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Risk perceptions, attitudes, and behavioral intentions to spend on experiences in the post-Corona crisis: Data from Italy, Denmark, China, and Japan
The dataset provides comprehensive cross-cultural data on individuals’ value priorities, risk perceptions, attitudes, and behavioral intentions to spend on experiences in the post-Corona crisis. The questionnaire was designed to incorporate several theoretical concepts around cultural psychology, to...
Autor principal: | |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9123219/ https://www.ncbi.nlm.nih.gov/pubmed/35607423 http://dx.doi.org/10.1016/j.dib.2022.108259 |
Sumario: | The dataset provides comprehensive cross-cultural data on individuals’ value priorities, risk perceptions, attitudes, and behavioral intentions to spend on experiences in the post-Corona crisis. The questionnaire was designed to incorporate several theoretical concepts around cultural psychology, tourism, and public health as well as specific questions about tourists’ behavioral intentions suggested by practitioners from the experience economy sector. The survey sample was collected based on quota sampling representative in terms of age, gender, and geography (gross sample) in the respective countries: China, Denmark, Italy, and Japan. The target sample was set as males and females the age of 18 years old or above in each country who have traveled abroad (either leisure or business) at least once within the past three years. The survey was conducted for the period between the 10th and 24th of July 2020 and collected a total of 4172 respondents divided into the four nationalities: Chinese (n = 1019), Danish (n = 1,028), Italian (n = 1014), and Japanese (n = 1111). Analyzed data are presented with mean, standard deviation, the minimum and maximum range of responses for the scale-based questions, and frequencies and proportions for the categorical questions. Raw data are accessible in ‘sav’ and ‘csv’ formats. |
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