Influence of online E-commerce interaction on consumer satisfaction based on big data algorithm

Abstract

With the rapid development of the times, people have entered the era of intelligence, and the application of big data algorithms is becoming increasingly widespread. The satisfaction of consumers in online shopping is closely related to communication and interaction during shopping. Based on this, this article studies the impact of online e-commerce interaction on consumer satisfaction based on big data algorithms. This article introduces the mediating variable of consumer satisfaction from the perspective of interaction, constructs a model between interaction and trust, and studies the internal impact mechanism of online interaction on consumer satisfaction in online shopping. This article takes the JD interactive shopping platform as the research object, and analyzes and explores the target consumer satisfaction of the women's clothing interactive shopping platform. Analyzed the impact of interaction on merchant qualifications and service satisfaction evaluations, store size, and logistics of purchased goods. The research results indicate that the normalization coefficients of the H1a and H1b pathways are 0.131 and 0.118, respectively, which are slightly smaller, indicating that the impact of perceived risk on consumer satisfaction is not significant. Meanwhile, the CR in H1 is a positive number and the direction of influence is positive, which is contrary to the assumption. Therefore, it is necessary to calibrate the initial model. After correction, the GFI value is 0.816, AGFI value is 0.825, RMSEA value is 0.042, TFI value is 0.930, CFI value is 0.955, PGFI value is 0.718, and PNFI value is 0.810. The degree is within an acceptable range. Therefore, when implementing interactive shopping, e-commerce companies need to create a good shopping environment for the implementation of interactive activities between sellers and customers. The impact of online e-commerce interaction based on big data algorithms on consumer satisfaction is a hot topic. Personalized recommendations can improve consumer satisfaction and loyalty, but data privacy and security issues are also receiving increasing attention. In addition, it is also necessary to consider the fairness and bias issues of the algorithm, as well as the transparency issues of data analysis and decision-making. On the premise of ensuring data privacy and security, it is necessary to improve the fairness and transparency of algorithms to improve consumer satisfaction and trust, and achieve sustainable development.