Journal of Air Transport Management Analysis 2022

A comparative performance analysis of airline strategic alliances using
data envelopment analysis
Hokey Min a, *, Seong-Jong Joo b
a James R. Good Chair in Global Supply Chain Strategy, Department of Management, BAA 3008C, College of Business Administration, Bowling Green State
University, Bowling Green, OH 43403, USA
b Department of Supply Chain Management, College of Business, Central Washington University-Des Moines, 2400 S. 240th Street, Des Moines, WA 98198,
USA
a r t i c l e i n f o
Article history:
Received 27 March 2015
Received in revised form
2 December 2015
Accepted 17 December 2015
Available online xxx
Keywords:
Airline strategic alliances
Performance measures
Data envelopment analysis
a b s t r a c t
As “open skies” agreements became more common among different countries and thus began to open up
international routes to further competition, the global airline industry has undergone accelerated
structural changes for the last two decades. These changes include the consolidation and expansion of
airline strategic alliances throughout different regions of the world. Though airline strategic alliances are
generally perceived to be a major driver for enhancing the operating efficiency and the subsequent
competitiveness of participating member airlines, the concrete evidence supporting such a perception is
still lacking in the literature. This paper is one of few attempts to evaluate the comparative efficiency of
the strategic alliances among global airlines and then assess the managerial impact of airline alliances on
the airline's comparative performances.
© 2015 Elsevier Ltd. All rights reserved.
1. Introduction
In the wake of prolonged world-wide recessions and sky-
rocketing oil prices, the airline industry lost $16 billion in 2008 and
$9.9 billion in 2009 (Zacks Equity Research, 2011). Although there is
a growing optimism for the revival of the airline industry with the
recent profit gains, the global airline industry has been hit hard by
rising fuel prices, instable yields, weak traffic volumes, security
hassles, and increased taxation for the last few years. To make it
worse, the competition in the global airline industry gets tougher
after a series of deregulations and “open skies” agreements across
the world that liberalized commercial aviation services and then
opened up international airports and transcontinental routes to full
competition. To survive in this deteriorating market condition,
many international flag carriers chose to consolidate their opera-
tions and created economies of scale through mergers and acqui-
sitions (M&A) due in part to changes in ownership laws and
freedom of the air. M&A of airlines, however, can backfire because it
may limit services to smaller regional routes, increase airfare,
create potential strife among integrated workers, raise cost asso-
ciated with increased frequent mileage rewards, and subject
combined airliners to antitrust scrutiny. As illustrated by the recent
mergers of Delta and Northwest in 2008, United and Continental in
2010, and Southwest and Air Tran in 2010, M&A is the continuing
trend of the airline industry. Despite its popularity and potential
benefits, many M&A efforts did not bring fruits to the merged
companies. Defying the conventional wisdom, many M&A attempts
did not go well as they were planned and might undermine the
performances of the merged companies (King et al., 2003). In fact,
the Weekly Corporate Growth Report reported that 70% of the M&A
failed to achieve its anticipated value and 60e80% of the M&A
underwent a slow and painful demise (Palmer, 2005).
Considering this high risk of M&A failures, airline strategic al-
liances (airline alliances hereafter) including code-sharing, equity
swaps, insurance pooling, and joint governance have become a
popular alternative to M&A. Generally, airline alliances refer to a
distinct form of the market entry mode which provides airlines
with a low-cost means of gaining access to new markets and local
infrastructure such as airports (Doz et al., 1990). One of the most
popular and simplest forms of airline alliances is code sharing
which is a commercial agreement between two airlines (operating
and marketing carriers) that allows an airline (marketing carrier) to
put its two-letter identification code on the flights of another
airline (operating carrier) as they appear in computer reservations
systems (US General Service Administration, 2011). For example,* Corresponding author.
E-mail addresses: [e-mail] (H. Min), [e-mail] (S.-J. Joo).
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Journal of Air Transport Management
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Journal of Air Transport Management 52 (2016) 99e110

Delta Airlines might have an agreement to operate flights for
Korean Airlines on a route to Detroit, Michigan. This flight would be
listed under Korean Airline's identification code (KE) but actually
operated by Delta Airlines. This code sharing agreement allows the
airline to expand its customer bases and service offerings without
additional resources (e.g., crews), equipment (e.g., airplanes), and
investment. Also, it helps code sharing partners improve its pas-
senger services through one-stop booking for connecting flights
and concerted service efforts (e.g., shared responsibility for
handling of missing luggage between multiple partnering airlines).
The prevalence of code sharing practices, however, raised some
concerns among consumer protectionists. With little guidance and
regulations, code sharing can be often confusing and not trans-
parent to passengers, because passengers often do not know
exactly which airline will operate their flights. The only exception is
that the U.S. DOT has begun to require airlines to state which airline
is flying a particular route. In addition, code sharing often forces the
passengers to change their planes at different gates in connecting
airports with additional security checkpoints and thus increases
hassles for confused passengers. Furthermore, code sharing may
increase the chance of monopoly for a certain route and leave no
alternative option for passengers. For instance, all the non-stop
flight services between San Francisco and Toronto are exclusively
operated by Air Canada due to its code sharing agreement with
other potential competitors such as United Airlines through Star
Alliances. Not to mention the aforementioned adverse impacts on
customer services, code sharing complicates airline branding
strategy, service differentiation strategy, pricing strategy, flight
scheduling/routing, baggage handling, and frequent flyer reward
systems. This added complexity can be a potential source of in-
efficiency for airlines. Other forms of airline alliances such as equity
swapping and insurance pooling require substantial financial
commitments in time of financial crisis, while joint governance
structures may limit independent decision making opportunities
and thus constraint aligned airlines' operational flexibility. As
illustrated above, there is a growing need to assess the true value of
airline alliances before jumping onto the bandwagon of airline al-
liances. This paper responds to such a need by systematically
measuring and then finding room for improvement in the
comparative (relative) operating efficiencies and service ratings of
airlines which are parts of key airline alliances using data envel-
opment analysis (DEA). This paper also compares the performances
of key airline alliances to those of the non-alliance group for their
competitive strengths and weaknesses, while identifying the po-
tential sources of inefficiency. Based on the DEA and post-hoc
statistical data analyses, this paper provides practical guidelines
for airlines which intend to retool and refine their alliance struc-
tures and practices.
2. Research background and relevant literature
Since deregulation of the U.S. airline industry in 1978 and
liberalization of the European airline industry in 1986 which gave
carriers greater freedom to operate on any routes and fares what-
ever the market would bear, a dramatic restructuring of the global
airlines industry has occurred. This restructuring led to the refor-
mulation of airlines' business strategies that can better cope with
unfettered free competition, elimination of route restrictions,
flexible airfares, and subsidies to “the Essential Air Service Pro-
gram” ensuring air services to small communities. The increasingly
popular business strategies adopted by the global airline industry
include: the focus on low-cost niche markets; discount pricing; the
development of hub-and-spoke networks; M&A among competi-
tors; and global strategic alliances. Despite the popularity and
benefit potentials of these strategies, it was not clear whether these
strategies actually worked well for airlines as they were intended.
With this in mind, this paper first examined what have been
studied in the past to assess the impacts of some of these strategies
on the airline performances and competitiveness.
2.1. Strategic choices
In line with Porter's research on generic business strategies,
airlines traditionally followed differentiation and (market) seg-
mentation strategies, with little pressure to contain costs (Porter,
1980). This is especially true prior to the enactment of airline
deregulation acts. Thus, cost leadership as a competitive strategy is
still a new but risky concept for airlines, as illustrated by the recent
business failures of notable discount carriers such as Skybus. In
addition, chronic industry challenges such as mounting oil prices,
labor strife, high bankruptcy rates, air safety concerns, and
heightened security in the wake of 9/11 put more pressure on air-
lines to find a way to improve operating efficiencies by controlling
costs. While some airlines such as Southwest Airlines have been
able to follow differentiation strategies and cost leadership strate-
gies simultaneously, many airlines continue to struggle with these
strategic tradeoffs.
Historically, prior studies on strategy formulation in the airline
industry fell into two categories: (1) strategic choices; (2) produc-
tivity measures. The first category includes the studies dealing with
classical strategic management topics such as cost leadership, dif-
ferentiation, deregulation, and market segmentation. For example,
using Porter's generic business strategies, Cappel et al. (1996)
theoretically evaluated strategy research as applied to the U.S.
airline industry. At that time, these authors found that airlines
pursuing a combination strategy of cost leadership and differenti-
ation attained a competitive advantage compared with airlines
adopting a singular strategic approach.
Subsequently, a number of low cost carriers (e.g., Southwest
Airlines, Jet Blue, and Spirit) gained attention. Cappel et al. (2003)
extended this research stream and examined the airline industry
structure in post deregulation in the European Community (EC) and
post 9/11 in order to determine whether the low cost strategy
would result in superior performance. Their theoretical question
was whether external events (deregulation and 9/11) would have a
temporary or permanent effect on the relationship between
financial performance and generic business strategy choices.
Alamdari and Fagan (2005) also observed that adherence to pure
low-cost strategy could lead to greater profitability than the
adoption of hybrid low-cost and differentiation strategy.
There are additional external factors that might affect the trend
toward the low-cost strategy. Customers who use the internet to
purchase airline tickets find lower fares than customers who use
travel agents. Research indicates the lower fares may be partially a
by-product of a broader and more thorough search (O'Connell and
Williams, 2005; Brunger and Perelli, 2009). Other studies have
examined the relationship between the low-cost strategy of new
entrants and changes in airline revenue management systems
(Gorin and Belobaba, 2004). These authors found that low-fare
airline entrants can lead to substantial revenue losses for the
incumbent carriers. However, both incumbents and low fare new
entrants alike benefit substantially from the use of revenue man-
agement systems. A comprehensive review of revenue manage-
ment and its development can be found in McGill and Van Ryzin
(1999).
Prince and Simon (2009) argued that much of the previous
research on airline competitive behaviors focused exclusively on
price and only recently researchers have begun to test non-price
forms of competition, e.g., service quality. These researchers
examined the relationship between multi-market contact and
H. Min, S.-J. Joo / Journal of Air Transport Management 52 (2016) 99e110100

service quality. Findings indicate that multimarket contact in-
creases delays and that this effect is greater for contacts on more
concentrated routes. Also concerned with customer service,
Scheraga (2004a) examined the relationship between operational
efficiency and customer service in a global study of thirty-eight
large international airlines. His research categorized areas of cost
savings into: (1) Passenger services such as meals, drinks, and other
services included in the fare, and (2) Cost of sales, such as selling
directly to the customers instead of using travel agents. As
mentioned previously, the Internet has been cited as an external
technological factor affecting the trend toward low-cost strategies
by airline companies (Buhalis, 2004; Brunger and Perelli, 2009).
2.2. The impact of airline alliances on airline productivity
In addition to the aforementioned literature focusing on the
airline's strategic choices, there is another stream of research that
addresses airline productivity issues (Schefczyk, 1993; Park, 1997).
This stream of research includes studies measuring the extent of
benefits of airline alliances and then assessing their impact on
airline productivity, market shares, firm value creation, and com-
petiveness. The following provides details of these studies.
As strategic alliance has emerged as a popular business strategy
for many airlines, numerous articles have identified the potential
benefits of forming strategic alliances. For instance, Wan et al.
(2009) identified five potential benefits of airline alliances. First,
alliances allow airlines to expand their market bases internation-
ally, while circumventing regulatory and legal barriers (Oum et al.,
1996; Park, 1997; Oum et al., 2001; Morrish and Hamilton, 2002).
Second, alliances provide cost saving potentials resulting from
sharing facilities, maintenance costs, and joint marketing (Iatrou
and Alamdari, 2005). Third, alliances may lead to traffic increases
for partner airlines, thus load factors and the subsequent revenue
may increase (Hannegan and Mulvey, 1995; Park, 1997; Wright
et al., 2010). Fourth, passengers may benefit from more flexible
schedules, shorter travel times, improved luggage handling, and
shared frequent flyer programs (Dennis, 2000). Fifth, alliances may
create more effective cooperation, resulting in the elimination of
direct competition (Vowles, 2000) or the improvement of firm
value (Wassmer and Meschi, 2011). In a nutshell, these studies are
predicated on the theoretical model that is graphically conceptu-
alized in Fig. 1.
An early study by Kleymann and Seristo (2001) highlighted the
need for trust in an alliance membership and its relationship to
efficiency. Their research also examined whether airline alliances
were efficiency-seeking or market-oriented (either offensively or
defensively). Oum et al. (2004) indicated that alliances involving
high-level cooperation were found to have a stronger positive effect
on both productivity and profitability than alliances involving low-
level cooperation. Jiang et al. (2008) referred to an alliance as inter-
outsourcing and indicate the goal was to improve efficiency for the
carriers and the travel experience for the customers. The inter-
outsourcing differs from M&A in that the partners choose to
cooperate only so long as it is mutually beneficial to each member.
Wagner et al. (2005) found that airlines involved in alliances
showed higher joint procurement activities than airlines that were
not involved in strategic alliances. Lin (2008a,b) examined the role
of code-sharing in market entry deterrence and its resulting impact
on passenger demand and airfares. On the other hand, Goh and
Uncles (2003) found that airline passengers did not recognize the
benefits of airline alliances such as seamless travel, transferable
priority status, and extended lounge access and thus airline alli-
ances did not give aligned airlines a competitive advantage over
their rivals. Defying the conventional wisdom, Armantier and
Richard (2008) also found that code sharing through airline
alliances did not improve consumer (passenger) welfare in terms of
destination access and flight frequencies.
Similar to the studies cited previously regarding differentiation,
Tiernan et al. (2008) argued that differentiation was the primary
motive for membership in international airline alliances; however
their research did not indicate significant differences in service
quality among the alliances they studied. Other studies (e.g.,
Brueckner and Whalen, 2000; Wan et al., 2009; Zou et al., 2011)
have measured the impact of airline alliances on airfares. For
example, Brueckner and Whalen (2000) verified that airline alli-
ances resulted in airfare reduction through joint pricing. Later, Wan
et al. (2009) found that the extent of impact of an alliance on air-
fares depended on the ability of an alliance to coordinate fares. Zou
et al. (2011) investigated whether cooperation led to higher fares,
or economics of density led to lower operating costs, then lower
fares. While their results were mixed, they indicated that two of the
major airline alliances appeared to charge significantly higher pri-
ces for through-tickets than the sum of segment fares on comple-
mentary routes. On the contrary, Gayle (2008) found no evidence
that airline alliances facilitated collusive behavior among aligned
airlines.
2.3. The evaluation of airline operating efficiency
Although there is a lack of research examining the consequences
of airline alliances from an operating efficiency standpoint, several
authors have attempted to measure operating efficiencies of air-
lines. For instance, to measure airline operating efficiency, Charnes
et al. (1996) introduced global efficient production functions in
evaluating the performances of Latin American airlines in the
presence of uncertainty using a Multiplicative-DEA model.
Following suit, Sengupta (1999) proposed an optimal control
theoretic view of the time path of capital inputs which minimized a
discounted sum of total input costs using a Data Envelopment
Analysis (DEA) model. Adler and Golany (2001) used principal
component analysis (PCA) in combination with DEA to analyze
efficient network configurations in Western European airlines.
Lapre and Scudder (2004) analyzed ten major airlines by separating
them into two groups based on geographic specialists and
geographic generalists. Using DEA and Tobit analysis, Scheraga
(2004b) investigated the structural drivers of airline operating ef-
ficiency in relation to the events of 9/11. More recently, Lin
(2008a,b) reviewed and analyzed previous airline studies in
terms of variables, terminologies, and models used to measure the
performances of Taiwanese domestic airlines using DEA. Barbot
et al. (2008) evaluated the comparative efficiency of 49 airlines
from the different part of the world using both DEA and total factor
productivity. They found that low-cost carriers were generally
more efficient than full-service carriers and labor and fleet utili-
zation tended to affect airline efficiency. Barros and Peypoch (2009)
evaluated the operating efficiency of selected airlines which
belonged to the Association of European Airlines (AEA) from 2000
to 2005 using the two-stage DEA. Similar to the finding of Barbot
et al. (2008), they discovered the influence of economies of scale
on airline operating efficiency. Based on the stochastic frontier
analysis Sj€ogren and S€oderberg (2011) observed that deregulation
improved airline productivity, while state ownership (or decreased
share of private ownership) had no significant effect on airline
productivity.
None of these earlier studies, however, examined the inter-
dynamics of airline alliances and operating efficiencies. In other
words, airline operating efficiencies and their relationship to stra-
tegic competitive advantage have not been studied well in the
existing literature. Considering the paucity of prior studies assess-
ing the impact of airline alliances on airline operating efficiency and
H. Min, S.-J. Joo / Journal of Air Transport Management 52 (2016) 99e110 101

the subsequent competitiveness, this paper is intended to create a
new knowledge base regarding the comparative efficiencies of
airline alliances with strategic insights.
3. Research methodology
3.1. Data envelopment analysis framework
DEA is a special application of linear programming based on the
frontier methodology of Farrell (1957). Since Farrell, a major
breakthrough for developing DEA was achieved by Charnes et al.
(1978) and by Banker et al. (1984). DEA is a useful approach for
measuring relative efficiency using multiple inputs and outputs
among similar organizations or objects. An entity that is an object
to be measured for efficiency is called a decision-making unit
(DMU). Because DEA can identify relatively efficient DMUs among a
group of given DMUs, it is a promising tool for comparative per-
formance analysis.
To elaborate, DEA can be employed for measuring the compar-
ative efficiency of any entity, which has inputs and outputs and is
homogeneous with peer entities in an analysis. According to a
recent DEA study performed by Haas and Murphy (2003), there is a
remedy for a group of entities that are non-homogeneous (e.g.,
manufacturing plants in the same industry producing different
products, airlines serving different customer bases in different re-
gions). In addition, DEA is applicable to DMUs with categorical and
uncontrollable (or environmental) input data such as air safety
regulations and tarmac rules (Athanassopoulos and Thanassoulis,
1995; Mahajan, 1991). Therefore, DEA can be applied to the wide
variety of DMUs without much restriction. DEA is designed to
identify the best practice DMU without a priori knowledge of which
inputs and outputs are most important in determining an efficiency
measure (i.e., score) and assessing the extent of inefficiency for all
other DMUs that are not regarded as the best practice DMUs (e.g.,
Charnes et al., 1978). Since DEA provides a relative measure, it
differentiates between inefficient and efficient DMUs relative to
each other. Thus, the best practice (most efficient) DMU is rated as
an efficiency score of one, whereas all other less efficient DMUs are
scored somewhere between zero and one. To summarize, DEA
determines the following (Sherman and Ladino, 1995):
 The best practice DMU that uses the least resources to provide
its products or services at or above the performance standard of
other DMUs;
 The less efficient DMUs compared to the best practice DMU;
 The amount of excess resources used by each of the less efficient
DMUs;
 The amount of excess capacity or ability to increase outputs for
less efficient DMUs without requiring added resources.
By denoting E0 as an efficiency score for the base DMU 0, DEA
can be mathematically expressed as:
Maximize E0 ¼
( PR
r¼1 u r0y r0
)
( PI
i¼1 vi0x i0
) (1)
subject to:
( PR
r¼1 u r0yrk
)
( PI
i¼1 vi0x ik
)  1 for all k (2)
ur0; vi0  d for all r; i; (3)
where
yrk: the observed quantity of output r generated by unit k ¼ 1, 2,
..., N,
x ik: the observed quantity of input i consumed by unit k ¼ 1, 2,
..., N,
Fig. 1. A conceptual model theorizing the link between airline alliance and efficiency.
H. Min, S.-J. Joo / Journal of Air Transport Management 52 (2016) 99e110102