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.

中文翻译：

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社会旅游业的可持续增长：使用异类旅行频率轨迹的增长混合建模方法

由于旅行活动已成为了解社会旅游业可持续增长的重要方式之一，因此越来越多的研究项目从宏观角度阐明了居民旅行数量（频率）与质量（体验）之间的关系。和微观观点。然而，很少有研究强调居民不能平等地获得旅行机会，特别是从纵向角度来看。当前的研究使用来自5054名韩国居民的四年纵向数据，将国内旅行者分为四个不同的类别。利用潜在生长曲线建模（LGCM）和生长混合物建模（GMM）找出（1）最佳类别数，（2）每个类别的纵向行进频率轨迹，（3）这四个阶层的独特的人口和旅行特征。这项研究为政策制定者在优化可利用资源以向相关目标人群提供可持续旅行机会时提供了一些实际意义。此外，还介绍了详细的分步分析教程，以将纵向潜变量分析扩展应用于旅游和酒店业，从而为相关利益相关者提供更多见解。