Journal of Air Transport Management Article 2022

Please develop a table of at least 25 articles Citation Research objectives Methodology Findings Conclusion/Future research Find answers to these questions 1- what are the important infrastructure needed in a company and a country to make e-commerce work 2- success factors for e-commerce adoption 3- e-commerce in airlines 4- code sharing 5- competiteve advantage 6- the personalization through e- commerce all those related chapter 2

The effect of code-sharing alliances on airline profitability
Li Zou* , Xueqian Chen1
College of Business,Embry-Riddle Aeronautical University,Daytona Beach,FL 32114,USA
a r t i c l e i n f o
Article history:
Received 20 May 2016
Received in revised form
30 August 2016
Accepted 15 September 2016
Keywords:
Codesharing
Airline global alliances
Profitability
a b s t r a c t
Code sharing and global alliances both have been increasingly adopted by airlines worldwide in recent
years.A growing number of airlines,therefore,are embedded in networks of multilateral “coopetitive”
(i.e.,cooperative,but competitive) relationships that influence their product offering,pricing strategies,
operating efficiency,market power,and their overallsuccesses.There has been considerable research
analyzing the benefits for airlines from joining globalalliances,including bilateralcode-sharing part-
nerships.However,the joint effect of code-sharing and global alliances on airline performance has not
been fully investigated.In this paper,we study how the use of code-sharing strategies and their struc-
tural embeddedness into globalalliances may impact airline performance.Using a unique dataset
compiled from Flight Global and Airline Business's Annual Airline Alliance Report, the paper empirically
investigates the joint benefits of code-sharing partnerships and global alliances on airline profitability.
The results based on a group of 81 airlines during the 2007e2012 period show that the profit margin of
an airline is positively associated with the number ofcode-sharing partners it has.Furthermore,the
profit margin gains from code-sharing are greater when an airline has a higher proportion of its code-
sharing partners in the same globalalliance; i.e.,allied code-sharing partners.Finally,we find no sig-
nificant evidence that the percent of comprehensive code sharing partnerships to total partnerships has
an impact on profit margin.
© 2016 Elsevier Ltd.All rights reserved.
1. Introduction
Code-sharing arrangements,the most common type of airline
alliance, rapidly developed in the US domestic airline industry after
industry deregulation in 1978 and on international routes by the
late 1980s (Dresner, 2010). Under a code-sharing arrangement, one
airline can use its designation code on a flight operated by a second
carrier. The seats on that flight can be marketed and sold by the first
airline either to provide regionalconnections to complement its
own network (i.e.,complementary alliance) or to reduce competi-
tion by having only one airline actually operate on the route (i.e.
parallel alliance). There are several benefits for airlines from
developing code-sharing partnerships. For example, through code-
sharing arrangements,a major hub-and-spoke airline can use the
flights operated by its regional affiliates or partners to feed traffic
from spoke cities to its hub cities,enabling the efficient and suc-
cessful operation of hub-and-spoke networks.Code-sharing ar-
rangements also allow an airline to expand its service network
without committing its own resources; for example,by extending
its route coverage to more internationaldestinations that other-
wise cannot be served under the restrictive regulatory framework
for international air transport.
The distinct advantages associated with code-sharing arrange-
ment prompt many global airlines to enter into such partnerships,
first at a bilateral leveland later evolving into multilateral,more
formalized group alliances. In 1989, Wings, the first global alliance,
was established,mainly based on cooperation between KLM and
Northwest. In 1997, Star Alliance,the largest and most mature
alliance,was formed by five core members,including United,Luf-
thansa,SAS,Air Canada and Thai Airways Intl.One year after the
formation of Star Alliance, American Airlines, British Airways,
Qantas, Canadian Airlines, and Cathay Pacific teamed together and
formed their own alliance e Oneworld.In 2000,Skyteam Alliance
was founded by Air France, Delta, Korean Air, CSA, and Aeromexico.
Following the merger between Air France and KLM,all the major
member airlines of Wings joined Skyteam.With the extinction of
* Corresponding author.College of Business,Embry-Riddle Aeronautical Univer-
sity 600 S.Clyde Morris Blvd.,Daytona Beach,FL 32114,USA.
E-mail address: [e-mail] (L.Zou).
1 Ms. Xueqian Chen is an undergraduate student in the accelerated MBA program
at the College of Business of Embry-Riddle Aeronautical University, and she helped
collect data for this project under the supervision of Dr. Li Zou during the spring of
2015.
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Journal of Air Transport Management
j o u r n a lhomepage: w w w . e l s e v i e r . c o m / l o c a t e / j a i r t r a m a n
http://dx.doi.org/10.1016/j.jairtraman.2016.09.006
0969-6997/© 2016 Elsevier Ltd.All rights reserved.
Journal of Air Transport Management 58 (2017) 50e57

Wings,the remaining three global alliances e Star,Oneworld,and
Skyteam,continue their growth and expansion,increasing airline
membership by 58% from 34 in 2004 to 54 in 2012.
With the increasing prevalence ofcode-sharing partnerships
and global alliances,a growing number of airlines,therefore,have
recently been embedded in networks of multilateral “coopeti-
tivity”, meaning the coexistence ofcooperation and competition
among allied partners that influence productofferings,pricing
strategies,operating efficiency,market power,and overall perfor-
mance.On one hand,more and more airlines have formed closer
and deeper partnerships with allied airlines in the same global
alliance to leverage their joint branding,joint marketing,resource
sharing,etc.,for potential revenue gains,cost savings,or both.On
the other hand,airlines also have developed and maintained their
bilateralalliance relationships with non-aligned airlines or even
with airlines from rival group alliances. For many airlines, including
both aligned and non-aligned carriers,the bilateralcode-sharing
strategy remains a driver of revenue growth and cost savings.
Though there has been much research analyzing the benefits for
airlines either from joining global alliances or from building bilat-
eral code-sharing partnerships,an examination of the joint effects
of code-sharing and global alliances on airline performance is still
unexplored. In this paper, we focus on the question: To what extent
are the impacts from code-sharing strategies on an airline's per-
formance moderated by its structural embeddedness (or the lack
thereof) into global alliances? Using data collected from Flight
Global and the Annual Airline Alliance Summary Reportresults
published by Airline Business,we empirically investigate the com-
bined effects of code-sharing partnerships and global alliances on
airline performance.The results based on a group of81 airlines
during the 2007e2012 period show that the profit margin of an
airline is positively associated with the number ofcode-sharing
partners it has.Furthermore,the profit margin gains from code-
sharing partnerships are greaterwhen an airline has a higher
proportion of its code-sharing partners in the same global alliance,
i.e.,allied code-sharing partners.Perhaps,due to methodological
limitations,we do not find significant evidence that the levelof
cooperation moderates the profitability benefits for code-sharing
partners from a code-sharing strategy.Nevertheless,our finding
that there are joint benefits from code-sharing partnerships and
global alliances provides valuable implications for airline man-
agementseeking to develop the most rewarding code-sharing
partnerships in the context of global alliances.
This paper contributes to the existing literature and to airline
alliance managementin three ways: First, we develop a new
construct,namely, allied code-sharing partner,to represent the
code-sharing partnership formed between airlines in the same
global alliance. This construct is then used to measure the extent of
an airline's allied code-sharing partnerships compared to its total
number of code-sharing alliances.Through our empirical analysis,
we find evidence suggesting thatan airline can gain a greater
profitability benefit when it increases its code-sharing partnerships
with allied airlines. For airlines that are already in the three global
alliances (i.e.,Star,Oneworld,and Skyteam Alliance),this finding
can help them decide whether to develop code-sharing partner-
ships with allied or non-allied airlines.For those non-aligned air-
lines that have existing code-sharing arrangements,our findings
may be valuable in helping them to choose the most beneficial
global alliance to join. Second, although there is empirical research
estimating the impact of code-sharing alliances on airline perfor-
mance, the moderating effect of the depth of code-sharing alliances
(measured by the extent of route integration through code-sharing
arrangements) is unexplored.In this paper,we develop a conve-
nient instrument to measure the depth of code-sharing arrange-
ments in order to estimate their potentially moderating effects.
Finally,we contribute to the existent literature on airline alliances
by using a panel dataset that includes a broad sample of airlines
varying in operating scale, geographic region, and alliance
engagement.
In the following section, a literature review is provided. Section
3 introduces our hypotheses.Data descriptions and the empirical
models are presented in Section 4.The results are summarized in
Section 5.The last section concludes the paper and discusses po-
tential for future research.
2. Literature review
Dresner and Windle (1996) examine the early development of
airline alliances and code-sharing arrangementsand suggest
several rationales for the adoption of such strategies by a growing
number of international airlines in the 1990s. For most airlines, the
primary consideration of forming alliances is to expand global route
coverage and serve on international routes that would otherwise be
impossible to offer due to legal and regulatory restrictions. Through
code-sharing arrangements,the most common type of airline alli-
ance,each of the two partner airlines can market and sellseats
under its code on the flights operated by the partner carrier.Such
an arrangement may generate revenue to partner airlines through
market expansion,traffic feeds,improved connectivity,multiple
listings on Computer Reservation System (CRS) screens, etc.
Moreover, code-sharing arrangements and other alliance activities
may help the partner airlines reduce the cost per passenger because
of increased traffic, joint advertising,equipment sharing, etc.
Therefore,it is expected that airlines may gain competitive ad-
vantages by code-sharing,and that carriers that do not enter into
these partnerships are ata disadvantage (Dresner and Windle,
1996).
There are various types of code-sharing partnerships. In the US
domestic airline industry, the first code-sharing arrangement were
formed between major US airlines and their smaller regional
partners that provided service on lower density routes,and fed
traffic onto mainline routes operated by the major airline. This type
of code-sharing arrangement,known as “complementary code-
sharing,” has become an essentialcomponentof the hub-and-
spoke systems in the US domestic airline industry (Ito and Lee,
2007). On international routes, a similar type of code-sharing
arrangement has been adopted by internationalairlines to con-
nect their route networks and provide seamless connections for
passengerstraveling from one country to another and flying
beyond gateway hubs to inland destinations in the foreign country.
In contrast, so-called “parallelcode-sharing”arrangementsare
developed between two airlines that competed on routes prior to
forming partnerships (Park,1997).The study of airfare effects of
parallel versus complementary alliances has been a research topic
over the last two decades (Yousseff and Hansen, 1994; Oum et al.,
1996; Park,1997; Park and Zhang,2000; Brueckner,2001,2003;
Brueckner and Zhang,2001; Ito and Lee,2007; Wan et al.,2009;
Zou et al.,2011; Gayle and Brown,2014).The extent of coopera-
tion associated with code-sharing arrangements on international
routes also varies depending on whether antitrustimmunity is
granted or not.With antitrust immunity,the partner airlines are
allowed to jointly set airfares and capacity,enabling them to have
greater cooperation in terms ofairfares,flight scheduling,mar-
keting and capacity adjustments (Dresner and Windle, 1996;
Brueckner,2003).
Given the prevalence of code-sharing partnerships and the po-
tential anticompetitive concerns over antitrust immunity for the
alliances,Brueckner (2003) develops an in-depth study to investi-
gate the joint airfare effects of code-sharing alliances and antitrust
immunity.As an extension ofhis earlier results (Brueckner and
L. Zou,X. Chen / Journal of Air Transport Management 58 (2017) 50e57 51

Whalen, 2000), Brueckner (2003) finds strong evidence that airline
cooperation through either code-sharing or antitrustimmunity
leads to airfare reductions for interline passengers on international
routes. However, the combined airfare reduction effects from code
sharing and antitrust immunity were found to be smaller than the
separate, individual effects. Such airfare reductions generate
further benefits for interline passengers above and beyond the
improved convenience and tighter connections associated with
code-shared flights.
Gayle and Brown (2014) compare the average airfare, traffic and
market share on routes connecting the 50 largest cities in the US
before and after the implementation ofalliances among Delta,
Continental, and Northwest Airlines in 2003 and find no statistical
evidence for collusive pricing on code-shared routes.Instead,a
traffic increase is found on routes where these three airlines have
code-sharing arrangements.The demand increase only existed on
routes where the joint market share between two allied airlines
was greater than 0.49 prior to their code-sharing alliance.Accord-
ing to the authors, the traffic stimulating effects can be attributed to
the enhanced opportunity for passengersto accumulate and
redeem frequent flier points with code-sharing partner airlines. In
other words,the formation of code-sharing partnerships seems to
bring additionalbenefits to airlines that already have a base of
customers that are loyal to either one of the partners prior to the
alliance.Through code-sharing alliances,loyalty may induce loy-
alty; thus,the total traffic of the code-sharing partners is greater
than the sum of the traffic of the individual airlines.
While there has been a generalconsensus abouthow code-
sharing alliances affect airfares in both the domestic and interna-
tional context,its overall impacts on airline performance are less
certain, with mixed findings in the existent literature. For example,
in their study of the three alliances formed between US airlines and
their European counterparts in the 1990s,including Continental
Airlines/SAS,Delta/Swissair,and Northwest/KLM, Dresner et al.
(1995) find that not all three alliances generate greaterthan
average increase in traffic and load factor in their markets.There-
fore, they conclude that the expected benefits from alliances are not
guaranteed,even after the partner airlines realign their route net-
works for improved connectivity. By contrast, in studying the code-
sharing alliances between US Air and British Airways and between
Northwest and KLM,Gellman Research Associates (1994),using a
different methodology,find evidence in support of the positive
traffic and revenue effects of these alliances. Their methodology is
based on passenger selection outcomes among alternative traveling
options,which are then employed to derive values placed by pas-
sengers on fares,flight time, connections,codesharing arrange-
ments, etc. Based in part on interview data and airline internal data,
the US General Accounting Office (1995) reports large traffic gains
and revenue increases for three strategic code-sharing alliances,
including Northwest/KLM (formed in 1992),USAir/British Airways
(1993), and United/Lufthansa (1994). As for the four regional code-
sharing alliances examined,two are found to result in modest in-
creases in traffic and revenue,including United/Ansett Australia
and United/British Midland,while the other two e Northwest/
Ansett Australia and TWA/Gulf Air fall, are not. According to the US
General Accounting Office (GAO, 1995), the majority of the 61 code-
sharing partnerships have a low degree of route integration at that
time and therefore, they are not profitable or long lasting.
Morrish and Hamilton (2002) provide a comprehensive review
of airline alliances and their impacts on airline performance in both
economic and non-economic terms.As Morrish and Hamilton
(2002) note,‘there is no conclusive evidence to date that major
airlines have been able to use global alliances to restrict competi-
tion and boost their own profitability.’They further suggest that
although alliance partnersmight experience some increase in
traffic,load factor,and productivity from alliances,these benefits
may be partially or even totally offset by greater frequencies and
lower airfares,thereby resulting in modest or little profit gains.
Pitfield (2007) echoes the view saying that an analysis using data
from the US Bureau Transportation Statistics ‘does not yield un-
ambiguous conclusions in accordance with theory or expectation’
about the positive impacts of code-sharing alliances on traffic and
market share of partner airlines. These inconclusive findings at the
route levelmay be due to the complexity of supply and demand
interactions,unidentifiable route characteristics,and/or diverse
regulatory and competitive environments (Pitfield, 2007).
The integration between airlines in code-sharing alliances may
be extensive, and distinct for different arrangements. Depending on
the specifics of each alliance,the market coverage ranges from
limited to comprehensive,and from point-specific to regional and
strategic.The scope of cooperation may include a variety of areas,
such as scheduling, route networks, operations, advertising,
frequent flyer programs, etc. As a result, it is necessary to take into
account the level and characteristics of code-sharing arrangements
when estimating the benefits to the partner airlines.For example,
in the study by Oum et al.(2004),the authors categorize alliances
into high-level and low-level by the degree of cooperation involved.
A high-level alliance involves network-level collaboration, through
which the allied airlines link their route networks. By contrast, the
collaboration in a low-level alliances only occurs at the route level
without combining the entire networks.The authors estimate the
impacts of alliances on airline productivity and profitability
focusing on the 108 alliances formed among the 22 leading inter-
national airlines during the 1986e1995 period.Their results sug-
gest that although alliances in general only lead to the
improvement of productivity (not profitability),strategic alliances
characterized by high-level cooperation contribute to both higher
productivity and greater profitability.
Yousseffand Hansen (1994) consider technicalefficiency and
market power as two primary factors leading to increased profit-
ability for airlines from participation in code-sharing alliances.
More specifically, improvement in technical efficiency will
contribute to unit cost reduction and can be accomplished through
traffic increase,route network consolidation, resource sharing,
flight scheduling optimization, and greater outputs in terms of the
quality and quantity of connecting services,etc.In addition to the
cost saving benefit, the formation of alliances, in general, is viewed
as a means for airlines to restrain competition, preclude rivalry, and
seek virtual monopoly status.Alliances allow carriers to retain
market power which otherwise might be threatened as the long-
standing regulatory regimes for air transportare replaced with
liberalization over time (Yousseff and Hansen, 1994).On the other
hand, the new entrants may also resort to code-sharing alliances as
a strategy to help strengthen their market power against the
dominant market leader.Using data for 56 airlines during the
1986e1993 period, Park and Cho (1997) find evidence showing that
the combined market share ofpartner airlines is more likely to
increase afterthe formation of code-sharing alliance and such
market share gains are higher when the alliance is formed between
two relatively new entrants on a route,and when the market has
fewer competitors and it is growing quickly.
There may be inconsistencies between theoreticalpredictions
and empirical results regarding the cost effects from code-sharing
alliances.In a survey conducted by Iatrou and Alamdari(2005)
among managers of alliance departments at 28 airlines belonging
to the four global alliances in 2002,the respondents,on average,
perceive cost to be least affected by alliances,compared to other
factors, such as fares, revenue, load factor, and traffic. Moreover, as
compared to outcomes such as traffic growth, load factor increases,
and revenue growth, cost reduction is cited much less as an
L. Zou,X. Chen / Journal of Air Transport Management 58 (2017) 50e5752

outcome of code sharing by the participating respondents. In sum,
Iatrou and Alamdari (2005) find that code-sharing is the most
effective type ofalliance for providing airlines distinctrevenue
benefits in terms of traffic and load factor increases. As for the cost
effects, the authors do not observe the same discernable benefits, as
widely acknowledged by airline alliance managers.The cost re-
ductions associated with alliances are nonexistent or very small, at
least in the short run. Through estimating a structural econometric
model, Gayle and Le (2013) decompose the cost effects of code-
sharing alliances atthe route level into three types: short-run
marginalcost,medium-to long-run sunk market entry cost,and
recurring fixed costs.Using a difference-in-differences estimation
methodology, Gayle and Le (2013) focus on the three-way alliances
among Delta, Northwest and Continental to investigate the alliance
effects in contributing to cost changes at these three airlines. Their
results suggest that the Delta-Northwest-Continentalalliance al-
lows the three partners to decrease their marginal costs, especially
when they have a dominant market presence at origin and desti-
nation endpoints,and to decrease their market entry sunk costs.
However, the three airlines are found to have increased fixed costs
following their alliances, implying that the overall cost effects from
alliances are uncertain because of these offsetting consequences.
The assessment of the profitability effects of code-sharing alli-
ances needs to take into account severaltradeoff,with consider-
ation to the following issues: First, “parallel” and “complementary”
code-sharing alliances have distinct effects in terms on traffic, load
factor, service, airfare, and revenue. The different outcomes are also
contingent upon whether the alliance is endowed with antitrust
immunity. Second, the traffic increase resulting from code-sharing
alliances may not be sufficient to offset the potential airfare
reduction and the incrementalcosts incurred.Thus, the overall
profitability effects cannot be assessed based on changes in traffic
or revenue alone.Third, the effects of code-sharing alliances may
not be the same across all routes because of distinct route-related
market structure and demand characteristics.Moreover, even
though partner airlines may win traffic from rival airlines due to
alliance agreements,the rival airlines in response may retaliate,
punitively,on overlapping routes with the partner airlines and
cause the loss of traffic for the partner airlines on some of their non-
codeshared routes. Therefore, it is necessary to examine the overall
effects ofcode-sharing alliances not only at the route level,but
throughout the network.
3. Hypotheses development
As stated above,it is necessary to consider both the potential
revenue and cost effects in a code-sharing arrangement for an ac-
curate assessment of its overall profitability.In contrast to a great
number of studies investigating revenue effects,empirical studies
on the cost effects of code-sharing alliances are very limited.As
noted by Gayle and Le (2013), the lack of the research, in part, is due
to the difficulty in disaggregating costdata to the route level.
Theoretically,the overall effect of alliances on the total cost of air-
lines (including marginal costs, market entry costs, and fixed costs)
may be uncertain.On one hand,code-sharing alliances may help
the two partner airlines consolidate their traffic by reducing or
eliminating duplicate flights.The practice of code sharing also en-
ables airlines to expand their route networks, optimize flight
schedules,and improve connecting services,thereby attracting
new passengers and increasing traffic. The increased traffic density
can lead to a marginal cost reduction. Moreover, an airline under a
code-sharing arrangementcan enter new markets by using the
flights of its partner airlines.Thus, the airline can avoid making
investments in route development, thereby saving costs associated
with market entry. On the other hand, to accommodate the
increased traffic resulting from code-sharing alliances,the two
partner airlines may have to incur costs acquiring baggage handling
facilities, airport gates, check-in counters, airplanes, etc., if there is a
traffic volume increase beyond their joint capacity. Because of these
offsetting effects,Gayle and Le (2013)further suggestthat the
overall impact on the totalcosts of partner airlines willbe small
after considering both the positive and negative forces. This view is
consistent with empirical findings in the studies by Goh and Yong
(2006),Gagnepain and Marin (2010),and Chen (2000).
The survey research by Iatrou and Alamdari (2005), based on the
questionnaires filled by alliance managers of28 airlines partici-
pating in Wings, Star, oneworld and Skyteam in 2002 also indicate
that cost is perceived to be least affected by the formation of alli-
ances,compared to severalother aspects of airline operations,
including airfares,load factor,revenue and traffic.
Using firm-level data for 10 US airlines from 1994 through 2001,
Goh and Yong (2006) estimate a truncated third-order translog cost
function and find that ‘ … alliances do appear to lower costs.
However,while the impacts are statistically significant,in eco-
nomic terms the magnitude appears to be immaterial.’On the
revenue side,the majority of the existent literature provides both
theoretical arguments and empirical support for the revenue ben-
efits from codesharing alliances.Thus, we propose the following
hypothesis.
Hypothesis 1. The operating margin ofan airline is positively
associated with the number of code-sharing partners it has.
The level of cooperation between two code-sharing partners can
be enhanced when the partners are in the same globalalliance.
Through sharing airport lounges,joint marketing,branding and
frequent flier programs, etc., allied airlines have more opportunities
to improve their connecting services involving code-shared flights,
and to make their services more seamless.Moreover,the service
standards followed by airlines in the same global alliances tend to
be more harmonized and consistent.Hence, code-share flights
offered by airlines in the same global alliance may be more
appealing to passengers,as compared to code-share flights by air-
lines not in the same global alliances. From the passenger’s
perspective, Goh and Uncles (2003) discuss five main benefits that
air travelers (especially business travelers) may be aware of when
choosing airlines in the same global alliance: “a) greater network
access; b) seamless travel; c) transferablepriority status; d)
extended lounge access; and e) enhanced frequent-flyer program.”
The first two can be regarded as benefits associated with code-
sharing partnerships,while the last three are benefits thatare
more relevant to global alliances in general. The offering of all these
benefits would be most likely when the code-sharing partnership is
formed between allied airlines.As outlined by Goh and Uncles
(2003),the full awareness of these benefits,and their actualde-
livery, would increase customer loyalty to a particular airline or
alliance group, a source of competitive advantage. In line with this
argument, we hypothesize that profitability gains would be greater
as an airline increases its code-sharing partnership with airlines in
the same global alliance.
Hypothesis 2. The operating margin ofan airline is positively
associated with the percentage ofallied code-sharing partners (i.e.,
those in the same global alliance as the focal airline) it has compared
its total number of code-sharing partners.
For two airlines in a code-sharing relationship, the more routes
that are code-shared, the more integrated are their route networks.
On the supply side,the increased number of code-shared routes
enables the partner airlines to add more destinations.This en-
hances the combined route networks, bringing greater efficiencies
and cost saving benefitsfrom improved traffic density to the
L. Zou,X. Chen / Journal of Air Transport Management 58 (2017) 50e57 53

partners.There are also several demand-side benefits to having a
greater scope of code-sharing practices.According to Yousseff and
Hansen,1994, the quality and quantity of connecting services
may increase because ofimproved scheduling coordination.As
partner airlines have more routes codeshared with each other,the
extended route network and will entice passengers to pay higher
airfares as a result.Moreover,the benefits to passengers from the
joint frequent flier programs offered by partner airlines are multi-
plied as code-sharing airlines increase the extent oftheir route
integration.Finally,due to passengers’incomplete information,a
large route network can provide an airline with some marketing
advantages as it is more likely to be selected by passengers.More
specifically,the US GAO,(1995) writes the following while discus-
sing potential factors influencing the benefits for participating
airlines from code-sharing alliances.
‘The extent to which airlines participating in alliances benefit from
them varies greatly and depends on the (1) geographic scope of the
code-sharing arrangement,(2) levelof operating and marketing
integration achieved by the airlines,and (3) agreement between
the airlines on how to divide revenues.’
Thus, our Hypothesis 3 is the following:
Hypothesis 3. The operating margin ofan airline is positively
associated with the percentage of comprehensive code-sharing part-
ners (i.e., those having a comprehensive code-sharing partnership with
the focal airline) compared to its total number of code-sharing
partners.
In Hypothesis 3, the comprehensive code-sharing partnersrefer
to those having ten or more code-shared routes. As explained in the
following section,the data we use in the analysis is derived from
the annual Airline Alliance Survey report published by Airline
Business.The annual survey categorizes the type of code-sharing
alliances as limited or comprehensive,with limited code-sharing
alliances defined as agreements with up to 10 routes covered and
comprehensive agreements with at least 10 routes covered.
4. Data and methodology
The data for our analysis is mainly collected from FlightGlobal
and the Airline Alliance Survey published by Airline Business.In its
annual Airline Alliance Survey report,the Airline Business maga-
zine provides a list of codesharing agreements for each member
airline of the Star, Skyteam and Oneworld Alliances. In addition, the
report provides code-sharing agreement information for a group of
leading non-aligned airlines. The code-sharing agreements
compiled in the report are relevant to allairlines featured in the
Airline Business Top 200 Passenger Rankings list. The alliance survey
data is supplemented by airline performance and operating char-
acteristics drawn from FlightGlobal.
Using panel data for 81 airlines from 2007 to 2012, we estimate
the effects on profit margin to a focal airline from: a) the number of
codesharing partners;b) the percentage of allied codesharing
partners; and c) the percentage ofcomprehensive codesharing
partners,while controlling for other variables,such as load factor,
passenger yield,unit cost,operating scale (measured by ASK),and
global alliance membership. During the study period, the number of
airlines in the Star Alliance increased from 22 to 27, in the Skyteam
Alliance from 12 to 17, and remained 11 in the Oneworld Alliance. In
our initial sample, there are a total of 92 airlines, including 24 in Star,
19 in Skyteam,and 11 in Oneword.The remaining 38 are non-
aligned airlines.After the exclusion of airlines with missing per-
formance data, our final sample covers 81 airlines with a total of 486
annual observations from 2007 through 2012,including 136 ob-
servations for Star Alliance,83 for Skyteam,66 for Oneworld,and
201 for the non-aligned group. Table 1 provides the descriptions for
key variables as well as summary statistics.
Table 2 presents the correlations between all key variables.
Figs.1e3 present a comparison of average code-sharing char-
acteristics,including the number of code-sharing partners,the
percentage of allied code-sharing partners,and the percentage of
comprehensive code-sharing partners among airlines in Star,Sky-
team,Oneworld,and non-aligned airlines during the 2007e2012
period.
The following empiricalmodel is developed to test our three
hypotheses.
lnðProfit Marginit Þ ¼a0 þ a1 Number ofCPit
þ a2 Percent ofAllied CPit
þ a3 Percent ofComprehensive CPit
þ a4 lnðLoad Factorit Þ
þ a5 lnðPassenger Yieldit Þ
þ a6 lnðUnit Costit Þ þ a7 lnðASKit Þ
þ a8 Alliance Dummiesit þ εit (1)
In the regression model, the dependent variable is the operating
profit margin for a focalairline i in year t. To test the three hy-
potheses,we develop three variables for measuring the scope and
extent of code-sharing characteristics and the airline's embedd-
edness in the global alliances.These three independent variables
are: (1) Number of code-sharing partners (denoted as Number of
CPit for Airline i in year t); (2) The percentage of allied code-sharing
partners (denoted as Percent of Allied CPit for Airline i in year t); and
(3) The percentage of comprehensivecode-sharing partners
(denoted ad Percent ofComprehensive CPit for Airline i in year t).
Several control variables are included,such as Load Factor,Passen-
ger Yield,Unit Cost,and Available-seat kilometers (representing the
total traffic volume the focal airline has in a given year). The Alliance
Dummies are included as indicatorvariables to control for the
alliance-specific effects.
5. Results
The model, Eq. (1), is estimated using a random generalized
least square (GLS) procedure. The estimation results are presented
in Table 3.Because of the nature of the panel data,we cannot as-
sume that the effort terms in Eq.(1) are independent and identi-
cally distributed,and thus results based on ordinary least square
(OLS) estimation would be vulnerable to issues such as hetero-
scedasticity,serial correlation and within panelor contempora-
neous correlation across the panel. To detect these potential
concerns,we first run the Breusch-Pagan/Cook-Weisberg test to
check if heteroscedasticity is present.The results (c 2(1) ¼ 570.52,
p ¼ 0.0000) strongly reject the null hypothesis of homoscedasticity.
In addition,the results from the Woodridge test (F(1, 52) ¼ 2.216,
p ¼ 0.1426) fail to reject the null hypothesis of no autocorrelation.
Considering our use of a relatively small panel with a large number
of airlines, we use the random GLS estimation for its data efficiency
and better fit.The results are summarized as follows.
In Model 1, the effect of the number of code-sharing partners on
operating profit margin is estimated while controlling for variables
such as load factor, passengeryield, unit cost, available seat-
kilometers,and airline global alliance status.The coefficient for
Number of Codesharing Partners is found to be positive (¼0.014) and
highly significant (p < 0.001),suggesting that operating margin is
positively related to the number of code-sharing partners,
L. Zou,X. Chen / Journal of Air Transport Management 58 (2017) 50e5754

consistent with Hypothesis 1.More specifically,the results reveal
that an airline will increase its profit margin by 0.14% with an
addition of 10 code-sharing partners. The estimated coefficients for
most control variables are statistically significantand show ex-
pected signs. In particular, Load Factor and Passenger Yield are foun
to have positive effects on profitability whereas Unit Cost is nega-
tively related to profit margin.The coefficient for available seat-ki-
lometers is found statistically insignificant suggesting that
operating scale by itself may not impact an airline's profitability
after taking into account the effects from unit cost, load factor, and
passenger yield.Since the estimation ofModel 1 is based on a
complete set of 81 airlines including those members of a global
alliance,the regression results for the dummy variables repre-
senting global alliances status indicate that compared to non-allied
airlines, only member airlines of Oneworld, on average, have greater
profitability.The other two global alliances (i.e.,Star and Skyteam)
do not directly impact the profitability of their member carriers, on
average.
In Model 2, the variables,Number of code-sharing partners and
Percent of Allied Code-sharingPartners,are both included as
explanatory variables in estimating profit margin. The inclusion of
Percentof Allied Code-sharing Partnersis necessary for testing
Table 1
Variable description and descriptive statistics.
Variable Description Mean (Std.
Dev.)
Profit margin The operating profit margin (i.e.,EBIT/Operating revenue) for airline i in year t 0.012 (0.099)
Number of code-sharing partners The number of code-sharing partners airline i has in year t 14.07 (7.48)
Percent of allied code-sharing partnersThe percentage of code-sharing partners that are allied partners of airline i in year t 0.61 (0.17)
Percent of comprehensive code-sharing
partners
The percentage of code-sharing partners that have comprehensive codesharing arrangements with airline i in
year t
0.61 (0.17)
Unit cost ($) The total operating expenses per available seat mile (CASM) for airline i in year t 0.094 (0.029)
Yield ($) The total passenger yield (Cents/RPMs) including scheduled and non-scheduled passenger services 0.104 (0.034)
Load factor (%) The total RPMs divided by the total ASMs including both scheduled and non-scheduled revenue services operated
by airline i in year t
0.742 (0.103)
Total ASKs (million) The total available seat kilometers for passenger services incurred by airline i in year t 58,137
(63,175)
Table 2
Correlation matrix for key variables.
1 2 3 4 5 6 7 8
1. Profit margin 1.00
2. Number of code-sharing partners 0.07 1.00
3. Percent of allied code-sharing partners 0.11 0.19 1.00
4. Percent of comprehensive code-sharing partners 0.05 0.13 0.37 1.00
5. Unit cost 0.14 0.32 0.03 0.06 1.00
6. Yield 0.02 0.17 0.05 0.09 0.89 1.00
7. Load factor 0.24 0.25 0.02 0.42 0.10 0.27 1.00
8. Total ASKs 0.02 0.34 0.05 0.53 0.01 0.10 0.51 1.00
0
2
4
6
8
10
12
14
16
18
20
2007 2008 2009 2010 2011 2012
Avg. # of codesharing partners of airlines in Star
Avg. # of codesharing partners of airlines in Skyteam
Avg. # of codesharing partners of airlines in Oneworld
Avg. # of codesharing partners of non-aligned airlines
Fig. 1. The average number of codesharing partners.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
2007 2008 2009 2010 2011 2012
Avg. % of allied of codesharing partners of airlines in Star
Avg. % of allied of codesharing partners of airlines in Skyteam
Avg. % of allied of codesharing partners of airlines in Oneworld
Fig. 2. The average percentage of Allied codesharing partners.
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
2007 2008 2009 2010 2011 2012
Avg. % of comprehensive codesharing partnership for airlines in Star
Avg. % of comprehensive codesharing partnership for airlines in Skyteam
Avg. % of comprehensive codesharing partnership for airlines in Oneworld
Avg. % of comprehensive codesharing partnership for non-aligned airlines
Fig. 3. The average percentage of comprehensive codesharing partnership.
L. Zou,X. Chen / Journal of Air Transport Management 58 (2017) 50e57 55

Hypothesis 2. As can be seen from Column 3 of Table 3, this newly
added variable has a positive (¼0.226) and moderately significant
(p < 0.10) coefficient,suggesting that the higher the proportion of
allied code-sharing partners an airline has,the greater its profit
margin. Thus, Hypothesis 2 is supported. The estimation results for
other control variables are similar to those found in Model 1.
Considering that Model 2 is estimated using data only for airlines in
the three global alliances,the default case for comparison is
member airlines of Oneworld.Therefore,the negative and signifi-
cant coefficients for Star and Skyteam can be explained as an indi-
cation that among the three global alliances, members of Oneworld,
on average,have higher profit margins than their counterparts in
Star and Skyteam.
To test Hypothesis 3, the variable Percent of Comprehensive Code-
sharing Partners is incorporated as a regressor, along with Number of
Code-sharing Partners, in Model III. Consistent with the findings from
Models I and II, the coefficient for Number of Code-sharing Partners is
positive (¼0.014) and highly significant (p < 0.001).However,the
variable representing the level of code-sharing partnership, Percent
of Comprehensive Code-sharing Partners,is positive (¼0.027)but
insignificant, suggesting that an airline can enhance its profitability
through increasing the number of code-sharing partners without
regard to whether the partnership is comprehensive or limited.
Though this result does not support Hypothesis 3,the conclusion
may be circumspect given limitations in the variable,Percent of
Comprehensive Code-sharing Partners, used for measuring the extent
of code-sharing partnership.In Model IV, we include all the three
alliance variables and other control variables.The results are very
similar to those found in Models I-III.
In summary, our empirical results validate the performance
benefits for an airline from developing more code-sharing part-
nerships with other airlines. Moreover, profitability gains for
partner airlines resulting from their code-sharing partnerships are
found to be higher when the two partners are airlines in the same
global alliance.Hence,for an airline planning to develop code-
sharing partners,it is optimal to choose those in the same global
alliance for greater operating margin improvement.Overall,the
implication from this study assures airline managementof the
performance benefits from joining global alliances in conjunction
with forming code-sharing partnership with allied carriers.
6. Conclusions,implications and limitations
In this paper,we investigate the impacts of code-sharing alli-
ances on an airline's operating margin.Using panel data for 81
major airlines from 2007 through 2012, we find evidence showing
that there is a highly significant and positive relationship between
the number of code-sharing partners and an airline's operating
margin. This result supplements the findings of Iatrou and
Alamdari (2005) based on their survey data that code-sharing al-
liances provide airlines with significant revenue gains but provide
few cost impacts. From a practical perspective, our results provide
confidence to managers to increase their use of code-sharing alli-
ances as a strategy for enhanced profitability,consistent with the
recent trend in the airline industry. Take Etihad Airways as an
example. Although the airline has not joined a global alliance, it has
formed a growing number of code-sharing alliances with other
carriers in recent years.According to the article “Etihad uses part-
nership to beat 2011” from Airline Business magazine (Bonnassies,
2012), Etihad Airways had two code-sharing partners in 2008
contributing to 1% of the airline's revenue.By 2012,it had thirty-
five code-sharing arrangements with thirty-five airlines account-
ing for 19% of the airline's total revenue.
Moreover, the results indicate a statistically significantand
positive association between operating margin and the percentage
of code-sharing arrangement with allied airlines.This finding has
two important managerial implications. First, for airlines in a global
alliance,it suggests that the benefits from code-sharing arrange-
ments will be amplified as they develop more code-sharing part-
nerships with their existent allies in the global alliance. The positive
association between greater profit margin and a higher percentage
of allied code-sharing partners is consistent with the finding by
Oum et al. (2004) that there is no substantialprofit benefits for
airlines in global alliances unless the alliances involve some high-
level cooperation.It can be shown from our study that through
code-sharing partnerships,allied airlines in global alliances can
develop a higher level of cooperation for greater profit gains.
Therefore,for an airline that is already participating in one of the
three global alliances, the implication is that it will benefit more if it
chooses those allied airlines in the same global alliance as its code-
sharing partners.On the other hand, for airlines that are not
members of global alliances,it may be beneficial for them to
consider joining the global alliance that contains the greatest
number of their existent code-sharing partners.As suggested by
our results,through globalalliances allied code-sharing partners
may develop a higher level of operating and marketing integration,
and thus the profit margin benefits from code-sharing partnership
can be enhanced.
Finally,consistent with previous literature,we find differential
performance among the three global alliances.Member airlines of
Oneworld appear to have a greater operating margin,on average,
than those in the Star and Skyteam Alliances. The use of
Table 3
The random-effects GLS estimation results for Ln (operating profit margin).
Independent variable Model I Model II Model III Model IV
Number of code-sharing partners 0.014*** (0.004) 0.017*** (0.005) 0.014*** (0.004) 0.016*** (0.005)
Percent of allied code-sharing partners 0.226* (0.140) 0.256* (0.146)
Percent of comprehensive code-sharing partners 0.027 (0.100) 0.126 (0.157)
Ln (Load factor) 2.297*** (0.274) 1.865*** (0.397) 2.286*** (0.278) 1.887*** (0.399)
Ln (Passenger yield) 1.761*** (0.122) 1.945*** (0.152) 1.762*** (0.122) 1.935*** (0.152)
Ln (Unit cost) 1.997 *** (0.125) 2.202 *** (0.165) 2.001 *** (0.126) 2.187 *** (0.166)
Ln (ASKs) 0.011 (0.031) 0.009 (0.036) 0.013 (0.032) 0.002 (0.038)
Star alliance 0.071 (0.074) 0.351 *** (0.105) 0.077 (0.077) 0.332 *** (0.106)
Skyteam alliance 0.053 (0.068) 0.273 *** (0.101) 0.056 (0.069) 0.263 *** (0.099)
Oneworld alliance 0.162* (0.088) 0.162* (0.088)
Constant 0.946 ** (0.371) 1.086 ** (0.465) 0.938 ** (0.374) 1.176 ** (0.472)
No. of observations 338 232 338 232
Wald c 2 300.69 196.59 300.29 194.80
Prob >c 2 0.0000 0.0000 0.0000 0.0000
Overall R2 0.35 0.30 0.34 0.31
The numbers in parentheses are standard errors of coefficients.
*** Significant at 0.01 level; ** Significant at 0.05 level; * Significant at 0.1 level.
L. Zou,X. Chen / Journal of Air Transport Management 58 (2017) 50e5756

comprehensive code-sharing partnerships, however, is not found to
have the expected positive effect on an airline's operating margin. It
is important to note that this insignificant result may be due to the
limited measurement we employ for comprehensive code-sharing
arrangements.Future research could develop an alternative vari-
able to represent in a more precise manner the geographic scope of
a code-sharing arrangement between partner airlines.To achieve
this goal, the use of the data detailing code-sharing agreements at
the route level would be very helpful.
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