Mask-Wearing Onboard Airplanes During COVID-19 - Identifying Passenger Segments Using Cluster Analysis
Main Article Content
Abstract
The COVID-19 crisis has had an unprecedented impact on air traffic, but its long-term effect on how people view and conduct air travel could be more significant. While some studies have identified factors that can affect the perception of COVID-19 and behaviors in transportation, few have examined factor impact at the segment level, especially with respect to mask-wearing as a protective measure onboard airplanes during COVID-19. This study fills the research gap by identifying passenger segments with different perceptions of mask use onboard airplanes, and which factor in the theory of planned behavior (TPB) model can be best distinguished in the different segments. Survey data was collected from MTurk for cluster analysis, which considered the comfort of mask-wearing, risk avoidance, and information-seeking jointly as the cluster variates. The analysis led to the formation of three passenger segments: 1) Comfort First – most attention was paid to the comfort of mask-wearing; 2) Risk Avoider – the greatest importance was given to avoiding the risk and searching for information about COVID-19; and 3) Balanced Group – having a balanced view on the importance of the three cluster variables. The multinomial logistic regression analysis showed that the three passenger segments differed most in their attitude toward masks, suggesting that attitude, compared to the other two TPB constructs (subjective norms and perceived behavioral control) can best predict the cluster membership of air travelers with regards to mask-wearing onboard airplanes. The findings provide useful guidance for the airline industry to recover safely and effectively from the COVID-19 pandemic, and may aid in preparations for future pandemics.
Keywords:
Mask-wearing, COVID-19, Cluster analysis, Multinomial logistic regression, Air travel, Attitude toward mask
Article Details
How to Cite
YU, Pan Jing; DAHAI, Liu.
Mask-Wearing Onboard Airplanes During COVID-19 - Identifying Passenger Segments Using Cluster Analysis.
Medical Research Archives, [S.l.], v. 11, n. 8, aug. 2023.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/4342>. Date accessed: 04 dec. 2024.
doi: https://doi.org/10.18103/mra.v11i8.4342.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
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3. Abboah-Offei M, Salifu Y, Adewale B, Bayuo J, Ofosu-Poku R, Opare-Lokko EBA. A rapid review of the use of face mask in preventing the spread of COVID-19. International Journal of Nursing Studies Advances. 2021;3(3):100013. https://doi.org/10.1016/j.ijnsa.2020.100013
4. Tao Z, Dong J, Culleton R. The use of facemasks may not lead to an increase in hand–face contact. Transbound Emerg Dis. 2020;67(6), 3038-3040. https://doi.org/10.1111/tbed.13698
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6. Irfan M, Akhtar N, Ahmad M, et al. Assessing Public Willingness to Wear Face Masks during the COVID-19 Pandemic: Fresh Insights from the Theory of Planned Behavior. International Journal of Environmental Research and Public Health. 2021;18(9):4577. https://doi.org/10.3390/ijerph18094577
7. Sun Y, Qin B, Hu Z, et al. Predicting mask-wearing behavior intention among international students during COVID-19 based on the theory of planned behavior. Annals of Palliative Medicine. 2021;10(2):55-55. https://doi.org/10.21037/apm-20-2242
8. Hair, JF, Black, WC, Babin, BJ, Anderson, RE. Multivariate data analysis, New Jersey: Cengage; 2019.
9. Chen HT, Chao CC. Airline choice by passengers from Taiwan and China: A case study of outgoing passengers from Kaohsiung International Airport. Journal of Air Transport Management. 2015;49:53-63. https://doi.org/10.1016/j.jairtraman.2015.08.002
10. Fu X, Juan Z. Empirical analysis and comparisons about time-allocation patterns across segments based on mode-specific preferences, Transportation. 2016;43:37-51. https://doi.org/10.1007/s11116-014-9561-2
11. Kuljanin JM, Kalić. Exploring characteristics of passengers using traditional and low-cost airlines: A case study of Belgrade Airport. JATM. 2015;46:12-18. http://doi.org/10.1016/j.jairtraman.2015.03.009
12. Wartelle A, Mourad-Chehade F, Yalaoui F, Chrusciel J, Laplanche D, Sanchez S. Effect of the COVID-19 pandemic lockdown on non-COVID-19 emergency department visits in Eastern France: Reduced risk or avoidance behavior? Public Health in Practice. 2021;2:100109. https://doi.org/10.1016/j.puhip.2021.100109
13. Dzimińska P, Drzewiecki S, Ruman M, Kosek K, Mikołajewski K, Licznar P. The Use of Cluster Analysis to Evaluate the Impact of COVID-19 Pandemic on Daily Water Demand Patterns. Sustainability. 2021;13(11):5772. https://doi.org/10.3390/su13115772
14. García S, Parejo A, Personal E, Ignacio Guerrero J, Biscarri F, León C. A retrospective analysis of the impact of the COVID-19 restrictions on energy consumption at a disaggregated level. Applied Energy. 2021;287:116547. https://doi.org/10.1016/j.apenergy.2021.116547
15. Hartono A, Ishak A, Abdurrahman A, et al. COVID-19 Pandemic and Adaptive Shopping Patterns: An Insight from Indonesian Consumers. Global Business Review. Published online June 18, 2021:097215092110135. https://doi.org/10.1177/09721509211013512
16. Theriot A, Urrutia-Alvarez N, McKinley EM. An Analysis of Pandemic Panic Buying Motivators among Undergraduate College Students Using Mind Genomics Cognitive Science. Psychology. 2021;12(09):1457-1471. https://doi.org/10.4236/psych.2021.129092
17. Ahmed MS, Aurpa TT, Anwar MM. Detecting sentiment dynamics and clusters of Twitter users for trending topics in COVID-19 pandemic. Cherifi H, ed. PLOS ONE. 2021;16(8):e0253300. https://doi.org/10.1371/journal.pone.0253300
18. Enders AM, Uscinski J, Klofstad C, Stoler J. On the relationship between conspiracy theory beliefs, misinformation, and vaccine hesitancy. Jonason PK, ed. PLOS ONE. 2022;17(10):e0276082. https://doi.org/10.1371/journal.pone.0276082
19. Lang R, Benham JL, Atabati O, et al. Attitudes, behaviours and barriers to public health measures for COVID-19: a survey to inform public health messaging. BMC Public Health. 2021;21(1). https://doi.org/10.1186/s12889-021-10790-0
20. Sheng X, Ketron SC, Wan Y. Identifying consumer segments based on COVID ‐19 pandemic perceptions and responses. Journal of Consumer Affairs. 2021;56(1). https://doi.org/10.1111/joca.12413
21. Kopsidas A, Milioti C, Kepaptsoglou K, Vlachogianni EI. How did the COVID-19 pandemic impact traveler behavior toward public transport? The case of Athens, Greece. Transportation Letters. Published online March 23, 2021:1-9. https://doi.org/10.1080/19427867.2021.1901029
22. Budd T, Suau-Sanchez P, Halpern N, Mwesiumo D, Bråthen S. An assessment of air passenger confidence a year into the COVID-19 crisis: A segmentation analysis of passengers in Norway. Journal of Transport Geography. 2021;96:103204. https://doi.org/10.1016/j.jtrangeo.2021.103204
23. Gray L, MacDonald C, Tassell-Matamua N, et al. Wearing one for the team: views and attitudes to face covering in New Zealand/Aotearoa during COVID-19 Alert Level 4 lockdown. J Prim Health Care. 2020;12(3):199. https://doi.org/10.1071/hc20089
24. Howard MC. Gender, face mask perceptions, and face mask wearing: Are men being dangerous during the COVID-19 pandemic? Personality and Individual Differences. 2021;170:110417. https://doi.org/10.1016/j.paid.2020.110417
25. Cheok GJW, Gatot C, Sim CHS, et al. Appropriate attitude promotes mask wearing in spite of a significant experience of varying discomfort. Infection, Disease & Health. 2021;26(2):145-151. https://doi.org/10.1016/j.idh.2021.01.002
26. Lorian CN, Grisham JR. Clinical implications of risk aversion: An online study of risk-avoidance and treatment utilization in pathological anxiety. Journal of Anxiety Disorders. 2011;25(6):840-848. https://doi.org/10.1016/j.janxdis.2011.04.008
27. Bruine de Bruin WB, Bennett D. Relationships Between Initial COVID-19 Risk Perceptions and Protective Health Behaviors: A National Survey. American Journal of Preventive Medicine. Published online May 2020. https://doi.org/10.1016/j.amepre.2020.05.001
28. Hopfer S, Fields EJ, Lu Y, et al. The social amplification and attenuation of COVID-19 risk perception shaping mask wearing behavior: A longitudinal twitter analysis. Gesser-Edelsburg A, ed. PLOS ONE. 2021;16(9):e0257428. https://doi.org/10.1371/journal.pone.0257428
29. Bento AI, Nguyen T, Wing C, Lozano-Rojas F, Ahn YY, Simon K. Evidence from internet search data shows information-seeking responses to news of local COVID-19 cases. Proceedings of the National Academy of Sciences. 2020;117(21):11220-11222. https://doi.org/10.1073/pnas.2005335117
30. Soroya SH, Farooq A, Mahmood K, Isoaho J, Zara S. From information seeking to information avoidance: Understanding the health information behavior during a global health crisis. Information Processing & Management. 2021;58(2):102440. https://doi.org/10.1016/j.ipm.2020.102440
31. Kim YJ, Cho J, Kang SW. Study on the Relationship between Leisure Activity Participation and Wearing a Mask among Koreans during COVID-19 Crisis: Using TPB Model. International Journal of Environmental Research and Public Health. 2020;17(20):7674. https://doi.org/10.3390/ijerph17207674
32. Buaphiban T, Truong D. Evaluation of passengers’ buying behaviors toward low cost carriers in Southeast Asia. Journal of Air Transport Management. 2017;59:124-133. https://doi.org/10.1016/j.jairtraman.2016.12.003
33. Hsiao CH, Yang C. Predicting the travel intention to take High Speed Rail among college students. Transportation Research Part F: Traffic Psychology and Behaviour. 2010;13(4):277-287. https://doi.org/10.1016/j.trf.2010.04.011
34. Pan JY, Truong D. Passengers’ intentions to use low-cost carriers: An extended theory of planned behavior model. Journal of Air Transport Management. 2018;69:38-48. https://doi.org/10.1016/j.jairtraman.2018.01.006
35. Si H, Shen L, Liu W, Wu G. Uncovering people’s mask-saving intentions and behaviors in the post-COVID-19 period: Evidence from China. Sustainable Cities and Society. 2021;65:102626. https://doi.org/10.1016/j.scs.2020.102626
36. Taylor S, Asmundson GJG. Negative Attitudes about Facemasks during the COVID-19 pandemic: the Dual Importance of Perceived Ineffectiveness and Psychological Reactance. Capraro V, ed. PLOS ONE. 2021;16(2):e0246317. https://doi.org/10.1371/journal.pone.0246317
37. Zhang B, Li Z, Jiang L. The Intentions to Wear Face Masks and the Differences in Preventive Behaviors between Urban and Rural Areas during COVID-19: An Analysis Based on the Technology Acceptance Model. International Journal of Environmental Research and Public Health. 2021;18(19):9988. https://doi.org/10.3390/ijerph18199988
38. Young SD, Goldstein NJ. Applying social norms interventions to increase adherence to COVID-19 prevention and control guidelines. Preventive Medicine. 2021;145:106424. https://doi.org/10.1016/j.ypmed.2021.106424
39. Barceló J, Sheen GCH. Voluntary adoption of social welfare-enhancing behavior: Mask-wearing in Spain during the COVID-19 outbreak. Shaman J, ed. PLOS ONE. 2020;15(12):e0242764. https://doi.org/10.1371/journal.pone.0242764
40. Aschwanden D, Strickhouser JE, Sesker AA, et al. Preventive Behaviors During the COVID-19 Pandemic: Associations With Perceived Behavioral Control, Attitudes, and Subjective Norm. Frontiers in Public Health. 2021;9. https://doi.org/10.3389/fpubh.2021.662835
41. Bao Y, Zhou KZ, Su C. Face consciousness and risk aversion: Do they affect consumer decision-making? Psychology and Marketing. 2003;20(8):733-755. https://doi.org/10.1002/mar.10094
42. DuBenske LL, Burke Beckjord E, Hawkins RP, Gustafson DH. Psychometric Evaluation of the Health Information Orientation Scale. Journal of Health Psychology. 2009;14(6):721-730. https://doi.org/10.1177/1359105309338892
43. Harvey J, Thorpe N, Caygill M, Namdeo A. Public attitudes to and perceptions of high speed rail in the UK. Transport Policy. 2014;36:70-78. https://doi.org/10.1016/j.tranpol.2014.07.008
44. Jing P, Juan Z. (2013). The Theory of Planned Behavior: The role of descriptive norms and habit in the prediction of inter-city travel mode choice, Journal of Convergence Information Technology. 2013;8(10):211-219.
45. Jing P, Juan Z, Gao LJ. Application of the Expanded Theory of Planned Behavior in Intercity Travel Behavior. Discrete Dynamics in Nature and Society. 2014;2014:1-10. https://doi.org/10.1155/2014/308674
46. Meertens RM, Lion R. Measuring an Individual’s Tendency to Take Risks: The Risk Propensity Scale. Journal of Applied Social Psychology. 2008;38(6):1506-1520. https://doi.org/10.1111/j.1559-1816.2008.00357.x
47. Oborne DJ, Clarke MJ. Questionnaire surveys of passenger comfort. Applied Ergonomics. 1975;6(2):97-103. https://doi.org/10.1016/0003-6870(75)90302-6
48. Park HS, Klein KA, Smith S, Martell D. Separating Subjective Norms, University Descriptive and Injunctive Norms, and U.S. Descriptive and Injunctive Norms for Drinking Behavior Intentions. Health Communication. 2009;24(8):746-751. https://doi.org/10.1080/10410230903265912
49. Raju PS. Optimum Stimulation Level: Its Relationship to Personality, Demographics, and Exploratory Behavior. Journal of Consumer Research. 1980;7(3):272. https://doi.org/10.1086/208815
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