Mask-Wearing Onboard Airplanes During COVID-19 - Identifying Passenger Segments Using Cluster Analysis

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Pan Jing Yu Liu Dahai


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: <>. Date accessed: 02 oct. 2023. doi:
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