“Beyond the growth limits of the ultra low cost
carrier business model: the Ryanair case”
Master of Business Studies
Strategy Track
-Draft version 7 -
Author: Hosselet, J.S.L.M. (Sjef) Studentnr.: 10400923
Supervisor: Prof. J.G. de Wit Second supervisor: Drs. E. Dirksen
1 Table of contents H1. Introduction ... 3 H2. Literature review ... 5 §2.1 Core concepts ... 5 §2.2 Hypotheses development ... 18 §2.3 Conceptual framework ... 21
H3. Research design and methods ... 23
§3.1 Research question and design ... 23
§3.2 Methods ... 23
H4. Case study: Ryanair ... 30
§4.1 Geographical overlap ... 30
§4.2 Direct and indirect cannibalization within a low density market ... 34
§4.3 Cases of geographical overlap and (in)direct cannibalization ... 38
§4.4 Summary ... 45
H5. Discussion... 48
§5.1 Discussion and scientific implications ... 48
§5.2 Managerial implications ... 52
§5.3 Limitations and future research ... 53
H6. Conclusion ... 55
References ... 57
Appendix ... 59
Appendix 1 - From LCC to Value Carrier to Ultra Low Cost Carrier, how do they compare? .... 59
Appendix 2 - Dispersion of airports with a radius of 100 km served by Ryanair ... 61
Appendix 3 - Dispersion of airports with a radius of 150 km served by Easyjet. ... 62
Appendix 4 - Matrix Stansted cluster ... 63
Appendix 5 - Matrix Malpensa cluster ... 68
Appendix 6 - Matrix Eindhoven cluster ... 71
Appendix 7 - Matrix Alicante cluster ... 74
Appendix 8 - Matrix Brie Champniers clusters ... 77
Appendix 9 - Matrix Araxos cluster ... 78
Appendix 10 - A Brief overview of Ryanair’s history ... 79
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Preface
The deregulation of the aviation sector in the 1970s in the US and the liberalization in the
1990s in Europe turned the sector from static to dynamic. The last two decades saw the end
and birth of several airlines. This trend continues. Due to deregulation it is possible for
airlines to have a wider choice in airports and destinations, allowing for competition on price.
Airlines such as: (in Europe), Ryanair, EasyJet, Air Berlin, Norwegian, Vueling, Aer Lingus,
Flybe, Wizz Air, Germanwings, Transavia etc. and (in the US), Southwest Airlines, JetBlue,
Airtran, Frontier, Spirit, Virgin America, Allegiant Air etc., took their chance. These airlines,
the so called low cost carriers (LCC), began competing with established airlines on short haul
flights. A recent example of the implications of the new dynamics is the possible introduction
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H1. Introduction
What was once started by Southwest Airlines, developed into a major competitive niche
market with many LCC airlines. However, there are signals of serious limits to the growth of
the LCC business model, such as the decrease of average flight frequencies and increasing
route density problems through the continuously adding of new routes (De Wit and Zuidberg,
2012). Building upon the research of De Wit and Zuidberg (2012), this thesis delves into the
process between increasing route density problems and saturation within the LCC business
model. In order to do so one airline is chosen as a case study: Ryanair. This airline is chosen
because it is the biggest European LCC. Moreover, it is one of the fastest growing airlines
within the European market. A second, more important reason is the fact that Ryanair was the
first airline in Europe to copy the business model of the pioneer of the LCC model: Southwest
(USA). Later, Ryanair proclaimed a new variation to this business model, the ultra low cost
carrier (ULCC) business model (Ryanair Holdings Plc., 2014). This is a specific variation
within the spectrum of the LCC model because of its pursuit of an ultra low cost strategy, by
cutting all the unnecessary frills and at the same time offering the lowest fare as possible.
According to De Wit and Zuidberg (2012), increasing route density problems as a
growth limit (which lead to saturation) leads to changes in the business model of LCC
airlines. It is interesting to investigate which factors influence the dynamics between route
density problems and saturation, and eventually the change in the LCC business model. In the
literature cannibalization is suggested as one of these influencers (Mason and Milne, 1994;
Morrison, 2001; Porter, 2008; Thelle Hvidt et al., 2012; Vowles, 2001). The questions and
limits of growth which emerged from the research conducted on the LCC business model by
De Wit and Zuidberg (2012), will be investigated in the light of the ULCC business model of
Ryanair. I will investigate if these limits of growth also apply to Ryanair’s model and what
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statements regarding saturation as a limit of the ULCC business model and to investigate what
factors are of influence.
The next chapter offers a more in depth explanation of the core concepts addressed in this
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H2. Literature review
This chapter will review the relevant literature on the (ultra) low cost carrier business model,
cannibalization and saturation within the secondary airports network. First I will discuss core
concepts of airline business models in general, the (U)LCC model, airport types, catchment
areas, cannibalization and saturation. Then I will summarize the research gaps and delineate
several hypotheses about the influence of cannibalization within catchment areas on Ryanair’s
ULCC business model.
§2.1 Core concepts
Airline business model
Every organization has a justification for its existence, its goal in “life”. For an airline this
could be earning on the air transport of people and cargo, by conducting transport in a way
which creates value and profit for the firm. But only a statement or definition which
determines a goal is not enough. How this goal has to be reached is an important question.
The how question is answered in the business model, it gives structure to the goal of the firm.
An example of a general approach to define a firm’s business model and its competitive
environment is the Product Organizational Architecture (POA) approach (Mason and
Morrison, 2008). This approach is displayed in figure 2.1 as a conceptual framework focused
on airlines. The framework is defined on the one hand by the product architecture, which is
focused on the core product in terms of consumer preferences (benefit drivers) and the
competitive environment (as defined by the market structure). On the other hand, the product
design focuses on the choices regarding the possible organizational structures (cost drivers).
These define the firm’s cost position. Both the product and organizational architecture have
the purpose to contribute to the creation and sustainability of profits (Mason and Morrison,
2008). Furthermore, a business model is linked to the strategy a company wants to follow in
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model. Porter (1985), proposed three strategies a company can follow to achieve competitive
advantage: (1) cost leadership, (2) differentiation and (3) focus. The differentiation strategy is
based on uniqueness of products. A firm selects one or more attributes which most of the
consumers in an industry perceive as important and uniquely positions itself to meet those
needs (Porter, 1985). When a firm uses the focus strategy, it takes a narrow competitive scope
within an industry. The firm selects a segment or group of segments in the industry and tailors
its strategy to serving them in order to exclude others (Porter, 1985).The cost leadership
strategy is used by firms to become the low cost producer in the industry. To do so, it must
find and exploit all resources at cost advantage (Porter, 1985). According to Porter (1985, pp.
13), “low cost producers typically sell a standard, or no frills, product and place considerable
emphasis on reaping scale or absolute cost advantages from all sources.”
According to Demil and Lecocq (2010), and Porter (1985), the concept of a business
model refers to the description of the articulation between different business model
components or “building blocks” to produce a unique proposition which can generate value
for consumers and thus for an organization. The idea that value leads to profit is one of the
cornerstones of this theory. Strategy is thus of influence on how these building blocks are
shaped. Following Demil and Lecocq (2010), a business model can be used in two ways. The
first one is the static approach; this approach sees the business model as a real model, a
blueprint. In this view, a business model synthesizes a way of creating value in business
(Demil and Lecocq, 2010). The second concept is the transformational approach, in this
approach the business model is a concept or a tool to address change and focus on innovation,
either in the organization, or in the business model itself (Demil and Lecocq, 2010). New
business models, like the LCC, have been acknowledged as radical innovations with the
potential to change whole industries (Demil and Lecocq, 2010). But innovations within a
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to a changing environment is key (O’Reilly III and Tushman, 2004). The transformational
approach implies changes in a business model. These changes can take place in only one or in
several components of the business model as shown in figure 2.1.
The low cost carrier business model
In the 1970s Southwest Airlines was the first successful LCC airline in the US. Its
LCC business model has been implemented by airlines in several markets (first in the US,
later in Europe and Asia) in order to compete with Full Service Airlines (FSAs) (Graham and
Vowles, 2006; Homsombat et al., 2014). In the literature the Southwest business model is
considered the original LCC business model (Alamdari and Fagan, 2005; Graham, 2013;
Homsombat et al., 2014; Klophaus et al., 2012). An LCC is defined as an airline which
provides low-fare air travel services by committing itself to the “cult of cost reduction” Figure 2.1: Scheme of product and organizational architecture of airlines.
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through point-to-point traffic, single aircraft types (usually Airbus 320 or Boeing 737 family),
high aircraft utilization, predominant use of so-called secondary or un-congested airports with
20-30 min aircraft turnaround time, direct sales of tickets especially over the airline’s own
website, only one one-way fare per flight available, single class cabins and competitive
employee wages with profit sharing. Furthermore the elimination of various “frills” such as
complimentary in-flight services, frequent flyer programs and seat assignment which are
offered by FSAs are important (Conrady et al., 2012; Doganis, 2010; Graham and Vowles,
2006; Homsombat et al., 2014). These aspects are depicted as components in figure 2.1. The
cutting of frills adheres to the low cost strategy of Porter (1980), and shapes the POA business
model in order to pursue the low cost strategy in air transport. However, the generic term
‘LCC’ now refers to many airlines, in recent years the industry has witnessed a widespread
departure from Southwest’s original model in pursuit of ensuring competitive advantage
(Alamdari and Fagan, 2005; Klophaus et al., 2012).
Ultra low cost carrier business model
In the previous paragraph it becomes clear not every airline has the same business
model. As Alamdari and Fagan (2005, pp. 383), state: “The extreme differences between
Ryanair’s service offering and fellow LCC, Frontier, are striking. Ryanair is the true ‘low cost
and no frill’ LCC,...”. Following out of this statement, Ryanair proclaims itself to be an ultra
low cost carrier (Ryanair Holdings Plc., 2013). This emphasizes the different types of
business models within the spectrum of the LCC. According to Mason and Morrison (2008,
pp. 84), there is a strategic explanation for the scattered spectrum: “when one airline
establishes a lowest-cost position in its product and organizational architecture, competitors
are forced to choose a different POA strategy.” On the product architecture (customer side),
Ryanair is focused on 3 important points according to Barrett (2004), Ryanair Holdings Plc.
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1. Inflight services items: No sweets, newspapers, free food or beverage services. No seat
allocation. No business class service. More seats per aircraft and a higher load factor.
2. Airport service items: Secondary airports are typically served. No interlining or connecting
journey tickets are issued. Passengers and baggage must be checked in at each airport on a
multi-sector journey. No airport lounge service.
3. Ticket restrictions: Tickets are not sold through travel agents. There are no company retail
tickets outlets; no frequent flyer program and strict penalties for “no show” passengers.
This is in line with Ryanair’s mission statement of making low fares widely available to the
travelling public (Ryanair Holdings Plc., 1999; Ryanair Holdings Plc., 2013).
At the organizational architecture (figure 2.1), these 3 points give focus to the ULCC
low cost base. To illustrate this influence an example is given: because of the restrictions on
inflight services, airport service items and ticket restrictions (e.g. no seat assignment), Ryanair
is able to have the quickest turnaround times on airports. This results in a high utilization of
airplanes. In order to achieve these focus points, Ryanair is primarily focused on secondary
airports due to the quick turnaround times but also because of the low airport charges and
preferential treatment. Yet, the LCC model in general is not that extreme. Easyjet for example
flies primarily from primary airports and seat allocation is possible (Alamdari and Fagan,
2005; Barett, 2004). There are several more examples in annex 1. To stay the cheapest is an
ongoing process of adaptation towards competitors and industry.
Airport types
Since airport charges can be up to 12% of the total costs of LCCs, the airport is of critical
importance in this type of business model (Warnock-Smith and Potter, 2005). Primary
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node which is part of a network, located in such a way as to facilitate connectivity through
spokes. The spoke can be defined as a route between a pair of airports of which at least one
(and in most occasions two) is a primary airport; the hub. These primary airports (or hubs) are
usually located near main urban areas (de Wit and Zuidberg, 2012; O’Kelly, 1998). These
airports are centers in their networks and are often used by FSAs in their intercontinental
network. In addition, the fact that these airports are situated near urban areas as a center
within a big network, implicates they are part of a high density market. Secondary airports
are usually situated further from main urban areas. In this thesis, secondary airports are
perceived as uncongested airports situated in regional areas, which can only be part as end
stations within a hub and spoke system and operate in a low density market (De Wit and
Zuidberg, 2012; O’Kelly, 1998). Furthermore, these secondary airports are often used in point
to point networks. In contrast with the hub and spoke system, the point to point network has
not one central place where an airline always returns to the hub (e.g. KLM’s homebase in the
hub and spoke system is Schiphol, Amsterdam). A point to point network can have several
central places, these are not hubs but are used as a base airport to return to. Both secondary
and primary airports can be base airports in a point to point network. Those base airports are
typified as airports where airplanes are stationed overnight.
In the past years secondary airports such as Hannover, Cologne-Bonn, Dusseldorf,
Eindhoven and Charleroi witnessed an increase in passenger numbers and destinations due to
the entrance of the LCC business model (Dobruszkes, 2013; Pantazis and Liefner, 2006).
Moreover, the growth of destinations and passenger numbers after the entrance of an LCC is a
widespread effect recognized at many other secondary airports (Pantazis and Liefner, 2006).
This effect is also defined as the “Ryanair effect” (Ryanair Holdings Plc., 2000).
The increase in passenger volume adds to the competitiveness of secondary airports. The
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airports which have less or none operating LCCs (Graham, 2013; Lieshout, 2012). The
combination of LCCs and the secondary airport attracts a whole new segment of customers
and a part of the segment of customers which normally would fly with FSAs from primary
hub airports (Hess et al., 2007; Pantazis and Liefner, 2006). For further analysis on the
secondary airports, as a component in the LCC business model, a definition for airport
catchment areas is necessary.
Airport catchment areas
The LCC business model attracts price sensitive customers (Hess et al., 2007; Pantazis and
Liefner, 2006). Through low ticket prices, the market for price sensitive customers has grown
on numerous secondary airports. This growth is connected to the previously mentioned
Ryanair effect. But this growth is not only identified as an effect in absolute numbers of
growing passengers and destinations, it is also expressed in the expansion of the catchment
areas of secondary airports. The catchment area of an airport can be defined as: The area
surrounding the airport from which it attracts its main portion of passengers and is defined by
criteria like distance, access travel time, number of defined spatial units or size of area,
destination, flight frequency and/or airfares and travel motive (Lieshout, 2012; Wilken et al.,
2005). The distance customers are willing to travel to an airport related to the price of the
ticket (airfare) defines them as the type of consumers. Consumers of the LCCs are the leisure
type travelers who accept higher access times, and thus distance, in order to get lower airfares
(Hess et al., 2007; Pantazis and Liefner, 2006).
The definition of a catchment area can be brought back to the basic central places
theory of Walter Christaller (Timmermans, 1980). In essence this theory states every central place has its own catchment area, with its own unique consumer base. This model is static of
nature and states consumers want to travel a certain amount of kilometers to the central place,
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can offer the same sort of products at the same price. For LCC consumers, price sensitivity is
an important factor when choosing a product (destinations). Differences in price and product
would enable size differences between catchment areas. However, because of the pursuit of
the cost strategies by LCCs, prices are assumed to be low compared to the FSAs. There might
be some differences in price between the types of LCCs due to the differences in business
models. But LCCs still attract the same type of customer who is demanding a cheap ticket as a
feature. Though, even when prices are assumed to be low, catchment area sizes could differ
between LCCs. Yet, focusing on catchment areas of one LCC (one airline). The price or
airfare would not matter anymore for the size of the catchment area, because there is no
competition on price within one airline. If the airfares are on the same level, consumers will
travel the same amount of kilometers to the central place.1 In the case of one LCC, catchment
areas are assumed not to differ by size through airfares, because there is a negligible
difference. However, since primary airports and secondary airports also differ on whether or
not they are operating in highly or low density markets, catchment areas can differ in market
density. In conclusion: catchment areas within an LCC airline are assumed to have the same
size but can differ in market density determined by the geographical location.
With the application of a radius of the maximum amount of kilometers consumers are
willing to travel, a catchment area of an airport can be geographically defined. In general, for
airports in Germany, Wilken et al. (2005), provides evidence: “We have found that airports
attract on average 31% of their traffic volume from an area with a radius of 25 kms, 56% from
an area with a radius of 50 kms and 72% of their traffic volume from an area with a radius of
75 kilometer’s around the airport.” Thelle Hvidt et al. (2012), showed in the Copenhagen
Economic report that airports present their catchment area as a radius of at least 100 kilometer
or 60 minutes driving time. However, an LCC can widen the catchment area of an airport
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since consumers are willing to travel further for a lower airfare. Moreover, most airports state
that their catchment area exceeds this minimum and 60 minutes seems therefore conservative
(Thelle Hvidt et al., 2012). This last statement is supported by the example of Schiphol and
Eindhoven, which are 90 minutes of travelling by car separated from each other. Though, both
airports are part of the same relevant market (Thelle Hvidt et al., 2012). Furthermore, several
studies conducted by the UK Civil Aviation Authority suggested that the catchment areas of
airports in the United Kingdom can be up to 2 hours driving time. These same reports state
that for some leisure passengers more than two hours may be appropriate (Thelle Hvidt et al.,
2012). The range of 2 hours driving time based on the provided evidence is plausible.
The application of the catchment area radius can be clarified from two angles. First, a
catchment area can be seen as a circle figure around an airport, assuming a homogeneous
network grid for access modes. It is calculated by taking the respondent in a survey who is
prepared to travel the furthest of all the respondents and apply this as a radius around the
airport (Lian and Rønnevik, 2011; Pantazis and Liefner, 2006). This results in a flat disc as a
catchment area. The second point of view, where the catchment area can be described as a
cone, adds up to the former. The theory of distance decay is based on the first law of
geography which states: “Everything is related to everything else, but near things are more
related than distant things (Tobler, 1970, p. 1).” In the case of the airports catchment area this
could mean that people who are living nearby an airport are more willing to travel to use the
airport than people who are living further away in the same catchment area. This results in a
cone shape catchment area. Distance decay can also be influenced by natural barriers. A place
can be nearby in absolute kilometers, but due to natural barriers like mountains the relative
distance is high. Nonetheless, several cases in Germany showed that people living further
away within a catchment area are the core customers of secondary airports (Pantazis and
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An alternative way to assess an airports’ catchment area is used by the European
Commission. In this case the approach is not focused only on the time the consumer is
prepared to travel to the airport, but also to the difference in the time between transferring to
the city from one airport and another substitute airport (Thelle Hvidt et al., 2012). This
approach acknowledges the importance of distance between catchment areas of airports in
general when using the radius in order to assess the size of an airports’ catchment area. When
the distance between two competing airports is not high enough, overlap of catchment areas
occurs (Pantazis and Liefner, 2006).
The route density problem, cannibalization and saturation
De Wit and Zuidberg (2012), emphasizes the route density problem within the LCC
business model. The problem is that due to organic growth and expansion of networks,
several routes offered by the same airline are overlapping with each other. This results in
“thinner” routes or the decline in occupancy of the planes serving on overlapping routes.
Another consequence, according to de Wit and Zuidberg (2012), is that the number of
scheduled flights on these routes are reduced (the frequency is reduced). The route density
problem forms a limit to the growth of the LCC business model. The amount of new routes
added are not less thin than the old routes and overlap of these routes results in market
saturation using this business model, as profitable routes are not available anymore. Market
saturation occurs in a point of time when a stage is reached in which nothing more can be absorbed or accepted (Oxford University Press, 1991). In this thesis it means that market
saturation is influenced by primarily adding thin or overlapping routes until the stage of
saturation is reached. In the LCC business model there is a limited number of routes which
can be used. According to the Wit and Zuidberg (2012), this is the reason LCCs in general are
moving towards primary airports. However, secondly, the adding of overlapping routes has an
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The adding of overlapping routes implicates that new airports are added in an already dense
network of the LCCs (De wit and Zuidberg, 2012). Following out of the articles of Vowles
(2001), and Morrison (2001), the “Southwest effect” in multi airport regions could play a role
in the process between the route density problem and saturation. These articles found that
when Southwest enters an airport within a region with several more airports, the pricing of
airlines serving the various airports in the region is affected. Because Southwest introduces a
low air fare, the competitors have to follow. However, the multi airport network as
emphasized on by Vowles (2001), consists of airports where only one is served by Southwest.
The other airports are not directly affected in their market share regarding passenger volumes
because Southwest attracts its own kind of customer (Vowles, 2001). But what if two airports
in such a region are served by Southwest? Geographical overlap of the airports’ catchment
areas is likely to occur within the network of Southwest.
The possibility of geographical overlap is also expressed in the findings in the research
of Wilken et al. (2005). It showed that in Germany, leisure travelers can generally choose
between 7 airports. In this case geographical overlap is likely to occur. Indeed, the distance
between airports is important as was already concluded from the Copenhagen Economic
report (Thelle Hvidt et al., 2012). Yet, distance is not important when airports serve unique
destinations and thus focus on separate parts of the consumers target group. It could possibly
even enlarge their catchment area if the airports would sell a unique destination (Lieshout,
2012). Thus, a destination can be identified as the part of the airport component in the
business model, which differentiates the served airport of an LCC airline from other
geographically overlapping airports (Dobruszkes, 2013).
When tickets with the same destination (or route) are sold in the same segment of
consumers within geographically overlapping catchment areas, it means destinations or routes
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theory. Thus, in the long term the overlapping of catchment areas could indicate
cannibalization between products. The cannibalization on products is different from airports
with geographical overlapping catchment areas. It can be defined as consumers who can
choose an alternative airport, situated in the same geographically catchment area of 2 hours
(direct cannibalization) or an alternative airport, situated in the same geographically
catchment area of 2 hours which offers an competitive destination or route (indirect
cannibalization) (Mason and Milne, 1994; Porter, 2008; Thelle Hvidt et al., 2012). In this
thesis cannibalization falls apart in two types of cannibalization: (1) direct and (2) indirect
cannibalization.
(1) According to Mason and Milne (1994), general cannibalization is identified by comparing
the market shares of two brands in the same segment and in what way one brand “eats”
market share of the other brand. “Brands compete within the marketplace for customers
(Mason and Milne, 1994, p. 165).” This is typified as direct cannibalization between two
different brands offering the same product within the same market, this can be compared with
the first part of the definition of cannibalization as mentioned above. In this thesis the
practical definition of direct cannibalization is: a route is offered by the same airline on a
direct competing route (e.g. Stansted-Malpensa also served by Stansted-Bergamo) (figure
2.2). To be clear in both directions there is direct cannibalization on the route, the opposite
direction from the example is the route from Bergamo and Malpensa both to Stansted.
(2) Cannibalization by substitution or indirect cannibalization. According to Porter (2008,
p. 84), the description of a substitute product includes that this product: “..performs the same
or similar function as an industry’s product by different means.” This definition is about
choosing an alternative product (not an alternative airport with the same product), in this case
another destination or route. The practical definition of indirect cannibalization used in this
Stansed-17
Malpensa also served by Luton-Bergamo) (figure 2.2). These are two separate routes, but due
to geographical overlap between catchment areas cannibalization still occurs.
Cannibalization seems to be an alarming and influencing factor for LCCs with regard
to the process between route density problems and saturation (De Wit and Zuidberg, 2012).
Within their own airline, cannibalization on routes or destinations is possible between airports
which overlap geographically by catchment area (Graham, 2013; Morrison, 2001; Vowles,
2001).
By going beyond the limit of growth of the LCC business model, this paragraph tried to
explore what might influence the supposed change in the business model and how the process
between the route density problem and saturation is moderated by cannibalization. To
illustrate this moderating effect three conditions can be recognized in the literature: 1.
Presence of geographical overlapping catchment areas, 2. Presence of direct and indirect
cannibalization on routes and 3. The airports are situated in a low density market. In the next
paragraph hypothesis will be developed for the ULCC model for Ryanair based on the
literature review in this paragraph. Figure 2.2: Direct and indirect cannibalization
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§2.2 Hypotheses development
Increasing airport density leads to geographical overlap
De Wit and Zuidberg (2012), indicate that the continuously adding of airports in Ryanair’s
route system due to the route density problem, eventually leads to saturation in the ULCC
business model. It is expected that adding these secondary airports will continue as Ryanair
plans to continue its activities on new and existing routes (Ryanair Holdings Plc., 2013).
Geographical density can occur when several of these airports are in each other’s catchment
area. In their article Pantazis and Liefner (2006), state that it is most likely catchment areas
will overlap (more) in the future. Moreover, partly the same segment of customers can choose
between two comparable airports served by Ryanair. Based on the theory there can be
hypothesized that an increase of secondary airports served by Ryanair will lead to more
overlap in catchment areas.
H1: The increasing density of airports will lead to a growing geographical overlap in
Ryanair’s network.
Geographical overlap leads to direct and indirect cannibalization
According to Thelle Hvidt et al. (2012), there is a growing trend in overlapping destinations
or routes (direct and in direct cannibalization) and the catchment areas of airports in Europe.
Some neighboring regional airports even have an overlap of 100% on destination or route
(Thelle Hvidt et al., 2012). Lieshout (2012), Thelle Hvidt et al., (2012), and Wilken et al.,
(2005), state that cannibalization stems from the geographical location of airports and their
catchment areas. Direct and indirect cannibalization, as defined in the theoretical framework,
can not exist without geographical overlap in airports catchment areas. Airlines operating
from these airports do not offer unique destinations or routes. Distance starts to matter when
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added. It is hypothesized that the geographical proximity of airports towards each other leads
to direct and indirect cannibalization in the network of Ryanair.
H2: Geographical overlap in the airport network of Ryanair leads to direct and indirect
cannibalization.
Direct and indirect cannibalization on routes within a low density market is a stimulus for saturation in the ULCC model
In paragraph 2.1 direct and indirect cannibalization are explained on the basis of marketing
theory. This theory by Mason and Milne (1994), described the cannibalization between two
products from one company within a market for a single product category. In addition, this
theory combined with Porter (2008), described cannibalization on the basis of substitute
products. In these markets, airports and their routes/destinations can be compared as products
offered by the same brand (an LCC) within the same product category. Lieshout (2012),
explains that each airport catchment area has its own market share. By offering the same
product (routes and destination) in each other’s catchment areas within a multi airport region,
market shares will overlap and influence each other and cannibalization can occur (Graham,
2013; Mason and Milne, 1994; Morrison, 2001; Vowles 2001). According to Mason & Milne
(1994), and Graham (2013), in the line with increasing airport density, lies saturation.
However, there is one condition that has to be met before cannibalization can have
implications for saturation within Ryanair’s business model. The airports which are
supposedly cannibalizing on routes are primary or secondary airports. Which means they lie
in a high density market (primary airports) or not (secondary airports). Cannibalization on the
basis of geographical overlapping catchment areas can occur. Notwithstanding, if there is a
high density market within these catchment areas, it does not necessarily have to mean this
will stimulate saturation. The market is dense enough to have the offered overlapping routes
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In addition, De Wit and Zuidberg (2012), identified saturation on routes in the
secondary airport market as a limit to the growth of the LCC business model. Furthermore, it
was found that Ryanair kept adding airports, routes and destinations to their network in order
to cope with the route density problem (Ryanair Holdings Plc., 2013; De Wit and Zuidberg,
2012). This stimulates geographical overlap and (direct and indirect) cannibalization even
more, it moderates the effect between route density problems and saturation. This means that
the adding of new airports which are not situated in a high density market, will only have
negative influence on the growth and profit of an LCC. Hence, the adding of more airports
will only lead to saturation quicker. It is hypothesized that direct and indirect cannibalization
on destination/route within a low density market, has a stimulating effect on saturation in the
secondary airport market for Ryanair’s ULCC business model.
H3: Direct and indirect cannibalization within a low density market stimulates saturation in
21
§2.3 Conceptual framework
The proposed hypotheses are combined in the conceptual model:
Light blue = The moderating variable of route cannibalization Black/white = Findings detracted from literature
Red = Occasions without moderating influence
A limited number of routes due to the route
density problem
Saturation Migration of ULCC to LCC ∆3: Low density market ∆2: Direct and indirect cannibalization on routes Stimulus for extra
saturation ∆1: Geographical overlap Yes Yes Increasing airport density No route cannibalization High density market
No stimilus
No geographical overlap / barriers are
22
Increasing airport density is fed by continuously adding of new routes and destinations
in Ryanair’s network. This leads to route density problems which leads to saturation in routes
and the secondary airport markets within the ULCC business model of Ryanair. If the
saturation is moving towards the point that it is not profitable anymore to add new routes,
which normally would lead to growth in the business model, every extra added route can be
prone for cannibalization since the network is becoming denser. The process of
cannibalization is a moderating variable which stimulates extra saturation next to the already
present saturation due to the route density problem. This stimulation is accelerated when 3
conditions for the moderating variable of route cannibalization are present: (∆1) Geographical overlap of airport catchment areas, (∆2) Direct and indirect cannibalization on routes within a (∆3) low density market. Increasing cannibalization will in the end lead to saturation quicker and thus to migration from the ULCC to the LCC business model.
23
H3. Research design and methods
§3.1 Research question and design
De Wit & Zuidberg (2012), suggested several factors which affect the LCC growth limits and
the influence these limits have on changes made by Ryanair to its business model. In this
thesis it is suggested that route cannibalization is one of these factors which affect the process
behind the growth limits and changes in the ULCC business model. This will be illustrated by
the case of Ryanair:
The goal is to describe and explain the relationship within the ULCC business model between
variables concerning route cannibalization on the one hand and variables of route density
problems and saturation on the other hand. The research is based on a deductive approach.
According to Saunders and Lewis (2012), this approach is followed by a study of a
quantitative nature. The theoretical propositions which resulted in the hypotheses will be
investigated in chapter 4 on the basis of quantitative research. This will be done with
quantitative data in order to investigate hypothesis 1, 2 and 3 answering the “how” in the
research question. When this part is answered, the results will be placed within a bigger
spectrum regarding the change in the ULCC business model and the theory of De Wit and
Zuidberg (2012) (chapter 5). The latter part has eventually the purpose to partly expand
existing theory on the (U)LCC model.
§3.2 Methods
Before describing on the methods used for this research, it is important to emphasize the
choice of methods is based on the substantiation of the moderating variable of route “How does the route cannibalization affect the relationship
between the route density problem and the change of the ULCC business model?”
24
cannibalization detracted from the literature. This moderating variable falls apart in 3 conditional variables (conceptual framework: ∆1, ∆2 and ∆3).
According to Thomas (2012), the choice for a case study is justified when the subject
is a piece of a bigger development which behavior is impossible to manipulate, as is the case
with Ryanair. Therefore, there is specifically chosen for a case study as the general
overarching method of research. Furthermore Ryanair is chosen as an organization to
investigate, because it is a key and topical case with a wide range of publicly accessible
information. Moreover, Ryanair is by far the biggest LCC with a market share of 20% in the
European market and it is the number 3 airline ranked by seat capacity behind Lufthansa and
Air France – KLM (CAPA: Centre for Aviation, 2013). Ranked by the amount of passenger
traffic on an international basis in 2014, Ryanair is the number 1 airline with 81.395.000
passenger according to the International Air Transport Association (Ryanair, 2014). It is
followed by Eeasyjet which transported 52.878.000 passengers. The first European FSA on
the third place is Lufthansa, with 50.739.000 passengers. Followed on the fourth place by the
first non-European airline: Emirates with 43.335.000 (Ryanair, 2014). These numbers show
the magnitude Ryanair has in the market. Therefore, a change in strategy could implicate
second order effects within the whole LCC sector. The choice and the importance of the case
study led to insert it directly into the research question (Ryan et al., 2002). Several methods
and tools are used within the case study in order to investigate the hypotheses.
The raw data used in the case study was retrieved from the Official Airline Guide
(OAG) database. The OAG is an independent platform for aviation information and
intelligence. It is composed of registered flight movements, departure/arrival times, served
airports etc. from all airlines over the world. The raw data was edited before investigating the
25
Hypothesis 1
The first part of the investigation is specifically focused on describing and explaining
the relation between the variables of hypothesis 1. This part has the goal to stress out the
proposed problem of the increasing density of secondary airports (continuously adding
secondary airports) and its geographical overlap in the network of Ryanair.
As described in paragraph 2, geographical overlap stems from the geographical
location of the airports and their catchment areas. Based on the airports Ryanair serves, maps
where created using raw data from the OAG database. This is done with a cartographic
software program: Arc Gis. This program is used to translate data and information in to maps.
A cartographic program is a relatively simple way to translate numbers and data into
customized geographic maps in a clear and comprehensive way. Because it is computerized it
is less prone for error than drawing it with other software which can create maps without the
background of data. Therefore it will contribute to reliability and viability of the research.
The airports served by Ryanair are found in the OAG database. Next, the longitude
and latitude coordinates of the airports, detracted from the International Air Transport
Association website, were uploaded in the program and applied to maps of Europe. The
airports marked with a green dot are secondary airports, those marked with a red dot are
primary airports. A variation of 100 kilometers to 150 kilometers is used as the radius of the
catchment areas. These maps are also developed for Easyjet in order to have an example of a
more general LCC business model.
In addition to the definitions for the types of airports in the literature review, the
airports are operational divided in the OAG database on the basis of the annual total offered
seats from a certain airport. Primary airports offer at least 10 million seats per year and
26
Hypothesis 2 and 3
The second part of the research is focused on investigating the relationship of the
several variables related to hypothesis 2 and 3.
In the OAG database all airports within a range of 150 kilometers from each other are
counted as geographical overlap. If these airports are served by Ryanair on the same route or
destinations, direct or indirect cannibalization occurs. In order to give a good overview and a
reference framework, this data is also provided for Easyjet. Easyjet is chosen because it is the
only other LCC which has a broad range of annual reports and online accessible information.
Though the case study on Ryanair is a snapshot trying to explain a relatively new
phenomenon, the data is investigated from a retrospective point of view. In order to explain
the relationship between the variables of geographical overlap, direct and indirect
cannibalization and market density on the one side (together the moderating variable of route
cannibalization) with the process between the variable of route density problem, saturation
and the move from ULCC to LCC on the other side, several tables and individual case studies
were developed.
Hypothesis 2 is investigated by comparing geographical overlap and direct and
indirect cannibalization. In order to compare the geographical findings with the data on direct
and indirect cannibalization, individual case studies are developed.
There are two reasons for using individual case studies. First, the relationship between
the variables regarding hypothesis 2 is highly important for the research in this thesis. Second,
the movement from secondary to primary airports is identified as an outcome of the
27
Ryanair. The type of airport is therefore an important part to research. In the individual case
studies and tables the airports are categorized as follows: Primary airports are indicated with a
P and/or a B for base airports, the airports with only a B or without a letter are both secondary
airports. The categories are based on the annual reports provided by Ryanair and on the raw
data from the OAG database. With the focus on the cluster around specific cases, clearer
conclusion can be drawn than with the focus on one airport.
In addition to the investigation of hypothesis 1, geographical overlap is also described
by counting the number of airports each particular airport has in its catchment area.
In order to compare within the case studies, the top 20 lists for direct and indirect
cannibalization is used. This variable is described by an overview of the top 20 of direct and
indirect cannibalization per airport over the periods 2000-2001, 2005-2006 and 2013-2014.
This is done separately for each airport, by manually counting the numbers of direct and
indirect cannibalizing routes compared to other airports. The list per period is composed by
arranging cannibalizing airports from big to small on the basis of cannibalizing routes. The
choice for the periods lies in the availability of the annual reports for the first and the last
period. The middle period is chosen because it was a central point before the economic crisis.
The exclusion of the periods regarding the economic crisis is to maintain objectivity and
reliability, since the crisis could have a moderating effect.
Six airports from the top 20 list are chosen to examine in further detail. Special
matrices per airport and its cluster are developed on the basis of detracted and processed data
from the OAG database (annex 4 to 9).2 Within these matrices three different categories of
airports with geographical overlap are focused on: (1) airport base, (2) primary airport and (3)
an airport which is not 1 or 2 but has a high level of geographical overlap. Per category the
2
28
airports are randomly selected. The primary and base airports are chosen out of the top 20 list
and the cluster airports are based on an airport with the maximum amount of airports within
its catchment area. Also, in the last category, an airport is chosen to indicate a restriction of
applying ranges of kilometers in the model. The following airport clusters where chosen:
Stansted, Malpensa, Eindhoven, Alicante, Brie Champniers and Araxos. Within each
category, airports within a range of 150 kilometers of the specific example airport are taken
into account in the separate case studies. Per route between departing (ORI_AP) and arrival
airports (DES_AP), is investigated if there are direct, indirect cannibalizing routes or routes
without cannibalization between airports.
Hypothesis 3 is investigated by comparing the descriptive numbers of saturation and
direct and indirect cannibalization within a low or high density market over the periods
2000-2001, 2005-2006 and 2013-2014.
Direct and indirect cannibalization is described in the same way as under hypothesis 2.
Saturation is based on the already found growth limit by De Wit and Zuidberg (2012). In this
thesis saturation for Ryanair is defined on the basis of the observation of the amount of direct
and indirect cannibalizing routes per airport, entering the top 20 lists of cannibalization over
the periods 2000-2001, 2005-2006 and 2013-2014. The overview of top 20 airports show how
many overlapping routes per airport have entered over the periods, but also how many new
airports have entered the top 20 between the periods. Because the type of airports is placed
behind the (new) airports in this list, it is also possible to see what type of airport is
cannibalizing and affects the process between the route density problem and saturation. The
type of airport indicates if an airport is in a low density market or not. The catchment area of a
primary airport is assumed to be located in an urban area with a high density market, this is
29
information regarding the “operating base airport” category was available. The lists of
operating base airports are only provided in the recent reports.
In addition to the 3 hypotheses, by comparing the move from ULCC to LCC with the outcome
of the prior hypotheses, the influence of the moderating variable of route cannibalization on
the move in the Ryanair business model is evaluated. This is done to place the results of this
30
H4. Case study: Ryanair
In this chapter the results are presented on the investigations of the relationships between the
several variables as presented in the conceptual model. Because hypothesis 2 holds the key in
the conceptual model as ∆2 and the question of how the relationship is affected, the hypotheses are presented in a different order: H1, H3 and H2.
Before the influence of direct and indirect cannibalization within a low density market
on the relationship (hypothesis 3) can be investigated in paragraph 4.2 and 4.3, there must be
certainty about the overlap between the geographical catchment areas as tested first with
hypothesis 1 (paragraph 4.1). Hypothesis 2 is presented last in the form of individual case
studies in paragraph 4.3.
In order to be able to focus in the discussion and conclusion part on the business
model of Ryanair and the move from ULCC to LCC regarding the influence of
cannibalization, the move will be reflected with the variables which are investigated in
paragraph 4.1 to 4.3. In the end of this chapter a brief summary on the findings is given per
hypothesis in paragraph 4.4. Furthermore, an overview of Ryanair’s history is given in annex
10.
§4.1 Geographical overlap
The present situation after the continuously adding of secondary airports and a growing
31
Figure 4.1: Dispersion of airports with a radius of 150 km operated by Ryanair
32
represented in figure 4.1.3 The figure gives an overview of current geographical overlap and
how substantial this situation is, it is covered with overlapping circles which represent the
airports catchment areas. A map with a range of 100 kilometers for Ryanair is provided in
annex 2. This map shows that even with a radius of 100 kilometers, there is substantial
geographical overlap.
To give a relative view on the dispersion of Ryanair’s airports, the same map was
attached for Easyjet in annex 3. In contrast to the airports served by Ryanair the map of
Easyjet shows less geographical density. Ryanair operates from 231 airports and Easyjet from
160 within the same area, which means the chance of overlapping catchment areas within
Ryanair’s network is bigger. Though, both maps show a high geographical density. The main
difference between the two figures is that Easyjet is not as present, with regard to the circles,
as Ryanair in the same area. Another interesting difference with regard to the served airports
is the amount of primary airports in both networks. Ryanair operates from 16 primary
airports, 6.9% of the airports in its
network. While Easyjet operates from
32 primary airports making up 20% of
its network. These percentages give a
good view of the differences between
Easyjet and Ryanair with regard to their
business models. The amount of primary
airports in Ryanair’s business model at
this moment, could emphasize the
movement from ULCC towards LCC
3 The growing density in the network is explained as the high amount of secondary airports with overlapping
catchment areas present in Ryanair’s network (figure 4.1.). This definition of density should not be confused with the definition of the route density problem.
Table 4.1: The frequency of airports operated by Ryanair which show geographical overlap with 0 to 8 other airport(s)
Number of
overlapping Airports N % cum % 0 32 13,9 13,9 1 55 23,9 37,8 2 41 17,8 55,6 3 39 17,0 72,6 4 22 9,6 82,2 5 14 6,1 88,2 6 16 7,0 95,2 7 8 3,5 98,7 8 3 1,3 100,0 230 100,0
33
since the secondary airports are an important cost reduction factor within the ULCC business
model. Moreover, in the companies annual reports since 2013 the focus is moved from
secondary airports towards operating from primary airports (Ryanair Holdings Plc., 2013).
The supposed adding of (primary) airports in the future could mean more congestion and thus
a lower utilization of aircrafts. This eventually leads to inefficiency and higher cost. It could
disturb the balance between the goal of operating with the lowest cost and flying as much and
efficient as possible in order to maximize profit.
Ryanair’s movement towards the LCC model (and saturation) could be explained by
the absolute numbers of geographical overlap. Figure 4.1 shows that most of the airports are
geographically overlapping. Table 4.1 shows numbers indicating how the geographical
overlap is distributed over the airports within Ryanair’s network. Furthermore, it shows how
many airports are directly overlapping, and with how many airports, within the same range.
This last point emphasizes the presence of geographical overlap even more. Though, as dense
as the dispersion of airports looks, most airports fall within the range of geographical overlap
of 0 to 3 airports (table 4.1).4 Even if the overlap is low (1 airport or higher) compared to the
maximum of 8, there exists still a full potential of direct and indirect cannibalization present.
The change of focus from Ryanair towards primary airports is partly explained by the route
density problem. Nonetheless, the subsequent geographical overlap supposedly caused by
adding secondary airports due to the route density problem, still does not explain directly why
Ryanair would move to primary airports. The geographical overlap does not state anything
about the presence of direct and indirect cannibalization. Yet, it does show the potential of
geographical overlap and it also is a condition which has to be present for routes to overlap
(or direct and indirect cannibalization). This is where the influence of the moderating variable
4 In the situation of a geographical overlap of 0, it means that an airports has no overlap with any other airport on
the map of figure 4.1. In the situation of geogaphical overlap with a number of 1, it means that one airport in this category has overlap with one other airport. The former explanation applies to the other numbers of overlapping airports in table 4.1 (column 1).
34
of cannibalization starts. The cannibalization and route density problem amplify each other in
the saturation of the ULCC business model. The part of the conceptual model and its
hypotheses between the geographical overlap and the move from ULCC to LCC is discussed
in the following paragraphs.
§4.2 Direct and indirect cannibalization within a low density market
In table 4.2., 3 top 20’s of direct cannibalizing routes and airports are provided.5 This table
shows direct cannibalization in the years 2000-2001, 2005-2006 and 2014. In
2013-2014 the number of routes of Stansted (STN) which are directly cannibalized is 96.What
strikes is the explosive growth in direct cannibalization and distribution over the airports. The
cannibalization in the period of 2000-2001 was concentrated around 2 or 3 specific airports
with Stansted (STN) as the steady number 1 in the top 20 list (table 4.2). Over the other 2
periods (2005-2006 and 2013-2014), Stansted (STN) remains on top of the list as other
(mainly secondary) airports enter the top 20 next to the doubled amount of primary airports in
the last period. This shows cannibalization is spreading through Ryanair’s route network and
can be affecting the relation between the route density problem and saturation since the
secondary airports lie in low density markets. Though, the number of routes per airport which
are cannibalizing can be a little nuanced for the geographical location of the airports presented
in the tables. If a certain airport has a cannibalizing route, it does not necessarily mean that the
cannibalization is caused by geographical overlap at the location of that certain airport. The
geographical overlap could also be only present at the destination. This could indicate for
example that Stansted (STN) is an airport which is serving routes to several other places in
Europe, but direct or indirect cannibalization on such a route is occurring due to geographical
overlap on the destination. Therefore the amount of unique (or overlapping) destinations is
important. However, the amount of overlap at Stansted (STN) could indicate the high density
35
of the market surrounding the airport. London is a big urban area, with high demand, more
routes are offered. So it seems indeed that not only the geographical overlap directly indicates
cannibalization, but it is also depending on the condition of the market density regarding the
location and type of airport. Yet, geographical overlap is needed as a condition for route
cannibalization. More and more cannibalizing primary airports and routes are entering the top
20, which indicates a movement from secondary towards primary airports. It implicates
Ryanair is indeed moving towards the general LCC business model, like for example Easyjet.
In order to make the comparison between Ryanair and Easyjet, the same top 20 for Easyjet is
shown in table 4.3.
Table 4.2: Top 20 airports by the number of directly cannibalized routes (N) over 3 periods (Ryanair)
Top 20 (2000-2001) Top 20 (2005-2006) Top 20 (2013-2014) Positon N Airport Position N Airport Position N Airport
1 27 STN (p) 1 63 STN (p) 1 96 STN (p/b)
2 13 DUB (p) 2 26 DUB (p) 2 54 CRL (b)
3 2 HHN 3 17 LPL 3 44 DUB (p/b) 3 2 MPL 4 15 EMA 4 40 AGP (p/b) 3 2 PGF 4 15 HHN 5 37 BGY (b) 6 1 AAR 6 12 CIA 5 37 EMA (b) 6 1 AOI 7 11 GRO 7 34 ALC (b) 6 1 BGY 7 11 SNN 8 33 EIN (b) 6 1 BHX 9 10 LTN 9 29 BLQ (b) 6 1 BOH 10 7 PSA 9 29 LPL (b) 6 1 BRS 11 6 BES 9 29 NRN (b) 6 1 CCF 11 6 BGY 9 29 PSA (b) 6 1 CRL 11 6 EIN 13 28 GRO (b) 6 1 CWL 14 5 CRL 14 26 MXP (p) 6 1 EBJ 14 5 FKB 15 25 BCN (p/b) 6 1 EDI 16 4 LGW (p) 15 25 BHX (b) 6 1 EIN 16 4 MJV 15 25 BRS (b) 6 1 FNI 16 4 TSF 15 25 MAN (p/b) 6 1 FRL 16 4 WRO 15 25 PMI (p/b) 6 1 GOA 20 3 CCF 15 25 TFS (b)
Source: OAG database
• (p) = primary airports; offer at least a minimum of 10 million seats per year
• Airports without (p) or (b) = secondary airports; offer at least a minimum of 4 million seats per year
• (b) = base airports and secondary airports; base airports are typified as airports where airplanes are stationed to let them stay overnight
36 Table 4.3: Top 20 airports by the number of directly cannibalized routes (N) over 3 periods (Easyjet)
Top 20 (2001-2002) Top 20 (2005-2006) Top 20 (2013-2014) Position N Airport Position N Airport Position N Airport
1 13 LTN 1 27 LGW (p) 1 73 LGW (p)
2 10 LGW (p) 2 19 LTN 2 37 LTN
3 4 AMS (p) 3 11 GVA (p) 3 34 GVA (p) 4 4 EDI 4 9 ALC 4 22 STN (p) 5 4 GLA 5 8 BRS 5 21 LYS 6 4 GVA (p) 6 7 EDI 6 18 DUS 7 2 AGP (p) 7 7 GLA 7 15 SEN 8 2 ATH (p) 8 6 BFS 8 13 BRS 9 2 BCN (p) 9 6 EMA 9 12 CDG (p) 10 2 BFS 10 5 AGP (p) 10 11 BFS 11 2 MAD (p) 11 5 FAO 11 11 GLA 12 2 NCE (p) 12 5 LPL 12 11 NCE (p) 13 2 ZRH (p) 13 5 NCE (p) 13 9 ALC 14 1 ABZ 14 5 NCL 14 9 AMS (p) 15 1 EMA 15 3 AMS (p) 15 9 NCL 16 1 INV 16 3 BCN (p) 16 9 PMI (p) 17 1 LPL 17 3 GNB 17 9 SXF
18 1 PMI (p) 18 3 ORY (p) 18 8 AGP (p)
19 0 ACE 19 3 PMI (p) 19 8 DUS (p)
20 0 ADB (p) 20 3 PRG (p) 20 7 EDI
Source: OAG database
The results show the spread of direct cannibalization between secondary and primary
airports is becoming equal for both airlines when comparing the periods in table 4.3. The
results show Easyjet already had cannibalizing primary airports over the three periods and at
Ryanair this process is slowly starting. Ryanair is moving within their business model on the
choice of airport category component with regard to the POA model. Easyjets direct
cannibalizing routes are growing at the same explosive pace as Ryanair’s, but Easyjet
operates from less airports than Ryanair. This results in a less geographical overlapping
network of airports as a first condition and thus less potential direct or indirect
37
For indirect cannibalization at Ryanair a similar table was made (table 4.4). It shows
that the indirect cannibalization has also grown explosively between 2005-2006 and
2013-2014. This means more airports have entered which show geographical overlap on both ends
of the route. However, for the indirect cannibalization to be affecting the relationship between
the route density problem and saturation, there should be a low density network. The situation
in table 4.4 shows that airports surrounded with a high density market have entered over the
period between 2005 and
2014. This means that the
cannibalization can
become severe. If
combined with the
growing number of
(primary) airports and the
amount of routes entering
the top 20 list, this
indicates cannibalization
is moderating the
relationship between the
route density problem and
saturation in an
amplifying way.
Direct and indirect cannibalization can become a problem in both the business models
of Ryanair and Easyjet. Yet, Easyjet services more primary airports making this organization
less prone to the consequences of low density markets. Since the amount of cannibalizing
primary airports in Ryanair’s network is growing parallel with the explosive growing amount Table 4.4: Top 20 airports by the number of indirectly cannibalized routes (N) over 2 periods (Ryanair)
Top 20 (2005-2006) Top 20 (2013-2014) Position N Airport Position N Airport
1 16 STN (p) 1 44 STN (p/b) 2 8 EIN 2 24 CRL(b) 3 7 LTN 3 23 EMA (b) 4 6 SNN 4 18 EIN (b) 5 5 LPL 5 17 ALC (b) 6 3 EGC 6 16 BLQ (b) 6 3 LGW 7 15 AGP (p) 6 3 LIG 7 15 BHX (b) 6 3 NOC 7 15 LPL (b) 6 3 ORK 7 15 MAN (p/b) 11 2 FNI 7 15 PSA (b) 11 2 GRO 12 14 BGY (b) 11 2 KIR 12 14 GRO (b) 11 2 POZ 14 13 TPS (b) 15 1 BGY 15 12 BCN (p/b) 15 1 BOH 15 12 MXP (p) 15 1 CCF 15 12 TRN 15 1 FKB 15 12 TSF 15 1 FRL 19 11 BRI (b) 15 1 HHN 19 11 BRS (b)
38
of secondary airports, it could indicate an accelerating influence of cannibalization on the
relationship between the route density problem and saturation within the business model.
Furthermore, another finding is almost all of the top 20’s direct and indirect cannibalizing
airports over the period 2013-2014 are operating bases for Ryanair. Both aspects will be
handled in the next paragraph by focusing on specific categories of airport clusters.
§4.3 Cases of geographical overlap and (in)direct cannibalization Primary airports (vs secondary airports)
Stansted (STN; England): In the direct and indirect cannibalization top 20 list for
Ryanair, Stansted (STN) is the solid leader with regard to cannibalization over the years. The
peculiarity here is that Stansted does not have a big amount of geographical overlap. It only
overlaps with four other airports. However, in the matrix detracted from the OAG database it
was found that the airports which are lying within Stansted its catchment area, have a
tremendous amount of cannibalizing routes (matrix annex 4). Especially the high amount of
indirect cannibalization is striking because it is an indication that cannibalization not only
occurs at Stansted but also at Ryanair’s destination airports from Stansted (STN).
To put this in the light of the theory in paragraph 2.1, this is a case of a possible
substitution opportunity in the traveler choice. A traveler can choose between two identical
products, which lead to cannibalization on routes/destinations. Though, the most
cannibalization with Stansted is to be found present at the secondary airports and not with for
example London Gatwick (LGW). Both Stansted (STN) and London Gatwick (LGW) are
primary airports. So, Ryanair does not let these two airports compete, because Ryanair offers
unique routes/destinations on both. It seems more likely in this case that the amount of
cannibalizing routes can be attributed to the primary airports since the density of the market is
higher. Operating from a high density market can mean cannibalization on the basis of
