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Procedia Economics and Finance 12 (2014) 329 – 333
Enterprise and the Competitive Environment 2014 conference, ECE 2014, 6–7 March 2014, Brno,
Czech Republic
Airline Alliances and Their Influence on Firm Performance
Natalya Kuzminykha, Pavel Zufana,*
a
Department of Management, FBE MENDELU, Zemedelska 1, 613 00 Brno, Czech Republic
Abstract
Literature on airline partnerships is growing, and has thus far mostly dealt with the price effects of airline consolidation. There is
no clear consensus, though, whether the alliances on international markets do bring benefits to the member companies. Therefore
the paper focuses on examination of the effects of alliances on size-indicators of airlines based on the panel data analysis of data
from 65 air-operators 14 of which are members of one of the three major alliances (Star Alliance, SkyTeam, OneWorld).
Panel data analysis examines the influence of alliance membership on turnover, total assets, and number of employees. Source
data are taken from the Amadeus database, and reflect 10-year time series of 2003-2012. Calculated values of determination
coefficients prove a very strong impact of an alliance membership on turnover and total assets, and relatively strong impact on
the number of employees of a particular allied company.
© 2014
2014 Elsevier
B.V. This
is an open
access article
©
The Authors.
Published
by Elsevier
B.V. under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
Selection and/or peer-review under responsibility of the Organizing Committee of ECE 2014.
Selection and/or peer-review under responsibility of the Organizing Committee of ECE 2014
Keywords: Partnership; Performance indicators; Panel data analysis; Alliance membership; Membership effects.
1. Introduction
Dynamic development of business environment, uncertainty about the future, but also the desire to actively shape
it contribute to development of a variety of approaches enabling formation of some secure points in the complex
environment. One of the frequently applied strategies focusing on gaining market power in general (through
extending the current distribution channels and getting to the new customers, extending the offer of products,
assuring a wider network of (service) operations etc.) is forming strategic alliances. Whereas the objectives ore
* Corresponding author. Tel.: +420-54513-2052; fax: +420-54513-2535.
E-mail address: [email protected]
2212-5671 © 2014 Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
Selection and/or peer-review under responsibility of the Organizing Committee of ECE 2014
doi:10.1016/S2212-5671(14)00352-9
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Natalya Kuzminykh and Pavel Zufan / Procedia Economics and Finance 12 (2014) 329 – 333
usually clearly set (rather often they include those mentioned in the previous sentence), evaluation of their reaching
– success of an alliance – is always complicated by dozens other influences than those of an alliance itself.
Alliance success has been examined by a number of authors, whose findings extend the current knowledge-base
with different pieces of the mosaic. Within this paper authors focus on alliances in airline industry, which is
mentioned by Porter (2008) as an industry, where almost no company earns attractive returns on investment – being
caused by intense competitive forces influencing the industry.
Flores-Fillol and Moner-Colonques (2005) conducted cost-benefit analysis of the network structure, which is
used in airline alliances. Passengers use the services of several airlines to arrive in their destination via this structure.
Cooperation can have 2 forms: code-sharing agreements and coordination of flight schedules. The basis of the
model developed by these authors is system of equations with the distance, flight time, transfer and the number of
passengers as key variables. Airlines in alliance become profitable if airlines offer differentiated services. The
authors conclude that joining of airlines in alliances is profitable, because it leads to a sufficiently high
differentiation of product and lower competition in the market. Joining in alliances allows the airlines to expand
their network without investing new resources.
Gagnepain and Marin (2010) examined the empirical model of airline alliances through price, demand, market
size, and length of the flight. Methodology of their research was based on networks of airlines. The model consists
of three levels: the first level is transport, the second level is technical function of the network in terms of size and
the third level is spending and effect of alliances. The model confirmed the findings of Borenstein (1989), Oum et
al. (1996), Brueckner and Whalen (2000), Brueckner (2001 and 2003), and Whalen (2007) – it concludes that
joining in alliances brings a reduction in the average cost of the flight and increases the number of passengers.
However, this is not affecting the technical costs.
Brueckner and Whalen (2000) also investigated loyalty of passengers to a particular airline. They primarily
focused, though, on a decision-making strategy when company – in spite of being already in an alliance – decides to
create additional alliance. They call this strategy a “Simultaneous game of alliances.” Research focuses on deriving
“equation of alternative”, and the incentive for creating alliance. Summarizing the above, joining an alliance is
expedient if companies have differentiated products. Furthermore, if the products are very similar company should
withdraw out of the alliance, because high competition arises.
Brueckner and Pels (2005) examined European alliances of airlines (Air France-KLM; Northwest-KLM and
SkyTeam) in the U.S. market. The analysis was based on the model of Brueckner and Spiller (1991). This model
asserts that any alliances are harmful to the consumer. Although, the merger is beneficial for airlines (high profit)
the merger’s effects are negative for consumers – expected reduction of tariffs does not happen in the most cases.
Forming an alliance also leads to a reduction of competition in the market, and consumers loose a chance to use the
services of other companies, which are not in the alliance.
Cravens et al. (2000) offer to use the balanced scorecard for measuring of alliances’ success. Designed template
includes four indicators: financial, customer, internal business processes, learning and growth. Nevertheless, the
work draws attention to the relationship between the partners in the alliance, strategic intent and management. Work
has theoretical character and offers a formal approach to the evaluation of the success of alliances. In this approach
the purposes of alliances and the assessment of results of activity are connected.
Sullivan and Coughlan (2004) researched horizontal alliances using a set of data analysis methods. The authors
conclude that the use of horizontal alliances can help mitigate transaction costs on entering a new market; success of
horizontal alliances depends on the size of firms and the level of market uncertainty; alliance partners play an
important role.
Baria and Constantatos (2006) suggest that among three airlines, two of which decide to cooperate, a strategic
alliance will give the partners higher profit as compared to a full merger. Park and Zhang (2000) find evidence for
increasing market power of the alliance members at their hubs, even though they suggest that this effect is offset by
cost savings that an alliance brings about.
2. Objectives and methods
Objective of the paper is to investigate the influence of alliance membership on selected size-related
characteristics of airlines. Investigation is done on a sample of 65 airlines in 10 years’ period (2003–2012).
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Natalya Kuzminykh and Pavel Zufan / Procedia Economics and Finance 12 (2014) 329 – 333
Economic data (turnover, total assets, number of employees) was taken from the Amadeus database of the Bureau
van Dijk, and included 14 alliance members and 51 independent flight operators in 2012 (in 2003 there were only 8
alliance members). Sample characteristics are described in Figure 1.
1
0
1
0
0
0
0
10000000
20000000
30000000
0
10000000
20000000
30000000
0
10000000
20000000
Turnover
30000000
0
10000000
20000000
30000000
Turnover
Graphs by Aliance
Graphs by Aliance
1
0
1
0
50000
0
100000
150000
0
0
10000000
20000000
30000000
0
10000000
20000000
30000000
0
50000
Turnover
Graphs by Aliance
100000
150000 0
50000
100000
150000
NoEmpl
Graphs by Aliance
Fig. 1. Relationship between key indicators and alliance membership (Amadeus data processed with STATA)
Even though there is an apparent difference between the absolute values given in Figure 1, the expected influence
of an alliance membership needs to be statistically evaluated to express its quantitative influence on operation of
airlines. This evaluation was done through panel data analysis, looking at the possible connection between an
alliance membership and a particular performance indicator.
In-line with the basic characteristics the paper is going to focus on testing the independence hypothesis related to
the sample of companies:
H: Company turnover, (total assets, number of employees) are independent on an alliance membership.
Testing was done through panel data analysis within the STATA program.
3. Results and discussion
Literature considers an alliance membership contribution to company performance characteristics obvious, and
current papers confirming these effects are very rare. Even though the effects also seem obvious from the data
presented in Figure 1, there still remains a question on how strong the influence really is in quantitative terms.
Performing panel data analysis through Stata program, authors reached the information presented in Table 1.
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Natalya Kuzminykh and Pavel Zufan / Procedia Economics and Finance 12 (2014) 329 – 333
Table 1. Results of panel data analysisof the sample (Amadeus data processed with STATA)
Coefficient
Std. error
t
p-value
Determination coeff.
Turnover/Alliance
Factor
5 229 133
354 492
14.75
0.000
0.9336
Total assets/Alliance
6 314 736
163 263
16.31
0.000
0.8598
Employees/Alliance
23 862
1 414
16.88
0.000
0.5178
The table shows that there are relatively high correlations between the particular indicators and alliance
membership, highest being in case of alliance influence on company turnover, and lowest being in the case of
number of employees. Values of t also confirm, that there is an effect of alliance membership, and for all variables
they show statistical significance of alliance membership.
Interpretation of the gained information and its generalization is difficult due to the relatively small sample of
selected companies (65, 14 of which being actual alliance members). This was caused by difficulties in data
availability, but the authors plan to extend the sample size by further searching for annual reports data to gain a
more comprehensive database.
Comparison with the literature is difficult, because there are no current papers focusing on this type of analysis,
and examined relations are just considered obvious. Quantifying the dependence between the key size-indicators,
though, should represent the first step in further analyses, and assuming this relationship should be quantitatively
confirmed.
4. Conclusion
Findings confirm the expectation of high benefits of alliance membership and significant positive effects on
particular size-indicators of analyzed companies. The positive impacts were quantified for the sample of 65 airlines.
Even though the sample is relatively small (having over 3000 airlines included in the Amadeus database), it
represents certain comparative basis, which will be further examined and extended.
Further analyses will concentrate on examining a bigger sample of companies, and also deeper analysis aiming
on capital structure influences, and performance indicators (profits, ROA, value added etc.).
This introductory step enabled to map the airline alliance market and get a more detailed perspective on the
situation of this industry, quantitatively confirming existence of expected relations between the selected indicators.
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