Tourist Spatiotemporal Behavior Pattern: The Case of Big Data Articles

Tourist behavior reflects their preferences, mental characteristics, decisions, and attitudes, initially formed in the mind and then manifested in physical movements. To identify tourist behavior inevitably requires examining their movements over time. Using the data collected from big data, the present study aimed to present a model for explaining the characteristics of tourist behavior and influential factors in their behavior based on their movements over time. As a developmental–fundamental inquiry, the study relied on an inductive approach and a qualitative metasynthesis design. The population of the study consisted of articles, books, and theses published and recorded in reputable domestic and foreign databases from 2000 to 2024. After three stages of screening, 78 articles were selected through the purposive sampling. The note-taking method was then used to collect the qualitative data from the identified sources. The data was then coded and categorized manually through MAXQDA software. Finally, the research proposed a model of tourist spatiotemporal behavior. The model is comprised of five components: spatial behavior, temporal behavior, the variables describing tourists, destination perception and selection, and innovation. The proposed model will contribute to the development and expansion of literature on tourist behavior models.