Factors Influencing Tourism Behavior after the COVID-19 Pandemic
Article Sidebar
Main Article Content
Abstract
Currently, COVID-19 has been declared an endemic disease. Most of the population has been vaccinated and public health has improved, leading to the relaxation of lifestyle-related restrictions by various agencies. Nevertheless, safety and hygiene remain critical concerns, particularly among individuals with underlying health conditions. As the pandemic has subsided, sectors across the economy, especially the tourism industry are striving to recover. This study aimed to analyze the factors influencing tourism behavior after the COVID-19 pandemic and to test the goodness of fit between the proposed structural model and empirical data. The research employed a self-administered questionnaire to collect data from a sample of 514 tourists, consisting of residents from Kalasin Province and visitors from other provinces. Data were analyzed using Structural Equation Modeling (SEM). The findings revealed that the COVID-19 pandemic significantly influenced post-pandemic tourism behavior. Although the initial model did not fit the empirical data according to the proposed hypothesis, after model adjustment to align with the local context of Kalasin Province, it was found that the pandemic had a statistically significant impact at the 0.01 level on Risk Management, Service Delivery, and Hygiene and Safety. The results of this study can be disseminated to the public and tourists to support effective risk management and travel planning emphasizing the importance of clean, hygienic, and safe travel destinations in preventing future disease outbreaks.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The information on the website, including images, audio, video, and text, will be allowed to be shared by citing the source, will not be used for commercial purposes, and will not be modified. (CC-BY-NC-ND)
References
กระทรวงการท่องเที่ยวและกีฬา. (2564). กองเศรษฐกิจการท่องเที่ยวและกีฬา.
กระทรวงสาธารณสุข. (2567). สถานการณ์ผู้ป่วย COVID-19 รายวัน. กระทรวงสาธารณสุข. Retrieved 6/05 from https://ddc.moph.go.th/covid19-dashboard/?dashboard=main
กรุงเทพธุรกิจออนไลน์. (2565). สิ่งที่จะเปลี่ยนแปลง เมื่อ "โควิด-๑๙" เป็น "โรคประจำถิ่น". กรุงเทพธุรกิจออนไลน์. Retrieved 6/05 from https://www.bangkokbiznews.com/social/986659
ChartExpo. (2025). 7-Point Likert Scale: Step by Step Guide. Microsoft Excel. https://chartexpo.com/blog/7-point-likert-scale#why-us-7-point-likert-scale
Daire Hooper, J. C., Michael Mullen. (2008). Structural Equation Modelling: Guidelines for Determining Model Fit Electronic Journal of Business Research Methods, 53-61.
Dedeoğlu, B. B. (2019). Shaping tourists' destination quality perception and loyalty through destination country image: The importance of involvement and perceived value. Tourism Management Perspectives, 29, 105-117. https://doi.org/https://doi.org/10.1016/j.tmp.2018.11.006
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.2307/3151312
Golob, T. F. (2003). Structural equation modeling for travel behavior research. Transportation Research Part B: Methodological, 37(1), 1-25. https://doi.org/https://doi.org/10.1016/S0191-2615(01)00046-7
Hair, J. F. B., W.C. Babin, B.J. (2010). Multivariate Data Analysis: A Global Perspective. Pearson Education. https://books.google.co.th/books?id=SLRPLgAACAAJ
Han, H., & Hyun, S. S. (2015). Customer retention in the medical tourism industry: Impact of quality, satisfaction, trust, and price reasonableness. Tourism Management, 46, 20-29. https://doi.org/https://doi.org/10.1016/j.tourman.2014.06.003
Hu, L. t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
Jomnonkwao, S., Sangphong, O., Khampirat, B., Siridhara, S., & Ratanavaraha, V. (2016). Public transport promotion policy on campus: evidence from Suranaree University in Thailand. Public Transport, 8(2), 185-203. https://doi.org/10.1007/s12469-016-0122-2
Kline, R. B. (2011). principles and practice of structural equation modeling. Guilford Press.
Loehlin, J. C. (1998). Latent variable models: An introduction to factor, path, and structural analysis, 3rd ed. Lawrence Erlbaum Associates Publishers.
Moon, H., & Han, H. (2018). Destination attributes influencing Chinese travelers' perceptions of experience quality and intentions for island tourism: A case of Jeju Island. Tourism Management Perspectives, 28, 71-82. https://doi.org/https://doi.org/10.1016/j.tmp.2018.08.002
Rahman, M. K., Gazi, M. A. I., Bhuiyan, M. A., & Rahaman, M. A. (2021). Effect of Covid-19 pandemic on tourist travel risk and management perceptions. PloS one, 16(9), e0256486. https://doi.org/10.1371/journal.pone.0256486
Steiger, J. H. (2007). Understanding the limitations of global fit assessment in structural equation modeling. Personality and Individual Differences, 42(5), 893-898. https://doi.org/https://doi.org/10.1016/j.paid.2006.09.017
Stylidis, D., Shani, A., & Belhassen, Y. (2017). Testing an integrated destination image model across residents and tourists. Tourism Management, 58, 184-195. https://doi.org/https://doi.org/10.1016/j.tourman.2016.10.014
Thailand, W. H. O. (2567). สถานการณ์โรคติดเชื้อไวรัสโคโรนา 2019 ในประเทศไทย. Retrieved 3/05 from https://www.who.int/thailand/news/detail/07-02-2024-update-on-covid-19-in-thailand--7-february-2024-THA
Ursachi, G., Horodnic, I. A., & Zait, A. (2015). How Reliable are Measurement Scales? External Factors with Indirect Influence on Reliability Estimators. Procedia Economics and Finance, 20, 679-686. https://doi.org/https://doi.org/10.1016/S2212-5671(15)00123-9
