A study on airlines’ responses and customer satisfaction during the COVID-19 pandemic

Abstract

The Coronavirus (COVID-19) pandemic outbreak has significantly impacted the airline industry worldwide. However, limited studies have systematically investigated the airlines' responses and customer satisfaction in the aviation industry during the COVID-19 pandemic. The present study attempts to address this knowledge gap.

The first aim of this study is to determine customers' satisfaction with the aviation industry during the COVID-19 pandemic. A questionnaire survey was conducted in China to investigate the Chinese passengers' satisfaction with 22 constructs in four stages: Pre-Flight, In-Flight, After-Arrival, and Others (Face mask requirement, HEPA filters, etc.). Second, this work explored the measures that will benefit the airlines by investigating the measures taken by 49 major airlines worldwide, especially considering the operational cost and passengers’ safety.

It was found that cabin selection and passengers who travelled after the start of COVID-19 were the groups that affected passengers’ satisfaction levels on responses. The top 3 satisfied measures were “Provide hygiene products for passengers and staff”, “A thermal scanner to monitor body temperature during check-in”, and “Disinfect the cabin after each flight, even for a previous flight of the connecting flight”. In contrast, the bottom 3 measures were “Protective clothing is required to board the plane”, “Adopt a special boarding method such as boarding in the order from back to front”, and “No in-flight meals and drinks (only snacks and water)”. Airlines’ responses primarily focused on reducing the operation cost, ensuring the safety and interests of the passengers and improving the income and cash of the company.

1. Introduction

Coronavirus (COVID-19) is a sheer devastating factor in the aviation and tourism industries. The airline industry has recorded constant and extraordinary growth in the past century. In this way, it has resisted some huge global catastrophes, for example, the 9/11 terrorist attacks in 2001 and the global financial crisis in 2008. Only a half-century after its commencement, the air travel market reached a milestone in 1987: one billion passengers in one year. Afterwards, it was exponentially developed for around two decades; it succeeded in surpassing 2 billion in 2005 and then 3 billion in 2013, and it reached even 4.5 billion passengers in 2019. Two factors have principally altered the international travel status: low airfares and a growing population of prosperous middle-class people. Air travel market share abruptly increased to 58% by 2019, which means 14% more than this number 20 years ago (UNWTO, 2020).

Suddenly in early 2020, the aviation industry encountered a critical point induced by the rapid outbreak and spread of COVID-19. After its primary identification in Wuhan, China, it started to spread to 218 countries in a short period (Lai et al., 2020). It infected over one million people worldwide by April 2020. Then, in November of the same year, 53 million people were identified as confirmed cases, with around 1.38 million deaths (World Health Organization, 2020a). Countries set up travel restrictions for their people, and, in addition to that, people were reluctant to travel, fearing COVID-19. This condition seriously and suddenly damaged the aviation and tourism sectors (Zahraee et al., 2022). Tourists were more likely to delay or cancel their travels to prevent them from being infected by COVID-19 (Reisinger and Mavondo, 2005). This stance has been internalized in the attitudes of today's tourists, and they are still trying to avoid high-risk destinations, which has an adverse impact on the tourism sector (Zhang et al., 2020). The existing research studies show that the global airline industry has been hit hard by the COVID-19 pandemic, and the demand for airline business has shrunk significantly. After the declaration of the pandemic by the World Health Organization (World Health Organization, 2020a) on 11 March 2020, many countries have closed their borders or issued travel bans in succession. Many international routes have been cancelled due to policy influences, and the aviation market has almost collapsed. Lange (2020) notes that at the worst point in April, two-thirds of the world’s fleet of aircraft was grounded, 90% of aviation business did not keep operation anymore, and even more so for international operations, where it was even more hovering around 98%. Airlines tried to restore routes in the shortest time, but the continuous outbreak of COVID-19 has made the disaster continue. Compared with September 2019, the number of flights of most airlines dropped significantly in September 2020, and the average reduction rate is 50%. The Revenue Passenger Kilometres (RPKs) of domestic routes initially decreased by 80% and then showed a clear upward trend (IATA, 2020b). The decline rate increased to 40% in September. The impact on cargo was even smaller, the lowest trough was only a 20% decline rate, and then it continued to rise. Finally, the cargo flight only dropped by about 10%.

Airlines still have to pay for aircraft storage and maintenance costs in a hostile environment and face cash flow interruption. The disparity between income and expenditure has deteriorated the economic situation of airlines. In March and April, international RPKs experienced a cliff-like decline, with a rate of decrease approaching 100%. Even in September, RPK was still at a low level, with a rate of change of around 89% (IATA, 2020a).

This work tries to fill the knowledge gap by bridging the operational cost, passengers’ safety, and satisfaction with the airlines by collecting the measures taken by 49 major airlines worldwide and collecting the responses through a questionnaire survey of the airline passengers. This work would determine the measures airlines have taken to deal with COVID-19 and analyze passengers' satisfaction with 22 measures in four stages: Pre-Flight, In-Flight, After-Arrival, and Others (Face mask requirement, HEPA filters, etc.). Based on the previous studies, it can be seen that most of the existing studies usually list which airlines have taken which measures without integrating with the passengers' perceptions. This paper explores the possibility of combining airlines' measures with passengers' satisfaction and thus provides robust evidence to airlines in their future decisions.

The rest of this article is structured as follows. The literature review section discusses the impact of different viruses and COVID-19 on the global aviation industry and the impact of travel restrictions on the tourism/aviation industry. The airline response measure and passengers’ satisfaction criteria are discussed. Then, the methodology section describes the method used and the data collected for the present paper. Afterwards, the empirical findings are presented and analysed in the results and discussion section. Finally, conclusions from the paper are presented.

2. Literature review

2.1. Effect of viruses and COVID-19 on the global aviation industry

According to scholarly findings, air transport has a considerable impact on spreading the pandemic worldwide (Wilder-Smith et al., 2003). In addition, several studies have maintained that airline travel has the potential to affect the spread of many viruses, e.g., Severe Acute Respiratory Syndrome (SARS) (McLean et al., 2005), influenza (Grais et al., 2003), and Ebola (Bogoch et al., 2015). SARS infected 37 countries (8000 cases), whereas the Middle East respiratory syndrome infected as many as 27 countries (2494 cases) (Oztig and Askin, 2020). In these cases, the transmission deteriorated partly by those who took flights at the time of the case. Another investigation asserted that the avian flu (H5N1) outbreak spread to about 60 countries, killed nearly 191 people, and reduced around 12 million tourist arrivals within the Asia Pacific (Wilder-Smith, 2006).

SARS negatively influenced people’s inclination to travel (Wen et al., 2005). This finding was confirmed by research conducted by Kuo et al. (2008), which reported a considerable shrinkage in the number of visitors arriving in SARS-affected countries. With the help of the econometric method, Rosselló et al. (2017) attempted to quantify the influence of various pandemics on visitors’ arrivals. Their findings showed that pandemics meaningfully decrease the number of visitors. For instance, the number of visitor arrivals decreased by 47% after the spread of malaria. Another study reported that foot and mouth diseases had reduced the tourism receipts in the UK (Blake et al., 2003). The above evidence clearly shows that aviation and tourism are two sectors of high vulnerability to infectious disease outbreaks because of their contact-intensive and face-to-face nature and the high mobility rate of both people and goods.

Though COVID-19 exceeded all the former pandemics (it spread to over 200 countries), the scientific reports indicated that the aviation industry significantly contributed to this situation (Sun et al., 2020). This disease led to an extraordinary economic disaster for airline operators on a global scale. In March 2020, the global traffic level fell by 21% compared to the same month in 2019. Then, an unexpected escalation was observed, resulting in more contraction as the global traffic levels dropped by 66% by April 2020 and declined by 69% by May (UNWTO, 2020). This considerable drop occurred when people became aware of the fact that the disease could kill infected people in a short time after infection. Estimations indicated that international tourist arrivals would decrease by 70% in 2020, that is, the loss of 700$ million in the number of visitors and 730$ billion loss in the inbound tourism market (UNWTO, 2020). Statistics indicate that, in 2020, the COVID-19 induced loss was eight times more than the loss during the Global Financial Crisis in 2008–2009 (UNWTO, 2020). The aviation sector suffered from the same loss; in 2020, the airline passenger revenues dropped by about 69% that accounting for around US$421 billion loss in comparison with 2019 (IATA, 2020b). The aggregated loss was estimated to be about US$118 billion, which is more than four times higher than the losses in this sector due to the global financial crisis of 2009 (IATA, 2020b). Historically, COVID-19 is recognized as the most serious threat to companies working in the aviation sector (Amankwah-Amoah, 2020); this impact could even last until 2024 (IATA, 2020c). Recovery may occur optimistically in mid-2022 or delay up to 2026 most pessimistically (Gudmundsson et al., 2020). Several scholars have even stated that the pandemic could cause the collapse of the entire international tourism market (Thams et al., 2020).

Table 1 summarizes several researchers who have paid attention to the impacts of COVID-19 on the air travel industry; however, the literature lacks empirical research on the influence of COVID-19 on operating costs and passengers’ safety and satisfaction.

Table 1.

Summary of COVID-19 investigation in the Aviation industry, including the present study.

Author	Objective	Approach	Time
Gallego and Font (2021)	To implement an approach for the early detection of reactivation of tourist markets to help mitigate the effects of the COVID-19 crisis, using Skyscanner data on air passenger searches	• Big Data	✓ November 2018–December 2020
Gössling et al. (2020)	To compare the effects of COVID-19 to previous epidemic/pandemics and other types of global crises and explores how the pandemic may change society, the economy, and tourism	• Review	✓ March 2020
Graham et al. (2020)	to assess the attitudes of ageing passengers by analysing air travel plans, examining the factors affecting future flying decisions, and evaluating the effect of the COVID-19 on perceived risks and experiences associated with flying	• Online survey of UK residents aged 65+	✓ 10 June 2020–15 June 2020
Hall et al. (2020)	To provide a comprehensive overview of pandemics and their effects	• Review	✓ April 2020
Iacus et al. (2020)	To collect and prepare data on air passengers traffic worldwide with the scope of analyze the impact of the travel ban on the aviation sector	• Historical Data	✓ First Quarter of 2020
Suau-Sanchez et al. (2020)	To estimate the medium- and long-term impacts of COVID-19 as seen within the aviation industry itself	• Empirical study	✓ January 2020– April 2020
Current Study	To investigate the effect of COVID-19 on operating costs and passengers’ safety and satisfaction.	• Empirical study	✓ 14 April 2021–7 May 2021

2.2. The effect of travel restrictions on the aviation/tourism industry

Both aviation and tourism industries have a high vulnerability to the outbreak of infectious diseases. With the start of COVID-19 spread among people globally, countries restricted travel and closed their borders to minimize the spread of the virus by limiting its import and export via tourists (Vaidya et al., 2020). Recent decades have witnessed increased affordability of air travel for people; consumers have been given a chance to select from numerous airlines. On the other hand, disease transmission could only be confined by limiting people’s travels and mobility in situations such as disease outbreaks. This typically prompts governments to enact the necessary legislation quickly.

As revealed by the reports UNWTO (2020), as many as 90 destinations partially or completely suspended inbound tourism, and 44 destinations closed their borders to certain countries of origin in case of the COVID-19 outbreak. Many governments have imposed lockdowns, travel bans, shutdowns, and stay-at-home directives to control the virus spread (Luo et al., 2020). However, these measures were taken into action in a highly uncoordinated and almost chaotic manner (Sun et al., 2020). Moreover, the inconsistent travel restrictions caused a significant reduction in the number of visitors intending to travel by air during the COVID-19 outbreak (Salari et al., 2020). According to UNWTO (2020), it was the first time international travel was limited in such a manner. Numerous people were either discouraged from travelling or informed that they could enter their destination country only if they followed a quarantine procedure lasting for up to 14 days at their own expense. Such conditions had wide-ranging consequences. As reported by Adrienne et al. (2020), by mid-April 2020, the air travel market dropped by 64%; meanwhile, around 17,000 aircraft were consigned to their shelters.

The travel restrictions mentioned above have obliged airlines to lower their flight operations as much as possible and cut costs. When the vaccination programs were implemented unevenly on a global scale, the accessible tools were confined to measures such as control and containment, including travel restrictions, quarantining, and social distancing (Petersen et al., 2020). The existing literature has empirically confirmed that travel restrictions and control measures can effectively minimize the spread of infectious viruses. The isolation of large cities played a significant role in controlling the SARS epidemic (Hufnagel et al., 2004). Brownstein et al. (2006) investigated the case of influenza spread in the United States and emphasized the prominence of flight restrictions. A significantly-delayed timeframe was reported before influenza peaked in 2001–2002 due to the reduction of flights. On the other hand, the conditions deteriorated in France, where any flight restriction was not imposed. Another study proposed a two-city dispersal model of avian influenza spread via air travel and asserted that control measures such as quarantine and isolation depend on the air travel rate, which refers to the proportion of air passengers in the population of the departure city. As a result, it can be said that restricting people’s travels plays an important role in decreasing the pandemic prevalence (Tuncer and Le, 2014).

On the other hand, some other studies indicate the ineffectiveness of travel restrictions in controlling the spread of infectious diseases. For instance, Cooper et al. (2006) showed that travel restrictions could not significantly delay the worldwide influenza pandemic spread because, initially, many people were infected, and the confirmed cases grew quickly. Likewise, another research argued that travel restrictions could lower the speed of virus spread only for less than 2–3 weeks (Ferguson et al., 2006). A population transmission model was used to investigate the relationships between travel restrictions and the COVID-19 spread (Chinazzi et al., 2020). According to their findings, the lockdown of Wuhan was not as effective as the global travel restrictions, which delayed the spread of the virus to other countries until mid-February 2020. Moreover, according to Borkowski et al. (2021), the virus can be spread on a domestic scale because of the regular daily mobility of people, such as going to school/work, carrying out social activities, and visiting hospitals. Nevertheless, this in not covered by the scope of the present paper that is mainly focused on the virus transmission via the cross-border flights. As a result, daily mobility of people at the domestic level is not incorporated into the analyses conducted in this research.

2.3. Effect of COVID-19 on passenger load and safety

From the perspective of global aviation, the industry net loss announced by airlines in 2020 was 118.5 billion, and RPKs & APK dropped by 66.3% & 57.6%, respectively. The passenger load factor is around 65.5%. The total number of flights in 2020 was 16.4 million, substantially lower than 38.9 million in 2019 (IATA, 2020b). In addition to the airline business affected, airline stock prices have also been hit by COVID-19. When Thailand reported the first case of infection outside China (January 13, 2020), the market did not show a significant decrease in accumulated abnormal returns. But with the outbreak of the Italian epidemic (February 21, 2020) and the WHO statement regarding the global pandemic, and the announcement of the US ban, travelers from 26 European countries/regions (March 11, 2020), global airline stock prices, fell sharply (Maneenop and Kotcharin, 2020). For some time in the future, the stock price will remain depressed. This phenomenon can be attributed to various factors: continuous blockades and traffic restrictions, cash consumption and downgrades of global rating agencies, and poor business prospects (Dube et al., 2021). In October and November of 2020, the aviation safety layer adopted the COVID-19 safety protocol and the arrival test at many destinations, coupled with the news of the successful development of the COVID-19 vaccine, which improved the airline’s performance and revenue. People's renewed trust in the aviation industry also increased stock prices (Dube et al., 2021).

2.4. Effect of COVID-19 on aviation-related industries

The Air Transport Action Group (ATAG) announced that 65.5 million jobs worldwide are supported by the aviation industry, including direct hiring of crew members, airport operators, airlines, etc. (IATA, 2020b). Service providers and indirect employment, such as fuel suppliers, construction companies, suppliers of aircraft companies, etc. The article proves that aviation is vital to every country's international trade and economic development (Serrano and Kazda, 2020). The disappearance of flights caused by the outbreak of COVID-19 has also dealt a heavy blow to these aviation-related industries. Air transportation depends on the upstream sector: airports, aircraft manufacturing, aircraft maintenance, etc. Usually, airlines and airports have a mutually beneficial relationship, and some airports rely heavily on one, two, or several companies that use it as a hub (OECD, 2020a). Aviation manufacturing and maintenance orders depend on the daily operation and loss of aircraft, and few flights make many aircraft grounded. Therefore, airlines must cancel aircraft purchase plans and reduce maintenance expenditures.

The downstream air transportation sectors rely on the flow of people and goods to stimulate economic activities, such as duty-free shops at airports, tourism, hotels, etc. The promulgation of travel bans and flight restrictions has made the number of visitors low. In transportation, aviation and other modes of transportation are interchangeable. For instance, with a well-developed railway network in China, high-speed rail can allow passengers to maintain a safe social distance while transporting many passengers. Although wide-body jets can still meet the conditions, airlines have to undertake higher costs. Besides, the number of destinations is limited by whether the landing airport is qualified to land large passenger aircraft. The air transport industry also faces increasing market pressure brought about by the increased availability of Internet connections. The connectivity established by video conferencing does not pose health risks associated with passenger transportation (Peoples et al., 2020).

2.5. Airline's response measures

Albers and Rundshagen (2020) made statistics on European airlines' strategic responses to COVID-19 from January to May 2020. The types of responses include retrenchment, persevering, innovating, and exit. The classification of response measures is based on the research of Wenzel et al. (2020) on the response plan of enterprises during the COVID-19 pandemic. Almost all airlines have made layoff decisions (Albers and Rundshagen, 2020), and some airlines have begun to retire their aircraft earlier than scheduled (Budd et al., 2020). Airlines have also made some countermeasures for passengers, such as requesting social distance between passengers and reducing physical contact between people. Some major airlines have introduced more flexible refund and change policies, enhancing their competitiveness in the aviation market (Chevtaeva and Guillet, 2021). Hygiene measures, mask use, and distancing have been proved effective in preventing coronavirus, while temperature screening was unreliable. Besides, in-airport rapid tests with telemedicine and facilities would be the appropriate future strategy at airports (Bielecki et al., 2020). Qatar Airways implements state-of-the-art safety and health measures, including personal protective equipment (PPE) for crew members, free protective kits, and disposable face masks for passengers. In addition, the airline is the first to deploy Honeywell International airlines with ultraviolet (UV) cabin systems that have further promoted sanitary measures on board (Athena Information Solutions Pvt. Ltd, 2020).

There are essential factors for passengers, such as safety and security, customer service, driver friendliness, and the quality of the passenger environment (Batarce et al., 2022). Airlines try new boarding methods to reduce or avoid interactions between passengers. By measuring the performance indicators related to the health of passengers and the boarding time indicators of single-door aircraft, they have evaluated the best use method at present, but due to the cost the time is too long, airlines are also placing greater emphasis on fast boarding times new methods (Milne et al., 2021). Many airlines have also begun to seek help from the government because of the COVID-19 pandemic. The government's assistance to airlines includes supporting loans, helping with capital restructuring, nationalization, and providing flight subsidies. In addition, the government's help may make the competition between airlines unfair, which is what the government needs to consider when assisting airlines (Abate et al., 2020). The main measures taken by the airlines against the business itself are mainly aimed at reducing expenses. As for the passengers, the airlines mainly aim to ensure their flight safety and avoid the spread of the pandemic on their flights. These previous studies are relevant to this report and will be contrasted with the findings of this report in anticipation of more accurate conclusions.

2.6. Passenger satisfaction with airlines

A search of Twitter keywords measured passenger satisfaction with individual airline flight cancellations, refunds, and other measures during the COVID-19 outbreak, with Southwest Airlines, for example, earning high scores (Monmousseau et al., 2020). By mid-October last year, most of the world's 20 major airlines had introduced mask and temperature testing requirements at the Pre-Flight stage, but less than half of them offered hygiene kits. Most airlines continue to require masks and Apply HEPA Filters during the In-Flight stage, and most airlines have set restrictions on the meals they serve (Bielecki et al., 2020). Passengers of different age groups expressed different levels of satisfaction with various aspects of airline service quality, with older passengers being more satisfied with international air travel services than younger passengers. In terms of gender, the results show that at the 10% significance level, male passengers are more satisfied with the safety and security of airlines than female passengers. The ANOVA test was used to determine whether there was a significant difference (Clemes et al., 2008).

3. Methodology

The critical design method of this research is an induction based on mixed data utilization, which includes identifying the research aim, questions, and scope; conducting a literature review and finding gaps; data collection and data analysis; getting results and discussing results. Besides, the theoretical framework is achieved by integrating literature sources. It consists of COVID-19 responses taken by the major airlines worldwide, an analysis of passengers’ differences in perception of measures adopted by airlines, and an evaluation of the responses. The ethics clearance has been acquired from the university. The questionnaires are all voluntarily completed by participants.

Data used in the report are both quantitative and qualitative. Quantitative data refers to passengers’ satisfaction among different responses, which is the primary data collected by the questionnaire. As it is not feasible to send the questionnaire to airlines and obtain their responses against COVID-19, the research collects 22 responses from passengers. The questionnaire used in this study is presented in Appendix. The 22 constructs were developed by considering the guidelines of regulators (e.g., Civil Aviation Safety Authority, Civil Aviation Administration of China, International Air Transport Association), and some other research in the aviation field, such as Bureau, 2020, Dube et al., 2021, Bielecki et al., 2020, Czerny et al., 2021, and the international air transport rating organisation such as Skytrax (2021). These 22 responses were widely adopted by Chinese airlines and implemented in four stages: Pre-Flight, In-Flight, After-Arrival, and Others (Face mask requirement, HEPA filters, etc.). The questionnaire was sent to passengers and asked for their satisfaction with each response. The choices of each response are from 1 to 5, representing the most negative attitude to the most positive attitude. The questionnaire survey was conducted online from April 14, 2021, to May 7, 2021, and the survey was mainly distributed through Chinese social media platforms such as WeChat.

The questionnaire collects passengers’ basic information under 12 items to determine whether there are differences between different groups of passengers (Age, Occupation, etc.). Finally, 449 of 500 questionnaires were considered meaningful after excluding repetitive and blank questionnaires. Qualitative data include categories of airline responses, which are the secondary data collected from three sources: airlines' official websites, airlines' annual reports, and credible internet sources. Keywords searching is the main qualitative data collection method (COVID-19, bankruptcy, layoff, etc.). Also, the annual reports of some airlines have a specific COVID-19 section from which the data could also be collected. This resulted in the systematic collection of airlines' responses to the COVID-19 pandemic from 49 airlines.

As for data analysis, IBM SPSS was applied to analyze quantitative data on passengers’ satisfaction. The quantitative analysis focused on two parts: one was to analyze average passengers’ satisfaction with each response by comparing the means of each response, and the other was to figure out whether there are differences between different groups of passengers on the same responses by using the One-Way ANOVA Test. One-Way ANOVA test consists of a series of tests to ensure completeness and preciseness, including Variance Homogeneity Test, ANOVA Test, Welch & Brown-Forsythe Test, and Tamhane T2 Post Hoc Tests in sequential. Homogeneity of variance is assessed using Levene's Test for Equality of Variances. In order to meet the assumption of homogeneity of variance, the p-value for Levene's Test should be above 0.05. If Levene's Test yields a p-value below 0.05, then the assumption of homogeneity of variance has been violated. ANOVA test is a type of statistical test used to determine if there is a statistically significant difference between two or more categorical groups by testing for differences of means using a variance. Another Key part of ANOVA is that it splits the independent variable into 2 or more groups. Welch and Brown-Forsythe ANOVA compares three or more sets of unpaired measurements (data expressed using an interval or ratio scale), assumed to be sampled from a Gaussian distribution but without assuming that the groups have equal variances. The tests of between-subjects effects help in determining the importance of a factor; therefore, the Tamhane T2 Post hoc test is used. It reveals the differences in model-predicted means for each pair at factor levels (Ross and Willson, 2018). When it comes to qualitative analysis of responses taken by airlines, responses are firstly fit into a table to directly show what responses have been taken by each airline in different areas. Then, responses are classified into three groups (reduce the operation cost, ensure the safety and interests of passengers, and improve the company's income) to illustrate the common-adopted responses of airlines in different areas.

4. Results and discussion

4.1. Impact of pandemic policies on airlines and passengers

The whole aviation industry was affected by COVID-19. The change in the behaviour of passengers following the COVID-19 crisis and travel restrictions resulted in a dramatic drop in demand for airline services. The cost of health-related measures and operating costs are likely to increase in the short-run for both airlines and airports because of additional health and safety requirements (e.g. disinfection, PPE, temperature checks or viral tests) before they can be passed on to consumers. Moreover, social distancing measures (if implemented for air transport) could force a reduction in the passenger load factor (i.e. the number of seats that can be occupied during a flight) by up to 50% (OECD, 2020b).

Both the airlines and passengers are affected by the pandemic policies, which are obviously different among countries. Mainland China, for example, had almost full control of the pandemic and tried to contain the virus at a relatively early stage. In many other countries, the target is to “flatten the curve”, so that the outbreak is contained at a level that the healthcare system can handle while essential economic activities can be restored early. As a result, the aviation industry should focus on preventing infection at airports and on-board aircraft, with capacity and flight frequency “reactively” adjusted in response to travel demands (Czerny et al., 2021).

Following the COVID-19-induced crisis, British Airways (BA) decided to bring forward its decision to discontinue Boeing 747 fleets as part of its recovery strategy. The airliner was once dubbed the “Queen of the Skies”, the “most recognizable” among the public as well as the preferred choice of global airlines for long-haul routes (Specia, 2020). Likewise, Cathay Pacific reassured their customers of measures being taken by highlighting the “intensifying disinfection of aircraft after landing, making cabin crews don gloves and masks, removing blankets, magazines and pillows, and adding safeguards to the in-flight food and drink service” (Lee, 2020).

As the crisis unfolded, many airlines started moving towards introducing some elements of in-flight social distancing, compulsory temperature checks and demanding that passengers put on masks (Lee, 2020). In line with WHO’s Guide to Hygiene and Sanitation in Aviation, some of the operational responses emphasized enhanced cleaning and disinfection, which covers airports and service providers (World Health Organization, 2020b). In addition, it re-emphasized post-event cleaning procedures and disinfecting contaminated surfaces following notification of suspected cases (World Health Organization, 2020b).

In an attempt to avert a second outbreak in China, the government limited inter-China flights for both Chinese and foreign airlines by allowing just one flight a week, and each flight was not to exceed 75% capacity (BBC, 2020). The International Air Transport Association, in collaboration with the World Health Organization, have developed guidelines to guide cabin crew and airport workers, e.g. captains are required to inform air traffic control of suspected communicable disease (IATA, 2020c). The Chinese government attempt to micro-manage the airlines and airport services to achieve their policy objectives and to deal with the conflicting needs for improving international connectivity for economic/social reasons and tightly controlling the spread of COVID-19 virus cases (Czerny et al., 2021).

4.2. Airlines’ strategic responses to the COVID-19 pandemic

After an investigation of the response of 49 airlines to the outbreak of COVID-19, a conclusion can be reached that airlines’ responses to the outbreak of COVID-19 can be broadly classified into three categories according to their purpose: to reduce the operation cost, to ensure the safety and interests of the passengers and to improve the income and cash of the company. Table 2 summarizes the different responses of airlines to the COVD-19 pandemic by classifying airline responses into 9 categories. In the table, if a response has been taken to a particular category, it is referred to as “Y”, or else it is left blank. According to Table 2, it is concluded that reducing flights is a measure taken by all airlines to reduce operating costs. This is arguably what airlines have been forced to do because of a sharp drop in passenger travel demand during the outbreak. Most of the airlines in the survey have reduced the salary of their employees or furloughed their workers to reduce labour costs. By contrast, fewer airlines lay off workers. In fact, most airlines reduced their staff numbers by taking many measures, including laying off. As a result, the number of staff of almost all airlines worldwide decreased. Many employees are retrained, which is possible because many airlines believe the outbreak of the COVID-19 pandemic will have a limited impact on the aviation market after the end of the epidemic and cutting too many jobs is not suitable for their future growth. Among the airlines surveyed, many in Europe and the Americas have opted to retire aircraft early to save operating costs. By contrast, few of the airlines in East Asia took the step of decommissioning their aircraft. Many airlines' annual reports show that many airlines did not stop buying planes during the COVID-19 outbreak.

Table 2.

Airlines' different responses to the COVID-19 pandemic (Source: authors’ compilation of 49 major airlines’ annual reports in 2020).

Methods: Lay off Retire/Ground aircraft Cut pay level Government/institution support Cut/Suspend flights Add passenger’s safety measures Change/improve the change fee system Add cargo flights Bankruptcy/Corporate Restructuring
	1	2	3	4	5	6	7	8	9
Asia:
Aeroflot				Y	Y	Y	Y
AirAsia	Y	Y	Y	Y	Y	Y	Y	Y
Asiana			Y	Y	Y			Y
Air China					Y	Y	Y	Y
All Nippon Airways			Y	Y	Y
China Eastern Airlines			Y		Y	Y		Y
Cathay Pacific	Y	Y	Y		Y
China southern Airlines			Y		Y		Y	Y
EVA Air					Y	Y		Y
Emirates			Y		Y	Y	Y
Etihad Airways					Y	Y			Y
Hainan Airlines				Y	Y		Y		Y
Indigo	Y	Y	Y		Y	Y	Y
Juneyao Airlines					Y		Y	Y
Japan Airlines			Y	Y	Y		Y	Y
Korean Air			Y	Y	Y	Y		Y
Malaysia Airlines			Y		Y	Y			Y
Oman Air					Y	Y	Y	Y
Royal Jordanian					Y	Y
Thai airways			Y		Y	Y			Y
Spring airlines			Y		Y		Y
Singapore Airlines		Y	Y	Y	Y		Y
Vietnam airline					Y	Y		Y
Vistara	Y	Y	Y		Y	Y	Y
Europe:
Air France	Y		Y	Y	Y	Y
Air Portugal		Y	Y		Y	Y	Y
Austrian Airlines		Y	Y		Y	Y	Y	Y
British Airways	Y	Y	Y	Y	Y	Y
EasyJet			Y		Y	Y
Euro wings		Y			Y
KLM	Y		Y	Y	Y
Lufthansa	Y		Y		Y	Y	Y
Turkish Airlines					Y	Y	Y
Americas:
Aero Mexico					Y				Y
American Airlines		Y	Y	Y	Y	Y		Y
Air Canada	Y		Y		Y	Y	Y
Air Transat	Y	Y	Y	Y	Y	Y			Y
Allegiant			Y	Y	Y	Y
Delta Airlines	Y	Y	Y	Y	Y	Y
Hawaii Airline		Y	Y	Y	Y	Y		Y
JetBlue	Y	Y	Y	Y	Y	Y
Latam airlines			Y		Y	Y
Southwest Airlines		Y	Y	Y	Y	Y	Y
United airlines	Y		Y	Y	Y	Y
Africa:
Air Mauritius			Y		Y			Y	Y
Ethiopian airlines					Y	Y	Y
Rwanda Air			Y		Y
Oceania
Air New Zealand	Y		Y	Y	Y		Y	Y	Y
Qantas	Y	Y	Y	Y	Y

According to the survey, Asian airlines are more likely than their United States and Europe counterparts to increase their revenue by adding more cargo planes or converting passenger flights to freighters. This is because the industry in the Asia region has recovered quickly, and demand for cargo has increased, giving airlines a chance to make temporary changes. Not many airlines surveyed successfully sought financial assistance from governments and institutions, accounting for about 15 percent of all the airlines surveyed. But more than half of the carriers surveyed in the Americas have successfully sought financial assistance from governments or agencies.

As for the safety of passengers during the epidemic, the vast majority of airlines have taken more or fewer measures. Almost all airlines have put in place comprehensive measures to ensure passenger safety. Due to the different levels of bans issued by various countries during the epidemic, the flight cancellation rate increased significantly. To safeguard the interests of passengers, the vast majority of investigated airlines have introduced more humane ticket cancellation and change services. In particular, airlines in Asia, where nearly 80 percent of the airlines have similar policies to meet passenger travel needs. Only a handful of the airlines in the survey, less than 10 percent, declared bankruptcy or restructured. The vast majority of airlines did not stop operating and continued to do their business.

In the following sub-sections, the details of responses are discussed.

4.2.1. Reduce the operation cost

Due to the impact of the outbreak of the COVID-19 pandemic, there has been a significant decrease in the number of passengers who travel by air, leading to a dramatic decline in all airlines worldwide. To reduce operating costs to tide over COVID-19 epidemic, airlines have taken different measures to reduce operating costs during the epidemic. The main ways to reduce operating costs include layoffs, wage cuts, early decommissioning of aircraft, postponing aircraft orders, and significantly decreasing flight schedules. Layoffs and wage cuts are the fastest way for airlines to reduce operating costs. The drastic reduction in flight schedules is something the airlines have been forced to do.

4.2.2. Ensure the safety and interests of the passengers

As the COVID-19 epidemic gets more and more serious, airlines have to take strict measures to ensure the safety and interests of passengers to maintain their satisfaction of passengers. To maintain the safety of passengers before check-in, on aircraft, and after landing, many airlines have strengthened the training of their staff and provided passengers with necessary medical supplies. Some airlines also cooperate with medical institutions to provide travellers with epidemic prevention services. As for protecting the interests of passengers, many airlines have introduced more flexible ticket cancellations and rebooking policies to ensure passengers' normal travel.

4.2.3. Increase the revenue and cash flow of the company

Many airlines are looking for more ways to increase revenue and cash flow to keep operating the business during the epidemic and lay the foundation for market recovery. Some airlines tried to turn their passenger flights into cargo flights. The revenue from flying thousands of cargo flights has greatly eased their financial constraints. Some airlines have increased cash flow by raising capital and seeking government assistance. These assistances can help airlines increase their cash flow temporally.

4.3. Average satisfaction score of responses

This section analyses the average satisfaction score of different airline responses, including an analysis of each stage and the overall analysis. The data was collected from 449 participants in the questionnaire. The average score from the participants to each response is shown in Table 3, Table 4 . Table 3 contains the average score for four stages, while Table 4 illustrates the satisfaction ranking score for all 22 responses.

Table 3.

Satisfaction Ranking (each stage).

Stage	Response	Score	Rank	Average
Pre-Flight	Provide hygiene products for passengers and staff	4.09	1	3.90
	A thermal scanner to monitor body temperature during check-in	4.07	2
	Regularly check the health of employees	4.00	3
	Keep a safe distance when boarding	3.97	4
	The COVID-19 nucleic acid negative certificate is required to allow boarding	3.96	5
	Each passenger needs to be seated one seat apart	3.77	6
	Adopt a special boarding method, such as boarding in the order from back to front	3.68	7
	Protective clothing is required to board the plane	3.66	8
In-Flight	Temperature monitoring on the plane	3.99	1	3.85
	Masks are required throughout the flight	3.93	2
	Apply HEPA filters on the aircraft (remove over 99.97% of particles characterized by diameter of 0.3 μm or larger)	3.93	3
	Social distancing is required on the plane	3.88	4
	Each passenger needs to be seated one seat apart	3.77	5
	It is not allowed to line up to go to the toilet, and the crew will disinfect the toilet after every-one has used the toilet	3.73	6
	No in-flight meals and drinks (only snacks and water)	3.72	7
After-Arrival	Disinfect the cabin after each flight, even for a previous flight of the connecting flight	4.06	1	3.99
	Crew members take 14 days of isolation after working on flights passing through risk areas	4.01	2
	Disembark in batches	3.88	3
Others	Standard of COVID-19 travel information	4.04	1	4.00
	Free refund and change policy	4.02	2
	Flight frequency has dropped, while on-time rate has risen	4.00	3
	The latest information on COVID-19 is visible on the airlines' official websites	3.99	4

Table 4.

Satisfaction Ranking (All).

Response	Score	Rank
Provide hygiene products for passengers and staff	4.09	1
A thermal scanner to monitor body temperature during check-in	4.07	2
Disinfect the cabin after each flight, even for a previous flight of the connecting flight	4.06	3
Standard of COVID-19 travel information	4.04	4
Free refund and change policy	4.02	5
Crew members take 14 days of isolation after working on flights passing through risk areas	4.01	6
Regularly check the health of employees	4.00	7
Flight frequency has dropped, while on-time rate has risen	4.00	8
Temperature monitoring on the plane	3.99	9
The latest information on COVID-19 is visible on the airlines' official websites	3.99	10
Keep a safe distance when boarding	3.97	11
The COVID-19 nucleic acid negative certificate is required to allow boarding	3.96	12
Masks are required throughout the flight	3.93	13
Apply HEPA filters on the aircraft (remove over 99.97% of particles characterized by diameter of 0.3 μm or larger)	3.93	14
Social distancing is required on the plane	3.88	15
Disembark in batches	3.88	16
Quick health test before boarding	3.77	17
Each passenger needs to be seated one seat apart	3.77	18
It is not allowed to line up to go to the toilet, and the crew will disinfect the toilet after every-one has used the toilet	3.73	19
No in-flight meals and drinks (only snacks and water)	3.72	20
Adopt a special boarding method, such as boarding in the order from back to front	3.68	21
Protective clothing is required to board the plane	3.66	22

The satisfaction score of the after-arrival and additional preventive measures, which are rated at 3.99 and 4.00, is higher than the pre-flight and in-flight periods. The lowest score, rated as 3.85, is shown in the in-flight period.

Providing hygiene products for passengers and staff, with an average rating of 4.09, is most acceptable to participants at the pre-flight stage. However, the participants are most unsatisfied with wearing protective clothing and adopting the boarding order, with an average rating of 3.66 and 3.68, respectively. Based on the average satisfaction score of in-flight responses, the participants' satisfaction with temperature monitoring gets the highest rating of 3.99. But for suspending the in-flight services, such as meals and drinks, and the restriction of toilet use, the participants provide the lowest rating of 3.72 and 3.73, respectively.

With regard to the after-arrival response, the participants are most satisfied with the disinfection of the cabin, and the average score is 4.06. The batched disembarkation is rated only 3.88, the lowest average score in the after-arrival response. As for other stages, the standard of COVID-19 travel information is rated 4.04, the highest average score in the table. The latest COVID-19 details on the official website get the lowest score, a rating of 3.99.

Overall, in Table 4, participants are most satisfied with the supply of hygiene products during the pre-flight period and a rate of 4.09. Wearing protective clothing is the most unsatisfactory measure among 22 responses, which only get a 3.66 score.

4.4. Difference between passengers on satisfaction

This section aims to determine whether there are differences between the satisfaction of different passengers’ groups on responses at four stages (Pre-Flight, In-Flight, After-Arrival, and Others). One-Way ANOVA Test analysis is applied in addressing this question. As the number of participants is 449, larger than 385, the test is regarded as meaningful at a 95% confidence level. The first step of One-Way ANOVA is to test the Homogeneity of Variances. Groups that meet the homogeneity of variance test (Sig. > 0.05) will be able to perform the subsequent ANOVA Test, while groups that violate the homogeneity of variance test (Sig. ≤ 0.05) will perform Welch & Brown-Forsythe Test instead of the ANOVA Test.

Table 5 shows the groups of indicators that meet the homogeneity of variance test (Sig. > 0.05). Gender, Nationality, Travel Frequency, and Education all meet the test of Homogeneity of Variances at all four stages (Pre-Flight, In-Flight, After-Arrival, and Others). In addition, Occupation, Income, and Travel Purpose all meet the Sig. > 0.05 condition only at the After-Arrival stage. The Frequent Flyers Points (FFP) members group meets the condition at all three stages except Pre-Flight. Finally, the Cabin Selection group only had Sig. > 0.05 at the other stage. Hence, the ANOVA test will be used to test these groups to see if there is a significant difference.

Table 5.

Test of Homogeneity of Variances (Sig. > 0.05).

Test of Homogeneity of Variances	Std. Deviation								F	Sig.
Gender	Type	Male (N = 206)				Female (N = 243)
	Pre-Flight	0.85				0.93			1.604	0.206
	In-Flight	0.94				0.95			0.013	0.910
	After Arrival	0.91				0.91			0.331	0.565
	Others	0.86				0.92			1.410	0.236
Nationality	Type	Chinese (N = 447)				Non-Chinese (N = 2)
	Pre-Flight	0.90				0.71			0.072	0.788
	In-Flight	0.94				1.11			0.042	0.837
	After Arrival	0.91				0.24			1.149	0.284
	Others	0.89				0.00			1.972	0.161
Occupation	Type	Student (N = 63)	Business (N = 246)	Owners (N = 44)	Government (N = 34)	Private (N = 46)	Others (N = 7)	Retired (N = 9)
	After Arrival	0.69	0.98	0.98	0.82	0.9	0.49	0.5	1.066	0.382
Income	Type	≤3000 (N = 64)		3001–10 K (N = 252)		101000–20 K (N = 86)		>20 K (N = 47)
	After Arrival	0.7		0.97		0.79		0.98	1.343	0.26
Age	Type	≤18 (N = 7)	18–30 (N = 180)	31–40 (N = 105)	41–50 (N = 94)	51–80 (N = 61)		≥61 (N = 2)
	In-Flight	0.92	0.95	0.93	0.88	1.06		1.21	1.639	0.328
	After Arrival	1.36	0.87	0.86	0.95	0.98		0.24	0.805	0.425
Travel Frequency	Type	0–1 (N = 112)	2–4 (N = 185)	5–10 (N = 69)	51–60 (N = 61)	≥ 61 (N = 2)
	Pre-Flight	0.87	0.88	0.85	1.01	0.99			0.913	0.456
	In-Flight	0.88	0.92	0.93	1.03	1.26			1.859	0.117
	After Arrival	0.92	0.86	0.97	1.02	0.63			1.178	0.32
	Others	0.93	0.83	1.01	0.94	0.49			1.991	0.095
Education	Type	Senior High School or Lower (N = 47)		College (N = 160)		Bachelor (N = 211)	Master and Above (N = 31)
	Pre-Flight	1.07		0.94		0.81	0.98		2.062	0.105
	In-Flight	0.99		0.96		0.91	0.97		0.035	0.991
	After Arrival	0.97		1.00		0.85	0.66		1.957	0.12
	Others	0.76		0.98		0.85	0.95		2.531	0.057
Travel Purpose	Type	Business (N = 47)	Visiting family (N = 107)		Holiday (N = 186)	Study (N = 62)	Others (N = 6)
	After Arrival	0.95	1.01		0.78	1.04	0.69		1.942	0.102
FFP members	Type	Yes (N = 170)				No (N = 279)
	In-Flight	1.01				0.9			2.385	0.123
	After Arrival	0.94				0.89			0.04	0.842
	Others	0.93				0.88			0.503	0.479
Cabin Selection (class)	Type	Economy (N = 219)			Business (N = 140)	First (N = 90)
	Others	0.82			0.95	0.98			0.534	0.587

In contrast to Table 5, Table 6 shows which groups did not meet the test of Homogeneity of Variances (Sig. < 0.05). Groups of Travelled or not after COVID-19 and Travel Frequency (After COVID-19) do not satisfy the test of Homogeneity of Variances at any of the four stages. The data from the other groups are also presented below, all of which will be subjected to the Welch & Brown-Forsythe Test to determine if there are significant differences.

Table 6.

Test of Homogeneity of Variance (Sig. ≤ 0.05).

Test of Homogeneity of Variances	Std. Deviation								F	Sig.
Occupation	Type	Student (N = 63)	Business (N = 246)	Owners (N = 44)	Government (N = 34)	Private (N = 46)	Others (N = 7)	Retired (N = 9)
	Pre-Flight	0.34	1.01	0.98	1.06	0.65	3.86	0.25	9.536	<0.001
	In-Flight	0.45	0.96	1.12	0.12	1.07	0.37	0.33	7.238	<0.001
	Others	0.5	0.93	0.84	1.19	1.01	0.49	0.45	4.463	<0.001
Income	Type	≤3000 (N = 64)		3001–10 K (N = 252)		101000–20 K (N = 86)		>20 K (N = 47)
	Pre-Flight	0.36		0.95		0.84		1.16	11.739	<0.001
	In-Flight	0.48		0.96		0.92		1.27	13.425	<0.001
	Others	0.5		0.94		0.98		0.91	4.461	0.004
Age	Type	≤18 (N = 7)	18–30 (N = 180)	31–40 (N = 105)	41–50 (N = 94)	51–80 (N = 61)		≥61 (N = 2)
	Pre-Flight	0.25	0.83	0.92	0.8	1.18		0.88	5.403	<0.001
	Others	0.19	0.77	0.94	1.06	0.92		0.88	3.460	0.002
Travel Purpose	Type	Business (N = 47)	Visiting family (N = 107)	Holiday (N = 186)	Study (N = 62)	Others (N = 6)
	Pre-Flight	1.17	0.94	0.77	0.73	0.39			10.174	<0.001
	In-Flight	1.07	1.05	0.82	0.94	0.35			4.044	0.003
	Others	1.01	0.99	0.72	1.01	0.78			3.881	0.004
FFP members	Type	Yes (N = 170)				No (N = 279)
	Pre-Flight	0.01				0.82			9.913	0.002
Travelled or not after COVID-19	Type	Yes (N = 170)				No (N = 279)
	Pre-Flight	0.96				0.3			26.579	<0.001
	In-Flight	1				0.44			19.903	<0.001
	After Arrival	0.95				0.57			6.231	0.013
	Others	0.94				0.49			9.647	0.002
Travelled frequency after COVID-19	Type	0–1 (N = 123)		2–4 (N = 105)		5–10 (N = 135)	≥11 (N = 86)
	Pre-Flight	0.65		0.8		1.09	0.97		12.966	<0.001
	In-Flight	0.76		0.88		1.06	1.06		5.26	0.001
	After Arrival	0.8		0.93		0.82	1.13		4.450	0.004
	Others	0.62		0.97		1.07	0.8		11.786	<0.001
Cabin Selection (class)	Type	Economy (N = 219)			Business (N = 140)	First (N = 90)
	Pre-Flight	0.74			1.00	1.07			8.776	<0.001
	In-Flight	0.64			1.20	1.06			47.824	<0.001
	After Arrival	0.93			0.81	0.99			4.355	0.013

After the Test of Homogeneity of Variances, all groups are distributed to the corresponding ANOVA Test or Welch & Brown-Forsythe Test. Same as the previous test, groups are regarded as having no significant difference if Sig. > 0.05, and regarded as having a significant difference if Sig. ≤ 0.05. Then, the Tamhane T2 Post Hoc Test is performed on groups having significant differences to figure out the exact group pairs that are different. In the ANOVA test, all significance is greater than 0.05, which means that there is no significant difference between each group in the data taken for the ANOVA test (Table 7 ).

Table 7.

ANOVA Test.

Test of Homogeneity of Variances	Std. Deviation								F	Sig.
	Pre-Flight	3.91 ± 0.85				3.89 ± 0.93			0.118	0.731
	In-Flight	3.89 ± 0.94				3.82 ± 0.95			0.535	0.465
	After Arrival	3.98 ± 0.91				4.00 ± 0.91			0.055	0.815
	Others	4.05 ± 0.86				3.98 ± 0.92			0.665	0.415
Nationality	Type	Chinese (N = 447)				Non-Chinese (N = 2)
	Pre-Flight	3.90 ± 0.90				3.38 ± 0.71			0.686	0.408
	In-Flight	3.86 ± 0.94				3.21 ± 1.11			0.923	0.337
	After Arrival	3.99 ± 0.91				3.50 ± 0.24			0.574	0.449
	Others	4.02 ± 0.89				3.00 ± 0.00			2.575	0.109
Occupation	Type	Student (N = 63)	Business (N = 246)	Owners (N = 44)	Government (N = 34)	Private (N = 46)	Others (N = 7)	Retired (N = 9)
	After Arrival	4.04 ± 0.69	3.93 ± 0.98	3.97 ± 098	4.09 ± 0.82	4.04 ± 0.9	4.38 ± 0.49	4.19 ± 0.5	0.331	0.565
Income	Type	≤3000 (N = 64)		3001–10 K (N = 252)		101000–20 K (N = 86)		>20 K (N = 47)
	After Arrival	4.06 ± 0.7		3.96 ± 0.97		4.13 ± 0.79		3.78 ± 0.98	1.739	0.158
Age	Type	≤18 (N = 7)	18–30 (N = 180)	31–40 (N = 105)	41–50 (N = 94)	51–80 (N = 61)		≥61 (N = 2)
	In-Flight	3.84 ± 0.92	3.8 ± 0.95	3.93 ± 0.93	3.91 ± 0.88	3.81 ± 1.06		3.29 ± 1.21	0.515	0.765
	After Arrival	3.57 ± 1.36	3.93 ± 0.87	4.03 ± 0.86	4 ± 0.95	4.11 ± 0.98		3.5 ± 0.24	0.823	0.534
Travel Frequency	Type	0–1 (N = 112)	2–4 (N = 185)	5–10 (N = 69)	51–60 (N = 61)	≥ 61 (N = 2)
	Pre-Flight	3.85 ± 0.87	3.88 ± 0.88	4.01 ± 0.85	3.95 ± 1.01	3.77 ± 0.99			0.4860	0.746
	In-Flight	3.82 ± 0.88	3.84 ± 0.92	3.94 ± 0.93	3.96 ± 1.03	3.38 ± 1.26			1.313	0.264
	After Arrival	3.96 ± 0.92	4.03 ± 0.86	3.96 ± 0.97	3.98 ± 1.02	3.76 ± 0.63			0.356	0.84
	Others	3.86 ± 0.93	4.06 ± 0.83	4 ± 1.01	4.09 ± 0.94	4.21 ± 0.49			1.274	0.279
Education	Type	Senior High School or Lower (N = 47)		College (N = 160)		Bachelor (N = 211)	Master and Above (N = 31)
	Pre-Flight	3.82 ± 1.07		3.94 ± 0.94		3.89 ± 0.81	3.84 ± 0.98		0.276	0.843
	In-Flight	3.95 ± 0.99		3.91 ± 0.96		3.78 ± 0.91	3.93 ± 0.97		0.827	0.479
	After Arrival	4.01 ± 0.97		3.92 ± 1.00		4 ± 0.85	4.17 ± 0.66		0.356	0.54
	Others	4.11 ± 0.76		3.92 ± 0.98		4.05 ± 0.85	4.06 ± 0.95		0.888	0.447
Travel Purpose	Type	Business (N = 47)	Visiting family (N = 107)		Holiday (N = 186)	Study (N = 62)	Others (N = 6)
	After Arrival	3.97 ± 0.95	3.93 ± 1.01		4.08 ± 0.78	3.85 ± 1.04	3.83 ± 0.69		1.021	0.396
FFP members	Type	Yes (N = 170)				No (N = 279)
	In-Flight	3.84 ± 1.01				3.86 ± 0.9			0.037	0.847
	After Arrival	4 ± 0.94				3.98 ± 0.89			0.043	0.836
	Others	3.99 ± 0.93				4.02 ± 0.88			0.136	0.713
Cabin Selection (class)	Type	Economy (N = 219)			Business (N = 140)	First (N = 90)
	Others	4 ± 0.82			4.02 ± 0.95	4.04 ± 0.98			0.103	0.902

Table 8 illustrates groups that take Welch & Brown-Forsythe Test. Table 7 and Table 8 illustrate groups with no significant difference (Sig. > 0.05). Travel Purpose is the only indicator that does not have a significant difference in all four stages. Travel Frequency (After COVID-19) has no significant difference in Pre-Flight, In-Flight, and Others. Occupation, Income, and Travelled After COVID-19 are indicators that do not significantly differ in the same three stages (Pre-Flight, In-Flight, and After-Arrival). Besides, Age, FFP members, and Cabin Selection showed a similar trend in that they do not significantly differ in Pre-Flight.

Table 8.

Welch & Brown-Forsythe Test (Sig. > 0.05).

Test of Homogeneity of Variances		Std. Deviation								F	Sig.
Occupation	Type		Student (N = 63)	Business (N = 246)	Owners (N = 44)	Government (N = 34)	Private (N = 46)	Others (N = 7)	Retired (N = 9)
	Pre-Flight	Welch	3.90 ± 0.34	3.84 ± 1.01	3.93 ± 0.98	3.86 ± 1.06	4.24 ± 0.65	3.86 ± 0.37	3.89 ± 0.25	2.061	0.075
		Brown-Forsythe								2.059	0.061
	In-Flight	Welch	3.92 ± 0.45	3.89 ± 0.96	3.69 ± 1.12	3.76 ± 1.23	3.78 ± 0.92	3.84 ± 0.37	3.92 ± 0.33	0.417	0.864
		Brown-Forsythe								0.539	0.778
	Others	Welch	4.16 ± 0.92	4.02 ± 0.93	4.06 ± 0.84	3.6 ± 1.19	3.99 ± 1.01	4.18 ± 0.49	3.94 ± 0.45	1.45	0.216
		Brown-Forsythe								1.928	0.079
Income	Type		≤3000 (N = 64)		3001–10 K (N = 252)		101000–20 K (N = 86)		>20 K (N = 47)
	Pre-Flight	Welch	3.87 ± 0.36		3.89 ± 0.95		4.02 ± 0.84		3.78 ± 1.16	0.842	0.473
		Brown-Forsythe								0.858	0.465
	In-Flight	Welch	3.9 ± 0.48		3.84 ± 0.96		4.01 ± 0.92		3.59 ± 1.27	1.53	0.209
		Brown-Forsythe								2.006	0.116
	Others	Welch	4.15 ± 0.5		3.99 ± 0.94		3.93 ± 0.98		4.07 ± 0.91	1.563	0.201
		Brown-Forsythe								0.965	0.41
Age	Type		≤18 (N = 7)	18–30 (N = 180)	31–40 (N = 105)	41–50 (N = 94)	51–80 (N = 61)		≥61 (N = 2)
	Pre-Flight	Welch	4.01 ± 0.25	3.9 ± 0.83	3.96 ± 0.92	3.97 ± 0.80	3.68 ± 1.18		3.5 ± 0.88	0.756	0.6
		Brown-Forsythe								1.202	0.343
Travel Purpose	Type		Business (N = 47)	Visiting family (N = 107)	Holiday (N = 186)	Study (N = 62)	Others (N = 6)
	Pre-Flight	Welch	3.73 ± 1.17	3.98 ± 0.94	3.9 ± 0.77	4.04 ± 0.73	3.63 ± 0.39			1.901	0.13
		Brown-Forsythe								1.806	0.128
	In-Flight	Welch	3.81 ± 1.07	3.81 ± 1.05	3.88 ± 0.82	3.91 ± 0.94	3.69 ± 0.35			0.497	0.738
		Brown-Forsythe								0.304	0.875
	Others	Welch	3.99 ± 1.01	3.94 ± 0.99	4.11 ± 0.72	3.92 ± 1.01	3.71 ± 0.78			1.194	0.33
		Brown-Forsythe								1.040	0.39
FFP members	Type		Yes (N = 170)				No (N = 279)
	Pre-Flight	Welch	3.84 ± 1.01				3.93 ± 0.82			1.037	0.309
		Brown-Forsythe								1.037	0.309
Travelled or not after COVID-19	Type		Yes (N = 170)				No (N = 279)
	Pre-Flight	Welch	3.9 ± 0.96				3.92 ± 0.30			0.152	0.697
		Brown-Forsythe								0.152	0.697
	In-Flight	Welch	3.84 ± 1.00				3.96 ± 0.44			2.546	0.112
		Brown-Forsythe								2.546	0.112
	Others	Welch	3.99 ± 0.94				4.12 ± 0.49			2.466	0.119
		Brown-Forsythe								2.466	0.119
Travelled frequency after COVID-19	Type		0–1 (N = 123)		2–4 (N = 105)		5–10 (N = 135)	≥11 (N = 86)
	Pre-Flight	Welch	3.92 ± 0.65		3.95 ± 0.8		3.78 ± 1.09	4 ± 0.97		0.896	0.444
		Brown-Forsythe								1.198	0.31
	In-Flight	Welch	3.87 ± 0.76		3.89 ± 0.88		3.82 ± 1.06	3.83 ± 1.06		0.145	0.933
		Brown-Forsythe								0.148	0.931
	After arrival	Welch	4.06 ± 0.8		4 ± 0.93		4.03 ± 0.82	3.81 ± 1.13		1.061	0.367
		Brown-Forsythe								1.338	0.262
Cabin Selection (class)	Type		Economy (N = 219)			Business (N = 140)	First (N = 90)
	Pre-Flight	Welch	3.89 ± 0.74			3.91 ± 1.00	3.91 ± 1.07			0.021	0.979
		Brown-Forsythe								0.018	0.982

Table 9 illustrates the groups taking Welch & Brown-Forsythe Test with significant differences (Sig. ≤ 0.05), which required Tamhane T2 Post Hoc Tests to analyse further where the significant difference was produced. As the indicator Travelled After COVID-19 consists of only two groups, the post hoc test is unnecessary. The two groups are regarded as having significant differences in the After-Arrival stage.

Table 9.

Welch & Brown-Forsythe Test (Sig. ≤ 0.05).

Test of Homogeneity of Variances		Std. Deviation							F	Sig.
Age	Type		≤18 (N = 7)	18–30 (N = 180)	31–40 (N = 105)	41–50 (N = 94)	51–80 (N = 61)	≥61 (N = 2)
	Others	Welch	4.57 ± 0.19	4.07 ± 0.77	3.97 ± 0.94	3.91 ± 1.06	4.03 ± 0.92	3.63 ± 0.88	7.563	0.003
		Brown-Forsythe							1.221	0.337
Travelled or not after COVID-19	Type		Yes (N = 170)				No (N = 279)
	After arrival	Welch	3.95 ± 0.95				4.22 ± 0.57		0.152	0.697
		Brown-Forsythe							0.152	0.697
Travelled frequency after COVID-19	Type		0–1 (N = 123)		2–4 (N = 105)		5–10 (N = 135)	≥11 (N = 86)
	Others	Welch	4.12 ± 0.62		3.92 ± 0.97		3.88 ± 1.07	4.16 ± 0.80	2.811	0.04
		Brown-Forsythe							2.825	0.038
Cabin Selection (class)	Type		Economy (N = 219)			Business (N = 140)	First (N = 90)
	In-Flight	Welch	3.97 ± 0.64			3.66 ± 1.2	3.87 ± 1.06		4.047	0.016
		Brown-Forsythe							3.903	0.021
	After arrival	Welch	3.9 ± 0.93			4.14 ± 0.81	3.96 ± 0.99		3.539	0.031
		Brown-Forsythe							3.0821	0.047

Table 10 illustrates the result of the Tamhane T2 Post Hoc Test, group pairs whose Sig. is less than or equal to 0.05 are marked grey. Age ≤18 significantly differs from age groups of 18–31, 31–40, 41–50, and 51–60 in the “Others stage”. As for Cabin selection, economy class significantly differs from business class in stages of In-Flight and After-Arrival. Thus, Table 11 concludes all groups with significant differences in specific stages.

Table 10.

Tamhane T2 Post Hoc Tests.

Tamhane T2 Post Hoc Tests
Group	Stage	Group (I)	Group (J)	Mean Difference (I-J)	Sig.
Age	Others	≤18	18–30	0.51	<0.001
			31–40	0.6	<0.001
			41–50	0.66	<0.001
			51–60	0.54	0.004
Travel Frequency (After COVID-19)	Others	0–1	2–4	0.2	0.357
			5–10	0.24	0.142
			≥11	−0.04	1.00
		2–4	2–4	−0.2	0.357
			5–10	0.04	1.00
			≥11	−0.24	0.339
		5–10	2–4	−0.24	0.142
			5–10	−0.04	1.00
			≥11	−0.28	0.157
		≥11	2–4	0.04	1.00
			5–10	0.24	0.339
			≥11	0.28	0.157
Cabin selection (Class)	In-Flight	Economy	Business	0.31	0.016
			First	0.11	0.763
	After- Arrival	Economy	Business	−0.24	0.027
			First	−0.06	0.956

Table 11.

The Summery of Selected Groups.

Group	Stage	Group (I)	Group (J)
Age	Others	≤18	18–30
			31–40
			41–50
			51–60
Cabin selection (Class)	In-Flight	Economy	Business
	After- Arrival	Economy	Business
Travel (After COVID-19)	After- Arrival	Yes	No

4.5. Discussion

After investigating the measures taken by 49 airlines, following insights could be obtained. First, airlines worldwide have adopted many measures to reduce expenditures, but few have been implemented to increase revenue. The conditions that can bring profits to airlines basically rely on government policies, such as economic assistance and open borders. Improving cargo services, an essential source of income that could practice by airlines, also requires cooperation with the government or other institutions. Such uncontrollable factors are unforeseeable compared to reducing expenditures, which is regarded as a controllable response.

The second point is that many words frequently appear in all the news released by 49 airlines, which seem ordinary but can subconsciously consolidate the company's image. In past research, it was found that different vocabulary represents what the company wants to express. For example, through keywords such as ‘volunteer’, ‘medical’, ‘food’, and ‘necessities’, the image of medical and charity responsible for society is enhanced, and through the collective pronoun ‘we’, a bond of unity between management, employees, and customers is established. It was found that airlines around the world are using public relations to bring confidence to stakeholders and passengers.

Measures such as travel restrictions, isolation, and social distance planning are detrimental to airline profitability, but airlines have to adopt some measures because of government regulations. These measures include reducing flight plans and arranging strict and complex check-in procedures. Many airlines seek to minimize the loss of market capacity, route networks, customer base, and customer trust built up over the years before the COVID-19 outbreak to prepare them for recovery.

Most airlines only have enough cash to make up for about two months of lost revenue (IATA, 2020b). Most governments placed a high priority on maintaining connectivity in air transport. As a result, almost all major airlines received government support. In addition, many airlines in developed countries have received financial assistance (IATA, 2020a). Most airlines have tried to seek financial aid, but many failed. However, some people are concerned that some countries may abandon the policies of liberalization and deregulation, which could jeopardize important progress in levelling the playing field. In addition, the financial assistance received by these airlines is limited to a certain extent; these assistances help the airlines survive. Moreover, offering aid to maintain airline operations creates a promising future for the airlines and the aviation market. It is conducive to economic recovery and production recovery after the outbreak of the COVID-19 pandemic is over because these financial assistances prevent millions of employees of airlines around the world from being fired.

The rapid production recovery in Asia and the increase in cargo demand have led airlines to add cargo flights. According to their annual reports, some airlines have added thousands of extra cargo flights over six months. Many of these cargo flights carry medical materials, so these flights are profitable for the airlines and contribute to the fight against the epidemic.

According to the data above, different measures will benefit the airlines differently. Reducing flight plans, reducing the salary of the employees and managers, and retiring aircraft can help airlines reduce their operating costs. Some measures to guarantee passengers’ safety and interests can improve the passengers’ satisfaction. Adding cargo flights and seeking financial assistance can improve the company’s income and cash flow. Most airlines reduced flight plans and reduced the salary of the employees and managers. Some airlines changed passenger flights to cargo flights or retired aircraft. Not many airlines laid off workers or declared bankruptcy.

Findings showed that passengers were generally satisfied with the airlines' response measures, with even the least satisfactory measures scoring an average of over 3. The measure most satisfied by passengers is providing hygiene products for passengers and staff, while passengers are least satisfied with the measure of protective clothing required to board the plane. In addition, for each flight stage, passengers are most satisfied with the “Other stage” and least satisfied with the “In-Flight stage”.

From the questionnaire, it can be seen that for travellers who were surveyed, providing hygiene products for passengers and staff and a thermal scanner to monitor body temperature during check-in were the two measures they were most satisfied with at the Pre-flight stage. However, according to previous research (, only a small number of airlines worldwide provide hygiene products. A 2021 study (Bielecki et al., 2020) shows that of the 20 major airlines surveyed, three airlines from China – China Southern, China Eastern, and Air China – all provide hygiene products. The remaining 17 airlines do not provide hygiene products. Considering that China has the best COVID-19 outbreak control and the fastest recovery in the airline industry, providing hygiene products might be an idea that other airlines worldwide could learn from it. Also, as this measure has received a very high level of satisfaction, applying this measure will enhance the feeling of safety for passengers travelling by air after the outbreak. Sixteen of the 20 airlines test passengers' body temperatures before take-off, while only one airline, Southern Airways, tests passengers' temperatures during the flight. Our survey shows that passengers are very satisfied with this measure, which means that most airlines are doing a good job of checking their temperature.

Passengers were most satisfied with temperature monitoring during the In-Flight stage, but only China Southern offered this service in previous studies. Passengers are most unhappy with the fact that airlines do not provide regular meals, and as can be seen from previous studies, almost all airlines have placed restrictions on eating on board. Airlines could consider adding temperature monitoring services and offering a bit of high-calorie ready-to-eat food. Regarding passenger satisfaction with the airline's response strategies, airlines may need to provide onboard temperature monitoring and hygiene products to enhance passenger satisfaction.

The ANOVA test results illustrate that “Age”, “Cabin Selection”, and “Travelled after COVID-19” are the groups that affect passengers’ satisfaction levels on responses. An analysis of previous research (Clemes et al., 2008) assessed whether passengers with different socio-demographic characteristics have a different perception of airlines' service quality in many aspects. Age was also the group that was significant at a 5% level when assessing passengers’ perception of safety or security. Therefore, it could be concluded that people of different ages are significantly different when facing airline safety issues such as COVID-19 responses. It is evident that people who have not travelled after COVID-19 did not know COVID-19 responses; thus, the different groups of travelled after COVID-19 could be ignored. As there are significant differences in the perception of airline response strategies for different age groups and cabin selections, airlines need to consider passengers' age composition and other cabins for developing COVID-19 measures. This may lead to adopting response strategies that are more likely to satisfy passengers.

4.6. General recommendations and strategies

Airline firms are trying to cope with the COVID-19 crisis that has significantly affected their financial viability. As statistics predict, the aviation sector cannot be recovered in a few years. Airlines require to resume their operations with a low number of customers and under strict regulations. Generally, domestic flights have taken the first place; after that, the border gates have been opened. Then, short-haul international flights and long-haul flights started their operations. A demand shock was induced worldwide by COVID-19 for passenger flights because of the travel confinements and people’s unwillingness to travel in such risky conditions. On the contrary, there was a surge in demand for cargo flights to rapidly transport the required medical equipment, PCR tests, vaccines, etc. Moreover, there were some restrictions on the supply of “belly cargo” carried on passenger flights, which increased the demand for cargo flights. Because of the inadequate financial arrangements taken into action, quarantine measures, the reduction of passenger demand, etc., airline companies have not succeeded yet in reaching their pre-2019 conditions. Some measures taken into action during the COVID-19 crisis will have long-term influences. As the restrictions on international travel should still be obeyed, changes to networks and fleets would not be at the desired level, at least in the near future. Airlines will continue to be suffered from such issues. Nevertheless, these effects could be recovered sooner if countries' relevant authorities allow international travel to be opened to those individuals vaccinated for COVID-19. During the recovery course, airline companies need to avoid quickly increasing the seating capacity because passenger demand may not rapidly return. Many countries have some constraints still taken into action. Airlines must adjust their staff's salaries as the passenger numbers and capacities gradually approach the levels before the COVID-19 crisis. During the post-COVID-19 period, cargo transportation may continue to be a remarkable source of revenue for airlines. The airline operators of adequate size will be able to incorporate cargo units, and as such, post-COVID-19 aviation services may differ from those before the COVID-19 crisis.

5. Conclusion

This research explored airlines’ responses and customer satisfaction in the aviation industry during the COVID-19 pandemic by collecting and analyzing the organization-level responses adopted by airlines and analyzing passengers’ satisfaction with individual-level responses during flights. The study classified organization-level responses into three categories: reduce the operation cost, ensure the safety and interests of the passengers, and increase the revenue and cash flow. It was found that all airlines took “reduce the operation cost” responses, such as cutting flights and reducing employees’ salary, while few airlines adopted responses of increasing the revenue and cash flow of the company. Importantly, airlines in different areas adopted considerably different responses. Airlines in developed countries have usually received financial support, while many airlines failed to be assisted by governments. Besides, in Asia, where production is recovering rapidly, airlines adopt some responses to increase revenue and cash flow, such as adding cargo flights. However, airlines cannot adopt such responses in other areas where COVID-19 is still severe. As for responses to ensure the safety and interests of the passengers, all airlines have taken measures that substantially depend on local government policies. Thus, this research conducted a questionnaire survey to analyze the satisfaction of responses in the Chinese market.

With a sample of 449 questionnaires, which collected passengers’ basic information and their satisfaction with responses in four stages, the airlines’ individual-level responses for passengers were ranked based on passenger satisfaction. It was found that passengers’ satisfaction varied among different COVID-19 measures adopted by airlines. For example, among 22 measures considered in this study, the top 3 measures that passengers were satisfied with were “ Provide hygiene products for passengers and staff”, “A thermal scanner to monitor body temperature during check-in” and “Disinfect the cabin after each flight, even for the previous flight of the connecting flight”. In contrast, the bottom 3 measures were “Protective clothing is required to board the plane”, “Adopt a special boarding method such as boarding in the order from back to front” and “No in-flight meals and drinks (only snacks and water). As China is one of the best countries to overcome COVID-19, the questionnaire is a suitable reference for airlines struggling with COVID-19. Moreover, the “others stage” is the most satisfying of the four stages, while the “In-Flight stage” is the least. The ANOVA and Welch & Brown-Forsythe test found significant differences in travelers' satisfaction between different age groups with the same measure. Therefore, airlines will have to consider this difference in the future.

The results of this study provide airline operators with a better understanding of how customers assessed the quality of airline service during the COVID-19 pandemic and what operators can do in future to improve customer satisfaction post-COVID. Airline operators can develop strategies to focus on key COVID-19 measures that can improve passengers’ satisfaction without compromising passengers’ health and safety. As found in this study, there are opportunities to improve the in-flight stage COVID-19 measures, especially the measures like “each passenger needs to be seated one seat apart”, “It is not allowed to line up to go to the toilet, and the crew will disinfect the toilet after every-one has used the toilet” and “No in-flight meals and drinks (only snacks and water)”. Likewise, airlines should attempt to meet passengers' travel expectations and measures in different cabins (economy, first class and business) as cabin selection groups affected passengers’ satisfaction level in responses.

The data in the questionnaire is limited to Chinese travellers and may not be representative of the views of travellers from other countries around the world. In addition, the number of travellers under 18 and over 60 surveyed in our questionnaire was small, and these smaller samples may not be representative of this age group. Future studies could focus on how the world's mainstream airlines respond to COVID-19 and whether the responses positively impact the airlines themselves. When evaluating whether the strategy adopted by an airline is effective, please consider combining various performance indicators of the airline with building a model that can rate the airline and score the airline's overall performance during the COVID-19 period. This report may help small and medium-sized airlines learn from the responses of large airlines and help airlines around the world adopt more satisfactory responses to passengers. In addition, it may also enable them to consider the differences between different passengers.

Limited studies have systematically investigated passengers’ satisfaction in four stages of air travel: Pre-Flight, In-Flight, After-Arrival, and Others (Face mask requirement, HEPA filters, etc.). As found in this study, through a systematic understanding of passengers’ satisfaction with the COVID-19 measures adopted by airlines in those four stages, there is an opportunity for the airline operator to improve their market share of air travel by improving post-COVID-19 and service measures that can influence passengers' satisfaction. Further, it provides a benchmarking resource for airline operators in case of a similar pandemic in future. Complementing the present study, future research could focus on passengers’ perceptions and satisfaction with new technology that could enhance some of the COVID-19 measures explored in this study, especially for the in-flight stage (e.g., the use of cleaning robots or ultraviolet light and antimicrobial cabin cleaning for toilets and cabins, and the use of application controlled in-flight entertainment systems).

From the investigation of measures adopted by 49 airlines, it can be expected that several significant changes will occur post-COVID-19, for instance, security measures and new operational standards. Airlines may need to re-plan their networks, crew, fleet, and cash flow to adapt well to such changes and for a future pandemic. Another significant aspect of demand recovery is that airline operators need to explore the factors leading to the reduction of passengers’ confidence and ways to restore it.

Author contributions

All authors: Conceptualization, Investigation, Data Collection and Compilation, Methodology, Software, Formal analysis, Seyed Mojib Zahraee: Writing – original draft, Hongwei Jiang: Supervision, Writing – review & editing, Nirajan Shiwakoti: Supervision, Writing – original draft, Writing – review & editing.

Conflict of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix.

Customer satisfaction rating on airline response to COVID-19

1. Basic Information

Gender	Male □ Female □
Nationality	Chinese □ Non-Chinese □
Occupation	Student □ Business □ Owner □ Government sector □ Private Sector □ Others □ Retired □
Income (Monthly RMB)	≤3000 □ 3001–10000 □ 10001–20000 □ ≥20000 □
Age	≤18 □ 18–30 □ 31–40 □ 41–50 □ 51–60 □ ≥60 □
Education	Senior High or lower □ College□ Bachelor□ Master and above□
Annual travel times by air (before the COVID-19 pandemic)	0–1 □ 2–4 □ 5-10□ 11–20 □ ≥21□
Travel purpose	Business □ Visiting friends and relatives □ Holiday □ Study □ other □
Are you Airlines FFP (Frequent Flyers Points) members?	Yes □ No □
Have you ever travelled by air after the start of COVID-19 pandemic?	Yes □ No □
IF YES, How many times have you travelled by air after the outbreak of the pandemic?	0–1 □ 2–4 □ 5-10□ ≥11 □
Which class do you prefer when you travel by air?	Economy class □ Business class□ First class□

2 Airline safety response questions

Please rate your satisfaction with the different policies implemented by airlines in response to the COVID-19 pandemic based on your actual situation and feelings. 1: very dissatisfied. 2: dissatisfied 3: neutral. 4: satisfied. 5: very satisfied.

Questions	Satisfaction
Measures taken by airlines for the pre-flight phase

1.1 The COVID-19 nucleic acid negative certificate is required to allow boarding	1	2	3	4	5
1.2 A thermal scanner to monitor body temperature during check-in	1	2	3	4	5
1.3 Provide hygiene products for passengers and staff	1	2	3	4	5
1.4 Regularly check the health of employees	1	2	3	4	5
1.5 Protective clothing is required to board the plane	1	2	3	4	5
1.6 Keep a safe distance when boarding	1	2	3	4	5
1.7 Adopt a special boarding method, such as boarding in the order from back to front	1	2	3	4	5
1.8 Quick health test before boarding	1	2	3	4	5
The measures taken by the airline for the in-flight phase
2.1 Masks are required throughout the flight	1	2	3	4	5
2.2 Social distancing is required on the plane	1	2	3	4	5
2.3 No in-flight meals and drinks	1	2	3	4	5
2.4 Apply HEPA filters on the aircraft (remove over 99.97% of particles characterized by diameter of 0.3 μm or larger)	1	2	3	4	5
2.5 Each passenger needs to be seated one seat apart	1	2	3	4	5
2.6 Temperature monitoring on the plane	1	2	3	4	5
2.7 It is not allowed to line up to go to the toilet, and the crew will disinfect the toilet after every-one has used the toilet	1	2	3	4	5
The measure taken by airline for the after-arrival phase
3.1 Crew members take 14 days of isolation after working in flights passing through risk areas	1	2	3	4	5
3.2 Disinfect the cabin after each flight, even for a previous flight of the connecting flight	1	2	3	4	5
3.3 Disembark in batches	1	2	3	4	5
Other measures taken by airlines
4.1 Visibility of COVID-19 information on home page	1	2	3	4	5
4.2 Standard of COVID-19 travel information	1	2	3	4	5
4.3 Free refund and change policy	1	2	3	4	5
4.4 Flight frequency has dropped, while on-time rate has risen	1	2	3	4	5