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 population of Korea was 50,955,782. Except for Jeju Island, the administrative district consisted of eight metropolitan cities and eight provinces. From February 9 to 21, 2022, we conducted a preliminary survey after completing the translation, correction, and review of the questionnaire items consulted 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 had traveled to Jeju to assess the effects and causal relation on the intention to revisit Jeju Island. A total of 27 questions consisting of 6 sets of main factors were measured on a Likert 7-point scale, and we added seven questions related to socio-demographic characteristics. The questionnaire was written as an online questionnaire, and the measurement tools for each factor can be referred to in Appendix A. 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, particularly useful for complex models with multiple regression and latent variables, was utilized to compare estimated and observed values and to define the relationships between variables. The measurement model was first accurately defined, then its validity, reliability, and normality were evaluated. We used Consistent PLS-SEM (PLSc-SEM) for its compatibility with standard factor models and its ability to 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 data set and reflective structure model.