Abstract
In the new consumption era, the popularization and application of information technology has continuously enriched residents’ consumption channels, gradually reshaping their consumption concepts and shopping behaviors. In this paper, Hohhot is taken as a case study, using open-source big data and field survey data to theorize the characteristics and mechanism of residents’ shopping behaviors in different segments of consumers based on geography. First, communities were divided into five types according to their location and properties: main communities in urban areas (MCs), historical communities in urban areas (HCs), high-grade communities in the outskirts of the city (HGCs), mid-grade communities in urban peripheries (MGCs), and urban villages (UVs). On this basis, a structural equation model is used to explore the characteristics of residents’ shopping behaviors and their influencing mechanisms in the new consumption era. The results showed that: (1) The online shopping penetration rate of residents in UVs and HCs is lowest, and that of residents in HGC is highest. (2) The types of products purchased in online and offline shopping by different types of community show certain differences. (3) From the perspective of influencing mechanisms, residents’ characteristics directly affect their shopping behaviors and, indirectly (through the choice of community where they live and their consumption attitudes), their differences in shopping behaviors. Different properties of communities cannot directly affect residents’ shopping behaviors, but they can affect them indirectly by influencing consumption attitudes and then affect such behaviors. Typical consumption attitudes of the new era, such as shopping for luxuries and emerging consumption, have the most significant and direct influence on shopping behaviors, as well as an intermediate and variable influence.
Highlights
Information and communication technologies (ICT) have experienced a persistent increase in usage over the past decades, the latest Grand View Research report [1] estimated a worldwide online shopping market of about 190 billion USD, predicting a composed annual growth rate of 24.8% from 2020 to 2027
Equation (5) is the structural model part of the structural equations model (SEM), which describes the causal relationship between the latent exogenous variable and the latent endogenous variable assumed in the research model; β is the relationship between the endogenous variables represented by the random connection matrix; Γ is the exogenous change in the influence of a quantity on the endogenous variable represented by the direct random effect matrix; And ξ represents the vector formed by the residual term
Residents in mid-grade communities in urban peripheries (MGCs) and HGCs have the largest proportion of online shopping for beauty care and books, indicating that these types of community are highly dependent on online shopping, and that e-commerce has penetrated their daily shopping (Figure 5)
Summary
Information and communication technologies (ICT) have experienced a persistent increase in usage over the past decades, the latest Grand View Research report [1] estimated a worldwide online shopping market of about 190 billion USD, predicting a composed annual growth rate of 24.8% from 2020 to 2027. It takes Hohhot as the research object, and, using residential data and residents’ shopping behavior questionnaires, classifies communities, systematically analyzing the characteristics of residents’ shopping behaviors in the new consumption era. It adopts a structural equations model (SEM) to illustrate the relationships among residents’ characteristics, community attributes, consumption attitudes and shopping behaviors in the new consumption era. This provides a scientific reference for the development of urban commerce.
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