AUTHOR=Gan Muyi , Ouyang Yao 

TITLE=Study on Tourism Consumer Behavior Characteristics Based on Big Data Analysis

JOURNAL=Frontiers in Psychology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2022.876993

DOI=10.3389/fpsyg.2022.876993

ISSN=1664-1078

ABSTRACT=In terms of scenic marketing, the research of big data also plays an important role in the precise marketing of scenic spots. This paper focuses on the big data related to scenic spots as the research object, and explores the relationship between various subdivision big data and the number of tourists in scenic spots. The difference and influence of the consumption behavior of the secondary consumption items in the scenic area, to find the potential of the scenic area's business growth, so as to promote the continuous and stable growth of the scenic area's sales and tourism economy. Using the relevant theories and analysis methods such as consumption behavior, big data, and tourism consumption behavior, the content mainly focuses on the establishment of the analysis model of the number of tourists in the scenic spot, the data collection, the estimation of the model parameters, the various types of big data in the big data. The calculation of the contribution rate of the data to the number of tourists in the scenic spot, the difference analysis of the secondary consumption items of different types of tourists in the scenic spot, etc. Results show that a multi-objective analysis model is established based on the relevant econometrics theories, and an optimization plan is proposed after the multicollinearity diagnosis of the model; establish a DEA model of the difference and influence of different types of tourists' consumption behavior in scenic spots, and study the consumption behavior characteristics of different types of tourists when they purchase secondary consumption items in scenic spots; the econometric model is used to analyze the big data, adjust the linear relationship of some variables, and then adopt the method of gradually introducing variables combined with the consumer theory, and determine the number of daily tourists as the explained variable, the number of IP, Baidu index, and the virtual value of the weekend. Dummy variables for variables, bounce rate, and air pollution as explanatory variables.