Bibliometric knowledge mapping of consumers’ inferring shopping experience in live E-commerce platform on data mining

Abstract

This study utilizes bibliometric knowledge mapping to guide the development of consumer behavior within a theoretical framework in live E-commerce environments. This analysis is based on data collected by existing customers on platforms such as Taobao, JD.com, Mogujie, Xiaohongshu, and Jumei Youpin. The literature survey chart is based on 2000 articles. The resulting theoretical concept shares the same structure as the stimulus-organism-response (S-O-R) model of consumer behaviors. The stimulus has social, technical, stream and viewer factors. The social stimulus includes social influence, and interaction among the live anchor and the consumers. The stream factor is represented by vicarious learning. The technical stimulus consists of performance and effort expectancies, the synchronicity of live session and system, and system and service quality. The viewer stimulus consists of impulsive buying tendency and innovativeness of consumers. Multilayer perceptron neural network (MLP-NN), which integrates many variables in bibliometrics, has become an effective means to guide Structural Equation Modeling (SEM) analysis and configuration, thus generating a knowledge base. The study offers many practical and theoretical implications, for instance, numerous theories are found fit to explain the roles of social stimuli such as cognitive development theory. Trust and enjoyment are found to significantly influence consumers’ flow state, which implies the working of cognitive appraisal theory, as an expanded insight into the flow theory of consumer behaviors. In addition, addiction to live is another factor that is significantly critical to influencing the impulsive buying of consumers.