The Adoption of Protective Health Behaviors During the COVID-19 Pandemic in Thailand

Main Article Content

Kedwadee Sombultawee
Sakun Boon-itt
Vipa Bussanit

Abstract


The objective of the research was to investigate acceptance of public health recommendations regarding COVID-19 in Thailand. The hypothesized framework included COVID-19 knowledge, communication, perceived risk, and perceived government response as predictors, with protective health behavior adoption as the outcome variable. A sample of Thai residents selected from across the country (n = 322) completed an online survey, which was analyzed using a structural equation modelling technique. Findings showed that COVID-19 knowledge influenced perceived risk (β = .20, p < .001), communication behavior (β = .16, p < .001), and government response (β = .17, p < .001), perceived risk (β =  .47, p < .001), communication behavior (β = .54, p < .001), and government response (β = .52, p < .001), influenced adoption of protective health behaviors; and that the effect of knowledge on protective health behaviors was partially mediated by perceived risk, communication behavior and government response. These findings illustrated that protective health behavior of Thai residents against COVID-19 was influenced by perceived risk, communication surrounding COVID-19, and perceptions of government response. The main implication is that simply providing more knowledge about COVID-19 is insufficient to improve public health response. Instead, individuals need to understand their risk, through accurate communication and a strong government response to encourage adoption of protective health behavior. Academically, the research provided insight into protective health behavior, especially in relation to government response. However, more research is needed, especially regarding adoption of new and changed behavioral recommendations and the potential for resistance.



Article Details

How to Cite
Sombultawee, K., Boon-itt, S., & Bussanit, V. (2021). The Adoption of Protective Health Behaviors During the COVID-19 Pandemic in Thailand. The Journal of Behavioral Science, 16(3), 72–83. Retrieved from https://so06.tci-thaijo.org/index.php/IJBS/article/view/251641
Section
Research Articles

References

Abuza, Z. (2020). Explaining successful (and unsuccessful) COVID-19 responses in Southeast Asia. The Diplomat. https://thediplomat.com/2020/04/explaining-successful-and-unsuccessful-covid-19-responses-in-southeast-asia/

Baron, R. M., & Kenny, D. A. (1986). The Moderator-Mediator Variable Distinction in Social Psychological Research. Conceptual, Strategic, and Statistical Considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173

Basol, M., Roozenbeek, J., Berriche, M., Uenal, F., McClanahan, W. P., & Linden, S. van der. (2021). Towards psychological herd immunity: Cross-cultural evidence for two prebunking interventions against COVID-19 misinformation. Big Data and Society, 8(1). https://doi.org/10.1177/20539517211013868

Berg, M. B., & Lin, L. (2020). Prevalence and predictors of early COVID-19 behavioral intentions in the United States. Translational Behavioral Medicine, 10(4), 843–849. https://doi.org/10.1093/tbm/ibaa085

Brennen, J. S., Simon, F. M., Howard, P. N., & Nielsen, R. K. (2020). Types, Sources, and Claims of COVID-19 Misinformation. Oxford University Press. https://reutersinstitute.politics.ox.ac.uk/types-sources-and-claims-covid-19-misinformation

Brennen, J. S., Simon, F. M., & Nielsen, R. K. (2021). Beyond (Mis)Representation: Visuals in COVID-19 Misinformation. International Journal of Press/Politics, 26(1), 277–299. https://doi.org/10.1177/1940161220964780

Brown, T. A. (2015). Confirmatory factor analysis for applied research. The Guilford Press.

Chaoguang, H., Feicheng, M., Yifei, Q., & Yuchao, W. (2018). Exploring the determinants of health knowledge adoption in social media: An intention-behavior-gap perspective. Information Development, 34(4), 346–363. https://doi.org/10.1177/0266666917700231

Chookajorn, T. (2020). Evolving COVID-19 conundrum and its impact. Proceedings of the National Academy of Sciences of the United States of America, 117(23), 12520–12521. https://doi.org/10.1073/pnas.2007076117

Chu, H., & Liu, S. (2021). Integrating health behavior theories to predict American’s intention to receive a COVID-19 vaccine. Patient Education and Counseling, 104(8), 1878–1886. https://doi.org/10.1016/j.pec.2021.02.031

Ciotti, M., Ciccozzi, M., Terrinoni, A., Jiang, W. C., Wang, C. B., & Bernardini, S. (2020). The COVID-19 pandemic. Critical Reviews in Clinical Laboratory Sciences, 57(6), 365–388. https://doi.org/10.1080/10408363.2020.1783198

Dang, H. L. (2021). Social media, fake news and the COVID-19 pandemic: Sketching the case of Southeast Asia. Aktuelle SüdostasienforsChung (Current Research on Southeast Asia), 14, 37–58. https://doi.org/10.14764/10.ASEAS-0054

Faasse, K., & Newby, J. (2020). Public Perceptions of COVID-19 in Australia: Perceived Risk, Knowledge, Health-Protective Behaviors, and Vaccine Intentions. Frontiers in Psychology, 11(September), 1–11. https://doi.org/10.3389/fpsyg.2020.551004

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2016). Multivariate Data Analysis (7th ed.). Pearson.

Hoffmann, R., & Lutz, S. U. (2019). The health knowledge mechanism: evidence on the link between education and health lifestyle in the Philippines. European Journal of Health Economics, 20(1), 27–43. https://doi.org/10.1007/s10198-017-0950-2

Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

Kenny, D. A., Kaniskan, B., & McCoach, D. B. (2015). The Performance of RMSEA in Models With Small Degrees of Freedom. Sociological Methods and Research, 44(3), 486–507. https://doi.org/10.1177/0049124114543236

Kowalski, R. M., & Black, K. J. (2021). Protection Motivation and the COVID-19 Virus. Health Communication, 36(1), 15–22. https://doi.org/10.1080/10410236.2020.1847448

Lazarus, J. V., Ratzan, S., Palayew, A., Billari, F. C., Binagwaho, A., Kimball, S., Larson, H. J., Melegaro, A., Rabin, K., White, T. M., & El-Mohandes, A. (2020). COVID-SCORE: A global survey to assess public perceptions of government responses to COVID-19 (COVID-SCORE-10). PLoS ONE, 15(10 October), 1–18. https://doi.org/10.1371/journal.pone.0240011

Lee, M., Kang, B. A., & You, M. (2021). Knowledge, attitudes, and practices (KAP) toward COVID-19: a cross-sectional study in South Korea. BMC Public Health, 21(1), 1–10. https://doi.org/10.1186/s12889-021-10285-y

Lindley, D. V, & Scott, W. F. (1984). New Cambridge Statistical Tables (2nd ed.). Cambridge University Press.

Loomba, S., de Figueiredo, A., Piatek, S. J., de Graaf, K., & Larson, H. J. (2021). Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA. Nature Human Behaviour, 5(3), 337–348. https://doi.org/10.1038/s41562-021-01056-1

Mackinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology, 58, 593-614. https://doi.org/10.1146/annurev.psych.58.110405.085542

Mohd Hanafiah, K., & Wan, C. D. (2020). Public knowledge, perception and communication behavior surrounding COVID-19 in Malaysia (Issue May). https://doi.org/10.31124/advance.12102816

Namwat, C., Suphanchaimat, R., Nittayasoot, N., & Iamsirithaworn, S. (2020). Thailand’s Response against Coronavirus Disease 2019: Challenges and Lessons Learned. Outbreak, Surveillance, Investigation and Response (OSIR) Journal, 13(1), 33–37. http://osirjournal.net/index.php/osir/article/view/174

Nowak, B., Brzóska, P., Piotrowski, J., Sedikides, C., Żemojtel-Piotrowska, M., & Jonason, P. K. (2020). Adaptive and maladaptive behavior during the COVID-19 pandemic: The roles of Dark Triad traits, collective narcissism, and health beliefs. Personality and Individual Differences, 167(May), 110232. https://doi.org/10.1016/j.paid.2020.110232

Osterrieder, A., Cuman, G., Pan-ngum, W., Cheah, P. K., Cheah, P. K., Peerawaranun, P., Silan, M., Orazem, M., Perkovic, K., Groselj, U., Schneiders, M. L., Poomchaichote, T., Waithira, N., Asarath, S. A., Naemiratch, B., Ruangkajorn, S., Skof, L., Kulpijit, N., MacKworth-Young, C. R. S., … Cheah, P. Y. (2021). Economic and social impacts of COVID-19 and public health measures: Results from an anonymous online survey in Thailand, Malaysia, the UK, Italy and Slovenia. BMJ Open, 11(7), 1–12. https://doi.org/10.1136/bmjopen-2020-046863

Pan-ngum, W., Poomchaichote, T., Peerawaranun, P., Kulpijit, N., Osterrieder, A., Waithira, N., Mukaka, M., Naemiratch, B., Chanviriyavuth, R., Asarath, S. at, Ruangkajorn, S., Kannika, N., & Cheah, P. Y. (2021). Perspectives on public health interventions in the management of the COVID-19 pandemic in Thailand. Wellcome Open Research, 5, 1–24. https://doi.org/10.12688/wellcomeopenres.16293.3

Pennycook, G., McPhetres, J., Zhang, Y., Lu, J. G., & Rand, D. G. (2020). Fighting COVID-19 Misinformation on Social Media: Experimental Evidence for a Scalable Accuracy-Nudge Intervention. Psychological Science, 31(7), 770–780. https://doi.org/10.1177/0956797620939054

Prasetyo, Y. T., Castillo, A. M., Salonga, L. J., Sia, J. A., & Seneta, J. A. (2020). Factors affecting perceived effectiveness of COVID-19 prevention measures among Filipinos during Enhanced Community Quarantine in Luzon, Philippines: Integrating Protection Motivation Theory and extended Theory of Planned Behavior. International Journal of Infectious Diseases, 99, 312–323. https://doi.org/10.1016/j.ijid.2020.07.074

Ranjit, Y. S., Shin, H., First, J. M., & Houston, J. B. (2021). COVID-19 protective model: the role of threat perceptions and informational cues in influencing behavior. Journal of Risk Research, 24(3–4), 449–465. https://doi.org/10.1080/13669877.2021.1887328

Roozenbeek, J., Freeman, A. L. J., & van der Linden, S. (2021). How Accurate Are Accuracy-Nudge Interventions? A Preregistered Direct Replication of Pennycook et al. (2020). Psychological Science, 32(7), 1169-1178. https://doi.org/10.1177/09567976211024535

Roozenbeek, J., Schneider, C. R., Dryhurst, S., Kerr, J., Freeman, A. L. J., Recchia, G., Van Der Bles, A. M., & Van Der Linden, S. (2020). Susceptibility to misinformation about COVID-19 around the world: Susceptibility to COVID misinformation. Royal Society Open Science, 7(10). https://doi.org/10.1098/rsos.201199

Rosi, A., van Vugt, F. T., Lecce, S., Ceccato, I., Vallarino, M., Rapisarda, F., Vecchi, T., & Cavallini, E. (2021). Risk Perception in a Real-World Situation (COVID-19): How It Changes From 18 to 87 Years Old. Frontiers in Psychology, 12(March), 1–8. https://doi.org/10.3389/fpsyg.2021.646558

Soper, D. (2020). A priori sample size calculator for structural equation models. Free Statistics Calculators. https://www.danielSoper.com/statcalc/calculator.aspx?id=89

Sylvester, S. M. (2021). COVID-19 and Motivated Reasoning: The Influence of Knowledge on COVID-Related Policy and Health Behavior. Social Science Quarterly, Pre-press. https://doi.org/https://doi.org/10.1111/ssqu.12989

Taghrir, M. H., Borazjani, R., & Shiraly, R. (2020). COVID-19 and iranian medical students; A survey on their related-knowledge, preventive behaviors and risk perception. Archives of Iranian Medicine, 23(4), 249–254. https://doi.org/10.34172/aim.2020.06

Tyupa, S. (2011). A theoretical framework for back-translation as a quality assessment tool. New Voices in Translation Studies, 7(1), 35–46. https://core.ac.uk/download/pdf/53121361.pdf

Velavan, T. P., & Meyer, C. G. (2021). COVID-19: A PCR-defined pandemic. International Journal of Infectious Diseases, 103, 278–279. https://doi.org/10.1016/j.ijid.2020.11.189

Westland, J. C. (2010). Lower bounds on sample size in structural equation modeling. Electronic Commerce Research and Applications, 9(6), 476–487. https://doi.org/10.1016/j.elerap.2010.07.003

World Health Organization. (2021). WHO Coronavirus Dashboard. https://covid19.who.int/

Zheng, D., Luo, Q., & Ritchie, B. W. (2020). Afraid to travel after COVID-19? Self-protection, coping and resilience against pandemic ‘travel fear.’ Tourism Management, 83(October 2020), 104261. https://doi.org/10.1016/j.tourman.2020.104261

Zhong, B. L., Luo, W., Li, H. M., Zhang, Q. Q., Liu, X. G., Li, W. T., & Li, Y. (2020). Knowledge, attitudes, and practices towards COVID-19 among chinese residents during the rapid rise period of the COVID-19 outbreak: A quick online cross-sectional survey. International Journal of Biological Sciences, 16(10), 1745–1752. https://doi.org/10.7150/ijbs.45221