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The Effects of Aviation Accidents on Public Perception toward an Airline

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  • Japan Transport and Tourism Research Institute

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This research studies on the social influences and public perception to aviation accidents and airlines. When an accident happens, the media usually exaggerates the consequences and people may worry about airline safety management, resulting in loss of passengers and social panic. We selected the TransAsia Airways GE222 Accident in Taiwan, which occurred on July 23, 2014 as a study case, and adopted event study method to observe stock price fluctuation and to find the relationship with the media index. Moreover, an online survey was conducted for Taiwanese people to investigate their perception about the airline considering different cases, but there was the GE235 Accident happened during our survey, so after hypotheses testing, the results show that respondents who participated in the survey before and after the GE235 Accident, are significantly different because the latter witnessed an accident again, their image, safety perception, trust and willingness to use are strongly affected.
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The Effects of Aviation Accidents on Public Perception toward an Airline
Chen-Wei LI a, Veng Kheang PHUN b, Mio SUZUKI c, Tetsuo YAI d
a,b,c,d Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of
Technology, Tokyo, 226-8502, Japan
a E-mail: li.c.ab@m.titech.ac.jp
b E-mail: kheangphun.aa@m.titech.ac.jp
c E-mail: mios@enveng.titech.ac.jp
d E-mail: tyai@enveng.titech.ac.jp
Abstract: This research studies on the social influences and public perception to aviation
accidents and airlines. When an accident happens, the media usually exaggerates the
consequences and people may worry about airline safety management, resulting in loss of
passengers and social panic. We selected the TransAsia Airways GE222 Accident in Taiwan,
which occurred on July 23, 2014 as a study case, and adopted event study method to observe
stock price fluctuation and to find the relationship with the media index. Moreover, an online
survey was conducted for Taiwanese people to investigate their perception about the airline
considering different cases, but there was the GE235 Accident happened during our survey, so
after hypotheses testing, the results show that respondents who participated in the survey
before and after the GE235 Accident, are significantly different because the latter witnessed
an accident again, their image, safety perception, trust and willingness to use are strongly
affected.
Keywords: Aviation Accident, Public Perception, Social Influences
1. INTRODUCTION
Even with the advancements in aeronautical technology and weather forecasting, aviation
accidents still cannot be avoided. We still hear news about aircraft crashes, loss of control and
disappearance due to human errors (e.g. pilot and maintenance error), bad weather,
mechanical failure or sabotage. According to Aviation Safety Network (ASN), a
Netherlands-based online aviation database, the seriousness of aviation accidents can be
classified into accident, hijack, incident, other occurrence, unfiled occurrence, write-off and
hull-loss. Most aviation accidents are fatal, and involve other political problems, so it always
causes huge public responses and concerns.
However, aircraft is proven to be the safest among all transport modes, but why do they
always cause a big social panic and have an influence on economic performances? Even
though they are also rare, crash events are nearly always catastrophic. Besides, the media
tends to misrepresent the accident causes and usually lacks accurate safety knowledge, giving
rise to negative spillover effects not only to air transport users but also to the society.
Individual safety perception toward airlines is a key to selecting which airline to use.
Objective safety may not be an adequate measure for passengers because they cannot
correctly comprehend it, so perceived (subjective) safety may be more relevant to them. The
origin of safety perception toward airline companies may come from accident history, tangible
elements and operation performances of airline service, airfare and media as well as rumor
influences.
Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015
2347
The purpose of this research is to clarify the degree to which accidents bring about
consequences such as public perception change and social influences. We selected TransAsia
Airways GE222 Accident in Penghu, Taiwan, which occurred on July 23, 2014 as a study case.
There are four airlines (i.e. UNI Air, Mandarin Airlines, TransAsia Airways and Far Eastern
Air Transport) operating the routes from the two biggest airports in Taiwan mainland, Taipei
Songshan Airport (TSA) and Kaohsiung International Airport (KHH), to Magong Airport
(MGZ) in Penghu. The passenger demand as well as the load factor data from Civil
Aeronautics Administration, MOTC in 2013 and 2014 are compared in Figures 1 to 4.
For TransAsia Airways, the number of passengers carried and load factor
(passenger/seat) in August 2014 abruptly decreased following the accident. People may have
stopped using the airline because they mistrust it and have safety concerns about it, thus we
would like to find to what degree they are influenced and to quantify the impact.
0
20000
40000
60000
80000
100000
120000
140000
Apr May Jun Jul Aug Sep Oct
Passenger Number
2013 Passenger Number (TSA&KHH - MGZ)
TransAsia Mandarin
Uni FarEast
Figure 1. Passengers carried in 2013
0
20000
40000
60000
80000
100000
120000
140000
Apr May Jun Jul Aug Sep
Passenger Number
2014 Passenger Number (TSA&KHH - MGZ)
TransAsia Mandarin
Uni FarEast
Figure 2. Passengers carried in 2014
55.0%
60.0%
65.0%
70.0%
75.0%
80.0%
85.0%
90.0%
95.0%
100.0%
Apr May Jun Jul Aug Sep Oct
Load Factor
2013 Load Factor (TSA&KHH - MGZ)
TransAsia Mandarin
Uni FarEast
Figure 3. Load factor in 2013
55.0%
60.0%
65.0%
70.0%
75.0%
80.0%
85.0%
90.0%
95.0%
100.0%
Apr May Jun Jul Aug Sep
Load Factor
2014 Load Factor (TSA&KHH - MGZ)
TransAsia Mandarin
Uni FarEast
Figure 4. Load factor in 2014
2. LITERATURE REVIEW
2.1 Safety Perception to Aviation Accidents
The aviation market is a highly competitive environment. The delivery of high-quality service
to airline passengers is important for the airline's survival, competitiveness, profitability and
sustained growth (Suki, 2014). Even though fatal aviation accidents are extremely rare, the
rapid growth in aviation industries has caused increasing exposure to risk. Airlines need to
understand what passengers expect in order to better serve their demands and achieve the
highest level of satisfaction. Liao (2014) pointed out that airlines especially do not want to
arouse any fears or unpleasant feelings in their passengers because excellent service quality
can increase levels of customer satisfaction, and retain consumers (Hu et al., 2009).
Considering passengers’ safety perception toward the airline as one of the service quality
would offer a perspective for airlines, enabling them to identify passengers' behaviors and
Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015
2348
expectation for better market segmentation. The results will bring a significant importance to
airline strategic management to improve its airline service quality, customer satisfaction and
safety management efficiently.
Safety perception is subjective. Each person has his own level of safety concerns, so
someone may feel that a situation is acceptable while another may not feel the same. It is also
possible that even it is physically safe, people might feel mentally unsafe. The reason why
causes them have this phenomenon is due to individual risk assessment. To be more specific,
the definition of risk is a combination of the probability and consequence. Sjoberg (1998)
concluded that perceived risk consists of 3 factors: cognitive (probability), emotional (worry),
and consequences. If we want to assess the risk perception on the psychological side (Backer,
2009), a psychometric model can be used to determine the factors (Slovic, 1987).
Airline safety perception is controlled by several factors such as individual personal
traits, cultural background, knowledge, diverse backgrounds, and the environment they are
staying. Fyhri and Backer-Grondahl (2012) investigated the relationship between risk
perception in transport and personality, and they defined personality as an individual’s
enduring patterns of thought, feelings and behavior. Moreover, You et al. (2013) tested the
relationship of pilots' locus of control among risk perception and safety operation behaviors.
Risk perception is also determined by cultural variations such as nationality. Lund and
Rundmo (2009) examined the cultural differences in risk perception and attitudes toward
traffic safety and risk, taking behavior in Norway and Ghana, and their results found
differences between two countries. Gill and Shergill (2004) surveyed businesses and
individuals throughout various sector of the aviation industry in New Zealand, and indicated
that people think safety education, training and rules role are important for the organizations
to improve safety management, but pilots and aviation industry experts think luck is highly
related, which is totally different from view of the public. External factors like environment
and facilities also affect risk perception. Han (2013) found that all passengers and airline
employees want to avoid risk from potential safety hazards because air quality, temperature,
layout and amenity have an effect on people's feelings.
Slovic (1987) also showed the discrepancy of risk assessment between different groups.
An ordering of perceived risk for 30 activities or technologies for four groups were tested.
Experts consider X-rays to be risky and nuclear power as not, while the public think
oppositely. Experienced experts adopt objective risk assessment to evaluate dangers, while
most people rely on risk perception influenced by experiences, media, insufficient knowledge
and other factors.
2.2 Social Influences of Aviation Accidents
Aviation accidents cannot be 100% avoided, but it is possible to minimize the loss associated
with accidents such as by reducing social panic. Aside from aviation disasters, terrorist
attacks and economic crisis also affect aviation market performances.
The most direct and immediate effect that we can see after accident occurring is stock
price fluctuation. Stock market reaction is a suitable connection to understand passenger
choice behavior. Goh et al. (2014) used ESM (event study method) and CAPM (capital asset
pricing market) model to realize investors' and market confidence after financial crisis.
Flouris and Walker (2005a), Flouris and Walker (2005b) and Walker et al. (2005) also
adopted ESM to examine economic influences by checking short- and long- term stock
performance of airlines and aircraft manufactures after aviation disasters and terror attacks.
Crisis management is also used in other fields. MacDonald et al. (2012) used
descriptive research method to establish what the government could do to increase public
Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015
2349
confidence in their vaccine system. Results indicated that despite the evidence showing
vaccines are safe and effective, public concerns continue because beliefs rather than facts and
evidence confirm the safety of vaccines. This is similar to airline safety. For an airline that has
no recent disastrous incidents and has passed safety standards, people still tend to trust their
safety perception and think otherwise. In the field of food safety, de Jonge et al. (2008) built a
structural model to compare public food safety perception and consumer confidence in
Canada and the Netherland. Seo et al. (2014) used ESM to develop an effective food crisis
management strategies and to measure changes in stock prices, associated with the release of
news.
In other words, people tend to believe themselves rather than experts in issues such as
aviation accidents, food crisis or vaccine confidence, and thus have a huge perception gap
from experts. The only way to eliminate this gap is to inform and educate people about risk
through risk communication and risk management, and to help them overcome excessively
high risk perception.
3. ANALYSIS AND HYPOTHESES DEVELOPMENT
3.1 Aviation Accident Stakeholders
The structure of multiple involved stakeholders of aviation accidents is drawn in Figure 5,
which is a revised version of the original diagram by the Ministry of Transportation and
Communications, ROC. Light lines connect the stakeholders in normal case, and dashed lines
refer to coordination. In the figure, we focus on the route of post-accident, which is drawn by
heavy lines, and highlight the important stakeholders such as airline, the media, the public,
passenger, and economy & society. We can also find that these listed stakeholders are
interactively related. When an accident happens, the media announces the news to the public,
and then they become concerned about this issue.
Accident
Investigator
NGO
Military Passenger
Civil Aviation
Bureau
Ministry of
Transport International
Association
Report
Monitor Report
Requirement
Report
Monitor
Demand
Service
Audit
Report
Educate
Coordinate
Data
Report
Rescue
Rescue
The Congress
Law
Law
Navigation
Service
Empower
Economy
& Society
The public
Information
Impact
Publicize
Information
Motivation Impact
Coordination
Normal case
Post-accident
Media
Environment
Airline
Emission
Figure 5. Involved stakeholders of aviation accidents
To investigate the impact of accidents, we selected one real accident for our case study -
the GE222 Accident, which occurred at 19:06 on July 23, 2014. The operator was TransAsia
Airways ATR 72-500, and the number of fatalities were 4 crew members and 44 passengers of
Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015
2350
total 58 occupants. The flight was from Kaohsiung International Airport (KHH) to Magong
Airport (MZG), located in an isolated island in Taiwan Strait. The aircraft was out of flight
path in en route phase before landing at Magong Airport because of bad weather. A sudden
vertical wind shear caused the aircraft to lose lift force, which resulting the aircraft to be
destroyed and crash onto local houses.
3.2 Social Influences of Aviation Accidents
The event study method (ESM) is a statistical technique to study stock price fluctuation
caused by unexpected events, and can be used to quantify short-term impacts. Crisis
management research has used ESM to explore the societal impacts of the release of new
information or occurrence of unique events by measuring changes in stock prices (Seo et al.,
2014; Walker et al., 2005). The assumption of the methodology is that the abnormal returns
(ARs) are the result of the announcement and that no other event occurrs on the same day.
)ln(
1,
,
,
=ts
ts
ts P
P
R or
1,
1,,
,
=ts
tsts
ts P
PP
R, for t=[-i,-1] and iN-{1} (1)
where, s : stock,
t : day,
Rs,t : the returns of the stock s on day t,
Ps,t : the closing price of stock s on day t.
Returns of the stock is computed as the difference between Ps,t and Ps,t-1. The
coefficients α and β are estimates of the parameters obtained via ordinary least squares (OLS)
regression where Rm,t represents for market return on day t given one period, t=[-i,-1]. The
stock returns are regressed against the return of market index to remove overall market effects.
The date of the event is denoted as t=0.
ttmts
RR
εβα
+×+=
,,
, for t=[-i,-1] and iN-{1} (2)
tmts RER ,, ˆ
×
+
=
β
α
, for t=[1, j] and jN-{1} (3)
where,
α, β : estimates,
m : market,
εt : error term,
Rm,t : the returns of the market on day t,
ERs,t : expected return of stock s on day t.
Abnormal return on day t (ARs,t) is the subduction of real stock return and expected
return. Note that abnormal returns are returns over and above the return predicted by general
market trends on a given day. Finally we can accumulate abnormal returns within given event
window to get cumulative abnormal return (CARs).
tststs
ERRAR
,,,
= , for t=[-i,j] and i, jN-{1} (4)
=
ttss
ARCAR
,
, for t=[-i,j] and i, jN-{1} (5)
Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015
2351
where, ARs,t : abnormal return of stock s on day t,
CARs : cumulative abnormal return of stock s.
TransAsia Airways’ closing price and market return were retrieved from homepage of
Taiwan Stock Exchange Corporation. The next trading day of TransAsia Airways aircraft
crash is July 24, 2014 as t=0. We collected data for a period, t=[-61, -1], 60 trading days of
pre-event data to determine the trend by using OLS, where t=-61 is April 25, t=-1 is July 22,
2014. To estimate the relationship between selected stock (TransAsia Airways) and whole
Taiwan stock market performances, we can get β=0.0476, α=0.001, and ERt = 0.001 +
0.0476
×
Rmt. Abnormal returns are the subtraction of expected returns for t=[-61, 47] and real
returns as illustrated in Figure 6, where t=47 is September 30, 2014. As we can see, there is a
big drop at t=0, indicating a big retreat on stock market. CARs is accumulation of ARs, as seen
in Figure 7. Before t=0, CARs had already been decreases, and the negative growth trend
becomes more serious after the accident.
-6%
-4%
-2%
0%
2%
4%
6%
-70 -50 -30 -10 10 30 50
AR
day
-30%
-25%
-20%
-15%
-10%
-5%
0%
5%
-70 -50 -30 -10 10 30 50
CAR
day
Figure 6. Abnormal Returns, t=[-61,47] Figure 7. Cumulative ARs, t=[-61,47]
3.3 Relationship of Media Exaggeration and Social Impact
The media has a strong influence on consumers, such as in purchase behavior, perception,
trust, and self-identification. For instance, Fang et al. (2012) found that during the avian
influenza outbreak in Taiwan in 2004, the fear of chicken product consumption lowered risk
tolerance and amplified public risk perception and anxiety through the repeated mass media
transmission of information. To confirm aviation disaster causes, Walker et al. (2014)
accessed various news services data source including Lexis/Nexis, Bloomberg and Reuters,
and then determined what causes were reported in the initial news reports about the accident
in order to ensure the direct influences to investors by using ESM. Yadavalli and Jones (2014)
checked influences on consumer behaviors caused by positive media portrayal and negative
media coverage about lean finely textured beef (LFTB) in the US. They found that consumers
rely on news media to direct their food purchase decisions, and discussion of the LFTB
controversy aroused curiosity in readers, causing them to seek further information on the topic.
To quantify the media impact, a long-term tracing quality-adjusted approach to construct a
weighted media index can be used. The media index is computed by summing the number of
news articles across each news source per day and calculating an expected value based on
percent of consumer readership per news source and the number of total news articles.
In this research, we accessed two news databases (udndata.com and KMW) and two
newspaper official websites pertaining to the four biggest newspapers in Taiwan: United Daily
News Series, China Times Series, Liberty Times, and Apple Daily. To observe short-term
influences to society from news releases of the aircraft crash, we used the keywords “Penghu
Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015
2352
Aircraft Accident” and “TransAsia” to collect all related news from July 23 to September 30,
2014 and made one database.
All related news (920 articles) were collected to get the number of news articles during
the said period. According to Figure 8 which shows the media index of the GE222 Accident,
we can understand that most news articles are focused on the first week and close to zero after
one month. The peak occurs on July 24 (230 articles), which is the day after the accident, and
we based on this day to standardize all data.
0
50
100
150
200
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
22-Jul 1-Aug 11-Aug 21-Aug 31-Aug 10-Sep 20-Sep 30-Sep
Number of News Articles
Media Index
230
24-Jul
Figure 8. Media index and news article of the GE222 Accident
3.4 Hypotheses Development
In order to figure out the direct relationship between media influences and social impacts, we
would like to compare abnormal returns and news release. Abnormal returns in Figure 6 are
similarly standardized using July 24 as the base, and then converted to absolute values. Figure
9 presents a graph that directly compares abnormal returns and number of news releases to
represent the influences to the society from a combination of stock performances and the
media. We found that they have similar trends especially in the first week, and it is almost
corresponded. For this reason, we can judge that ESM, which is used to evaluate social panic,
is related to media influences, and simultaneously we can say media exaggeration did affect
the public cognition. As a result, we would also like to make hypotheses to clarify whether
people are affected by aviation accidents, and that will be explained in next section.
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
22-Jul 29-Jul 5-Aug 12-Aug 19-Aug 26-Aug 2-Sep 9-Sep 16-Sep 23-Sep 30-Sep
Absolute value of AR News Releases
r = 0.75
Figure 9. Comparison of media and stock performance
Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015
2353
4. METHODOLOGICAL APPROACH
4.1 Implementation of Online Survey
In order to test the hypotheses, we adopted a 23-item online survey for Taiwanese citizens
from January 27 to February 16, 2015. The purpose is to realize what people consider about
aviation accidents, and to clarify the degree to which people may perceive toward an airline
that has recent accidents. It is feasible and reasonable to collect airline preference and
evaluation data by online survey, because most of the people purchase air tickets through the
Internet. Moreover, talking about aviation accidents to passengers in the airport is not morally
allowed, so if we implement face-to-face questionnaire, it is highly possible to be rejected.
4.2 Research Instruments
The questionnaire consists of four sections. Firstly, we investigated their previous flight
experience for domestic and international routes, and preference and usage of four airlines in
Taiwan, UNI Air, Mandarin Airlines, TransAsia Airways and Far Eastern Air Transport. Then
we selected TransAsia Airways GE222 Accident in July 2014 as a case study target, and asked
people about their image and identity, safety perception, trust and willingness to use and
recommend toward TransAsia Airways using a Likert 5-point scale (i.e. 1: very low, 2: low, 3:
medium, 4: high, 5: very high). In the second section, there are two cases representing before
and after the GE222 Accident. We would like to understand people’s considerations toward
TransAsia Airways before the accident, so we let them recall the previous situation and rate it
as case 1 in this study, while their considerations after half year of the GE222 accident were
asked in case 2. In third section, we asked about their safety perception toward aviation
accidents regarding to accident record impact, media impact, willingness to use the airline and
confidence of safety knowledge with a Likert 5-point scale to rate the level of agreement (i.e.
1: strongly disagree, 2: disagree, 3: neither agree nor disagree, 4: agree, 5: strongly agree).
Socio-economic information including gender, age, monthly income, civil status and
education level were retrieved in last section.
0
15
30
45
60
75
90
105
120
17-Jan19-Jan22-Jan24-Jan27-Jan29-Jan1-Feb 3-Feb 6-Feb 8-Feb11-Feb13-Feb16-Feb
Person
Respondent/day
GE222 Accident
23-Jul 2014
i=1
Group 1 i=2
Group 2
j=1
Case 1 j=2
Case 2
Respondent/day
GE235 Accident
4-Feb 2015
0
23-Jan
Figure 10. Distribution diagram of respondents and accidents
Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015
2354
However, during the course of the survey, another TransAsia aircraft crash (GE235
accident) occurred in Taipei on February 4, 2015. Thus, respondents who participated in the
survey after the second aircraft crash would have different feelings, perception and response
due to the recent catastrophic accident and media exaggeration. Therefore, the respondents
were separated into two groups according to answering period, which means before and after
the GE235 Accident, as group 1 and group 2 respectively. Figure 10 shows the distribution
diagram of respondents and accident period. Originally we only planned to implement survey
for group 1, but since the second accident occurred unexpectedly, we decided to continue
collecting data. As we can see clearly, the questions we asked are regarding case 1 and case 2,
and respondents are composed of group 1 and group 2.
4.3 Hypothesis Description
The methodological approach used in this study is hypotheses testing. We intended to testify
whether aviation accidents have an impact on public perception toward airline companies for
different groups under different cases, so six hypotheses in Figure 11 are described.
G1S1 (μ11k)
G2S1 (μ21k)
G1S2 (μ12k)
G2S2 (μ22k)
H1
H2
H3
H6
H5 H4
Figure 11. Configuration of 6 hypotheses
μijk stands for mean of category k in case j for group i., for i= 1, 2 (G1 and G2 in brief),
j=1, 2 (S1 and S2 in brief) and k=1, 2, 3, 4 (image & identity, safety perception, trust and
willingness to use and recommend, as IMG, SAF, TRU, and WLN in short). The range of μijk
is from 1 (very low) to 5 (very high).
Public attitudes toward the airline may change if an accident happens because its
occurrence would decrease their product confidence, alter brand image and identity, and also
let them reevaluate the airline again. People may attribute the cause of accidents to operation
and safety management, and arouse emotional distrust to the airline. Since the airline has a
bad record once, people may be afraid of repeated occurrences again, thus their willingness to
use and recommend the airline would be affected. The two hypotheses for group 1 and group
2 respectively are given below:
H1. An accident has a negative effect on the airline assessment from the viewpoint of public
perception on the base of group 1. (μ11k >μ12k)
H2. An accident has a negative effect on the airline assessment from the viewpoint of public
perception on the base of group 2. (μ21k >μ22k)
Regardless of groups, perception in case 1 should be equal because there were no
accidents before the GE222 Accident, so when respondents recall their previous consideration,
they are supposed to judge TransAsia Airways in the same standard. However, another recent
Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015
2355
accident (the GE235 Accident) could have had an influence on group 2 respondents, which
implies that their criteria of perception could be biased and sentimental at that moment as
group 2 directly experience the seriousness of aircraft crash again through the media. In a
similar way, the evaluation of TransAsia Airways after the GE222 Accident would be
interfered with the GE235 Accident, we suppose that respondents may deepen their reactions
and turn to be more pessimistic due to the recently happened accident. For these reasons, we
assume that for group 1 and group 2, their perception in case 1 and case 2 are different due to
the latest aviation accident involving the same airline.
H3. A more recent accident has a negative effect on the airline assessment from viewpoint of
public perception on the base of case 1. (μ11k >μ21k)
H4. A more recent accident has a negative effect on the airline assessment from viewpoint of
public perception on the base of case 2. (μ12k >μ22k)
When a tragic event breaks out, most of the people may be shocked and closely follow
the news. Most of time, they would have an overwhelmed reaction upon hearing about it, then
they would calm down gradually and adapt to the negative change until they can accept the
truth. The period of adaptation to an aviation accident would vary across individuals and
people would likely have additional influences from another recent accidents. In line with this,
another hypothesis is proposed.
H5. A more recent accident has a stronger effect than a less recent accident on the airline
assessment from viewpoint of public perception. (μ21k >μ12k)
To sum up H1 to H5, the level of influence caused by aviation accidents for different
groups under different cases can be specified.
H6. Both accidents have a negative effect on the airline assessment from viewpoint of public
perception. (μ11k >μ21k >μ12k >μ22k)
Table 1. Hypotheses and statistical methodology
H0 HA Methodological approach
H1
μ11k =μ12k
μ11k >μ12k t-test: paired two sample for means
H2
μ21k =μ22k
μ12k >μ22k
H3
μ11k =μ21k
μ11k >μ21k (1) F-test: two-sample for variances
(2) t-test: two-sample assuming equal/unequal
variances
H4
μ12k =μ22k
μ12k >μ22k
H5
μ21k =μ12k
μ21k >μ12k
H6
- μ11k >μ21k >μ12k >μ22k
Combination of H1 to H5
We adopted a statistical method to prove the hypotheses. Null hypothesis (H0) and
alternative hypothesis (HA) for H1 to H6 are listed as Table 1. It should be noted that we
examined paired samples in H1 and H2 because we checked two variables given one group,
while in H3, H4 and H5, we had to compare the difference between two samples, group 1 and
group 2. Moreover, we conducted F-test for H3, H4 and H5 in advance to check whether their
variances are equal or not. In this study, after implementing one-tailed t-test, if p-value is
smaller than level of significance, which normally uses 5%, null hypothesis can be rejected,
and then we can accept alternative hypothesis.
Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015
2356
5. RESULTS
A 3-week intensive online survey was conducted from January 27 to February 16, 2015 for
Taiwanese citizens. We didn’t provide incentives for them to prevent double submission, and
finally we received 393 effective samples for data analysis. Owing to answering date, the
respondents were divided into two groups according to their response date: (1) group 1 refers
to those who answered the survey before February 4 (the date of the GE235 accident), and
comprises of 202 samples; and (2) group 2 refers to those who answered after the said date,
and is composed of 191 samples.
5.1 Descriptive Statistics
A demographic profile of total respondents including gender, age, civil status, education level
and monthly salary (NTD) for groups 1 and 2 is summarized in Table 2. In this table, we can
find that most of respondents are young (aged 21-30) and highly educated with advance
diplomas. This could be due to the fact that this survey was conducted online, which was
chosen because it is common to purchase air tickets online nowadays. We also examined the
correlations between aviation safety perception and age, income and education level in Table
4, eventually found there is no relationship. Thus, this survey has targeted general people, and
can be used to understand their safety consciousness toward aviation industry regardless of
their socio-economic statuses.
Table 2. Demographic profile of respondents (group 1/group 2)
Category Item Number Category Item Number
Gender Male 117/109
Education level
Junior & Primary 003/003
Female 085/082
Senior 007/007
Age <21 001/001
Junior college 021/020
21-30 120/113
University 076/074
31-40 044/042
Graduate School 095/087
41-50 022/021
Monthly salary
(NTD) <20,000 046/043
51-60 012/011
20,000~39,999 057/055
>60 003/003
40,000~59,999 063/061
Civil status Married 060/057
60,000~79,999 020/019
Single 142/134
>79,999 016/013
Table 3. Flight experience of respondents (group 1/group 2)
Variable Item Number Variable Item Number
Annual flight
frequency* 0 21/021
Annual flight
frequency*
(domestic)
0 157/126
<1 71/065
<1 032/044
1-2 55/053
1-2 011/006
2-3 24/023
2-3 000/005
3-4 23/021
3-4 001/005
>4 08/008
>4 001/005
Frequent used or
favored airline UNI Air 125/127
Total TransAsia
used times* 0 115/112
Mandarin 029/024
1-2 058/052
TransAsia 026/016
3-4 015/007
Far Eastern 004/007
5-6 011/013
None/Others 018/017
>6 003/007
*Round trip is counted as 1.
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The flight experience of respondents such as annual flight frequency (total and
domestic), frequently used or favored airline, and frequency of use of TransAsia Airways are
shown in Table 3. The GE222 Accident is a domestic route, so the four airlines operating in
the domestic market are selected. Most respondents fly international routes instead of
domestic routes, but these airlines except Far Eastern also operate overseas routes, so it is
merely a matter of airline choice for respondents. For those who have not taken an aircraft,
they are still potential customers, thus their responses are important too.
5.2 Difference of Two Groups
To understand public safety perception toward the air industry, we tested their agreement of
the statements regarding aviation accidents, as shown in Table 4. This table compares group 1
and group 2, their consciousness regarding to accident record impact, media impact,
willingness to use the airline, and confidence of safety knowledge. We adopted Likert 5-point
scale to rate level of agreement (i.e. 1: strongly disagree, 2: disagree, 3: neither agree nor
disagree, 4: agree, 5: strongly agree). From the results, we can find that group 2 are more
sensitive to aviation accidents than group 1. They tend not to trust the airline especially if an
accident occurs to the same company again, and further decrease their willingness to use it.
Correlation test also reveals that safety perception is independent of age, income and
education level for both group 1 and group 2. It means that no matter how different their
socio-economic statuses are, all respondents have a common criteria for evaluating safety
aviation consciousness.
Table 4. Consciousness of aviation accidents of respondents (group 1/group 2)
Item description Average
Standard
deviation
Correlation
Age Income Education
Accident record impact
1. The safety level of airlines with
accident records is the same as that of
airlines without accident records.
2.40/2.26
0.99/1.01 0.00/-0.09
-0.04/0.05 -0.21/-0.15
2. An airline with a more recent accident
is less reliable than an airline with a less
recent one. (i.e. airline with accident 5
years ago is less reliable than airline with
accident 6 years ago)
2.87/2.71
1.00/1.01 -0.04/0.03
0.00/-0.08 0.00/0.06
3. I am discouraged to purchase a flight
ticket from an airline with more frequent
accidents even it is cheap.
3.70/3.83
1.05/0.99 -0.05/-0.04
-0.02/-0.09
0.09/0.10
4. I don’t care about flying with an airline
that has had a number of casualties. 2.29/2.28
1.05/1.02 0.07/-0.03
-0.04/0.09 -0.21/-0.09
5. Even if an accident occurs, good
company response and attitude make me
feel that the airline involved is reliable.
4.07/3.90
0.70/0.78 -0.07/-0.02
-0.01/-0.06
0.07/0.10
Media impact
1. I feel dreadful when I watch the news
coverage of aviation accidents on TV. 3.85/3.71
0.82/0.99 0.07/-0.02
-0.01/-0.02
-0.13/0.09
2. I believe the information and
discussions regarding aviation accidents
and airline safety rankings on the Internet.
3.60/3.25
0.73/0.87 -0.12/0.00
0.06/0.09 0.19/0.15
3. I am influenced by family members,
relatives and friends to not use airlines
with bad safety records to make them less
worried.
3.79/3.78
0.90/0.87 -0.01/-0.04
-0.06/-0.03
-0.02/0.15
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Airline use willingness
1. I do not want to buy or may sell (if
owned) stock of an airline that I distrust. 3.65/3.72
0.80/0.87 0.20/0.13 0.05/0.09 -0.05/0.06
2. I will use the airline even my safety
perception toward it is bad. 2.36/2.37
0.94/0.95 -0.11/-0.01
-0.01/0.09 0.03/0.05
3. I wouldn’t recommend an airline that I
distrust. 4.12/4.03
0.62/0.76 -0.05/-0.11
0.01/-0.09 0.09/0.05
Confidence of safety knowledge
1. I am familiar with airline safety
management and know what to do in
emergency.
3.02/2.88
0.93/0.95 0.01/-0.01
0.04/0.06 0.05/-0.08
5.3 Hypotheses Testing
Table 5 shows the average and standard deviation of μijk for i = 1, 2, j = 1, 2 and k = 1 to 4.
Respondents used 5-point Likert scale to evaluate TransAsia Airways about image & identity,
safety perception, trust and willingness to use and recommend in case 1 and case 2.
Table 5. Average and standard deviation in 2 cases for 2 groups
Case 1 Case 2
IMG SAF TRU WLN
IMG SAF TRU WLN
Group 1
(202) AVG
3.04
3.04
3.05
2.94
2.50
2.42
2.39
2.29
STD
0.70
0.68
0.70
0.79
0.82
0.84
0.85
0.86
Group 2
(191) AVG
2.90
2.87
2.86
2.82
1.97
1.84
1.85
1.79
STD
0.72
0.74
0.74
0.77
0.80
0.81
0.79
0.82
As we can see, given that a rating of 1 is very low and 5 is very high, the range of each
average number μ11k is 2.94 to 3.05, μ12k is 2.29 to 2.50, μ21k is 2.82 to 2.90 and μ21k is 1.79 to
1.97. There are significant differences among those data, so we tested the six hypotheses
outlined in Section 4.3 and examine the results. We adopted paired sample t-test for H1 and
H2. For H3, H4 and H5, because they covered different samples, F-test was conducted at first
respectively, and found variances of H3 and H4 were the same but variances of H5 were
unequal. After that, two-sample t-test assuming equal/unequal variances was implemented to
examine the significance.
Table 6. Summary of hypotheses testing
H0 HA Significance
IMG (k=1) SAF(k=2) TRU(k=3) WLN(k=4)
H1 μ11k =μ12k μ11k >μ12k ** ** ** **
H2 μ21k =μ22k μ12k >μ22k ** ** ** **
H3 μ11k =μ21k μ11k >μ21k * ** **
H4 μ12k =μ22k μ12k >μ22k ** ** ** **
H5 μ21k =μ12k μ21k >μ12k ** ** ** **
H6 - μ11k >μ21k
>μ12k >μ22k Confirmed Confirmed Confirmed Partially
confirmed
** Significant at 1% level; * Significant at 5% level.
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Null hypothesis (H0), alternative hypothesis (HA) and p-value for H1 to H6 are listed as
Table 6. After conducting one-tailed t-test for H1 to H5, it was found that all p-values indicate
greatly significant at 1% or 5% level, so null hypotheses can be rejected, and then accept
alternative hypotheses. Examination result of WLN in H3 is 0.075, which means there is no
significant difference between μ114 and μ214 at 5% level, but it can be accepted at 10% level.
That is to say, willingness to use TransAsia Airways is almost the same before the G222
Accident, but slightly affected by the G235 Accident. H6 is the combination of H1 to H5, and
thus we can fully confirm the hypotheses for IMG, SAF and TRU, and partially confirm it for
WLN.
5.4 Discussion
The survey outcomes show that accidents significantly affected public perception toward
TransAsia Airways, and that the more recent accident had stronger influences than the
previous one, so for this reason, group 2 could not fairly evaluate the airline at the same
criteria as group 1. The findings in this research can help the government and airlines to
observe the effects on stock fluctuation, which is related to the release of news, so they could
take some countermeasures to prevent getting worse. Besides, airline companies can also
realize the consequences of repeated accidents will result in loss of customers, so they have to
implement safety management at the highest level. Finally, results indicate that public
perception is an important element in air transport management and dominates airline choice
behavior, since their image & identity, safety perception, trust are low, willingness to use and
recommend would be low as well.
6. CONCLUSIONS AND RECOMMENDATIONS
6.1 Conclusions
As time passes by, the worries are healed and gradually forgotten. We investigated public
perception change by assuming different cases for different groups of respondents. The
purpose of this study is to clarify the degree to which an accident would bring about
consequences, e.g. public perception change and accident consciousness, and we observed
this from the perspective of normal people. The concept of this research is innovative because
it takes multiple stakeholders (i.e. airlines and passengers) into account, and the degree of the
impacts are quantified by numerical data.
Our study has clearly indicated the strong correlation between the media and public
reactions, which can be reflected by stock price fluctuation. Besides, the hypothesis testing
results show that the accidents do have a strong impact on public perception. Their general
safety consciousness and evaluation to TransAsia Airways are totally different for group 1 and
group 2 and in case 1 and case 2. With time passing, the impact would decrease, people might
gradually adapt to occurrence of the accidents, and rebuild confidence toward the airline;
however, it will deteriorate again if an accident repeatedly occurs.
This study has employed a hypothesis testing approach to quantify negative influence of
aviation accidents to the public and revealed reasonable results. Aviation accidents cannot be
predicted, so there is no research collecting similar data, which is a unique and exclusive
point in our survey. The outcomes will be a typical research which analyzes the direct
influence of just happened aviation accidents.
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6.2 Recommendations
Aviation accidents arouse a huge social panic and involve multiple stakeholders, so it is
important to know and deal with the crisis. This study could serve as a constructive reference
for the government and airline companies to deal with crisis management because the results
have quantified the level of the seriousness and provide an estimation method to know the
consequence of accidents. Airlines may also make more efforts to implement safety
management in order to prevent accidents from happening. Media exaggeration about the
accident could monitor airline safety performance but also might hamper the development of
air transport market.
This is a creative and useful research which greatly matches current needs. We can
conclude that the decrease of air transport passengers after aviation accidents is attributed to
safety perception, which reduces their willingness to choose the airline. For our future work,
we can apply the outcomes of this study to foresee the change of air demand and estimate
how long the public may adapt to one accident by using duration analysis. Moreover, finding
the components that cause aviation safety perception and how these components affect criteria
of airline choice behaviors still need to be further discussed.
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Chapter
The aviation industry is an important sector in contributing to a nation’s economy and its growth and development. The continuous improvement in the aviation industry with the adoption of new technology has developed trust and confidence amongst people all over the world. However even with such improvement and technological advances embedded in the aviation industry, accidents still cannot be avoided. An aviation accident is characterized as an incident that is directly related to an aircraft’s service. Aviation accidents are often the result of a number of causes and contributory factors, many of which have a human dimension to them. In some cases, human error is a factor in as high as 70% of aircraft accidents (Feggetter 2007). Hence, the challenge for accident investigators is how best to identify and mitigate the causal sequence of events leading up to an accident. The Human Factors Analysis and Classification System (HFACS) is a general human error framework originally developed and tested within the USA military as a tool for investigating and analysing human causes of aviation accidents (Li et al. 2008). The applicability of HFACS to the analysis of large-scale datasets of incidents and accidents has also been demonstrated in both civil and military aviation environments in Taiwan, India and Australia. Further, this framework has been extended and adapted to analyse the underlying human factors causes in accidents involving remotely piloted aircraft, aviation maintenance and railroad accidents. In Asia, Singapore has a strong and robust economy and operates one of the world’s most successful and recognisable airlines. It is therefore surprising that little is known about human error in the aviation context. Thus, one purpose of the present study is to assess the utility of the HFACS framework as an error analysis and classification tool for accidents/incidents in the aviation industry in Singapore. Specifically, HFACS will be applied to commercial aviation accident and incident records maintained by the Transportation Safety Investigation Bureau (TSIB) of the Ministry of Transport (MOT). The TSIB is the air, marine and rail accidents and incidents investigation authority in Singapore. Its mission is to promote transport safety through the conduct of independent investigations into air, marine and rail accidents and incidents. A comprehensive review of 75 accidents and incidents that occurred between October 2000 and December 2019 in Singapore has just been undertaken. This is currently being analysed within a HFACS context and the findings will be reported here. Thus, the utility of the HFACS framework will be appraised, an overview of the 75 accidents will be given, and more specific detail relating to human error will be reported. At a later stage, these findings will be compared with other countries in Asia where similar data is available. Researchers claim that the HFACS framework bridges the gap between theory and practice by providing safety professionals with a theoretical tool for identifying and classifying human errors in aviation mishaps (Wiegmann and Shappell 2001a, b, c). The system focuses on both latent and active failures and their interrelationships, thereby facilitating the identification of the underlying causes of human error. The findings from this research will allow safety managers, administrators and other aviation professionals working in airlines, airports and aviation regulatory organizations to analyse their safety programmes’ strengths and weaknesses. This will also contribute to the build-up of a comprehensive database in Southeast Asia. Finally, this study is unprecedented in Singapore and will offer new insights into the nature and trends of human factors in aircraft accidents and incidents.
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