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Airline choice for domestic flights in vietnam application of multinomial logit model

UNIVERSITY OF ECONOMICS

ERASMUS UNVERSITY ROTTERDAM

HO CHI MINH CITY

INSTITUTE OF SOCIAL STUDIES

VIETNAM

THE NETHERLANDS

VIETNAM –THE NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

AIRLINE CHOICE FOR DOMESTIC FLIGHTS IN VIETNAM:
APPLICATION OF MULTINOMIAL LOGIT MODEL
BY

TRAN PHUOC THO
MASTER OF ARTS IN DEVELOPMENT ECONOMICS


HO CHI MINH CITY, December 2016


UNIVERSITY OF ECONOMICS

INSTITUTE OF SOCIAL STUDIES

HO CHI MINH CITY

THE HAGUE

VIETNAM

THE NETHERLANDS

VIETNAM - NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

AIRLINE CHOICE FOR DOMESTIC FLIGHTS IN VIETNAM:
APPLICATION OF MULTINOMIAL LOGIT MODEL
A thesis submitted in partial fulfilment of the requirements for the degree of
MASTER OF ARTS IN DEVELOPMENT ECONOMICS

By

TRAN PHUOC THO

Academic Supervisor:
TRUONG DANG THUY

HO CHI MINH CITY, December 2016


ACKNOWLEDGEMENT

First of all, I would like to express my gratitude supervisor Dr. Truong Dang Thuy of the Vietnam – The
Netherlands Programme (VNP) at Ho Chi Minh City University of Economics for his patience,
enthusiasm, and immense knowledge. He not only guided me to the right direction but also continuously
supported in overcoming a lot of obstabcles in my research.


Second, I would like to thank all of the respondents for spending their time to answer the questions in my
survey. They contribute significantly in collecting data for my study. Without their participation, I am
sure that the survey could not be conducted successfully.
Finally, my sincere thanks also go to my family and my friends for encouraging me throughout two years
of study as wel as throughout the process of researching and writing this thesis.
Thank you.
Tran Phuoc Tho
December, 2016

I


ABBREVIATIONS
RUM

Random Utility Model

SP

Stataed Preference

RP

Revealed Preference

VNA

Vietnam Airline

VJ

Vietjet Air

BL

Jetstar Pacific

LCC

Low cost carrier

II


ABSTRACT
In 2015, Vietnam witnessed the booming of airline industry. The participation of low cost
carriers makes the airline market more and more competitive. Understanding the behavior of
passengers is essential for any carriers to make their strategic policies.
This study employs the multinomial logit model with the data of 122 respondents to investigate
the impacts of characteristics of passengers as well as attributes of airlines on the airline choice.
The characteristics of passengers include age, gender, marital status, education, and income
whereas the attributes of airlines consist of price, number of flights of airlines, punctuality,
comfort of seat space, and quality of check in service.
A stated preference survey is conducted online from 16th to 23rd of October 2016 to collect the
data of 122 respondents, who used to travel by air at least one time before. They are required to
finish three tasks. The first task is providing their information, such as age, gender, marital
status, education, and income. The second one is evaluating about the quality of services of the
three airlines, including Vietnam Airline, Vietjet, and Jetstar. The final part is hypothetical
scenarios of fifteen domestic routes given along with the prices of airlines for the respondents to
choose one of the three airlines.
Jetstar is chosen as the base outcome, the results of multinomial logit model suggest that
characteristics of airlines have relationships with the ratios of probability of chosing Vietnam
Airline or Vietjet over probability of chosing Jetstar, except for the satisfaction of customers
about staff at the check in counter. When comparing one airline and the based airline (Jetstar),
the attributes of the third airline is also necessary to be taken into consideration. In general, a
good judgment of service of an airline makes the odds ratios of that airline and the base
increased. In contrast, a good evaluation of the based carrier or of the other airline makes the
odds ratios declined. Besides that, income has positive association with probability of choice
Vietnam Airline and Vietjet but negative relation with Jetstar, holding other variables constantly.

III


TABLE OF CONTENTS
Contents
ACKNOWLEDGEMENT ............................................................................................................................. I
ABSTRACT.................................................................................................................................................. B
TABLE OF CONTENTS:............................................................................................................................ IV
LIST OF TABLES ....................................................................................................................................... VI
LIST OF FIGURES .................................................................................................................................... VII
INTRODUCTION ........................................................................................................................................ 1
1.1.

Problem statement ......................................................................................................................... 1

a.

Overview of airline industry ......................................................................................................... 1

b.

Airline industry in Vietnam .......................................................................................................... 1

1.2.

Research objectives ....................................................................................................................... 3

1.3.

Research questions ........................................................................................................................ 4

1.4.

Scope of the thesis ........................................................................................................................ 4

1.5.

Structure of thesis ......................................................................................................................... 4

LITERATURE REVIEW ............................................................................................................................. 5
2.1

Theoretical review ....................................................................................................................... 5

a.

Random Utility Model (RUM) ..................................................................................................... 5

b.

Reveal Preference & Stated Preference survey ............................................................................. 7

2.2.

Empirical review ........................................................................................................................... 8

RESEARCH METHODOLOGY ................................................................................................................ 13
3.1. Stated preference method ................................................................................................................. 13
3.2. Questionnaire and survey process .................................................................................................... 14
3.3. Attributes of airlines ........................................................................................................................ 16
3.4. Model specification .......................................................................................................................... 18
DATA & EMPIRICAL RESULTS............................................................................................................. 23
4.1. Data .................................................................................................................................................. 23
4.2. Empirical results .............................................................................................................................. 31
a.

Controlling variables ................................................................................................................... 35

b.

Attributes of airline ..................................................................................................................... 37
IV


c.

Effect of different routes ............................................................................................................. 38

CONCLUSION ........................................................................................................................................... 41
REFERENCES .............................................................................................................................................. i
APPENDIX ................................................................................................................................................... v

V


LIST OF TABLES
Table 3.1. Summary of hypothetical scenarios in survey: ................................................. 15
Table 3.2. Attributes of airline: .......................................................................................... 17
Table 3.3. Prices and numbers of flights by routes of carriers .......................................... 20
Table 3.4. Description of variables: ................................................................................... 21
Table 4.1. Social demographic characteristics ................................................................... 27
Table 4.2. Estimation results of multinomial logit model ................................................. 32

VI


LIST OF FIGURES

Figure 3.1. The screen of the online survey ................................................................................ 16
Figure 4.1. Airline Choice for Destinations................................................................................ 24
Figure 4.2. Frequency Of Income ............................................................................................... 25
Figure 4.3. Willingness to pay for routes.................................................................................... 26
Figure 4.4. Check-In Service Evaluation .................................................................................... 28
Figure 4.5. Cabin Crew Service Evaluation ............................................................................... 28
Figure 4.6. Food & Drink Onboard Evaluation ......................................................................... 29
Figure 4.7. Inflight Seat Space Evaluation ................................................................................. 29
Figure 4.8. On-time Performance Evaluation ............................................................................ 30
Figure 4.9. Schedules Delay Evaluation ..................................................................................... 30
Figure 4.10. Predicted probability of airline choice and income ............................................... 35
Figure 4.11. Predicted probability of airline choice and age..................................................... 36

VII


CHAPTER 1

INTRODUCTION
1.1.

Problem statement
a. Overview of airline industry

In 2015, the world’s aviation industry achieved the highest net profit in history, 33 billion
dollars. It is nearly double when compared to a net profit of 17.4 billion dollars in 2014.
Particularly, the aviation industry in Asia Pacific obtained net profit of more than 5.8 billion
dollars. In addition, region of Asia Pacific accounted for 31% of global passengers, while Europe
and North America is 30% and 26%, respectively. It is noted that low cost carrier has transported
over 950 million passengers, approximately 28% of those who are scheduled passengers (IATA
report, 2016).
According to The International Air Transport Association (IATA), number of air travelers is
forecasted to increase nearly double, from 3.8 billion in 2016 to 7.2 billion in 2035. IATA also
announces the five fastest growing markets that have the most additional passengers per year for
over the next 20 years, including China, US, India, Indonesia, and Vietnam. In detail, Vietnam
may have 112 million new passengers for a total of 150 million. Moreover, IATA also stated that
Vietnam is one of the seven countries which have fastest growth in aviation industry. Besides
that, Vietnam Government pays much attention to infrastructure which is one of the most critical
components of air transport sector. Vietnam’s planning is to have 26 airports by 2020;
particularly Long Thanh International Airport will be ready by 2020.
b. Airline industry in Vietnam
The Vietnam airline industry, which was administered by Ministry of Transport and Civil
Aviation Authority of Vietnam, has witnessed rapid growth in 2015 compared to the figures in
2014. The whole market served 40.1 million of passengers and transported 771 thousand tons of
cargo. In particular, transportation of domestic carriers is 31.1 million passengers, increased by
21%. This positive sign with the falling of crude oil price of 30% in 2015 are stimulus for airline
carriers to continue reducing fares in order to meet the demand of transportation of passengers.
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It could be said that airline industry in Vietnam has a potential market due to many reasons.
First, population of Vietnam is more than 90 million. Thus, demand of traveling is very high.
Moreover, in the recent years, income of Vietnamese is increasing so that the demand of transit
of people is also higher day by day. People have many options to choose means of transports not
only faster but also safer. Although there are some disasters of airline in 2014 in the world, it
seems that traveling by air is the safest way. According to IATA Safety Report, there were 12
fatal accidents in the total of 73 accidents, which caused to 641 fatalities on over the world in
2014. This is not a high proportion when comparing to about 33 billion passengers in 2014
(IATA Annual Review 2015). Moreover, air travel helps people save much time. For examples,
it takes about two days to transit by train from Ho Chi Minh City to Ha Noi while only two hours
by air. Finally, thanks to internet, e-commerce is more and more popular. People can stay at
home, and buy tickets with the cheap price at the time of promotion of carriers.
In 1956, the Government established the Vietnam Civil Aviation Department. At that time, there
were only five aircrafts to serve some domestic flights. In 1993, Vietnam Airlines was set up as a
national carrier. Until 1995, by gathering 20 aviation enterprises, Vietnam Airlines Corporation
was born and the airline itself is the core business. Now, Vietnam Airlines is operating an
extensive network of domestic and international services to Southeast and North Asia, Europe
and Australia. In July 2016, ANA Holding Inc became a strategic shareholder after purchasing of
an 8.77% stake. Vietnam Airlines claimed that, under the restructure plan, it will keep on to
divest the shareholding of state to 75%. Skytrax, organization of the leading airline and airport
rating of the world, certified that Vietnam Airlines is a 4-star airline.
Vietjet Air, an international low cost carrier, was the first privately owned airline in Vietnam.
Although Vietjet Air was approved to operate in November 2007, it launched the first flight in
December 2011, with only 3 aircrafts. Up to 2015, Vietjet had 29 aircrafts with 28 domestic
routes and 12 international routes. As planning of Vietjet in 2016, it will have 42 aircrafts to
meet the demand of travel and open more 3 domestic and 5 international fleets.
Another airline is Jetstar Pacific Airlines JSC. This airline was founded in 1990 as Pacific
Airlines and commenced operations in 1991 with charter cargo services under control of
Vietnam Airlines Corporation. In 2005, it began to operate in passenger service. In 2007, Qantas
Airway Limited bought a portion of Pacific Airlines’ shares and changed it as model of low cost
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carrier. It officially became a part of Jetstar network in 2008, named Jetstar Pacific. In 2012,
Vietnam Airlines purchased a 70% stake, so up to now Qantas is having only 30% stake in the
company.
Vietnam Air Services Company (VASCO) is one of a subsidiary of Vietnam Airlines
Corporation. From 2004 to now, VASCO has transported passengers from Tan Son Nhat Airport
to Southern airport such as Ca Mau, Con Dao, Rach Gia, Can Tho and many other routes.
Besides of service flight, VASCO also plays a role as a multi functioning airline and providing
maintenance service for private aircrafts.
In summary, there are four domestic carriers are operating in Vietnam at present, including
Vietnam Airlines, Vietjet, Jetstar, and VASCO. In the past, there were another two airlines used
to operate: Indochina Airlines and Air Mekong. Due to difficulty in finances, Indochina Airlines
claimed to stop all of the flights after one year in operation in 2009. Similarly, because of loss in
business, Air Mekong had to halt commercial flights in 2013. Until January in 2015, it is
officially revoked by The Ministry of Transport.
There are many literatures about the theory of customer behavior and empirical studies about
airline choice of passengers. The annual report of IATA (The International Air Transport
Association) in 2015 shows the answers of the passengers with the question “What is the first
reason for choosing an airline?” It is found that nonstop flight (15%) and lowest fare (14%) are
the reasons why customers choose an airline while recommended by travel agent and in-flight
service is just accounted for 4% and 3%, respectively. However, in Vietnam, airline industry has
just been booming in the recent years so there are not many researches focus on this topic.
Knowing the preference of passengers is necessary for both aviation firms and foreign investors.
It helps not only the three carriers have policies that are suitable for Vietnamese people but also
investors in evaluate the airline market to make decision in investing or not.
1.2.

Research objectives

This study uses stated preference survey and employs the multinomial logit model to identify the
factors that have impacts on airline choice of passengers. These factors include the
characteristics of both airline and air travelers. This study is expected to provide information on

3


factors affecting the choice of passengers, and thus provide information for carriers in identifying
their target market segments and efficiently improving their services.

1.3.

Research questions

There are two questions are proposed. First, what are attributes of airlines that giving impacts on
travelers in deciding which airline to fly? Second, what are demographic factors of air travelers
that have influence on their airline choice?
1.4.

Scope of the thesis

Although there are four carriers in Vietnam airline market, this research examines the airline
choice of three carriers, including Vietnam Airline (VNA), Vietjet (VJ), and Jetstar (BL).
VASCO is excluded from the choice set since VASCO just operate in the Southest with short
flight, for example from Sai Gon to Ca Mau, Rach Gia, Con Dao. Moreover, the main business
of VASCO is providing maintenance service for aircrafts, not transporting passengers. Therefore,
the market share of VASCO is very small so the elimination of VASCO is not a severe problem.
1.5.

Structure of thesis

The rest of the study includes four chapters. Chapter 2 reviews not only the theory of random
utility, stated preference and reveal preference data but also the empirical study of choice model
in airline industry. The third chapter presents methodology research with description of
questionnaire, process of survey, and empirical model. Chapter 4 describes in detail the data
collected from the survey and gives the results of model. Finally, chapter 5 concludes main
results and limitations of the study.

4


CHAPTER 2

LITERATURE REVIEW
This chapter first introduces the economic literature of individual choice, which is the foundation
for empirical studies in analyzing choices of economic agents, including air travellers. The
chapter then provides a review of empirical studies that analyzed choice of passengers among
carriers. Based on these reviews, a model is set up to analyze the choices of air travelers among
the three airlines: Vietnam Airlines, Vietjet, and Jetstar.
2.1

Theoretical review
a. Random Utility Model (RUM)

Random Utility Model is commonly used to represent individual choice behavior. Thurstone
(1927) first introduced a law of comparative judgment and originally developed the terms of
psychological stimuli, which leads to the result of binary probit model now. This is a model of
whether the respondents could get the different level of stimulus. The stimuli concept was further
developed as utility by Marschak (1960). The random utility model implies that the decision
maker may know the utility of each choice alternative but the researcher may not know it fully.
Therefore, it is necessary to take uncertainty into account. This leads to the result that the model
of utility consists of two parts, deterministic and random components. Deterministic components
could be observed and interpreted by the analyst while random components are unknown. There
are four main causes of uncertainty that Manski (1977) identified, including measurement errors,
the use of proxy variables, unobserved of attributes of the choicer and unobserved attributes of
the alternatives.
Discrete choice models are based on the random utility theory and other assumptions. It is
assumed that the decision-makers choose among a finite choice set, which are collectively
exhaustive and mutually exclusive alternatives and they select the alternative that brings the
highest utility. With every alternative, the deterministic factors of utility are stated as a function
of attributes, for example a linear function. The probability of selecting an alternative of an
individual is the outcome of the choice model. Besides that, random components are also the key
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factors. The difference of assumptions of the distribution of the error terms causes many forms of
choice models. According to Train (2009), the main models include logit, GEV, probit and
mixed logit model.
First, logit model is assumed that the error terms is iid extreme value. The term of iid means
independent, identically distributed (Train, 2009). It is assumed that the unobserved factors are
not correlated and have the same variance with alternatives. This assumption, on the one hand, is
restrictive, on the other hand, makes the choice probability have a very convenient form. This
convenience makes the logit model used popularly; however, in some situations, the assumption
of un-correlation over alternatives could be not appropriated. The sequences of choices over time
are also derived under the independence assumption. This means that each choice does not
depend on the others. Thanks to the convenient form, most of the researchers utilize this model
to examine many aspects of air choice behavior. (Escobari & Mellado (2014); Warburg (2005);
Yoo & Ashford (1996))
Second, to avoid the assumption of independence in logit model, generalized extreme value
models or GEV which imply a generalization of the distribution of extreme value were
developed (Train, 2009). The generalization allows the relationship of unobserved factors and
alternative. It could be seen as a special case of logit model when this correlation does not exist.
The less or more flexibility of the correlations depend on the kinds of GEV model. For instance,
a comparatively simple GEV classifies the alternatives in many groups, called nests. The
unobserved factors are assumed to have the same correlation with alternatives in the same nest
but no correlation with ones in the others nests. Hess (2008) employs nested logit model to
establish model of air travel behavior. Pels et al (2001) also use nested logit model to describe
the passenger concerning in airports and airlines.
Third, probit can deal with three limitation of logit model. Train (2009) shows the restrictions of
logit model, including not representing random taste variation, IIA property and correlation
between unobserved components and alternatives. However, probit model assumes that errors
terms are normally distributed. Therefore, the only limitation of probit model is that, in some
cases, unobserved factors may not have normal distribution.

6


Finally, mixed logit permit the unobserved factors to have any distribution. In this model,
unobserved factors could be divided in two parts. One part includes all of the heteroskedasticity
and correlation while the other part is iid extreme value. It is noted that the first part could obey
any distribution, not excluding non-normal distribution. Adler et al (2005) apply mixed logit
model to develop itinerary choice model. The research of Warburg (2005) employs both of
multinomial logit model and mixed logit to understand the flight choice behavior of passengers.
In reality, there are many other discrete choice models specified for specific purposes by
researchers. These models are often established by incorporate the concepts of other models. For
example, a mixed probit could be obtained by breaking down the observed components as in
mixed logit, yet, the second part is normal distributed in lieu of extreme value distributed. By
acknowledging the motivation and derivation of these models, researchers are able to determine
the model that is suitable for a specific situation to achieve the goals of their studies.
b. Reveal Preference & Stated Preference survey
There are two main kinds of surveys which are conducted to analyze the behavior of customers,
including revealed preference (RP) and stated preference (SP) survey. RP data provide
information about the preferences in a real choice environment. This brings the primary
advantage of RP data, actual behavior of respondent. However, it is difficult to do trade-off
analysis with RP data (Bhat & Sardesai, 2004). Moreover, for new alternatives introduced in the
new market, it could not handle the models with RP data (Whitaker et al, 2005). According to
Yoo and Ashford (1996), there are three practical limitations of RP data. First, it is not enough
variation for some interesting variables to calibrate a statistical model. Second, researchers face
to difficulty with estimating model that reflects the trade-off ratios due to the correlations of
explanatory variables. Finally, to calibrate statistical models, it is necessary to carried out very
large surveys to obtain enough observations. Therefore, not many researchers employ this
method of survey in modeling choice behavior of customers. Carrier (2008) use RP data of a
booking data so that the study does not include the non-booked travel alternatives, such as
income, purpose of travel,…Escobari and Mellado (2014) collect data from the online travel
agency and use posted priced and the changes of inventory to explain the demand of flights.

7


In contrast, in SP survey, the hypothetical scenarios are designed to understand the stated
responses of the interviewers. Thus, SP data could reduce the limitation of RP data. According to
Collins et al. (2012), with SP data, it is possible to reproduce the output of behavior, such as
willingness to pay. In addition, by conducting SP survey, it is able to explore the choice behavior
of consumers regarding the alternatives that do not exist. Nevertheless, SP data has limitation
that the respondents may be uninterested or careless in a survey, or may express their own
opinions about the context of survey rather than give information about a new product usage
(Warburg, 2006). Besides that, decision making in hypothetical situation easily leads to the result
of bias because people may not do as what they say. In practical, most of the researchers use SP
survey for modeling choice behavior. Adler et al (2005) do SP survey to analysis trade-offs in air
itinerary choice while Collins et al (2012) use the interactive stated choice survey to investigate
the behavior of air travelers. Wen and Lai (2010) and Proussaloglou and Koppelman (1999) also
use SP data to examine air carrier choice of passengers.
In general, due to the full complement of RP and SP data, there are estimation techniques to be
developed to combine these data sources to deal with limitation of each type of data. It is
suggested that the most effective way is to use both of method. RP is useful for forecasting
demand or realistic purposes while SP is useful for system planning purpose (Yoo & Ashford,
1996). Similarly, to present model of itinerary choice, Atasoy and Bierlaire (2012) use mixed
dataset of RP and SP. The mixed data enable the study to succeed in estimating elasticity of price
in demand model.
2.2.

Empirical review

There are several studies that examine all the different aspects of airline choice behavior. For
instances, the researches of Basar and Bhat (2004), Hess and Polak (2005), and Pathomsiri and
Haghani (2005) investigate the airport choice in multi-airport regions. Besides that, some papers
focus on not only airport choice but also other aspects of travel. Ndoh et al. (1990) study airport
choice and route choice of passengers whereas Furiuchi and Koppelman (1994) examine the
passengers’ destination choice and airport choice. In addition, there are a few studies pay
attention to air traveler choice rather than airport choice, such as the research of Chin (2002),
Algers and Beser (2001), Proussaloglou and Koppelman (1999), and Yoo and Ashford (1996).

8


The multinomial logit model of choice is utilized in most of the studies mentioned above. Other
studies, such as Ndoh et al. (1990), Furiuchi and Koppelman (1994), and Pels et al. (2001) use
the nested logit model to estimate the multidimensional and spatial choices of air travelers.
However, the papers that attempt to consider the issues of behavior or effects in air travel choices
employ the mixed multinomial logit model (Hess & Polak, 2005; Pathomsiri & Haghani, 2005).
Moreno (2006) uses the multinomial logit model to address airline choice for domestic flights in
São Paulo. There were 1,923 passengers interviewed at the departing lounges of São PauloGuarulhos International Airport (GRU) and São Paulo-Congonhas Airport (CGH). It is believed
that airline choice is the result of the tradeoff due passengers have to face with flight cost, flight
frequency, and performance of airline. Thus, three types of variables are tested. First, variables
associated with cost are the lowest and highest fare. The second type of variables is those
associated with flight frequency, including the existence of connections or stops, travel period,
and the day of the week. Finally, age of airline is used to be proxy of performance of airline. This
study finds that the lowest fare is the best explained variable of airline choice. Besides that,
senior passengers seem to pay more attention to airline age than junior passengers. In the same
way, Nason (1981) conducts a stated preference survey to ask respondents to make a choice of
airline among a list of airlines. By employing multinomial logit model, the research considers
airline choice as a function of attributes of airline service as well as characteristics of passengers.
With revealed preference survey, Prossaloglou and Koppelman (1995) examine airline choice of
passengers who depart from Dallas and Chicago in the US. In multinomial logit model,
independent variables are schedule convenience, reliability, fares, city pair presence, market
presence, and frequently flyer program of membership. The results show that the attractiveness
of carriers and its market share are positively associated with program of frequently flyer.
Similarly, Nako (1992) explores the choice of airlines of business travelers as a function of the
frequently flyer program of airlines. It is concluded that frequently flyer programs affect
positively on demand of airline. Similarly, Prossaloglou and Koppelman (1999) investigate the
passengers’ choice of airline, flight, and fare class by using logit model. The authors consider
that air travelers are rational decision makers, who tend to choose the alternative brings the
highest utility. The explanatory variables include fare class, fare price, presence of carrier
market, service quality, frequent flyer participation of travelers, and flight schedules. Moreover,
9


the study use separate models to estimate for different groups, such as business and leisure
passengers. These models are based on stated preference data, which is collected by a two-tier
survey. First, initial data involving in the characteristics of passengers, such as previous trip,
purpose of trip, address, membership of frequent flyer are collected via mail survey. Second, a
sample of mail survey respondents is chosen randomly to be interviewed by phone. The
questionnaire is designed to simulate the search of individual for air travel options and their
selection among alternatives like during a real process of booking air tickets. The results suggest
that behavior of leisure and business travelers are significant different. Leisure travelers are more
price-sensitive but less time-sensitive than business travelers. Furthermore, businessmen pay
more attention to frequent flyer programs and they are also willing to pay more to fly with their
most preferred airlines.
In contrast, Pels et al. (2001) also use separate models for business and leisure traveler but the
results suggest that the difference between two groups is very small. The authors utilize the
nested logit model to examine the preferences of passengers in concerning airports and airlines.
In this research, the nests defined by airports as well as the nest defined by airlines are
considered in detail. This empirical study use data of the survey in San Francisco Bay Area in
1995. Furthermore, it is implied that an airline has two types of competitors: ones operate in the
same airport and the others operate in other airports because access time to the airport are
significant for both leisure and business travelers.
Besides that, Warburg (2005) says that it is valuable to understand the passengers’ flight choice
behavior and predict air travel demand. The study helps the carriers give appropriate pricing
policy and predict air travel demand in new routes. In 2001, Warburg conducted stated
preference survey which consists of 119 business and 521 non-business passengers. The
respondents were passengers who reported their most recent domestic flight. They had to make
10 binary choices between the actual flight and hypothesis flight, which was 10 itinerary
alternatives with the same departure and arrival place. Therefore, Warburg (2005) claims that
there is not existence of universal choice set. This could be explained that travelers have different
flight itineraries so there is ability of different choice set for each passenger. The study employs
both multinomial logit model and mixed logit model to examine the behavior of two groups of
passengers: business and non-business travelers. Similarly to the results of Prossaloglou and
10


Koppelman (1999), in multinomial logit model, the business people seem to be more sensitive to
time while non-business ones are more sensitive to fare and men are more sensitive to fare than
women.
Moreover, the study of Yoo and Ashford (1996) investigates the flight choice behavior of
Korean. The respondents were who had long distance international air trips, which took more
than 10 hours air journey time. By employing logit model for both RP and SP data, the
researchers also want to do comparative analysis of RP and SP survey. Surveys were conducted
at the passenger terminal of Kimpo International Airport in Seoul, in Oct 1993 for RP Survey
and in August 1994 for SP survey. Total number of samples was equal in RP and SP data. The
research gives the result that passengers paid more for Korean airline than foreign airline and
Korean residents paid more than foreign residents. Likely, Escobari and Mellado (2014) estimate
the demand of international flights by using a unique dataset with information of flight choices,
prices, and characteristics of non-booked flights. The data collected from the online agency
“expedia.com”, consist of 317 flights from 6 carriers between 19 and 24 Dec, from New York to
Toronto and vice versa. The prices and inventory changes for the flights departed from 19 to 24
Dec, 2008 were recorded. The study focuses on one way and non-stop flights. The findings show
that if the price increases 10% in 100 seat aircraft, the quantity demand decrease by 7.7 seats.
For revealed preference survey, Ukpere et al. (2012) investigate the determinants of airline
choice making in the Nigerian domestic air transport. With the questionnaire follows Likert scale
of ranking, data are collected to obtain both socio-economic characteristics and attributes of
airline. The socio-economic characteristics include sex, age, marital status whereas the airline
attributes consist of comfort, on-board service, fare, frequency, behavior of crew, and power of
monopoly. These determinants could have effects on passengers in choosing airlines at the
selected airports. By using the nested logit model, the findings show that all of these variables
are significant, that means they effect on making decision of customers. The authors also
recommend that airline should charge competitive fares and make their products distinct from
others to attract more air travelers. In constrast, Adler et al. (2005) do stated preference survey
on the internet in 2003 to collect the detail information of about 600 individuals who have just
paid for domestic air trip. The aim of this study is to understand the tradeoffs that an individual
faces to when choosing itinerary choices. The characteristics of itineraries in the survey include
11


airline carrier, airport, fare, flight times, on-time performance of carrier, the time difference
between the expected arrival time and schedule arrival time. By employing mixed multinomial
logit model, it is found that the effects of these characteristics of the service are statistically
significant. However, the limitation of the study of Adler et al (2005) is that it does not examine
the effects of characteristics of demographics and trip on the choice of individuals.

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CHAPTER 3

RESEARCH METHODOLOGY

This chapter presents the research methods, including the identification of the airlines attributes
that may affect traveler’s choice, the data collection methods, and the analytical model.
3.1

Stated preference method

According to Whitaker et al. (2005), stated preference (SP) method, a technique widely used to
understand behavior of decision makers, plays an important role in forecasting demand of
different types of airlines services. Traditionally, analysis of demand employs revealed
preference (RP) data, which is choices and decision making take place in actual environment.
Yet, approaching of RP has some practical limitations. First, it is largely associated with costs of
survey. Moreover, based on RP data, new alternatives, which may be proposed in the market in
future, could not be handled in the models.
Wen and Lai (2010) suggest that although approaching of reveal preference collects data in real
choice, it may be inappropriate since passengers often do not consider carefully all of attributes
of all possible airlines. However, the stated choice approach could analyze how individual would
respond to hypothetical choice situations, which are comprised a set of alternatives and attributes
with their levels. Recently, this method has been applied generally in airline choice and other
choice problems.
It is said that in research of travel behavior, there are two types of stated response (Hensher,
1994). First, a respondent is asked to identify his or her preferences in alternatives. This task
usually aims to find out a scale of metric, which is a rating scale or a rank ordering scale. A
rating scale is scale designed to obtain information about both of quantitative and qualitative
attributes. Likert scale and 1 to 10 rating scale are commonly used in researches. However, a
rank ordering scale has a little bit of difference. With a rating task, individuals are able to order
alternatives that listed so it could give the view of their degrees of preferences. The study of
Warburg et al (2006), Adler el al (2005) are typical examples of using rank ordering scale in
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survey of airline choice. Second, a respondent is required to take one of the listed alternatives.
This is named as first preference choice task. It is important to address types of response strategy
at the beginning of conducting an SP survey since it defines the outputs.
The survey of this study applies the first preference choice task. In this task, based on the airfares
of airlines for a specific route, each respondent is required to choose one of three airlines:
Vietnam Airlines (VNA), Vietjet Air (VJ), and Jetstar (BL). According to Hensher (1994), SP
data has an appealing feature that is ability to view the stated response as the counterpart of
reveal preference. It is because in reality, individuals decide to select one option after
considering a set of alternatives carefully. Many researchers utilize this method in their studies,
such as Wen & Lai (2010), Hong (2010). In the SP survey of Wen & Lai (2010), air travelers
face to a choice set of three carriers: China Airlines, EVA Airways, and JAA for Tapei – Tokyo
route whereas four airlines: China Airlines, EVA Airway, Cathay Pacific, and Dragon for Tapei
– Hong Kong route. Similarly, Hong (2010) conducts an SP survey which the task of
respondents is select one of three airlines: British Airways, Air France, and Easyjet.
3.2

Questionnaire and survey process

The questionnaire of this survey that is showed detail in the Appendix consists of three parts.
The first section is the questions about social demographic information and primary purpose of
trip. In the second part, respondents evaluate the quality of services of airlines, including attitude
of staff at check in counter, attitude of flight attendants, in-flight food and drink, seat space, and
on-time performance. For carriers that they have never had experience, there is an available
choice for them “I have never used this service before”. Finally, fifteen hypothetical situations
are presented. Each case is a specific route that departs from Tan Son Nhat Airport to others 15
domestic airports, is presented in Table 3.1. The hypothetical scenario is that if an individual has
travel by air, with the airfare as listed, which airline he or she could choose. In addition,
respondents also reveal their possible purpose of trip and the highest price that they willing to
pay for a ticket of each route. However, if respondents think that they would never go to one
place in future, they could choose option as “I will never go there” and skip the remaining
questions to move to the new situation.

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Table 3.1. Summary of hypothetical scenarios in survey:
Route
From Sai Gon To
Operation of airline
Vietnam Airline
Vietjet
1
Ha Noi
x
x
2
Da Nang
x
x
3
Vinh
x
x
4
Nha Trang
x
x
5
Da Lat
x
x
6
Hue
x
x
7
Thanh Hoa
x
x
8
Buon Me Thuot
x
x
9
Pleiku
x
x
10
Phu Quoc
x
x
11
Hai Phong
x
x
12
Tuy Hoa
x
13
Quy Nhon
x
x
14
Dong Hoi
x
x
15
Chu Lai
x
Note: x: having at least one flight in a day

Jetstar
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x

At the beginning, a pilot-test was conducted at the air ticket agency to identify the determinants
that have influence on decision of customers in purchasing air tickets. There were 18 customers,
who had just bought air ticket, were asked to list all of the possible factors affect their choice.
These factors consist of fare, schedules, on time performance, quality of staff service, and
comfort of seat onboard. After that, this survey was proceeded online from 16th to 23rd of
October 2016. SurveyMonkey, online survey development software is employed to design the
questionnaire. Figure 3.1 is the print screen of the online survey. The link to access this
questionnaire is sent to air travelers via mail as well as posted public on social media network,
such as Facebook and Zalo. The target respondents are those who have traveled by air before and
used to fly with at least one of airlines: VNA, VJ, and BL. It is noted that they may not have
experiment with all of three airlines. Because of loyalty of customers, some people tend to use
only service of their favorite airline. Therefore, it is not easy to find out a person who has
chances to fly with all of three airlines.

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3.3

Figure 3.1. The screen of the online survey
Attributes of airlines

In service industry, customer satisfaction is one of the most important determinants of
customer’s retention. Fornell et al. (1994) suggest that customer satisfaction brings benefits
because it means that the company gets back fewer complaint, thus it make the cost of dealing
with failure also decreased. According to Zeithaml and Bitner (1996), satisfaction of customers
is made up of a number of factors, including price, term and condition, quality of products and
services, and personal characteristics. Quality of service is not the only crucial factor of customer
loyalty since customers usually have trend to consider trade-off relationship between costs and
benefits (Lee & Cunningham, 1996). Hence, price is also a key factor of consumer satisfaction.
Furthermore, the research of Athanassopoulos et al. (2001) imply that customer behavior
responds to customer satisfaction in one of the three ways, including staying with the existing
providers, participating in word-of-mouth communicating, or changing service providers. In
airline industry, beside of price and quality of service, there are many determinants that affect on
selection of airlines, including schedule time, frequency of flights, aircraft types, number of seats

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