Tourism Customer Attitudes During the COVID-19 Crisis

Ivanka Vasenska () and Blagovesta Koyundzhiyska-Davidkova
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Ivanka Vasenska: South-West University “Neofit Rilski”
Blagovesta Koyundzhiyska-Davidkova: South-West University “Neofit Rilski”

A chapter in Culture and Tourism in a Smart, Globalized, and Sustainable World, 2021, pp 295-309 from Springer

Abstract: Abstract Tourism is an escape door to a better, nicer, cleaner and most of all peaceful world—the world of dreamed and long-awaited vacation, which rejuvenates with positive emotional experiences for the time our next escape. In the era of global tourism, every whim can be satisfied at a very reasonable price, every destination is relatively affordable, and every tourist is a citizen of the world. Although the tourism development was on cloud nine, there were at least a dozen thunder clouds during the past few decades, such as economic, social and political crises and unrests, pandemics, terrorists’ attacks, natural and man-made disasters, climate change and last but certainly not least the current pandemic caused by the SARS-CoV-2, responsible for the outbreak COVID-19. Could this be the extinction of the global tourism or at least the end of its golden ages? Therefore, the aim of the report is to establish Bulgarian tourists’ attitudes towards travelling before and after the COVID-19 crisis. The elaborations are made by the computer language Python and a Web-based interactive computing environment for creating documents—Jupyter Notebook, in order to answer the above question, analysing a survey implementing statistical models and tools. The research data are primary and were derived of a questionnaire survey of 121 individual respondents. Consequently, the research results outline the extent to which customers incline to travel before, in the post-self-isolation period and after the crisis. The survey can be used as a model for data analysis of tourist’s attitudes towards during crises.

Keywords: Tourism customers attitudes; Risk travelling; Crises; Python in Jupyter Notebook; Data analysing (search for similar items in EconPapers)
JEL-codes: C8 D19 D80 Z3 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-030-72469-6_19

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DOI: 10.1007/978-3-030-72469-6_19

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