A New Business Model: Low Cost Carriers (The Case of...

Global Review of Research in Tourism, Hospitality and Leisure Management (GRRTHLM)

An Online International Research Journal (ISSN: 2311-3189)

2018 Vol: 4 Issue: 1

608 www.globalbizresearch.org

A New Business Model: Low Cost Carriers (The Case of EasyJet)

I. Rouby,

Faculty of Tourism and Hotels,

Alexandria University-Egypt.

E-mail: [email protected]

___________________________________________________________________________

Abstract

In the last three decades, a new phenomenon dominated the airline industry; the emergence

of Low Cost Carriers (LCC). This new business model was first introduced by Southwest

Airline, and then it spread to include Europe then Asia. Their lower fares are considered their

main competitive advantage. These airlines manage to lower their fares by adopting highly

efficient operational strategies which help them cutting their costs and smoothly adapting to

market changes.

The objective of this research is to examine the business model of LCCs with a thorough

review of the literature at the first stage. The second stage involves using time series analysis

to forecast the number of passengers for easyJet, a successful British Low Cost Carrier which

started off in 1995. The data analyzed in this study was provided by the secondary data of the

annual reports of easyJet. The significance of the variable “Air Passenger Traffic Worldwide”

via “Total Passenger Traffic easyJet” was addressed with a linear regression.

The results show, that there is a great potential for easyJet to expand in the future in terms

of passenger traffic. As a result of some external factors like the increasing tendency for

travelling around the world, the rise of the middle class, the emergence of new markets,

economic instabilities pushing some scheduled airlines out of the market and internal factors

like the constant developments in easyJet’s fleet, it is predicted that it will achieve greater

traffic growth in the future.

Key Words: easyJet, Forecasting, LCC, Scheduled Airlines, Time series, Tourism Demand





1. Introduction

The term Low Cost Carriers (LCCs) or Low Cost Airlines (LCA) can be referred to as “any

carrier with low ticket prices and limited services regardless of their operating costs” (Macario

et.al, 2008)

Low cost carriers (LCC) have grown in the last three decades and have become a tempting

alternative to Full Service Airlines (FSA). Low Cost Carriers utilize a business model that

reduces operational costs. In order to compensate revenue loss in tickets, they may charge

customers for auxiliary services like meals, priority boarding and baggage. This new type of

service airlines is one of the fastest growing economic segments and at times, LCCs were the

only sector growing in periods of economic and political uncertainty (Eurocontrol, 2017).

According to (InterVISTAS, 2013), these carriers have been demonstrating sustained

growth, with gradually increasing fleet sizes, number of passengers served, revenues, and in

many cases profits. It is typical to start operating with three or so aircraft, and then steadily add

capacity. Their growth rates in their first years may reach 100% which then soothes to 30-60%

at the two to eight year point.

LCCs have played a fundamental role in the expansion of the aviation industry in the last

decades and it is expected that their growth will continue. Since the emergence of Southwest

Airlines in the U.S., there have been several attempts to implement LCCs business models but

with no success. That was due to government barriers, airport and other infrastructure

constraints and lack of knowledge of how to successfully implement the new business model.

Nowadays, LCCs are one of the major components of the aviation market. Their rise can be

referred to several reasons. Firstly, due to market liberalization in many countries in addition

to air service agreements, LCCs seized the opportunity to offer innovative services and lure

new customers looking for air services with low prices. In 2015, the European LCCs captured

41% of the seat capacity in Europe. In Africa, although market access barriers are high, the

share of LCCs within the region is at 9% while in Asia, the LCCs share accounted for 23%.

Secondly, LCCs managed to offer what potential air service customers value and responded

to customer needs, namely good quality for lower prices. Thirdly, LCCs have responded

quickly to market conditions and understood that keeping a competitive advantage requires a

ruthless force to cut costs, increase revenues, and maximize efficiency.

LCCs were able to compete with FSAs, because they were able to lower their costs as they

don’t offer the same level of amenities, they don`t manage the same degree of connectivity of

FSAs and they don’t have to bear legacy costs. As passengers nowadays don’t need network

connectivity for all their journeys or don’t need service amenities, these travelers are willing to

substitute the FSAs product for LCC trips. If FSAs were to lower their costs in response to LCC

strategies, they would decrease their revenues.





The objective of this research is to examine the business model of LCCs, which is considered

the key driver to cost reductions giving them the opportunity for lower-priced offerings. The

research also highlights the reaction of FSAs towards the fierce invasion of LCCs of the air

service market. In addition, the potential for future growth of traffic for easyJet was tested using

time series forecasting.

This research is divided into two parts. The first part consists of a thorough review of the

literature to examine the new business model of LCCs versus FSA (Full Service Airlines). The

second part, covering the methodology of research, was dedicated to hypotheses testing. It

comprised of applying times series forecasting on the secondary data of monthly passenger

traffic of easyJet covering the period from January 2004 till February 2018. A linear regression

analysis was conducted to simulate cause and affect relationship between variables “Air

Passenger Traffic Worldwide” and “Total Passenger Traffic easyJet”. Also Pearson’s

correlation coefficient was calculated to examine direction and magnitude of association

between variables “crude oil prices” and “total air passengers worldwide” and “total passenger

traffic easyJet”. The research hypothesized the following:

H1: There is a significant relationship between easyJet air passenger traffic and number of air

passenger traffic worldwide.

H2: There is a potential for growth in the future for easyJet in terms of passenger traffic.

H3: There is a significant relationship between average crude oil prices and total air

passengers worldwide and total passengers of easyJet.

2. Literature Review

2.1 Changing Business Environment

It is crucial to examine the changing market environment within which the aviation industry

has been operating and which has affected its development in recent years.

Liberalization of the economy means “to free it from direct or physical controls imposed by

the government”. The most affecting trend which revolutionized the structure and operating

models of the aviation industry was the liberalization of international air transport.

Consequently, many governments allowed the emergence of new domestic and/or international

airlines able to compete directly with their established national carriers. For example, state of

the European Union (EU) can benefit from open skies with unconstrained market access to any

routes for airlines and the elimination of all capacity, price controls and ownership limitations

(Cento, 2009).

The decline in traffic growth rates had also affected the development of air transport. In

absolute traffic terms a 2.0 % growth in 2004 compared to a 12 per cent rise in the late 1960s

implies a noteworthy slow-down in the traffic growth rate.





Also the decline in yield – that is the average revenue produced per passenger-kms or tonne-

kms carried was one of the factors significantly affecting aviation industry.

Air transport is considered a capital intensive industry with small marginal profits.

Therefore, airlines always try to keep their operational cost as low as possible as there are some

costs that cannot be controlled by management like fuel costs (The World Bank, 2014). Porter

(2008) explained that the airline industry has the lowest average return on invested capital

(ROIC) if compared to a list of selected industries. The average ROIC for airlines was a mere

6.9% between 1992 and 2006, where top performing sectors, had an average ROIC above 40%.

Therefore, the LCCs business strategies focused primarily on reducing operating costs which

consequently intensified the competition with FSAs which are burdened by high legacy and

operational costs.

2.2 Key Elements of the New Business Model

In their study Francis et al. (2006) developed a typology, distinguishing among five broad

types of low cost carriers:

a. The Southwest copy-cats – Airlines that started from scratch as LCCs.

b. Subsidiaries – Airlines that have been set up as subsidiaries of “legacy” airlines and emerged

to get back a part of the market of legacy airlines that was taken away by LCCs.

c. Cost cutters – Are long established “legacy” airlines which are trying to adopt cost cutting

strategies like charging passengers for meals or offering low cost one way tickets.

d. Diversified charter carriers – Low-cost subsidiaries developed by charter airlines.

e. State-subsidized companies – These are not true low cost carriers but they act in the market

as if they were. They are financially supported by government ownership or subsidies allowing

them to offer low fares without the need to cover their long run average costs.

LCCs are characterized by a business model that relies primarily on some key elements.

These include:

Service Offering: The unbundling of fares is one of the characteristics of the low-cost airline

business model, which concentrates on separating the product into distinct elements (Fageda et

al., 2015). These elements are sold separately. This results in cost reductions and offers

opportunities for revenues. Food and beverage for instance are offered for an extra charge. Most

LCCs have no pre-assigned seating arrangements and operate on a first-come, first-served basis.

However, some LCCs such as easyJet have started issuing “speedy boarding” tickets that can

be purchased in advance. Also LCCs have firm rules concerning luggage weights per

passengers. Some tickets don’t include any checked-in luggage and have to be purchased

separately (Vidiovic et al., 2006.)

Point-To-Point versus Hub-and-Spoke Structures: The majority of LCCs focus on point to

point routes in contrast to “hub-and spoke” system adopted by FSAs. The “Spoke” flights





assemble the passengers in one or more “hub” airports where passengers continue their flights

onward from these hubs. This operation is very expensive because of the excessive use of

infrastructure such as ground equipment and runways. Furthermore, in times of congestion,

diseconomies of scale may arise because hubs have to deal with these congestions leading to

higher fuel consumption and labor costs (Vidiovic et al., 2013).

LCCs in contrast are able to reduce costs by offering point-to-point routes, benefiting from

economies of scale and enhanced utilization of facilities and employees.

Usage of Secondary Airports: LCCs usually use secondary airports for the following

reasons: lower airport charges, the availability of slots, and reduced congestion. In addition to

that, LCCs manage to get incentives from remote airports. Local authorities recognize that

LCCs are considered a potential driver for social and economic developments, and are willing

to provide financial help such as tax exemption and marketing support (Soyk et al., 2017).

Additional cost reduction factors include the use of a standardized fleet of cost-efficient and

fuel-saving aircrafts, allowing a better flexibility of crew assignment and savings in training,

qualification and stock of spare parts (Franke, 2004). In addition to standardized service, free

services on board are omitted and intermediaries are bypassed. Reservations are made online

(easy-Jet sells 95% of its seats in this way) or by telephone. Offering point-to-point tickets with

no connections simplifies luggage handling and increases planes turnaround. No pre-assigned

seating, no frequent flyer programs and non-integration into alliances is the rule. In addition,

no compensation to passengers is offered in the form of a hotel reservation or transfer to another

airline if a flight is cancelled or delayed. They get additional revenues by offering direct or

indirect services such as car rentals or hotel reservations. They rent advertising possibilities on

board or on the Internet. Some figures show that the LCCs workers are paid less than their

fellow workers in the FSAs although having a heavier workload (Gillen et al., 2004).

The following table (1) summarizes key elements of the business models adopted by LCCs

and FSAs.

Table 1: Key Elements of Business Models of LCCs and FSAs

Feature LLC Strategy FSA Strategy

Costs Simple processes to reduce costs.

Reduced training, servicing as well

as crew and maintenance costs.

Collective agreements and network service

result in complex processes. Thus, the

adoption of continuous reforms and

improvement management strategies are

only initiatives with no strategic alterations.

Hubs Hubs are largely results of market

sizes.

Hubs are strategic assets.

Connections Connection opportunities are

sometimes offered but aren’t a

primary product dimension.

Hubs are built to maximize the number of

possible connections with shortest possible

connection times. This results in the need for





extra resources (e.g., more gates, more

aircrafts) to accommodate large demands.

Interline

connectivity

Interlining is rare. It requires

significant additional costs and

systems investments.

Interline principles are a core concept for

FSAs. Considerable investments are made to

handle interline connectivity.

Code share Some cases exist, but are relatively

rare and not a major focus of the

carrier.

A key strategic practice which allows the

FSA to sell a network of services.

Secondary

airports

Some LCCs have used this as an

important business strategy for

cheaper airport fees while others

have combined the use of secondary

with primary airports.

Outsourcing of most non-flying jobs

(ground handling, call centers,

aircraft maintenance).

FSAs often serve secondary markets in

regions from their hubs.

Alliances

Not observed. A key strategic development to meet

consumer demand for service from large

network carriers.

Class of service

Generally one class, but in some

cases business class seating and

amenities are provided. No seat

allocation for faster boarding.

Largely based on two classes, business and

economy and sometimes first class on

international and oceanic flights.

Single

aircraft type

.

LCCs typically operate a single

aircraft type to maximize aircraft

utilization. Some indication that

LCCs may adopt large regional jets.

In order to serve as many markets as possible

and connect them, the adoption of multiple

aircraft types, with each suited to different

route lengths and traffic densities is needed.

Service

strategies

LCCs mainly focus on bare bones

services. Increasingly LCCs are

adding some service enhancements,

such as lounges, frequent flyer

programs, however, these are

provided on a fee for use.

FSAs typically offer a range of value added

services.

Customer

Service

orientation

Most LCCs have successfully

developed a strong customer service

culture.

Adequate customer service culture among

employees.

Product

bundling

The LCC offers a core product at a

low price.

Additional services, such as lounge

access, meals and beverages are

typically sold. Frequent flyer

rewards are offered when a higher

fare is booked.

FSAs offer bundled air services as a matter of

strategic choice and legacy. This will include

features such as lounge access at no charge

for frequent travelers, on board meals on

medium and long haul flights, and frequent

flyer rewards.

Seating features Often very dense some do not have

pre-assigned seating

Varies by route, international routes offer a

larger seating area, pre-assigned seating

dominates.

Distribution

channels

Preference is online and direct sales

to save the commissions of

intermediaries

Increasing movement towards online and

direct sales to reduce commissions to third

parties.

Personnel LCCs use a minimum number of

cabin crew usually performing

multiple tasks. Lower wage scales if

compared to FSAs and wages are

usually performance- based.

Adequate number of cabin crew and high

average wages with unionization.





Ticket policies No rebooking and no refund Flexibility for rebooking or refund and

sometimes for some fees.

Source: InterVISTAS (2013) and Macário et al. (2008)

A prerequisite for a sustainable success of LCCs are low cost operations. As explained in

Magill (2014), a low cost pattern can save up 35% to 50 % costs through operational and

managerial features (Figure1). The traditional airlines are also trying to reduce costs as well but

they lag far behind the LCCs. The philosophy of FSAs is to focus on passengers with a

willingness to pay.

Figure 1: Unit Cost Advantage is Derived from Many Factors

Source: Magill (2014)

Some trends worldwide acted as catalysts to intensify the Low Cost Carrier phenomenon.

These include regulatory framework; degree of entrepreneurship; density of population and

relative wealth; travelling culture; airport availability; adherence to internet facilities. There is

a direct relation between these factors and the development of societies and development life

cycle of Low Cost Airlines.

The strategy of price discriminations is used by many industries especially in transport

industries to reduce costs. This is favorable to use, when separating markets can yield more

profits than keeping markets combined. This strategy depends on the elasticities of demand of

clustered markets. Customers in rather inelastic sub-market are charged a higher price, while

consumers in the relatively elastic sub-market are charged a lower price (Investopedia, 2018).

According to Dhingra et al. (2018) both FSAs and LCCs engage in price discrimination

schemes, in the sense that travelers purchasing tickets at different points in time before a flight,

or with different restrictions on the use of the ticket, pay different prices

LCCs have removed the barriers of the need to purchase a return ticket in order to get lower

prices. They also removed the minimum and maximum stay requirements which were in

addition to low priced one way tickets considered a huge leap in the air pricing methodologies.





According to Macario et al., 2008, so far most LCCs have tried to avoid mutual competition.

For instance, Ryanair, focused on smaller markets and regional airports, while easyJet is

focusing on bigger markets and primary airports. Three scenarios may arise. LCCs will

continue to avoid competition or a price war will be set off or else consolidation and the

emergence of alliances will take place. Historical experience in the Air Transport business

points to consolidation and the possible emergence of alliances.

Evidence of competition between air transport services and other modes of transport exist.

Chen et al. (2015) found a substantial increase in competition between air transport services

and rail services. The two main United States’ road companies were forced to cut prices in order

to reduce the shift of passenger to the LCCs. The rail company - Amtrak - for instance had to

cut prices and introduced special deals for passengers.

The high speed train (HST) in Europe intensified the competition because it is the only land

transport resolution than can directly compete in term of travel times for routes up to 500km.

There has also been evidence of cooperation between airlines and HST. For example, Charles

de Gaulle airport in Paris is directly served by the TGV network enabling Air France to

compete, for passengers living in the Brussels region.

Two main operating approaches for LCCs in the market are either to open new markets or

to enter old established markets competing with other airlines. Ryanair, follows primarily the

first strategy. They first identify untapped markets, and then they search for secondary airports

to serve these routes. In addition, they look for the support of authorities looking for fiscal

incentives. Governments usually support such initiatives to induce social and economic

development. The other strategy is being followed by easyJet. This airline prefers to penetrate

large markets competing with other airlines by offering high frequencies and lower fares.

LCCs have a high load factor up to 80%, accommodation of a larger numbers of passengers,

paying cheaper taxes since they use secondary airports, short haul flights without connections,

reducing the negative effect of air transport on the environment by consuming less fuel thus

decreasing emissions of harmful substances into the environment. Some 25% of Asian inter

region seats are flown by these airlines, in Europe it is over 40%. Today, LCCs represent

approximately 40% of the “Available Seat Kilometers” flown between European countries or

domestically (Eurocontrol, 2017).

The following table (2) demonstrates the application of Porters five forces model on LCCs

and the justification for the level of strength that was associated to each factor.





Table 2: The Effect of Porters’ Five Forces on LCCs Market Penetration

Porters five forces Strength Justification

Threat of new entrants Medium Deregulations and demand growth can be the

drivers for more potential players although some

markets like the European market are already

saturated. Some other markets like the Middle

East with a limited number of LCCs show more

potential for new entrants.

Threats of Substitutes Medium to

Low

There is a threat for substitution in markets where

train network is developed and has a high

network of connectivity. But again in these

markets long haul routes are to be accomplished

by air services.

Nevertheless, in markets such as the Middle East,

where train network is in its infancy state, rail

network is in no direct competition with LCC’s.

Bargaining Power of

Customers

High The reason that a customer will opt for an LCC is

its cheap price. They can shift from one airline to

another due to lower price offerings.

Bargaining Power of

Suppliers

Medium The aircraft supply side is monopolized by two

major manufacturers, namely Airbus and Boeing.

However the competition between the two

competitors is severe. This allows airlines to get

competitive pricing and good servicing when

purchasing an aircraft.

Also fuel companies pose some pressure on

LCCs. Air service labor has also a bargaining

power as it comes from the fact that there are no

substitutes for several classes of employees such

as pilots and mechanics

Rivalry among Competitors High The major selling advantage of LCCs is their low

prices, which has resulted in a high competition

in the airline industry, where legacy airlines are

fighting to protect themselves from losing their

market share. Also competition among LCCs

does exist.

The business model of FSAs differs from that of LCCs. FSAs focus on offering their

customers high level of connectivity, therefore they were engaged in alliances to allow this kind

of connectivity, to coordinate schedules, reduce connection times and maximize the number of

cities that can be linked together. Furthermore, FSAs have relatively high costs of labor and

other service inputs. These legacy costs include labor compensation programs, productivity

agreements, supplier relationships, and consumer expectations (InterVISTAS, 2013).

The biggest disadvantages of hub-and-spoke model adopted by FSAs are the complexity of

connecting flights in the given time frame and high utilization during peak periods (Vidovic et

al.; 2013).





To counter the threat of LCC’s, Full Service Carriers designed sophisticated revenue

management softwares to separate leisure passengers from business travelers which enabled

them to optimize revenue from every passenger. FSAs also tried to defend their market share

by offering similar low prices of a new entrant, who was trying to operate from their hubs.

Another strategy adopted by FSAs to keep their market share against aggressive competition

from LCCs was adding supplementary flights or using larger aircraft on routes entered by LCCs

to keep the load factor of the new entrant at an unachievable level. FSAs lower the fares until

new entrants depart from these hubs, then FSAs start raising their prices again and reducing

their capacity. This procedure has made it difficult for LCCs to compete with FSAs in their

hubs (Barbot, 2004).

However, some authors explained, that many FSAs aim to provide a high level of service

quality to improve customer satisfaction to replace the generic reputation of LCCs as low fares’

providers. Balcombe et al. (2009) show that not only price but also service quality triggers the

passengers’ behaviors as well. This has led to the emergence of a hybrid model combining

together features of FSAs and LCCs business models in an attempt to sustain their business and

market share.

2.3 The Emergence of a Hybrid Model

Due to major changes in the global economy and consumer trends affecting directly the

airline industry, FSAs and LCCs went through many changes.

On the one hand, FSAs are trying to reduce their costs in order to compete with LCCs. On

the other hand, LCCs are trying to tackle new markets once dominated by legacy airlines. Thus,

a new operating model, the hybrid model emerged which combines elements from both legacy

airlines and Low Cost Carriers. Some LCCs are adopting business models that include shifting

to primary airports, start hub and spoke activities, offering meals and other in-flight services as

well as entering alliances. As a result, FSAs are also trying to change and adapt their focus. A

hybrid model is a current business model that comprises the best features of both the legacy

and low-cost business models in one, balancing costs which is the focus of LCCs and value

which is the focal point of FSAs (Avram, 2017).

This model uses a mix of narrow- and wide-body aircraft models, utilizes third-party

aggregators, uses intermediaries, practices limited code sharing, facilitates interlining and flies

short- and long-haul and Inter-regional flights (Sabre Holdings, 2010).

The saturation of some LCCs markets and the slowdown in its growth forced them to shift

to business strategies traditionally used by full-service airlines; i.e. fare bundling, connecting

flights and code sharing (de Wit et al.; 2012). Other studies (Henrickson et al., 2016;

Dobruszkes et al., 2017) explained that LCCs are increasing their operations in main airports

alongside regional airports. However, other studies highlighted redirections in some other

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respects such as employee productivity, homogeneous fleets, non-stop service and branding

strategies (Daraban, 2012; Taylor et al., 2013).

A large number of FSAs have implemented features from the LCC business model. They

started eliminating hot meals for flights. They have also simplified their fare structure and

implemented profit sharing plan for employees (e.g. Delta). Some of the FSAs have introduced

their own Low Cost Airlines (Gillen et al., 2004).

Low Cost Carriers have also adopted features from the Full Service Carriers in order to

differentiate their products including: Frequent flyer programs (Southwest); hub and spoke

network systems (AirTran); in-flight entertainment (Frontier); and multiple aircraft type

(JetBlue). As a result the line between Low Cost Carriers and Full Service Carriers has

diminished substantially in terms of product offerings. However, LCCs still have a cost

advantage over FSA although offering almost same products (Barbot 2004).

A study by Bland (2014) showed that the price is the major advantage for LCCs. Some other

studies examined other factors besides price such as booking convenience, in-flight services,

schedule, safety consideration, and airline image (Chang et al., 2013; Diggines, 2010; Davison

et al., 2010). The consumer decision theory suggests that the price isn’t the single factor upon

which consumers base their decisions (Blythe, 2013). There are other attitude and behavioral

factors which play a major role in the decision making process of LCC passengers.

There aren’t any more a clear cut business strategies differentiating LCCs and legacy airlines

(Lohmann et al., 2013, Jean et al., 2016). Several studies (Francis et al., 2006; Mason et al.,

2007; Tsoukalas et al., 2008) showed that business models are converging. According to Tay

et al. (2013) airlines are no longer easily categorized as either Low Cost Carriers (LCCs) or

Full Service Network Carriers (FSNCs), as airlines are now merging the features of LCCs and

FSNCs to expand their target demand and survive increasing competition.

2.4 Air Transport, LCCs and Tourism

Air transport and tourism demand are two intertwined areas with important

interdependencies. The tourism industry was a driving force of many developments in air

transport as new business models like charter flights emerged. On the other hand air transport

encouraged the emergence of new tourism destinations or tourism forms like long haul

excursions. Therefore, shaping a clear strategic development of airline policy and air access

strategy seems to be necessary for destination development. In addition, a corresponding

assessment and understanding of the business models of destinations is inevitable for air service

growth.

Air traffic proved to be resilient to global economic, environmental and political crisis. The

world annual traffic took an upward trend from 1955 to 2015 with small fluctuations. This gives

an indication, that air services can outstand and survive global shocks. The figures also show,


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that passenger traffic growth was outperforming the GDP growth in the period form 2010-2016

(Airbus, 2016).

There is a close relation between growth of the tourism demand worldwide and the

emergence of LCCs. LCCs have made air travel available to all budgets and enabled tourists to

reallocate their expenditures and spend more at destinations (Ferrer-Rosell et al., 2017).

A study by Eugenia-Martin et al. (2016) examined the hypothesis of whether low-cost travel

savings from tourists’ place of origin are transferred, at least partially, to higher tourism

expenditures at the destination. The results showed that LCC travelers make higher tourism

expenditures at the destination compared to scheduled-flight tourists. However, if the

comparison is made to charter-flight tourists, those savings are not always transferred as

additional expenditures at the destination.

2.5 Background about EasyJet

EasyJet is a low-cost European airline, which offers low fares by using operational

efficiency on point-to-point routes. Their business model makes travel affordable for a wider

range of passengers and drives growth and returns for shareholders. EasyJet has a strong capital

base, with market capitalisation of £4 billion and a net cash position of £213 million at 30

September 2016. EasyJet uses a modern Airbus fleet, in addition to the new fuel efficient

A320neo aircraft. This provides easyJet with operating and maintenance advantages.

EasyJet’s workforce reached over 10,000 people, including 2,865 pilots and 6,516 cabin

crew members as at 30 September 2016. EasyJet uses innovative technological solutions to

maintain its cost leadership.

Through its successful digital strategy, it implements customer relationship management

techniques based on a sophisticated analysis of customer databases to increase customer

loyalty. Consequently, in 2017, 74% of its seats were booked by returning customers. In

addition, the carrier recently launched a frequent flyer program “Flight Club” which aims to

identify and retain loyal customers with a scheme that makes travel with easyJet even easier.

Its digital leadership is the key differentiation from its competitors. Their award winning

application has been downloaded 18.3 million times in 2016, an increase of 30% on last year.

20% of its bookings are accomplished on ApplePay, their mobile application. Passengers are

also increasingly using mobile boarding passes, which has increased 63% year-on-year.

Their strategy entails targetting business passengers, growing the number of passengers by

6% to 12.5 million, with September 2016 a record month for easyJet. They attract business

passengers by using primary airports in large economic markets, with high frequencies and

attractive flight timings (easyJet, 2016).





3. Methodology

Time series forecasting refers to the use of the historical observations of time series to

predict the value at a future time. A primary concern of time series analysis is the development

of forecasts for future values of the series (Britannica Library, 2007).

Monthly data of passenger traffic of easyJet for the period January 2004 to February 2018

was used for the purpose of analysis. Basic time series analysis was used to predict future

values. Each of these time series is an aggregation of three components: (i) Trend, (ii) Seasonal,

and (iii) Random. In order to make further investigations into the behavior of the time series

data, each time series was decomposed into its three components. Moving averages (MA) and

centered moving average (CMA) were used to estimate the trend component. Furthermore the

seasonal component was estimated. In order to forecast the trend in the future a regression

model was applied. Accuracy of forecasting measures was tested using the Mean Absolute

Percentage Error (MAPE).

The linear regression approach mathematically simulates cause and effect relationships.

Thus, a linear regression analysis was conducted to show the relationship between the

independent variable “total air passengers worldwide” and dependent variable “total passengers

easyJet”. Also a correlation analysis between average “crude oil prices” and “total passengers

worldwide” and “total passengers of easyJet” was conducted to show whether and how strongly

these pairs of variables are related.

4. Results

4.1 Decomposition Results

Table 3 and 4 present the numerical actual and forecasted values of the time series data with

the random component.

Table 3: Actual and Forecasted Values of the Time Series Data with the Random Component

Year Actual Forecast e Year Actual Forecast e Year Actual Forecast e

Jan-04 1,683,699

1,594,234.09 89464.91

Nov-06 2,554,143

2,472,708.18 81434.82

Sep-09 4,422,021

4,376,316.83 45704.17

Feb-04 1,864,970

1,721,686.31 143283.69

Dec-06 2,638,279

2,570,543.80 67735.20

Oct-09 4,219,096

4,194,317.87 24778.13

Mar-04 1,996,790

2,031,901.99 35111.99

Jan-07 2,573,451

2,385,950.04 187500.96

Nov-09 3,351,187

3,308,622.39 42564.61

Apr-04 1,947,675

2,144,879.30 197204.30

Feb-07 2,646,775

2,565,062.39 81712.61

Dec-09 3,399,305

3,431,447.67 32142.67

May-04 2,092,709

2,302,450.34 209741.34

Mar-07 3,094,588

3,013,876.87 80711.13

Jan-10 3,142,629

3,177,666.00 35037.00

Jun-04 2,241,252

2,381,793.80 140541.80

Apr-07 3,133,725

3,167,722.64 33997.64

Feb-10 3,390,523

3,408,438.47 17915.47

Jul-04 2,413,367

2,606,461.63 193094.63

May-07 3,345,465

3,386,081.26 40616.26

Mar-10 3,964,399

3,995,851.74 31452.74

Aug-04 2,459,735

2,663,262.53 203527.53

Jun-07 3,440,639

3,488,301.30 47662.30

Apr-10 3,490,599

4,190,565.98 699966.98

Sep-04 2,355,324

2,507,309.81 151985.81

Jul-07 3,723,004

3,801,915.87 78911.87

May-10 4,258,675

4,469,712.18 211037.18

Oct-04 2,405,073

2,415,697.71 10624.71

Aug-07 3,706,354

3,869,401.94 163047.94

Jun-10 4,537,959

4,594,808.81 56849.81

Nov-04 2,120,948

1,915,432.04 205515.96

Sep-07 3,438,261

3,628,714.02 190453.02

Jul-10 5,021,838

4,997,370.11 24467.89

Dec-04 2,134,787

1,996,607.89 138179.11

Oct-07 3,344,916

3,482,869.81 137953.81

Aug-10 5,203,165

5,075,541.36 127623.64





Jan-05 2,083,852

1,858,139.41 225712.59

Nov-07 2,888,133

2,751,346.25 136786.75

Sep-10 4,774,991

4,750,118.23 24872.77

Feb-05 2,168,985

2,002,811.67 166173.33

Dec-07 2,899,349

2,857,511.76 41837.24

Oct-10 4,582,632

4,550,041.90 32590.10

Mar-05 2,573,070

2,359,226.95 213843.05

Jan-08 2,761,583

2,649,855.36 111727.64

Nov-10 3,694,514

3,587,260.46 107253.54

Apr-05 2,438,194

2,485,827.08 47633.08

Feb-08 3,240,767

2,846,187.75 394579.25

Dec-10 3,657,186

3,718,415.63 61229.63

May-05 2,551,619

2,663,660.65 112041.65

Mar-08 3,730,134

3,341,201.82 388932.18

Jan-11 3,743,593

3,441,571.31 302021.69

Jun-05 2,586,889

2,750,629.64 163740.64

Apr-08 3,553,023

3,508,670.42 44352.58

Feb-11 3,833,667

3,689,563.83 144103.17

Jul-05 2,847,598

3,004,946.38 157348.38

May-08 3,877,960

3,747,291.57 130668.43

Mar-11 4,435,624

4,323,176.70 112447.30

Aug-05 2,903,404

3,065,309.00 161905.00

Jun-08 4,112,951

3,857,137.14 255813.86

Apr-11 4,717,402

4,531,513.76 185888.24

Sep-05 2,743,221

2,881,111.21 137890.21

Jul-08 4,467,268

4,200,400.61 266867.39

May-11 4,739,436

4,830,922.49 91486.49

Oct-05 2,734,931

2,771,421.75 36490.75

Aug-08 4,585,739

4,271,448.42 314290.58

Jun-11 4,952,305

4,963,644.64 11339.64

Nov-05 2,297,895

2,194,070.11 103824.89

Sep-08 4,197,643

4,002,515.42 195127.58

Jul-11 5,427,112

5,395,854.85 31257.15

Dec-05 2,372,333

2,283,575.84 88757.16

Oct-08 3,959,194

3,838,593.84 120600.16

Aug-11 5,543,961

5,477,587.84 66373.16

Jan-06 2,316,749

2,122,044.72 194704.28

Nov-08 2,985,826

3,029,984.32 44158.32

Sep-11 5,181,839

5,123,919.63 57919.37

Feb-06 2,373,706

2,283,937.03 89768.97

Dec-08 3,111,388

3,144,479.72 33091.72

Oct-11 4,939,904

4,905,765.93 34138.07

Mar-06 2,754,705

2,686,551.91 68153.09

Jan-09 2,839,617

2,913,760.68 74143.68

Nov-11 3,821,355

3,865,898.53 44543.53

Apr-06 2,848,065

2,826,774.86 21290.14

Feb-09 3,018,910

3,127,313.11 108403.11

Dec-11 4,135,562

4,005,383.59 130178.41

May-06 2,939,361

3,024,870.96 85509.96

Mar-09 3,494,312

3,668,526.78 174214.78

Jan-12 3,728,514

3,705,476.63 23037.37

Jun-06 2,990,169

3,119,465.47 129296.47

Apr-09 3,776,582

3,849,618.20 73036.20

Feb-12 3,976,741

3,970,689.19 6051.81

Jul-06 3,168,012

3,403,431.12 235419.12

May-09 3,948,416

4,108,501.88 160085.88

Mar-12 4,629,241

4,650,501.66 21260.66

Aug-06 3,146,216

3,467,355.47 321139.47

Jun-09 4,146,609

4,225,972.97 79363.97

Apr-12 5,124,597

4,872,461.53 252135.47

Sep-06 3,011,145

3,254,912.61 243767.61

Jul-09 4,661,068

4,598,885.36 62182.64

May-12 5,423,726

5,192,132.80 231593.20

Oct-06 2,935,355

3,127,145.78 191790.78

Aug-09 4,800,336

4,673,494.89 126841.11

Jun-12 5,434,763

5,332,480.47 102282.53

Table 4: Actual and Forecasted Values of the Time Series Data with the Random Component

(Continued) Year Actual Forecast e Year Actual Forecast e Year Actual Forecast e

Jul-12 5,860,272

5,794,339.60 65932.40

Oct-14 5,835,145

5,972,938.02 137793.02

Jan-17 4,745,630

5,025,003.22 279373.22

Aug-12 5,873,948

5,879,634.31 5686.31

Nov-14 4,386,296

4,701,812.75 315516.75

Feb-17 5,337,949

5,376,315.99 38366.99

Sep-12 5,451,217

5,497,721.04 46504.04

Dec-14 4,634,977

4,866,287.46 231310.46

Mar-17 6,334,753

6,287,126.46 47626.54

Oct-12 5,245,201

5,261,489.96 16288.96

Jan-15 4,022,253

4,497,192.59 474939.59

Apr-17 7,116,731

6,577,200.43 539530.57

Nov-12 4,116,576

4,144,536.60 27960.60

Feb-15 4,491,425

4,814,065.27 322640.27

May-17 7,512,545

6,998,184.33 514360.67

Dec-12 4,339,836

4,292,351.55 47484.45

Mar-15 5,490,337

5,632,476.54 142139.54

Jun-17 7,720,090

7,176,659.65 543430.35

Jan-13 3,878,640

3,969,381.95 90741.95

Apr-15 6,006,371

5,895,304.87 111066.13

Jul-17 8,174,606

7,786,763.32 387842.68

Feb-13 4,112,186

4,251,814.55 139628.55

May-15 6,490,974

6,275,763.71 215210.29

Aug-17 8,219,017

7,889,866.68 329150.32

Mar-13 4,872,934

4,977,826.62 104892.62

Jun-15 6,559,802

6,438,987.98 120814.02

Sep-17 7,718,714

7,366,728.06 351985.94

Apr-13 5,253,610

5,213,409.31 40200.69

Jul-15 7,036,470

6,989,793.83 46676.17

Oct-17 7,519,759

7,040,110.12 479648.88

May-13 5,609,351

5,553,343.10 56007.90

Aug-15 7,064,931

7,085,773.73 20842.73

Nov-17 5,350,245

5,537,726.96 187481.96

Jun-13 5,537,275

5,701,316.31 164041.31

Sep-15 6,610,844

6,619,125.25 8281.25

Dec-17 5,884,304

5,727,191.33 157112.67

Jul-13 5,976,704

6,192,824.34 216120.34

Oct-15 6,398,796

6,328,662.05 70133.95

Jan-18 5,159,860

5,288,908.54 129048.54

Aug-13 6,101,344

6,281,680.78 180336.78

Nov-15 4,807,922

4,980,450.82 172528.82

Feb-18 5,552,255

5,657,441.35 105186.35

Sep-13 5,714,152

5,871,522.44 157370.44

Dec-15 4,848,258

5,153,255.42 304997.42

Mar-18

6,614,451.41





Oct-13 5,530,091

5,617,213.99 87122.99

Jan-16 4,276,821

4,761,097.90 484276.90

Apr-18

6,918,148.21

Nov-13 4,255,978

4,423,174.68 167196.68

Feb-16 4,932,212

5,095,190.63 162978.63

May-18

7,359,394.63

Dec-13 4,490,538

4,579,319.50 88781.50

Mar-16 5,728,114

5,959,801.50 231687.50

Jun-18

7,545,495.48

Jan-14 4,021,678

4,233,287.27 211609.27

Apr-16 6,369,877

6,236,252.65 133624.35

Jul-18

8,185,248.07

Feb-14 4,232,325

4,532,939.91 300614.91

May-16 6,861,040

6,636,974.02 224065.98

Aug-18

8,291,913.15

Mar-14 5,107,676

5,305,151.58 197475.58

Jun-16 6,937,601

6,807,823.81 129777.19

Sep-18

7,740,529.46

Apr-14 5,787,833

5,554,357.09 233475.91

Jul-16 7,506,939

7,388,278.58 118660.42

Oct-18

7,395,834.15

May-14 6,054,249

5,914,553.41 139695.59

Aug-16 7,513,592

7,487,820.20 25771.80

Nov-18

5,816,365.03

Jun-14 6,098,364

6,070,152.14 28211.86

Sep-16 7,513,592

6,992,926.65 520665.35

Dec-18

6,014,159.29

Jul-14 6,434,284

6,591,309.09 157025.09

Oct-16 6,842,699

6,684,386.08 158312.92

Jan-19

5,552,813.86

Aug-14 6,612,075

6,683,727.26 71652.26

Nov-16 4,947,060

5,259,088.89 312028.89

Feb-19

5,938,566.71

Sep-14 6,143,974

6,245,323.84 101349.84

Dec-16 5,579,978

5,440,223.38 139754.62

Figure 2 plots the overall time series for the passenger traffic for easyJet for the period

January 2004 – February 2018 in addition to the forecasted figures. The plot shows, that the

time series have an increasing trend. The peak season for the passenger traffic of easyJet is in

August, where a gradual decline afterwards is to be noticed till February, when it takes again

an upward movement till August. The trend values have consistently increased over the period

2004 – 2018.

The first step of the forecasting work was to breakdown the time series data into trend,

seasonal and random components. This will help to show the overall trend of passenger traffic

and furthermore it can indicate seasonality patterns; i.e. during which month passenger traffic

is highest. The random component will throw some light on the unpredictability pattern of the

data.

A multiplicative model was used and showed more appropriate. This model is used when the

magnitude of the seasonal pattern increases as the data values increase, and decreases as the

data values decrease. The formula of the Multiplicative Model is as follows:

Y (t) = T (t) × S (t) × I (t).

Here Y (t) is the observation and T (t), S (t), and I (t) are respectively the trend, seasonal, and

irregular variation at time t.

Multiplicative model is based on the assumption that the three components of a time series

are not necessarily independent and they can affect one another (Adhikari et al., 2013).





Figure 2: Actual and Forecasted Time Series Values for EasyJet

The following figure (3) shows the results obtained by time series decomposition work. The

three components of the time series are shown separately so that their relative behavior can be

visualized. According to Adhikari et al. (2013), trend is a long term movement in a time series.

Seasonal variations in a time series are fluctuations within a year during the season. Irregular

or random variations in a time series are caused by unpredictable influences, which are not

regular and also do not repeat in a particular pattern.

The plotting of the decomposition components show, that there is an obvious seasonal

pattern in the data, the trend value increased over the period of 2004-2018 and the random

component shows considerable fluctuations in its values.

It is natural for the aviation sector to have a dominant seasonal component as the flow of

leisure travelling reaches its peak in summer because of the holidays. The tendency for

travelling declines from August to February when it again starts to increase gradually in

February.

0

10,00,000

20,00,000

30,00,000

40,00,000

50,00,000

60,00,000

70,00,000

80,00,000

90,00,000

Jan

-04

Jul-

04

Jan

-05

Jul-

05

Jan

-06

Jul-

06

Jan

-07

Jul-

07

Jan

-08

Jul-

08

Jan

-09

Jul-

09

Jan

-10

Jul-

10

Jan

-11

Jul-

11

Jan

-12

Jul-

12

Jan

-13

Jul-

13

Jan

-14

Jul-

14

Jan

-15

Jul-

15

Jan

-16

Jul-

16

Jan

-17

Jul-

17

Jan

-18

Jul-

18

Jan

-19

Traffic Easyjet Yt

Forecast





Figure 3: Decomposition of time Series Data of EasyJet

Source: Wessa (2018), Free Statistics and forecasting Software

A forecast for the month starting March 2018 to February 2019 based on time series data

from January 2004 to February 2018 was conducted using forecasting techniques and

calculations. It can be observed (Figure 2 and Table 4 and 5) that the forecasted values closely

match the actual values even when the forecast horizon is long (12 months). This clearly shows

that the model with trend and multiplicative seasonal components is very effective in

forecasting monthly indices during the period.

In order to test the accuracy of forecasting method, mean absolute percentage

error (MAPE) was calculated. The mean absolute percentage error (MAPE), also known

as mean absolute percentage deviation (MAPD), is a measure of prediction accuracy of a

forecasting method in statistics, for example in trend estimation. It usually expresses accuracy

as a percentage, and is defined by the formula:

Where is the actual observations time series, is the estimated or forecasted time

series and is the number of non-missing data points (Hyndman et al., 2014). The MAPE test

value was 3.7%. This measure represents the percentage of average absolute error occurred.

https://en.wikipedia.org/wiki/Statistics

https://en.wikipedia.org/wiki/Trend_estimation





4.2 Linear Regression Analysis

The figures of the World Bank related to “Air Passenger Traffic” and “The Passenger Traffic

of easyJet” (derived from the annual reports of easyJet) in the period 2004 to 2016 were used

for the linear regression analysis (Table 6).

Table 6: Average Annual OPEC Crude Oil Price from 1960 to 2017 (in U.S. Dollars per Barrel),

Total Air Passengers and Total Traffic EasyJet

Year Total Air

passengers

(million) *

Easy Jet Total

air passengers

(million) **

Avr. Crude oil price

(U.S dollars)***

2004 1890 25 36.50

2005 1970 30 50.59

2006 2070 33 61.00

2007 2209 38 69.04

2008 2208 44 94.10

2009 2250 46 60.86

2010 2628 49 77.38

2011 2790 55 107.46

2012 2894 59 109.45

2013 3048 61 105.87

2014 3227 65 96.29

2015 3464 69 49.49

2016 3696 75 52.89

Source: * World development indicators (2018)

** Annual Reports easyJet

*** Statista (2018)

The output of linear regression of variables Total Air Passengers Worldwide and Total

Air Passengers easy Jet is shown in Table 7.

Table 7: Output Linear Regression Total Air Passengers Worldwide and

Total Air Passengers Easy Jet SUMMARY OUTPUT

Regression Statistics

Multiple R 0.979267087

R Square 0.958964028

Adjusted R

Square

0.955233485

Standard

Error

3302277.787

Observations 13

ANOVA

df SS MS F Significance

F

Regression 1 2.80322E+15 2.80322E+15 257.0574967 5.6425E-09

Residual 11 1.19955E+14 1.0905E+13

Total 12 2.92318E+15

Coefficients Standard

Error

t Stat P-value Lower 95% Upper 95% Lower

95.0%

Upper

95.0%





Intercept -17487193.08 4328877.426 -4.039660023 0.00194961 -27014988.05 -7959398.115 -27014988.05 -

7959398.115

Total air

pass.

worldwide

0.025678132 0.001601579 16.03301271 5.6425E-09 0.022153081 0.029203183 0.022153081 0.029203183

Linear Regression: Total Air Passengers Worldwide and Total Air Passengers easy Jet

Microsoft Excel 2010 was used to generate tables of output for linear regression analysis.

The explanatory variables are shown with their standard errors and statistical significance.

* Determining how well the model fits (Model summary table):

The R shows the correlation between the observed and predicted values of dependent

variable. The correlation coefficient is 0.9792 which indicates a strong positive relationship

between the two variables.

R-Square – shows the proportion of variance in the dependent variable (Total Air pass.

easyJet) which can be explained by the independent variable (Total Air pass. World). The

R-square value is 0.9589 which indicates that the independent variable (Total Air pass. World)

explains 95% of the variability of the dependent variable (Air pass easyJet).

** Statistical significance: (ANOVA table):

The F-ratio in the ANOVA table tests whether the overall regression model is a good fit for

the data. The table shows that the independent variables statistically significantly predict the

dependent variable as F= 257, p < .0005 (i.e., the regression model is a good fit of the data).

***Parameter estimates (Coefficients table):

The following output is obtained from the Coefficients table:

The Model column shows the predictor variables. Coefficient stands for the values for the

regression equation for predicting the dependent variable from the independent variable. The

coefficient for (Total Air pass easyJet) is 0.025. So for every 1% increase in (Total Air pass.

World), a 2.5 % increase in (Total Air pass easyJet) is predicted, ceteris paribus (holding all

other variables constant).

- t- values and Sig.: These are the t-statistics and their associated p-values used in testing

whether a given coefficient is significantly different from zero. As p is < .05 it can be deduced

that the coefficients are statistically significant, i.e. (Total Air pass. World) can predict the

variable (Air pass. easyJet).

4.3 Correlation

Table 6 shows the average crude oil prices from 2005 till 2016. Also “total air passengers

worldwide” and “total passenger traffic easyJet” are displayed. This secondary data was used

to conduct correlation analysis to test whether there was a relationship between the variables

and how strong this relationship is if existed.

Correlation between variable total air passengers and average fuel prices: The value of R

is 0.2623. Although technically a positive correlation, the relationship between the variables is





weak (i.e. the nearer the value is to zero, the weaker the relationship). The value of R2, the

coefficient of determination, is 0.0688. The p-Value is 0.386628. The result is not significant

at p < 0.05.

Correlation between variable total passengers easyJet and average fuel prices: The value

of R is 0.3772. Although technically a positive correlation, the relationship between the

variables is weak The value of R2, the coefficient of determination, is 0.1423. The p-Value is

0.203887. The result is not significant at p < 0.05.

5. Discussion

The review of the literature showed, that the phenomenon of LCCs is leading the air service

market nowadays and is threatening the existence of FSAs. The business model adopted by

LCCs consists of lowering ticket prices by charging the passengers for auxiliary services. They

also adopt strategies to lower their operational costs like using secondary airports, offering

short-haul flights, using e-marketing strategies and omitting legacy costs. The usage of new

homogeneous medium sized fleet usually results in lower fuel, maintenance and personnel

costs. Higher seat density in aircraft results in lower unit costs for almost all cost categories. In

addition, delays can be decreased by using smaller secondary airports. Point-to-point flights

without connections maximize turnarounds and focuses on high utilization of aircraft. The

“free-seating” principle also contributes to the reduction of operating costs because it

encourages passengers to board the plane earlier and additionally reduce delays. Smaller

airports usually charge lower fees than larger primary airports and are willing to contribute in

the promotion of new routes. Finally, the unit costs are reduced by online selling, and as well

by eliminating all forms of free in-flight services such as catering, entertainment during the

flight.

The business model adopted by LCCs showed evidences of success for these carriers and

made it possible for them to sustain their business and achieve revenues.

The time series forecasting method used based on the secondary data of the passenger traffic

of easyJet provided an accurate forecast (MAPE = 3.7%). Hence, this forecasting method can

be used to forecast the total passenger traffic at least in the short term. This result can be useful

to decision makers of easyJet as it helps them to identify their potentials for growth in the future

and also pinpoint seasonal variations as the data showed a significant seasonal pattern. The

results also indicate, that easyJet should pay more attention in low seasons, where the traffic is

low. This could be accomplished through the increase of marketing efforts and pricing

strategies to increase traffic flows. An increasing trend in passenger traffic is shown, which

indicates the great potential for easyJet for expansion in the future.

Applying a linear regression showed the following: The regression model is a good fit for

data as F= 257, p < .0005. The coefficient of determination (R-Square) is the proportion of





variance in the dependent variable which can be explained by the independent variables. R-

square value is 0.958 which shows that the independent variable explains 95% of the variability

of the dependent variable. The coefficient value for (Air pass easyJet) is 0.025. Thus, for

every 1% increase in “Total Air pass. World”, a 2.5% increase in “Air pass easyJet” is

predicted and vice versa (holding all other variables constant).

The correlation analysis showed, that there is a weak relationship between “average crude

oil prices” and “air passengers worldwide” and “air passengers easyJet”. This indicates that air

traffic isn’t severely influenced by the rise or fall of fuel prices because of fuel hedging

agreements. Although the rise of fuel prices, where fuel consumes the majority of operational

costs, may affect ticket prices, the analysis showed that this had a little effect on passenger

traffic worldwide.

Based on the results of statistical analyses, hypothesis 1 and 2 are accepted and hypothesis 3

is rejected.

IATA forecast (IATA, 2017) indicates that total air passengers worldwide will nearly double

to reach 7.8 billion in 2036. This growth requires partnerships and alliances to be strengthened

between players in the aviation industry, communities and governments to expand and

modernize infrastructure including runways, terminals, and ground access to airports. This

expansion in passenger numbers requires innovative solutions to challenges facing air services

and air traffic management needs urgent transformations to cut delays, costs and emissions.

Growth will also depend on trade liberalizations and visa facilitations.

6. Conclusion

There are some common features among LCCs. Nevertheless, the analysis showed that there

is no clear cut Low Cost Carrier business model adopted by all carriers. Although most LCCs

strategies include elements like charged service offerings, the use of secondary airports, short-

haul point-to-point routes, low distribution costs, high labor utilization, the models diverge

considerably in their offering and operating practices. Different industry strategies and business

cultures have a considerable impact on their business models.

The emergence of LCCs had major impacts on the air service industry. Some elements

supported the rise of LCCs like air liberalization and deregulation of air service industry. Some

LCCs copied the business model of Southwest and others adopted combined business

strategies.

The rise of the middle class and urbanization was a driving force for the support of LCCs

and also their sustainability. It is worth to mention, that the success of LCCs can be referred to

their great adaptability to market changes. LCCs have been the catalysts for economic growth

in some neglected regions and have put these regions on the map of airline destinations.





Furthermore, it stimulated tourist inflow in some regions that were considered previously out

of reach.

FSAs were highly affected by the emergence of LCCs. They lost a huge part of their

customer base due to lower price strategies implemented by LCCs. Their ability to lower their

prices to compete with LCCs is associated with their ability to reduce their base of high costs

to compensate the decline in their yields.

A new model emerged, the hybrid model which combines the cost-saving methodologies of a

pure Low Cost Carrier with the service, flexibility and route structure of a full-service carrier.

LCCs are trying to gain an additional market share by increasingly flying from main airports

to attract customers from traditional scheduled airlines and some LLCs are experiencing

connections. This goes parallel with new FSA policies, which consist of unbundling their offers

by excluding some services, which used to be included like baggage allowance and meals.

Many traditional full-service carriers created new products, restructured and streamlined their

processes and reduced costs.

The growth in air service in the future will depend on developing innovative market

solutions to gain a solid market share.

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