Dataset on Motivation and Revisitation in Jeju for Post-Pandemic Implications
Description
• This dataset provides insights into tourist behavior during a pandemic, which can help develop effective tourism marketing strategies and improve destination management. • Researchers can use this data to analyze the impact of pandemics on tourism, compare with other regions, or use the variables to study related psychological aspects of travel behavior. • The dataset includes detailed demographic information that can be used for segmentation analysis and to tailor marketing strategies to different tourist profiles.
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1. Data collection As of January 2022, the total population of Korea was 50,955,782. The administrative districts, excluding Jeju Island, consist of eight metropolitan cities and eight provinces. The population aged 20 to 65 was 34,523,518. From February 9 to 21, 2022, we conducted a preliminary survey after completing the translation, correction, and review of the questionnaire items in consultation with tourism experts. We commissioned a research company to survey about 40,000 panelists nationwide. Nine hundred thirty participants submitted final analyzable responses without missing data in the survey. The authors adhered to all ethical guidelines, including the Personal Information Protection Act (PIPA) of the Republic of Korea, and obtained consent from all participants following the Act. All participants traveled to Jeju Island to assess the effects and causal relations of their intention to revisit it. 27 out of 32 questions, consisting of 6 sets of main factors, were measured on a 7-point Likert scale. Additionally, seven questions related to socio-demographic characteristics were included. The questionnaire was administered as an online survey, and the measurement tools for each factor can be referred to in the survey file (docx). 2. Analyzing method In this study, we employed IBM® SPSS® 24.0 for frequency and factor analysis, and SmartPLS 4.0.8.5 for reliability, validity, and structural equation modeling (SEM) analyses. SEM is particularly useful for complex models with multiple regression and latent variables, allowing comparison of estimated and observed values to define relationships between variables. The measurement model was first accurately defined, followed by evaluations of its validity, reliability, and normality. We used Consistent PLS-SEM (PLSc-SEM) because it is compatible with standard factor models and can adjust for parameter attenuation in composite models based on the theoretical foundation provided by Nunnally and Bernstein (1993). PLSc-SEM is particularly effective for large-scale samples or complex indicator structures, making it suitable for our study's extensive dataset and reflective structure model.