradually tilted to the central and western inland areas, becoming an important platform for the development of the e-commerce economy in developing area.
The Taobao towns in China exhibit a prominent north-south orientation and belt-like spatial distribution pattern, with the center of gravity gradually shifting towards the northwest direction.
Based on a comprehensive review of previous research findings (Wang & Wang, 2020), this study considers the number of Taobao towns in each Chinese province in 2021 as the dependent variable. Independent variables, including GDP, cargo volume, express business volume, road network density, digital village index, the number of enterprises engaging in e-commerce trading activities, and the regional innovation and entrepreneurship index, are selected. These variables are transformed into quantitative types through a reclassification method to analyze the factors influencing the spatial distribution of Taobao towns using the GeoDetector. The q-value indicates each factor’s explanatory power on the density of provincial distribution of Taobao towns.
As shown in Table 4, the express business volume, which characterizes the degree of improvement of regional e-commerce infrastructure, has the greatest influence, followed by the value of GDP, which characterizes the regional economic development, and the innovation and entrepreneurship index, which characterizes the regional entrepreneurial atmosphere, and the p-value of all three is infinitely close to 0. This indicates that the e-commerce industry is highly correlated with the industrial base, entrepreneurial concentration, and construction of supporting facilities.
A possible reason is due to the Chinese traditional rural society is a society of acquaintances (Guo, 2022; Yamauchi, 2007). When people have more discretionary funds and a good local industrial foundation, under the leadership of individual entrepreneurial models, rural areas will have a “herd effect” to promote the development of rural industries (Soluk et al., 2021; Wu et al., 2017; Lei & Liu, 2017), which will bring imitation from other potential rural entrepreneurs. As a form of the economy with the bottom of the pyramid (BOP) people as the main employees, rural e-commerce has the characteristics of low technology content and low investment risk (Zhao et al., 2021; Gao & Liu, 2020), which will be more attractive to entrepreneurs and easier to form entrepreneurial clusters. When the development of regional rural e-commerce reaches a certain stage, the enhancement of the regional innovation and entrepreneurship atmosphere can provide innovative “soil” for rural e-commerce and give rise to the emergence of new business models. On the one hand, it provides conditions for the development of new modes of rural e-commerce through the construction of a new digital innovation network of “Internet + entrepreneurial services” in rural areas, and the penetration of digital technology in all aspects of rural e-commerce logistics and services. On the other hand, the development of regional innovation and entrepreneurship can help smooth the circulation channels of urban and rural innovation resources, and through the introduction of capital, talent, land, and other factors, provide basic support for rural technological innovation and research and development, thus promoting the transformation and upgrading of rural e-commerce. At the same time, the improvement of technological innovation can accelerate the construction of smart agriculture, accelerate the process of matching rural information technology, promote the digital transformation of the rural industry, improve the efficiency and quality of agricultural production, and promote the upgrading of the rural industrial chain, which in turn will bring a positive impact on the development of rural e-commerce in the supply chain. In essence, the sustainable development of rural e-commerce is significantly bolstered by a comprehensive entrepreneurial support system, robust innovation, a conducive entrepreneurial atmosphere, and targeted financial investment across the region.
Because the express industry is a key booster of rural e-commerce, rural e-commerce can, in turn, promote the growth of the express business. The two are mutually beneficial and mutually reinforcing relationships. At present, the logistics and warehousing industry has touched more than 30,000 towns and villages, with a coverage rate of 97%, and the prosperous development of the rural e-commerce industry can provide fertile ground. “Report on the work of the government (2022)” (General Office of the State Council of China, 2022) mentions the need to vigorously develop rural logistics, open up production, consumption, circulation, consumption of various links, and consolidate the construction of the county business system. In the future, with China’s high-quality economic development more robust, and the international and domestic logistics system more perfect, Taobao towns will embark on a more robust development path.
In addition, the explanatory power of cargo volume, digital village index, and the number of enterprises with e-commerce trading activities in the number of Taobao towns is strong. Firstly, cargo volume shows the production volume of industry and agriculture and market activity of an area to a certain extent; the number of enterprises with e-commerce trading activities reflects the social atmosphere of e-commerce development in an area; both play a secondary positive role in promoting the development of Taobao towns through market radiation and functional spillover effects. Secondly, the digital village, i.e., a new model of economic development based on big data, mobile Internet, Internet of things (IoT), and cloud platforms in the context of rural revitalization, aims to integrate digital informatization into all aspects of rural production and life ecology, which generally includes four aspects of rural digital infrastructure, rural economic digitization, rural governance digitization, and rural life digitization. Among them, digital infrastructure is mainly measured by the coverage rate of devices, the depth of commercial and financial reach, and the degree of platform construction of data and information resources; the economic digitalization index involves the scale of production bases, the developed evaluation of supply chains, the scope of e-commerce marketing promotion, and the degree of digitalization of inclusive finance; digitalization of governance refers to the coverage of public governance means; digitalization index of life, i.e., the degree of digitalization of consumption, cultural tourism, education, health, services, etc.
The operating mechanism of digital countryside empowering rural e-commerce development is reflected in the interaction and influence of the supply side and the demand side. On the supply side, digital countryside-enabled rural e-commerce development plays a role by improving the efficiency of resource allocation, reducing transaction costs, enhancing information transparency, and expanding the scope of the market. Through digital technology, agricultural producers can directly bring their products to the market, avoiding the intermediate links in the traditional channels, and at the same time, expanding the scope of sales of agricultural products, enabling products in rural areas to have wider access to the urban market, and facilitating the continued penetration of e-commerce scale into rural areas. On the demand side, the digital countryside brings the impact of improved consumer convenience, increased product diversity, enhanced information symmetry, and price transparency. Improved consumer convenience can promote the circulation of rural consumer goods and urban consumer goods, and the development of rural e-commerce platforms can help bring together more suppliers, which can help rural consumer goods go out and also provide conditions for urban consumer goods to come in, thus diversifying overall consumer demand. In addition, the development of digital villages can make product information more transparent, so that consumers can have a more comprehensive understanding of product quality, price, and other information, which enhances the confidence and efficiency of purchasing.
At present, the digital village indices in the western and northeastern areas lag behind those in the eastern and central areas, and there are still large regional differences in the results of village construction, making it difficult for this economic development model to take maximum advantage. In the future, it is worth exploring how to realize the deep integration of digital technology and agriculture, how to apply the results of digital village construction to the growth of Taobao towns, and how to promote rural e-commerce in backward areas to realize the economic transformation of agricultural products, and how to realize the common prosperity of the rural e-commerce industry.
It is worth noting that the q-statistic of the road network density is not significant, the reason is that China has basically formed a “ten vertical and ten horizontal” comprehensive transport channel as the backbone of a smooth internal and external comprehensive three-dimensional transportation network, and gradually entering a new phase of accelerating the construction of a modernized comprehensive transport system. Traffic conditions are not the limiting factor for the emergence and development of Taobao towns, but the strengthening factor.
As shown in Table 5, according to the results of the spatial distribution of Taobao towns interaction factor detection, there are no independent or weakening factors, all showing bi-factor enhancement. The development of Taobao towns is not the result of one factor alone but depends on the combined effect of the economic base, social environment, market atmosphere, transportation conditions, infrastructure construction, and so on.
From villages to towns, from scattered points to networks, it is not only an upgrade of space scale but also an evolution of steady-state and sustainable development, as well as a choice of innovative entrepreneurship system with a better ecological niche (Liang et al., 2017). China’s e-commerce industry is in a critical period of transition from the “new industry” to the “new normal”, the urgent need for more standardized and efficient market players. At present, the focus on Taobao villages has gradually shifted to the observation of their disappearing trend (Lan et al., 2022; Peng & Ding., 2021), but there is still a lack of a systematic approach to the upgrading path of rural e-commerce economy. In terms of spatial analysis, starts from two aspects of agglomeration characteristics and development evolution trends, and completely analyzes the data from 2014 to 2021, concluding that Taobao towns are spatially multi-core agglomeration and cluster development mode, and the development center of gravity gradually tilts to the inland, which is both verification of the existing studies on the spatial layout of Taobao towns (Ma et al., 2017). At the same time, the trend of shifting the center of gravity of Taobao town development is also consistent with Luo, Qiao (2021) findings on Taobao villages, as Taobao towns evolved from Taobao villages’ development, and there are similar patterns between the two.
For the eastern area, the relatively developed manufacturing industry and perfect infrastructure are fertile ground for the development of the rural e-commerce industry to emerge, so taking a Taobao village as a transitional form to promote the integration of rural resources with e-commerce and gradually develop Taobao towns is its useful path choice. For the central and western areas, where the development is relatively lagging, and the villages are decaying and hollowed out, the support of village administrative units for Taobao village’s development may not be enough to support its stable development. Therefore, for small and medium-sized towns in central and western areas, Taobao towns, as leaders of the rural camp of the e-commerce economy, have a superior level of public governance and stronger scale benefits, catering to the requirements of industry chain integration and resource elements integration in the operation of rural e-commerce in the Internet era, and deserve more attention and promotion.
Taken together, the development of Taobao towns is influenced by a combination of social and economic factors. The rising level of regional GDP has provided rural residents with more purchasing power, driving the expansion of e-commerce transactions and creating a virtuous market cycle. At the same time, a sound rural freight and express road network improves logistics efficiency and reduces the spatial gap between urban and rural areas, prompting the spatial expansion of rural e-commerce. The elevated Digital Countryside Index has made it easier for rural residents to access the Internet, increasing the likelihood that they will participate in e-commerce. In the process, enterprises responded to the market trend and increased their e-commerce business, which stimulated their motivation to improve service and product quality. The increase in the regional innovation and entrepreneurship index has provided the soil for innovation in rural e-commerce and promoted the emergence of new business models. It is worth noting that the development of both Taobao towns and Taobao villages cannot be separated from grassroots and local forces. For example, Jiangsu’s Shaji Town and Guangdong’s Junpu Village, two places popular with e-commerce, are largely indispensable to the government’s financial and policy support for infrastructure, e-commerce industrial parks, low-interest loans, and spatial planning and design of villages (Li, 2017). These factors, in interaction, have shaped the development pattern of rural e-commerce in China at multiple levels and dimensions.
In the future, with the current Internet penetration rate of nearly 60% in rural areas, the central and western areas should continue to strengthen the construction of infrastructures such as the IoT and transportation, and explore the concepts of “getting through the last kilometer of the villages” and “shared logistics”, continue to promote the projects of “one village, one product” and “one county, one industry” of agricultural products into the city (Natsuda et al., 2012), recognize the importance of e-commerce talents, give full play to the advantages of the labor force, and implement the talent attraction plan and the young migrant workers’ return home entrepreneurship support plan, create a regional soft environment suitable for the development of e-commerce industry. At the same time, in areas with a good light industry foundation and strong business atmosphere, give priority to the development of the e-commerce economy, promote the live broadcasting mode, make full use of the operation mode in the streaming media era, create Internet celebrity economy, cultivate Internet thinking, promote the upward movement of agricultural products and capital transformation, extend the e-commerce industry value chain, and finally achieve the goal of common prosperity.
It can be predicted that Taobao towns will become the core unit of e-commerce development in the future, gradually developing from pure e-commerce economic business to an industrial form with comprehensive supporting facilities and a fully optimized rural governance system. Globally, this innovative model combining China’s rural revitalization and digital agriculture may lead to the global digital transformation of agriculture, providing China’s experience in poverty eradication and sustainable rural development for the world’s poor in the mobile Internet era.
Based on the research results, the practical implications of this study lie in the following aspects: First, the research results provide evidence-based insights that can assist policymakers in formulating effective strategies to promote the development of e-commerce in rural areas. By identifying key factors such as logistics capabilities and regional innovation and entrepreneurship abilities, policymakers can design targeted interventions and support policies to stimulate the growth of the e-commerce ecosystem in underdeveloped areas. Second, e-commerce companies and platforms can utilize the conclusions of the study to tailor their rural expansion strategies. Understanding the spatial distribution and evolution patterns of Taobao villages enables these companies to identify potential market expansion areas and develop customized services for rural entrepreneurs and consumers. Third, rural entrepreneurs and small business owners can gain valuable insights from the study on how to leverage the opportunities brought by e-commerce. Analyzing the contributing factors to the success of Taobao towns can guide these entrepreneurs in making informed decisions about their business models, marketing strategies, and logistics management. Moreover, understanding the importance of digital skills and innovation may encourage rural entrepreneurs to seek training and resources to enhance their competitiveness in the online marketplace.
It is important to note that the development of rural e-commerce, as exemplified by Taobao Towns, is still at a nascent stage. There is a deficiency of statistical data and information at the village and town scale, which, to some extent, hampers the analysis of Taobao Town’s micro-impact mechanisms. In the future, field research will be conducted to introduce more comprehensive indicators for the study and collect more comprehensive micro-statistical data on Taobao towns in China, in order to explore the development path and characteristics of rural e-commerce in China in a more in-depth manner, to realize the analysis of the micro-mechanisms of rural e-commerce, and to enhance the practical applicability of research findings.
A Taobao town is an emerging e-commerce agglomeration model developed based on a Taobao village, which provides new ideas for transforming small and medium-sized cities and plays an irreplaceable role in promoting employment and helping rural areas revitalize. The spatial clustering pattern and temporal evolution trend reflect the development direction of rural e-commerce in China. This research synthesizes various spatial analysis methods, tries to portray the development pattern and spatial layout of Taobao towns, and clarifies the development law of town e-commerce in the context of the Internet era, with the following specific research findings:
The spatial expansion of Taobao towns across China has exhibited consistent growth over successive years, delineating a distinctive “T-shaped and three-center” spatial configuration characterized by a gradual decrease from the southeast to the northwest. Remarkably, Taobao towns display a discernible pyramidal distribution across regions, wherein fewer provinces host densely concentrated Taobao towns, predominantly situated within coastal urban agglomerations, contrasted by a fragmented and sporadic dispersion across inland areas.
Regarding influential determinants, factors such as business volume, GDP, regional innovation, and entrepreneurship index emerge as pivotal contributors to the evolution of Taobao Towns, exerting significant influence on their spatial distribution. Complementary to these primary drivers, metrics including cargo volume, digital village index, and the prevalence of enterprises engaged in e-commerce activities play a supportive role in fostering the advancement of Taobao towns. Furthermore, transportation infrastructure serves as an augmenting catalyst in the genesis and maturation of Taobao towns.
The temporal evolution of Taobao towns in China unfolds across three distinct developmental stages. The inaugural phase, spanning from 2014 to 2016, witnessed an era of explosive growth coinciding with the designation of rural e-commerce as a strategic industry within the nation. This period laid the foundation for the burgeoning development of rural e-commerce. Subsequently, the second stage, spanning from 2017 to 2018, marked a phase of steady growth, characterized by a transition “from quantity to quality”. Finally, the third stage, spanning from 2019 to 2021, signifies a period of lean development, during which rural e-commerce matured, fostering the emergence of several refined industrial chains and development models through the accumulation of early-stage experiences. This period witnessed sustained expansion in both incremental growth and stock enhancement, culminating in the contemporary rural e-commerce landscape.
The socio-economic data used in this paper can be found in the appendix of the paper. Other relevant datasets generated during and/or analyzed during the current study are available to researchers conducting similar research, provided they sign a confidentiality agreement. For those seeking to utilize the data, they can contact the corresponding author at any time.
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X.C. and W.H. conceived the study, while W.H. and J.Z. developed the methodology. J.Z. and W.D. conducted the investigation. W.H. wrote the original draft, which was reviewed and edited by X.C., W.H. and Z.J.
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