**4. Methodology**

This research aims to give an overview of the airline industry's issues regarding the growth, development, and sustainability of the airline industry. This is combined with how airlines are currently performing and what LCC criteria they meet, so that can be compared generally in which airlines can sustain future issues.

The structure of the research consisted of five processes, as shown in Figure 2. The processes were supplied with information through input from literature, annual reports, and databases. Analysis of the literature revealed the definition of LCCs and describes the issues faced in the airline industry, and forms a list for the main characteristics of LCCs. The issues faced were mapped into the Ishikawa diagram, where the issues were grouped by the affected factors of the airline process. The main characteristics of the LCCs provide input to the first TOPSIS, which compares the business models of the airlines on how they are meeting the listed LCC-characteristics. Annual reports and data from databases form the input of the second TOPSIS, which focused on growth, EBITDA, and efficiency of the airline. The output of both TOPSIS processes were mapped into a quadrant diagram, which shows how the airlines are meeting the characteristics and how successful they are.

**Figure 2.** Diagram of research structure. Compiled by the authors.

The airline development issues described above were put into an Ishikawa diagram (see Figure 1) for providing an overview. For an airline, it is then important to see how it can withstand some of the issues, and what can be affecting the airlines. For example, The International Air Transport Association (IATA) (2018) says that airlines try to counter the fuel price increase not only with fuel hedging but with fuel efficiency provided by new, efficient aircraft. Therefore, an airline has a low average fleet age, preferably. Moreover, airlines that mainly fly to secondary airports generally have more space to grow there than at a major European hub. Therefore, it is important to see how the airlines operate and what their success is in the environment of 2017 (financial results, yearly averages) and 2018 (fleet characteristics, operating model). What can be seen from IATA's statement is that multiple criteria have to be met by an airline to withstand issues in the industry. Focus is on how well the airlines are meeting the criteria of the operating model of an LCC, which is combined with a criteria analysis upon the airlines' success.

The methodology used to assess if the airline is meeting the LCC definition criteria and the LCC development's success is the technique for order preference by similarity to the ideal solution (TOPSIS) from where the outcome leads to a ranking of alternatives, in this case, the airline's business model. The assessment exists of different criteria and numbers and it can, therefore, be seen as fuzzy data analysis. This needs to be approached by a multiple criteria decision method (MCDM). Normally, TOPSIS is meant for ranking alternatives in decisions, but it can be used for ranking models, too. Hence, the TOPSIS method was based on a set of criteria and situations. TOPSIS is preferred over simple additive weighting since it can handle negative data in its calculations. This is required since airlines can decline, make losses, and sell aircraft. Based on the fuzzy dataset, the methodology TOPSIS was chosen to rank the airlines to identify the successful airlines in the market. This led to an output that shows if meeting LCC criteria can lead to more successfulness and lower vulnerability. The two rankings were presented together in a quadrant diagram. In Table 1, the criteria used in the two TOPSIS analyses in Tables 2–5 are presented. In this way, the split was done between the earlier mentioned criteria [29].

The TOPSIS analysis was based on retrieved data regarding meeting the LCC criteria, as well as the airlines' successfulness in growth (in passengers), efficiency (seat occupancy, fleet age), and EBITDA. The data used for the analyses are presented below in Tables 2 and 4. This was the input to the TOPSIS calculations on meeting the LCC requirements in Table 2 and the success in Table 4. TOPSIS requires normalized data for the statistics, so the numbers in the table needed to be normalized by the following formula [29]:

$$N\_{ij} = \frac{X\_{ij}}{\sqrt{\sum\_{i=1}^{m} X\_{ij}^2}} \, \, \, \tag{1}$$

where *N* is the normalized number, *X* is the given number, *i* is the value of the alternative in relation to the criterion, and *j* marks the criterion involved in the calculation.

Since TOPSIS is based on weights, the normalized numbers in the table were multiplied by the assigned weight given for the concerned criterion. After this multiplication, the highest and the lowest numbers were visible. After this, the positive and negative ideal per criterion were calculated. Before this, what is positive and negative needed to be established. A high EBITDA margin is positive, but a high average fleet age is negative. The following formula was used to calculate the positive and negative ideal [29]:

Positive:

$$V^{+} = \begin{pmatrix} V\_1^{+} \ \vdots \ V\_2^{+} \ \vdots \ \dots \ \vdots \ V\_n^{+} \end{pmatrix} = \left( \begin{pmatrix} \max\{i\} \\ \frac{i}{j \in I i} \end{pmatrix} \begin{pmatrix} \min\{i\} \\ \frac{i}{j} \in I i \end{pmatrix} \right) \tag{2}$$

Negative:

$$V^{-} = \begin{pmatrix} V^{-}\_{1} \ \vdots \ V^{-}\_{2} \ \vdots \ \dots \ \vdots \ V^{-}\_{n} \end{pmatrix} = \left( \begin{pmatrix} \min\stackrel{i}{i}j\\\ j \in fi \end{pmatrix} \begin{pmatrix} \max\stackrel{i}{i}j\\\ j \in fi \end{pmatrix} \right) \tag{3}$$

where *V*<sup>+</sup> is the positive ideal and *V*− is the negative ideal.

When the positive and negative ideals per criterion were calculated, the TOPSIS method proceeded with the calculation of the distance of each alternative to the positive and negative ideal. In this case, each airline's normalized weighted number for each criterion was compared to the positive and negative ideal by using the following formulas:

Distance to Positive:

$$S\_i^+ = \sqrt{\sum\_{j=1}^n \left(V\_{ij} - V\_j^+\right)^2} \tag{4}$$

Distance to Negative:

$$S\_i^- = \sqrt{\sum\_{j=1}^n \left(V\_{ij} - V\_j^-\right)^2} \tag{5}$$

where *Si*+ is the distance to the positive ideal solution and *Si*− is the distance to the negative ideal solution.

The goal of the application of the TOPSIS was to provide an order preference for similarity to the ideal solution. In this case, it is how the airlines are meeting the LCC criteria and how successful the airlines are, as mentioned in Table 1. For this final calculation, this formula was used [29]:

$$P\_{\bar{i}} = \frac{S\_{\bar{i}}^{-}}{S\_{\bar{i}}^{-} + S\_{\bar{i}}^{+}} \; \prime \tag{6}$$

where *Pi* is the biggest similarity to the negative solution.

The closest result to a value of one is the most similar to the best solution, which in this research is the airline that meets the highest number of LCC criteria and that is the most successful in terms of profitability, growth, network size and fleet age. The closest to 0 is the most similar to the worst solution, which is a vulnerable airline for eventual issues that occur, which means an airline can have an old fleet or is making losses at the moment. TOPSIS is a method to compare alternatives based on criteria [29]. In this research, airlines form the "alternatives."

The LCC criteria mentioned by Doganis (2009) and airlines was assessed by Klophaus, Conrady, and Fichert (2012). Miranda, Baltazar, and Silva (2016) formed the base for the analysis of success. The EBITDA margin was chosen since this provides the operating profit margin, not including taxes, interests, or depreciation and amortization of the airline of the revenue they make [13]. It was also chosen since it does not include taxation since that can be different in each country.


#### **Table 1.** Criteria for TOPSIS analysis.

All the criteria mentioned were based on the definitions given on what a real LCC or ULCC should be like. Since defining a secondary airport is hard, it was chosen to have a limited look at the airports serving Europe's two most populated functional urban areas, which are London and Paris [30]. Airlines can differ from them in their strategy. However, in that way, they were not meeting the real definitions or requirements. Definition criteria were equally weighted, whereas successfulness criteria, i.e., profitability, growth, and the number of destinations offered were given a higher weight. It was based on an assumption of what is important.

Klophaus, Conrady, and Fichert (2012) assessed 20 airlines for their simple LCC index, based on the 20 largest LCC, ranked by capacity [31]. Some of these airlines no longer exist. Other airlines focus on one country, rather than having bases in more countries. Within this research, the airlines Ryanair, easyJet, Eurowings, Norwegian, Vueling, Wizz Air, airBaltic, and Transavia were chosen based on the following factors:


The quadrant diagram shows the positions of the different airlines regarding successfulness and meeting the LCC criteria. The axes are successful, vulnerable (vertical), and "hybrid carrier/FSC or True" LCC. The quadrants wherein the airlines can be located:


This shows how the different operating models of the airlines can lead to successfulness.

#### **5. Results—Data Gathering and Processing by Applying TOPSIS**

For the assessment in TOPSIS, whether airlines are meeting LCC requirements, it is important to systemize the knowledge and to construct the table. In the case of yes-or-no answers to the question, if a requirement is met, then the answer will be 1 (yes) and 0 (no). Fleet similarity (as of 30 November 2018) and the different LCC characteristics are described in Table 2. Wizz Air, Transavia, and Vueling are

the only airlines that operate a complete one-type fleet of only Airbus A320-family- and Boeing 737-family-type aircraft. Operating a one-type fleet is one of the main characteristics of an LCC and provides several operational benefits in maintenance, training, and flexibility [11]. The data of flying from London Heathrow and or Paris Charles de Gaulle shows that only three of the eight airlines avoid these airports [32]. By browsing for a flight from each assessed airline, the main booking option via the airline's website was ranking the highest with the best price offer. The flights used for this research were:


Skyscanner is a neutral website that aims to discover the lowest prices on flights, and the cheapest options for the flights were in the first place in the offers for the same flight. Eurowings, Vueling, and Transavia flights are also code-sharing, mainly with (partners of) their parent companies, such as Lufthansa Group, IAG, and Air France-KLM.

Via the airline websites, it became clear that every airline first provides the one-way services, and showed the frequent flyer programs [41–48]. In the case of Eurowings, Vueling, and Transavia, it is the same as their parent company. The analysis of airline website contents also revealed that food and drinks are not available "for free" on short- and medium-haul flights of the airlines. Ryanair and easyJet changed their business model slightly [49,50]. They started providing connections and cooperation with other long-haul carriers. This is part of changed and adjusted strategies.


**Table 2.** Input for TOPSIS LCC criteria. Source: compiled by the authors.

The data in Table 2 and the result of the calculation in Table 3 prove that Wizz Air is meeting all the LCC requirements, whereas airBaltic is meeting only the ones that every researched airline is meeting. Wizz Air is in this way ranking first, followed by Ryanair and easyJet, that both started following some different principles in the time between the publication of Klophaus, Conrady, and Fichert (2012) and today. Wizz Air is, according to this research, the single "true" LCC. The TOPSIS calculations in Tables 4 and 5 show which airline is the most successful, both financially and in terms of growth. The calculation towards the results in Table 3 can be found in Appendix A.

The data presented in Table 4 show that all of the airlines grew in 2017. This varied between 6.5% and 77%. However, the 77% for Eurowings was caused by the acquisition of the remains of the fallen Air Berlin [61]. The most noticeable data in Table 3 are the aircraft on order by Wizz Air, the complete fleet replacement by airBaltic, the high load factor, and the high EBITDA margin of Ryanair. Norwegian is the only airline in this research that reported a loss, which means that its operating costs are exceeding the revenue made. All of the data upon the criteria are placed in Table 4. In Table 5 the

results of the calculations can be found, whereas the calculations of the successfulness can be found in Appendix B.


**Table 3.** Results of TOPSIS LCCcriteria. Source: compiled by the authors.

**Table 4.** Input for successfulness TOPSIS. Source: compiled by the authors, see footnotes.


1 Ryanair, easyJet, and Wizz Air work with a broken fiscal year (FY). The profit margin given is the one of FY that covered the most of 2017. 2 Eurowings is put in the same group as Brussels Airlines in Lufthansa Group's Annual Report. The load factor given in this table is the one of Eurowings and Brussels Airlines combined. 3 Eurowings grew by 77%, mainly due to Lufthansa group's acquisition of the parts of Air Berlin.


**Table 5.** Results successfulness TOPSIS. Compiled by the authors.

From this TOPSIS calculation and its result in Table 5, it can be concluded that Ryanair has the highest rank, whereas Norwegian is ranked last. This ranking tells that Ryanair is the best performing LCC, by being the most similar to the positive ideal solution, which is a growing, profitable, (fuel-) efficient airline. This is mainly caused by its high EBITDA, as well as a young, nearly single-type fleet of aircraft, as well as its high seat occupancy. Newer aircraft are more fuel e fficient and have a single-type fleet that allows the company to save on crew (all the crew able to operate all the aircraft in the fleet) and maintenance costs (the maintenance crew only needs to have knowledge on one aircraft type and the airline does not need to maintain a stock of di fferent aircraft types). High seat occupancy is a key performance indicator for airlines since this determines how well the provided capacity is used to create revenue. Norwegian is the only airline making a loss amongs<sup>t</sup> this group and had to sell new aircraft to pay bills. Noticeable is that the "subsidiary LCCs," such as Eurowings, Vueling, and Transavia, are not amongs<sup>t</sup> the highest in meeting LCC criteria and also not in the successfulness TOPSIS. A probable reason for this is that they mainly operate out of main airports, operate an older fleet, and have lower load factors than the airlines that are meeting more LCC criteria. The older fleet can cause less efficient aircraft and higher vulnerability when the oil price rises. In Figure 3, the data presented shows how the airlines compare to each other, based on the overall score on both TOPSIS analyses. Wizz Air and Ryanair are the least vulnerable to the industry's problems, given their successfulness and growth (perspectives). Norwegian is the most vulnerable.

**Figure 3.** Quadrant diagram results of TOPSIS Source: compiled by the authors.

From the compiled quadrant diagram, it can be concluded that Ryanair and Wizz Air are the two airlines that are the most successful and meet the most LCC criteria. Also located in the "True LCC"–Successful quadrant, easyJet is the fourth most successful airline in this research. Based on this final result, it can be stated that the chances for a listed LCC to be successful are mainly based on how many criteria they meet to become a true LCC. Independence also seems to have an influence, as both Eurowings and Transavia are on the vulnerable side. Vueling is the only airline that is not meeting the majority of the LCC criteria and is a successful airline. Concerning the theory, it can be stated that airlines that are based on characteristics, which can be considered as "True LCC", are in the European market and are more likely to be successful than LCC airlines that are more focused on a hybrid model. The exact cost savings and profits have to take place in the case studies of the airlines. However, a single-type, young aircraft fleet seems to be more profitable than operating different aircraft types, as all the airlines that are on the "Successful" side have this in common. Additionally, high seat occupancy seems to be contributing to the success of the airline as well.
