Impact of COVID-19 Pandemic on Singapore’s Aviation Industry

DOI: https://doi.org/10.21203/rs.3.rs-1505332/v1

Abstract

COVID-19, a global pandemic, is proving a significant threat to various sectors globally, including the aviation industry. There has been limited research on the Singapore aviation industry, their responses and preparedness and the impact of COVID-19 during and post-pandemic. With such a gap in the literature, this study aims at understanding the impact that the pandemic has specifically on Singapore’s aviation industry both current and in the future while focusing on the existing mitigation measures. A survey questionnaire was used to collect quantifiable data from airport and non-airport workers to analyse various attributes, such as the perception of mitigation and air travel demand. The results concluded that attitudes towards pandemic and mitigation measures significantly led to better expectations of air travel recovery. It is to be noted that beyond domestic and global attitudes, rebound of international travel essentially relies on borders to be opened and multiple countries’ collaboration and coordination.

1. Introduction

Singapore's aviation sector stands as a key pillar for its economic growth, with Changi airport being the hub of major worldwide airlines. The sector has been experiencing a positive performance; for example, in 2018, 18.5 million visitors came to Singapore, about 300% of the Singaporean population. In the same year, a total of 36.1 million passengers were carried through this sector, an illustration of significant contribution to the economy. With its significant contribution both directly and indirectly, the sector supported more than 375,000 jobs in 2019 with a gross domestic product contribution of US$36.6 billion, where $22.1 billion contributed directly and $14.5 billion from tourism spending supported by aviation (Enterprise Singapore 2020). Despite such a positive performance illustration, the sector has been facing several challenges, with the recent one being the effects of the COVID-19 pandemic.

Corona virus, also termed COVID-19, is an infectious viral disease caused by Corona virus, SARS-CoV-2 (Hui et al., 2020). The disease was first appeared in December 2019 in Wuhan, China and has since then spread globally. Early hypotheses by health officials link the disease to a seafood market operating in Wuhan city. The virus spreads via contact with droplets released by an infected person when coughing or sneezing. After exposure to the virus, the symptoms are manifested within 14 days; and these symptoms include cough, fever, headache, difficulty breathing/shortness of breath, loss of taste or smell, amongst others. With the combination of other underlying health problems and old age, the virus is likely to kill quickly, considering the minimal guarantee of vaccines introduced in the market. Therefore, as (Ibn-Mohammed et al., 2021) described, the virus is greatly causing immense disruption on various sectors with great negative influences.

The pandemic's disrupting forces resulted mainly from the fear of the unknown with this new virus spread and the mitigation measures established to curb the spread, such as lockdowns and travel restrictions. As the numbers of infections increased, it prompted more restrictions that further escalated the global and national scale impacts.

2. Literature Review

The literature review attempted reports the impact of the pandemic on the economy and aviation sector from past pandemic epidemiology research on people attitude, mitigation policies, and post-pandemic recovery plans. The foundational understanding of past significant consequences from pandemic fallouts would be relevant for this research paper as it seeks to find similarities or differences in the outcomes. The literature could provide inspirations for the measurements and analysis to derive results from the quantitative research. Moreover, gaps or conflicts could be from the literature that would increase the research's weight in reporting the pandemic effects on the aviation sector, particularly for trade-dependent countries such as Singapore.

COVID-19 as a Pandemic

COVID-19 is proving to be a greatly destructive pandemic compared to previous global or regional pandemics recorded in history.(Qiu et al., 2018) describe on SARS 2003, which was another strain of corona virus though with limited impacts on a global scale. Despite this pandemic, limited mitigation measures were established on various sectors, including the aviation industry. COVID-19 cannot be compared to SARS 2003 in the same scale because the infection and death rate far superseded and prolonged for at least a year. This had hit Singapore's economy significantly as well. Hence, with such a more persistent pandemic that has infected and killed millions, it is critical to evaluate the severity of the pandemic's impact on the most susceptible sector of the economy, the airline industry.

In a comprehensive scoping review of 65 research articles by (Adhikari et al., 2020), coronavirus was identified as a disease within the virus category, which lead to several symptoms, including difficulty in breathing, fever, lung infection and pneumonia. As a reference, this literature confirmed that it was common globally in animals, and only a few cases have been pointed to affect human beings. In December 2019, it was reported that there was a coronavirus disease outbreak in Wuhan, China, which was linked to a severe acute respiratory syndrome coronavirus2 referred to as SARS-CoV-2. The World Health Organisation (WHO) declared the disease name as COVID-19 (Harapan et al., 2020). By the end of January 2020, WHO declared the diseases as a global pandemic.

This situation differed greatly from a similar pandemic that arose from China in 2003, known as SARS. Over the decade, the virus mutated. Nonetheless, comparing COVID-19 with SARS highlighted the immense scale of damage the current pandemic has. Wilder-Smith, Chiew, and (Lee et al., 2020) emphasized that it took a mere 8 months to contain SARS and was largely contained within Asian countries such as Hong Kong, whereas the COVID-19 infections rate far exceeded that of SARS with more than 82,000 cases within a matter of 2 months since the beginning of 2020.

Impact on Economy and Aviation Industry

In a recent assessment of COVID-19 impact on air transport, (Suau-Sanchez et al., 2020) established that the aviation industry was being affected much more than other industries due to the pandemic. The report indicated that 98% of worldwide passenger revenues as of End-March 2020 were lost. The global airline industry suffered a heavy shock as most countries went on lockdown and travel restrictions both domestically and internationally until July 2020.

Given the severity of COVID-19 to be much more potent than SARS 2003, a literature review of the economic impact from SARS would provide much foresight into similar or worse outcomes for COVID-19. In 2003 when there was SARS epidemic breakout, all economic contributors were affected. In the findings of a study by (Wilder-Smith, 2006), it is argued that the SARS significantly impacted travelling and tourism. Many governments were ill-prepared for the disease back then, and cosmopolitan cities such as Hong Kong were among the hardest hits.

(Suau-Sanchez et al., 2020) reported a similar comparison of COVID-19 and SARS with variations of impacts. This literature indicated similarities in impacts that include grounding aeroplanes, job losses, reduced profits, and operational losses. A close focus indicates that as of May 2003, SARS had affected Asia-Pacific monthly revenue passenger kilometres by 35% lower than the number before the crisis. In this study, COVID-19 is indicated to have affected up to 98% of the former operations.

A comparative analysis of COVID-19 with other pandemics and global crises such as climate change by (Gössling et al., 2020) indicated the trend of impact, as shown in the pandemic curve illustrated in Fig. 3.1. From this illustration, the literature study indicated the possibility of further impacts post-pandemic that could also be significant. This information would clarify the post-COVID nature of impacts that are likely to face the aviation industry. Tourism especially was vulnerable to COVID-19 due to social distancing, and aviation will almost cease, just like during the World War period. This comparison could be exaggerated and hence, justify the need to evaluate the pandemic's impact on the resumption of air travel in Singapore more accurately.

Mitigation Measures

When studying the pandemic of such levels, (Wilder-Smith, 2006) concluded that the investments should focus on infection control and screen capacities at the healthcare system entry points. The findings are also supported by (Anderson et al., 2020) that China's success case containing the virus despite being the earliest to be affected. China's strict and broad application of social distancing rules, isolation of infected people, and full lockdown of cities collectively helped to prevent uncontrollable transmission of COVID-19 amongst the populace. After the virus got contained, China was able to open almost all the domestic travel.

Singapore has employed similar tactics in theory to contain the virus. (Ng et al., 2020) with the Centres for Disease Control and Prevention in the United States reported the list of swift and decisive actions that Singapore has utilised, which included surveillance, early detection and isolation with a reduction in the 7-day moving average from onset to isolation in hospital, air travel restrictions and public education in the bid to bring community cases down. However, the effects were dismal, especially for the virus's early onset as clusters were forming (Lee et al., 2020).

Noteworthy in comparison to China, Singapore's measures were similar in theory but less restrictive in their implementation. Moreover, in extension to (de Bruin et al., 2020) findings that other than just simply timely first response, having high citizenship awareness, knowledge, and acceptance of roles and responsibilities for social distancing measures were critical to the imposed measures' success. Could the attitudes towards the mitigation measures be different in China and Singapore? Could the people expect the different intensity of mitigation measures? These questions would have to be studied to understand the people's attitudes in Singapore towards COVID-19 and mitigation measures. Furthermore, these attitudes would impact the expectations of air travel (Gössling et al., 2020). Therefore, this study's research question matters, and the people's attitude matters as the efficiency and success of health policies depend on the people able to protect themselves, follow experts' advice, and obey rules (Lee et al., 2020).

Attitude during the pandemic

The importance of attitudes towards mitigating the pandemic is further supported by research by (Yap et al., 2010), who focused on understanding how best to manage influenza pandemic in Singapore through modifying behavioural changes following an outbreak in 2009. The study determined the variability in practices, attitudes and knowledge among various groups concerning the pandemic. It further recommended that the general public be educated to improve their practices of managing future pandemics. It also studied influenza perceptions in the context of healthcare workers and general personnel, distinguishing between airport staffers and non-aviation workers. However, this study's results were contradicted by another study by (Honarvar et al., 2020), who assessed the risk perceptions, attitudes, and practices of Iran adults towards the COVID-19 pandemic and surprisingly concluded that practices, perceptions and knowledge about COVID-19 amongst the Iranian adults are not related. Even if people were informed of the danger of sending sick patients to the hospital at the risk of infections, people still did so without many protective measures taken. The conflicting pieces of evidence justify an investigation into the context of Singapore's pandemic on how knowledge and attitudes could influence people's behaviours and especially air travel behaviours.

Post Pandemic Recovery

The literature has established shreds of evidence that there is an expected lasting impact on the economy and aviation sector. The severity of the impacts depends on the mitigation measures and receptivity towards these measures as well. More reviews of literature that studied post-pandemic periods found that countries who could return to normality or drivers of life pre-pandemic faster saw a corresponding faster rebound in tourist arrivals such as Hong Kong and United States (Mao et al., 2010). A deeper study into China’s domestic airline demand and industry for COVID-19 suggested similar predictions found that airlines in open economies would be severely disrupted from normality and would likely require capital injection such as bailouts to survive (Czerny et al. 2020).

(Mao et al., 2010). applied the cusp catastrophe model as a theoretical foundation to learn about the mechanics of post-pandemic recovery in tourism in Japan, Hong Kong and the United States. The results highlighted that the cusp catastrophe model explained that fear and perceived risk about the pandemic mitigation measures would slow down the recovery pace, whereas more confident populations recover faster.

The literature provided extensive foundational theories and methodologies that are significantly relevant to this paper’s objectives. Starting from the cusp catastrophe model, the prospects of returning to normality in the context of pandemics, as researched by (Mao et al., 2010) can be influenced by the attitudes towards the pandemic and mitigation measures. Concurrently, the mitigation measures' effectiveness depends on the attitudes towards these measures as explored by (Yap et al., 2010). Referencing to this model is relevant to pandemic studies and also the quantitative questionnaire methodology by (Yap et al., 2010) on Singapore's SARS period, the current study investigates the context of the COVID-19 on the aviation sector to answer the research question of how attitudes and mitigation measures could influence expected air travel.

Problem Statement

The study's background illustrates a direct connection between the COVID-19 cases and the effect on flight frequency, which translates to the effect on the performance of the industry during the pandemic. Since the first case was reported, there has been no guarantee on the complete ending of this pandemic and so the possibility of resultant effects continuing on the aviation industry. Considering that the aviation industry depends on global performance, it is thus influenced greatly by global changes, market dynamics and shifts across the world, which continue affecting the industry. Singapore's aviation industry is not isolated, which attracts the interest of understanding the possible impact of the pandemic. Further, it raises concerns about the industry's performance after the pandemic through the response actions undertaken within the industry.

Despite this worrying trend on the global and national (Singapore) aviation industry, there has been no research focused on understanding the Singapore aviation industry, their responses and preparedness and the impact of COVID-19 during and post-pandemic. Being the main airport in Singapore and an international airport hub, Changi airport could help understand the Singapore aviation industry with the pandemic's influence. The results could potentially serve to improve the preparedness and decisive actions to be taken to protect the aviation sector should another pandemic happens in the future.

3. Research Methodology

The study was developed to understand the impact of the pandemic on the aviation industry, the following research questions based on the perception and actions of passengers are used as the focus of the study:

Research Aim and Objectives

This research aims at investigating the impact of COVID-19 on the Singapore aviation industry. In this context, the present study was conducted with the following research objectives,

Research Hypothesis

This research tests the hypothesis with the focus on each objective as follows,

Conceptual Framework

Integrating (Mao et al., 2010) and (Yap et al., 2010) frameworks, the following model has been used in this study to understand how have Singaporeans felt about the pandemic, effectiveness of the mitigation measures, and these attitudes on the expectation of air travel recovery.

Research Gap

Literature related to pandemics, including SARS, was comprehensively reviewed. Past confidence in the effectiveness of mitigation measures such as early lockdowns on good post-pandemic recovery for certain countries provides optimism and forward-looking expectations to Singapore for COVID-19. However, the concentration of literature in the European airports for COVID-19 presents a gap in understanding the current situation on COVID-19, especially on the attitudes and travelling demand amongst Singaporeans in the aviation drought brought about by the yearlong pandemic. Given the conflicting evidence from different literature on the association of attitudes towards post-pandemic recovery, could (Yap et al., 2010) 's results be consistent with the COVID-19 period? Moreover, given the importance of the aviation sector to Singapore's economy, it is paramount to investigate the potential and possibility of recovery in travelling demand shortly. Therefore, this research would extend further on existing literature foundational theories and methodologies for past pandemic and examine the current reception of the people's mitigation measures and the expectations that people have on the aviation outlook quantitatively.

Sample Design

The research methodological design considered different milestone deliverables for the study, which included: awareness/sensitisation program, data collection/gathering relevant information, data comparison and valuation, analysis and recommendations. The study adopted an explanatory research approach to understand people attitudes to the pandemic, mitigation measures and expectations for travelling. The descriptive cross-sectional research design and quantitative study method were chosen given that other researches are using secondary public data sources, so this study aims to obtain primary data from ground respondents to more accurately assess the attitudes and knowledge level of the Singaporeans about COVID-19. Survey method has been used to collect data. The research instrument used was a structured online questionnaire. Questionnaires are good research methods as they could generate a genuine perception towards COVID 19 with the participant’s privacy, they are easy to conduct and broad coverage (Wright, 2005). The questionnaire quantified the attitudes, expectations and behaviours of the respondents. This field research method was faster and captured a more widely distributed population at a lower cost with the ability to prove or disprove assumptions. However, the questionnaire could generate dishonest feedback due to self-promotion bias or any privacy concerns.

The study engaged both female and male aviation staff and normal workers in Singapore as the study population. The population age range that responded were between 18 and 65 years, who would likely have travelled at least 3 times in 2019/2020. The respondents' diversity ensured robust research and analysis into the connection between travelling impact and COVID-19 influence of the aviation sector. The respondents were selected via convenience sampling technique, chosen through the first point of contact, word-of-mouth and referrals in lieu that random sampling and other sampling methods would be much costlier in terms of time and resources. This sampling was preferred because it was simple to use with quick ability to identify any possible errors arising. Using a 95% confidence level, 5% margin of errors, and 2,631,300 as the population size, this study's ideal sample size would be 385. The total respondents were 425, which exceed the target.

Data collection process

Data collection was undertaken through a questionnaire, which is shared online via SurveyMonkey software platform. Though the approach might not be considered serious by other participants, or some might not easily access the shared online links (Wright, 2005), it still helped collect data without face-to-face interaction, which would be an issue COVID-19 situation. This study's focus was on Singapore and Changi airport, where staff and the public are randomly sampled conveniently over a month. The duration sufficed for obtaining the required targeted sample size for robustness in the results. The questionnaire comprised of multiple-choice questions. The questions included Dichotomous, Nominal, Ordinal questions. Scaling, rating and ranking questions were asked as well.

Measurements

The questionnaire had closed-end and open-end questions, which was classified as follows,

  1. Demographic information

    1. Standard demographic variables such as age were asked to better group the respondents for analysis

    2. Asked about the travelling pattern for the year 2020 to provide an inductive understanding of the current travelling demand and travelling profile of the respondents

  2. Attitudes towards pandemic measures

    1. Captured the attitudes towards the pandemic

  3. Demand attitudes

    1. Captured the expectations to resume travelling shortly

    2. Evaluated if the participants felt that their air travel demand and livelihood has been affected significantly by the pandemic

  4. Attitudes towards mitigation measures

    1. Measure the attitudes towards the level of preparation by the aviation sector against the pandemic

Validity and Reliability

The questionnaire interviewed 5 healthcare and aviation management professionals who are the subject matter experts on the questionnaire design and questions. The construct validity was reviewed with statisticians to ensure that the questions were designed appropriately. Internal validity is also fulfilled because this study followed the scientific method's standard steps and logically collected responses from participating individuals. The sample size was statistically robust, with 425 respondents and hence achieved external validity that it can be generalized across a larger population.

A pilot study was done, and reliability analysis was conducted through Cronbach's alpha measure for each section of the research instrument to understand each section's reliability. Table 1 showed that all scores are greater than 0.73, which is the minimum score for the questions to be considered as reliable (Bryman, 2016). However, given that convenience sampling was employed, there remained a risk for sample bias. Moreover, given that the questionnaire prompted the respondents to perceive fear and knowledge, the respondents could have self-promotion bias on the results.

Table 1

Reliability Analysis

Questions

Cronbach’s Alpha on Standardised Items

N of items

Attitudes towards the pandemic

0.75

4

Attitudes towards the mitigation measures

0.80

2

Impacts and effects of the pandemic

0.78

4

Expectation of the travelling pattern

0.83

2

Reliability Score for all items together

0.85

12

4. Results

This research has arrived at the significant findings obtained from the research using correlational analysis, t-test, chi-square test and regression analysis. In total 425 respondents have participated in the questionnaire and returned a well-distributed result. Hypothesis testing was conducted to validate and answers the research objectives and overarching research questions.

Demographic Profile of the participants

The age groups of the participants are from 18 to 50 years old and above. The majority of the participants' age groups are above 50 age range as seen from Table 2 which were reported by the media and healthcare professionals as the more vulnerable groups against COVID-19. The distribution is left-skewed for the age. The people who worked in the aviation sector, henceforth known as airport staff that the questionnaire has surveyed is about 50% more than those who do not work in the aviation industry. Some respondents still flew more than 3 times in 2020, which is about 169 of the entire sample as shown in Table 3.

Table 2

Age-wise classification of the participants

Age

Number of Respondents

Percentage (%)

18–25 years

22

5.2

26–33 years

69

16.2

34–41 years

76

17.9

42–49 years

121

28.5

50 and above

137

32.2

Total

425

100%

Table 3

Aviation Workers vs Non-Aviation Workers

Type of Occupation of the Respondents

Number of Respondents

Percentage (%)

Non-Aviation Workers

169

39.8

Aviation Workers

256

60.2

Total

425

100%

Pandemic Attitudes

Regarding information awareness about the pandemic, it can be seen as in Table 4 that a small 0.5% of respondents self-assessed themselves to be not informed about the COVID-19 pandemic. A higher proportion of respondents of 12.9% are not aware of the healthcare facility to seek treatment for COVID-19 infections, as evident from Table 5. Despite a high awareness and knowledge of treatment accessibility, the worry for being infected by COVID-19 is also high, with 50% of respondents believing they are likely to obtain COVID-19. Those who think that they are highly likely to be infected with COVID-10 is double that of those who otherwise feel highly unlikely.

Table 4

Respondents’ Knowledge on COVID-19

Age

Number of Respondents

Percentage (%)

Highly Informed

341

80.2

Not informed at all

2

0.5

Somehow informed

81

19.1

Total

425

100%

Table 5

Respondents’ Knowledge on COVID-19 Treatment

Knowledge of healthcare facility or isolation centre to seek medical services if infected with COVID-19

Number of Respondents

Percentage (%)

No

55

12.9

Yes

368

86.6

Total

425

100%

However, knowledge is not equivalent to security. Most respondents are worried that they could be infected. In Table 6, around 84% of the respondents expressed some level of fear over infections. Although perfect information could mitigate misinformation and people are well-educated on the virus, they remain fearful of their health given the ease of infections. Even if COVID-19 does not present a serious health concern, the potential economy and livelihood consequences weigh heavily on people’s mind with at least a quarter of respondents are very much worried of the impacts from COVID-19 from Table 7.

Table 6

Respondents’ Infection Fears on COVID-19 Infection Possibility

COVID-19 Infection Possibility

Number of Respondents

Percentage (%)

Highly Likely

44

10.4

Highly Unlikely

20

4.7

Likely

202

47.5

Unlikely

156

36.7

Total

425

100%

Table 7

Extent of Respondents’ Worry over COVID-19 Impact

Worry over COVID-19 Impact

Number of Respondents

Percentage (%)

Extremely Worried

69

16.2

Not at all worried

7

1.6

Not so worried

61

14.4

Somewhat worried

165

38.8

Very much worried

123

28.9

Total

425

100%

Expectation of Travelling

From the respondents, 82.8% of the respondents have not flown more than three times in 2020 (Table 11), which is a drastic fall, given that 84.2% travelled on average 0.5 times per month (Table 10). The recovery of the airline industry would likely not happen in the near term. Over 93% of people are uncertain when they would fly again (Table 8), and 43.1% is confident that they would not be travelling anytime soon. Travelling has been resumed only 7.1% of sample.

Table 8

Air Travel Resumptions Expectations of the Respondents

Resumption of Air Travel

Number of Respondents

Percentage (%)

Already resumed

30

7.1

Not likely to resume soon

183

43.1

Uncertain

86

20.2

Yet to resume

126

29.6

Total

425

100%

The expectation of not flying does not owe to fear of infections, though. At least 88.9% felt that the safety measures to prevent airline travel infections are at least adequate. This high confidence and trust in the health and safety measures suggest that airline travel remains depressed due to a more significant lack of demand for business and leisure travel. However, the result offers a glimpse of hope. 73.2% of respondents shared that their air travel plans have been at least moderately affected by Covid-19 (Table 9). The inconvenience and temporary obstacle to flying would likely observe a quick surge in demand once conditions improve for air travel globally.

Table 9

Air Travel Fallout from COVID-19

Degree of impact in travel via air transport due to COVID-19

Number of Respondents

Percentage (%)

Minimum

114

26.8

Moderately

115

27.1

Very much

196

46.1

Total

425

100%

Table 10

Air Travel Number of Respondents Pre COVID-19

Air travel in a month

Number of Respondents

Percentage (%)

(0–1)

358

84.2

(2–4)

55

12.9

(5–10)

10

2.4

Daily

2

0.5

Total

425

100%

Table 11

Air Travel Number of Respondents During COVID-19

Air travel in a month has been more than 3 times

Number of Respondents

Percentage (%)

No

352

82.8

Yes

72

16.9

Total

425

100%

Table 12

Air Travel Perceptions by Airport Staff

Travelling trend from the start of the 1st quarter to the end of the 2nd quarter

Number of Respondents

Percentage (%)

Constant all through

14

3.3

Decreasing all through

118

27.8

Decreasing all constant

40

9.4

Decreasing then increasing

33

7.8

Increasing all through

3

0.7

Increasing then constant

12

2.8

Increasing then decreasing

36

8.5

Unable to comment

168

39.7

Total

425

100%

Those working in the airline industry experienced first hard to hard-hit to business and their job. 45.9% of those airport staff surveyed observed that the travel demand has decreased from January to July 2020 (Table 12). This suggests that more than half of the airport staff does not see a consistent worsening of the situation (Table 13). The demand could remain still and pick up gradually.

Table 13

Air Travel Fallout Perceptions by Airport Staff

Effects of COVID-19 on the aviation industry

Number of Respondents

Percentage (%)

Devastating

152

35.8

Mild

3

0.7

Moderate

21

4.9

Normal

1

0.2

Severe

79

18.6

Total

425

100%

Attitudes towards Mitigation Measure

From Table 14, it could observe that only 37.6% of respondents are very supportive of the measures enforced in aviation to protect consumers from aviation. This relates to the low expectations of resumption air travelling, given that safety is a critical consideration for those travelling. Similarly, about a third of respondents (32.5%) felt that the industry is sufficiently prepared to manage COVID-19 from Table 15.

Table 14

Attitudes on Mitigation Measures Adequacy

Adequacy of measures enforced in the aviation industry to guarantee safety from COVID-19 infections

Number of Respondents

Percentage (%)

Minimum

42

9.9

Moderate

218

51.3

None

3

0.7

Very much

160

37.6

Total

425

100%

Table 15

Attitudes on Aviation Sector Preparedness

Preparedness of the aviation industry in managing cases of COVID-19

Number of Respondents

Percentage (%)

Highly prepared

138

32.5

Least prepared

51

12.0

Moderately prepared

228

53.6

Not prepared at all

8

1.9

Total

425

100%

Hypotheses Testing

Attitudes between Airport and Non-Airport Workers

Comparative analysis between the airport and non-airport staff would provide insights to suggest that airport staff has been affected significantly different from that of non-airport staff.

\({H}_{0}\) : There is no significant difference across people working and those not working in the aviation sector in terms of their attitudes towards the measures to mitigate COVID-19.

\({H}_{1}\) : There is a significant difference across people working and those not working in the aviation sector in terms of their attitudes towards the measures to mitigate COVID-19.

A chi-square test found insufficient evidence to reject the null hypothesis as there was no significance at the 10% confidence level that airport staff differed from non-airport staff in terms of their attitudes towards the mitigation measures and preparedness against COVID-19 for the aviation industry from Tables 16 and 17. Airport staff were neither more confident, nor dismal compared to non-airport staff in reacting to the response of the aviation sector. There was a consensus in the population that the airline industry was moderately prepared for COVID-19 and adopted moderate measures to manage the crisis. Next, is to investigate the impacts of the COVID-19 on airport versus non-airport staff.

Table 16

Adequacy Attitudes between Airport and Non-Airport Workers

Adequacy of measures enforced in the aviation industry to guarantee safety from COVID-19 infections

Airport Staff

No

Yes

Minimum

14

28

Moderate

92

126

None

2

1

Very much

60

100

Pearson Chi-square

2.602^

Likelihood Ratio

2.592

N of Valid Cases

425

^ 4 cells (40.0%) have expected count less than 5. The minimum expected count is .80.

Table 17

Preparedness Attitudes between Airport and Non-Airport Workers

Preparedness of the aviation industry in managing cases of COVID-19

Airport Staff

No

Yes

Highly prepared

52

86

Least prepared

19

32

Moderately prepared

95

133

Not prepared at all

3

5

Pearson Chi-square

0.746^

Likelihood Ratio

0.747

N of Valid Cases

425

^ 2 cells (25.0%) have expected count less than 5. The minimum expected count is 3.18.

COVID-19 Impacts Fear between Airport and Non-Airport Workers

\({H}_{0}\) : There is no significant difference across people working and those not working in the aviation sector in terms of the expected impacts from COVID-19.

\({H}_{1}\) : There is a significant difference across people working and those not working in the aviation sector in terms of the expected impacts from COVID-19.

Table 18 result showed that there was a significant difference between an airport and non-airport employees in the anxiety expected from COVID-19 on themselves. Notably, airport staff had less proportion of those who are only somewhat worried about more being very or extremely worried. Those the other two higher categories did not differ significantly from non-airport staff, the lower count for somewhat worried was statistically significant at a 5% confidence level. Airport staffs generally were more worried.

Table 18

COVID-19 Impacts Fear between Airport and Non-Airport Workers

Worry over COVID-19 Impact

Airport Staff

No

Yes

Extremely Worried

22

47

Not at all worried

1

6

Not so worried

27

34

Somewhat worried

78

87**

Very worried

41

82

Pearson Chi-square

10.209**

Likelihood Ratio

10.522

N of Valid Cases

425

*** Significant at 0.01 level, ** Significant at 0.05 level, * Significant at 0.1 level

^ 2 cells (20.0%) have expected count less than 5. The minimum expected count is 2.78.

Regression Analysis on Travelling Expectations

An analysis of the relationship between variables is being conducted using regression methods.

\({\varvec{H}}_{0}\) : The adequacy of mitigation measures on COVID-19 has no significant impact on making air travel resume to normal.

\({\varvec{H}}_{1}\) : The adequacy of mitigation measures on COVID-19 has significant impacts on making air travel resume to normal.

Table 19

Regression Output on Travelling Expectations

Dependent Variable: Travelling Ordinal

Standardized Coefficients

Infections Fear Ordinal

0.249***

Impact Worry Ordinal

-0.135**

Mitigation Adequacy Ordinal

0.214***

Preparedness Ordinal

-0.076

Travelling Patterns Effects Ordinal

-0.084

Knowledge Ordinal

-0.017

R Square

0.090

Adjusted R Square

0.068

*** Significant at 0.01 level, ** Significant at 0.05 level, * Significant at 0.1 level

From Table 19 result, it could see that aviation adequacy significantly predicted the future travelling expectations at 1% confidence level. Respondents having a higher adequacy attitude towards aviation measures registered 0.214 units increase in the expectation to resume travelling. Hence, the null hypothesis is rejected with sufficient evidence.

5. Discussion

Surprisingly, there was no sufficient evidence found that airport staff and non-airport staff differed in their attitudes towards the mitigation measures and the aviation sector’s preparedness against pandemics. Yet, the airport staff were more worried at a 5% confidence significance level than the economic and job loss fallout from COVID-19. The resolution of this perceived gap in attitudes could be that both groups register high similar positivism, registered at 88.9% from Table 14, on the nation's mitigation measures. Hence, research objective 1 was achieved given that the knowledge and fear of COVID-19 were measured and was found to be significantly higher for airport staff. The findings from this study affirmed previous literature, cusp catastrophe model, that positive attitudes towards mitigation measures correspond to faster recovery albeit, in this study, the expectation of faster recovery was tested. The aviation adequacy significantly predicted better future travelling expectations at 1% confidence level from Table 19. Research objectives 2 and 3 were also attained from the regression analysis with better attitudes towards mitigation measures is associated with forward-looking hopes that travel resumption will happen soon. Therefore, the overarching research question has been thoroughly investigated through this research and insights have been derived descriptively on Singaporeans’ knowledge and attitudes towards the pandemic and mitigation measures’ effectiveness and their expectations of air travel recovery. The correlational impacts have been tested statistically and aligned with previous literature from SARS 2003 and similar cities like Hong Kong and Japan.

The results found that airport staff were more worried than non-aviation workers, and this informs the policymakers to mitigate this worry with more economic and health support for these workers that are also at the frontline battling with COVID-19. Policymakers have uncovered the attitudes of COVID-19 more accurately across two segments (airport and non-airport staff), addressing Research Objective 1. Next, it is to manage the expectations and assure the public that effective management can aid the aviation sector's recovery. Better attitudes towards mitigation measures were associated with the expectation of travel resumption. This encourages more decisive and in-time measures to cope with a future pandemic such that the national health policies can draft a standard just-in-time protocol. Further research on the standard protocol could be conducted to evaluate the feasibility and effectiveness of the proposed measures. Research objectives 2 and 3 are answered from the regression results on the factors that impact future travelling demand expectations. This would inform the necessary professionals to prioritize the critical factors in managing attitudes and outcomes.

Research Implications

The results suggested a close similarity to other literature in other parts of the world and comparable to the findings from SARS 2003. Regardless of the scale of the pandemic, positive receptions to the mitigation measures would encourage a more positive outlook on recovery. This research would contribute to the academic understanding of the relationship between attitudes towards mitigation measures and the association with expectations of air travel recovery. The airport staff significantly higher worry of the fallout was reasonably expected and in line with other research on the fear and dangers of frontline personnel albeit the aviation sector was more of the victim of the mitigation measures’ reduction in mobility. The results further contribute to policymaking by informing policymakers on keeping the people informed and knowledgeable about policies, treatment options and COVID-19 facts. These are drivers that encourage the positive attitudes towards measures implemented by the national leaders handling the virus. Moreover, the positive outlook would encourage policymakers to place more confidence in the aviation sector recovery and attract investments to be prepared for a post-COVID-19 recovery when the time comes.

Future Research Directions

The research could be potentially expanded in multiple ways. One direction is to expand the scope of the investigation beyond the aviation sector. Other sectors were reported by the media to be affected and could potentially be worse than tourism such as the nightlife businesses. Furthermore, the investigation into the attitudes of the respondent in this current study was conducted from a self-reporting questionnaire that could be affected by self-promotional bias. Other methodological approaches could be tested such as blind study or include reverse-coded questions. Further comparative studies with other cities such as China top tier cities, Hong Kong or Japan could be investigated to identify similarities across attitudes, mitigation measures and air travel resumption. Domestic and international travel recovery would differ and hence the investigation of other neighbourhood countries’ international travel recovery would be critically related to Singapore’s international recovery. The research survey, given that it is applying convenience sampling, has the largest proportion of respondents in the age 50 years old group range. Future research with more time, resources and even incentives could explore more extensive and robust sampling strategies such as random or stratified sampling. The statistical evidence would likely be more generalizable.

Conclusions

News and reports suggested that aviation, tourism would only be expected to recover beyond 2021 or even later. Singapore Airlines announced a massive retrenchment exercise after more than 80% of their capacity grounded. This study was conducted amid the COVID-19 pandemic, where lockdown and travel restrictions were issued globally for the entire year of 2020. The domestic COVID-19 cases skyrocketed in the early half of 2020. However, the government has provided an enormous stimulus to the aviation sector. Moreover, the mitigation measures by the government have intensified after community cases rose. How effective are the mitigation measures and could attitude towards these measures influence the effectiveness and thereby expectations of air travel to resume earlier is debatable. These questions are a subset of the overarching research question interested in how Singaporeans felt towards the pandemic, effectiveness of the mitigation measures, and expected air travel to recover. The research question focused on the aviation sector given the severe impacts felt from the pandemic as established from past researches in other airports such as Croatia. This research has been guided by literature that established the cusp catastrophe model theoretical foundation and methodological approach adopted by (Yap et al., 2010) team. Literature related to SARS 2003 was comprehensively reviewed and past confidence in the effectiveness of mitigation measures such as early lockdowns on good post-pandemic recovery for certain countries provide the optimism and forward-looking expectations to Singapore for COVID-19.

However, the concentration of literature in the European airports for COVID-19 presents a gap in understanding the current situation on COVID-19 especially on the attitudes and travelling demand amongst Singaporeans in the aviation drought brought about by the yearlong pandemic. Therefore, this paper has contributed to the pandemic impact analysis on the Aviation industry and have addressed the research question and objectives. The knowledge and fear of COVID-19 were measured and tested to be significantly higher for airport staff, thereby fulfilling research objective 1 and contributing to the research question's attitude perceptions. The reported results also further affirmed previous literature that positive attitudes towards mitigation measures correspond to faster recovery, albeit, in this study, the expectation of faster recovery was tested addressing research objectives 2 and 3. The overarching research question has been thoroughly examined through this research paper on Singaporeans’ knowledge and perceptions towards the pandemic and mitigation measures’ effectiveness and their expectations of air travel recovery.

Declarations

Ethical approval

The procedures used in this study adhere to the principles of the Declaration of Helsinki. The study was conducted according to the guidelines of the Declaration of Helsinki and is approved by the Ethics Committee of authors’ affiliating institution.

Informed consent

All study participants provided informed consent.

Competing interests 

The authors declare no competing interests.

Funding: 

This research received no external funding.

Author Contributions: 

All of the authors contributed to conceptualization, formal analysis, investigation, methodology, and writing and editing of the original draft. All authors have read and agreed to the published version of the manuscript.

Acknowledgements

None

Availability of data and materials

The authors confirm that all data generated or analysed during this study are included in this published article.

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