Investigation of Attitudes and Behaviors towards Covid 19 with Theory of Planned Behavior

Abstract

Objective: In the COVID-19 pandemic, it is necessary and crucial for local authorities to be aware of the prospective behavior and perception of individuals so that health authorities can effectively respond and be prepared for the pandemic. This study has focused on researching people's attitudes and behaviors towards COVID-19 within the context of theory of planned behavior (TPB).

Methods: In the study, initially, a research model was designed within the scope of TPB, and then in order to analyze the relationships in the model, a data collection tool was developed. Ultimately, the fit of the proposed modeling and the test of hypotheses were conducted through using partial least squares structural equation modeling (PLS-SEM).

Results: As a result of the research, it was found out that as the anxiety about COVID-19 soared the intention to go out was decreased, while the behavior of taking precautions increased. Also, it was indicated that as the intention of going out was decreased by one point, the behavior of taking measures against the transmission of COVID-19 increased by 0.28 points.

Conclusions: In the study, it could be stated that the model proposed within the scope of TPB fits well with the perceived attitudes and behaviors towards COVID-19. It was determined that as the anxiety towards COVID-19 increased, the intention of going out was decreased and the behavior of taking measures increased.

Keywords

• Covid -19
• Theory of Planned Behavior (TBP)
• Anxiety
• Behavior
• Attitude

COVID-19’a Yönelik Tutum ve Davranışların Planlı Davranış Teorisiyle Araştırılması

Abstract

Amaç: COVID-19 pandemisinde, sağlık otoritelerinin etkili bir şekilde yanıt verebilmesi ve pandemiye hazırlıklı olabilmesi için yerel yetkililerin bireylerin olası davranış ve algılarının farkında olması gerekli ve çok önemlidir. Bu çalışma, Planlı Davranış Teorisi (PDT) bağlamında insanların COVID-19'a yönelik tutum ve davranışlarını araştırmaya odaklanmıştır.

Yöntem: Çalışmada öncelikle PDT kapsamında bir araştırma modeli tasarlanmış ve ardından modeldeki ilişkileri analiz etmek için bir veri toplama aracı geliştirilmiştir. Sonunda önerilen modellemenin uyumu ve hipotez testi, kısmi en küçük kareler yapısal eşitlik modellemesi (KEKK-YEM) kullanılarak gerçekleştirilmiştir.

Bulgular: Araştırma sonucunda COVID-19 endişesi arttıkça dışarı çıkma niyetinin azaldığı, önlem alma davranışının arttığı tespit edildi. Ayrıca dışarı çıkma niyeti bir puan azaldığı için COVID-19 bulaşına karşı önlem alma davranışının 0,28 puan arttığı belirlendi.

Sonuç: Araştırmada, PDT kapsamında önerilen modelin COVID-19'a yönelik algılanan tutum ve davranışlarla uyumlu olduğu söylenebilir. COVID-19'a yönelik kaygı arttıkça dışarı çıkma niyetinin azaldığı ve önlem alma davranışının arttığı belirlendi.

Keywords

• Covid 19
• Planlı Davranış Teorisi (PDT)
• Kaygı
• Tutum
• Davranış

References

1. Agarwal V. A/H1N1 Vaccine Intentions in College Students: An Application of the Theory of Planned Behavior. Journal of American College Health 2014; 62(6): 416-424. http://dx.doi.org/10.1080/07448481.2014.917650
2. Ahorsu DK, Lin CY, Imani V, Saffari M, Griffiths MD, Pakpour AH. The Fear of COVID-19 Scale: Development and Initial Validation. Int J Ment Health Addict 2020; 27: 1-9. http://dx.doi.org/10.1007/s11469-020-00270-8.
3. Armitage CJ, Conner M. Distinguishing perceptions of control from self-efficacy: predicting consumption of a low-fat diet using the theory of planned behavior. J Appl Soc Psychol. 1999; 29: 72–90. https://doi.org/10.1111/j.1559-1816.1999.tb01375.x.
4. Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process 1991; 50: 179–211.
5. Bandura A. Health promotion from the perspective of social cognitive theory. Psychol Health 1998; 13(4):623‐649.
6. Cornally N, Ann Deasy E, McCarthey G, et al. Student nurses’ intention to get the influenza vaccine. Br J Nurs. 2013; 22: 1207–1211.
7. Fornell C, Bookstein FA comparison analysis of two structural equation models: Lisrel and pls applied to market data. In C. Fornell (Ed.), A Second Generation of Multivariate Analysis (pp. 290–312). New York: Greenwood, 1982.
8. Fornell C, Lacker DF. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 1981;18: 39-50.

9. Fornell C. A national customer satisfaction barometer: the Swedish experience. Journal of Marketing 1992; 56(1): 6–21.
10. Fornell C, Cha J. Partial least squares, In R. Bagozzi (Ed.), Advanced methods of marketing, 52-78. Cambridge: Blackwell, 1994.
11. Fornell C, Johnson M, Anderson E-W, Cha J, Bryant B.E. The American Customer Satisfaction Index: Nature, Purpose, and Findings. Journal of Marketing 1996; 60(4): 7-18.
12. Garfin DR, Silver RC, Holman EA. The novel coronavirus (COVID-2019) outbreak: Amplification of public health consequences by media exposure. Health Psychol. 2020; doi: 10.1037/hea0000875.
13. Gritsenko V, Skugarevsky O, Konstantinov V, Khamenka N, Marinova T, Reznik A, Isralowitz R. (2020). COVID 19 Fear, Stress, Anxiety, and Substance Use Among Russian and Belarusian University Students. International Journal of Mental Health and Addiction. https://doi.org/10.1007/s11469-020-00330-z.
14. Hair Joseph F, Tatham R.L, Anderson R-E, Black W. Multivariate data analysis with readings. 5th Edn. Englewood Cliffs, NJ: Prentice Hall,1998.
15. Hair JF, Ringle CM, Sarstedt M. PLS-SEM: Indeed a silver bullet. The Journal of Marketing Theory and Practice 2011; 19(2): 139-152.
16. Hair JF, Hult G, Tomas M, Ringle C- M, Sarstedt M. A primer on partial least squares structural equation modeling (PLS-SEM). SAGE Publications, Inc., 2013.
17. Hekler EB, Lambert J, Leventhal E, Leventhal H, Jahn E, Contrada RJ. Commonsense illness beliefs, adherence behaviors, and hypertension control among African Americans. Journal of Behavioral Medicine (2008), 31: 391-400.
18. Hagger M.S, Orbell S. A meta-analytic review of the common-sense model of illness representations. Psychology and Health 2003: 18: 141-184.
19. Jung S. Exploratory factor analysis with small sample sizes: acomparison of three approaches. Behav Proceses (2013), 97: 90-95.
20. Kline R.B. Principles and Practice of Structural Equation Modeling. Guilford Press, New York, 2011.
21. Kumar A, Nayar R. COVID 19 and its mental health consequences. Journal of Mental Health 2020. https://doi.org/10.1080/09638237.2020.1757052.
22. Li S, Wang Y, Xue J, Zhao N, Zhu T. The Impact of COVID-19 Epidemic Declaration on Psychological Consequences: A Study on Active Weibo Users. International Journal of Environmental Research and Public Health 2020; 17: 2032; https://doi.org/10.3390/ijerph17062032.
23. Lin C-Y. Social reaction to ward the 2019 novel coronavirus (COVID-19). Social Health and Behavior 2020; 3(1):1–2.
24. Marčinko D, Jakovljević M, Jakšić N, Bjedov S, Mindoljević D. A. The Importance of Psychodynamic Approach during COVID-19 Pandemic. Psychiatr Danub. 2020, 32(1):15-21. https://doi.org/10.24869/psyd.2020.15.
25. Meng R, Li J, Wang Z, Zhang D, Liu B, Luo Y, Hu Y, Yu C. The Chinese version of the Perceived Stress Questionnaire: development and validation amongst medical students and workers. Health Qual Life Outcomes 2020 ; 18(1):70. https://doi.org/ 10.1186/s12955-020-01307-1.
26. Myers LB, Goodwin R. Using a theoretical framework to determine adults’ intention to vaccinate against pandemic swine flu tin priority groups in the UK. Public Health 2012, 126: 53–56. https://doi.org/10.1016/j.puhe.2012.05.024.
27. Qazi A, Qazi J, Naseer K, Zeeshan M, Hardaker G, Maitama JZ, Haruna K. Analyzing situational awareness through public opinion to predict adoption of social distancing amid pandemic COVID-19. J Med Virol. 2020; Apr 8. https://doi.org/ 10.1002/jmv.25840.
28. Qazi A, Tamjidyamcholo A, Raj RG, Hardaker G, Standing C. Assessing. consumers' satisfaction and expectations through online opinions: expectation and disconfirmation approach. Comput Human Behav. (2017); 75: 450‐460.
29. Qian M, Wu Q, Wu P. et al. Psychological responses, behavioral changes and public perceptions during the early phase of the COVID-19 outbreak in China: a population based cross-sectional survey. Journal of Medical Virology 2020. https://doi.org/10.1101/2020.02.18.20024448.
30. Satici B, Gocet-Tekin E, Deniz ME, & Satici S. A. Adaptation of the Fear of COVID-19 Scale: Its Association with Psychological Distress and Life Satisfaction in Turkey. International Journal of Mental Health and Addiction 2020. https://doi.org/10.1007/s11469-020-00294-0.
31. Schermelleh- Engel-K, Moosbrugger H. Evaluating The Fit Of Structural Equation Models: Test Of Significance And Descriptive Goodness Of-Fit Measures, Methods of Psychological Research- Online 2003; 8(2): 23-74.
32. Schneeweiß, H. Models with latent variables: LISREL versus PLS. Statistica Neerlandica, 1991; 45(2): 145–157.
33. Wang Y, Di Y, Ye J, Wei W. Study on the public psychological states and its related factors during the outbreak of coronavirus disease 2019 (COVID-19) in some regions of China. Psychology, Health & Medicine 2020. https://doi.org/10.1080/13548506.2020.1746817.
34. Wu JT, Leung K, Leung GM. Nowcasting and forecastingthe potential domestic and intirnational spread ofthe 2019-nCoV outbreak originating in Wuhan, China:a modelling study Lancet 2020; 395:689-697.
35. Vinzi VE, Trinchera L, Amato S. PLS path modeling: from foundations to recent developments and open issues for model assessment and improvement. In: V. Esposito Vinzi, 2010.
36. Yuan S, Liao Z, Huang H, Jiang B, Zhang X, Wang Y, Zhao M. Comparison of the Indicators of Psychological Stress in the Population of Hube, 2020.
37. Province and Non-Endemic Provinces in China During Two Weeks During the Coronavirus Disease 2019 (COVID-19) Outbreak in February 2020. Med Sci Monit. 2020; 26. https://doi.org/10.12659/MSM.923767.