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
The Asia Pacific aviation industry achieved a net profit of over $5.8 billion, highlighting its significant growth This region accounts for 31% of global passenger traffic, surpassing Europe’s 30% and North America’s 26%, positioning Asia Pacific as a key player in the aviation market Additionally, low-cost carriers in the region have transported more than 950 million passengers, representing approximately 28% of scheduled air travel passengers, according to the 2016 IATA report.
According to the International Air Transport Association (IATA), the number of air travelers is projected to nearly double from 3.8 billion in 2016 to 7.2 billion by 2035, highlighting substantial growth in the aviation industry IATA has identified the five fastest-growing markets—China, the US, India, Indonesia, and Vietnam—that will contribute the most additional passengers over the next two decades Specifically, Vietnam is expected to see an increase of 112 million new travelers, reaching a total of 150 million passengers This data underscores the rising demand for air travel in emerging markets and signals significant opportunities for the aviation sector worldwide.
Vietnam is among the seven fastest-growing countries in the aviation industry, highlighting its significant development potential The Vietnamese government is heavily investing in air transport infrastructure, which is a key component of the sector’s growth The country’s strategic plan includes developing 26 airports by 2020, with the ambitious Long Thanh International Airport scheduled for completion within the same timeframe This robust infrastructure development aims to boost Vietnam’s airline industry and enhance its connectivity on a global scale.
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
In 2015, a 21% increase was observed despite a 30% decline in crude oil prices, which served as a stimulus for airline carriers to lower fares This reduction aimed to meet the growing passenger transportation demand, making air travel more affordable and accessible during that period.
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
In recent years, rising incomes in Vietnam have led to increased demand for transit options People now have multiple transportation choices that are not only faster but also safer Despite global airline accidents in 2014, air travel remains considered the safest mode of transportation According to the IATA Safety Report, there were 73 total aviation accidents worldwide in 2014, resulting in 12 fatal crashes and 641 fatalities.
2014 This is not a high proportion when comparing to about 33 billion passengers in 2014
Air travel significantly saves time, with flights from Ho Chi Minh City to Hanoi taking only about two hours compared to two days by train, enhancing travel efficiency Additionally, the rise of the internet has boosted e-commerce, allowing people to conveniently purchase tickets online at discounted prices during promotional periods, making travel more accessible and affordable.
Vietnam Civil Aviation Department was established in 1956, initially operating with only five aircraft for domestic flights In 1993, Vietnam Airlines was founded as the national carrier, and by 1995, it evolved into the Vietnam Airlines Corporation, consolidating 20 aviation enterprises with the airline as its core business Today, Vietnam Airlines boasts an extensive network of domestic and international routes across Southeast Asia, North Asia, Europe, and Australia In July 2016, ANA Holding Inc became a strategic shareholder by acquiring an 8.77% stake, with plans to reduce state ownership to 75% through ongoing divestment Recognized as a 4-star airline by Skytrax, Vietnam Airlines continues to enhance its reputation for quality service.
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
Starting with just three aircraft in December 2011, Vietjet expanded rapidly to operate 29 aircraft by 2015, serving 28 domestic routes and 12 international destinations Looking ahead to 2016, Vietjet planned to increase its fleet to 42 aircraft to meet growing travel demand and expand its network with three additional domestic routes and five new international flights.
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 has acquired a significant stake in Pacific Airlines, transforming it into a leading low-cost carrier This strategic investment aims to expand route networks, enhance operational efficiency, and offer affordable travel options to customers The partnership signifies a major step in Pacific Airlines' growth trajectory, positioning it as a competitive player in the budget airline industry.
3 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
Since 2004, VASCO Corporation has been providing passenger transportation services from Tan Son Nhat Airport to various southern destinations, including Ca Mau, Con Dao, Rach Gia, Can Tho, and several other routes, establishing itself as a trusted name in the region's travel industry.
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 are the major airlines currently operating in Vietnam Previously, Indochina Airlines and Air Mekong also served the market but faced financial difficulties Indochina Airlines ceased all operations after just one year in 2009 due to financial struggles, while Air Mekong halted its commercial flights in 2013 and was officially revoked by the Ministry of Transport in January 2015.
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
A 2015 survey by the Association reveals that the primary reasons passengers choose an airline are nonstop flights (15%) and the lowest fares (14%), while recommendations by travel agents and in-flight service receive much less consideration at 4% and 3%, respectively Despite the rapid growth of Vietnam's airline industry in recent years, there is limited research focusing on passenger preferences and decision factors in this market.
Knowing the preference of passengers is necessary for both aviation firms and foreign investors
This article highlights how the three carriers offer policies tailored specifically for Vietnamese customers, ensuring their needs are met effectively Additionally, it provides valuable insights for investors by evaluating the airline market, helping them make informed investment decisions.
Research objectives
This study utilizes a stated preference survey and applies the multinomial logit model to identify key factors influencing passengers' airline choices These factors encompass both airline characteristics and air traveler demographics The findings aim to provide valuable insights into passenger preferences, supporting airline marketing strategies and service improvements.
4 factors affecting the choice of passengers, and thus provide information for carriers in identifying their target market segments and efficiently improving their services.
Research questions
This article explores two key questions: first, what airline attributes influence travelers' decisions when choosing an airline? Factors such as service quality, price, flight schedules, and airline reputation significantly impact passenger preferences Second, it investigates the demographic factors that affect airline selection, including age, income, travel frequency, and travel purpose Understanding these variables helps airlines tailor their marketing strategies to better meet the needs of diverse traveler segments Overall, both airline attributes and demographic factors play crucial roles in shaping passengers' airline choices, providing valuable insights for industry stakeholders.
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 because it operates exclusively in the Southeast region with short flights, such as from Sai Gon to Ca Mau, Rach Gia, and Con Dao Additionally, VASCO's primary focus is providing aircraft maintenance services rather than passenger transportation As a result, VASCO's market share is minimal, and its elimination does not pose a significant impact on the overall market.
Structure of thesis
This study is structured into four chapters Chapter 2 reviews key theories such as random utility, stated preference, and revealed preference data, along with an empirical analysis of choice models in the airline industry Chapter 3 details the research methodology, including the questionnaire design, survey process, and the empirical model used Chapter 4 presents the collected data and discusses the results of the model Finally, Chapter 5 summarizes the main findings, discusses study limitations, and offers concluding insights.
This chapter explores the economic literature of individual choice, forming the foundation for empirical studies on decision-making by economic agents, including air travelers It reviews key research analyzing passenger preferences among different airline carriers, providing insights into factors influencing airline selection Building on this review, a comprehensive model is developed to analyze the airline choice behavior of travelers among Vietnam Airlines, Vietjet, and Jetstar, offering valuable insights for the airline industry and marketing strategies.
Theoretical review
Random Utility Model is commonly used to represent individual choice behavior Thurstone
In 1927, a law of comparative judgment was introduced, laying the foundation for the development of psychological stimuli concepts that eventually led to the binary probit model, which assesses whether respondents perceive different levels of stimuli Marschak (1960) further advanced this concept by developing the stimuli idea into the utility framework, suggesting that decision-makers may be aware of the utility associated with each choice, although researchers may not have complete knowledge of these utilities The random utility model thus implies that while individuals know their preferences, researchers must account for the unobservable aspects of utility when analyzing decision-making behavior.
Accounting for uncertainty is essential in utility modeling, which comprises both deterministic and random components Deterministic elements are observable and can be interpreted by analysts, whereas random components remain unknown According to Manski (1977), four primary sources of uncertainty include measurement errors, the use of proxy variables, unobserved attributes of the decision-maker, and unobserved attributes of the alternatives Understanding these sources helps improve the accuracy and robustness of choice models.
Discrete choice models, grounded in random utility theory, assume that decision-makers choose from a finite set of mutually exclusive and exhaustive alternatives, selecting the option that provides the highest utility These models represent utility as a combination of deterministic factors, typically modeled as functions of attributes, such as linear functions, alongside random components that capture unobserved influences The probability of an individual selecting a particular alternative is derived from these models, reflecting both observable attributes and random variability in preferences.
The assumptions regarding the distribution of error terms significantly influence the choice of model in discrete choice analysis According to Train (2009), key models used in this field include logit, generalized extreme value (GEV), probit, and mixed logit models, each differing based on their underlying error distribution assumptions.
The logit model assumes that error terms are independently and identically distributed (iid) extreme value, meaning the unobserved factors are uncorrelated and have the same variance across alternatives, which simplifies the choice probability into a convenient mathematical form This assumption makes the logit model widely popular among researchers for analyzing air choice behavior, despite being restrictive as it may not always accurately reflect situations where alternatives are correlated or choices over time depend on previous decisions The independence assumption also implies that each choice is made independently of others, facilitating straightforward analysis of decision-making patterns in various studies.
To address the limitations of the traditional logit model's assumption of independence, Generalized Extreme Value (GEV) models were developed to extend the distribution of extreme values (Train, 2009) GEV models enable the modeling of correlations between unobserved factors and alternatives, capturing more complex choice behaviors When these correlations are absent, GEV models reduce to the standard logit model, making them a flexible and comprehensive tool for discrete choice analysis.
The flexibility of correlations in GEV models varies depending on the model type Simple GEV models categorize alternatives into multiple groups called nests, where unobserved factors are assumed to have consistent correlations within the same nest but no correlation across different nests Hess (2008) utilized the nested logit model to analyze air travel behavior, demonstrating its effectiveness in capturing choice patterns Similarly, Pels et al (2001) applied the nested logit model to study passenger preferences related to airports and airlines, highlighting its relevance in transportation modeling.
Probit models address three key limitations of the logit model, including the inability to represent random taste variation, the IIA property, and correlations between unobserved components and alternatives (Train, 2009) Unlike the logit model, which assumes independence of irrelevant alternatives, probit assumes that error terms are normally distributed, providing greater flexibility in modeling correlated choices However, the main limitation of the probit model is its reliance on the assumption of normally distributed unobserved factors, which may not hold true in all cases.
Mixed logit models allow for unobserved factors to follow any distribution, capturing heteroskedasticity and correlation, while the remaining unobserved factors are assumed to be iid extreme value This flexibility enables modeling of non-normal distributions of unobserved variables, offering a more comprehensive understanding of travel behavior Adler et al (2005) utilized the mixed logit model to develop an itinerary choice model, demonstrating its applicability in travel demand analysis Additionally, Warburg (2005) employed both multinomial logit and mixed logit models to analyze passenger flight choice behavior, highlighting the effectiveness of mixed logit in capturing unobserved heterogeneity in mode selection.
Many discrete choice models are tailored for specific research purposes by incorporating concepts from other models, such as using normal distributions in mixed probit models instead of extreme value distributions found in mixed logit Understanding the motivation and derivation of these models enables researchers to select the most appropriate model for their particular study objectives Additionally, methods like Revealed Preference and Stated Preference surveys play a crucial role in capturing consumer preferences and informing model selection.
There are two main types of customer behavior surveys: revealed preference (RP) and stated preference (SP) RP data captures actual consumer choices in real-world situations, offering the advantage of reflecting true behavior, but it poses challenges for trade-off analysis and modeling new market alternatives Conversely, SP surveys allow for the evaluation of hypothetical scenarios and new options, making them useful for exploring potential market responses when RP data is unavailable or limited.
Yoo and Ashford (1996) identify three practical limitations of revealed preference (RP) data: limited variation for certain variables, difficulties in estimating trade-off ratios due to correlated explanatory variables, and the need for large surveys to gather sufficient observations As a result, few researchers utilize RP data for modeling customer choice behavior Carrier (2008), for instance, used booking data that did not include non-booked travel options like income and travel purpose Similarly, Escobari and Mellado (2014) analyzed data from online travel agencies, focusing on posted prices and inventory changes to explain flight demand.
In SP surveys, hypothetical scenarios are carefully designed to capture respondents' stated preferences, providing valuable insights into their choices This approach helps mitigate some limitations associated with RP data by enabling researchers to explore potentially unrealized options and future behaviors As a result, SP data can offer more flexible and comprehensive understanding of consumer preferences, improving the accuracy of market analysis.
According to Collins et al (2012), SP data allows researchers to accurately reproduce behavioral outcomes, such as willingness to pay Conducting SP surveys also enables the exploration of consumer choice behavior for hypothetical alternatives that do not currently exist However, SP data has limitations, as respondents may be uninterested, careless, or more focused on sharing their opinions about the survey context rather than providing genuine feedback on new product usage.
Decision making in hypothetical scenarios often leads to biased results because individuals may not act as they claim (Warburg, 2006) In practice, most researchers utilize stated preference (SP) surveys to model choice behavior For example, Adler et al (2005) used SP surveys to analyze trade-offs in air itinerary choices, while Collins et al (2012) employed interactive SP surveys to understand air traveler behavior Additionally, Wen and Lai (2010), along with Proussaloglou and Koppelman (1999), used SP data to examine passengers' air carrier preferences, demonstrating the importance of SP surveys in transportation choice modeling.
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) investigates airport choice in multi-airport regions, highlighting the complexity of passengers’ decision-making processes Several studies extend beyond airport selection to explore various aspects of travel behavior, such as route choice and destination preferences Ndoh et al (1990) focus on both airport choice and route selection of passengers, providing insights into travel patterns Similarly, Furiuchi and Koppelman (1994) examine passengers’ destination choices alongside their airport preferences Additionally, some research emphasizes broader air traveler choice behavior, with Chin (2002) contributing to this understanding These studies collectively enhance our knowledge of factors influencing traveler decisions in the air transportation industry.
Research by Algers and Beser (2001), Proussaloglou and Koppelman (1999), and Yoo and Ashford (1996) highlights significant findings in their respective fields, demonstrating the importance of integrating academic insights to enhance understanding Staying updated with the latest thesis downloads and academic resources is crucial for ongoing research, accessible through platforms like Gmail for seamless collaboration and information sharing.
The multinomial logit model is widely used in many studies to analyze choice behaviors, providing a robust framework for understanding decision-making processes In addition, research by Ndoh et al (1990), Furiuchi and Koppelman (1994), and Pels et al (2001) employs the nested logit model to effectively estimate multidimensional and spatial choices of air travelers These models are essential tools in transportation research for capturing complex travel preferences and route selection patterns.
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-
This study examines airline choice at Guarulhos International Airport (GRU) and São Paulo-Congonhas Airport (CGH), highlighting that passengers typically weigh flight cost, frequency, and airline performance in their decision-making Key variables analyzed include the lowest and highest fares, flight frequency factors such as connections, travel period, and day of the week, and airline age as a proxy for performance The findings reveal that the lowest fare is the most significant factor influencing airline choice, with senior passengers placing greater importance on airline age compared to junior passengers Additionally, a prior study by Nason (1981) used a multinomial logit model and stated preference surveys to demonstrate that airline attributes and passenger characteristics collectively shape airline selection preferences.
Prossaloglou and Koppelman (1995) used a revealed preference survey to analyze airline choices among passengers departing from Dallas and Chicago in the US Their study employed a multinomial logit model, considering factors such as schedule convenience, reliability, fares, city pair presence, market presence, and frequent flyer program membership The findings indicate that the attractiveness of carriers and their market share are positively influenced by the presence of frequent flyer programs, highlighting the importance of loyalty programs in airline selection.
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, tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
This study employs separate models to estimate travel preferences for business and leisure passengers, based on detailed stated preference data collected through a two-tier survey process Initially, a mail survey gathers passenger characteristics such as trip purpose, previous travel experience, address, and frequent flyer membership, while a subsequent random sample of respondents participates in phone interviews The questionnaires are designed to simulate the real-world airline booking experience, allowing passengers to select among various travel options Findings indicate significant behavioral differences between leisure and business travelers; leisure travelers are more sensitive to prices but less affected by travel time, whereas business travelers place greater importance on frequent flyer programs and are willing to pay more for their preferred airlines.
Pels et al (2001) employed separate models for business and leisure travelers; however, their findings indicate that the differences between these groups are minimal The study utilizes a nested logit model to analyze passenger preferences related to airports and airlines, providing valuable insights into travel behavior.
This research provides a comprehensive analysis of nesting behaviors, focusing on both airport-defined nests and airline-defined nests An empirical study was conducted using survey data from the San Francisco Bay Area to understand the dynamics and significance of these nests in the transportation ecosystem The findings highlight the importance of considering multiple perspectives when evaluating nesting patterns within the aviation industry.
In 1995, it was recognized that airlines face two primary types of competitors: those operating within the same airport and those operating at different airports Access time to the airport plays a crucial role for both leisure and business travelers, making airport proximity a significant competitive factor Understanding these dynamics is essential for developing effective airline strategies in a competitive market.
Understanding passengers’ flight choice behavior and predicting air travel demand are essential for airlines to optimize their pricing strategies and plan for new routes, as highlighted by Warburg (2005) His 2001 study, which involved a stated preference survey of 119 business and 521 non-business passengers reporting their most recent domestic flight, provides valuable insights into consumer preferences This research helps carriers develop targeted pricing policies and accurately forecast demand for upcoming routes.
This study examines ten binary choices between actual flights and hypothetical flight options, each representing different itineraries with the same departure and arrival points Warburg (2005) argues that there is no universal choice set in airline selection, as travelers have diverse preferences and different available itineraries To analyze passenger behavior, the research employs both multinomial logit and mixed logit models, focusing on two groups: business and non-business travelers The findings highlight the variability in flight choices across different passenger segments, emphasizing the importance of considering individual preferences in airline decision-making.
According to Koppelman (1999), in a multinomial logit model, business travelers tend to be more sensitive to time, whereas non-business travelers are more influenced by fare Additionally, men are generally more sensitive to fare changes than women, highlighting differing sensitivities based on trip purpose and gender.
Moreover, the study of Yoo and Ashford (1996) investigates the flight choice behavior of
This study focuses on travelers who undertake long-distance international flights exceeding 10 hours, analyzing their preferences using both Revealed Preference (RP) and Stated Preference (SP) data Surveys were conducted at Kimpo International Airport in Seoul, with the RP survey carried out in October 1993 and the SP survey in August 1994, ensuring an equal number of samples for both methods Employing a logit model to compare RP and SP data, the research reveals that passengers are willing to pay more for Korean airlines compared to foreign airlines, highlighting key insights into airline choice behavior.
Korean residents pay higher prices for international flights compared to foreign residents According to Escobari and Mellado (2014), the demand for international flights was estimated using a unique dataset that included detailed information on flight choices, prices, and characteristics of non-booked flights This data was collected from an online agency, providing valuable insights into consumer behavior and pricing disparities across different resident groups.
“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
Stated preference method
According to Whitaker et al (2005), the stated preference (SP) method is a widely used technique for understanding decision makers' behavior and plays a crucial role in accurately forecasting demand for various airline services While the SP method provides valuable insights, traditional demand analysis primarily relies on revealed preference (RP) data, which reflects choices made in real-world environments and enhances the reliability of demand forecasts.
Limitations of Revealed Preference (RP) methods include high survey costs and challenges in accommodating future market alternatives, which may not be captured effectively in the models based solely on RP data.
Wen and Lai (2010) highlight that while reveal preference methods collect data from actual decisions, they may be limited because passengers often do not fully consider all airline attributes Alternatively, the stated choice approach allows researchers to analyze how individuals respond to hypothetical scenarios, which include a set of alternatives and varying attribute levels This method has gained popularity and is now widely applied in airline choice research and other decision-making contexts.
It is said that in research of travel behavior, there are two types of stated response (Hensher,
In survey research, respondents are typically asked to identify their preferences among various alternatives, aiming to establish a measurement scale such as a rating or rank ordering scale Rating scales, including Likert scales and 1 to 10 scales, are commonly used to capture both quantitative and qualitative attributes of preferences Unlike rating scales, rank ordering scales require individuals to order the listed alternatives, providing insights into their relative degrees of preference These methods are essential for accurately assessing consumer preferences and attitudes in market research.
Warburg et al (2006) and Adler et al (2005) are exemplary studies that utilize rank ordering scales to analyze data effectively These methods are commonly employed in research to prioritize or rank preferences, perceptions, or other ordinal data Incorporating rank ordering scales enhances the accuracy and clarity of qualitative and quantitative analyses Understanding their application is essential for researchers aiming to establish clear hierarchies within their datasets, ultimately improving the robustness of their research findings.
14 survey of airline choice Second, a respondent is required to take one of the listed alternatives
In a choice experiment, the first preference choice task is essential, as it plays a crucial role in shaping survey outcomes Addressing response strategies at the outset of conducting a stated preference (SP) survey is vital because it determines the reliability and accuracy of the results Understanding different response strategies helps ensure that the data collected reflects true preferences, thereby enhancing the validity of the survey findings.
This study utilizes a first preference choice task to gather insights Respondents are presented with airline options based on airfares for a specific route and are asked to select their preferred airline This approach helps to identify consumer preferences and sensitivities related to airfare variations, providing valuable data for understanding market behavior.
Vietnam Airlines (VNA), Vietjet Air (VJ), and Jetstar (BL) are prominent carriers in the aviation industry According to Hensher (1994), stated preference (SP) data effectively reflects individuals’ decision-making processes by capturing their choices among a set of alternatives This method is widely used in research, as demonstrated by Wen & Lai (2010) and Hong (2010), who utilized SP surveys to analyze traveler preferences In their study, Wen & Lai (2010) presented air travelers with choices among different airlines, such as China Airlines, EVA Airways, and JAA for the Taipei – Tokyo route, and among four airlines including Cathay Pacific and Dragonair for the Taipei route.
– 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.
Questionnaire and survey process
The questionnaire of this survey that is showed detail in the Appendix consists of three parts
The survey begins by collecting respondents’ sociodemographic information and their primary purpose for travel Participants then evaluate airline service quality, including staff attitude at check-in, flight attendants’ service, in-flight food and beverages, seat comfort, and punctuality, with an option to indicate if they have no experience with certain services The questionnaire presents fifteen hypothetical scenarios involving routes from Tan Son Nhat Airport to various domestic airports, asking which airline they would choose based on listed fares Respondents also disclose their intended trip purposes and the maximum ticket price they are willing to pay for each route If participants believe they will never visit a particular destination, they can select "I will never go there" and skip subsequent questions for that scenario.
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 the key factors influencing customers' air ticket purchasing decisions Among 18 recent customers asked to identify all factors affecting their choices, the study aimed to understand the main determinants shaping consumer behavior in airline ticket purchases.
The key factors influencing passenger experience include fare prices, flight schedules, on-time performance, quality of onboard staff service, and seat comfort An online survey was conducted to gather passenger feedback, which took place from the 16th to the 23rd of [month].
In October 2016, an online survey was developed using SurveyMonkey to gather insights from air travelers The survey link was distributed via email and shared publicly on social media platforms such as Facebook and Zalo to reach a broad audience The target respondents were individuals who had previously traveled by air and had flown with at least one of the airlines: Vietnam Airlines (VNA), VietJet Air (VJ), or Bamboo Airways (BL) Due to customer loyalty, many travelers tend to fly exclusively with their preferred airline, making it challenging to find participants who have experienced all three airlines This survey aims to understand passenger preferences and airline experiences within this context.
Figure 3.1 The screen of the online survey
Attributes of airlines
Customer satisfaction is a critical factor in the service industry that directly influences customer retention and loyalty According to Fornell et al (1994), higher customer satisfaction reduces complaints and lowers the costs associated with service failures Zeithaml and Bitner (1996) identify key components of customer satisfaction, including price, service quality, terms and conditions, and personal characteristics While service quality is essential, customers often weigh trade-offs between costs and benefits, making price a significant determinant of overall satisfaction (Lee & Cunningham, 1996).
Research by Athanassopoulos et al (2001) indicates that customer behavior in response to satisfaction typically falls into three categories: remaining loyal to existing providers, engaging in positive word-of-mouth, or switching to alternative service providers In the airline industry, factors influencing airline selection extend beyond price and service quality, including schedule timing, flight frequency, aircraft types, and the number of seats available.
17 of airline Many researchers investigate the effects of airline attributes on carrier choice, which are summarized as in Table 3.2
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
Hong (2010) tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Model specification
This study is based on Manski's (1977) Random Utility Model, which assumes that air travelers are rational decision-makers aiming to maximize their utility Passengers typically choose the airline that offers the highest perceived benefit, highlighting the importance of factors influencing airline selection Understanding this behavior provides valuable insights into consumer preferences and helps airlines optimize their services to attract more customers.
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
𝜀 : 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 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:
In airline choice modeling, a traveler selects airline n when the utility difference satisfies the condition 𝜀 𝑖𝑚 < 𝑉 𝑖𝑛 − 𝑉 𝑖𝑚 + 𝜀 𝑖𝑛, where 𝜀 𝑖 represents the error term The probability of choosing a specific airline can be estimated based on the distribution of these error terms The multinomial logit model assumes that the error terms are independent and identically distributed extreme value variables, which facilitates the estimation process and interpretation of airline choice behavior.
(Gumbel distribution) According to Train (2009), the function of probability could be rewritten as below:
In a multinomial logit model, the total probability of choosing among three airlines sums to 1 However, Gujarati (2011) notes that it is not possible to identify the three individual choice probabilities independently Typically, one airline is designated as the base or reference category, with its coefficients set to zero; for example, if airline 3 is the base (α3 = 0 and β3 = 0), the choice probabilities for the other two airlines can be derived 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:
𝑃 𝑖3 = 1 − 𝑃 𝑖1 − 𝑃 𝑖2 (3) tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
This model examines whether X, a vector of variables—including independent and controlling factors—are related to carrier choice The independent variables analyzed include price, flight frequency, and routes, which are identified as key factors influencing passengers' decision-making These factors are tested as hypotheses in the third part of the survey to understand their impact on carrier selection.
This study collects respondent information and their evaluations of airline services through the first and second sections of the questionnaire The variables analyzed in this research, detailed in Table 3.4, provide key insights into factors influencing customer perceptions and satisfaction.
This article highlights that airline prices and flight frequencies are sourced from real business data collected online Specifically, the survey analyzes the average prices for each route offered by different airlines in November, providing an accurate reflection of current market trends.
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
15 Chu Lai - 5.5 6.52 - 3 1 tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
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 article discusses the flight frequencies of different airlines on various routes VNA (Vietnam Airlines) operates multiple flights per day, indicated by the parameter "freqvn." Similarly, VJ (VietJet Air) maintains its flight frequency with "freqvj," while BL (Bamboo Airways) schedules flights according to "freqbl." Monitoring these flight frequencies provides insights into the daily operational capacity of each airline, emphasizing the significance of route-specific flight schedules in optimizing airline network efficiency and meeting passenger demand This information is essential for travelers planning their trips and for industry analysis of airline operations.
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
On-time performance is a key indicator of airline efficiency, with metrics such as ontvn_pun indicating whether VNA's previous flights departed on schedule, and ontvj_pun reflecting similar data for VJ flights Additionally, ontbl_pun tracks the punctuality of BL flights, providing a comprehensive view of airline punctuality Maintaining high on-time performance is crucial for customer satisfaction and operational effectiveness For further details or to submit your inquiries, please contact us via email at vbhtj mk@gmail.com Ensure your thesis or master's dissertation is up-to-date by consulting the latest data and analysis on airline punctuality.
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
Customer feedback indicates that staff at the check-in counter, including VNA, VJ, and BL personnel, are often perceived as unfriendly Guests have expressed dissatisfaction with their interactions, highlighting the need for improved customer service professionalism Ensuring friendly and welcoming staff can enhance the overall travel experience and boost airline reputation Additionally, some users have shared concerns about difficulty accessing academic resources online, emphasizing the importance of user-friendly digital platforms for students and graduates.
This chapter presents the summary of data collected form the stated preference survey as well as the regression results of multinomial logit model.
Data
Table 4.1 provides a detailed overview of the characteristics of social demography The study initially included 135 respondents; however, 13 participants did not complete the survey, leading to their exclusion from the data analysis Each respondent evaluated 15 scenarios to select their preferred airline Figure 4.1 illustrates airline choices across various hypothetical destination scenarios surveyed The data suggests notable preferences for airlines in destinations such as Ha Noi, Da Nang, and Phu.
Destinations with the highest travel demand are those where over 98% of respondents intend to visit in the future Vietnam Airlines (VNA) shows the lowest preference across all routes, notably lacking flights on the Sai Gon – Tuy Hoa and Sai Gon – Chu Lai routes, leading to their exclusion from these survey scenarios Overall, VietJet Air (VJ) is the most preferred airline in most routes, particularly for the Sai Gon to Ha Noi and Da Nang routes.
Over 60% of respondents prefer VJ, making it the most popular airline overall However, in specific destinations like Da Lat and Hue, Blue Sky Airlines (BL) emerges as the leading choice among travelers This indicates regional preferences and highlights the importance of airline reputation in different areas Understanding these trends can help airlines tailor their marketing strategies to target specific customer segments more effectively.
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)
There are 122 respondents in the data includes 84 females (68.85%) and 38 males (31.15%)
Most respondents in the survey are single, accounting for approximately 70%, and the survey was conducted online, targeting young people with easy internet access The average age of participants is around 27 years old, with ages ranging from 20 to 46, while two respondents chose not to disclose their age Regarding education, 83 respondents (68%) have a university degree, 32 (26.23%) hold a master's degree, and only one individual has completed high school The majority are employed by companies, with about 68% (83 respondents) working as employees, whereas only one person is self-employed or a freelancer Over 40% of respondents reported that they usually travel by air for both leisure and business trips Income levels among respondents mostly fall within the range of 8 to 10 million VND per month.
DADHANPQCCXR DII HUI BMV UIH VII TBB VCL PXU HPHVDH THD
Airline Choice for Destinations - Depart from Sai Gon
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The survey evaluates airline service quality based on key factors such as check-in processes, cabin crew attitudes, and onboard food and beverage quality, rated on a three-point scale: not good, good enough, and very good (Figures 4.4 to 4.7) To assess flight punctuality, respondents answer whether their previous flights with three different airlines were on time (Figure 4.8) and whether they find on-time performance acceptable (Figure 4.9) Additionally, respondents who have never used airline services can indicate “I have not used the service before,” ensuring comprehensive data collection on customer experiences and perceptions.
In general, approximately 36 percent of people in this survey said that they have not flied with
Jetstar Pacific before, the highest rate among of three airlines This contrasts with Vietnam
Most people praise the quality of service provided by Vietnam Airlines, rating it as good or very good In particular, the check-in process and cabin crew are widely regarded for their professionalism and friendliness, contributing to a positive flying experience.
Vietnam Airlines has relatively low customer friendliness ratings, at only 1.64% and 0.82%, respectively However, regarding punctuality, nearly 60% of respondents agree that Vietnam Airlines flights depart on time, indicating a positive perception of their punctual services Additionally, approximately 74% of passengers find the airline's delayed flight rate acceptable, reflecting satisfactory overall punctuality performance.
Vietjet Air has the highest number of unpunctual flights (up to 62%) and more than 30% says that it is unacceptable for schedule changes of Vietjet Air
The survey also assesses respondents' willingness to pay for air tickets on various routes, such as from Sai Gon to Ha Noi Figure 4.8 illustrates the average amount respondents are willing to spend, providing valuable insights into consumer pricing expectations for different destinations.
Income (Mill VND/month) tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
The survey indicates that 26% of respondents are willing to pay for transportation routes, with a clear preference for higher fares on long-distance trips such as Ho Chi Minh to Hanoi and Hai Phong Conversely, travelers are willing to pay less for shorter routes like Ho Chi Minh to Nha Trang, reflecting differing willingness to pay based on trip distance.
Figure 4.3 Willingness to pay for routes
(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)
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 )
Destinations - Departure from Saigon tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Notes: 122 respondents, in which 2 not reveal their age
Demographic Characteristics Number of respondents Percentages (%)
Both of Leisure and Business 53 43.44
Age Mean: 27.075 Min: 20 Max: 46 tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Figure 4.4 Check-In Service Evaluation
Figure 4.5 Cabin Crew Service Evaluation
VN VJ BL tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Figure 4.6 Food & Drink Onboard Evaluation
Figure 4.7 Inflight Seat Space Evaluation
VN VJ BL tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Figure 4.8 On-time Performance Evaluation
VN VJ BL tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Empirical results
According to Gujarati (2011), odds ratios measure how much one choice is preferred over another by comparing the probability of selecting alternative i to that of alternative j, the base outcome A positive coefficient indicates that increasing the variable boosts the odds of choosing option i over j, suggesting higher utility for the decision-maker when selecting i Conversely, a negative coefficient signifies that a one-unit increase in the variable decreases the odds of choosing i relative to j, implying a preference for j over i Proper interpretation of these coefficients helps understand decision-making preferences in choice models.
In a multinomial logit model, relative risk ratios (RRRs) are derived by exponentiating the model's coefficients (e^coef) These RRRs indicate that a one-unit increase in an independent variable will modify the relative risk of outcome j compared to the base outcome by a factor equal to the RRR Understanding RRRs is essential for interpreting how changes in predictors influence the likelihood of different outcomes in multinomial logistic regression analysis.
Table 4.2 presents the results of multinomial logit models examining airline choice behavior Model 1 analyzes only control variables, while Model 2 incorporates airline-specific attributes such as price and flight frequency Model 3 further includes route information to assess its impact on decision-making Jetstar Pacific (choice 3) serves as the reference category in all models, with Vietnam Airlines and Vietjet Air designated as choices 1 and 2, respectively These models reveal how airline attributes and route factors influence passenger preferences, providing valuable insights for targeted marketing strategies.
It is noted that there are 122 respondents who are required to make decision in 15 scenarios
Each response to a scenario is considered a single observation, with a total potential of 1,830 observations if all respondents answer all 15 scenarios However, when respondents indicate no demand to travel from Sai Gon to a particular destination, they are not asked to choose an airline in those scenarios, reducing the total observations to 605 for the regression analysis The detailed results are provided 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
Da Lat 0.874 2.396 0.189 tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
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
The analysis indicates that the seat space of VJ significantly impacts passenger comfort, with discomfort levels rated at -1.659 (p < 0.001), suggesting insufficient legroom contributes to passenger dissatisfaction Additional factors such as overall cabin design and seating arrangements further influence comfort levels, emphasizing the importance of optimizing seat space in vehicle design to enhance passenger experience Improved seat capacity can lead to increased customer satisfaction and better travel experiences.
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
Note: *** p