Affiliations: School of Economics and Management, Jiaozuo University, Jiaozuo, Henan 454000, China | E-mail: [email protected]
Correspondence:
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Corresponding author: School of Economics and Management, Jiaozuo University, Jiaozuo, Henan 454000, China. E-mail: [email protected].
Abstract: In the background of the widespread development of big data technology, the actual behavior of e-commerce consumers is affected by different consumer environment and consumer demand. This article conducts in-depth inquiry and analysis of the behavior of different e-commerce users, and more accurately conducts a comprehensive analysis and effective assessment of various factors that affect consumer behavior. The factors and measures of consumer behavior are comprehensively judged to get the evaluation model closer to consumer behavior. By conducting a comprehensive full-process research on different e-commerce platforms, consumer behavior, consumption habits, and consumption processes, a comprehensive full-process research, more effective channel methods and dependence on consumers’ purchase of goods, conduct a comprehensive research other factors of behavior, such as transportation logistics, environmental conditions, advertising, and other factors, conduct comprehensive judgment and analysis, and provide important theoretical basis and certain research in order to promote and improve the orderly development of consumer behavior and e-commerce environment. The research adopts modeling and analysis methods, through the evaluation of the research model, it can be seen that under the background of big data, e-commerce consumer behavior presents the trend of data, diversification and complexity. Factors affecting consumer behavior are more diverse and complex. It effectively expands the research field of e-commerce consumer behavior analysis, and provides scientific reference for the development of e-commerce.
Keywords: Big data technology, e-commerce platform, consumer, consumer behavior, analysis and research, online shopping platform, e-commerce users, behavior, data mining
