Strategic alliances

Strategic alliances
as a competitive strategy
How domestic airlines use alliances
for improving performance
James Rajasekar
College of Commerce and Economics, Sultan Qaboos University,
Muscat, Sultanate of Oman, and
Paul Fouts
Ageno School of Business, Golden Gate University, San Francisco,
California, USA
Abstract
Purpose – The purpose of this paper is to examine how domestic airlines benefit when they have
code sharing arrangements with international carriers.
Design/methodology/approach – The data for this research study have been collected primarily
from three sources. The first database, the digest of statistics no. 400 is from International Civil Aviation
Organization (ICAO) based in Montreal, Canada. The second source of data comes from the Airline
Business database. The third source of data for this research study is from Official Airline Guide (OAG).
Ten years of data from 1994 to 2004 are collected from the databases of ICAO, Airline Business and also
from individual airlines. Data such as the revenue passenger miles (RPMs) and load factor are obtained
from the ICAO database and data such as alliance pattern are culled from the Airline Business database.
Findings – This research study reveals that code sharing agreements between a domestic and
international airline will benefit the former by way of increased RPMs, passenger load factor (PLF),
and market share. However, the coefficients of the hypothesized variables suggest that the initial
gains achieved by the domestic airlines by way of increased RPMs start to erode in the long run. Thus,
a domestic airline must form a code sharing agreement with an international airline at the earliest, so as
to get the initial increase in RPMs. The effect of code sharing on the market share of domestic airlines
is explicit and consistent throughout this research study. The second dimension in the code sharing is
the multiple alliances between domestic and international airlines. Multiple alliances refer to an airline
having more than one code sharing agreement with international carriers. The third factor in this
sequence of hypotheses is equity investment by international carriers in domestic airlines. The
relationship between equity investment and its influence on the performance of the targeted firm is
always an interesting topic explored by both the academic researchers and practitioners. However, in
this study, the regression results do not support the hypothesis. That means that mere equity
investment by international carriers in domestic airlines may not result in increased RPMs, load factor
and the market share for domestic airlines. The interesting finding in this particular section is the
influence of the large size of the alliance partners on all the three dependent variables; RPMs, PLF, and
the market share. Therefore, we can conclude that if both the airlines are large enough and they form
code sharing agreements, then this may result in increased RPMs, PLFs, and market share for the
domestic airlines. Similarly, the study supports the premise that if the partners are unequal, then the
domestic airlines may not be able to increase the RPMs, load factor, and the market share.
Originality/value – This paper reveals that code sharing arrangements reached earlier in the
competition is better as the benefits tend to reduce after a certain period of time.
Keywords Strategic alliances, Competitive strategy, Airlines
Paper type Research paper
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1056-9219.htm
Alliances
as a competitive
strategy
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International Journal of Commerce
and Management
Vol. 19 No. 2, 2009
pp. 93-114
q Emerald Group Publishing Limited
1056-9219
DOI 10.1108/10569210910967860

Introduction
One of the most notable trends in recent years has been the growth in the popularity of
alliances between competitors, prospective competitors, and even supply chain members.
Hence, investigating what drives one firm to cooperate with another firm has been an
interestingtopicforresearchers forwelloveradecade. Anumberofempiricalstudieshave
documented the unprecedented growth of inter-firm cooperative agreements in a variety
of industries, and much has been made of the beginning of a new era of cooperation in
which firms seek partnerships in several facets of their operations (Lei and Slocum, 1991;
Chang, 1995).
The international air transport industry plays a key role in the development of the
world economy, stimulating exchanges between countries and facilitating international
economic relations. The liberalization of market access in Europe, North America, and
other regions of world air transport has added a new dimension to competition and
customer orientation in the airline industry. This has resulted in airlines revising their
growth and competitive strategies to survive and prosper (Berry, 1994). Airline growth
and competitive strategies not only include cost cutting measures and better revenue
management tools, but also strategic alliances with other airlines. Airlines form
alliances to gain access to global networks within the constraints of the current bilateral
air services agreement (ASA) system. In many cases, they have entered into code
sharing agreements to maintain or expand network coverage, and international code
sharing has now become part of bilateral negotiations.
Airlines use alliances as a means to achieving global service networks, getting
access, and establishing identities in new markets without providing aircrafts, and
providing services which would be unprofitable if operated alone. On the other hand,
consumers have demonstrated a preference for dealing with airlines with large service
networks to minimize their cost of travel, to get better services, and to take advantage
of more attractive frequent flyer programs. Alliances can also lead to better access at
congested airports, where landing restrictions, lack of landing and take-off slots, and
other constraints would otherwise exist. Moreover, alliances are theorized to reduce
costs through economies of scale associated with joint marketing, maintenance, ground
facilities, training, computer reservation systems, and through elimination of
duplication and redundancy in operation (Oum et al., 1996; Borenstein and Rose,
1995). Thus, the overall aim of airline alliances is considered to be enhancing partner
airlines’ competitive position and also achieving higher profits for each of the partners.
Though many surveys and research have been done on strategic alliances in the
airline industry in general, the positive or negative effects of alliances on the domestic
airlines is under researched. A domestic airline is defined as a carrier which flies within
the borders of a particular country. This study is aimed at filling that gap of knowledge
by systematically studying and analyzing the benefits accrued or not accrued by
domestic carriers while having strategic alliances in the form of code sharing with
international carriers. The term benefit in this research study is defined by increased
revenue passenger miles (RPMs), load factor, and market share, as these three
variables are used to measure the productivity and profitability of an airline.
Theoretical framework and hypotheses
There are many theoretical frameworks dealing with strategic alliances. For reasons
ranging from vertical disaggregation (Powell, 1990), shrinking product life cycles
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(Ohmae, 1989), growing capital investment requirements (Doz, 1988), to the desire to
increase competitiveness through organizational learning (Senge, 1992), companies
are forming alliances at an ever increasing rate. Popular theories that have been
applied to strategic alliances include transaction cost economics (Williamson, 1985),
game theory (Parkhe, 1993), exchange theory (Gulati, 1995), strategic behavior model
(Hagedoorn, 1993), dialectical model (Das and Teng, 1996), and resource-based view of
the firm (Eisenhardt and Schoonhoven, 1996). Hamel et al. (1989) also asserted that
alliances can be used to learn the skills of the other partner. The authors claimed
that if there was not a mutual learning process, then the alliances would become just
outsourcing agreements without using the full potentials of the strategic alliance
concept.
In this context, air carriers increasingly use strategic alliances to take full advantage
of the economies of scale that internationalization offers. As seen in the study
conducted by Park (1997), over the last two decades, air transport has undergone major
changes owing to the increasing globalization of the industry. One reason for this
evolution is the liberalization of markets, which is characteristic of many industries in
the late twentieth century. Consequently, the competitive landscape of many
industries, including the airline industry, is changing significantly. Thus, airlines now
have an opportunity to penetrate formerly inaccessible markets, but they are, at the
same time, also confronted with new entrants into their markets and risk losing a
considerable percentage of their market share to newly formed low cost airlines. As
evidenced from Schaeffer (1998) study airline alliances are creating trends that are
consolidating control of flights in 82 percent of the market share.
On the other hand, Douglas (2005) found that airline sales teams identify alliance
membership as a critical sales tool, but the airline passengers did not consider the
alliance membership as equally valuable. This research study is focused on the impact of
the alliances on national or domestic airlines. A domestic airline is defined as a carrier,
which only flies within the borders of a particular country. Generally, the airline
industry can be analyzed using various factors such as traffic, costs, load factor, labor,
fuel, and maintenance. However, the starting point in analyzing a passenger airline is its
traffic. Though many standards to measure the performance of airlines are available,
RPMs or revenue passenger kilometers are preferred by airlines, as well as regulators, as
this measure is superior as an analytical tool because airline revenues most closely
correspond with RPM/RPK levels.
According to Youssef and Hanson (1994), the rate of change in RPKs can be used as
a yardstick to determine whether a carrier is gaining or losing market share. Iatrou and
Skourias (2005) also investigated the effects of alliances on the performance of airlines
and concluded that alliances helped increase the RPKs of airlines with alliances by
9.4 percent. Wang et al.’s (2004) study also supports that alliances between airlines
significantly increase the traffic volume and market share for the airlines within the
alliance. In recent years, start-up regional and charter carriers typically outperformed
the major airlines in RPK growth because their traffic base is much smaller. The term
successfulness in this hypothesis is defined as a proportionate increase in revenue
passenger kilometers of each carrier in the alliance. Ever since the USA deregulated its
airline industry in 1978, other countries have also followed a suit. Many countries in
the European Union have deregulated their airline industries, followed by
South American nations. Earlier, the national carriers had an advantage of not
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having real competition due to government controls, the protection of ASAs, and entry
barriers in the aviation industry. However, since worldwide deregulation of the airline
industry, they now face stiff competition from foreign and other newly start-up
domestic carriers, especially on the low cost model (low cost airlines). This is also
evidenced by the study conducted by the Avmark Aviation Economist (1996) that
found increased liberalization which began in the early 1980s and increased
competition that made it more difficult for domestic airlines to survive. Hence:
H1a. Domestic airlines will have increased RPMs if they have code sharing
agreements with international carriers.
H1b. Domestic airlines will have increased passenger load factor (PLF) if they have
code sharing agreements with international airlines.
H1c. Domestic airlines will have increased market share if they have code sharing
agreements with international airlines.
Single vs multiple alliances
Gomes-Casseres (1993) defines alliance networks as those that have loose collections of
firms with disparate interests and capabilities. Since the competitive behavior of an
alliance network is driven by the nature of collaboration among members inside the
group, each alliance partner should be chosen with care so as to provide the necessary
skills to the alliance as a whole. He also asserts that the alliance networks not only
reshape rivalry in an industry but also create collective competition. This new structure
and dynamics of competition depend on the collective behaviors of allied firms. Hence,
the number of member firms in an alliance group affects how the group competes as a
single entity. Having more members, for example, may give the alliance network access
to a broader range of capabilities; a larger overall alliance size also tends to make it
harder for the group to unite behind a common strategy (Gomes-Casseres, 1994). Hwang
and Burgers (1997) affirm that the alliance networks will only be successful if there is an
advantage to combining the capabilities of two or more firms. He also claims that for this
to occur, each firm must be unable to develop internally the capability offered by the
other firm and the combination of capabilities must yield a total value that isgreater than
if the capabilities were used separately.
According to a study conducted by International Civil Aviation Organization
(ICAO) (1996), airlines that are party to a broader alliance have clearly benefited from
the code sharing agreement in terms of additional traffic and extra revenue, although
this has mainly been at the expense of other carriers. A wider alliance permits each
partner to reach more destinations and to take advantage of hub-and-spoke efficiencies
much like those that have driven the development of America’s domestic airline market
(Oum et al., 1996). Although each of the partners in the alliance remains independent,
the alliance has been projected as a single company for the purpose of attracting
passengers.
Through multiparty alliance, airlines are believed to be utilizing new programs
which help with work force utilization, fleet rationalization, flight planning, electronic
ticketing, and determining optimal fare and route structures (Spitz, 1998; Tretheway and
Oum, 1992). Although many major airlines can service new routes, without a supporting
international hub network they cannot provide profitable service on many
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international routes. However, this can be achieved by having a partner feeding traffic
through a hub and thereby generating numerous benefits including stimulation traffic.
This will not only lower a carrier’s operating costs dramatically, but also allow the
carrier to lower fares and increase flight frequency without the need to make substantial
investment in additional aircraft (Borys and Jamison, 1989). The partners may also
realize further cost savings by sharing cargo and passenger terminal facilities,
integrating frequent flyer programs, consolidating sales and administrative operations,
combining information technologies, and coordinating advertising, and engaging in
joint procurement where feasible:
H2a. Multiple alliances foster increased RPMs for domestic airlines than airlines
that have alliances with one partner.
H2b. Multiple alliances foster increased PLF for domestic airlines than airlines that
have alliances with one partner.
H2c. Multiple alliances foster increased market share for domestic airlines than
airlines that have alliances with one partner.
Equity vs non-equity alliances
Research by Hagedoorn and Narula (1996) indicates that international alliances are more
equity-oriented, whereas a disproportionate share of domestic alliances is of
a contractual nature. From both a transaction cost economics perspective and a
strategic management perspective this preference can be explained in terms of the cost
of monitoring and keeping control over a long distance agreement. As domestic alliances
are formed in a familiar environment, equity control is probably less prevalent in order
to monitor the agreement than in the case of international alliances where the familiarity
with the behavior of partners is expected to be smaller. Enforcing a contract in an
unfamiliar environment is rather difficult compared to enforcing partial control through
an alliance in which equity-sharing gives a firm at least some degree of ownership
advantages (Dunning, 1994).
Transaction cost economists have classified the governance structures of alliances
in terms of their use of equity ownership (Vonortas, 1990). Equity alliances, as defined
by transaction cost economists, take one of two forms (Teece, 1986). They can either be
organized as an equity joint venture, which involves the creation of a new and
independent jointly owned entity, or they can come about when one of the partners
takes a minority equity position in the other partner or partners. Transaction cost
economists justify treating equity joint ventures and minority equity investments as
a single category on the grounds that “a direct equity investment by one firm into
another essentially creates an equity joint venture between one firm’s existing
shareholders and the new corporate investor” (Pisano and Teece, 1989). In both types,
the effective shared equity stakes of the firms vary case by case. The important point is
that beyond a certain threshold, the shared ownership structure effectively deters
opportunistic behavior. Generally, an airline can purchase only a minority holding in
a carrier based in another country and this creates a sort of halfway house, which often
defeats the original purpose of the deal (Doganis, 1991).
The Mountford and Tacoun (2004) study identified a total of 502 alliances during
2004 and of these 56 alliances or 10.8 percent involved equity, a drop of 14.9 percent
compared to 2003. However, this decline in equity alliances as a percentage is partly
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attributable to the steep increase in the number of airline alliances. Some significant
new developments, such as alliances between North American carriers and
South American carriers, show that equity is in fact creeping back into favor as a
means to cement an alliance. The other body of literature suggests that by providing
access to new markets with better service to customers and thus reducing costs, the
alliances might probably succeed, with or without equity. Many studies also support
the view that equity participation in an alliance does improve the performance and
survival rate (Youssef and Hanson, 1994; Ramirez, 1998):
H3a. Alliances involving equity investment by international airlines in domestic
carriers will have increased RPMs for domestic airlines compared to those
without the equity arrangement.
H3b. Alliances involving equity investment by international airlines in domestic
carriers will have increased PLF for domestic airlines compared to those
without the equity arrangement.
H3c. Alliances involving equity investment by international airlines in domestic
carriers will have increased market share for domestic airlines compared to
those without the equity arrangement.
Partners’ size, performance, and alliance patterns – large partners
The combined forces of globalization and privatization are leading drivers in the present
surge of strategic alliances. Selection of the right partner has become one of the most
difficult and crucial tasks for top management at the airlines. Well-structured
partnerships can create highly profitable and beneficial global networks,
but, conversely, ill-conceived alliances can threaten the very existence of the
participating carriers. While some of the alliance failures may be attributed to
changes in business conditions, inappropriate partner selection underlies a number of
alliance failures (Borenstein and Rose, 1995). While studies have found organizational
compatibility to be an important determinant of marketing alliance success (Das and
Teng, 1996), they do not address the partner selection issue. In an exception, Doz (1988)
proposed that marketing alliances be conceptualized, designed, and managed from the
perspective of the customer. They recommend that the usage complementarity that
exists between goods and services be used as a basis for determining partner selection.
Hence, airlines must evaluate many parameters such as size, geographical location,
network comparison, fleet, partnership goals, and long-term strategic visions before
they select a partner for code sharing agreement. To achieve this process in a most
efficient manner, some consulting companies have even developed a computer model to
select airline partners. The computer simulated program uses parameters mentioned
above to short list airline carriers by ignoring unqualified partners, thereby cutting top
management’s valuable time in the selection process. The basic foundation of a good
relationship is the choice of the right partner. But what are the characteristics of the right
partner? According to Gulati (1995) the partner selection process should first identify
organizations whose needs, skills, and resources are completely complementary to those
of the large firm. A second selection criterion is the choice of a partner that is financially
stable and well managed.
Bleeke and Ernst (1994) categorized strategic alliances into six distinct types. They
are: collisions between competitors (direct competitors as partners), alliances of the
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weak (two or more weak companies form alliances), disguised sales (a weak company
joins a strong company, usually a direct competitor), bootstrap alliances (a weak
company joins a strong company, usually a competitor with a complementary
product), evolutions to a sale (sale of one partner), and alliances of complementary
equals (involves two complementary partners). The current alliance agreements
between the airline carriers also resemble one or more of the above pattern categorized
by Ernst and Bleeke. Hence:
H4a. Code sharing arrangements between a large domestic carrier and a large
international carrier will increase the RPMs for domestic airlines.
H4b. Code sharing arrangements between a large domestic carrier and a large
international carrier will increase the PLF for domestic airlines.
H4c. Code sharing arrangements between a large domestic carrier and a large
international carrier will increase the market share for domestic airlines.
Partners’ size, performance, and alliance patterns – weak or unequal partners
As discussed in the previous section, partners’ size and performance is one of the
significant factors that influence the successful outcome of the proposed alliances. Just
like alliances between large partners produce significant benefits for the newly formed
alliances, if an alliance is formed between two weak or unequal partners, i.e. either of
the partners is small or weak, then it may not produce as many benefits as the first type
of alliance. This particular theory was also adopted from the works of Bleeke and Ernst
(1994). Alliances between large partners bring more benefits to the alliances because
they bring different sets of synergies and opportunities, especially when they
complement each other. These synergies and opportunities can be explored and used
by both the partners for the successful operation of the alliance firm.
The alliance literature suggests that viewing alliances as learning opportunities provide
an alternative to mutual alliance value creation. Alliances can provide firms with access to
the embedded knowledge of other organizations. This access creates the potential for firms
to internalize partner skills and capabilities. Hennart (1988) referred to this process as
grafting, the process by which organizations increase their store of knowledge by
internalizingknowledgenotpreviouslyavailablewithintheorganization.Inanalliance,two
or more organizations are brought together because of their complementarity and their
differences. However, when small partners strike alliances with large partners, the above
logic may not work in many cases because the weak or unequal partner’s motive and
ambition may be diametrically different than the large partner. Hence:
H5a. Code sharing arrangement between domestic and international carriers that
are small or unequal in size will not increase the RPMs for the domestic
airlines.
H5b. Code sharing arrangement between domestic and international carriers that
are small or unequal in size will not increase the PLF for the domestic
airlines.
H5c. Code sharing arrangement between domestic and international carriers that
are small or unequal in size will not increase the market share for the
domestic airlines.
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Research design and methodology
The data for this research study have been collected primarily from three sources. The
first database, The digest of statistics no. 400, is from ICAO based in Montreal, Canada.
This digest contains statistics of all the domestic airlines in the world such as RPMs,
cost per mile, miles flown by each flight, number of passengers carried, and PLF as
reported by the contracting states. The second source of data comes from the Airline
Business (2004) database. This database contains information on all alliance
agreements in the airline industry such as airlines that have alliance(s) with other
carriers, the year in which the alliance was formed, number of partners in an alliance,
type of alliance, and whether it is an equity or non-equity alliance agreement. The third
source of data for this research study is from Official Airline Guide (OAG). This
database contains information on detailed schedule information of flights for each and
every destination in the world. OAG identifies a code sharing flight by placing a delta
(D) mark in the table. Hence, this database has been used to identify airlines that have
code sharing agreements.
Ten years of data from 1994 to 2004 were collected from the databases of ICAO,
Airline Business, and also from individual airlines. Data such as the RPMs and load
factor were obtained from the ICAO database, and data such as alliance pattern were
culled from the Airline Business database. The control variables used in this research
study are the age of the airline and previous code sharing experience, and the data for
the same were obtained from individual airlines.
As can be seen from Table I, only 59 airlines satisfied all the conditions necessary to
be a part of the sample list, hence these airlines were chosen as a sample: domestic
airlines with code sharing agreements accounted for 37 carriers: North America (five
carriers), South America (12 carriers), Europe (12 carriers), and Asia (eight carriers).
Likewise, domestic airlines without code sharing agreements accounted for 22 carriers:
North America (four carriers); South America (six carriers), Europe (nine carriers), and
Asia (four carriers).
The primary research question of this study is to investigate the effect of
code sharing on the RPMs, PLF, and the market share of domestic airlines. Hence, code
sharing is the independent variable for H1a, H1b, and H1c. The effect of multiple
alliances on firms’ performance is a relatively under researched area in the strategic
alliance literature. Many studies that investigated the effect of multiple alliances on
firm performance have not focused on the airline industry. Therefore, multiple
alliances is the independent variable for H2a, H2b, and H2c. Multiple alliances refer to
domestic airlines having more than one code sharing agreements with the international
carriers.
The effect of equity investment on firms’ performance is a well researched and most
attempted topic in the alliance literature. As seen in the literature review, many authors
support the view that firms that have invested in equity in other firms have more
commitment than others. Consistent with this typology, H3a, H3b, and H3c were
formed and equity investment was made the independent variable for all
these hypotheses.
The term large partners refer to size of the airlines on the basis of each partner’s
market share in their own territories. This particular variable was used in conjunction
with Bleeke and Ernst’s typology (1994). The authors claim that alliances between large
partners will not only increase the lifespan of the alliance firm, but are also more fruitful
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than other types of alliances. Following this logic, H4a, H4b, and H4c were formed with
the independent variable, large partners.
The independent variable, unequal partners, for the last set of hypotheses in this
research study is also derived from the works of Bleeke and Ernst focusing on weak or
unequal size of the partners. Unequal in this context refers to weak or an alliance partner
who is not comparable in market size with the other partner.
The dependent variables used in this research study are RPMs, passenger load
factor (PLF), and the market share of domestic airlines. The RPM is one of the tools
used by the airline industry to measure the productivity of an airline. Hence,
performance increase in the domestic airlines should result in increased RPMs for
domestic airlines. The increase (D) in RPMs was calculated for every year for every
airline by finding the difference between a given year and its preceding year (x 2 x1) as
a percentage. The load factor is considered to be a key measure of capacity utilization
by the airline industry experts. As a first step, the increase (D) in load factor was
calculated for every year for each airline in the study by calculating the differences
between a year and its preceding year (x 2 x1) as a percentage.
The third dimension in the dependent variable is the market share of domestic
airlines. It was hypothesized that all the independent variables will influence the market
share of the domestic airlines so that the latter will have increased market share, as a
result of forming code sharing agreements with international carriers.
USA South America Europe Asia
1. Air Wisconsin 9. Austral Airlines 27. Finaviation 48. East West Airlines
2. Aloha Airlines 10. Lade 28. Kar Air 49. Bourgaq Indonesia
3. Midwest Express 11. Laer S.E. 29. Coast Air K/S 50. Mandala Airlines
4. Mesa Airlines 12. Brasil-Central 30. Wideroe 51. Archana Airways
5. Trans States
Airlines
13. Nordeste 31. Sata 52. Jet Airways
6. Atlantic Southeast 14. Rio-Sul 32. Binter Canararias 53. NEPC Airlines
7. American Trans
Air
15. Tam 33. Binter Medit 54. Sahara India
Airlines
8. Southwest
Airlines
16. Pantanal 34. SPANAIR S.A. 55. Turkey
17. Tavaj 35. AVIACO 56. Skywest Airlines
18. TABA 36. Linjeflyg 57. Dirgantara Air
Services
19. Intercontinantal
Columbia
37. Gill Airways 58. Vayudoot
20. Aerosanta 38. Isles of Scilly 59. Arkia
21. CATA 39. Air Stord
22. Dinar 40. Air Nostrum
23. Interbrazil Star 41. Augsburg Airways
24. Passaredo 42. Air Inter
25. Aces 43. ATI
26. Imperial Air 44. Air Bristol Ltd
45. Community Express
46. Jersey European
Airways
47. Knightair
Table I.
Domestic airlines selected
for study
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Age of the airlines, previous code sharing experience, regional RPMs of domestic
airlines and regional load factor of domestic airlines are control variables used in this
research study. Age in this discussion refers to the total number of years an airline is in
business. The age and experience of firms in a strategic alliance as well as in an
international joint venture (IJV) make a significant contribution in creating wealth for
both partners. This is because when firms get older they become more successful and
efficient in conducting business. Following this logic, the age of partners was
incorporated as a control variable in all the hypotheses.
The term previous code sharing experience refers to the past code sharing experience
of domestic and international airlines. The strategic alliance literature (Simon, 1999)
supports the view that if a firm has previous alliance experience, then it will lead to
further success when it forms new alliance agreements in the future. The airline industry
is a cyclical industry which can be affected by the growth and decline of a country’s
economy. When this happens in a country, the domestic airline industry is also affected.
Hence, in order to capture the regional airline industry’s effect on the individual airlines,
the regional load factor was calculated for the whole region in which a domestic airline
is operating. This was done by adding the load factors of the entire domestic airlines
operating in a given region – year by year for the entire period of the study. For similar
reasons, the regional RPMs were also calculated.
The descriptive statistics of the sample (Table II) provides mean and standard
deviation values for all the variables used in this study. As the correlation matrix
(Table III) for all the variables is not highly correlated, the researcher decided to use all
these variables.
Testing of hypotheses and analysis of variables
H1 predicts that the domestic airlines will have increased RPMs if they have a code
sharing agreement with international airlines. An international airline is defined as
a foreign carrier in this research study. While domestic airlines strictly carry passengers
within a country, the international airlines fly passengers out of a country from a specific
international airport. A significant level of 0.05 has been chosen for comparing the
F-ratio and other value in each hypothesis. If the individual values are equal to or less
Variables Mean SD Min. Max.
Age of the domestic airlines 22.028 15.325 2 63
Code sharing 1.151 0.359 1 2
Equity investment 4.825 5.453 0 19
Large partners 0.144 0.353 0 1
Load factor of individual airlines 0.028 0.177 20.279 1.5
Market share 45.764 15.233 12 91
Market share – difference 0.063 0.05 20.091 0.214
Previous code sharing experience 3.906 2.98 0 11
Regional passenger load factor 0.018 0.12 20.199 1.09
Regional revenue passenger miles 0.266 0.701 20.734 9.329
Revenue passenger miles of individual airlines 0.87 5.273 20.734 66.527
Unequal partners 0.633 0.483 0 1
Note: N ¼ 59
Table II.
Descriptive statistics
of variables
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12345678910
1.Ageofthedomesticairlines
2.Largepartners0.12
3.Equityinvestment0.1820.28
4.Loadfactorofairlinesas%increase20.080.0120.09
5.Loadfactor–regionas%increase20.050.0320.060.71
6.Marketshare–actual0.210.670.020.030.05
7.Marketshareas%increase20.0920.240.1020.010.0420.33
8.Previouscodesharingexperience0.190.260.010.080.060.3120.23
9.Revenuepassengermilesofairlinesas%increase20.1320.0420.060.150.0120.150.0920.08
10.Revenuepassengermilesofregionas%increase20.160.1420.150.220.250.0820.040.040.26
11.Unequalpartners0.0620.540.040.0120.0820.3120.0120.080.0420.08
Notes:N¼59;correlationsabove0.09orbelow20.09aresigniﬁcantat5percentlevel
Table III.
Pearson’s correlation
matrix of variables
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than the standard value (0.05), then the hypothesis will be supported – otherwise it
will not be supported. As shown in Table IV, the probability of the F-ratio (0.004) and
the probability of the independent variable for this hypothesis, code sharing is
significant (0.016).
There were four control variables used in the regression equation for this hypothesis.
They are: age of the airlines, previous code sharing experience, market share, and RPMs
of the region. Age in this discussion refers to the total number of years an airline is in
business. Conventional wisdom suggests that the age of firms in a strategic alliance as
well as in an IJV makes significant contribution in creating wealth for both partners
which will lead to the success of the alliance. This is because when firms get older, they
get experience and become more mature in transactions with other businesses.
Following this logic, the age of partners was incorporated as a control variable in all the
hypotheses.
However, the age as a control variable was not significant in any of the hypotheses in
this research study. Hence, this research study does not support that the age and
experience of a partner firm in a strategic alliance plays a significant role in the
successful operation of the alliance including creating wealth for the partners. However,
we can conclude that code sharing does increase the RPMs of domestic airlines if they
have code sharing agreements with international carriers. The data also suggests that
not only domestic airlines benefit through alliances, but international airlines also share
equal benefits. This is because alliances between domestic and international carriers
allow both sides to enhance their attractiveness and brand loyalty through improved
frequent flyer programs, increased efficiency, and low fares. Therefore, H1a is
supported.
H1b predicts that domestic airlines that are part of code sharing agreements with
international carriers will increase their load factor more than domestic airlines that are
not part of code sharing agreements with international carriers. As shown in Table IV,
the probability of the F-ratio for this hypothesis is significant (0.000). However, one
cannot judge the validity of a hypothesis just by the F-ratio alone. Hence, in regards to
the probability of code sharing, the independent variable was taken into account. This
value stands at 0.004, which is also significant. This result indicates that there is
a significant relationship between increase in load factor and code sharing. Hence, H1b
is supported. Based on the regression results, it is supported that domestic airlines may
increase their PLF, if they have code sharing agreements with international carriers.
Similarly, international airlines also increase their PLF when they have code sharing
arrangements with domestic carriers.
The third dimension in H1 is the effect of code sharing on the market share of
domestic airlines. Consistent with Park’s (1997) typology, it is predicted in H1c that
domestic airlines will increase their market share if they have code sharing agreements
with international carriers. As shown in Table IV, the probability of the F-ratio is very
significant (0.000) for this hypothesis, which directs us to compare the probability of
the independent variable, code sharing which is also significant (0.017). Therefore,
it appears that there is a positive relationship between increase in the market share of
both domestic and international airlines and code sharing arrangements with each
other. Therefore, H1c is supported.
Based on the regression results, we can deduce that code sharing between domestic
and international airlines may help increase the RPMs, PLF, and market share of both
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Dependentvariables
Independent
variablesRPMPLFMSRPMPLFMSRPMPLFMSRPMPLFMSRPMPLFMS
S.No.HypothesisH1aH1bH1cH2aH2bH2cH3aH3bH3cH4aH4bH4cH5aH5bH5c
1Code
sharing0.091
*
0.014
*
0.023
*
2Multiple
alliances0.451
*
0.009
*
20.002
**
3Equity
investment20.00520.0010.001
4Large
partners0.005
*
20.001
*
0.002
**
5Unequal
partners0.164
*
0.023
*
20.012
*
6Previous
code
sharing
experience20.0660.00320.003
*
20.0910.00320.002
*
20.0730.00220.00320.0730.00220.003
*
20.0730.00220.003
*
7Ageofthe
domestic
airlines20.0290.0000.00020.0180.0000.00020.0020.0000.00020.0020.0000.00020.01720.0010.000
8Market
shareofthe
domestic
airlines20.03820.00120.001
***
20.046
*
0.00020.001
***
0.049
*
0.0000.0010.049
*
0.0000.001
*
20.048
*
0.00020.001
***
9RPMsofthe
region1.74
***
5.434
***
5.485.480
***
5.502
***
10Loadfactor
oftheregion1.089
***
1.002
***
1.003
***
1.003
***
1.012
***
11Constant4.0740.0170.092.460.0160.1142.7390.0160.1082.7390.0160.1082.54520.0130.125
12R2
0.0570.5620.1840.0750.5010.1070.0740.5020.120.0740.5020.1070.0740.5060.12
13F-ratio3.529
**
55.086
***
12.901
***
3.919
**
37.015
***
6.389
***
3.845
***
37.119
***
7.108
***
3.845
***
37.119
***
6.362
***
3.853
***
37.726
***
7.117
***
Notes:*
p,0.05;**
p,0.01;***
p,0.00;allone–tailtests,whereA,revenuepassengermiles;B,passengerloadfactor;C,marketshare
Table IV.
Regression analysis
of the variables
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the airlines in general, domestic airlines in particular. The statistical tests reveal that
the international airlines also benefited in the form of increased passenger miles, PLF,
and market share when compared to other international airlines that do not have code
sharing arrangements with domestic carriers. However, the extent to which an
international airline will benefit from an alliance depends on the geographic scope of
the agreement, the degree of operational and marketing integration between partners,
and the division of revenue among partners in the code sharing routes. The control
variable and previous code sharing was found to be not only significant but also
negatively correlated with the independent variable, code sharing. This suggests that
airlines should form code sharing agreements earlier which would give better results
and productivity than if formed later. This is because the effects of code sharing
agreements tend to erode after a certain period of time due to other environmental
factors and competitive scenarios in the industry.
Consistent with the Hwang and Burgers (1997) typology that emphasizes that
multiple alliances will be more beneficial than single firm alliances, it was predicted in
H2a that domestic airlines that have multiple alliances would be more able to increase
their RPMs than airlines that have single firm alliances. As can be seen from Table IV
the F-ratio for this hypothesis is calculated to be 3.919 giving the probability of 0.002,
which is significant. Similarly, the coefficient of the independent variable for this
hypothesis, multiple alliances also stands at 0.003 which is significant, suggesting that
there is a significant relationship between multiple alliances and RPMs. Hence, H2a is
supported.
H2b predicts that domestic airlines will be able to increase their PLF if they have
code sharing agreement with more than one international airline instead of having code
sharing agreement with only one international airline. As the regression test reveals,
the probability of F-ratio is significant (0.000). Likewise, the coefficient of multiple
alliances is also significant (0.004), which is the independent variable for this
hypothesis. Hence, H2b is supported.
H2c predicts that domestic airlines will be able to increase their market share if they
have code sharing agreements with multiple international carriers than airlines that
have single firm alliances. As shown in Table IV, the F-ratio for this hypothesis is 6.389
giving the probability of 0.000 which is very significant. The correlation of the
independent variable for this hypothesis, multiple alliances is also significant at 20.002.
Therefore, H2c is supported. This result suggests that there is a positive, significant
relationship between airlines having multiple alliances and increase in market share.
Based on the regression results, it can be suggested that multiple alliances between
domestic and international airlines lead to domestic carriers capturing higher market
share than airlines that have alliances with single firms.
In summary, H2a, H2b, and H2c are supported. As the web of multi-carriers
alliances evolves into a multiplicity of agreements and negotiated agreements,
many carriers now find themselves members of multiple alliances, resulting in highly
complex network systems. Eventually, the same complex networks appear to help
increase the RPMs, PLF, and the market share of domestic airlines.
It was predicted in H3a that domestic airlines that have more percentage of equity
investment by international airlines will increase the RPMs more than airlines that do
not have equity investment. As shown in Table IV, though the F-ratio is very significant
with the value of 3.845, the coefficient of the independent variable, equity investment is
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not significant (20.005). This suggests that there is a negative correlation between
percentage of equity investment by international airlines in domestic carriers and
increase in RPMs of domestic airlines. Hence, H3a is not supported. This means that
domestic airlines are unlikely to increase their RPMs simply by inviting international
airlines to participate by equity investment.
The second dimension of H3 is the influence of equity investment by international
carriers in the domestic carriers that also have code sharing agreements with the former.
Following the above logic, it was predicted in H3b that if international airlines have more
equity investment (in percentage) in domestic airlines, it will help the latter increase the
PLF compared to other domestic airlines that do not have equity agreement, but have
code sharing agreement. As can be seen from Table I, though the F-ratio is significant
(37.119), the coefficient of the independent variable, equity investment, is not significant
(20.001). This suggests that there is a negative correlation between equity investment
by international airlines in domestic airlines and an increase in PLF of domestic airlines.
Hence, H3b is not supported.
According to Gomes-Casseres (1993), mutual equity investment by partners or by one
of the partners will increase the market share of a firm. He concludes that this is achieved
because both the firms’ interests are at stake and the partner who invested in equity
takes a more aggressive approach in promoting the interests of the firm in which it
invested equity. Consistent with this typology, it is predicted in H3c that domestic
airlines that have more equity investment (as a percentage) from international carriers
will increase market share compared to airlines that do not have equity investment.
The calculated F-ratio is significant (7.108) with the probability of 0.002. However, the
coefficient of the independent value, equity investment is not significant with the value
of 0.230 (Table IV). This suggests that there is no significant relationship between higher
percentage of equity investment by international airlines and increase in the market
share of domestic airlines. Hence, H3c is also not supported.
According to Bleeke and Ernst (1994), there are six types of strategic alliances, one
being the “alliance of complementary equals”. The authors define this type of alliance
as the one that involves two strong and complementary partners that remain strong
during the course of the alliance. Consistent with Bleeke and Ernst’s typology,
I predicted in H4a that a code sharing agreement between a large domestic and a large
international airline (based on market share held by each carrier in the region served)
would increase the RPMs for the domestic airline because of the synergies and
opportunities the large international airline brings along with it.
As per the regression test (Table IV), the calculated F-ratio for this model stood at
3.969 which gave it a significant probability of 0.002. Similarly, the coefficient of the
independent variable for this hypothesis is also significant (0.003). This means that
there is a relationship between size of the partner airlines and increase in RPMs.
Therefore, H4a is supported.
H4b is focused on the load factor of domestic airlines with reference to the size of the
alliance partners as independent variable and it predicted that if a domestic carrier is
large in size (on the basis of market share) and if it has code sharing agreements with
large (on the basis of market share) international airline(s), then the domestic carrier
will be able to increase its PLF because of the synergies and opportunities the large
international carrier provides. Based on the regression testing, the F-ratio is calculated
to be 36.884 which is significant at 0.000 (Table IV). The coefficient of the independent
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variable for this hypothesis, large partner, is also significant (20.001), suggesting that
there is a significant relationship between size of the partner airlines and increase in
PLF. Therefore, H4b is supported.
H4c predicts that if a domestic carrier is large in size (on the basis of market share)
and if it has code sharing agreement with large (on the basis of market share)
international airline(s), then the domestic carrier will be able to increase its market
share because of the synergies and opportunities the large international airline brings
along with it. As shown in Table IV, the calculated F-ratio for this model is 7.108 which
is significant at 0.000. The coefficient of the independent variable for this hypothesis,
large partners, is also significant (0.002), suggesting that there is a positive and
significant relationship between size of the partner airlines and increase in market
share of the domestic airlines. Therefore, H4c is supported. Hence, it can be concluded
that if both the airlines – domestic and international – are large in nature, then it may
not only result in increased number of RPMs, but will also result in increased number
of PLFs and market share.
Though it seems that H2 and H4 are inconsistent with each other, the premises on
which H2 was developed is different than H4. H2a, H2b, and H2c predicted that
a domestic airline which has alliances with more than one international airline would
benefit from increased RPMs, PLF, and market share. Here, the domestic airline is
opting for multiple alliance arrangements because no single international airline holds
majority of the market share in the region where a domestic airline is a major operator.
However, H4a, H4b, and H4c were developed based on the premises that there is
a large domestic and a large international airline available to form the alliance. It also
factored in the availability of smaller airlines (both domestic and international carriers)
to compete against the alliance. In this scenario, the hypothesized variables were tested
and supported by the statistical tests, i.e. code sharing arrangement between a large
domestic airline and a large international carrier would result in increased RPMs, PLF,
and market share.
H5a predicts that if one of the partners is weak or unequal in size, then the domestic
airline will not be able to increase the RPMs. As shown in Table IV, the F-ratio for this
hypothesis is 3.853 which gave the probability of 0.002 which is very significant.
Similarly, the coefficient of the independent variable for this hypothesis, unequal
partners, is 0.005 which is also significant. Therefore, this result suggests that there is
a significant relationship between the size of the partners and increase in RPMs.
Therefore, H5a is supported.
H5b predicts that code sharing agreement between small or unequal partners (both
domestic and international) will not result in increased PLF for the domestic airlines
because each partner have their own synergies and strengths that cannot complement
each other. As shown in Table IV, the F-ratio for this hypothesis is 37.726 which gave
the probability of 0.000 which is very significant. Likewise, the coefficient of the
independent variable for this hypothesis, unequal partners is also significant (0.004).
This result suggests that there is a significant relationship between the size of the
partners and increase in PLF. Therefore, H5b is supported.
In a similar pattern discussed earlier, none of the control variables were found to be
significant, suggesting that there were no positive correlation between weak or
unequal partners. On the other hand, it was proved that if the one of the partners in the
airline alliances is unequal or weak, then that would negatively affect the alliance
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outcome by not resulting in increased RPMs, increased PLFs, and increased market
share, despite the partners having previous code sharing experience and general
business experience.
When a weak or unequal domestic airline has code sharing agreements with an
international airline that services the same region as the domestic airline, the alliance
may not produce fruitful outcomes. This could be because a large international airline
prefers a code sharing agreement with an available large domestic carrier so that both
the partners can feed traffic to each other. Following this logic, it was predicted in H5c
that alliances between weak or unequal carriers will not result in increased market share
for the domestic airlines because each partner has their own synergies and strengths
that cannot complement each other. This hypothesis was tested using the variable,
unequal partners which was derived from the actual market share controlled by both
domestic and international airlines.
As shown in Table IV, the calculated F-ratio for this hypothesis is 7.117 and the
probability is 0.000 which is significant. Moreover, the coefficient of the independent
variable for this hypothesis, unequal partners, is also significant (0.013). This suggests
that there is a significant relationship between the size of the partners (unequal) and
increase in market share. Therefore, H5c is supported. The control variable, previous
code sharing experience has negative coefficient in this hypothesis. This means
that the earlier the code sharing agreements between domestic and international
airlines, the better the benefits for the domestic airlines by way of increased market
share.
As seen consistently through this particular data analysis, market share is
a prominent dependent variable in a code sharing agreement. If one of the alliance
partners is weak or unequal then the domestic airline will not be able to increase the
market share. So care must be taken before a domestic airline decides to align with
an international airline that is weak or unequal in size with regard to the market
share.
Findings, recommendations, and limitations
The objective of this study was to investigate the effects of code sharing on RPMs,
PLF, and the market share of domestic airlines when they have code sharing
agreements with international carriers. The specific reason for focusing on domestic
airlines was discussed earlier in this paper, i.e. while many studies supported the
positive (and negative) influences of code sharing on the international airlines, research
on domestic airlines were few. Hence, the focus of this paper is how domestic airlines
benefit exclusively from the alliances, though the results also suggest that
international airlines do benefit along with domestic airlines. The secondary
objective of this study is to analyze the effects of multiple alliances, equity investment
by international carriers in the alliance partner’s firm, size of the partners (large), and
unequal partner size on the dependent variables of this research study – RPMs, PLF,
and the market share of domestic airlines.
The regression results of H1 supported our premise that code sharing agreements
between a domestic and international airline will benefit the former by way of
increased RPMs, PLF, and marker share. However, the coefficients of the hypothesized
variables suggested that the initial gains achieved by the domestic airlines by way of
increased RPMs start to erode in the long run. Thus, a domestic airline must form
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a code sharing agreement with an international airline at the earliest, so as to get the
initial increase in RPMs.
Following the same logic that appeared in H1a, H1b was focused on the effect of
code sharing on the load factor of the domestic airlines. As discussed earlier, this
hypothesis was also supported because the test results were found to be significant.
The results reveal that the domestic airlines will be able to increase their PLF if they
form code sharing agreements with international carriers. This particular result is very
important to any airline, because the load factor is an important mechanism to measure
an airline’s productivity. The higher the load factor, the higher the profitability of the
airline will be. Since this research study suggested that the domestic airlines would
be able to increase their load factor by forming a code sharing agreement with
international carriers, code sharing must be used as one of the strategic tools not only
to increase the RPMs, but also to increase the load factor which will eventually lead to
better performance and productivity.
The effect of code sharing on the market share of domestic airlines is explicit and
consistent throughout this research study. The regression analysis supported H1c
which posited that code sharing will increase the market share for domestic airlines if
they have strategic alliance with international airlines. As discussed earlier, code
sharing agreements between domestic and international airlines not only resulted in
increased RPMs and PLF for domestic airlines, but also increased the market share of
domestic airlines. This particular finding will help the domestic airlines to adopt the
same strategy that Japanese multinational corporations follow when they venture into
foreign markets. Japanese companies focus on capturing market share rather than the
profitability of the product itself when launching a new product or entering into a new
market. In the long run, this helps the company to entrench itself strongly in the
market and so the company slowly turns this dominant position into profitability of the
product, as its competitors are way behind in market share. Using the same strategy,
domestic airlines can first increase the market share and, thereafter, they will be able to
increase their RPMs and the load factor.
The second dimension in the code sharing is the multiple alliances between
domestic and international airlines. Multiple alliances refer to an airline having more
than one code sharing agreement with international carriers. As discussed earlier,
multiple alliances did increase the RPMs, load factor, and the market share of domestic
airlines. Therefore, domestic airlines should have multiple alliances with international
carriers so that they can achieve the optimal productivity through increased RPMs,
PLFs, and market share.
The third factor in this sequence of hypotheses is equity investment by
international carriers in domestic airlines. The relationship between equity investment
and its influence on the performance of the targeted firm is always an interesting topic
explored by both the academic researchers and practitioners. Consistent with the
typology that corroborates the view that investing firms will have more commitment
and interest in the targeted firms, the next set of hypotheses were formed. H3a, H3b,
and H3c predicted that equity investment by international carriers would increase the
RPMs, PLF, and market share of the domestic airlines. However, as discussed earlier,
the regression results did not support all the three variables, That means equity
investment by international carriers in domestic airlines may not result in increased
RPMs and load factor and market share.
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The fourth dimension in the hypothesis is size of the partners – both domestic and
international airlines. As discussed earlier, this classification of the alliance pattern
was adopted from the works of Bleek and Ernst, which focused on alliances between
strong or large alliance partners. Following this logic, the next set hypotheses was
formed and they predicted that code sharing agreements between large domestic and
large international airlines would result in increased RPMs, load factor, and market
share.
The interesting findings in this particular section was the influence of the large size of
the alliance partners on all the three dependent variables; RPMs, PLF, and the market
share. Therefore, we can conclude that if both the airlines are large enough and they
formed code sharing agreements, then it may result in increased RPMs, PLFs, and
market share for the domestic airlines. Only in this section, the load factor is
significantly influenced by the independent variable; in this case, size of the partners.
This could be because the independent variable, size of the partners, is a dummy
variable and derived from the actual market share of both the alliance partners. So, we
can construe that both the airlines control a major portion of market share that must
have brought the increased traffic that would have ultimately resulted in increased PLF.
The last dimension in the hypothesis is unequal size or weak partners. Following the
same logic as the equal partners described in the above paragraph, H5a, H5b, and H5c
were formed which predicted that if one of the airlines is unequal in size or even weak on
the basis of market share, then the domestic airlines will not be able to increase the
RPMs, load factor, and the market share, respectively. As expected, the unequal or weak
partners did not influence the dependent variables.
One of the important findings of this study is the significant influence of code sharing
on the market share of the domestic airlines which has direct managerial implications on
the airline management. As seen consistently in the analysis chapter, every form of code
sharing (multiple alliances, large partners, and unequal partners) influences the market
share significantly, except equity investment. The domestic airlines can significantly
increase the market share if they can form code sharing agreements with international
carriers which will eventually result in increased RPMs and load factor. This is because
whichever airline controls the market share usually transports passengers more than its
nearest competitor in a region which will ultimately benefit it by way of increased RPMs
and load factor.
However, consolidating the market share in the airline market may attract anti-trust
or regulatory actions from the governments. As with mergers and other forms of
agreements between carriers, code sharing agreements have the potential to be
significantly anti-competitive, because of the consolidation taking place in the domestic
airline market which will eventually limit the passengers’ choice of selecting routes.
As long as the domestic airlines create new services, improve existing services, and
lower costs and increase efficiency for the benefit of the traveling public, the
governments may not interfere with the operations of the airlines. Just like any other
marketing alliance, domestic airlines must take a long-term view, match firms’ cultures
and styles when forming alliances, define the measures for success, insist on and
practice open communication, and pursue multiple alliances if they intend to succeed in
the market place. Also, the alliance would be stronger if the benefits were distributed in a
balanced way to all partners of an alliance. This would ensure the sustainability of
alliance as strategic tool.
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Limitations and scope for further research
Collection of data for this research involved a complex set of procedures. There has
been no systematic collection of data by international or national agencies that would
enable a thorough analysis in relation to the impact of domestic airlines. The lack of
publicly available and reliable data, except contacting the individual airlines
themselves, makes it difficult to conduct a comprehensive analysis of the impact of
these alliances and, in particular, the long-term effects of alliances on the domestic
airline industry. The recent growth in airline alliances, and the likely continuation of
this trend, makes it imperative that there should be a better process for collecting data
and monitoring the effects of code sharing agreements. Better data would enable the
researchers to more fully assess the benefits and costs of airline alliances and promote
a better understanding of the circumstances under which alliances may affect
competition and enhance the ability of airlines to exercise their market power. This
would provide a better foundation for an analysis of the effectiveness of existing
policies in targeting potential anti-competitive behavior and market power created
through airline alliances.
In some cases, where the national governments own a majority stake in national
flag carriers, the governments heavily influence the flag carriers by way of dictating
with whom the national carriers must have alliance. Given these circumstances, the
national flag carriers cannot have code sharing agreements with the best possible
international airline or the airline of their choice. Therefore, a longitudinal study can
be done focusing on these issues which would enhance our knowledge in
understanding the cross cultural airline alliances. Future studies on code sharing
practices in the domestic airline industry should include the routes in the African
subcontinent as this study did not include this region. The recent worldwide
deregulation of the airline industry fueled startup carriers which had never existed
earlier. As the majority of these airlines came into being only after 2000, future
research study including these carriers would be appropriate.
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Corresponding author
James Rajasekar can be contacted at: raja@squ.edu.om
IJCOMA
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