Predicting consumer behaviour in tourism industry: Comparing Structural Equation Modelling (SEM) and multiple regression. Issue 3 (April 2020) - Suhartanto, D; Kusdibyo, L; Chen, B; Dean, D; Setiawati, L
Abstract: The Indonesian creative tourism industry is currently flouring and competitive. In this competitive industry, the industry players' capability to predict their tourist behaviour is imperative. However, research to examine method of predicting tourist behaviour in this industry is limited. This study is designed to assess the applicability of SEM compared to Multiple Regression to predict tourist behaviour. This study uses a survey of 403 tourists from tourism attractions in Bandung. The model of tourist loyalty behaviour was assessed and compared using software AMOS (SEM) and SPSS (Multiple Regression). The results show that the model of tourist behaviour tested by using SEM has a sound Goodness of Fit Index. Further, the coefficient of determination of tourist behaviour in the SEM model is higher compared to that of multiple regression model. In addition, this study confirms the effect of experience quality on tourist behaviour. This study reveals that applying SEM has offered a better prediction on tourist behaviour compared to Multiple Regression. This finding improves the theoretical and managerial understanding on the application of SEM in tourism industry.