Integrating Cultural Sustainability into Tourism Management Models: A Structural Equation Modeling Approach

Abstract

This research will apply sustainability of cultural tourism in tourism management models by exploring its effects on tourist satisfaction and behaviour intention on cultural destinations. The study conceptualises cultural sustainability as a strategic determinant of a perceived service quality, visitor satisfaction, and revisit intention grounded on the concepts of the theory of sustainable tourism and the views of cultural authenticity. The research design was quantitative and the research instruments used were a structured questionnaire to be distributed among the tourists visiting specific cultural sites, and gathered data was analysed using multivariate methods to provide a valid sample. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to test the proposed model. The measurement model was found to have good strength of reliability and validity where the composite reliability values were greater than accepted by guidelines and the average variance extracted (AVE) rated higher than 0.50. The results of structural models show that cultural sustainability has a significant and positive impact on the perception of service quality and tourist satisfaction, and satisfaction is the predictor of revisit intention and mediates the correlation between cultural sustainability and loyalty effects. The model describes a significant variance in major endogenous constructs as the model has a strong predictive power. In practise, the results demonstrate the significance of cultural preservation, authenticity, and community participation in the strategies of managing tourism in order to promote the satisfaction of visitors and longevity of the destinations on the long-term competitiveness. The work makes contributions to the existing body of literature on tourism management by providing empirical support to cultural sustainability as a key construct in structural tourism frameworks and its explanatory-predictive value research by applying a strong PLS-SEM methodology.