Beach Tourism: A Structural Equation Model with R

Beach tourism is a primary contributor to the Gross Domestic Product of many nations, and its economic return is a key driver for economic growth of destinations and surrounding communities. It has been proven that there is a positive correlation and causation between level of satisfaction and probability of return and recommendation, especially via word of mouth, one of the most effective source of information. In order to improve intention to return, it is then crucial to identify factors that contribute most to visitors’ satisfaction. Beach tourists are becoming more environmentally conscious, are looking for higher quality products and services, are seeking unique experiences, and are disregarding mass tourism. Beach tourism marketers should then target smaller specific segments of tourists based on their demographic profiles and preferences and avoid causing overtourism. Since beach tourism is also responsible for beach degradation, that subsequently lowers tourist’ satisfaction and intent to return, beach managers should offer tourism targeted to small groups of motivated travellers and protect the beach environment. In this paper, inspired by current literature on the subject, a structural equation model is proposed to predict tourists’ satisfaction and intent to revisit by the statistical analysis of well designed surveys. The model involves five constructs regarding tourist’s intent to return, overall experience satisfaction, facility satisfaction, beach satisfaction and perception of cost. Few manifest variables regarding respondent demographics and beach characteristics are also considered and linked to overall experience satisfaction in the regression part of the model. Following results published in recent scientific papers, a data set composed of 2000 complete surveys has been simulated. The proposed structural equation model has been fitted to data. The measurement model explaining the connections between latent constructs and their manifest measurable components is presented by a paths diagram. The model has been evaluated by fit indices and compared to a simpler model. Graphical representations of the estimated structural models are provided. Data simulation, model definition and fitting, and graphical presentations have been made with the R software and some ot its specific packages. The aim of the paper is to show how the open source R software may help in conducting targeted and effective investigations and effectively communicate the results to stakeholders