Multiple Regression Analysis for Tourism Management: A Path of Integration between Tourist Satisfaction Enhancement and Business Operations

Abstract

In recent years, the tourism industry has gradually developed into a strategic pillar industry of the national economy and has become one of the important industries for development in China. In order to help tourism management, the study utilized socio-economic data and selected 13 indicators of tourism development driving factors. Principal component analysis was applied to extract the main driving factors affecting tourism development. A multiple linear regression model was used to study the interrelationship between tourism development and various driving factors, and the results of principal component analysis were verified by the goodness-of-fit test and the significance test of regression coefficients. The test of the fitted linear regression coefficient of determination R² = 0.994 indicates that the model fits the data well. The quantitative model of tourism development was established as F1=-6.18012+2.083ZLX1+0.496LX2+0.145ZLX3+1.154ZLX4. That is, for every 1% increase in socio-economic conditions, tourism service capacity, tourism resource attractiveness and infrastructure development, tourism development increases by 2.083%, 1.154%, 0.496% and 0.145% respectively. In addition, the study starts from the perspective of business operation and proposes to enhance employee training, improve employee welfare and good customer relations as a way to improve tourist satisfaction.