No abstract

The tourism industry is one of the largest and fastest growing economic sectors in the world and has contributed to world economic growth. Since the tourism industry is among those directly related to the environment, green tourism has an important role in the environment management system, especially practices that have the ability to reduce negative impacts onto the environment. Green initiatives are considered part of the programs adopted in many parts of the tourism sector. This article examines the influence of green practices and a green image towards customer satisfaction and customer loyalty. To test the theoretical framework, some 385 data were analysed using Structural Equation Modeling (SEM). Results revealed that green practices have stronger effects on customer satisfaction compared to a green image. The moderation test indicated that tourists’ educational background moderates the relationship between green practices and customer satisfaction, where the effect among tourists with a high education level is higher compared to those with a low education level. Further analysis showed that green practices and a green image lead to customer loyalty indirectly through customer satisfaction. The results present many implications towards theory and practice as far as the tourism industry is concerned.

No abstract

The purpose of this study was to model the travel attraction in the education area of Gunung Pangilun, Padang City. Primary data was collected by random sampling including 17 variables over 200 questionnaire responses. Crosstabs technique and multiple linear regression analysis were used to understand the trip attraction patterns in the study area. The result of the crosstabs analysis indicates a relationship between genders (X10), destination (X14), the mode of transportation used (X8), reasons for choosing the mode (X9), distance (X6), travel time (X7), and congestion (X17) to the total number of trips. Through multiple linear regression techniques, the amount of trip attractions (Y) is successfully explained through the Building Floor (X4), Trip Distance (X6), and Age (X11) variables. The model prediction is Y = -32,404 + 0,012 X4 -3,899 X6 + 7,110 X11.

No abstract