Problem statement
In 2015, the aviation industry achieved a record net profit of $33 billion, nearly doubling the $17.4 billion profit from 2014 The Asia Pacific region alone contributed over $5.8 billion to this total, accounting for 31% of global passengers, while Europe and North America represented 30% and 26%, respectively Notably, low-cost carriers transported over 950 million passengers, making up approximately 28% of scheduled travelers, according to the IATA report from 2016.
The International Air Transport Association (IATA) projects that the number of air travelers will nearly double from 3.8 billion in 2016 to 7.2 billion by 2035, highlighting Vietnam as one of the fastest-growing aviation markets Specifically, Vietnam is expected to welcome 112 million new passengers, bringing its total to 150 million The Vietnamese government is prioritizing infrastructure development, a key factor in the air transport sector, with plans to establish 26 airports by 2020, including the anticipated Long Thanh International Airport.
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
In 2014, the aviation market served 40.1 million passengers and transported 771,000 tons of cargo, with domestic carriers accounting for 31.1 million passengers, reflecting a 21% increase The subsequent 30% drop in crude oil prices in 2015 provided a positive stimulus for airlines, encouraging them to lower fares to meet growing passenger transportation demands.
The airline industry in Vietnam holds significant potential due to several factors With a population exceeding 90 million, the demand for travel is substantial Additionally, rising incomes among Vietnamese citizens have led to an increasing preference for air travel, which is perceived as both faster and safer compared to other modes of transportation Despite global airline disasters in 2014, air travel remains the safest option, as highlighted by the IATA Safety Report, which recorded 12 fatal accidents out of 73 total incidents, resulting in 641 fatalities worldwide.
In 2014, air travel accounted for a small fraction of the 33 billion passengers reported by the IATA However, it significantly reduces travel time, with flights from Ho Chi Minh City to Ha Noi taking just two hours compared to two days by train Additionally, the rise of e-commerce, facilitated by the internet, allows consumers to conveniently purchase airline tickets from home, often at discounted promotional rates.
Established in 1956, the Vietnam Civil Aviation Department initially operated with just five aircraft for domestic flights The national carrier, Vietnam Airlines, was founded in 1993, and by 1995, it evolved into Vietnam Airlines Corporation, incorporating 20 aviation enterprises Today, Vietnam Airlines boasts a comprehensive network of domestic and international routes across Southeast and North Asia, Europe, and Australia In July 2016, ANA Holding Inc acquired an 8.77% stake, becoming a strategic shareholder As part of its restructuring plan, Vietnam Airlines aims to reduce state ownership to 75% Recognized by Skytrax as a 4-star airline, Vietnam Airlines continues to enhance its reputation in the global aviation industry.
Vietjet Air, Vietnam's first privately owned international low-cost carrier, was established in November 2007 and launched its inaugural flight in December 2011 with just three aircraft By 2015, the airline expanded its fleet to 29 aircraft, servicing 28 domestic and 12 international routes Looking ahead to 2016, Vietjet plans to increase its fleet to 42 aircraft to accommodate growing travel demand and introduce three additional domestic and five international routes.
Jetstar Pacific Airlines JSC, originally founded as Pacific Airlines in 1990, began operations in 1991 with charter cargo services under Vietnam Airlines Corporation It transitioned to passenger services in 2005 and became part of the Jetstar network in 2008 In 2012, Vietnam Airlines acquired a 70% stake in the airline, leaving Qantas with a 30% ownership.
Vietnam Air Services Company (VASCO), a subsidiary of Vietnam Airlines Corporation, has been operating since 2004, offering passenger transport from Tan Son Nhat Airport to various southern destinations, including Ca Mau, Con Dao, Rach Gia, and Can Tho In addition to its passenger services, VASCO functions as a multifunctional airline, providing maintenance services for private aircraft.
Currently, Vietnam has four domestic airlines: Vietnam Airlines, Vietjet, Jetstar, and VASCO Previously, Indochina Airlines and Air Mekong also operated in the country; however, Indochina Airlines ceased operations in 2009 due to financial difficulties, while Air Mekong suspended its flights in 2013 after facing business losses The Ministry of Transport officially revoked Air Mekong's license in January 2015.
Numerous studies have explored customer behavior theories and empirical research on airline choice According to the 2015 IATA annual report, passengers primarily select airlines for nonstop flights (15%) and the lowest fares (14%), while recommendations from travel agents (4%) and in-flight services (3%) are less influential However, the Vietnamese airline industry, which has recently experienced significant growth, lacks extensive research on this subject Understanding passenger preferences is essential for aviation companies and foreign investors alike.
The policies established by the three carriers are tailored to meet the needs of the Vietnamese population, providing valuable insights for investors assessing the airline market to inform their investment decisions.
Research objectives
This study utilizes a stated preference survey and the multinomial logit model to analyze the factors influencing passengers' airline choices, focusing on both airline characteristics and traveler preferences The findings aim to shed light on the determinants of passenger selection, enabling carriers to better understand their target market segments and enhance their service offerings effectively.
Research questions
This article explores two key questions regarding airline selection: first, what attributes of airlines influence travelers' decisions on which airline to choose; and second, what demographic factors affect air travelers' airline preferences Understanding these elements is crucial for airlines aiming to attract and retain customers.
Scope of the thesis
This research focuses on the airline choices among three major carriers in Vietnam: Vietnam Airlines (VNA), Vietjet (VJ), and Jetstar (BL) VASCO is not included in this analysis due to its limited operations in the Southeast, primarily offering short flights such as those from Sai Gon to Ca Mau, Rach Gia, and Con Dao Additionally, VASCO's primary business revolves around aircraft maintenance rather than passenger transport, resulting in a minimal market share that justifies its exclusion from the study.
Structure of thesis
The study is organized into four chapters Chapter 2 explores the theories of random utility, stated preference, and revealed preference data, along with an empirical analysis of choice models in the airline industry Chapter 3 outlines the research methodology, detailing the questionnaire design, survey process, and empirical model used Chapter 4 provides an in-depth examination of the survey data collected and presents the results of the model analysis Finally, Chapter 5 summarizes the key findings and discusses the limitations of the study.
This chapter begins by exploring the economic literature on individual choice, which serves as the basis for empirical studies examining the decisions made by economic agents, such as air travelers It then reviews various empirical studies focused on passenger choices among different airlines Building on these insights, a model is developed to analyze the preferences of air travelers when selecting between Vietnam Airlines, Vietjet, and Jetstar.
Theoretical review
Random Utility Model is commonly used to represent individual choice behavior Thurstone
In 1927, a law of comparative judgment was introduced, leading to the development of psychological stimuli concepts that culminated in the binary probit model, which assesses respondents' reactions to varying levels of stimuli Marschak (1960) expanded on this concept by framing stimuli as utility within the random utility model, suggesting that while decision-makers may understand the utility of each choice, researchers may not fully grasp it due to inherent uncertainties This model comprises both deterministic components, which are observable and interpretable by analysts, and random components, which remain unknown Manski (1977) identified four primary sources of uncertainty: measurement errors, reliance on proxy variables, unobserved attributes of the chooser, and unobserved attributes of the alternatives.
Discrete choice models, grounded in random utility theory, assume that decision-makers select from a finite set of mutually exclusive and collectively exhaustive alternatives to maximize their utility Each alternative's utility is influenced by deterministic factors, often represented as a linear function of various attributes The choice model predicts the probability of an individual selecting a particular alternative, incorporating random components as crucial factors Variations in assumptions regarding the distribution of error terms lead to different types of choice models, including logit, generalized extreme value (GEV), probit, and mixed logit models, as highlighted by Train (2009).
The logit model assumes that error terms are independent and identically distributed (iid), indicating that unobserved factors are uncorrelated and share the same variance across alternatives While this assumption provides a convenient form for calculating choice probabilities, it can be restrictive and may not always be appropriate in certain situations where alternatives are correlated Additionally, the model operates under the independence assumption, meaning that each choice is made without influence from previous selections Due to its simplicity, the logit model is widely used by researchers to analyze various aspects of air travel behavior (Escobari & Mellado, 2014; Warburg, 2005; Yoo & Ashford, 1996).
To address the independence assumption in logit models, generalized extreme value (GEV) models were developed, allowing for a broader distribution of extreme values (Train, 2009) This generalization facilitates the exploration of relationships between unobserved factors and alternatives, functioning as a specific case of the logit model when no correlation exists The flexibility of these correlations varies based on the GEV model type; for example, a simpler GEV categorizes alternatives into groups known as nests, where unobserved factors are correlated within the same nest but not across different nests The nested logit model has been utilized by Hess (2008) to analyze air travel behavior and by Pels et al (2001) to examine passenger dynamics in airports and airlines.
The probit model effectively addresses three key limitations of the logit model, as highlighted by Train (2009) These limitations include the inability to account for random taste variation, the independence of irrelevant alternatives (IIA) property, and the correlation between unobserved components and alternatives In contrast, the probit model assumes that error terms follow a normal distribution However, it is important to note that the probit model's primary limitation arises in situations where unobserved factors do not conform to a normal distribution.
Mixed logit models allow for unobserved factors to follow any distribution, which can be divided into two components: one accounting for heteroskedasticity and correlation, and the other being independent and identically distributed (iid) extreme value Notably, the first component can adhere to any distribution, including non-normal distributions Adler et al (2005) utilized the mixed logit model to create an itinerary choice model, while Warburg (2005) employed both multinomial logit and mixed logit models to analyze passenger flight choice behavior.
Researchers have developed various discrete choice models tailored for specific applications, often integrating concepts from existing models For instance, a mixed probit model can be derived by decomposing observed components similar to mixed logit, but with the second component following a normal distribution instead of an extreme value distribution Understanding the motivations and derivations of these models enables researchers to select the most appropriate model for their particular needs, thereby enhancing the effectiveness of their studies Additionally, both revealed preference and stated preference surveys play a crucial role in this process.
Surveys analyzing customer behavior primarily fall into two categories: revealed preference (RP) and stated preference (SP) surveys RP data reflects actual consumer behavior in real choice environments, providing valuable insights However, it presents challenges for trade-off analysis and is inadequate for modeling new market alternatives Yoo and Ashford (1996) identify three limitations of RP data: insufficient variation in key variables for statistical calibration, difficulties in estimating trade-off ratios due to variable correlations, and the need for large sample sizes to gather enough observations As a result, many researchers avoid using RP surveys for modeling customer choice behavior For instance, Carrier (2008) utilized RP data from booking records, omitting non-booked travel alternatives, while Escobari and Mellado (2014) analyzed data from online travel agencies to understand flight demand through posted prices and inventory changes.
Stated Preference (SP) surveys are designed to capture the stated responses of interviewers, offering a way to mitigate the limitations of Revealed Preference (RP) data According to Collins et al (2012), SP data can effectively reproduce behavioral outputs, such as willingness to pay, and allows researchers to explore consumer choice behavior regarding non-existent alternatives However, SP data is not without its challenges; respondents may exhibit disinterest or provide opinions rather than relevant information about new product usage (Warburg, 2006) Additionally, decision-making in hypothetical scenarios can lead to bias, as individuals may not act in accordance with their stated preferences Despite these limitations, researchers frequently utilize SP surveys for modeling choice behavior For instance, Adler et al (2005) analyze trade-offs in air itinerary choices using SP surveys, while Collins et al (2012) employ interactive stated choice surveys to study air traveler behavior Other studies, such as those by Wen and Lai (2010) and Proussaloglou and Koppelman (1999), also leverage SP data to investigate passenger choices among air carriers.
To effectively address the limitations of both Revealed Preference (RP) and Stated Preference (SP) data, it is essential to develop estimation techniques that integrate these data sources Utilizing both methods is recommended, as RP is valuable for forecasting demand and realistic applications, while SP serves a critical role in system planning.
In their 2012 study, Atasoy and Bierlaire utilized a mixed dataset of revealed preference (RP) and stated preference (SP) to enhance the model of itinerary choice This combination of data allowed for a successful estimation of price elasticity within the demand model.
Empirical review
Numerous studies have explored various facets of airline choice behavior, particularly focusing on airport selection in multi-airport regions, as seen in the works of Basar and Bhat (2004), Hess and Polak (2005), and Pathomsiri and Haghani (2005) Additionally, some research delves into broader travel aspects, with Ndoh et al (1990) analyzing both airport and route choices, while Furiuchi and Koppelman (1994) investigate destination and airport selection Furthermore, several studies emphasize air traveler choices beyond just airport selection, including notable research by Chin (2002), Algers and Beser (2001), Proussaloglou and Koppelman (1999), and Yoo and Ashford (1996).
The multinomial logit model is commonly used in studies of air travel choices, while the nested logit model has been employed by researchers like Ndoh et al (1990), Furiuchi and Koppelman (1994), and Pels et al (2001) to analyze the complex and spatial decisions of air travelers In contrast, studies focusing on behavioral aspects and effects in air travel preferences often utilize the mixed multinomial logit model, as demonstrated by Hess & Polak (2005) and Pathomsiri & Haghani (2005).
Moreno (2006) employs the multinomial logit model to analyze airline choice for domestic flights in São Paulo, interviewing 1,923 passengers at São Paulo-Gru Airport (GRU) and São Paulo-Congonhas Airport (CGH) The study suggests that airline selection is influenced by trade-offs among flight cost, frequency, and airline performance It examines three variable types: cost-related variables, including the lowest and highest fares; flight frequency variables, such as connections, travel periods, and days of the week; and airline age as a performance proxy The findings indicate that the lowest fare significantly influences airline choice, with senior passengers prioritizing airline age more than younger travelers Similarly, Nason (1981) utilizes a stated preference survey and the multinomial logit model to evaluate airline choice based on service attributes and passenger characteristics.
Prossaloglou and Koppelman (1995) conducted a revealed preference survey to analyze airline choices among passengers departing from Dallas and Chicago, employing a multinomial logit model that incorporated variables such as schedule convenience, reliability, fares, city pair presence, market presence, and frequent flyer program membership Their findings indicated a positive correlation between the attractiveness of carriers and market share with frequent flyer programs Similarly, Nako (1992) found that frequent flyer programs significantly enhance airline demand among business travelers In a subsequent study by Prossaloglou and Koppelman (1999), the authors utilized a logit model to assess airline, flight, and fare class selections, asserting that air travelers are rational decision-makers seeking the highest utility Their analysis included variables like fare class, fare price, service quality, frequent flyer participation, and flight schedules, and they differentiated between business and leisure travelers using a two-tier survey method The results revealed distinct behavioral differences, with leisure travelers being more price-sensitive and less time-sensitive than their business counterparts, who showed greater attention to frequent flyer programs and a willingness to pay more for preferred airlines.
Pels et al (2001) employ distinct models for business and leisure travelers, revealing that the differences between the two groups are minimal Their research utilizes a nested logit model to analyze passenger preferences regarding airports and airlines.
This research thoroughly examines the nests identified by both airports and airlines, utilizing empirical data gathered from a survey conducted in the San Francisco Bay Area.
In 1995, it was noted that airlines face two categories of competitors: those operating within the same airport and those at different airports This distinction is crucial, as access time to the airport plays a significant role for both leisure and business travelers.
Understanding passengers' flight choice behavior is essential for predicting air travel demand, as highlighted by Warburg (2005) This research assists airlines in developing effective pricing strategies and forecasting demand for new routes In 2001, Warburg conducted a stated preference survey involving 119 business and 521 non-business passengers, who reported their most recent domestic flight experiences.
The study presents ten binary choices between actual flights and hypothetical flights, analyzing ten itinerary alternatives with identical departure and arrival locations Warburg (2005) asserts that a universal choice set does not exist, as travelers possess varying flight itineraries, leading to unique choice sets for each passenger To investigate the behavior of two distinct groups, the research utilizes both the multinomial logit model and the mixed logit model.
In Koppelman's (1999) multinomial logit model, it was found that business individuals exhibit a greater sensitivity to time, whereas non-business individuals prioritize fare costs Additionally, the study revealed that men are more sensitive to fare compared to women.
The study by Yoo and Ashford (1996) examines the flight choice behavior of Koreans who undertook long-distance international air trips exceeding 10 hours Utilizing a logit model for both revealed preference (RP) and stated preference (SP) data, the researchers aimed to conduct a comparative analysis between the two survey types Surveys were carried out at Kimpo International Airport in Seoul, with the RP survey conducted in October 1993 and the SP survey in August 1994, ensuring equal sample sizes for both The findings revealed that passengers were willing to pay more for Korean airlines compared to foreign airlines, with Korean residents showing a higher willingness to pay than foreign residents Additionally, Escobari and Mellado (2014) estimated the demand for international flights using a unique dataset that included information on flight choices, prices, and characteristics of non-booked flights, collected from an online agency.
Expedia.com offers 317 one-way, non-stop flights from New York to Toronto and back, operated by six carriers between December 19 and December 24, 2008 The analysis reveals that a 10% increase in ticket prices for a 100-seat aircraft results in a decrease in demand by 7.7 seats.
A study by Ukpere et al (2012) explores the factors influencing airline choice among passengers in Nigeria's domestic air transport, utilizing a Likert scale questionnaire to gather data on socio-economic characteristics such as sex, age, and marital status, alongside airline attributes like comfort, service quality, fare, frequency, crew behavior, and monopoly power The nested logit model reveals that these variables significantly impact passenger decisions at selected airports, leading the authors to recommend competitive pricing and distinct product offerings to attract more travelers In contrast, Adler et al (2005) conducted a stated preference survey online, gathering data from approximately 600 individuals who had recently purchased domestic air tickets to analyze the trade-offs involved in itinerary selection, considering factors like airline carrier, airport, fare, flight times, and on-time performance Their findings, derived from a mixed multinomial logit model, indicate that these service characteristics are statistically significant; however, the study's limitation lies in its failure to examine the effects of demographic and trip-related factors on individual choices.
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.
Stated preference method
The stated preference (SP) method, as highlighted by Whitaker et al (2005), is a crucial tool for understanding decision-making behaviors and forecasting demand for various airline services Unlike the revealed preference (RP) approach, which relies on actual choices made in real-world scenarios, the SP method overcomes the limitations of RP, particularly the high costs associated with surveys and the inability to incorporate potential future alternatives into demand models.
Wen and Lai (2010) argue that while revealed preference methods gather data from actual choices, they may not accurately reflect passenger decision-making, as travelers often overlook various attributes of airlines In contrast, the stated choice approach effectively examines how individuals would react to hypothetical scenarios featuring different alternatives and attribute levels This method has gained popularity in analyzing airline selection and other decision-making challenges.
It is said that in research of travel behavior, there are two types of stated response (Hensher,
In survey research, respondents first identify their preferences among alternatives using either a rating scale or a rank ordering scale Rating scales, such as the Likert scale or a 1 to 10 scale, gather both quantitative and qualitative data, while rank ordering scales allow individuals to prioritize alternatives based on their preferences Studies by Warburg et al (2006) and Adler et al (2005) exemplify the use of rank ordering scales in airline choice surveys Additionally, respondents are asked to select their top choice from the listed alternatives in a first preference choice task Understanding response strategies at the outset of a stated preference (SP) survey is crucial, as it significantly influences the study's outcomes.
This study employs a first preference choice task survey, where respondents select one airline from three options: Vietnam Airlines (VNA), Vietjet Air (VJ), and Jetstar (BL), based on specific airfares for a designated route Hensher (1994) highlights the advantage of stated preference (SP) data, which mirrors revealed preferences, as individuals make choices after careful consideration of available alternatives This methodology has been utilized by various researchers, including Wen & Lai (2010) and Hong (2010) For instance, Wen & Lai (2010) presented air travelers with a choice between three airlines for the Taipei-Tokyo route and four for the Taipei-Hong Kong route, while Hong (2010) focused on a selection among British Airways, Air France, and Easyjet in his SP survey.
Questionnaire and survey process
The survey questionnaire, detailed in the Appendix, is divided into three sections The first section gathers demographic information and the primary purpose of the trip The second part asks respondents to evaluate airline service quality, focusing on staff attitudes at check-in and during the flight, in-flight food and drink, seat space, and on-time performance For airlines they have not used, respondents can select "I have never used this service before." The final section presents fifteen hypothetical scenarios involving flights from Tan Son Nhat Airport to various domestic airports, as shown in Table 3.1 Respondents indicate their airline choice based on the listed airfare, their travel purpose, and the maximum ticket price they are willing to pay for each route If they believe they will never travel to a specific destination, they can choose "I will never go there" to bypass subsequent questions.
Table 3.1 Summary of hypothetical scenarios in survey:
Route From Sai Gon To Operation of airline
Note: x: having at least one flight in a day
A pilot test was conducted at an air ticket agency to identify key factors influencing customer decisions when purchasing air tickets Eighteen recent customers listed determinants such as fare, schedules, on-time performance, staff service quality, and onboard seat comfort Following this, an online survey was conducted from October 16 to 23, 2016, using SurveyMonkey to design the questionnaire The survey link was distributed via email and shared on social media platforms like Facebook and Zalo, targeting air travelers who had flown with at least one of the airlines: VNA, VJ, or BL It is important to note that due to customer loyalty, many individuals may only use their preferred airline, making it challenging to find respondents with experience across all three airlines.
Figure 3.1 The screen of the online survey
Attributes of airlines
Customer satisfaction is a critical factor for retention in the service industry, as it leads to fewer complaints and reduced costs associated with addressing failures (Fornell et al., 1994) Key elements influencing customer satisfaction include price, terms and conditions, product and service quality, and personal characteristics (Zeithaml & Bitner, 1996) While service quality is essential, customers often weigh the trade-off between costs and benefits, making price a significant contributor to overall satisfaction (Lee & Cunningham, 1996) Research by Athanassopoulos et al (2001) indicates that customer satisfaction can influence behavior in three ways: remaining with current providers, engaging in word-of-mouth communication, or switching service providers In the airline industry, factors such as price, service quality, schedule times, flight frequency, aircraft types, and seating capacity play vital roles in determining airline choice, as highlighted in various studies.
Price Cost of a route (return fare) Continuos data Warburg (2005)
Cost of a route (one-way fare) AUD1600,
Average fare for each route Higher 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 the travel day
Flights per day Pereira et al (2007)
Number of flights per week Yoo & Ashford
Percentage of on time flight itinerary 50%, 60%, 70%,
Percentage of on time flight itinerary 50%-99% Adler et al (2005)
On time service schedules Sometimes delay,
Seat space on board Seat pitch 31", 32", 34" Collins & Hess
(2012) Passenger's evaluation of seat Very uncomfortable
Comfort No; yes Pereira et al (2007)
Check in service Passenger's evaluation of check in service
Very uncomfortable Comfortable enough Very comfortable
Kindness of employees Not very polite and friendly Very polite and friendly
Model specification
This study utilizes the Random Utility Model established by Manski (1977), positing that air travelers act rationally to maximize their utility Passengers are inclined to choose the airline that offers them the greatest utility, as represented in the model.
𝑈 𝑖𝑛 = 𝑉 𝑖𝑛 + 𝜀 𝑖𝑛 = 𝛼 𝑛 + 𝛽 𝑛 𝑋 𝑖 + 𝜀 𝑖𝑛 Where U: Utility level of passenger
V: Portion of utility (observed utility), and 𝑉 = 𝛼 + 𝛽𝑋
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 𝑌 𝑖𝑛 , so:
𝑌 𝑖𝑛 = 1, if 𝑈 𝑖𝑛 is maximum or 𝑈 𝑖𝑛 > 𝑈 𝑖𝑚 (m = 1, 2, 3, and m ≠ n)
Let 𝑃 𝑖𝑛 = Pr( 𝑌 𝑖𝑛 = 1) be the probability of choosing airline n, and probability of individual i choosing carrier n is calculated as below:
An air traveler selects airline n when the condition 𝜀 𝑖𝑚 < 𝑉 𝑖𝑛 − 𝑉 𝑖𝑚 + 𝜀 𝑖𝑛 is met This indicates that the distribution of the error term 𝜀 𝑖 can be estimated probabilistically The multinomial logit model operates under the assumption that the error terms are identically and independently distributed according to an extreme value distribution.
(Gumbel distribution) According to Train (2009), the function of probability could be rewritten as below:
In a multinomial logit model, the total probability of selecting three airlines by an individual sums to 1 However, as noted by Gujarati (2011), the three probabilities cannot be identified independently To simplify the analysis, one airline is typically chosen as the reference or base choice, with its coefficients set to zero For instance, if the third airline is selected as the base (α3 = 0 and β3 = 0), the probabilities for the three airlines can be calculated accordingly.
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:
This study examines the relationships between various independent and controlling variables, represented by the vector X, and carrier choice The independent variables include price, flight frequency, and routes, which are hypothesized in the third section of the survey to influence respondents' decisions Meanwhile, controlling variables, such as respondents' demographics and their evaluations of airline services, are gathered from the first two sections of the questionnaire A comprehensive list of the variables utilized in this research is detailed in Table 3.4.
In November, the average prices of flights for various routes were analyzed based on real-time data from airlines, highlighting the current pricing trends and frequency of flights available online.
2016 whereas the frequency of flights is the actual number of flights that each carrier has in a day Table 3.3 is the summary of value of independent variables
Table 3.3 Prices and numbers of flights by routes of carriers
Route From Sai Gon To Price (100,000 VND) Number of flights
Vietnam Airline Vietjet Jetstar Vietnam Airline Vietjet Jetstar
Type of variable Variables Denotation Unit Description
3 = Jetstar Independent variables Price pricevn 100.000 VND Airfare of VNA pricevj 100.000 VND Airfare of VJ pricebl 100.000 VND Airfare of BL
The frequency of airlines on specific routes is crucial for travelers, with Vietnam Airlines (VNA) offering a certain number of daily flights, while VietJet Air (VJ) provides a different frequency Additionally, Bamboo Airways (BL) also contributes to the daily flight options available on these routes Understanding the daily flight frequencies of VNA, VJ, and BL can help passengers make informed decisions about their travel plans.
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
The on-time performance of various airlines is reflected in their previous flight departures VNA has consistently shown that its flights departed on time, while VJ has also maintained a record of timely departures Similarly, BL has demonstrated reliable on-time performance with its past flights.
Type of variable Variables Denotation Unit Description
Seat space seavn_ufr (Dummy) 1 = Seat space of VNA is uncomfortable seavj_ufr (Dummy) 1= Seat space of VJ is uncomfortable seabl_ufr (Dummy) 1= Seat space of BL is uncomfortable
The check-in experience at various airlines has been reported as unsatisfactory due to unfriendly staff interactions Passengers have noted that VNA, VJ, and BL airline representatives at the check-in counters exhibit unwelcoming behavior, impacting the overall customer service experience.
This chapter presents the summary of data collected form the stated preference survey as well as the regression results of multinomial logit model.
Data
The characteristics of social demography are detailed in Table 4.1, which includes data from 135 respondents, with 13 eliminated for incomplete answers Each participant evaluated 15 scenarios to select an airline, as illustrated in Figure 4.1, highlighting that Ha Noi, Da Nang, and Phu Quoc are the most sought-after travel destinations, with over 98% of respondents planning to visit them in the future Vietnam Airlines (VNA) had the lowest selection percentage across all routes, notably receiving no choices for the Sai Gon – Tuy Hoa and Sai Gon – Chu Lai routes due to the absence of flights Consequently, VNA was excluded from these scenarios Overall, VietJet Air (VJ) emerged as the preferred airline in most scenarios, particularly for the Sai Gon to Ha Noi and Da Nang routes, where over 60% of respondents favored VJ However, for routes like Da Lat and Hue, Bamboo Airways (BL) was the top choice among respondents.
Figure 4.1 Airline Choice for Destinations
(Notes: HAN: Ha Noi, DAD: Da Nang, VII: Vinh, CXR: Nha Trang, DII: Da Lat, HUI: Hue, THD: Thanh Hoa, BMV: Buon Me Thuot, PXU:
Pleiku, PQC: Phu Quoc, HPH: Hai Phong, TBB: Phu Yen, UIH: Quy Nhon, VDH: Dong Hoi, VCL: Chu Lai)
The survey included 122 respondents, comprising 84 females (68.85%) and 38 males (31.15%) Approximately 70% of the participants are single, and the majority are young individuals with easy access to the internet, resulting in a median age of around 27 years, with ages ranging from 20 to 46 Notably, two respondents chose not to disclose their age Education levels reveal that 68% of participants hold a university degree, 26.23% have a master's degree, and only one individual completed high school Most respondents (68%) are employed by companies, while just one is self-employed Regarding travel preferences, over 40% indicated they typically travel by air for leisure and business purposes Additionally, the income range for most respondents falls between 8 to 10 million per month.
DADHANPQCCXR DII HUI BMV UIH VII TBB VCL PXU HPHVDH THD
Airline Choice for Destinations - Depart from Sai Gon
The survey evaluates airline service quality, focusing on check-in efficiency, cabin crew attitudes, and onboard food and drink quality, rated as not good, good enough, or very good Additionally, respondents assess flight punctuality by answering questions about their past experiences with three airlines and their acceptance of the airlines' on-time performance For individuals who have not used any airline services, an option is provided to indicate their lack of experience.
A recent survey revealed that approximately 36% of respondents have never flown with Jetstar Pacific, marking it as the least experienced airline among the three surveyed In contrast, Vietnam Airlines received high marks for service quality, with the majority of participants rating it as good or very good Notably, only 1.64% of respondents found the check-in service unfriendly, and a mere 0.82% felt the same about the cabin crew Additionally, around 60% of respondents believe that Vietnam Airlines flights generally depart on time, with 74% considering the rate of delayed flights acceptable Conversely, Vietjet Air reported the highest incidence of unpunctual flights, with 62% of respondents noting delays, and over 30% deeming schedule changes unacceptable.
The survey additionally asks respondents to indicate their willingness to pay for air tickets on various routes, such as from Sai Gon to Ha Noi As shown in Figure 4.8, the mean values reflect the average price respondents are ready to spend on these flights.
Respondents demonstrate a willingness to pay more for long-distance trips, such as those from Ho Chi Minh City to Hanoi and Hai Phong, while showing a lower willingness to pay for shorter journeys, like the route from Ho Chi Minh City to Nha Trang.
Figure 4.3 Willingness to pay for routes
Bài viết này đề cập đến các địa điểm du lịch nổi bật tại Việt Nam, bao gồm Hà Nội (HAN), Đà Nẵng (DAD), Vinh (VII), Nha Trang (CXR), Đà Lạt (DII), Huế (HUI), Thanh Hóa (THD), Buôn Ma Thuột (BMV), Pleiku (PXU), Phú Quốc (PQC), Hải Phòng (HPH), Phú Yên (TBB), Quy Nhơn (UIH), Đồng Hới (VDH) và Chu Lai (VCL) Mỗi địa điểm mang đến những trải nghiệm văn hóa và thiên nhiên độc đáo, thu hút du khách trong và ngoài nước Việc khám phá các thành phố này không chỉ giúp bạn hiểu rõ hơn về lịch sử và con người Việt Nam, mà còn tận hưởng cảnh đẹp hùng vĩ và ẩm thực phong phú.
HAN DAD VII CXR DII HUI THD BMV PXU PQC HPH TBB UIH VDH VCL
M e an o f Wi lli n gn e ss to p ay ( 1000 VN D )
Notes: 122 respondents, in which 2 not reveal their age
Demographic Characteristics Number of respondents Percentages (%)
Both of Leisure and Business 53 43.44
Figure 4.4 Check-In Service Evaluation
Figure 4.5 Cabin Crew Service Evaluation
Figure 4.6 Food & Drink Onboard Evaluation
Figure 4.7 Inflight Seat Space Evaluation
Figure 4.8 On-time Performance Evaluation
Empirical results
According to Gujarati (2011), odds ratios represent the likelihood of choosing one alternative over another, defined as the ratio of the probability of selecting option i compared to option j, the base outcome A positive coefficient indicates that an increase in the variable enhances the odds of choosing option i over option j, thus increasing the decision maker's utility for option i Conversely, a negative coefficient suggests that an increase in the variable decreases the odds of choosing option i over option j, implying that option j is preferred over option i.
In the multinomial logit model, relative risk ratios (RRR) are derived by exponentiating the multinomial logit coefficients (e^coef) These ratios indicate how a unit change in an independent variable affects the likelihood of outcome j relative to the base outcome, with the expected change represented by the corresponding parameter factor.
Table 4.2 presents the findings from the multinomial logit models, with Model 1 focusing solely on controlling variables Model 2 incorporates two airline attributes: price and frequency, while Model 3 further includes routes to assess the impact of these factors In this analysis, Jetstar Pacific (choice 3) serves as the reference point, with Vietnam Airlines and Vietjet Air represented as choice 1 and choice 2, respectively.
The study involved 122 respondents making decisions across 15 scenarios, resulting in a potential total of 1,830 observations However, if respondents indicated a lack of demand to travel from Sai Gon to a specific destination, they were not prompted to choose an airline for that scenario Consequently, a total of 605 observations were utilized for the regression analysis, with detailed results available in the Appendix.
Table 4.2 Estimation results of multinomial logit model
Dependent variable: choice Coef rrr P>|z| Coef rrr P>|z| Coef rrr P>|z|
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
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
Dependent variable: choice Coef rrr P>|z| Coef rrr P>|z| Coef rrr P>|z|
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
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
Dependent variable: choice Coef rrr P>|z| Coef rrr P>|z| Coef rrr P>|z|
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
Choice = 3: Jetstar Pacific (BL) - Base outcome
This research explores the connection between airline selection and passenger characteristics, revealing consistent findings across three models The analysis indicates that individual characteristics are statistically significant at the 10% level, with the exception of career factors Notably, male passengers and higher income levels positively influence airline choice, while age, marital status, and education level negatively impact it Specifically, female passengers are less likely to choose VNA or VJ over BL compared to males, whereas married individuals show a higher likelihood of preferring VNA over BL compared to singles, when other factors are held constant.
The positive income coefficient suggests that individuals with higher incomes are more likely to prefer VNA or VJ over BL, assuming other variables remain constant As illustrated in Figure 4.10, there is a clear relationship between income levels and the predicted likelihood of selecting airlines; specifically, as income increases, the probability of choosing VNA and VJ rises, while the likelihood of selecting BL decreases.
Figure 4.10 Predicted probability of airline choice and income
Pre d ict e d p ro b a b ili ty
Pr(VNA) Pr(VJ) Pr(BL)
Research indicates that as age and years of schooling increase, the likelihood of choosing VNA or VJ over BL decreases Specifically, older individuals or those with more educational experience are more inclined to select BL instead of VNA or VJ.
Figure 4.11 Predicted probability of airline choice and age
Figure 4.12 Predicted probability of airline choice and school year
Pre d ict e d p ro b a b ili ty
Pr(VNA) Pr(VJ) Pr(BL)
Pre d ict e d p ro b a b il it y
Pr(VNA) Pr(VJ) Pr(BL)
Figures 4.11 and 4.12 illustrate the correlation between the predicted probability of choosing an airline and factors such as age and school year As both age and school years increase, the likelihood of selecting VJ and BL airlines rises, in contrast to the trends observed with VNA.
The on-time performance of airlines significantly influences passenger preferences for airline alternatives, particularly at a 5% level of significance Specifically, the punctuality of VJ indicates that past experiences of delayed flights lead to a decreased likelihood of choosing VJ over BL Consequently, negative impressions from VJ’s performance result in passengers favoring BL instead.
Passengers are more likely to choose VJ over BL if they have had negative experiences with the punctuality of VNA or BL flights Additionally, the on-time performance of airlines is a significant factor at a 10% level when comparing VNA and BL, although VNA's on-time performance remains consistent across three models.
VJ in model 1 It may be understood that performance of VNA does not affect on the choice of VNA
In-flight seat space discomfort significantly impacts passengers' preferences for airlines, as uncomfortable seating can deter future flights with the same airline This factor shows a statistically significant effect at the 10% level, with the exception of the uncomfortable seat variable for VJ in Table 4.2 When comparing VJ to BL, both VNA and VJ exhibit negative coefficients for uncomfortable seat space, indicating that the likelihood of choosing VNA or VJ increases when passengers find their seating comfortable, thereby making VNA and VJ more favorable options than BL.
This study investigates the connection between the quality of check-in services and the perceived utility of alternative airlines It is anticipated that negative evaluations of unfriendly service will adversely affect customer choices However, the findings reveal that all related variables are insignificant at the 10% level, indicating that respondents' assessments of check-in services do not influence their decision-making regarding airlines.
Model 2 employs the multinomial logit model to analyze airline attributes and control variables, revealing that airline prices are statistically significant at the 1% level, except for Vietjet's price in Panel 1 of Table 4.2 The negative coefficient for Vietnam Airlines (VNA) indicates that an increase in its ticket price decreases the odds of choosing VNA over other options, as passengers tend to prefer Jetstar when VNA's fares rise Similarly, Panel 2 shows that an increase in Vietjet's airfare reduces the odds of selecting VJ over BL under the same conditions Conversely, Jetstar's positive price coefficients in both panels suggest that a higher price for BL increases the likelihood of choosing either VNA or VJ over BL, assuming other variables remain constant Additionally, VNA's higher airfare negatively impacts the odds of choosing VJ over BL, leading passengers to favor BL instead.
The frequency of flights, which refers to the number of daily flights offered by each airline on a specific route, plays a crucial role in passenger preferences Airlines with a higher frequency of flights are generally favored, as this provides travelers with more options In a comparison between VietJet (VJ) and Bamboo Airways (BL), VJ's flight frequency shows a positive correlation with passenger choice Conversely, Vietnam Airlines (VNA) exhibits a negative and significant relationship, suggesting that an increase in VNA's daily flights diminishes the likelihood of passengers choosing VJ over BL Similar trends are observed when comparing VNA and BL.
The relationship between airline pricing and consumer choice is illustrated in Figures 4.13, 4.14, and 4.15, demonstrating that as prices rise, the likelihood of selecting a particular airline decreases Specifically, when Vietnam Airlines (VNA) raises its prices, the probability of choosing both VNA and Bamboo Airways (BL) declines, while the preference for VietJet Air (VJ) increases This shift indicates that consumers are more inclined to purchase tickets from VJ over BL when VNA's fares are elevated.
Model 3 is the multinomial logit model of controlling variables and categorical variable (routes)