1.1. Background
Consumption is considered the central process of the economy in economic theory [
1]. Residents’ consumption consists of the goods and services used by individual households or the community to satisfy their individual or collective needs or wants. Residents’ consumption refers to the extent to which resources meet the needs of people’s survival, development, and enjoyment in the consumption process of physical products and services [
2,
3].
The contribution rate of residents’ consumption expenditure to economic growth in 2019 was 57.76% [
4], which exceeded the sum of the contributions of investment and export. Consumption has increasingly become an important means to promote economic growth. Conforming to the development trend of consumption, further tapping consumption potential, and continuously expanding consumption demand are important conditions for maintaining the sustained and healthy development of China’s economy and for improving people’s quality of life.
The term digital economy was first mentioned in Japan and proposed by Don Tapscott in 1995 [
5]; it is driven by the convergence of information, computing, and communication, which lead to the extensive growth of e-commerce, new competitive strategies, and changes in business processes and organizational structures [
6]. With the appearance of the digital economy, scholars have proposed new definitions and characteristics for it. For instance, the digital economy is a special economic form, and its nature is that goods and services are digital trade-in forms [
7]. A digital economy is an economy in which the production, sales, and consumption of goods and services depend on the network of electronic means based on an intermediary information flow [
8]. It is an economy that operates through digital technology, including technical facilities, and e-commerce [
9]. The digital economy’s key resource is information, a series of economic and social activities carried out by people through the Internet and related technologies [
8,
10]. Compared to agriculture, industry, and other industries, the digital economy is an emerging industry [
11], and owing to its short development period, the digital economy system is not perfect [
12]. The OECD (2020) defined the digital economy as all economic activities that are based on or through the application of digital input for their enhancement [
13]. With the transformation from the traditional economy to the digital economy, how the digital economy affects residents’ consumption in China has become an important topic. The digital economy is an economic model succeeding the agricultural economy and industrial economy, with the network as the carrier, digital information as the element, online service as the model, and sharing economy as the direction [
14].
At present, the digital economy has penetrated all fields of society, causing major changes in the economic environment and form. By 2020, the scale of China’s digital economy had reached CNY 39.2 trillion, and the proportion of the GDP jumped from 27% in 2015 to 38.6% in 2020 [
15]. In the first half of 2021, the online retail sales of physical goods reached CNY 5026.3 billion, a year-on-year increase of 18.7%, accounting for 23.7% of the total retail sales of social consumer goods [
16]. It can be seen that the digital economy is comprehensively reconstructing the development mode and pattern of China’s economy. Therefore, strengthening the research on the effect of the digital economy on residents’ consumption expenditure and unearthing the trends and laws of consumption development are important ways to promote the sustainable growth of consumption. It has important theoretical and practical significance for revealing the internal mechanism of the digital economy on residents’ consumption [
17].
1.2. Literature Review
Some scholars have studied the impact of the digital economy on household consumption from different perspectives, such as its impact on upgrading household consumption and consumption structure [
18,
19,
20,
21,
22], consumer behavior [
23,
24,
25,
26], consumption level, circulation field [
4,
27], and upgrading the industrial structure [
28]. Some studies have focused on the influence of the digital economy on demand and consumption [
2,
29,
30,
31,
32,
33,
34,
35], business models [
36,
37,
38,
39], and background characteristics of the digital economy [
40,
41]. As for how the digital economy influences residents’ consumption, it leads to changes in the quality, content, price, and accurate matching of residents’ consumption [
14,
24,
42]; the Internet eliminates the problem of asymmetric consumption information and accelerates the upgrading of residents’ consumption [
4,
18,
40,
43,
44]. The changes in consumer psychological demand, consumer motivation, and consumption mentality are the internal causes of the changes in the characteristics of residents’ consumption behavior against the background of the digital economy [
32,
37,
45]. The resident network under the digital economy is more inclined to geographical space and cost saving, and online consumption produces a community effect [
35,
46,
47].
The spatial effect is an important concern of many scholars, and this paper comments on the relevant literature from two aspects: the research subject and research methods.
In terms of the subject of spatial effect, research in this area is still in its infancy. Some scholars have used panel data to explore the impact and spatial effects of the development of digital finance on residents’ consumption [
48,
49]. The empirical results have shown that there is a significant spatial spillover effect on residents’ consumption. Digital finance can promote residents’ consumption levels in this region, and there is also a significant positive spillover effect [
50,
51,
52]. Zhang and Tu (2017) [
53] empirically studied the differential impact of Internet finance and the development of various fields on Chinese residents’ consumption behavior and structure. Tan, Li, and Zhu (2022) found that Internet penetration significantly reduced the degree of regional consumption differences in China [
54]. Their results indicate that Internet penetration can narrow the regional consumption gap by alleviating the degree of income differences and reducing the consumer price index. This inhibitory effect showed distinct heterogeneity owing to different income levels, geographical distribution, and development characteristics. Wang (2022) analyzed the spatial autocorrelation between the digital economy and consumption upgrading, further discussing the spatial agglomeration characteristics of consumption upgrading; the results indicate that there is an obvious spatial autocorrelation between the digital economy and consumption upgrading [
55]. Wei (2022) analyzed the topological characteristics and driving factors of provincial residents’ consumption spatial spillover network. The results show that the spatial spillover network has the characteristics of neighborhood spillover and club convergence; moreover, spatial adjacency, residents’ disposable income, urbanization level, consumer credit, and consumption environment similarity have significant driving effects on the spillover correlation of the consumption level [
56]. Xu (2021) theoretically analyzed the role of digital economic development in influencing residents’ consumption inequality and empirically examined the cracking effect of digital economic development on regional consumption inequality by using the dynamic panel model. The research discovered that the development of the digital economy has different cracking effects on consumption inequality in different regions [
47]. Shen (2020) empirically tested the promotion effect of “Internet + retail” on consumption upgrading and its regional differences. The research found that “Internet + retail” has a significant positive effect on the scale and quality of consumption in the Yangtze River delta, and there are also significant regional differences [
57]. Li and Huang (2022) empirically examined the direct impact of the digital economy on service consumption and the spatial spillover effect by using the spatial Durbin model. The research showed that the development of the digital economy can not only promote the growth of local residents’ service consumption expenditure, but also have a significant positive spatial spillover effect on residents’ service consumption expenditure in neighboring areas [
58]. Hu (2020) studied the logical mechanism of the impact of mobile payment on Chinese residents’ consumption. It was found that mobile payment can reduce the transaction costs of consumers, ease the mobility constraints of residents, and reduce the psychological loss of consumers when they pay; it can then improve the consumption intention and stimulate residents’ consumption. The research results also show that mobile payment has a positive pull effect on the total consumption expenditure of Chinese residents, and the impact of mobile payment on the consumption of Chinese residents is heterogeneous in different regions, on different income levels, and at different ages [
59]. Jiao and Sun (2021) used the extended linear expenditure model (ELES) to measure consumption upgrading and explored the link between digital economic development and consumption upgrading. The research found that digital economic development plays a leading role in promoting consumption upgrading; the development of the digital economy realizes consumption upgrading by improving residents’ ability to cope with risks, alleviating their current liquidity constraints, and improving network-based convenient services. Moreover, the development of the digital economy has a positive spatial spillover effect on consumption upgrading, and both the direct effect and the total effect are positive [
44]. Based on the essential function of consumer finance, Ma and Han (2017) analyzed the impact of Internet consumer finance on the consumption behavior of urban residents in China. The research results show that the generation and development of the Internet consumer finance model can positively promote the consumption behavior of urban residents in China; moreover, there are regional differences in the impact of Internet consumer finance on the consumption behavior of urban residents in China [
60].
In terms of experimental methods, previous studies have used many mainstream ones such as: the fixed effect model [
57,
58,
61], system GMM [
44,
47,
54,
62], quantile and intermediary effect model [
48,
55], simultaneous equation model and OLS [
55,
59], static and dynamic panel GMM method [
60,
61], propensity score matching [
2,
59], ELES model and dynamic spatial Durbin model [
44,
51], gravity center analysis, spatial autocorrelation analysis [
55], spatial panel measurement, geographically weighted regression [
50], the spatiotemporal double fixed effect spatial Durbin model [
52], VAR model and complex network analysis method [
56], and the Thiel index and dynamic panel model [
47,
58], etc.
However, there are gaps in the existing relevant literature. First, most empirical studies merely emphasize the spatial effect of the digital economy’s impact on residents’ consumption, while ignoring the analysis of spatiotemporal evolution, which is limited and challenging when reflecting the characteristics and internal mechanism of the spatiotemporal difference in an intuitive and accurate way. In the actual operation of the social economy, the digital economy itself has the characteristics of crossing region and time, so a spatiotemporal analysis must be supplemented. Second, most existing studies on the impacts of residents’ consumption differentiation are based on traditional statistical methods, which cannot demonstrate and explain the complex spatiotemporal impacts, for instance, when many driving factors have the effect of interaction enhancement. This paper fills the research gap by combining the spatial and temporal evolution of residents’ consumption in the context of the digital economy using a nonlinear statistical method. Therefore, we selected the panel data of 31 provinces in China from 2012 to 2020 and used Moran’s I and Geodetector to empirically analyze the spatiotemporal effect produced by the process of residents’ consumption led by the digital economy, so as to provide a reference for the coordinated development of the regional economy.
Accordingly, this paper focuses on the following issues: (1) What spatiotemporal patterns and effects on residents’ consumption development against the background of the digital economy at the provincial level in China can be found by mining features of residents’ consumption expenditure? (2) What are the driving factors affecting residents’ consumption in the context of the digital economy? What are the driving factors of interaction? How do their driving mechanisms perform? (3) How do we provide differentiated policy suggestions for effective delivery of residents’ consumption in the context of the digital economy at the provincial level in China based on the two findings above?
1.3. Research Methods and Paper Organization
To resolve these questions, the research consisted of five steps. Step 1 involved the research question, where the research objectives and problems of this paper were put forward and confirmed based on the background analysis and literature review. Step 2 concerned the study area, variables selection, and data processing. Step 3 dealt with research methods, where the spatial cluster, global Moran’s I, and local Moran’s I of residents’ consumption at the provincial level were calculated by Stata 16, ArcGIS 10.2, and Python 3.7. Step 4 was the analysis of the results, where the spatiotemporal characteristics of residents’ consumption were reflected by spatial differentiation analysis, spatial cluster analysis, and spatial autocorrelation analysis. The driving factors of residents’ consumption were calculated by Geodetector based on its factor detection, ecological detection, and interaction detection methods. Step 5 presented our conclusions and suggestions, where the conclusions were drawn based on results, and policy recommendations put forward on optimizing the development of residents’ consumption according to the analysis of different regions.
The remainder of our paper is organized as follows.
Section 2 demonstrates the study area and data source.
Section 3 explains variable selection, research models, and methods.
Section 4 analyzes the data and explores the spatial differences in residents’ consumption using global and local spatial autocorrelation analysis with scatter plots.
Section 5 analyzes the driving factors of spatial differences in residents’ consumption using Geodetector. Finally,
Section 6 concludes the paper and proposes policy implications.