Abstract
By using keywords crawled by big data as a survey reference, this research applied latent category clustering method and binary logistic regression model analysis method to analyze the differences in community group buying behaviors of residents from different city scale and summarize the shopping behavior and features of different types of residents, for the purpose of offering advice on different marketing methods for different types of urban residents, so as to realize the precise marketing of community e-commerce and promote the further development of the industry.
Highlights
Shopping channels mainly include Meituan selection optimization, Xingsheng Optimal, and related stores and convenience stores, and these products are delivered to home to improve online shopping efficiency; a supply chain is formed in this way
Small-scale urban residents account for a larger proportion of the two consumer behavior categories, Class2 and Class3, which is because, in small-scale cities, community group buying is more popular and has a wider audience
Rough latent category cluster analysis, the 287 urban residents who have participated in community group purchases are roughly divided into 4 types of consumption behavior, among which the consumption behaviors of urban residents in the Class3 and Class4 categories have obvious grouping characteristics
Summary
Existing research mainly analyzes the factors that affect consumers’ online shopping willingness and rarely involves consumer community group buying willingness and behavior based on the perspective of city scale. Erefore, by making community group buying behavior as the carrier and city scale as the perspective, this research applied latent category cluster analysis, chi-square test, and binary logistic regression model to study the impact of different factors on urban residents’ consumption behavior. Starting from the current background, the authors put forward the research topics, consulted the research situation in China and abroad, further determined the entry point of the research, used Octopus crawler software to collect data, and calculated the data to form cloud map and analyze which online shopping modes are preferred by residents and how this influences online shopping behavior, products, and other information and obtained the characteristics of consumer shopping behavior. Among the interviewees in this survey, 287 have participated in community group buying, accounting for 42.7% of the total
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