A Multiple Regression Model To Predict Tourists’ Satisfaction Index

Abstract

The tourism industry appears to be one of the fastest growing industry all over the world. This growth can boost a nation’s economy. Nonetheless, much effort is needed for a nation to harness the socioeconomic benefits attributed to the growth of its tourism sector. One of the ways though which a country can capitalize in the growth of tourism is by use of mining Big Data for insight to improve its strategic approach to boosting tourism. In this regard, this paper reviewed several yet relevant past studies about tourism and its socioeconomic implications. Based on the findings in these reviewed literature, this paper acquired specific socioeconomic data and developed a multiple regression model to predict tourists’ satisfaction. As hypothesized, GDP per capita, social support, health life expectancy, freedom, generosity, and corruption perception, part of a nation’s socioeconomic indicators, can be used to predict a tourist’s satisfaction. The paper concluded that it is possible to predict tourists’ satisfaction with the developed model. Moreover, Big Data can be mined and its insight used to advise tourists, an approach that can boost a nation’s tourism industry.