University of Groningen Faculty for Business and Economics
MSc Business Administration Strategic Innovation Management
Multilateral Airline Alliances: Competitive Advantage or
Mission to Survive?
Competition in the Airline Industry Illustrated by Strategic Groups
By: Shira Daliah Goldberg
20th of June 2017
Supervisor: Dr. Charlie Carroll Co-Assessor: Dr. Killian J. McCarthy
Abstract
Going it alone is not a strategy that many airlines follow within the European airline industry. Rather, they form multilateral alliances and try to offer customers a wider route network than their competitors. This study looks at how this influences competition, the performance of multilateral alliances and the performance of airlines without affiliation in comparison to multilateral alliances. A dataset comprised of 25 European carriers offers the foundation for the cluster analysis and the formation of strategic groups. The findings show that the industry can be split up in three groups: (1) pure low cost carriers, (2) mainly low cost carriers and (3) full service carriers. Between these groups only marginally significant performance
differences exist. The performance differences across alliances and the group of airlines without alliance affiliation show that alliance membership does not necessarily lead to better performance. For airline managers this means that they should position their airline within the industry in a way that yields the best possible competitive advantages depending on their business model, their assets and the complementary resources of other airlines.
Keywords: airline industry; alliance networks; firm performance; multilateral alliances;
Introduction
“If you want to be a millionaire, start with a billion dollars and launch a new airline.”
- Sir Richard Branson (Greenberg, 2001)
The airline industry is generally struggling to maintain profitability (Czipura & Jolly, 2007). Scholars find reasons for this in the changes, which the industry has undergone over the past thirty years. Especially with the liberalization of the European airline market in 1986, more and more airlines have emerged and thus increased competition (Min & Joo, 2016). Consequently, airlines had to differentiate themselves to stay competitive on individual firm level. Because of the regulated aviation market, airlines could not easily expand and access foreign markets. They had to form alliances to achieve growth abroad (Lazzarini, 2007; Wright, Groenevelt & Shumsky, 2010; Janawade, 2012; Topaloglu, 2012).Hence, Airlines came together and formed multilateral strategic alliances. These can be defined as an “interfirm link that involves exchange, sharing or co-development” (Gulati, 1995, p.86). When this interfirm link exists between two or more firms it is termed a multilateral alliance (Li, Eden, Hitt, Ireland, Garrett, 2012). Further, on this group level, their goal was to offer customers unique benefits in form of frequent flyer programs (FFP), coordinated luggage transfer in case of stopover, lounge access and most importantly well organized and seamless transfers (Star Alliance, 2017). More specifically, alliances were supposed to offer
competitive advantage “by offering the widest range of destinations, the greatest choice of frequencies and the shortest journey times” (Dennis, 2000, p.75). By forming such a
multilateral strategic alliance in the airline industry, airlines did not only offer benefits to their customers but they also gained benefits themselves by being able to tap into the resources of their alliance partners. This improves the flow between nodes of their network, increases convenience regarding flight frequencies and raises the level of connectivity due to joined carriers (Gudmundsson & Lechner, 2006).Over time, other airlines caught up on these benefits of having access to “resources otherwise not attainable” (Gudmundsson & Lechner, 2006, p.153). As a result, on industry level, changes occurred in industry structure and competition. These changes are reflected in SkyTeam with 20 members worldwide
In 2015, Star Alliance was leading with 23% of total scheduled air traffic worldwide by Revenue Passenger-Kilometer (RPK), followed by SkyTeam with a share of 20.4% and Oneworld with 17.8%. Within Europe, there were 17.6% of all of international scheduled RPK prevailing and each airline and therewith each alliance competes for them (IATA, 2017). Analyzing an industry according to its strategic groups can capture such competition
constellations. Hence, to see how these alliances shape the industry layout, this paper will perform a cluster analysis. The outcome will helpto see what competition looks like within the European airline industry and more specifically, what role multilateral airline alliances play therein also in terms of performance differences.
The remainder of this paper will first take a closer look on the European airline industry, namely what exactly makes up multilateral airline alliances and how these are competing within the industry. Then the concept of strategic groups will be introduced and applied to this specific industry to analyze the conduct and competition of European airlines. Thereby, this study takes a system theory point of view, which will uncover in what way all levels (i.e. firm level, group level and industry level) are interdependent (Short, Ketchen, Palmer & Hult, 2007).
The Airline Industry
To offer better understanding of how multilateral strategic airline alliances are constructed within the European airline industry, an overview is given on the three
multilateral strategic alliances Oneworld, SkyTeam and Star Alliance and the airline industry in the following section before the hypotheses are derived from the current status quo of strategic groups and multilateral strategic airline alliances.
Multilateral Airline Alliance Characteristics
ground facilities as well as ground handling activities (Oum, Taylor & Zhang, 1993; Park, 1997; Park & Cho, 1997; Li, 2000; Dennis, 2000; Gudmundsson & Rhoades, 2001; Evans, 2001; Weber, 2005; Holtbrügge, Wilson & Berg, 2006; Janawade 2012). Therein, their work is organized by global distribution systems that allow for them to coordinate their flights and passenger transfer as well as rebooking with ease because of coordinated flight schedules (Oum et al., 1993; Park, 1997; Park & Cho, 1997; Li, 2000; Dennis, 2000; Pels, 2001; Weber, 2005; Lazzarini, 2007; Janawand, 2012). Schedules are integrated by the joint investment in the development of Information Technology (IT), which supports the coordination of each member’s individual information systems (Li, 2000; Gudmundsson & Rhoades, 2001; Morrish & Hamilton, 2002; Janawade, 2012).
Besides the joint coordination, alliance members also make use of so-called block space agreements where one airline buys a block of seats from a flight operated by another airline. The buying airline is then responsible for selling the booked seats by making use of its own marketing code but is also allowed to keep the revenue produced by selling the block of seats (Park, 1997; Park & Cho, 1997, Park & Zhang, 1998; Li, 2000; Fan et al., 2001;
Gudmundsson & Rhoades, 2001; Morrish & Hamilton, 2002; Weber, 2005; Ito & Lee, 2007; de Man, Roijakkers & de Graauw, 2010;Janawade, 2012).
Also, all three alliances offer lounge access to their customers. Star Alliance with over 1000 lounges worldwide offers the most compared to Oneworld with around 650 and
SkyTeam with around 670 (Oneworld, 2017; SkyTeam, 2017; Star Alliance, 2017). Besides shared lounge access, all three alliances work with standardized luggage regulations that make luggage transfer in case of customer stopover efficient and easy to manage.
How the multilateral airline alliances manage such interdependent networks will be elaborated on in the following paragraph.
Alliance Route Networks
As mentioned earlier, airline alliance members take over specific geographic areas and so complement each other in reaching their objective of covering more countries and cities worldwide than their competitors could possibly cover without the joint resources of an alliance (Nedvědová, 2014). This represents an organizational challenge and alliances manage it by switching from point-to-point connections to a hub-and-spoke coordination. This means that instead of connecting medium airports directly, they are indirectly connected via bigger hub airports. Thereby, they are able to reduce costs and create more efficient fleet utilization for each alliance members by coordinating small aircrafts for the shorter spoke routes and larger aircrafts for the connections of hubs (Reynolds-Feighan, 2001; Burghouwt & de Wit, 2003; Carroll, 2017). Hub-and-spoke coordination includes the spatial and temporal
components of airlines’ flight offerings and so makes well coordinated travels served by multiple airlinespossible (Nedvědová, 2014). In an airline alliance, every member airline has one or a few hubs via which they operate. It should be noted that these hubs never overlap within an alliance, as this would not offer additional value to an alliance. Table 1offers an overview of each European airline’s hubs and therewith the collection of European hubs within each of the three main multilateral alliances. This shows that alliance members complement each other and make integrated coordination of each of their flight schedules possible.
Table 1
Overview of European Airlines, their Alliance Affiliation and their Hubs/Bases
Alliance Airline Hubs/Bases
Oneworld
Air Berlin Berlin, Düsseldorf
British Airways London
Finnair Helsinki
Iberia Madrid
SkyTeam
Air Europa Madrid
Air France Paris
Alitalia Rome, Milan
KLM Amsterdam
Star Alliance
Aegean Airlines Athens, Larnaca, Thessaloniki
Austrian Airlines Vienna
Lufthansa Frankfurt, Munich
Scandinavian Airlines Copenhagen, Oslo, Stockholm
Swiss Zurich, Geneva, EuroAirport (Basel, Mulhouse,
No Alliance Affiliation
Air Lingus Dublin
Condor Frankfurt, Munich
Easyjet Gatwick, London Luton
Flybe Birmingham, Manchester
Monarch London Luton, Birmingham, Leeds, Manchester
Norwegian Helsinki, London, Oslo
Ryanair London-Stansted, Dublin
Transavia Amsterdam, Eindhoven, Rotterdam The Hague
Virgin Atlantic Airways London Heathrow/Gatwick, Manchester
Vueling Barcelona, Rome
Furthermore, it gives way to operate with so-called wave-systems. Wave-systems include the coordination of spatial and temporal components, which means that all incoming flights are connected to all outgoing flights via a hub airport (Nedvědová, 2014). Therewith, airlines work more efficiently and can manage higher amounts of passengers in less time and so reduce costs (Button, 2002). Single airlines without alliance affiliation are able to work by hub-and-spoke as well but will by themselves not be able to cover the same amount as alliances with several hubs can. Also the goal of point-to-point connections lies more in picking the most profitable routes rather than trying to cover a lot of geographic area. Instead, this is what they leave for multilateral airline alliances, as they differentiate themselves over a wide network.
The three main multilateral alliances Oneworld, SkyTeam and Star Alliance are already successful in providing well-coordinated international indirect flights (Wang, 2014;
Janawade, Bertrand, Léo & Philippe, 2015). For instance, within Oneworld, the European airlines are Air Berlin, British Airways, Finnair and Iberia. All four of these airlines operate via hubs and their main hubs can be found within their country of origin (Oneworld, 2017). Oneworld’s European airlines’ hubs complement each other in the sense that they make it possible for customers to travel from point A to point B well coordinated and without long layovers. Thereby, each airline can be seen as a specialist for a specific geographic region and so connects almost every part of Europe within one alliance (Oneworld, 2017b). But not only Oneworld has mastered this complementary route network. In fact, SkyTeam as well as Star Alliance show similar coverage and just by the number of European airlines that cover routes within and also outside of Europe Star Alliance with seven European carriers should be able to offer the most frequent routes compared SkyTeam as well as Oneworld with only four European carriers (Oneworld, 2017; SkyTeam, 2017; Star Alliance, 2017).
also bring “diseconomies in terms of excessive peaking or extended aircraft turn-around and connection times” (Dennis, 2000, p.75).
Nevertheless, alliances’ route networks demonstrate their biggest competitive advantage if it is managed well and outperforms rival alliances in terms of routes and flight frequency offered. This takes competition within the airline industry from airline level up to alliance level.
No Multilateral Alliance Affiliation
Customers’ Perceived Benefits
The benefits that customers experience through code-sharing agreements as well as the general coordination of airlines within multilateral alliances are wider and better-managed route networks and the therewith-seamless transfers, check-ins and FFP reciprocity (Dennis, 2000; Weber, 2005). Customers are not aware of all of these benefits to the same extent. FFP reciprocity and a wider route network is something that Goh and Uncles (2003) find
customers to be most aware of and trace it back to the promotional efforts of multilateral airline alliances. Code-sharing seems to be charged with opacity when it comes to the benefits customers perceive to originate from it. Customers rather perceive it as unpleasant to fly with an airline they did not book a seat with without being informed about it previously.
Furthermore, customers were not able to find great differences in the benefits that the main alliances Star Alliance and Oneworld offered. This lead to researchers questioning whether FFPs are effective in installing a loyal customer base (Goh & Uncles, 2003). Nevertheless, Weber (2005) finds that not all customers attach the same importance to the same benefits. For example, Asian customers find convenience to be more important than the possibility to collect frequent flyer miles relative to customers from other regions.
How customers perceive an airline is further influenced by its brand and alliance affiliation has definitely a big impact on its image. Wang (2014), for instance, found that an airlines’ revenue as well as passenger volume increased right after joining one of the three multilateral airline alliances. This shows that brand perception is an important factor that should be considered by airlines that want to stay competitive in this highly dynamic industry. An alliance’s brand is further crucial for an individual member as it gets endorsed also
through other alliance members and thus can have a substantially bigger bearing than one airline’s brand by itself (Weber, 2003; Wang, 2014). Thereby, scholars have classified alliance membership as part of the business strategy of carriers that should help them to differentiate themselves from low-cost airlines “in terms of the quality of the service
provided” (Tiernan, Rhoades & Waguespack, 2008; Wang, 2014, p.54). Janawade, Bertrand, Léo and Philippe (2015) trace this back to these so-called ‘meta-services’ being the most innovative way of offering transportation services.
performance differences across the found strategic groups as well as across alliances and also compared to carriers that are not affiliated with an alliance.
Strategic Groups
Strategic group membership stems from similar strategic views and from how firms are affected by external influences that come from their dynamic environment (Porter, 1979). Therewith, firms can be grouped into several clusters that all together describe an industry’s structure and thereby help to understand the prevailing competition (McGee & Thomas, 1986).Strategic groups are mostly identified by cluster analyses, which have been missing significance testing up until now (Carroll, 2017). This missing support of strategic groups’ existence has been the reason why scholars have been criticizing the construct and labeled it solely an invention of algorithms (Hatten & Hatten, 1987). This criticism is also a reason for the declining interest in strategic group research (Cattini, Porac & Thomas, 2017).
Nevertheless, research has shown that strategic groups can offer an explanation of performance differences of firms from one and the same industry (Porter, 1979; McGee & Thomas, 1986; Fiegenbaum & Thomas, 1990). Thereupon, Dranove et al. (1998) say that strategic groups exist if group-level characteristics influence the individual firms
independently of firm- or industry-level effects. These group-level effects are said to be a byproduct of firms’ interactions within the strategic group and in order to preserve these effects and the strategic group itself over time it is necessary to have robust mobility barriers (Dranove et al., 1998). Porter (1980) has also emphasized that mobility barriers are necessary to perpetuate financial performance differences among strategic groups as this protects high performing strategic groups from outsiders.These mobility barriers can be defined in three different subgroups: (I) market-related strategies, (II) industry supply characteristics and (III) characteristics of firms (McGee & Thomas, 1986).
and also its own level of financial performance (Porter, 1979; Fiegenbaum & Thomas, 1990; Short et al., 2007).
As the European airline industry is already marked by airlines that can be grouped according to their multilateral strategic alliance affiliation, which offer monetary benefits in form of cost reduction (Iatrou & Alamdari, 2005; Min & Joo, 2016) as well as their business model types (Daft & Albers, 2015), the industry is expected to be divided into groups that follow similar strategies. This leads to the following hypothesis.
Hypothesis 1: There are significant strategic groups within the airline industry.
In general, only because there are statistically significant strategic groups it does not entail that there are also statistically significant performance differences across these groups. This is mainly due to the fact that even though there are distinct groups that each follow distinct business strategies it does not give any indication on how efficient and effective these strategies are implemented by group members. Hence, even though members of a group have similar strategic orientations this does not entail that they all must have the same level of performance (Cool & Schendel, 1988). Still, performance differences can occur across strategic groups but not necessarily because of strategic group affiliation. There are plenty of reasons for performance differences (e.g. market regulations, inflation and dynamic industrial environments) that cannot be fully incorporated in the analysis of clusters.
Nevertheless, because alliances offer the previously derived competitive advantages at least compared to airlines without alliance affiliation, there should be significant performance differences across strategic groups. Furthermore, because no performance variables are used within the cluster analysis, the found clusters will be free of the bias that would be caused through including performance variables in the cluster analysis. Hence, reliable and significant performance differences are predicted. On this reasoning I build the following hypothesis.
Hypothesis 2: There are financial performance differences across strategic groups.
Multilateral Strategic Airline Alliances
there is not inevitably any form of direct interaction between group members (Hatten & Hatten, 1987). Thus, multilateral alliances that are made up solely by members of the same industry and which include unavoidable interaction between alliance members are a fitting complement for the concept of strategic groups. Furthermore, because these alliances are all composed of firms that are all active within the same industry they are necessarily
competitors. Such alliances in the airline industry can be defined as “any collaborative arrangement between two or more carriers involving joint operations with the declared
intention of improving competitiveness and thereby enhancing overall performance” (Morrish & Hamilton, 2002, p.401). This kind of interaction can be termed coopetition (Nalebuff, Brandenburger & Maulana, 1996; Czakon & Dana, 2013) and offers the question why firms would partner with their direct competitors. With the ever-increasing competition in the global business environment firms are having a hard time securing competitive advantages on their own. Hence, they form strategic alliances to create superior value through the joint usage of resources (Dyer & Singh, 1998; Das, 2006; Min & Joo, 2016). In particular, the
cooperation between several partners offers competitive advantages through investments in relation-specific assets, through knowledge exchange, through the combination of
complementary resources and through lower transaction costs because of more efficient governance (Dyer & Singh, 1998). Hereby, such an alliance network entails opportunities to create, capture and appropriate more value than if a company would work by itself (Czakon & Dana, 2013). Especially in a coopetition setting where alliances are formed by competitors, the strategic orientation is set on creating higher rents appropriating as much as possible from them (Okura, 2007; Czakon & Dana, 2013), reducing costs (Button, 2002; Min & Joo, 2016) and, especially in the airline industry, covering as much geographic space as possible
(Nedvědová, 2014).
the routes they offer into the alliance. Having these resources overlap, meaning that all alliance members would offer the same routes and operate via the same hubs, would not yield any benefits. Rather, it would make alliance formation redundant.As the analysis is twofold and the data on each airline’s routes and hubs will only be included in the next step, these complementary resources can be excluded from the deviation of the following hypothesis. Building on this line of reasoning the following hypothesis is formulated.
Hypothesis 3: Multilateral strategic airline alliances are comprised of airlines that belong to the same strategic group.
Furthermore, because alliance membership can be seen as a key strategic move that should offer competitive advantages in form of joint marketing (Tiernan et al., 2008; Wang, 2014), it is anticipated that this yields performance differences at least between the group of alliances and the group of airlines that are not affiliated with an alliance. Additionally, alliance memberships also offers monetary benefits in terms of cost reduction and increase in efficiency and thus represent a competitive advantage compared to airlines without alliance affiliation (Gudmundsson & Rhoades, 2001; Morrish & Hamilton, 2002). Still, alliance might differ in their performance as the ability to efficiently internalize a strategy differs from airline to airline (Cool & Schendel, 1988). Another factor that might influence alliances’ performance is their route network and therewith especially the number of flights per day, the efficient utilization of aircrafts and flight coordination via hubs at preferably primary airports (Min & Joo, 2016). On the one hand, by offering such geographic coverage alliances might outperform competitors without alliance affiliation in terms of customer benefits offered. On the other hand, airlines that work with point-to-point systems and focus on the more profitable routes might be more profitable in general compared to alliances that try to cover all routes, even the less profitable ones. Of course, single airlines are not able to compete as easily on service relative to airline alliance members and thus might have to compete on price, which would cut their profit margin and could lead to a less profitable performance. Besides these factors, alliances’ costs can also increase through administrative costs that airlines without alliance affiliation do not have to the same degree as they have to coordinate a much smaller number or airlines, routes and flights.
that they should at least perform significantly different. Derived from these arguments the following hypotheses are formulated.
Hypothesis 4a: There are financial performance differences between multilateral strategic alliances.
Hypothesis 4b: There are financial performance differences between multilateral strategic alliances and the group of airlines that are without alliance affiliation
Methodology Data and Sample
The data used in this analysis stems from a dataset published by Daft and Albers (2015). It contains information about 26 European airlines and their business model configurations, which consist of items on corporate core logic, on the configuration of the corporate core logic and on the companies’ specific assets. As this study wants to identify the performance difference that follows each company’s conduct, not all the variables are practical for this analysis. To identify variables that are most significantly related to each company’s conduct within the European airline industry a factor analysis was performed, which lead to nine independent variables being used in the remainder of the analysis. Table 2 shows an overview of the nine variables, which were selected from the existent dataset. All of these variables were z-transformed to standardize the dataset and represent the independent variables in this study.
Table 2
Independent variables taken from Daft and Albers (2015)
No. Item Explanation Scale/Measure
1 Hub
Flights can be coordinated via hub or just point-to-point. The variable from Daft and Albers (2015) is transformed into a dummy variable
Point-to-point = 0, Hub-and-spoke = 1
2 Utilization Average usage of fleet Average flight hours per aircraft per day 3 Routes Routes that one airline offers
worldwide Number of routes offered
4 In-Flight
Entertainment (IFE)
Music, video and internet supply for customers
[no IFE, shared music supply, shared video supply, internet on own device, shared video and internet on own device, individual IFE,
individual IFE with internet]
6 Brand Brand presentation ranging from conventional to extravagant
[Conventional = 1, rather conventional = 2, rather extravagant = 3, extravagant = 4] 7 Fleet Uniformity Fleet uniformity at airline level Hirschman-Herfindahl-Index (HHI) of aircraft
families
8 Fleet age Average age of fleet In years
9 Primary Airport Access Primary airports in Europe Percentage of flights at primary airports
To account for the dependent variable financial performance, prior literature (Faems, De Visser, Andries & Van Looy, 2010; Jiang, Tao & Santoro, 2010) suggests profit margin as a valuable measure. Profit margin is defined as the percentage of net profit (loss) divided by revenue. As Carroll (2017) has already provided profit margins for 22 of the 26 companies, Orbis database was used to acquire missing data. Only for three of the missing four firms have I been able to find data. Thus, the dataset will be comprised of 25 European airlines in total.
Analysis
To identify strategic groups within the dataset a hierarchical cluster analysis was conducted using the statistic software SPSS and the Wards method with syquared Euclidean distance. The clusters’ significance is reassured by conducting a permutation test as well as a Monte Carlo test with the two using 999 iterations. Both tests create a null distribution for the clustering statistics. The permutation test does so by randomly shuffling the variables while preserving all data characteristics and ensuring a reduction to the level associated with
of the Monte Carlo test lies in the possible overlooking of peculiarities within the data that could influence the clustering statistics. Similarly, as the Monte Carlo test repealed the weaknesses of the permutation test, the permutation test repeals the peculiarity weakness of the Monte Carlo test by working solely with the original data (Carroll, 2017). Therefore, these tests complement each other nicely and are thus the right choice in this type of analysis to ensure that the found clusters are not merely a statistical occurrence but rather actual groups where members can be identified according to similar strategic orientations (Carroll, 2017). After distinguishing clusters, the next step in the analysis was conducting the (M)ANOVAS, which included the strategic groups that originated from the cluster analysis and the financial performance variable profit margin. Firstly, a one-way ANOVA was conducted for each of the cluster solutions that were found significant in the permutation test with the dependent variable profit margin, to see which of the cluster solutions are significant with regard to their financial performance differences across strategic groups. The reasoning behind going further with the results from the permutation test will be described in the results section. Secondly, the significant cluster solutions were analyzed using a MANOVA in combination with the dependent variables profit margin to check for performance differences across the significant cluster solutions.
After the cluster analysis and the (M)ANOVAs have been conducted the outcome is compared with the publicly available data of each airline alliance (i.e. Star Alliance, SkyTeam and Oneworld) as well as the airlines’ individual websites to see which members of the same alliance can be found within the same strategic group and which can be found in different strategic groups. Once strategic group members were matched with alliances a one-way ANOVA is conducted to see whether there are significant performance differences between alliances with regard to profit margin. Another one-way ANOVA is run to check for
performance differences not only between alliances but also with the group of airlines that have no alliance affiliation. Again, this is done by using profit margin as dependent variable.
What results these analyses produced will be described in detail in the following section of this paper.
Findings and Results
the cluster solutions two to five were found to be significant on a 5%-level by the permutation test. Cluster solutions six to eight can be seen as marginally significant on a 10%-level. Table 3
P-values of permutation test and Monte Carlo test with 12 Cluster Solutions
No. of Cluster Solution Permutation Test
(1-tailed) MC test (2-tailed) 1 1.000 0.480 2 0.030 0.042 3 0.012 0.018 4 0.029 0.010 5 0.044 0.010 6 0.059 0.008 7 0.051 0.010 8 0.072 0.012 9 0.109 0.010 10 0.144 0.010 11 0.135 0.018 12 0.090 0.046
The permutation test shows significant results for several reasons. One of them is correlation between the variables that were used in the analysis. These correlations can be found in Table 4.
Table 4
Pearson Correlation of Dependent and Independent Variables
1 2 3 4 5 6 7 8 9 10 Profit Margin 1.000 Utilization 0.204 1.000 Routes 0.432* 0.235 1.000 IFE -0.164 -0.112 -0.406* 1.000 Self-Check-in 0.180 0.255 0.405* -0.261 1.000 Brand 0.125 0.104 -0.094 -0.028 -0.301 1.000 Fleet Age -0.287 -0.020 -0.202 -0.066 -0.142 -0.239 1.000 Fleet Uniformity 0.283 -0.099 0.194 -0.138 0.014 0.451* -0.504* 1.000
Primary Airport Access -0.096 0.000 -0.169 0.029 -0.052 -0.416* 0.280 -0.290 1.000
Hub -0.152 -0.020 -0.038 -0.011 0.095 -0.529** 0.259 -0.431* 0.282 1.000
** p < 0.05, * p < 0.10
uncorrelated set of variables it can be said that this test is free from the bias of correlating variables. Hence, this multi-method approach ensures that cluster can be identified even if they are very close to each other (Carroll, 2017).
The results of the permutation test show that not all cluster solutions are significant but that cluster solutions 2 to 5 can be derived as significant on the 5%-level. In the case of cluster solution 2, this implies that the European airline industry can be categorized in two groups, namely low cost (LC) carriers and full service (FS) carriers. Cluster solution 3 offers a more detailed categorization in which the group of LC carriers is split into pure LC carriers and mainly LC carriers but also next to the third cluster of FS carriers. Cluster solution 4 divides the industry into mainly LC carriers, pure LC carriers FS carriers and additionally small FS carriers. In cluster solution 5, the industry is divided into the same strategic groups as in cluster solution 4. Only small FS carriers are represented by two strategic groups, which lead to a total amount of five strategic groups in this cluster solution. Additionally, it has to be noted that, even though cluster solutions 6-8 and 12 show significant results on the 10%-level, a categorization into more than five strategic groups within the industry does not equal a meaningful separation as only five different business model categories exist (Daft & Albers, 2015; Carroll, 2017). In sum, based on the significant cluster solutions, it can be concluded that there is support for hypothesis 1.
Build upon the findings of hypothesis 1, one-way ANOVAs were run with each of the four cluster solutions (i.e. cluster solutions 2-5) and combined with the dependent variable profit margin to see whether there are performance differences across strategic groups of each cluster solution. The results shown in Table 5 offer an overview of the outcome. Thereupon, it became apparent that only cluster solution 3 shows significant differences, whereas cluster solution 2, 4 and 5 show no significant differences. In particular, cluster solution 3 shows significant performance differences on a 10%-level for the dependent variable profit margin. Thus, hypothesis 2 is partially supported.
Table 5
One-Way ANOVAs with Cluster Solutions 2-5 and the Dependent Variable Profit Margin
Cluster Solution 2 Cluster Solution 3 Cluster Solution 4 Cluster Solution 5 n Strategic Group 1 8 5 5 5 n Strategic Group 2 17 17 10 4 n Strategic Group 3 3 3 6 n Strategic Group 4 7 3 n Strategic Group 5 7 Σ n 25 25 25 25
p-Value for Profit Margin 0.101 0.091 0.195 0.310
To test hypothesis 3 the analysis was continued with cluster solution 3 for the following reasons: (1) it offers the lowest p-values regarding the permutation test, (2) significant
performance differences were found for the dependent variable profit margin and (3) the separation into the carrier categories FS carrier, pure LC carrier and mainly LC carrier draws the most accurate picture of todays European airline industry.
The found clusters of cluster solution 3 are shown in a scatter plot, which can be found in Graph 3. It shows that cluster 1’s group centroid, which is comprised of mainly LC
carriers, is closer to the FS carriers categorized in cluster 2’s group centroid compared to group centroid of cluster 3, which stands for pure LC carries. Further, both, cluster 1 and 2’s centroids, are approximately the same distance away from the centroid of cluster 3.
Graph 3. Scatter plot of the three-cluster solution using canonical discriminant functions
cluster 1 represents mainly LC whereas cluster 2 shows FS carriers and cluster 3 contains pure LC carriers. It is noteworthy to point out the differences in the number of strategic group members: cluster 2, which represents the FS carriers, contains 17 airlines, the mainly LC carriers in cluster 1 are represented by five airlines and the last cluster, cluster 3 consists only of three members that can be categorized according to the pure LC business model.
According to the alliance networks, it is observable that, except for Air Berlin as outlier, all multilateral alliances were composed of airlines that can be found within the same strategic group, which is categorized by FS carriers. In other words, the found strategic group
affiliations of Star Alliance’s as well as SkyTeam’s member airlines offer support for hypothesis 3. As there is one outlier (i.e. Air Berlin), who represents 25% of Oneworld’s European member airlines, hypothesis 3 is only partially supported.
Further, it is interesting to see that airlines that are not affiliated with an alliance are split up in all of the three different clusters.
Table 6
Cluster Solution Three and each Cluster’s corresponding Airlines sorted by Alliances
Alliance Cluster 1 Cluster 2 Cluster 3
SkyTeam Air Europa
Alitalia Air France
KLM
Oneworld Finnair Air Berlin
British Airways Iberia
Star Alliance Aegean Airlines
Turkish Airlines TAP Portugal Scandinavian Airlines Lufthansa SWISS Austrian
No Alliance Transaviaa Condora Ryanair
Flybe Virgin Atlantic Airwaysa Easyjet
Monarch Air Lingusa
Norwegian Vuelinga
a = Airlines that are not part of either of the alliance but are partner with an airline that is member of an alliance.
Table 7
One-Way ANOVAs with Alliance Networks/Airlines without Alliance Affiliation and the Dependent Variable Profit Margin
Mean Profit Margin p-Value
Oneworld - 4.2900
SkyTeam - 4.1950
Star Alliance 2.5943
Airlines without Alliance Affiliation 2.2850
Between Alliance Networks 0.066
Between Alliance Networks & Airlines without Affiliation 0.068
Table 7 shows, that there are significant performance differences across alliance networks as well as across the group of European airlines without alliance affiliation with regard to the airlines’ profit margin. Thereby, the results offer support for hypotheses 4a and 4b. Furthermore, it becomes apparent that two of the alliances, namely Oneworld and SkyTeam, appear to be rather similar in terms of all of their European members average profit margin. What stands out is, that Star Alliance and the group of airlines without alliance affiliation also perform quite similar with regard to their group members’ profit margin.
Overall, the results show that strategic groups exist within the European airline industry. Moreover, the findings show that these strategic groups perform significantly different. In addition, it can be said that members of certain alliance networks are likely to be positioned within the same strategic group, whereas airlines with no alliance affiliation are spread across several strategic groups. Finally, it becomes visible that significant performance differences exist between alliance networks as well as between alliance networks and airlines with no alliance affiliation.
Discussion and Conclusion
Even though the performance influence of alliance membership on airlines’
European airlines. The produced significant cluster solutions were further analyzed in terms of their performance differences where only the 3-cluster solutionyielded marginally
significant differences. Thus, cluster solution 3 was used for the remainder of the analyses and illustrated the industry split up in three strategic groups, namely: mainly LC carriers, FS carriers and pure LC carriers. Almost all members of the main multilateral airline alliances were found within the group of FS carriers but still there were significant performance differences found across alliance networks as well as across the group of airlines without alliance affiliation, which was represent in all three of the strategic groups.
Thereby, this study contributes on the one hand to literature on the airline industry and especially on its alliances. On the other hand it also contributes to strategic group literature as it drives the significance testing of strategic groups further by making use of the permutation test as well as the Monte Carlo test and thus shows that strategic groups are not merely an invention of algorithms like scholars have thought in the past (Hatten & Hatten, 1987). For instance, Dranove et al. (1998) believed that differences in firms’ conduct, interaction between firms and therewith true group-effects were necessary for strategic groups to exist. Additionally, they said that true group-effects exist if group characteristics influence the firm’s “performance independently of firm-level and industry-level effects” (p.1029). Further, to perpetuate strategic groups, mobility barriers were seen as a necessity.
permutation test. Hence, it can be concluded that actual strategic groups can be found within the European airline industry as predicted.
In case strategic groups were found within the industry it was hypothesized that there would also be significant performance differences across strategic groups. Performance differences were not expected because of significant strategic groups per se. This is because performance differences can generally not be traced back to strategic group membership as there might be performance differences but there might also be no performance differences (Carroll, 2017). Rather, if strategic groups show performance differences it is more likely that these can be traced back to monetary benefits that alliance membership offers through shared resources (e.g. facilities, marketing and maintenance) (Button, 2002; Iatrou & Alamdari, 2005; Wang, 2014; Min & Joo, 2016). Specifically, because a large number of carriers within the European airline industry are affiliated with an alliance and can thus tap into these
benefits, performance differences were expected. Just like performance increases were observable right after airlines joined a multilateral alliance (Wang, 2014), more long-lasting beneficial effects were anticipated. Hence, the found marginally significant performance differences in terms of each strategic groups’ average profit margin could be explained by the alliance affiliation and more specifically also because of the different strategic group
compositions. In detail, the number of carriers found within each of the groups differed with 5 in the first, 17 in the second and 3 in the third. The five group members of the first group all showed the characteristics of mainly LC carriers, the second and also biggest group
comprised FS carriers and the third group, with just three members displayed the group of pure LC carriers. Therefore, performance differences can also be traced back to the differing size of each strategic group and to the biggest group being the one that includes all alliance members except for one outlier (i.e. Air Berlin). This is in line with Porter’s (1979)
affected by exogenous factors on industry level such as changes in oil prices, new market entries and the deregulation of the European airlines market (Burghouwt & Huys, 2012; Klophaus & Fichert, 2016; Carroll, 2017).
Returning to table 6, another perspective on the three-cluster solution can be taken from a business model point of view. Daft and Albers (2015) found a converging trend of airlines’ business models and hybridization was also found by Klophaus, Conrady and Fichert (2012). This entails that airlines that were originally categorized as pure LC carriers adopt more and more features of FS carriers like for example FFP, coordination via hubs and global
distribution systems of tickets (Klophaus & Fichert, 2016). In this study, a convergent trend of European airlines’ business models is recognizable in separation of the industry’s strategic groups. The smallest of all groups with only three members represents pure LC carriers. An increase can be seen in the number of mainly LC carriers in the second biggest strategic group. Here, airlines have the basic concept of pure LC carriers but then also offer additional service features to not only compete on price. The third, and therewith biggest cluster
represents airlines with a FS carrier business model. Hence, the findings that most airlines show similar strategic orientations and the consequently similar business models are in line with the growing similarity that Daft and Albers (2015) could observe in their findings.
Furthermore, the finding that almost all European alliance members, no matter if Oneworld, SkyTeam or Star Alliance, are found in the same strategic group shows that alliances generally offer the same. In particular, all alliance members have hubs via which they operate, all alliance members offer a joint FFP and coordinate their route network jointly through code-sharing between all alliance members. By taking a closer look on their
constellations it becomes apparent that they do, in fact, all follow the same business strategies but that their route offerings show explicit differences. The hubs of each alliance member do not overlap with any of the other alliance members from the same alliance but also almost never with the hubs of airlines that belong to another alliance (see Table 1). Therewith, alliances ensure that they compete by offering wide networks but that they secure competitive advantages by having hubs in different locations and preferably primary airports. More specifically, table 1 shows that Star Alliances with the most European carriers consequently also has the most hubs spread over Europe and thus offers a denser network than the
competing alliances Oneworld and SkyTeam.
Even though almost all alliance members were also identified as FS carriers and therewith as members of the same strategic group, performance differences were
Mainly because of the differing sizes of alliances compared to each other and also to the group of airlines without alliance affiliation, because of the increasing amount of market share that alliances took away from other competing airlines by increasing their passenger volumes and flight frequencies (Janawade, 2012) and because alliances increased airlines’ efficiency and thus have the possibility to offer lower fares and better services (Oum & Park, 1997; Morrish & Hamilton, 2002). The expected performance differences were confirmed in the analysis. Nevertheless, they do not paint the distinct picture of alliances performing better than the group of airlines without alliance affiliation. Rather, performance differences across the different groups of airlines were significant but not in the sense that alliances performed on average better compared to the group of airlines without alliance affiliation. However, Star Alliance with the most airline members and hence the widest route network was found to perform best according to the average profit margin of its members. As this alliance is the oldest of the three it might be that it has the ability to manage its processes more efficiently than other alliances and so produces economies of scale (Sørensen & Stuart, 2000).When comparing Oneworld’s average performance with SkyTeam’s average performance not much of a big difference is observable. This might be traced back to the number of carriers their alliances contain and thus their ability to spread their network all over Europe. With only four European carriers compared to Star Alliance’s seven it is harder to stay competitive for Oneworld and SkyTeam.
In addition, within Star Alliance, Lufthansa and Turkish Airlines provide the largest portion of the alliance’s capacity when looking at its European member airlines. Just as Lazzarini (2007) has found that these types of member airlines profit most from an alliance it is observable that both airlines also perform superior according to their profit margin and their alliance’s average profit margin. In the case of Oneworld and SkyTeam this kind of
correlation between contribution and benefit capturing could not be observed. This might be because of the low amount of smaller carriers that provide additional capacity to their alliance.
The comparatively good average performance of the group of airlines without alliance affiliation might be traced back to the outliers who are represented by Ryanair with an
coordinate its flight network via hubs but uses point-to-point connections and therewith serves shorter routes without coordinated stopovers. Hence, it does not need a high variety of
different aircrafts and so has the ability to work more efficient. Also, their average fleet age is comparatively low as they lease most of their fleet (Carroll, 2017).Furthermore, the airline does not go into the direction of a hybrid, as they do not offer any additional services to their customers for free and mainly sell their tickets through online channels. Thereby, Ryanair offers the rawest form of transportation and shows that the hybridization of airlines might not be the right path to profitability for single airlines. These findings go in line with Barbot, Costa and Sochirca (2008) research that showed higher levels of efficiency for LC carriers. Moreover, Min and Joo (2016) concluded that airlines without alliance affiliation generally did not perform worse than airlines, which are member of an alliance, and further, that airlines performances did not increase after they joined an alliance.
Overall, it can be concluded, that alliance membership might yield performance differences but it is not certain what kind of differences and depends on many different factors on firm level, alliance or group level as well as on industry level. This shows again, that there is high competition in a market that shows not much room for profitability (Czipura & Jolly, 2007). Looking at the individual airline level, the alliance level and the strategic group level as well as on the overall competition within the European airline industry, a system theory point of view is chosen in this paper. Short et al. (2007) describes all these levels as interdependent and thus as having an influence on firms’ performances. This can be supported by the current study, as there were significant performance differences across strategic groups as well as across airline alliance networks and the group of airlines without alliance affiliation.
From a managerial point of view, the findings of this study imply that joining an alliance can be beneficial but does not necessarily have to be. Thus, managers should ensure that they position their airline within the industry in a way that yields the best possible competitive advantages depending on their business model, their assets and the
complementary resources of other airlines. If an airline shows perfectly complementary routes to an alliance’s route network they might increase they number of passengers by joining the alliance. If they do not possess the necessary resources, they might be better off
differentiating themselves via special services or very low fares if possible.
in this analysis. Further limitations can be found in the following and therewith last part of the current paper.
Limitations and Future Research
No research is without limitations. In this particular setting improvements could be made regarding the number of airlines that were considered as the focus was merely on European airlines. Multilateral airline alliances are composed of a number of international airlines, which were not taken into consideration. Therefore, the results’ generalizability is limited to the European market. Not at last because the data used in the analysis is from only 2012 and as the airline industry is a very dynamic one, changes could have occurred over the past five years. More up-to-date data might yield differing results and because of the
dynamism of this industry a repetition of this research could provide helpful insights into the competition in today’s airline industry. Further, research in the direction of a longitudinal study that takes worldwide alliance members into account might also provide a different outlook on the competition and its dynamics within the industry.
Additionally, airlines are not only involved in multilateral alliances but also in bilateral alliances. These were not considered in this research an thus might offer another component that could be included in the analysis of the airline industry and would therewith also draw an even more detailed picture.
Furthermore, airlines offer several other services like for example cargo transportation besides passenger transportation. Their alliance agreements might differ in this regard and therefore the influence of multilateral cargo airline alliances on the competition within the industry might also differ. Further examination of this type of competition could yield interesting results.
In addition, the dataset used in this research did not include all variables available from Daft & Albers and thus only shows partial business model configurations. Future research might seek to fill this gap by including more data in the analysis.
References
Aegean Airlines (2017). Destinations. Retrieved from Airlines https://en.aegeanair.com/discover/destinations/ on 03-06-2017. Airberlin (2017). Destinationen. Retrieved from
https://www.airberlin.com/de/site/landingpages/map.php on 03-06-2017.
Air Europa (2017). FlightMaps. Retrieved from http://ux.fltmaps.com/en on 03-06-2017. Air France (2017). Air France Network. Retrieved from
http://www.airfrance.fr/FR/en/local/guidevoyageur/reseau/reseau_airfrance_airfrance.ht m on 03-06-2017.
Alitalia (2017). Destinations. Alitalia’s Routes and Destinations. Retrieved from http://flights.alitalia.com/en_us/ on 03-06-2017.
Austrian (2017). Destinations. Retrieved from http://os.fltmaps.com/en on 03-06-2017. Barbot, C., Costa, Á., & Sochirca, E. (2008). Airlines performance in the new market context:
A comparative productivity and efficiency analysis. Journal of Air Transport
Management, 14(5), 270-274.
British Airways (2017). Out route network. Retrieved from
https://www.britishairways.com/en-gb/information/flight-information/our-route-network on 03-06-2017.
Button, K. (2002). Debunking some common myths about airport hubs. Journal of air
transport management, 8(3), 177-188.
Burghouwt, G., & de Wit, J. (2005). Temporal configurations of European airline networks. Journal of Air Transport Management, 11(3), 185-198.
Carroll, C. (2017). Significant Clustering: Implications for Strategic Groups Research. Manuscript in preparation.
Cattani, G., Porac, J. F., & Thomas, H. (2017). Categories and competition. Strategic
Management Journal, 38(1), 64-92.
Cool, K. & Schendel, D. (1988). Performance differences among strategic group members.
Strategic Management Journal, 9(3), 207-223.
Czakon, W., & Dana, L. P. (2013). Coopetition at Work: how firms shaped the airline industry. Journal of Social Management/Zeitschrift für Sozialmanagement, 11(2). Czipura, C., & Jolly, D. R. (2007). Global airline alliances: sparking profitability for a
troubled industry. Journal of business Strategy, 28(2), 57-64.
Das, T. K. (2006). Strategic alliance temporalities and partner opportunism. British Journal of
Management, 17(1), 1-21.
de Man, A. P., Roijakkers, N., & de Graauw, H. (2010). Managing dynamics through robust alliance governance structures: The case of KLM and Northwest Airlines. European
Management Journal, 28(3), 171-181.
Dennis, N. (2000). Scheduling issues and network strategies for international airline alliances. Journal of Air Transport Management, 6(2), 75-85.
Dranove, D., Peteraf, M., & Shanley, M. (1998). Do strategic groups exist? An economic framework for analysis. Strategic Management Journal, 1029-1044.
Dyer, J. H., & Singh, H. (1998). The relational view: Cooperative strategy and sources of interorganizational competitive advantage. Academy of management review, 23(4), 660-679.
Evans, N. (2001). Collaborative strategy: an analysis of the changing world of international airline alliances. Tourism management, 22(3), 229-243.
Fan, T., Vigeant-Langlois, L., Geissler, C., Bosler, B., & Wilmking, J. (2001). Evolution of global airline strategic alliance and consolidation in the twenty-first century. Journal of
Air Transport Management, 7(6), 349-360.
Faems, D., De Visser, M., Andries, P., & Van Looy, B. 2010. Technology alliance portfolios and financial performance: value-enhancing and cost-increasing effects of open
innovation. Journal of Product Innovation Management, 27: 785-796.
Fiegenbaum, A., & Thomas, H. (1990). Strategic groups and performance: the US insurance industry, 1970–84. Strategic Management Journal, 11(3), 197-215.
Gayle, P. G. (2008). An empirical analysis of the competitive effects of the Delta/Continental/Northwest code-share alliance. The Journal of Law and
Economics, 51(4), 743-766.
Goh, K., & Uncles, M. (2003). The benefits of airline global alliances: an empirical
assessment of the perceptions of business travelers. Transportation Research Part A:
Policy and Practice, 37(6), 479-497.
Gudmundsson, S. V., & Lechner, C. (2006). Multilateral airline alliances: Balancing strategic constraints and opportunities. Journal of Air Transport Management, 12(3), 153-158. Gudmundsson, S. V., & Rhoades, D. L. (2001). Airline alliance survival analysis: typology,
strategy and duration. Transport Policy, 8(3), 209-218.
Gulati, R. (1995). Does familiarity breed trust? The implications of repeated ties for contractual choice in alliances. Academy of management journal, 38(1), 85-112. Greenberg, P. Why JetBlue will be different. MSNBC, New York, NY.
Hatten, K. J., & Hatten, M. L. (1987). Strategic groups, asymmetrical mobility barriers and contestability. Strategic Management Journal, 8(4), 329-342.
Holtbrügge, D., Wilson, S., & Berg, N. (2006). Human resource management at Star Alliance: pressures for standardization and differentiation. Journal of Air transport
management, 12(6), 306-312.
IATA (2017). India sees Highest Domestic Market Growth in 2015. Retrieved from http://www.iata.org/pressroom/pr/Pages/2016-07-05-01.aspx on 17-06-17.
Iatrou, K., & Alamdari, F. (2005). The empirical analysis of the impact of alliances on airline operations. Journal of Air Transport Management, 11(3), 127-134.
Ito, H., & Lee, D. (2007). Domestic code sharing, alliances, and airfares in the US airline industry. The Journal of Law and Economics, 50(2), 355-380.
Janawade, V. (2012). Consumer perceived value of international networked services: an exploratory study of the case of an airline Alliance. International Business
Research, 6(2), 20.
Janawade, V., Bertrand, D., Léo, P. Y., & Philippe, J. (2015). Assessing ‘meta-services’: customer's perceived value and behaviour. The Service Industries Journal, 35(5), 275-295.
KLM (2017). Destinations. Retrieved from
https://www.klm.com/travel/us_en/destinations/index.htm on 03-06-2017. KLM (2017a). Worldwide locations. Retrieved from
https://www.klm.com/travel/cu_en/prepare_for_travel/at_the_airport/lounges/worldwid e_locations.htm on 04-06-2017.
Klophaus, R., Conrady, R., & Fichert, F. (2012). Low cost carriers going hybrid: Evidence from Europe. Journal of Air Transport Management, 23, 54-58.
Klophaus, R., & Fichert, F. (2016). Serving Transfer Passengers: An Evolving Business for European Low-Cost Carriers. In The 3rd International Aviation Management
Conference 2016 (p. 89).
Lazzarini, S. G. (2007). The impact of membership in competing alliance constellations: Evidence on the operational performance of global airlines. Strategic Management
Journal, 28(4), 345-367.
Li, D., Eden, L., Hitt, M. A., Ireland, R. D., & Garrett, R. P. (2012). Governance in multilateral R&D alliances. Organization Science, 23(4), 1191-1210.
Li, M. Z. (2000). Distinct features of lasting and non-lasting airline alliances. Journal of Air
Transport Management, 6(2), 65-73.
Lin, E. T. (2008). Route-based performance evaluation of Taiwanese domestic airlines using data envelopment analysis: a comment. Transportation Research Part E: Logistics and
Transportation Review, 44(5), 894-899.
Lufthansa (2017). Lufthansa Route Map. Retrieved from
http://www.lufthansa.com/us/en/Route-Map on 03-06-2017.
McGee, J., & Thomas, H. (1986). Strategic groups: theory, research and taxonomy. Strategic
Management Journal, 7(2), 141-160.
Min, H., & Joo, S. J. (2016). A comparative performance analysis of airline strategic alliances using data envelopment analysis. Journal of Air Transport Management, 52, 99-110. Morrish, S. C., & Hamilton, R. T. (2002). Airline alliances—who benefits?. Journal of Air
Transport Management, 8(6), 401-407.
Nohria, N., & Garcia-Pont, C. (1991). Global strategic linkages and industry structure.
Strategic management journal, 12(S1), 105-124.
Okura, M. (2007). Coopetitive strategies of Japanese insurance firms a game-theory approach. International Studies of Management & Organization, 37(2), 53-69. Oneworld (2017). Member Airlines. Retrieved from
https://de.oneworld.com/Mitgliedsfluggesellschaften-3jj/Überblick on 13-03-2017. Oneworld (2017a). Lounge-Zutritt. Retrieved from
https://de.oneworld.com/VFP-3uw/Zutritt-zu-Lounges on 04-06-2017.
Oneworld (2017b). Oneworld Interactive Network Map. Retrieved from
http://onw.innosked.com/(S(lbv5p1z4heftztgk1kbbjzse))/default.aspx?show=MAP on 13-06-17.
Oum, T. H., Park, J. H., Kim, K., & Yu, C. (2004). The effect of horizontal alliances on firm productivity and profitability: evidence from the global airline industry. Journal of
Business Research, 57(8), 844-853.
Oum, T. H., & Park, J. H. (1997). Airline alliances: current status, policy issues, and future directions. Journal of Air Transport Management, 3(3), 133-144.
Oum, T. H., Taylor, A. J., & Zhang, A. (1993). Strategic airline policy in the globalizing airline networks. Transportation Journal, 14-30.
Park, J. H. (1997). The effects of airline alliances on markets and economic
welfare. Transportation Research Part E: Logistics and Transportation Review, 33(3), 181-195.
Park, N. K., & Cho, D. S. (1997). The effect of strategic alliance on performance: A study of international airline industry. Journal of air transport management, 3(3), 155-164. Pels, E. (2001). A note on airline alliances. Journal of Air Transport Management, 7(1), 3-7. Porter, M. E. (1979). The structure within industries and companies' performance. The Review
of Economics and Statistics, 214-227.
Porter, M. E., & Strategy, C. (1980). The Free Press. New York.
Reynolds-Feighan, A. (2001). Traffic distribution in low-cost and full-service carrier networks in the US air transportation market. Journal of Air Transport
Scandinavian Airlines (2017). Destinations. Retrieved from https://www.flysas.com/en/flight-to/ on 03-06-2017.
Short, J. C., Ketchen, D. J., Palmer, T. B., & Hult, G. T. M. (2007). Firm, strategic group, and industry influences on performance. Strategic Management Journal, 28(2), 147-167. SkyTeam (2017). Retrieved from https://www.skyteam.com/de on 13-03-2017.
SkyTeam (2017a). Route Map. Retrieved from
https://applications.skyteam.com/routemapper/?_ga=2.1058229.588640719.149735046 9-1047000364.1496405482#/destinations/FCO on 13-06-17.
Smith, K. G., Grimm, C. M., Wally, S., & Young, G. (1997). Strategic groups and rivalrous firm behavior: Towards a reconciliation. Strategic Management Journal, 149-157. Sørensen, J. B., & Stuart, T. E. (2000). Aging, obsolescence, and organizational
innovation. Administrative science quarterly, 45(1), 81-112. Star Alliance (2017). Star Alliance Member Airlines. Retrieved from
http://www.staralliance.com/en/member-airlines on 13-03-2017. Star Alliance (2017a). Airport Lounges. Retrieved from
http://www.staralliance.com/en/lounges on 04-06-2017.
Star Alliance (2017b). Map of Destinations. Retrieved from http://star-alliance.innosked.com on 13-06-17.
SWISS (2017). Streckennetz. Retrieved from https://www.swiss.com/za/de/buchen/wohin-wir-fliegen/streckennetz.html on 03-06-2017.
TAP Portugal (2017). Our destinations. Retrieved from https://www.flytap.com/en-nl/destinations/destinations-map on 03-06-2017.
Tiernan, S., Rhoades, D., & Waguespack, B. (2008). Airline alliance service quality performance—An analysis of US and EU member airlines. Journal of Air Transport
Management, 14(2), 99-102.
Topaloglu, H. (2012). A duality based approach for network revenue management in airline alliances. Journal of Revenue and pricing management, 11(5), 500-517.
Turkish Airlines (2017). Flight destinations. Retrieved from
http://www.turkishairlines.com/en-int/flights-tickets/flights-destinations/?departure=istanbul&continent=all&class=economy on 03-06-2017. Weber, K. (2003). Consumer perceptions and behavior: neglected dimensions in research on
strategic airline alliances. Journal of Travel & Tourism Marketing, 13(4), 27-45. Weber, K. (2005). Travelers’ perceptions of airline alliance benefits and performance.
Journal of Travel Research, 43(3), 257-265.
