Sustainable Growth of Social Tourism: A Growth Mixture Modeling Approach Using Heterogeneous Travel Frequency Trajectories

Abstract

As travel activity has gained attention as one of the essential ways of understanding the sustainable growth of social tourism, a growing number of research projects have been conducted to elucidate the relationship between residents’ travel quantity (frequency) and quality (experience) in both macro and micro perspectives. Yet, very little research has highlighted that travel opportunities are not equally available to residents, especially a longitudinal perspective. The current study classified domestic travelers into four distinct classes using four years of longitudinal data from 5054 Korean residents. Latent growth curve modeling (LGCM) and growth mixture modeling (GMM) were employed to find out (1) the optimal number of classes, (2) the longitudinal travel frequency trajectory of each class, and (3) the distinctive demographic and travel characteristics of the four classes. This study provides some practical implications for policymakers when optimizing available resources for sustainable travel opportunities to relevant target sub-populations. Furthermore, detailed step-by-step analytic tutorials are also introduced for the extended application of longitudinal latent variable analysis in the tourism and hospitality fields, providing additional insights for relevant stakeholders.

Analysis of the correlation between the longitudinal trajectory of SOFA scores and prognosis in patients with sepsis at 72 hour after admission based on group trajectory modeling

“…For example, in the case of early sepsis in patients with renal dysfunction due to microcirculation or severe inflammation, younger patients demonstrated greater compensatory capacity, and the urine output may have not been markedly reduced. [20] , [37] , [38] However, due to their weak compensatory ability, the level of urine output in elderly patients was significantly decreased. In such cases, the scores of kidney function in older patients were higher than those in recorded in younger patients; however, patients in both groups suffered kidney damage.…”

“…In our study, the fitting basis of SOFA trajectory grouping based on GBTM model is mainly determined by the following indicators: [20] (1) Bayesian information criterion (BIC), e.g., BIC values closer to 0 indicate a better fitting effect. (2) Average post-test grouping probability (Avepp), in which the index reflects the post-test probability of each individual being classified into the corresponding trajectory subgroup, with >0.7 typically used as the acceptance criterion.…”