The aim of this research was to explore the aspects of airlines’ marketing performance in relation with their membership with global alliances, being either Star Alliance, Skyteam or Oneworld. This study emphasizes on consumer perception and brand value to conceptualize and measure brand equity in this particular field of study.

2. 1
ABSTRACT
Global Airline Alliances are three distinctive entities formed by more than sixty airlines,
improving their network and offering enhanced services to customers as main drivers.
Nowadays, member airlines dominate the market by transporting two-thirds of the international
traffic. The alliances footprint is now recognized by frequent travellers due to a substantial work
on brand recognition of its pretended added value to the end user. However, the dynamics and
quantified benefits to the member airlines remain unclear although numerous research papers
discussed the topic using different angles. Assessing the influence of alliances requires the
analysis of multiple dimensions. The specific issue of the marketing performance, one of the
key elements of member airlines viability has attracted the attention of the researcher.
In this dissertation, we provide an overview of the airline industry with specifically presenting
the Global Airline Alliances network and its evolution. The literature review discusses the
research carried out on airline alliances since their formation twenty five years ago with a focus
on the marketing performance aspects. The empirical research uses a target group of frequent-
flyer passengers. This study aims at identifying and analysing the marketing aspects by using
consumer perceived attributes and brand values as a tool of measurement.
Results concluded that although customers recognize the improvement of the core services and
values often marketed by the alliances, it lacks substantial acceptance of other related services
which would help improve the marketing performance of member airlines.
ACKNOWLEDGMENTS
First of all, I would like to express my gratitude to my tutor, Mr Jean-Yves Saulquin for his
guidance during the semester of the dissertation process. His comments and suggestions also
helped me to find a better approach to the topic studied.
Secondly, I am very grateful to the large number of anonymous forum users who took the time
to respond to my survey, even providing additional comments to help me with the process.
Lastly, I would like to thank my family and relatives for their continued support throughout this
research and more widely my entire scholarship.

3. 2
TABLE OF CONTENTS
ABSTRACT...........................................................................................................................1
ACKNOWLEDGMENTS ......................................................................................................1
TABLE OF CONTENTS........................................................................................................2
LIST OF TABLES .................................................................................................................3
LIST OF FIGURES................................................................................................................4
1. INTRODUCTION..........................................................................................................5
1.1. Research objectives and structure .............................................................................5
1.2. Research limitations .................................................................................................6
2. PRESENTATION OF THE AIRLINE INDUSTRY .......................................................7
2.1. History .....................................................................................................................7
2.2. Business models, markets and current trends ............................................................7
2.3. Future trends ..........................................................................................................10
2.4. Presentation of the Global Airline Alliances ...........................................................11
3. LITERATURE REVIEW..............................................................................................15
3.1. Global airline alliances as a strategy .......................................................................15
3.2. The diverse features of airline performance within alliances ...................................17
3.3. Analysing consumer perception of the alliances......................................................20
4. RESEARCH METHODS..............................................................................................23
4.1. Research aim and problem statement......................................................................23
4.2. Research hypothesis ...............................................................................................23
4.3. Choice of methodology ..........................................................................................25
4.4. Research design......................................................................................................26
4.5. Implementation ......................................................................................................28
5. RESULTS ....................................................................................................................30
5.1. Profile of survey respondents..................................................................................30

4. 3
5.2. Data analysis ..........................................................................................................39
5.3. Hypothesis verification...........................................................................................45
6. CONCLUSION ............................................................................................................47
6.1. Recommendations ..................................................................................................47
6.2. Theoretical contribution..........................................................................................48
6.3. Managerial contribution..........................................................................................48
6.4. Limitations and further research .............................................................................49
REFERENCES.....................................................................................................................50
APPENDIX 1: QUESTIONNAIRE LAYOUT .....................................................................54
APPENDIX 2: COMPUTATIONAL STATISTICS .............................................................66
LIST OF TABLES
Table 1: Ten largest airlines in terms of scheduled passenger carried – Own illustration using
2014 IATA data....................................................................................................................11
Table 2: Alliance member carriers list – Own illustration from April 2016 alliances websites
.............................................................................................................................................12
Table 3: Global alliances main figures - last updated 2013 (Wang, 2014) .............................26
Table 4: Pearson correlation coefficient test on travel classes................................................37

5. 4
LIST OF FIGURES
Figure 1: Star alliance network - Taken from Airline Route Mapper free software – Last updated
June 2014 .............................................................................................................................13
Figure 2: Skyteam network - Taken from Airline Route Mapper free software – Last updated
June 2014 .............................................................................................................................13
Figure 3: Oneworld network - Taken from Airline Route Mapper free software – Last updated
June 2014 .............................................................................................................................14
Figure 4: Aaker’s customer-based brand equity dimensions (Aaker, 1991) ...........................24
Figure 5: Proposed research model - Own illustration...........................................................25
Figure 6: Gender distribution (n=150, results are shown in numbers on the left and in percentage
on the right)..........................................................................................................................30
Figure 7: Age distribution (n=150, results are shown in numbers on the left and in percentage
on the right)..........................................................................................................................31
Figure 8: Country of residence (n=150, results in percentage)...............................................32
Figure 9: Country of residence breakdown by markets (n=150, results in percentage)...........33
Figure 10: Respondents working in the aviation industry (n=150, results are shown in numbers
on the left and in percentage on the right) .............................................................................34
Figure 11: Travel frequency (n=150, results in percentage)...................................................35
Figure 12: Travel frequency breakdown analysis (n=150, results in percentage) ...................36
Figure 13: Travel classes (n=150, results in percentage) .......................................................36
Figure 14: Category of most flights taken (n=150, results in percentage) ..............................38
Figure 15: Knowledge of Global Airline Alliances (n=150, results in percentage) ................38
Figure 16: Frequent-Flyer Program membership (n=150, results in percentage)....................39
Figure 17: Perceived improvement of airline alliances – Likert scale questions (n=150, results
in percentage).......................................................................................................................40
Figure 18: Mean value for Likert scale questions..................................................................40
Figure 19: Perceived values of member airlines - Semantic differential scale questions (n=150,
results in percentage)............................................................................................................43
Figure 20: Mean value for semantic differential questions ....................................................43

6. 5
1. INTRODUCTION
Global Airline Alliances are nowadays key drivers and part of the landscape of the airline
industry. This field has been continuously struggling with profitability mainly due to its
vulnerability to exogenous events which affects demand. In this context, many airlines and
especially alliance member airlines focus on quality of service, seeking to understand the
passenger perception as well as their expectations of the added values of global alliances.
Some research discuss the Global Airline Alliances contribution to the airline industry (Tiernan
et al., 2008; Kuzminikh and Zufan, 2014), as well as thesis with authors from various
backgrounds dealing with this topic. Here, the researcher, keen on aviation, was deeply
interested in the airline industry and especially the power of global alliances which led to this
study.
As Kalligiannis et al. (2006) mention, airline alliances are mostly considered as marketing
agreements between airlines, which leads us to aim at this aspect of performance in particular.
Thus, very few research papers focus on the assessment of the quality of service or the consumer
perceived values (Weber, 2005; Janawade, 2012, 2013). As there was a possible lack of
research, it was decided to provide an in-depth review of this specific topic.
1.1. Research objectives and structure
The research topic is entitled the influence of Global Airline Alliances on the marketing
performance of member airlines. The objective is to identify the relevant elements building
marketing performance and measure them thanks to the previous research on the topic and an
empirical study.
The relevant questions to address are:
- What are the main drivers of marketing performance originating from the alliances?
- How strong are these drivers in order to improve the marketing performance?
- How can alliances influence customers’ perceived attributes and values in order to
ultimately improve the brand equity of member airlines?

7. 6
This document is divided into six chapters. The first chapter, the introduction, portrays the
interest of the researcher to this topic, briefly discussing its background, the aim of the study,
its structure and the limitations. The second chapter, a presentation of the airline industry, brings
an overview of this sector, its history, its main actors, its future and introduces the three Global
Airline Alliances, laying down their importance in the airline industry today. The third chapter,
in the form of a literature review presents the research that has been done the last years on the
topic. The fourth chapter discusses the methods used to carry out the research, involving the
use of hypothesis and the construction of a model, the methodology and the survey design. The
fifth chapter displays the empirical results obtained, using basic to advanced computations and
discusses it. Finally, the sixth chapter presents the conclusions in the form of recommendations,
both theoretical and managerial contributions, limitations and suggestions for further research.
1.2. Research limitations
The research was limited to the study of member airline perceived attributes and values, only
focusing on the possible added value of global airline alliances and measuring its strength. The
aim was not to discuss all the aspects of marketing performance and to compare alliances with
each other. Thus, many service quality dimensions handled by the member airlines, their
attributes and values were taken out of the scope to only focus on the major ones and supposedly
the most influencing ones.
The survey targeted a certain demographical grouping for practical reasons. In order to find a
population well-aware of Global Airline Alliances, the cluster of frequent travelers, also named
frequent-flyers in this study was chosen. This population was targeted using online specialized
forums with a limited sample of 150 respondents.

8. 7
2. PRESENTATION OF THE AIRLINE INDUSTRY
2.1. History
It is commonly acknowledged that the first commercial aircraft flight occurred in 1914 in
Florida, USA. Since then, multiple milestones have been achieved. Air transport grew
exponentially during many years, although facing challenges that only slowed down its
development. Nowadays, civil air transport accounts for over 3.5 billion passengers each year
and 50,000 air routes with global revenues of $2,500 billion.
From the early 1910s towards the end of the Second World War, air transport did not grew
substantially as it could not impose its effectiveness due to technology constraints. Starting in
the 1950s, new technology made possible the development of comfortable, fast and cost
efficient journeys. Then, the 1960s saw the introduction of more efficient jet aircrafts such as
the Boeing 707 for long-haul and the Caravelle for medium-haul flights. Although air travel’s
average growth was much higher than the global GDP growth itself, it followed the economic
trends, experiencing major downturns being the two energy crisis in the 1970s and the 1991
Persian Gulf War which provoked major disruptions in air travel. Since the 1980s, the airline
industry remained at an average 5% annual growth rate globally. This period, as we will discuss
later, was a kick-off to deregulation policies and strategic alliances began to emerge. This also
sparked changes in the form of the decline of major airlines, merging one another. In the early
2000s, airlines also experienced issues in profitability due to the steep increase of oil prices.
Finally, despite two very strong economic downturns in the last ten years, which were the 2007-
08 financial crisis and the 2010 Eyjafjallajökull eruption ash cloud which caused a disruption
of most air travel services during seven days, the industry recovered and its growth remains
higher than the global economic growth.
2.2. Business models, markets and current trends
We can identify two main business models in the airline industry, in the form of so-called
traditional or full service network carriers and low-cost carriers (LCCs).

9. 8
LCCs offer lower fares, with very few services and most of the time using point-to-point transit,
in the contrary of traditional airlines using most of the time large airports named hub-to-spoke
transit. They are not part of any global alliances as it does not correspond to their business
model. In order to save costs, LCCs also often operate a single type of aircrafts (Ryanair only
uses B737s, Easyjet only A320 Family aircrafts) to lower maintenance costs, lower salaries,
single classes, no in-flight services, no seat assignments and direct sale of tickets using their
own website (Klophaus et al, 2012). However, during the past years, these business models
were constantly evolving as many traditional airlines tend to offer lower prices, depending on
the composition of local airline actors and many LCCs nowadays tend to offer extended
services, getting closer to the traditional airline model. Only remains the fact that major LCCs
do not operate intercontinental routes, as long-haul flights are hardly achievable using the LCC
model with its competitive advantages detailed beforehand (Tugores-García, 2012). According
to Morrell (2008), discussing the viability of long-haul LCC flights, the need for hub-to-spoke
networks to get more customers and the higher need of fuel to cover the distances could not
make the airline achieve a substantial cost reduction. However, today, with the reduction of fuel
costs, this theory could be reworked. To sum up, traditional airlines are still the main actors in
the long-haul market and they use this strength on the short and medium-haul market, using
hub airports and very often global alliances to gain more customers.
Another developing model should be showcased at this point, created by Persian Gulf airlines
such as Emirates and Etihad Airways, thus not part of a global alliance, and to a lesser extent
Qatar Airways, as the airline ended up joining Oneworld in 2013. These companies indeed
gained strong market shares with internationalization, using a geographical advantage. The
Persian Gulf is situated between Europe and Asia-Oceania which are the second and the third
biggest markets in the world. Therefore, using a strong partnership with local hub airports and
authorities, they can expand easily and absorb the majority of the traffic between these
continents. In comparison to many European airlines for example, which strive to open new
routes as their main European hub is striking with full capacity. In Middle-East, this is part of
a long-term strategy from local governments seeking for more economic independence to
enable a mix of revenue when the energy sector will not be following anymore. This role taken
by local authorities is criticized by other network airlines as they claim it ruins competition,
even claiming Gulf airlines get subsidies from local governments. However, it is commonly
acknowledged that these airlines have a competitive advantage in the form of cost reduction

10. 9
because of fuel proximity, lower salaries and good infrastructures for an easier expansion
(Tugores-García, 2012).
No matter the geographical market studied, on short and medium-haul flights, traditional
carriers have been disrupted by LCCs, one way or another. To respond to that, network carriers
either attempted to copy the LCC model or strongly differentiated from them. In the US, the
most mature market, network carriers tried to use the same techniques when new LCC entered.
After many developments and mergers between several airlines, it resulted in the formation of
four main airlines being American, United, Delta and Southwest. Together, they account for
87% of the US market (CNBC, 2013) and mainly offer what can be qualified as extended LCC
services but most of them using the hub-to-spoke model and do not market themselves as LCCs.
In contrary, in Europe, another model is mainly used which consists in creating a low-cost
subsidiary from the traditional carrier to counter LCCs. Most of them failed, although for
example Lufthansa’s Eurowings (formerly Germanwings) and lately Air France’s Hop! seem
to be well-settled in their market as of today.
However, the main attribute of network carriers is in their name, “network”. Thus, the
development of partnerships between airlines which resulted in the formation of the global
alliances, from which most traditional carriers are part of, a chapter which will be discussed
later, led to the acknowledgment that LCCs and traditional carriers are nowadays two different
models.
Speaking of current trends, profitability since the years 2008 and 2009 which was following the
economic crisis induced negative results. Then, net profits grew substantially from 2010 to
2015. This growth is also due to the strong decrease of fuel costs. In 2014, barrel prices started
at $130 and finished as low as $75. For 2015, it remained around this price, in 2016 it reached
less than $50. This had a strong impact on profitability because fuel represented in average 30%
of airlines’ operating costs beforehand. Subsequently, the market experienced a slight decline
in ticket prices, though only diminishing by 3% between 2013 and 2014 (IATA, 2015),
somehow encouraging customers to travel more. However, some major airlines, using the
process of hedging, which allows the company to fix prices before starting the business period,
had a negative impact as prices strongly decreased after commercial contracts were signed.
Thus, at the same time, US dollar was appreciated which had a negative impact on non-US
airlines. In terms of passenger load factor, between 2000 and 2014 it increased from 70% to
80%, with higher results in the US market. The cargo load factor did not improve the last 20

11. 10
years and remains a difficult business, although following world trade growth most of the time.
This is also due to much lower costs in ship transport and tendencies for protectionism in major
countries due to the recent economic downturns.
2014 and 2015 accidents might also had a negative impact on air travel due to customer
confidence at stake with the unbelievable events of the disappearance of a Malaysian Airlines
plane, the shot down of an aircraft of the same airline over Ukraine and a Russian one over
Egypt, and finally the Germanwings deliberate crash. This is also to remind this business area
mostly composed of tourism that the airline industry can be strongly impacted by either political
instability, terrorism or natural disasters. Therefore, aviation safety and security is a major
concern. Airlines advocate for better training, data analysis improvement and in partnership
with governments: passenger data management, improved security screening processes and
cybersecurity (IATA, 2015).
2.3. Future trends
According to the duopoly manufacturers, Airbus (2015) and Boeing (2015), following their
twenty-year market outlooks recently published, the average growth per year will remain the
same in the next twenty years, 4.4% according to Airbus and 4.9% for Boeing, above the GDP
growth. This can be also seen with the current manufacturing backlogs. Airbus, as of early
2016, has a higher number of orders with aircrafts value worth $1 trillion USD. This represents
around 6,700 airplanes. The combined number of orders for Airbus and Boeing is around
12,000. A strong majority of orders is composed of small to medium-range aircrafts such as the
A320 NEO family for Airbus and the B737-MAX for Boeing. All markets are expected to grow,
with substantially higher figures in Asia drove by China and India and to a lesser extent the
Middle-East. Future developments include lower carbon-emitting planes, following sustainable
development plans. Thus, air transport accounts for only 2% of global carbon emissions but
goals have been set to reduce this amount using various improvement leverages such as
technology, operations and infrastructure. New types of fuel like biofuel are currently in
development and may be an alternative soon. Although fuel consumption issues are nowadays,
strictly business-speaking, less important for airlines as fuel costs declined, major economic
and geopolitical studies are not categorical on the fact that long term trends will keep fuel costs
as low as we are experiencing nowadays. Anyway, currently delivered planes are already much

12. 11
more fuel efficient than the previous era. Other developments include a better regulation and
taxation principles as well as air traffic management, airport capacity and ground operations
improvements (such as slot management) to face the expected growth. Noise and waste
reduction are also a concern for local governments. Airlines and their trade unions with its main
actor the International Air Transport Association therefore need to work in partnership with
infrastructure providers.
It leads us to airline alliances which represent an added-value for these developments as certain
decisions taken and processes are mutualised which certainly allows faster changes and
reactivity of the actors.
2.4. Presentation of the Global Airline Alliances
Here, we briefly present the three Global Airline Alliances named Star Alliance, Skyteam and
Oneworld in terms of statistics and networks as of today. The history and the formation of the
alliances will be discussed later in the literature review.
According to the IATA (2015), below is a 2014 list of the ten biggest airlines in terms of
scheduled passenger carried for both international and domestic routes. A column has been
added to identify which carriers are part of the three Global Airline Alliances.
Rank Airline Passenger carried (thousands) Alliance membership
1 Delta Air Lines 129,433 SKYTEAM
2 Southwest Airlines 129,087 -
3 China Southern Airlines 100,683 SKYTEAM
4 United Airlines 90,439 STAR ALLIANCE
5 American Airlines 87,830 ONEWORLD
6 Ryanair 86,370 -
7 China Eastern Airlines 66,174 SKYTEAM
8 EasyJet 62,309 -
9 Lufthansa 59,850 STAR ALLIANCE
10 Air China 54,577 STAR ALLIANCE
Table 1: Ten largest airlines in terms of scheduled passenger carried – Own illustration using 2014 IATA data

13. 12
We can see that the three airlines in the table not part of the three global alliances are categorized
as Low-Cost Carriers. Southwest operates in the Americas and Ryanair and Easyjet in Europe.
Below is another table showing the current members of the three global alliances, as of April
2016. This data was computed using the alliances’ official websites.
Table 2: Alliance member carriers list – Own illustration from April 2016 alliances websites
Star Alliance (27 members) Skyteam (20 members) Oneworld (15 members)
Adria Airways Aeroflot Air Berlin
Aegean Airlines Aerolinas Argentinas American Airlines
Air Canada Aeromexico British Airways
Air China Air Europa Cathay Pacific
Air India Air France Finnair
Air New Zealand Alitalia Iberia
All Nippon Airways China Airlines Japan Airlines
Asiana Airlines China Eastern LAN Airlines
Austrian Airlines China Southern TAM Airlines
Avianca CSA Czech Airlines Malaysia Airlines
Brussels Airlines Delta Qantas
Copa Airlines Garuda Indonesia Qatar Airways
Croatia Airlines Kenya Airways Royal Jordanian
Egyptair KLM S7 Airlines
Ethiopian Airlines Korean Air SriLankan Airlines
EVA Air MEA Middle East Airlines
LOT Polish Airlines Saudia
Lufthansa Tarom
SAS Scandinavian Airlines Vietnam Airlines
Shenzhen Airlines Xiamen Air
Singapore Airlines
South African Airways
Swiss International Air Lines
TAP Portugal
Thai Airways
Turkish Airlines
United Airlines

14. 13
To allow a better understanding of the global alliances coverage, below is presented three maps
corresponding to Star Alliance, Skyteam and Oneworld full networks. The maps were extracted
from Airline Route Mapper, a free software which gathers all the air routes available in the
world, although the last version available is June 2014.
Star Alliance network:
Figure 1: Star alliance network - Taken from Airline Route Mapper free software – Last updated June 2014
Skyteam network:
Figure 2: Skyteam network - Taken from Airline Route Mapper free software – Last updated June 2014

15. 14
Oneworld network:
Figure 3: Oneworld network - Taken from Airline Route Mapper free software – Last updated June 2014
From these illustrations, using only a network point-of-view, we can observe that although all
the alliances cover the 5 continents, discrepancies appear in terms of geographical coverage.
For example, we can see that Star Alliance has more routes than the two others, it can be
explained through its higher number of partner airlines. Thus, we can see that Oneworld has an
advantage in South America as well as Oceania, but much less routes in Africa.

16. 15
3. LITERATURE REVIEW
Global Airline Alliances were created at the end of the 1990s which was a crucial onset in the
airline industry history. Similarly, in other fields such as tourism, interactions between
companies using the same patterns of what will be discussed here were developed during this
period (Lazzarini, 2008; Casanueva et al., 2014) sometimes referred in the literature as a form
of coopetition (Himpel, 2012).
Numerous research has been published to explain the role of those alliances and their influence
throughout the last twenty years. In addition, substantial research have been achieved with
empirical studies as those partnerships were growing, involving a large number of airlines
meaning a growing number of passengers. Assessing the multi-dimensional performance of
member airlines is a key element in order to determine the viability of these partnerships.
Determining and investigating the areas which lack sufficient data and interpretation is also the
objective of the study. This section reviews the relevant literature presenting the concept of
alliances, their strategy, their impact on airlines performance and to conclude a primary focus
will be made on the marketing factor and the related consumer perception.
3.1. Global airline alliances as a strategy
Global Airline Alliances are three main entities named Star Alliance, Sky Team and Oneworld
which are gathering more than sixty airlines as of 2015. This enterprise originated from legacy
carriers’ observation that operating in this field became more and more challenging due to
growing external competition mainly because of a deregulation of the air routes. These reforms,
undertaken by the United States and followed by the majority of OECD governments aimed at
improving efficiency and lowering prices by increasing competition (Gönenç & Nicoletti,
2000). Providing a global network of international routes, at the beginning, enabled companies
to reach more markets while maintaining their local presence (Tugores-García, 2012). The
development of hub airports as a business model also gathered dominant carriers to work
together, especially on transatlantic routes (Gillen and Morrison, 2005). Lazzarini (2008)
describes that airlines, which were already partnering through bilateral collaborative
agreements, decided to enhance their relationship by creating multilateral alliances and

17. 16
involving a centralized management team, brand development and technology platforms. As
Morrish and Hamilton (2002) state after the creation of those networks, the definition of an
alliance would be “any collaborative arrangement between two or more carriers involving joint
operations with the declared intention of improving competitiveness and thereby enhancing
overall performance”.
Further companies’ motivations underlying the creation of alliances are discussed by Evans
(2001). The author identifies several factors, starting with external drivers. The new
technologies of information with the spreading of what are called Computer Reservation
Systems allowed airlines to extend their market and use advanced marketing tools. As it was
pointed out above, economic restructuration mainly caused by market deregulation and a
growing global competition implied strategic changes for airlines. As internal drivers, the need
for sharing risks between several partners and achieving big economies of scale were reported.
Czipura and Jolly (2007) emphasize on the geographical strategies of alliances as a mean to
keep customers within the same network. Indeed, alliances seek to cover most parts of the
busiest air routes with an emphasis on transcontinental links. The aim for the airline being to
benefit from each other networks by offering new passengers to fly in their regional grid.
Soon after the creation of the three global alliances, Fan et al. (2001) describe the evolution of
global alliances by analysing the economic trends and industry forces at this period. The authors
infer that alliances would likely to remain at a small number but widely expanding as well as
the strong possibility for airlines to merge and become transnational “mega-carriers”. This latter
concept, from the definition given by the review, did not become real eventually. However
alliances are nowadays a key component of the airline industry. Evans (2001) also states that
partnering between airlines, instead of merging, was and remains most of the time the best
solution for entities due to regulations and legal restrictions. To this extent, one of the most
comprehensive studies on airline alliances is provided by Iatrou and Oretti (2007), from theory
to empirical research. The authors examine the opportunities and threats for airlines to join
partnerships from theoretical evidence and empirical data. They identify key areas to determine
the influence of alliances by achieving a survey on thirty airline members. It reveals that
enhancing airlines’ network is the best opportunity, followed by reward programs potentially
acclaimed by customers.

18. 17
As explained earlier, numerous bilateral agreements out of the main alliances also remain
between airlines and existed before the creation of global alliances. This process is called code-
sharing which enables carriers to sell tickets on their own name but flights are operated by their
partner. Thus, airlines can gain new markets without having to advertise for this. Global
alliances also use this process inside their market (Jangkrajarng, 2011). The author as well as
Gillespie and Richard (2011) also emphasize on deregulation granted on member airlines since
the liberalization process started in the 1990s and the Open Skies agreements signed between
the US and the EU. It allows alliances to gain competitive advantage which even let
monopolistic situations appear in some areas. The main factor is the antitrust immunity, from
the US antitrust law and granted by the US Department of Transportation which therefore let
partner carriers setting their most suitable fares, no matter the competitors. Moreover, other
instances such as the European Union did not provide any significant restrictions to this nor
legislate. However, this situation might change in the future as the International Civil Aviation
Organization claimed the necessity of a more equitable regulation. Bilotkach and Hüschelrath
(2011) advocate for a better control with an immunity granted to airlines only for a limited
amount of time.
3.2. The diverse features of airline performance within alliances
When covering the literature produced on firm’s multidimensional performance in the specific
field of airline management, a recent work to be highlighted would be the approach from Wu
and Liao (2014). The balance scorecard (BSC) and the Data Envelopment Analysis (DEA),
well-known models, are used to assess company performance on an operational viewpoint.
Indeed, in the study, the authors claim that traditionally, most evaluations only take into account
financial measures and only a few integrate the operational dimension, which can also be a
concern when forecasting future performance. Then, the computation of the two tools allow the
model to be more advanced, the BSC reviewing strategic financial and non-financial objectives
and the DEA focusing on productive efficiency with a higher mathematical assessment. This
model deployed on 38 carriers allows to compare companies with each other, identifying the
lack of efficiency in some areas and allocate resources in a different way. However, it does not
establish the input received from strategic alliances nor include the quality of service factor.

19. 18
On the other hand, the effect of alliances on airline performance is a further discussed topic and
the vast majority of studies conclude with a beneficial outcome. According to Park and Cho
(1997), with an investigation held before the creation of global alliances and therefore focusing
mainly on bilateral partnerships, it shows an increase of market-shares after entering joint
activities. Furthermore, a late study grouping 65 airlines achieved by Kuzminykh and Zufan
(2014) also reveals that airlines benefit from alliance memberships. The sample demonstrates
that turnover, assets and to a lesser extent the number of employees grow when joining a
network. In addition, Lazzarini (2007) also advocates that alliances have a positive result for
airlines. The author argues that benefits are greater for large carriers involved in large alliances.
Larger members are indeed more able to capture flows of traffic coming from partner airlines.
This phenomenon is regarded as positive externalities.
When analysing in a thorough way the components of performance, revenue management is a
main concern for airlines joining an alliance. As reported by Tugores-García (2012), this
department determines “how shared seat inventory on each airline’s code share flights can be
optimized to maximize revenue gains”. In addition, Vinod (2005) uses a specific terminology
to describe this, being the marketing carrier, whose job is selling the ticket and the operating
carrier, whose job is flying the passenger. Alliances develop their own agreements with the
objective to enhance each company’s revenue. In connection with this, Graf and Kimms (2011)
discuss the distribution of seats in the airplane between two or several partner airlines. The
authors analyse and suggest a different model by using a simulation of a booking process. This
enables airlines to have a better understanding of partnerships with the pros and cons for the
specific revenue management. Nevertheless, the model does not integrate any customer-choice
behaviour to their review. Grauberger and Kimms (2015) later created a model for alliance
member airlines. They emphasize on the competition that may arise between members and how
to distribute the revenue of code share tickets in order to maximize profit for both cooperating
firms.
As reported by Wang (2014), carriers need to respond as fast as possible to strong competition.
A strong brand equity is therefore very important compared to the past era. Moreover, focusing
on alliances, new ways of branding are developed, for example aircrafts painted in the network
livery. Being member of an alliance allow legacy airlines to detach from low-cost competitors
as well as regional carriers, providing a different level of quality at the airport and in the
airplane. It has become a huge component of companies’ business strategy (Tiernan et al.,

20. 19
2008). A survey carried out by Kalligiannis et al. (2006) on partner airlines’ marketing
managers investigate the potential brand equity conflicts that may arise between members.
Respondents declared that alliances should reinforce their mutual brand equity while keeping
their own brand values and remain cautious that every partner airline is on the same level in
terms of brand management. In connection with this, Sultan and Simpson (2000) suggest that
airlines choose their partners carefully in order to avoid differences of quality of service, which
might impair the brand equity and the reputation of the company, from which Park and Cho
(1997) also alerted in their study. Thus, as Himpel (2012) shows, some partner airlines gain
more than others in the process. As alliances are mainly a mean for companies to improve their
individual status, rivalry may emerge. Matei (2012) even speaks of a risk of internal
cannibalization if the network is not optimized as members can sometimes operate on the same
routes.
For an airline, managing its network is a crucial area and this is all the more important within
alliances. Himpel (2012) discusses the need for airlines to optimize their slot allocation, which
is the permission for an aircraft to land or take-off at a coordinated airport at a particular time
on a particular day. The goal of alliance members is therefore to focus on slots bringing the
biggest volume of traffic and being the most profitable. Lordan et al. (2015) analyse the global
alliances networks to define its coverage. It appears that Star Alliance is the most robust among
the three, all having nonetheless quite similar patterns. The authors indicate that airlines should
ensure that their coverage strongly matches the need of the alliance joined.
Speaking of how global alliances interact with each other, an investigation to be highlighted
would be the report of Tiernan et al. (2008) who discuss the service quality of Western airlines,
members of the three main alliances. Their model is based on indicators such as on-time
arrivals, baggage reports and flight cancellations. It shows no significant discrepancies between
alliances when statistics being taken overall, although gaps appear among member airlines
when compared one by one. This is mostly due to internal policies and events which affect the
companies but not significantly the alliances. In connection with this, Tsantoulis and Palmer
(2008) also show that as there can be substantial differences of quality between airlines at the
time they join the same alliance, there is very little evidence that this gap will shrink gradually
towards a mutual standard. It is all the more confusing that alliances should help in promoting
their brand, meaning the same quality of service. The researchers imply that several reasons
can lead to these remaining discrepancies of quality of service. First, member carriers do not

21. 20
mainly focus on promoting the same market, that is to say the same values. Second, alliances
were created to respond to several issues explained earlier and to a lesser extent to promote one
common brand. Third, their model might be not precise enough to detect these changes.
Joint activities are part of the process to mutualize services and thereby to save costs as well as
to create value. Czipura and Jolly (2007) divide alliances activities in two, being back office
and front office. Back office is still in development within partner airlines, being procurement
and logistics for fuel costs, catering and maintenance. Front office, though, is a core component
of alliances as it adds services and therefore provides more value for customers. One side is the
commercialization, being the code-sharing agreements. The other side is the production of
services which consists of common facilities ranging from lounges to a dedicated terminal,
baggage handling and mutualized frequent-flyer programmes. The authors suggest that in the
future, alliances should be more process-oriented and therefore bringing further economies of
scale with increasing joint operations, such as fuel purchasing, shared IT platforms and even
mutualized aircraft purchasing. Some of these suggestions are nowadays indeed already
effective in global alliances.
3.3. Analysing consumer perception of the alliances
From the consumer point-of-view, a general rationale would be that alliances enhance the
choice of destinations and services, provide seamless travel by reducing transfer times and
merge loyalty programmes (Flores-Fillol and Moner-Colonques, 2007).
As stated by Wang (2014), there are different types of customers using air travel. From the
leisure traveller, non-frequent user to the business traveller, owner of multiple mileage cards,
defined as a frequent-flyer; customer behaviour is different and companies need to adapt their
branding strategy to all those types of customers. Continuing on this aspect, Janawade (2012,
2013) investigates the attributes of the consumer perceived value within alliances using the
qualitative method known as participant’s discussion. Starting from a statement that member
airlines can undermine the overall service quality of the partnership, but also use it mainly to
increase their profit rather than improve their service, the author indicates that airlines should
provide more attention to customers. The study reveals that passengers are sensitive to

22. 21
multiparty joint-services enabled by the alliance, loyalty programs as a form of reward,
harmonisation and coordination of services between the member airlines, a better access to
information and finally the extended airline network. Furthermore, according to the author, at
the time of the review no other literature discussed the significance of the aforementioned
factors and previous researchers only focused on specific alliances or even airlines, which has
a different impact on the results. This research empowers marketers to understand how joint
strategies can be used to allow an effective output in order to maximize value to customers.
Thus, a quantitative study on this topic may bring additional outcomes.
When studying frequent-flyer passengers, a key target in the airline industry, Castillo-Manzano
and Lopez-Valpuesta (2014) point out the characteristics of this group with an extensive survey
implemented on the Spanish market. By gathering more than 37,000 answers they were able to
draw the portrayal of the average traveller. It suggests that frequent-flyers are often attracted by
rewards. However, the authors do not specifically study the influence of alliances on the
passengers. Similarly, an older survey by Weber (2005) took place at Hong Kong International
Airport, therefore focused on the Asian market and albeit a lower number of 800 respondents
compared to the hereinabove study. The results reveal a contrast between the two investigations
and as the author claims, it is also contrasting with previous studies held on a similar
background. Customers indeed emphasize on their convenience and the seamless travel
implemented by partnerships between airlines rather than gaining rewards and the expanded
network provided. Furthermore, it also concludes that passengers originating from different
parts of the world do not have the same point of view. For example, European and North-
American customers tend to be less concerned with the opportunities given by alliances than
Asian travellers.
Thus, Chen and Tseng (2010) study the airline brand equity from the customer’s perspective.
The authors, investigating on the Taiwanese market, find that the dominant factor is brand
loyalty while perceptual and behavioural dimensions seem to have a strong relationship.
However, there is no specific information regarding alliances. Similarly, Wang (2014) focuses
on frequent-flyer passengers well-aware of the global alliances, an angle that has not been
represented widely in previous studies. The author, using once again the Taiwanese market
explores the role of alliances in influencing brand equity, brand preference and purchase
intention from a customer perspective. However, the researcher explains that he did not focus

23. 22
on the organisational perspective, which might be valuable to deepen in order to fully
understand the alliance effectiveness.
To sum up, as proposed by Lin (2013), who studied the performance from a company point-of-
view, joining an alliance will create opportunities for the airlines. However, turning it in order
to improve the company’s performance is not immediate. It may take a long time and cost a
huge amount of money. Thus, alliances can also show weaknesses both from the company and
from the customer perspective. Airlines are indeed under pressure when they join a program
because of the changes in management and operations needed to comply with the other partners.
On the other hand, customers, especially frequent-flyers may have trouble understanding all the
different policies and disparities between airlines. As alliances regroup advantages, such as
miles rewards, they use their own calculation process which can mislead passengers (Wang,
2014).
Therefore, we can assume that although a trustworthy amount of research is available there are
several areas that might be interesting to deepen. The global alliance is also a more or less recent
concept which is constantly evolving following the airline industry growth. Indeed, nowadays
we can assist to two distinct phenomena: the influence of Low-Cost Carriers developing their
strength on local and regional markets and the emerging global carriers most of the time
established in Asia and the Arab states of the Persian Gulf, as some of them are not part of the
three alliances. This may already or in the future have a strong impact on the strategy and the
performance of alliance members. Hence, some literature which was up to date at the moment
may lack information nowadays and by all means accuracy. Therefore, the objective of this
research is to identify the components of marketing performance for global alliances member
airlines and to provide an analysis of the relationship between brand values, consumer
perception and airline alliances awareness, an area which lacks research. This is possible
through the customer’s perspective of perceived quality and brand value which influence brand
image and brand equity in the last stage. Accordingly, the following topics to investigate in
order to provide a theoretical and managerial interest would be:
- Defining the relevant components to assess member airlines marketing performance
- Focusing on the relationship between brand value and consumer perception
- Providing an in-depth analysis of these two components by studying the influence of
global airline alliances

24. 23
4. RESEARCH METHODS
4.1. Research aim and problem statement
The aim of this research is to explore the aspects of airlines’ marketing performance in relation
with their membership with global alliances, being either Star Alliance, Skyteam or Oneworld.
Thus, this study will emphasize on consumer perception and brand value to conceptualize and
measure brand equity in this particular field of study.
Therefore, the research topic is studying the influence of Global Airline Alliances on the
marketing performance of member airlines.
The following questions should be reminded:
- What are the main drivers of marketing performance originating from the alliances?
- How strong are these drivers in order to improve the marketing performance?
- How can global alliances influence customers’ perceived attributes and values in order
to ultimately improve the brand equity of member airlines?
4.2. Research hypothesis
Three research papers relating to the same topic written by Chen and Tseng (2010), Wang
(2014) and Lin (2015) were presented in the literature review. Interestingly, they are studying
consumer perception of airlines and/or alliances using quantitative surveys on the Taiwanese
market. It provides an interesting ground to explore further questions. Adding the investigations
achieved by Weber (2005) which indicates that Asian customers seem to have different
perceptions than Westerners and the qualitative study held by Janawade (2012), we can build a
research model by analysing the authors’ frameworks.
Chen and Tseng (2010) and Wang (2014) both mention widely accepted brand equity
conceptual frameworks from the work of Aaker (1991) and Keller (1993). This latter author
develops a consumer-based brand equity pyramidal model. It draws the steps to strengthen a
brand. For a company, the first step is to create awareness, then to meet customers’ needs on

25. 24
the performance, social and psychological level. Customers will then respond by judgments and
feelings and end up, at the top of the pyramid, with full loyalty and active engagement towards
the brand.
In relation to that, Aaker (1991) draws four dimensions of brand equity being brand awareness,
brand associations, perceived quality and brand loyalty.
Based on these two models, we can draw a conceptual model quite similar to the one developed
by Chen and Tseng (2010) study on airline customer-based brand equity and Wang (2014) study
on passenger purchase decision.
Compared to the existing models, brand awareness has been replaced by Global Airline
Alliances awareness as we want to study its influence on member carriers. Thus, we do not
want to study the brand loyalty, which goes with the brand preference, but only study the
alliances and their member airlines in general. Brand equity will not be studied as well as it
involves a comparison of the different airlines and Global Airline Alliances. Therefore, this
model comprises 3 hypothesis and is presented below.
Figure 4: Aaker’s customer-based brand equity dimensions (Aaker, 1991)

26. 25
H1: Member airline perceived quality is positively influenced by global airline alliances
awareness
H2: Member airline brand value is positively influenced by global airline alliances awareness
H3: Member airline perceived quality has a positive effect on member airline brand value
Previous research has partially proved the influence of airline alliances on perceived quality
and brand image, related to brand value of member carriers, although not focusing on the exact
same topics (Flores-Fillol and Moner-Colonques, 2007; Wang, 2014). Moreover, according to
Villarejo-Ramos and Sanchez-Franco (2005), this is the opinion of the consumer on product
quality which forms the measurement scale indicator of the brand quality perceived.
4.3. Choice of methodology
In this type of empirical study with several hypotheses to be verified and the aim being to
understand what customers do or think, a quantitative approach is the most suitable. The issues
are the validity and the objectivity of the results, and quantitative analysis has a strong physical
sciences background compared to qualitative research (Barnham, 2015). This can be verified
by the success of numerous studies discussing the same topic and presented earlier in this part
and in the literature review.
Figure 5: Proposed research model - Own illustration

27. 26
Thus, as Chen and Tseng (2010) explain, in order to be efficient in this kind of study, the
questionnaire should have well-structured parts following the path taken by the model. In this
case, it means starting with questions regarding the customer’s identification with his travel
habits or trip behavioural information and its awareness of Global Airline Alliances, his
appreciation of perceived quality, then brand value and finally the general information such as
demographic questions. Inside these parts, the questionnaire should be designed as related to
the different services encountered by the passenger, following a chronological order. As being
said, the last section will focus on the participant himself, being demographic information also
used for cross-referencing purposes. This is important to end the questionnaire by these personal
questions because it is less obnoxious for participants to give more personal data at this point
(Greenberg and Weiner, 2014). Finally, a pre-test is necessary to ensure its understandability
by future participants. This can be achieved by using people in the entourage of the researcher,
with some knowledge of the topic but not familiar with research methodology.
4.4. Research design
Data collection plan
When discussing the target population for this study, we can infer that it involves a large number
of passengers, as it can be seen in the table below. Passengers carried by member airlines are
counted in hundreds of millions each year. Even though we will narrow down the sample to
encompass only the frequent-flyer customers, this still comprises a very large community. Data
is difficult to obtain but close to 200 million people worldwide are at least member of a frequent-
flyer program (FFP), which can be taken as the total population.
Table 3: Global alliances main figures - last updated 2013 (Wang, 2014)

28. 27
Therefore and because of the short period of time and the very low budget, a selected sample
will have to be built. Thus, as the population is difficult to access, most of the time regrouped
in restricted areas or only accessible with the prior consent of private or public parties (airport
areas), a different approach should be used to carry out this research more easily, which has not
been used by researchers before. This should take the form of online questionnaires accessible
through English and French speaking social networks and websites specialized in this topic,
such as forums, although a self-administered survey on site was also being studied as it is the
model used by previous authors (Chen and Tseng, 2010; Wang, 2014).
Sample size and composition
Regarding the sample itself, when considering the previous studies similar to what needs to be
achieved here, surveys goes between 249 and 819 participants (Weber, 2005; Chen and Tseng,
2010; Wang, 2014). However, a smaller number of respondents is also possible, but the
confidence level will decrease (Leary, 2011). Normal approximation formula will be used to
determine the sampling error (Greenberg and Weiner, 2014). It seems that in this study we will
focus on 150 participants to obtain a decent confidence level. Adding to that, the survey will be
only submitted to a population who already know to a certain extent global alliances and their
role, meaning mostly the frequent-flyer passengers category.
In terms of sampling method, in this case the most related form is probability cluster sampling.
This approach uses natural groupings of possible respondents. However this method involves a
higher risk of sampling error and possible biased results.
Data analysis plan
The collected data will be analysed using EXCEL and XLSTATS software. Most of the
questions will take the form of both five-point Likert scale and semantic differential scale, being
attitude measurement methods. The first method usually ranges from strongly agree to strongly
disagree and is used to capture the intensity of the participant’s feelings. Both forms have
proved their efficiency for data collection and analysis in several previous surveys (Chen and
Tseng, 2010; Wang, 2014). For a better visual representation, Robbins and Heiberger (2011)

29. 28
recommend the stacked bar chart used in the analysis. The second measurement method is the
semantic differential scale which is a more enhanced technique used to measure opinions,
attitudes and values of the respondent using a bipolar scale of stimulus adjectives (Garland,
1990). As part of the probability samples, the correlation coefficient “indicates the degree to
which two variables are related to one another” (Leary, 2012). The most commonly used
measures are the Pearson or Spearman correlation coefficients, depending on parametric or non-
parametric statistics and ranging between -1 and +1, 1 indicating a perfect correlation between
the variables.
4.5. Implementation
The survey questionnaire is presented in Appendix 1. There are 4 different parts and the total
number of questions is 23.
The software used was Typeform.com. This questionnaire builder is a seamless, straight-
forward and nice-looking tool which was used to ensure that a maximum number of visitors fill
the survey. Thus, as the researcher did not know the respondents, not having any control on
them, this was a way to retain respondents until the end of the questionnaire. Respondents being
themselves mostly composed of well-educated and upper middle-class to high-net worth
individuals, the aim was to ensure no mistakes were made and no time wasted for the
respondents.
Two questionnaires were created, one in English and one in French, a particular attention was
made on the translation, with a strong accuracy as much as possible. The English one was
considered as the main survey and eventually gathered more than 95% of the total responds.
The French survey was only used for pre-testers and to cover a few French-speaking
respondents.
Flyertalk.com, BusinessTraveller.com and InsideFlyer.com forum sections were the three main
websites used for conducting the survey and eventually make up for more than 95% of the
responses gathered. These websites are major worldwide forums used by frequent-flyers to
exchange their point-of-view over the airline industry. The most popular website is
Flyertalk.com, created in 1998 which was ranked in average in the top 4000 websites worldwide

30. 29
for traffic according to Alexa Internet (2016), a commercial web traffic data and analytics
company.
Pre-testing was carried out using both acquaintances and anonymous people, French and non-
French speaking. One of the pre-testers was a family member of the researcher, established in
Singapore, which empowered the test as the research is of global reach. Other pre-testers to be
thanked were found on one of the forums and gave several feedbacks on both the questionnaire
framework and questions.
The response rate could not be measured as the traffic on the websites used are not available to
the general public. For the analysis, the results coming from the French survey were
transformed into English and added into the software to cope with the language used for the
final research paper.
Responses were gathered between April the 12th
, 2016 and April the 23rd
, 2016. The sample
totaled 165 respondents, of which 150 fully completed questionnaires were retrieved. Most of
the missing data was dealing with the absence of country of residence. Missing data
questionnaires were removed from the calculations, leaving 150 responses in total. This coped
with the initial target of 150 responses.

31. 30
5. RESULTS
Below, we present an analysis of the results obtained using different types of charts. In the first
part, the results from single computations will be detailed and in the second part, measurements
on multiple variables will be analyzed using statistical tests such as Pearson or Spearman
correlation test and Student t-test. Pearson correlation is more often used for nominal scales
whereas Spearman correlation is more appropriate for ordinal scales, such as Likert scale and
semantic differential scales. For hypothesis testing, as De Winter and Dodou (2010) explain,
for 5-point Likert items there is no significant difference in the results whether using Student’s
t-test and Mann-Whitney test, therefore we will use the t-test in this research paper. For Pearson
and Spearman correlation, the statistical correlation is significant if the coefficient is more than
0.5 and the p-value under 0.05; otherwise the correlation does not exist. For Student t-test for
independent samples, if the p-value is lower than the significance level alpha=0.05, one should
reject the null hypothesis H0, and accept the alternative hypothesis Ha. Chi-square test was not
used as there is no theoretical population available to compare with our data. We only
highlighted the possible bias for certain items.
5.1. Profile of survey respondents
Demographic information
a) Gender distribution
Figure 6: Gender distribution (n=150, results are shown in numbers on the left and in percentage on the right)

32. 31
Out of 150 respondents, 94% are males and only 6% are females. This is a possible bias in the
survey. This can be explained by the method used to gather responds, the Internet forums. Prior
to the survey, the researcher did not expect such a discrepancy between the two groups. In most
of the previous studies held, the number of males was as well superior to the number of females
but not to this extent. Similar studies like Weber (2005) ended up with 58% males and 42%
females. Castillo-Manzano & Lopez-Valpuesta (2014) who examined the typical frequent-
flyer, showed that a short majority of them were males, 53% in their study of 37,000 passengers.
b) Age distribution
The age distribution is much more consistent with the previous studies. As the age segmentation
cannot be found on the frequent flyer population and this was not the purpose of this study, it
has been decided to breakdown into three categories only. However, it would have been
interesting to add more categories in the range between 25-64 years old. 90% of the respondents
are in the middle-aged category. Previous research showed that frequent-flyers are mostly
composed of male adults between the age of 25 and 60 years old, with a college or university
degree and belonging to the upper middle-class, travelling for business purposes (Weber, 2005;
Chen and Tseng, 2010; Castillo-Manzano and Lopez-Valpuesta, 2014). Here, no questions were
asked on their education level and household income because this was not the central purpose
of the research and there are questions which we will discuss later dealing with the class of
travel they mostly use which can provide an indication on their wealth or their position within
their company.
Figure 7: Age distribution (n=150, results are shown in numbers on the left and in percentage on the right)

33. 32
c) Country of residence
The country of residence results show that more than one-third (42%) of the respondents live
in the United Kingdom, and 17% in the United States of America. An explanation can be that
most internet forum users are either based in the UK and in the US, as the language of reference
is English. Nationality has not been asked as the survey is rather more market-oriented than
culture-oriented.
Figure 8: Country of residence (n=150, results in percentage)

34. 33
To offer a better view on the results, a breakdown was made on the data from country of
residence which was then computed into markets. These are the same as shown in the alliance
networks part earlier in the document. With this chart, we observe that two-thirds (63%) of the
respondents live in Europe and 21% in the Americas. The other markets represented are not
significant. In previous studies, most of them were carried out at international airports, yielding
a higher rate of local residents. Weber (2005) gathered more than 44% of Asian residents as her
research was held at Hong-Kong International Airport and Castillo-Manzano & Lopez-
Valpuesta (2014) had a majority of Spanish respondents as the study was carried out in Spain.
This research may show additional results later in the analysis as the survey is dominated by
both Anglo-Saxon and Western countries residents. We can also make a parallel with the
alliances networks discussed earlier, as a majority of respondents live in areas where the three
alliances have more or less the same coverage or footprint. It ensures that the survey is assessing
all three alliances.
Figure 9: Country of residence breakdown by markets (n=150, results in percentage)

35. 34
d) Respondents working in the aviation industry
This question, not studied in previous research papers was used to ensure that not a significant
part of the respondents worked in the aviation industry, as a high figure might have introduced
bias in the results. Aviation professionals may have a different point-of-view of the airline
industry compared to the general population of frequent-flyers. At 10% of positive results, there
is no issue to be considered.
However, another question not asked is if the respondent is more a business traveler of a leisure
traveler. This could have been useful to determine the expectations of the two different
categories.
Figure 10: Respondents working in the aviation industry (n=150, results are shown in numbers on the left and in percentage on
the right)

36. 35
Travel habits
In this part, we asked general information about respondents’ travel habits in order to identify
several groups of frequent-flyers and use them afterwards in advanced computations.
a) Travel frequency
The travel frequency among respondents shows that most of them use air transport very
frequently with 99% who travelled at least once a year the last five years and more than two-
thirds (69%) who travelled at least once every month. To get a better view of the results, two
categories were constructed to show additional results, to be found in the next chart.
Figure 11: Travel frequency (n=150, results in percentage)

37. 36
In Weber (2005) research paper, travellers are qualified as frequent when they reach more than
seven trips a year. Frequent travelers can also be categorized if they own frequent-flyer
programs. In our study, we considered respondents to be regular users when travelling at least
once a month, which resulted in 69% of them.
b) Travel classes
Figure 12: Travel frequency breakdown analysis (n=150, results in percentage)
Figure 13: Travel classes (n=150, results in percentage)

38. 37
This type of question is unusual in the similar research papers. We tried to identify the
percentage of travel class users by asking whether they never, rarely or often travelled in either
Economy & Premium Economy, Business or First. The First class is not available in every
airline, most particularly during medium-range flights, but this question can be interesting to
categorize users and ensure that all travel classes users are represented and the perception of
added values is not biased. We find out that most respondents (92%) already flew in Business
class and more than half of them (51%) already flew in First class. This is very interesting to
have such a high percentage as a First class ticket is usually at least ten times the price of an
Economy class ticket. An explanation to this is that most respondents travel very often which
enable them to accumulate mile points and eventually get discounted or even get free First class
tickets.
To go further, a Pearson correlation coefficient test was used to determine if a correlation exists
between travel frequency and the use of First class. The test did not show a significant link (p-
value=0.148 at 0.05 significance level).
Another test was carried out to determine the utilization of the different classes. This shows a
significant correlation (p-value=0 at 0.05 significant level). We can observe that the more
Economy & Premium Economy is chosen by the respondent, the less Business and First will
be chosen. Thus, the more Business is chosen, the more First is also chosen, which demonstrates
two different clusters being Economy & Premium Economy on one hand and Business and First
on the other hand. These clusters might be used for further analysis in consumer perception.
Variables Economy Business First
Economy 1 -0,481 -0,539
Business -0,481 1 0,518
First -0,539 0,518 1
Table 4: Pearson correlation coefficient test on travel classes

39. 38
c) Category of most flights taken
Although this question is one of the most arbitrary to answer for the respondents, this reveals
that half of the respondents take mostly short and medium-haul flights and the other half mostly
long-haul flights. These two categories can be used to determine differences in perception
between the two groups. Long-haul and short and medium-haul flights can be very different in
terms of quality of service which can reveal different results.
d) Knowledge of Global Airline Alliances
Figure 14: Category of most flights taken (n=150, results in percentage)
Figure 15: Knowledge of Global Airline Alliances (n=150, results in percentage)

40. 39
This question allows us to ensure that all respondents have at least a basic knowledge of global
airline alliances. Although we knew that the method, using aviation-oriented forums for the
survey would decrease the percentage of non-useful responses, the results reveal that 91% of
them believe they have an extensive knowledge on the topic and the totality know global
alliances, which reinforce the reliability of the results obtained.
e) Frequent-Flyer Program membership
In addition to the last question, another one dealt with Frequent-Flyer programs to identify
whether the respondents held several, one or no memberships. This shows that 98% of the
respondents hold at least one membership, which also strengthens the research.
5.2. Data analysis
Data analysis from Likert scale questions
This part deals with the nine 5-point Likert scale questions on the perceived value
improvement of member carriers thanks to the Global Airline Alliances.
The next chart presents the most important attributes to the least important ones, as identified
by the respondents. A second chart presents the mean value obtained using a radar chart, with
the lower the number, the higher the grade (1 = strongly agree to 5 = strongly disagree, mean
value = 3).
Figure 16: Frequent-Flyer Program membership (n=150, results in percentage)

41. 40
1,80
1,90
2,16
2,42
3,213,25
3,31
3,32
3,34
More advantages on my
frequent-flyer programs
Improved lounge access and
quality
Better choice of destinations
and schedules
Improved overall travel
experience
Cheaper flight tickets
Improved cabin comfort and
entertainment
More safety
Improved service by flight
attendants
Improved catering
Mean value
Figure 17: Perceived improvement of airline alliances – Likert scale questions (n=150, results in percentage)
Figure 18: Mean value for Likert scale questions

42. 41
These results reveal that respondents value most importantly:
1. The additional advantages on the frequent-flyer programs (FFPs), (Mean value: 1.80)
2. The improved lounge access and quality (Mean value: 1.90)
3. The better choice of destinations and schedules (Mean value: 2.16)
Also asked, the improved overall travel experience question shows that for the respondents,
Global Airline Alliances slightly help member airlines to offer a better travel experience (Mean
value: 2.42).
This shows that respondents value most importantly the two basic advantages of airline
alliances, which are frequent-flyer programs to accumulate rewards and a better network with
extended destinations and schedules. The last item identified is the improvement of the lounges
access and quality, which is tightly linked to the development of global alliances and very used
in their marketing campaigns.
The other items, less important for the respondents, being ‘cheaper flight tickets’, ‘cabin
comfort and entertainment’, ‘safety’, ‘service by flight attendants’ and lastly ‘catering’ are not
the main features that are often marketed by airline alliances, but more often by airlines
themselves, using their own brand.
These results mostly cope with previous research held on this subject. Indeed, Weber (2005)
who studied the traveler’s perception of airline alliance benefits and performance showed that
the four highest ranked items for the frequent traveler cluster were:
1. Ease of transfers between flights,
2. One-stop checking,
3. Smoother baggage handling,
4. Ability to earn frequent flyer points
Therefore, two of the four items (1 and 4) are very close to the ones revealed in our study, being
additional advantages on the FFPs and better choice of destinations and schedules, although
Weber’s study was performed ten years before our research.
In addition to that, Wang (2014) also identified five leading items:
1. Greater network access,
2. Seamless travel,
3. Transferable priority status,

43. 42
4. Extended lounge access,
5. Enhanced FFP benefits
The three most important items can also be identified in Wang’s study (respectively: 1 and 2,
4, and 5).
To a lesser extent, Janawade (2012), who carried out a qualitative study, also identified two of
our three items named “extended service network” and “fringe benefits”.
Moreover, a Spearman correlation test showed statistically significant correlation between
many of the attributes (see table in Appendix 2), in particular:
1. Improved cabin comfort and entertainment with improved catering (coefficient 0.845,
p-value = 0)
2. Improved catering with improved service by flight attendants (coefficient 0.587, p-value
= 0)
3. Improved service by flight attendants with improved cabin comfort and entertainment
(coefficient 0.597, p-value = 0)
These three variables tightly linked together make sense because all these attributes are attached
to the flight experience itself and are usually performed at the same time, which is why a
majority of respondents gave a similar appreciation of these three attributes.
Another element to report is the significant correlation between improved overall travel
experience and all the other variables, which gives us an indication of accuracy of the answers.
Data analysis from semantic differential scale questions
This last part deals with the three questions using the semantic differential system, which
features two opposite words describing the perceived value in order to identify the brand
value of member airlines. The attributes chosen are very often part of Global Airline Alliances
brand image (set of beliefs) or at least in the same semantic fields.

44. 43
The next chart presents the most important attributes to the least important ones, as identified
by the respondents. A second chart presents the mean value obtained using a radar chart, with
the lower the number, the higher the grade (1 = good attribute to 5 = bad attribute, mean value
= 3).
Figure 19: Perceived values of member airlines - Semantic differential scale questions (n=150, results in percentage)
Figure 20: Mean value for semantic differential questions

45. 44
As we can see in the charts, the three values proposed obtain a slightly positive attribute, ranked
hereafter:
1. Trustful opposed to untrusting (Mean value: 2.43)
2. Caring opposed to neglecting (Mean value: 2.49)
3. Luxury opposed to frugality (Mean value: 2.56)
The three attributes are ranked very close to each other (between 2.43 and 2.56), which can be
observed in the radar chart (see figure 19). No research papers dealing with Global Airline
Alliances analyzed brand image using a semantic differential scale before.
Moreover, a Spearman correlation test (see Appendix 2) showed statistically significant
correlation between the three attributes (all p-values equal to 0). Therefore, the more the
respondents have a positive perceived value on one attribute, the more they will have a positive
perceived value on the other attributes, and inversely.
Other correlation and hypothesis testing
A final Spearman correlation test between the Likert scale and Semantic differential scale
variables (see Appendix 2) reveals that most attributes are significantly correlated together, with
trustful opposed to untrusting correlated with 8 out of 9 Likert scale attributes, caring opposed
to neglecting correlated with 9 out 9 Likert scale attributes and finally luxury opposed to
frugality correlated with 8 out 9 Likert scale attributes.
Another type of analysis was carried out to test different hypothesis, using a two-sample
Student’s t test, with a comparison of two means (see Appendix 2). First of all, using the two
most prominent markets in the study, Europe and the Americas, accounting respectively for
63% and 21% of the respondents, we compared European and American residents with the
Likert scale attributes. We find that only 1 out of 9 attributes, improved lounge access and
quality (p-value = 0.03) allows us to reject the null hypothesis and accept the alternative
hypothesis, meaning significant discrepancies between the two groups and a relationship
towards improved lounge access and quality: European residents give in majority a better grade
to improved lounge access and quality compared to the Americas residents subgroup. A similar
test was used to compare European and American residents with semantic differential scale
attributes. This shows that only 1 out of 3 values, trustful opposed to untrusting (p-value =

46. 45
0.012) allows us to reject the null hypothesis and accept the alternative hypothesis. Europeans
residents, once again, generally see trust as a perceived value for member airlines more often
than the American subgroup.
Moreover, we compared travel frequency groups we identified earlier, regular users and
occasional users with the Likert scale attributes (see Appendix 2). We find that 1 out of 9
attributes, improved lounge access and quality (p-value = 0.002) allows us to reject the null
hypothesis and accept the alternative hypothesis, meaning significant discrepancies between
the two groups: Regular users generally gave a better appreciation to improved lounge access
and quality compared to the occasional users which can be explained by the frequency of
utilization of lounge services more important for the regular users subgroup. We also compared
these groups with semantic differential scale values. All computations (p-values of 0.016, 0.012
and 0.004) allow us to reject the null hypothesis and accept the alternative hypothesis. In detail,
regular users tend to better acknowledge the perceived values of member carriers, being trust,
care and luxury compared to the occasional users.
A test was made to compare the First class subgroups, being often/rarely and never travelled
with Semantic differential scale attributes (see Appendix 2). This results in 1 out of 3 attributes,
luxury opposed to frugality (p-value = 0.034) allows us to reject the null hypothesis and accept
the alternative hypothesis, meaning significant discrepancies between the two groups.
Respondents travelling more often in First class tend to better acknowledge the perceived value
of luxury when flying with member carriers, compared to respondents who never travelled in
First class. Also to be noted, the test was not carried out using Business class as the subgroups
are too unequal (92% of the respondents already travelled in Business class).
Finally, no tests were carried out using gender, age category and knowledge of global airline
alliances due to highly unequal subgroups.
5.3. Hypothesis verification
H1: Airline perceived quality is positively influenced by global airline alliances awareness
In order to verify this hypothesis, we should start from the most important values identified
earlier in the research. Respondents were already aware of global airline alliances and were
asked to think about it before answering the questions. The identified values are: the additional

47. 46
advantages on the frequent-flyer programs, the improved lounge access and quality and the
better choice of destinations and schedules and most importantly, the improved overall travel
experience which has a mean value of 2.42, below 3. The other five values are ranked over the
mean of 3, from which we can understand that a majority of respondents do not think that global
airline alliances help improve these attributes. However, as being said, the three most important
values are core to the global alliances and have been found in many other research papers before
as mentioned earlier. This is therefore a partial hypothesis verification that member airlines
perceived quality is positively influenced by Global Airline Alliances awareness.
H2: Airline brand value is positively influenced by global airline alliances awareness
Similarly, starting from the mean values given for the perceived value of brand image, again,
respondents were already aware of global airline alliances and were asked to think about it
before answering the questions. The three values trust, care and luxury receive relatively
positive marks, at an average of 2.49, above the mean of 3. Therefore, we can infer that a
majority of respondents think that Global Airline Alliances help improve these values. Thus,
using the t-test computations made, we can identify that the more the respondents travel using
member airlines, the more they are likely to recognize brand values. This is a complete
hypothesis verification that member airline brand value is positively influenced by Global
Airline Alliances awareness.
H3: Airline perceived quality has a positive effect on airline brand value
Verifying this hypothesis involves the analysis of the correlation tests made between the
semantic differential scale questions, by extension brand values and the Likert scale questions,
and ultimately the member airline perceived quality. As revealed earlier, most attributes and
values are significantly correlated together. This means that a majority of respondents who gave
a good appreciation on the attributes also gave a good appreciation of perceived values. The
hypothesis is verified, airline perceived quality has a positive effect on airline brand values.

48. 47
6. CONCLUSION
The purpose of this research paper was to study whether certain components of the marketing
performance of member carriers were influenced by Global Airline Alliances. In order to
perform this analysis, two main components were investigated, consumer perception and brand
values. The data was collected using the survey method which is part of the quantitative studies.
The identification of these components and the path of this empirical research was only possible
because of a theoretical review of the previous work done on this specific topic which was
presented in the literature review. We will now discuss our findings in the form of
recommendations, theoretical and managerial contribution and finally we present the
limitations and the possible further research.
6.1. Recommendations
The theoretical overview showed that the impact of airline alliances on air travel has been
widely discussed the past 25 years. Areas such as the marketing performance and its
components have been more discussed lately but still lacks research. Many researchers focused
on airline service quality without taking into account the alliance factor, and other studied
specifically alliances in a general way. The aim was to provide discussions in a field where
service quality is a core driver of profitability and customer loyalty which is one of the main
priorities in the airline industry.
In this document, the empirical results and its analysis showed that the link between airline
alliances awareness, consumer perception and brand values has been established to a certain
degree. This is all the more interesting as the sample studied was composed of frequent-flyers,
one of the populations the most sensitive to changes and among the most loyal customers.
The following components or attributes, identified as additional advantages on the frequent-
flyer programs, improved lounge access and quality and better choice of destinations and
schedules have been identified as the most influenced by global alliances. These components
have also been identified by previous research, as highlighted in the discussions earlier. They
are the core attributes of alliances and are often marketed by them which definitely proves their

49. 48
added value to the industry. Moreover, brand values often marketed by member airlines,
identified as trust, care and luxury demonstrated to be attached to the member airlines. In
conclusion, we proved that Global Airline Alliances bring to a certain degree an added value to
the performance of their member airlines by ultimately improving perceived quality of service
and the brand values attached. However, results show that this influence is limited to direct and
most obvious factors only.
6.2. Theoretical contribution
The global alliances influence on member airlines, as we have seen, has been discussed by some
researchers. We can recall the works of Weber (2005) and Janawade (2012, 2013), the models
of Chen and Tseng (2010) and Wang (2014) which were reviewed in this document.
Researchers agreed that although global alliances certainly influenced member carrier’s
consumer perception and to a larger extent their brand equity performance, this area remains
difficult to assess due to the intangibility of many factors. However, using service quality
attributes and to a lesser extent brand values, previous research and the purpose of this thesis
offer models to determine the most important components which were highlighted in this study.
6.3. Managerial contribution
The first managerial contribution is to provide an understanding of the direct link between
consumer perceived attributes and brand values. Therefore, the marketing campaigns held and
the work on alliance brand image on one hand and the improvement of services made possible
with the creation of these networks have a real impact on consumer perception. As many
researchers highlighted and discussed before in this paper, there is a direct path starting from
consumer perception towards brand loyalty and brand equity of member airlines which also
leads to profitability at the end. In this industry often considered as uncertain, airline marketers
should maintain this link and reinforce their understanding of customer expectations. Thus,
managers might find a way to improve the influence of alliances on the other elements that are
generally not considered as improved such as cabin comfort and entertainment, service by flight
attendants and catering, three services where alliances could make a difference as they are

50. 49
tightly linked and performed together with a possibility of mutualisation between member
carriers. The two last areas identified, cheaper flight tickets and safety, may be more difficult
to improve as they are very sensitive to external factors and often considered out of the scope
of global alliances.
6.4. Limitations and further research
Although the survey results offered an interesting outcome and support previous theoretical and
empirical research, there are some limitations to be highlighted.
A wide range of components, values and attributes were Global Airline Alliances may have an
effect have not been studied. For example, improved baggage allowance was mentioned by one
of the respondents as an added value to a member airline and one of its criteria of choice. To a
similar extent, as another respondent explained, some routes part of the alliances networks
operate in a monopoly, which means that frequent-flyers living in the area have no other choice
but to travel with a specific alliance, which therefore may undermine the research as alliances
do not operate together in a strictly competitive market.
Further research may identify, discuss and measure other components of the so-called
marketing performance to provide a comprehensive study of the benefits. Moreover, other areas
of the performance measurements could be explored in depth, such as efficiency and
productivity. As we pointed out, although marketing is a core component of the viability of
member airlines, many more may bring an additional value such as the reduction of operational
costs which can be improved by the mutualisation of services and collaboration between
member airlines.

51. 50
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APPENDIX 1: QUESTIONNAIRE LAYOUT
Welcome screen:
Part 1 introduction:

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Question 1:
Question 2.a:

57. 56
Question 2.b:
Question 2.c:

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Question 3:
Question 4:

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Question 5:
Part 2 introduction:

60. 59
Question 6.a:
Question 6.b:

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Question 6.c:
Question 6.d:
Question 6.e:

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Question 6.f:
Question 6.g:
Question 6.h:

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Question 6.i:
Question 7.a:
Question 7.b:

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Question 7.c:
Part 3 introduction:

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Question 8:
Question 9:

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Question 10:
Question 11:
End of the questionnaire