Poisson Regression Analysis with Tourism Data: Analysis of The Effects of Foreign Visitors' Local Tradership and Shopping Perceptions on The Frequency of Visit

Authors : Hilmi Karaalioğlu, Adil Korkmaz

Pages : 1443-1456

Doi:10.29023/alanyaakademik.889572

View : 10 | Download : 4

Publication Date : 2021-09-30

Article Type : Research

Abstract :Increasing the frequency of visits by foreign visitors is an important phenomenon in today's world, where the contribution of tourism, which is accepted as a chimneyfree industry, to the economies of developing countries is increasing. This phenomenon can be possible if foreign tourists have positive holiday experiences, develop positive perceptions and attitudes about Alanya holiday, and as a result, tourist loyalty is formed. For this purpose, a survey was conducted on foreign tourists who have finished their holiday in Alanya and are in the process of airport transfer, and the variables that are predicted to be effective on their revisit intentions are measured. In this study, in order to reveal the relations between the frequency of visits of foreign tourists and the attitudes of these individuals towards the perceptions of local tradesmen, factors were determined by factor analysis first, and then the mathematical relationship between these factors and the dependent variable of visit frequency, which is the counting variable, was analyzed in the Poisson regression model. As a result of the analysis, it has been seen that the factor of "the attitude of the tourist towards positive tradesman behavior and good product quality" increases the frequency of tourist visits in Alanya destination. On the other hand, it has been determined that the factors "tourist's attitude towards the cooperation of tradesmen with brokers", "tourist's attitude towards negative shopping experience", "tourist's attitude towards hotel services" and "tourist's attitude towards product prices" have reducing effects on the frequency of visits.
Keywords : Poisson regression, Alanya Tourism, Count variable, Count Data, Overdispersion Tourist Loyalty