DETERMINANTS OF RURAL TOURISM DEVELOPMENT IN UKRAINE: EXPERT’S OPINION
Keywords:
rural tourism, PLS-SEM, expert’s opinion, Ukraine.Abstract
This study examines the key determinants of tourism infrastructure development
in rural areas of Ukraine through empirical research conducted in 2024. The investigation employed
a Computer-Assisted Web Interview (CAWI) methodology, utilizing a comprehensive questionnaire
comprising 33 substantive questions organized into six thematic groups: demographic and social
transformations, economic development, and agricultural changes and sustainable development,
rural tourism development, tourism-related infrastructure, and tourism resources including natural
assets and cultural heritage. Responses were measured using a 5-point Likert scale. The final
Ukrainian sample consisted of 105 respondents representing diverse stakeholder groups, with
scientists constituting the largest proportion (72.38%), followed by representatives from the tourism
economy (9.52%), officials (7.62%), farmers (5.71%), and representatives of tourism associations
and organizations (4.76%). The sample demonstrated gender imbalance favouring women
(70.48%), with the dominant age cohort being 40-49 years (34.29%). Educational attainment was
notably high, with 60.95% holding doctoral degrees and 22.86% possessing master's degrees.
Geographically, respondents represented various administrative regions across Ukraine, excluding
temporarily occupied territories such as the Autonomous Republic of Crimea, Sevastopol, and
occupied portions of Donetsk and Luhansk oblasts due to ongoing military operations and Russian
Federation occupation.
References
1. Butler, R. W. (Ed.). (2006). The Tourism Area Life Cycle, Vol. 1: Applications and
Modifications. Channel View Publications.
2. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In
G. A. Marcoulides (Ed.), Modern Methods for Business Research (pp. 295–336). Lawrence
Erlbaum Associates.
3. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.).
Lawrence Erlbaum Associates.
4. Dwyer, L., & Kim, C. (2003). Destination competitiveness: Determinants and indicators.
Current Issues in Tourism, 6(5), 369–414.
5. Efron, B., & Tibshirani, R. J. (1994). An Introduction to the Bootstrap. Chapman & Hall.
6. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with
unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
7. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to
report the results of PLS-SEM. European Business Review, 31(1), 2–24.
8. Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2021). Advanced Issues in
Partial Least Squares Structural Equation Modeling (2nd ed.). SAGE Publications.
9. Hayton, J. C., Allen, D. G., & Scarpello, V. (2004). Factor retention decisions in
exploratory factor analysis: A tutorial on parallel analysis. Organizational Research Methods,
7(2), 191–205.
10. Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36.
11. Nitzl, C., Roldan, J. L., & Cepeda, G. (2016). Mediation analysis in partial least squares
path modeling. Industrial Management & Data Systems, 116(9), 1849–1864.
12. Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle,
C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict.
European Journal of Marketing, 53(11), 2322–2347.
Published
Issue
Section
License
Copyright (c) 2026 New knowledge Journal of science

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.