Airline Choice for Domestic Flights in Vietnam: Application of Multinomial Logit Model

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

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

TRAN PHUOC THO Class 21 January 2017 Supervisor: TRUONG DANG THUY

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. 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. JEL Classification: D12, M31 Keywords: airlines, passengers, air travelers Abbreviations: VNA - Vietnam Airlines, VJ – Vietjet, BL - Jetstar

1. INTRODUCTION 1.1. Problem statement 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). 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. 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 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. 2. LITERATURE REVIEW 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. 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. 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

4 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. 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). 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 Paulo- Guarulhos 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

5 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. 3. RESEARCH METHOD 3.1 Stated preference method 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 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. Table 3.1. Attributes of airline: Attributes of Definition Level Researchers airline Price Cost of a route (return fare) Continuos data Warburg (2005)

Cost of a route (one-way fare) AUD1600, Collins & Hess AUD1900, (2012) AUD2200, AUD2500

Average fare for each route Higher price; Wen & Lai (2010) Medium price; Lower price

Fare of the chosen flight Continuos data Adler et al (2005)

Frequency of airline Number of flights/route/day Wen & Lai (2010)

Number of direct flights in Moreno (2006) the travel day

Flights per day Pereira et al (2007)

Number of flights per week Yoo & Ashford (1996) On time Percentage of on time flight itinerary 50%, 60%, 70%, Warburg (2005) performance 80%, 90%

Lateness timing 60 min late; Wen & Lai (2010) 30 min late; On time

Percentage of on time flight itinerary 50%-99% Adler et al (2005)

On time service schedules Sometimes delay, Hong (2010) Always consistent Seat space on board Seat pitch 31", 32", 34" Collins & Hess (2012)

Passenger's evaluation of seat Very uncomfortable Wen & Lai (2010) Comfortable enough Very comfortable

Comfort Little; yes Hong (2010)

Comfort No; yes Pereira et al (2007) Check in service Passenger's evaluation of check in Very uncomfortable Wen & Lai (2010) service Comfortable enough Very comfortable

Kindness of employees Not very polite and Hong (2010) friendly Very polite and friendly 3.3 Model specification This study follows the framework of Random Utility Model of Manski (1977) since air travelers are assumed to be rational to maximize their utility. Passengers tend to select the carrier that brings them the highest utility which has the form as below:

= + = + + Where U: Utility level of passenger V: Portion of utility (observed utility), and = + : Error terms (unobserved utility) X: vector of explanatory variables i : Passenger i n = 1, 2, 3 denoted for Vietnam Airline, Vietjet, and Jetstar, respectively.

It is reasonable to assume that the actual of choosing airline n is , so:

= 1, if is maximum or > (m = 1, 2, 3, and m ≠ n)

= 0, otherwise

Let = Pr( = 1) be the probability of choosing airline n, and probability of individual i choosing carrier n is calculated as below:

= Pr( + > + ) , ∀ ≠

= Pr ( − < − ) , ∀ ≠

= Pr ( < − + ) , ∀ ≠

Therefore, an air traveler i selects airline n when < − + . It is clearly that if the distribution of error , the probability could be estimated. The multinomial logit model is based on the assumption that the error terms are identical, independent distributed extreme value (Gumbel distribution). According to Train (2009), the function of probability could be rewritten as below:

() () = = ∑ () ∑ () Furthermore, it could be seen that the total of probability of choosing three airline of an individual is equal to 1. However, according to Gujarati (2011), it is unable to identify the three probabilities independently. It is common to select one choice as the reference or base choice in multinomial logit model. The coefficients of the base choice are set to be zero. If n = 3 is chosen as the base, and set α3 = 0 and β3 = 0, the probabilities of three airlines could be obtained:

= + + 1 = + + 1 1 = + + 1 The ratio of probability of choosing airline 1 and 2 over probability of choosing airline 3 (the base) is known as the odds ratios: = (*) = (**) Taking the natural log of (*) and (**), the log of the odds ratios are called the multinomial logit model, which have forms: = ln = ln = + (1)

= ln = ln = + (2)

= 1 − − (3) In this model, it is noted that X is the vector of variables, including independent variables and controlling variables, are investigated whether they have the relationships with carrier choice or not. Independent variables are composed of price, frequency of flights, and routes. These factors are mentioned as hypothesis in the third part of the survey for respondents making decision. Controlling variables which consist of information of respondents and their evaluation of airline service are collected through the first and the second section of the questionnaire. The list of variables used in this study is described in Table 3.2. Table 3.2. Description of variables: Type of variable Variables Denotation Unit Description

Dependent Choice choice 1 = Vietnam Airline variable 2 = Vietjet 3 = Jetstar Independent pricevn 100.000 VND Airfare of VNA variables Price pricevj 100.000 VND Airfare of VJ pricebl 100.000 VND Airfare of BL

freqvn Number of flights of a route in a day of VNA

Frequency of freqvj Number of flights of a route in airlines a day of VJ

freqbl Number of flights of a route in a day of BL

1 Ha Noi

2 Da Nang

3 Vinh

4 Nha Trang

Route 5 Di Linh

6 Hue

7 Thanh Hoa

8 Buon Me Thuot

9 Pleiku

Type of variable Variables Denotation Unit Description

10 Phu Quoc

11 Hai Phong

12 Tuy Hoa

13 Quy Nhon

14 Dong Hoi

15 Chu Lai

Controlling Age age Years variables Gender male (Dummy) 1 = Male Marital status single (Dummy) 1 = Single

Education schoolyear Years Number of schooling years Income income Million VND Average income per month Occupation job_emp (Dummy) 1 = company employees ontvn_pun (Dummy) 1 = VNA previous flights departed on time On time ontvj_pun (Dummy) 1 = VJ previous flights performance departed on time ontbl_pun (Dummy) 1 = BL previous flights departed on time seavn_ufr (Dummy) 1 = Seat space of VNA is uncomfortable seavj_ufr (Dummy) 1= Seat space of VJ is Seat space uncomfortable seabl_ufr (Dummy) 1= Seat space of BL is uncomfortable chevn_ufr (Dummy) 1= VNA staff at check in counter is unfriendly chevj_ufr (Dummy) 1= VJ staff at check in counter Check in service is unfriendly chebl_ufr (Dummy) 1= BL staff at check in counter is unfriendly

4. RESULTS

According to Gujarati (2011), the odds could tell how much this choice is preferred over that choice. The odds ratios are defined as the ratio of the probability of choosing alternative i and the probability of choosing alternative j, which is the base outcome. Besides that, the positive value of a coefficient suggests that a raise in this variable will increase the odds for choice i over choice j, when holding others variables constant. This indicates that choosing i increase the utility of decision maker in comparing with choosing j. Reversely, a negative coefficient of a regressor means that if this variable increases a unit, the odds for choice i over choice j will decrease, when holding others regressors constant. It implies that choosing j is preferred than choosing i. In multinomial logit model, the relative risk ratios (rrr) could be obtained by exponentiating the multinomial logit coefficients, e coef. The meaning of relative risk ratios is that for a unit change in independent variable, the relative risk ratios of outcome j over the base outcome is expected to change by a factor of that parameter. Table 4.2 reports the results of the multinomial logit models. Model 1 is the regression for only controlling variables. Model 2 adds two attributes of airlines including price and frequency of airline whereas mode 3 adds routes to see the effects of these factors. In these models, the choice of Jetstar Pacific (choice 3) is used as the reference. It is noted that choice 1 and choice 2 are denoted for choice of Vietnam Airlines and Vietjet Air, respectively. It is noted that there are 122 respondents who are required to make decision in 15 scenarios. Each choice in each scenario is considered as one observation. In other words, if all of respondents answer all of 15 scenarios, there would be 1,830 observations. However, in the survey, if the respondents say that they do not have demand to travel from Sai Gon to one place, they are not asked to make choice of airline in that scenarios. Therefore, there are 605 observations when running the regression. The results are presented in detail in Table 4.1. a. Controlling variables This research attempts to investigate the relationship between airline choice and characteristics of passengers. The results in three models are totally consistent to each other. With the scope of the observed sample, characteristics of individuals have statistical significance at the level of 10%, except for the career characteristic. Male and income variables have positive value of coefficient while variables of age, marital status, and school year are negative parameters. It is noted that marital status is not a significant factor when comparing VJ and BL. In detail, a

12 person, who is female, has lower odds of choice VNA or VJ over BL, so he/ she prefers BL than VNA or VJ comparing with a male, ceteris paribus. In contrast, a negative parameter of marital status suggests that a married person has a higher odds ratio of probability of VNA over probability of BL, thus he/ she prefers VNA than BL, in comparison with a single, holding other variables constant. Moreover, as expected, on time performance of airlines have positive effect on utility of airline alternatives. It is noted that at the 5% level of significance, punctual performance of airlines have statically significant when considering VJ and BL. In the panel 2 of Table 4.2, the positive coefficient of punctuality of VJ suggests that if passengers used to have a delayed flight of VJ in the past, the odds for choice VJ over choice BL also decrease. It means that after bad impression with VJ, passengers tend to prefer BL than VJ. On the contrary, the parameters of punctuality of BL and VNA are negative. This indicates that passengers tend to prefer choosing VJ than choosing BL if they ever had bad experiments with unpunctual flights of VNA or BL. In the same way, on time performance of airlines is significant at 10% when comparing between VNA and BL, except for the on time performance of VNA in three models and on time performance of VJ in model 1. It may be understood that performance of VNA does not affect on the choice of VNA. Furthermore, the discomfort of in-flight seat space is predicted to have negative impact on the utility of airline alternatives. It is because if passengers feel seat space small or make them uncomfortable onboard, they may have tendency of not flying with that airline again. The effect of this factor is statistically significant at 10% level, except for the variable of uncomfortable seat of VJ in the first panel of Table 4.2. In comparison VJ between BL, the coefficients of uncomfortable seat space of VNA and VJ are negative. This lead to the results that the odd for choice VNA or VJ over choice BL increase if passengers evaluate the seat of VNA or VJ is comfortable or in other words, choice of VNA or VJ are preferred than choice of BL. Finally, this study makes effort to examine the relationship between quality of check in service and utility of airline alternatives. Evaluation of unfriendly service is forecasted to have negative effect. Yet, all of variables of this determinant are insignificant at the 10% level. In the sample of this research, check in service evaluation of respondents does not impact on their decisions. a. Attributes of airline

Model 2 is the multinomial logit model for airline attributes and controlling variables. In considering price factor, it is observed that variables of prices of airlines are statistically significant at the 1% level of significance, except for the price of Vietjet in Panel 1 of Table 4.2. The coefficient of Vietnam Airline’s price variable is negative while of Jetstar is positive. The negative coefficient of price of VNA indicates that if ticket price of Vietnam Airline increases, the odd ratios of probability of choosing VNA and BL will decline when all else is equal. It is because people prefer Jetstar than VNA when VNA makes the fare higher. Similarly, Panel 2 of Table 4.2 shows the results for comparing the choice of VJ and BL. The negative parameter of variable of price of VJ implies that an increase in airfare of VJ makes the odd ratios of probability of choice VJ over BL reduced, in the same conditions. In contrast, the positive coefficients of Jetstar’s price in the two panels suggests that if air ticket price of BL is higher, the odd ratios of probability of choice VNA and BL or VJ and BL is also increased while others variables are kept constant. However, in comparison between VJ and BL, the price of VNA is also a significant factor. The negative sign indicates that if airfare of VNA is higher, the odds for choice VJ over choice BL is lower and people prefer BL than VJ. Besides that, frequency of flights, defined as the number of flights for a specific route in a day of each carrier, is also paid attention. It is expected that the airline is more preferred than the other if it has more flights. This could be explained that more flights mean more options for passengers to choose. When making comparison of VJ and BL, the coefficient of number of flights of VJ is positive as expected. The parameter of frequency of VNA is negative and significant indicate that if VNA open more flights in a day, the odds for choice VJ over choice BL is reduced. The results are also similar when comparing VNA and BL. The relationships between price and probability of airline choice are showed in Figure 4.13, Figure 4.14, and Figure 4.15. It is clearly that when price increase, the probability of chosing that airline will reduce. Notably, when price of Vietnam Airline is higher, not only probability of chosing VNA but also of BL is decreased while probability of chosing VJ is increased. It is because people prefer to buy air ticket of VJ than BL if VNA makes the price higher. b. Effect of different routes Model 3 is the multinomial logit model of controlling variables and categorical variable (routes). It is observed that the effects of destinations are significant at the level of 10%, except for some destinations such as Nha Trang, Thanh Hoa, Pleiku, Tuy Hoa, Chu Lai. When comparing VNA

14 and BL, Da Nang and Vinh are destinations that have positive coefficients whereas other places have negative parameters. Positive coefficient of Da Nang indicates that the odds ratios of probability of choice VNA over choice of BL is higher for the route of Da Nang, comparing to the route of Ha Noi.. Notably, in comparison of VJ and BL, all of significant factors of destinations have negative coefficients. A negative coefficient suggests that comparing to Sai Gon – Ha Noi, all other routes have lower odds ratio of choice VJ over BL, holding other variables constant.

Table 4.1. Estimation results of multinomial logit model

Model 1 Model 2 Model 3

Dependent variable: choice Coef. rrr P>|z| Coef. rrr P>|z| Coef. rrr P>|z| Choice = 1: Vietnam Airline (VNA)

Price of VNA (100,000 VND) -0.802*** 0.448 0.000

Price of BL (100,000 VND) -0.408 0.665 0.119

Number of flight of VNA 1.036*** 2.817 0.000

Number of flight of VJ -0.274** 0.760 0.034

Number of flight of BL 0.307*** 1.360 0.008

Age (years) 0.153 1.166 0.298

Male (Male = 1) -0.129** 0.879 0.017 -0.199*** 0.819 0.001 -0.181*** 0.834 0.003 Single (Single =1) 0.719* 2.052 0.064 0.921** 2.511 0.032 0.940** 2.559 0.030 School of year(years) -1.387*** 0.250 0.000 -1.699*** 0.183 0.000 -1.617*** 0.199 0.000 Income(Million VND) -0.316* 0.729 0.061 -0.533*** 0.587 0.005 -0.520*** 0.594 0.006 Job (Company Employee=1) 0.051* 1.053 0.067 0.084*** 1.088 0.009 0.069** 1.071 0.024 On time Performance of VNA (Punctuality = 1) -0.408 0.665 0.250 -0.471 0.624 0.233 -0.451 0.637 0.252 On time Performance of VJ (Punctuality = 1) -0.121 0.886 0.812 -0.507 0.602 0.375 -0.460 0.631 0.418 On time Performance of BL (Punctuality = 1) 0.738 2.092 0.111 1.229** 3.419 0.017 1.239** 3.451 0.017 Seat space of VNA (Uncomfortable =1) -0.748* 0.473 0.054 -0.992** 0.371 0.021 -0.970** 0.379 0.023 Seat space of VJ (Uncomfortable =1) -3.067* 0.047 0.052 -5.055*** 0.006 0.006 -4.224** 0.015 0.016 Seat space of BL (Uncomfortable =1) -0.562 0.570 0.369 -0.706 0.494 0.315 -1.079 0.340 0.120 Check in Service of VNA (Unfriendly =1) 1.747*** 5.737 0.000 2.463*** 11.738 0.000 2.443*** 11.508 0.000 Check in Service of VJ (Unfriendly =1) 14.854 2,824,890 0.983 14.151 1,398,023 0.975 15.727 6,764,777 0.987 Check in Service of BL (Unfriendly =1) -0.673 0.510 0.296 -0.770 0.463 0.291 -0.662 0.516 0.353 _cons 0.799 2.224 0.241 0.402 1.494 0.589 0.475 1.607 0.522

Model 1 Model 2 Model 3

Dependent variable: choice Coef. rrr P>|z| Coef. rrr P>|z| Coef. rrr P>|z| Route 8.443*** 4,644 0.010 17.285*** 32,126,763 0.000 14.238*** 1,526,380 0.000 Da Nang

Vinh 1.398* 4.049 0.077

Nha Trang 1.604* 4.970 0.061

Da Lat 0.874 2.396 0.189

Hue -2.240*** 0.106 0.003

Thanh Hoa -1.514** 0.220 0.029

Buon Me Thuot -0.528 0.590 0.558

Pleiku -1.632** 0.195 0.025

Phu Quoc 0.033 1.034 0.969

Hai Phong -1.476** 0.228 0.037

Tuy Hoa -0.675 0.509 0.314

Quy Nhon -14.286 0.000 0.981

Dong Hoi -1.946*** 0.143 0.010

Chu Lai -3.195*** 0.041 0.001

-14.502 0.000 0.981

Choice = 2: Vietjet Air (VJ)

Price of VNA (100,000 VND) -0.395*** 0.674 0.000

Price of VJ (100,000 VND) -1.103*** 0.332 0.000

Price of BL (100,000 VND) 1.416*** 4.122 0.000

Number of flight of VNA -0.375*** 0.687 0.001

Number of flight of VJ 0.309*** 1.362 0.002

Number of flight of BL 0.139 1.150 0.305

Model 1 Model 2 Model 3

Dependent variable: choice Coef. rrr P>|z| Coef. rrr P>|z| Coef. rrr P>|z| Age (years) -0.049 0.952 0.241 -0.104** 0.901 0.041 -0.074 0.928 0.130 Male (Male = 1) 0.892*** 2.439 0.002 1.271*** 3.566 0.000 1.285*** 3.616 0.000 Single (Single =1) -0.276 0.759 0.319 -0.597* 0.550 0.079 -0.440 0.644 0.179 School of year(years) -0.431*** 0.650 0.001 -0.678*** 0.508 0.000 -0.627*** 0.534 0.000 Income(Million VND) 0.070*** 1.072 0.002 0.110*** 1.116 0.000 0.092*** 1.096 0.000 Job (Company Employee=1) -0.145 0.865 0.599 -0.166 0.847 0.625 -0.138 0.871 0.671 On time Performance of VNA (Punctuality = 1) -0.750** 0.472 0.043 -1.140** 0.320 0.015 -1.078** 0.340 0.016 On time Performance of VJ (Punctuality = 1) 1.068*** 2.909 0.002 1.646*** 5.186 0.000 1.678*** 5.357 0.000 On time Performance of BL (Punctuality = 1) -0.588* 0.555 0.052 -0.823** 0.439 0.028 -0.894** 0.409 0.013 Seat space of VNA (Uncomfortable =1) -5.450*** 0.004 0.000 -7.594*** 0.001 0.000 -7.361*** 0.001 0.000 Seat space of VJ (Uncomfortable =1) -1.659*** 0.190 0.001 -1.751*** 0.174 0.005 -2.230*** 0.108 0.000 Seat space of BL (Uncomfortable =1) 2.307*** 10.045 0.000 3.148*** 23.295 0.000 3.161*** 23.602 0.000 Check in Service of VNA (Unfriendly =1) 14.356*** 1,717,379 0.983 14.097 1,325,579 0.976 15.813 7,371,118 0.987 Check in Service of VJ (Unfriendly =1) -0.463 0.629 0.397 -0.726 0.484 0.281 -0.572 0.564 0.361 Check in Service of BL (Unfriendly =1) -0.533 0.587 0.374 -0.906 0.404 0.192 -0.833 0.435 0.221 _cons 8.592*** 5,386 0.001 15.777*** 7,109,766 0.000 13.629*** 829,542 0.000 route

Da Nang 0.995 2.705 0.159

Vinh 0.654 1.923 0.409

Nha Trang -0.920 0.398 0.139

Da Lat -2.590*** 0.075 0.000

Hue -2.254*** 0.105 0.000

Thanh Hoa 0.308 1.360 0.643

Model 1 Model 2 Model 3

Dependent variable: choice Coef. rrr P>|z| Coef. rrr P>|z| Coef. rrr P>|z| Buon Me Thuot -1.955*** 0.142 0.001

Pleiku 0.529 1.697 0.443

Phu Quoc -1.769*** 0.171 0.002

Hai Phong -2.710*** 0.067 0.000

Tuy Hoa 0.804 2.233 0.239

Quy Nhon -2.295*** 0.101 0.000

Dong Hoi -3.720*** 0.024 0.000

Chu Lai 0.540 1.716 0.409

Choice = 3: Jetstar Pacific (BL) - Base outcome

Note: *** p<0.01, ** p<0.05, * p<0.1

5. CONCLUSION Based on the empirical findings, this study gives some recommendations for the three airlines. First, income has positive relationships with the probability of choosing Vietjet. At the beginning, as a low cost carrier, the strategy of Vietjet is aiming to low income customers who never travel by air before. But now, Vietjet should pay more attention to high income passengers. It is because when income is higher, probability of selecting Vietjet is also increased. Besides that, the results of survey imply that on time performance of Vietjet is not good since more than sixty percentages of respondents said that they used to have unpunctual flights with Vietjet. This is the highest percentage in among of three airlines. Therefore, Vietjet should have commitments with the flight schedules set up before. Second, Jetstar should continue to focus on low income passengers because income affects negatively on probability of choosing Jetstar. Low cost carriers make more revenue by developing ancillary products such as food and drink onboard, free duty goods, fee of seat selection. The results of survey suggest that more than fifty percentages of respondents said that they never use service of food and drink on Jetstar’s flights. Thus, Jetstar should consider the quality of food and drink as well as the category of products onboard to get more additional revenue. However, it is still existence of limitations in this study. First, the size of sample is small. 122 respondents are not enough to be considered as representative for all of passengers in airline market. Moreover, this survey does not examine other attributes of airlines such as fare class, aircraft type, number seats of aircraft, and flight time... Finally, limitation of stated preference survey is that it easily leads to bias results. It is because respondents may be uninterested in the survey so they may not have the same action in reality as in hypothesis situation.

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