Background Prior research has examined consumer willingness to fly in a variety of situations, including during disease outbreaks. However, to date, no study that we know of has identified what type of person is willing to fly during the COVID-19 pandemic. Methods Six hundred and thirty-two participants from the United States were asked to complete a survey designed to capture demographics, personality measures, emotional states and travel purposes. The data were collected in two stages in order to both develop a descriptive regression equation and a predictive model. Results Regression equations were created for both business and pleasure travel, and the following predictors were significant for both scenarios: perceived threat from COVID-19, agreeableness, affect, and fear. These models accounted for 66–67% of the variance in willingness to fly. Conclusion Airlines and governments could use these findings to help control the message to potential passengers on actions being taken to provide a safe flying experience, such as mask wearing policies and aircraft disinfectant procedures.

Within the last decade, numerous online populations, such as SurveyMonkeyH and Amazon's Mechanical TurkH (MTurk), have been established allowing researchers to gather data involving diverse populations. These resources offer an alternative to traditional laboratory settings hosted at universities, where many studies utilize students as the available and accessible population. While these online portals do provide new opportunities, they also contain unique advantages and disadvantages. This paper synthesizes the advantages and disadvantages of using online populations to conduct research in the aviation field. Some of the advantages are: easier access to new populations, larger sample sizes, more balanced ratio of genders, lower cost, more timely data collection, reliable data, and anonymity of participants. Some of the disadvantages are: unrepresentative samples, lower response rates, financial motivation issues, limited access to certain portals, limited length of study, non-behavioral data, and lack of follow-up data. In addition, the authors share their personal experiences of using these portals and summarize previous literature researching online populations.

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